diff --git a/.gitignore b/.gitignore index b2eb511..800fc84 100644 --- a/.gitignore +++ b/.gitignore @@ -148,4 +148,6 @@ app/logs/* *.pickle *.csv *.avi -*.json \ No newline at end of file +*.json +*.env* +config.backup.py \ No newline at end of file diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..d134880 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,22 @@ +FROM python:3.12-slim + +# Install uv. +COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/ + +# Copy the application into the container. +COPY . /app + +# Install the application dependencies. +WORKDIR /app +RUN mkdir /seg_cache +# 更新索引并安装替代包 +RUN apt-get update && apt-get install -y \ + vim \ + libgl1 \ + libglib2.0-0 \ + && rm -rf /var/lib/apt/lists/* + +RUN uv sync --frozen --no-cache + +# Run the application. +CMD ["/app/.venv/bin/fastapi", "run", "app/main.py", "--port", "80", "--host", "0.0.0.0"] \ No newline at end of file diff --git a/README.md b/README.md index 023ed87..6085b85 100644 --- a/README.md +++ b/README.md @@ -23,11 +23,11 @@ $ pip install mmcv==1.4.2 -f https://download.openmmlab.com/mmcv/dist/cu117/torch1.13/index.html -2. 启动服务器 +1. 启动服务器 $ uvicorn app.main:app --host 0.0.0.0 --port 8000 -3. 打开 http://127.0.0.1:8000/docs +2. 打开 http://127.0.0.1:8000/docs Docker 部署 --------------- diff --git a/app/api/api_attribute_retrieve.py b/app/api/api_attribute_retrieve.py index 2a5ad4d..aee5731 100644 --- a/app/api/api_attribute_retrieve.py +++ b/app/api/api_attribute_retrieve.py @@ -2,8 +2,7 @@ import json import logging from fastapi import APIRouter, HTTPException - -from app.core.config import DEBUG +from app.core.config import settings from app.schemas.attribute_retrieve import * from app.schemas.response_template import ResponseModel from app.service.attribute.config import const, local_debug_const @@ -35,13 +34,13 @@ def attribute_recognition(request_item: list[AttributeRecognitionModel]): """ try: for item in request_item: - logger.debug(f"attribute_recognition request item is : @@@@@@:{json.dumps(item.dict())}") - if DEBUG: + logger.info(f"attribute_recognition request item is : @@@@@@:{json.dumps(item.dict(), indent=4)}") + if settings.DEBUG: service = AttributeRecognition(const=local_debug_const, request_data=request_item) else: service = AttributeRecognition(const=const, request_data=request_item) data = service.get_result() - logger.debug(f"attribute_recognition response @@@@@@:{json.dumps(data)}") + logger.info(f"attribute_recognition response @@@@@@:{json.dumps(data, indent=4)}") except Exception as e: logger.warning(f"attribute_recognition Run Exception @@@@@@:{e}") raise HTTPException(status_code=404, detail=str(e)) @@ -67,10 +66,10 @@ def category_recognition(request_item: list[CategoryRecognitionModel]): """ try: for item in request_item: - logger.info(f"category_recognition request item is : @@@@@@:{json.dumps(item.dict())}") + logger.info(f"category_recognition request item is : @@@@@@:{json.dumps(item.dict(), indent=4)}") service = CategoryRecognition(request_data=request_item) data = service.get_result() - logger.info(f"category_recognition response @@@@@@:{json.dumps(data)}") + logger.info(f"category_recognition response @@@@@@:{json.dumps(data, indent=4)}") except Exception as e: logger.warning(f"category_recognition Run Exception @@@@@@:{e}") raise HTTPException(status_code=404, detail=str(e)) diff --git a/app/api/api_brand_dna.py b/app/api/api_brand_dna.py index 2133ee9..c1dd432 100644 --- a/app/api/api_brand_dna.py +++ b/app/api/api_brand_dna.py @@ -26,7 +26,7 @@ def seg_product(request_item: BrandDnaModel): } """ try: - logger.info(f"brand dna request item is : @@@@@@:{json.dumps(request_item.dict())}") + logger.info(f"brand dna request item is : @@@@@@:{json.dumps(request_item.dict(),indent=4)}") service = BrandDna(request_item) result_url = service.get_result() except Exception as e: @@ -36,7 +36,7 @@ def seg_product(request_item: BrandDnaModel): @router.post("/GenerateBrand") -def GenerateBrand(request_data: GenerateBrandModel): +def generate_brand(request_data: GenerateBrandModel): """ 通过prompt 生成 brand name ，brand slogan , brand logo。 创建一个具有以下参数的请求体: diff --git a/app/api/api_brand_dna_initialize.py b/app/api/api_brand_dna_initialize.py index 72c0a25..9388bdd 100644 --- a/app/api/api_brand_dna_initialize.py +++ b/app/api/api_brand_dna_initialize.py @@ -9,7 +9,6 @@ from apscheduler.schedulers.background import BackgroundScheduler from apscheduler.triggers.cron import CronTrigger from fastapi import HTTPException, APIRouter -from app.service.recommend.service import load_resources, matrix_data import pymysql from app.core.config import DB_CONFIG, TABLE_CATEGORIES, RECOMMEND_PATH_PREFIX from minio import Minio diff --git a/app/api/api_brighten.py b/app/api/api_brighten.py index cc5a03f..1cfeac2 100644 --- a/app/api/api_brighten.py +++ b/app/api/api_brighten.py @@ -5,10 +5,11 @@ import time from PIL import ImageEnhance from fastapi import APIRouter, HTTPException - +from minio import Minio +from app.core.config import settings from app.schemas.brighten import BrightenModel from app.schemas.response_template import ResponseModel -from app.service.utils.oss_client import oss_get_image, oss_upload_image +from app.service.utils.new_oss_client import oss_get_image, oss_upload_image router = APIRouter() logger = logging.getLogger() @@ -20,6 +21,9 @@ def increase_brightness(img, factor): return bright_img +minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE) + + @router.post("/brighten") async def brighten(request_item: BrightenModel): """ @@ -35,14 +39,14 @@ async def brighten(request_item: BrightenModel): """ try: start_time = time.time() - logger.info(f"brighten request item is : @@@@@@:{json.dumps(request_item.dict())}") - image = oss_get_image(bucket=request_item.image_url.split('/')[0], object_name=request_item.image_url[request_item.image_url.find('/') + 1:], data_type="PIL") + logger.info(f"brighten request item is : @@@@@@:{json.dumps(request_item.dict(),indent=4)}") + image = oss_get_image(oss_client=minio_client, bucket=request_item.image_url.split('/')[0], object_name=request_item.image_url[request_item.image_url.find('/') + 1:], data_type="PIL") new_image = increase_brightness(image, request_item.brighten_value) image_data = io.BytesIO() new_image.save(image_data, format='PNG') image_data.seek(0) image_bytes = image_data.read() - req = oss_upload_image(bucket=request_item.image_url.split('/')[0], object_name=request_item.image_url[request_item.image_url.find('/') + 1:], image_bytes=image_bytes) + req = oss_upload_image(oss_client=minio_client, bucket=request_item.image_url.split('/')[0], object_name=request_item.image_url[request_item.image_url.find('/') + 1:], image_bytes=image_bytes) brighten_url = f"{req.bucket_name}/{req.object_name}" logger.info(f"run time is : {time.time() - start_time}") except Exception as e: diff --git a/app/api/api_chat_robot.py b/app/api/api_chat_robot.py index c8bcf32..a05a1c2 100644 --- a/app/api/api_chat_robot.py +++ b/app/api/api_chat_robot.py @@ -30,9 +30,9 @@ def chat_robot(request_data: ChatRobotModel): } """ try: - logger.info(f"chat_robot request item is : @@@@@@:{json.dumps(request_data.dict())}") + logger.info(f"chat_robot request item is : @@@@@@:{json.dumps(request_data.dict(),indent=4)}") data = chat(post_data=request_data) - logger.info(f"chat_robot response @@@@@@:{json.dumps(data)}") + logger.info(f"chat_robot response @@@@@@:{json.dumps(data, indent=4)}") except Exception as e: logger.warning(f"chat_robot Run Exception @@@@@@:{e}") raise HTTPException(status_code=404, detail=str(e)) diff --git a/app/api/api_clothing_seg.py b/app/api/api_clothing_seg.py index e09b882..67fc782 100644 --- a/app/api/api_clothing_seg.py +++ b/app/api/api_clothing_seg.py @@ -42,7 +42,7 @@ def clothing_seg(request_item: ClothingSegModel): } """ try: - logger.info(f"clothing_seg request item is : @@@@@@:{json.dumps(request_item.dict())}") + logger.info(f"clothing_seg request item is : @@@@@@:{json.dumps(request_item.dict(),indent=4)}") server = ClothingSeg(request_item) result_url = server.get_result() except Exception as e: diff --git a/app/api/api_design.py b/app/api/api_design.py index 03e0b25..555819f 100644 --- a/app/api/api_design.py +++ b/app/api/api_design.py @@ -1,64 +1,77 @@ import json import logging -import os -from fastapi import APIRouter, HTTPException, UploadFile, File, Form, BackgroundTasks +import requests +from fastapi import APIRouter, HTTPException, BackgroundTasks -from app.schemas.design import DesignModel, DesignProgressModel, ModelProgressModel, DBGConfigModel, DesignStreamModel +from app.core.config import settings +from app.schemas.design import DesignModel, ModelProgressModel, DesignStreamModel, SAMRequestModel from app.schemas.response_template import ResponseModel -from app.service.design.model_process_service import model_transpose -from app.service.design_batch.service import start_design_batch_generate from app.service.design_fast.design_generate import design_generate, design_generate_v2 -from app.service.design_fast.utils.redis_utils import Redis +from app.service.design_fast.model_process_service import model_transpose router = APIRouter() logger = logging.getLogger() @router.post("/design") -def design(request_data: DesignModel, background_tasks: BackgroundTasks): +def design(request_data: DesignModel): """ - objects.items.transparent: - "transparent":{ - "mask_url":"test/transparent_test/transparent_mask.png", - "scale":0.1 - }, - mask_url 为空"" -> 单件衣服透明 - mask_url 非空"mask_url" -> 区域透明 + - **objects.items.transparent**: + ```json + "transparent":{ + "mask_url":"test/transparent_test/transparent_mask.png", + "scale":0.1 + }, + ``` + - **mask_url** 为空"" -> 单件衣服透明 + - **mask_url** 非空"mask_url" -> 区域透明 + - **transpose** 镜像模式 ,:"top_bottom"或"left_right" + - **rotate** 45, - 创建一个具有以下参数的请求体: - 示例参数： + - ** design 参数变更: + design detail 请求参数中 basic -> preview_submit 替换为design_type 可选参数 default ,merge (移除preview和submit) + design_type 参数说明: + defuault模式下 请求参数不变 + merge模式下 items -> 每个item需要新增 merge_image_path , merge_image_path为前端处理 print color等操作后的单件结果图 + + ** + + - 创建一个具有以下参数的请求体: + 示例参数： + ```json { "objects": [ { "basic": { "body_point_test": { "waistband_right": [ - 200, - 241 + 203, + 249 ], "hand_point_right": [ - 223, - 297 + 229, + 343 ], "waistband_left": [ - 112, - 241 + 119, + 248 ], "hand_point_left": [ - 92, - 305 + 97, + 343 ], "shoulder_left": [ - 99, - 116 + 108, + 107 ], "shoulder_right": [ - 215, - 116 + 212, + 107 ] }, "layer_order": true, + "design_type": "preview", "scale_bag": 0.7, "scale_earrings": 0.16, "self_template": true, @@ -67,14 +80,19 @@ def design(request_data: DesignModel, background_tasks: BackgroundTasks): }, "items": [ { - "businessId": 270372, - "color": "30 28 28", - "image_id": 69780, + "businessId": 2115382, + "color": "", + "image_id": 61686, "offset": [ 0, 0 ], - "path": "aida-sys-image/images/female/trousers/0825000630.jpg", + "path": "aida-sys-image/images/female/dress/0628000564.jpg", + "transpose": [ + 1, + 1 + ], + "rotate": 45, "print": { "element": { "element_angle_list": [], @@ -83,10 +101,30 @@ def design(request_data: DesignModel, background_tasks: BackgroundTasks): "location": [] }, "overall": { - "location": [], - "print_angle_list": [], - "print_path_list": [], - "print_scale_list": [] + "location": [ + [ + 53.0, + 118.5 + ] + ], + "print_angle_list": [ + 0.0 + ], + "print_path_list": [ + "aida-users/89/print/02d57aa8-f342-4e1d-b02c-b278f94dcfe6-3-89.png" + ], + "print_scale_list": [ + [ + 0.5, + 0.5 + ] + ], + "gap": [ + [ + 10, + 10 + ] + ] }, "single": { "location": [], @@ -100,104 +138,30 @@ def design(request_data: DesignModel, background_tasks: BackgroundTasks): 1.0, 1.0 ], - "type": "Trousers" + "seg_mask_url": "aida-clothing/mask/mask_9698b428-eb93-11f0-9327-0242c0a80003.png", + "type": "Dress" }, { - "businessId": 270373, - "color": "30 28 28", - "image_id": 98243, - "offset": [ - 0, - 0 - ], - "path": "aida-sys-image/images/female/blouse/0902003811.jpg", - "print": { - "element": { - "element_angle_list": [], - "element_path_list": [], - "element_scale_list": [], - "location": [] - }, - "overall": { - "location": [], - "print_angle_list": [], - "print_path_list": [], - "print_scale_list": [] - }, - "single": { - "location": [], - "print_angle_list": [], - "print_path_list": [], - "print_scale_list": [] - } - }, - "priority": 11, - "resize_scale": [ - 1.0, - 1.0 - ], - "type": "Blouse" - }, - { - "businessId": 270374, - "color": "172 68 68", - "image_id": 98244, - "offset": [ - 0, - 0 - ], - "path": "aida-sys-image/images/female/outwear/0825000410.jpg", - "print": { - "element": { - "element_angle_list": [], - "element_path_list": [], - "element_scale_list": [], - "location": [] - }, - "overall": { - "location": [], - "print_angle_list": [], - "print_path_list": [], - "print_scale_list": [] - }, - "single": { - "location": [], - "print_angle_list": [], - "print_path_list": [], - "print_scale_list": [] - } - }, - "priority": 12, - "resize_scale": [ - 1.0, - 1.0 - ], - "transparent":{ - "mask_url":"test/transparent_test/transparent_mask.png", - "scale":0.1 - }, - "type": "Outwear" - }, - { - "body_path": "aida-sys-image/models/female/5bdfe7ca-64eb-44e4-b03d-8e517520c795.png", - "image_id": 96090, + "body_path": "aida-sys-image/models/female/2e4815b9-1191-419d-94ed-5771239ca4a5.png", + "image_id": 67277, "type": "Body" } ] } ], - "process_id": "83" + "process_id": "89" } - """ - # logger.info(f"design request item is : @@@@@@:{json.dumps(request_data.dict())}") + ``` + """ + # logger.info(f"design request item is : @@@@@@:{json.dumps(request_data.dict(),indent=4)}") # data = generate(request_data=request_data) - # logger.info(f"design response @@@@@@:{json.dumps(data)}") + # logger.info(f"design response @@@@@@:{json.dumps(data, indent=4)}") # try: - logger.info(f"design request item is : @@@@@@:{json.dumps(request_data.dict())}") + logger.info(f"design request item is : @@@@@@:{json.dumps(request_data.dict(), indent=4)}") data = design_generate(request_data=request_data) - logger.info(f"design response @@@@@@:{json.dumps(data)}") + logger.info(f"design response @@@@@@:{json.dumps(data, indent=4)}") except Exception as e: logger.warning(f"design Run Exception @@@@@@:{e}") raise HTTPException(status_code=404, detail=str(e)) @@ -215,47 +179,48 @@ async def design_v2(request_data: DesignStreamModel, background_tasks: Backgroun "basic": { "body_point_test": { "waistband_right": [ - 200, - 241 + 203, + 249 ], "hand_point_right": [ - 223, - 297 + 229, + 343 ], "waistband_left": [ - 112, - 241 + 119, + 248 ], "hand_point_left": [ - 92, - 305 + 97, + 343 ], "shoulder_left": [ - 99, - 116 + 108, + 107 ], + "relation_type": "System", "shoulder_right": [ - 215, - 116 - ] + 212, + 107 + ], + "relation_id": 1020356 }, - "layer_order": true, + "layer_order": false, "scale_bag": 0.7, "scale_earrings": 0.16, - "self_template": true, + "self_template": false, "single_overall": "overall", "switch_category": "" }, "items": [ { - "businessId": 270372, - "color": "30 28 28", - "image_id": 69780, + "color": "209 196 171", + "image_id": 84093, "offset": [ - 0, - 0 + 1, + 1 ], - "path": "aida-sys-image/images/female/trousers/0825000630.jpg", + "path": "aida-users/89/sketchboard/female/Outwear/0943d209-7ce0-408c-bc61-83f15da94138.png", "print": { "element": { "element_angle_list": [], @@ -264,10 +229,23 @@ async def design_v2(request_data: DesignStreamModel, background_tasks: Backgroun "location": [] }, "overall": { - "location": [], - "print_angle_list": [], + "location": [ + [ + 0.0, + 0.0 + ] + ], + "print_angle_list": [ + 0.0, + 0.0 + ], "print_path_list": [], - "print_scale_list": [] + "print_scale_list": [ + [ + 0.0, + 0.0 + ] + ] }, "single": { "location": [], @@ -276,22 +254,20 @@ async def design_v2(request_data: DesignStreamModel, background_tasks: Backgroun "print_scale_list": [] } }, - "priority": 10, "resize_scale": [ 1.0, 1.0 ], - "type": "Trousers" + "type": "Outwear" }, { - "businessId": 270373, - "color": "30 28 28", - "image_id": 98243, + "color": "63 71 73", + "image_id": 100496, "offset": [ - 0, - 0 + 1, + 1 ], - "path": "aida-sys-image/images/female/blouse/0902003811.jpg", + "path": "aida-sys-image/images/female/blouse/0628001684.jpg", "print": { "element": { "element_angle_list": [], @@ -300,10 +276,23 @@ async def design_v2(request_data: DesignStreamModel, background_tasks: Backgroun "location": [] }, "overall": { - "location": [], - "print_angle_list": [], + "location": [ + [ + 0.0, + 0.0 + ] + ], + "print_angle_list": [ + 0.0, + 0.0 + ], "print_path_list": [], - "print_scale_list": [] + "print_scale_list": [ + [ + 0.0, + 0.0 + ] + ] }, "single": { "location": [], @@ -312,7 +301,6 @@ async def design_v2(request_data: DesignStreamModel, background_tasks: Backgroun "print_scale_list": [] } }, - "priority": 11, "resize_scale": [ 1.0, 1.0 @@ -320,14 +308,14 @@ async def design_v2(request_data: DesignStreamModel, background_tasks: Backgroun "type": "Blouse" }, { - "businessId": 270374, - "color": "172 68 68", - "image_id": 98244, + "color": "111 78 63", + "gradient": "aida-gradient/f69b98e8-4248-4f7a-98a2-21bac41bf3e0.png", + "image_id": 92193, "offset": [ - 0, - 0 + 1, + 1 ], - "path": "aida-sys-image/images/female/outwear/0825000410.jpg", + "path": "aida-sys-image/images/female/trousers/0825001160.jpg", "print": { "element": { "element_angle_list": [], @@ -336,10 +324,23 @@ async def design_v2(request_data: DesignStreamModel, background_tasks: Backgroun "location": [] }, "overall": { - "location": [], - "print_angle_list": [], + "location": [ + [ + 0.0, + 0.0 + ] + ], + "print_angle_list": [ + 0.0, + 0.0 + ], "print_path_list": [], - "print_scale_list": [] + "print_scale_list": [ + [ + 0.0, + 0.0 + ] + ] }, "single": { "location": [], @@ -348,31 +349,37 @@ async def design_v2(request_data: DesignStreamModel, background_tasks: Backgroun "print_scale_list": [] } }, - "priority": 12, "resize_scale": [ 1.0, 1.0 ], - "transparent":{ - "mask_url":"test/transparent_test/transparent_mask.png", - "scale":0.1 - }, - "type": "Outwear" + "type": "Trousers" }, { - "body_path": "aida-sys-image/models/female/5bdfe7ca-64eb-44e4-b03d-8e517520c795.png", - "image_id": 96090, + "body_path": "aida-sys-image/models/female/2e4815b9-1191-419d-94ed-5771239ca4a5.png", + "image_id": 67277, + "offset": [ + 1, + 1 + ], + "resize_scale": [ + 1.0, + 1.0 + ], "type": "Body" } - ] + ], + "objectSign": "65830966" } ], - "process_id": "83" + "process_id": "4802946666428422", + "requestId": "1d1e7641-0d62-4da2-adc0-b4404910723c", + "callback_url": "https://api.aida.com.hk/api/third/party/receiveDesignResults" } """ try: # 异步 - logger.info(f"generate_image request item is : @@@@@@:{json.dumps(request_data.dict())}") + logger.info(f"generate_image request item is : @@@@@@:{json.dumps(request_data.dict(), indent=4)}") background_tasks.add_task(design_generate_v2, request_data) except Exception as e: logger.warning(f"design Run Exception @@@@@@:{e}") @@ -380,30 +387,76 @@ async def design_v2(request_data: DesignStreamModel, background_tasks: Backgroun return ResponseModel() -@router.post('/get_progress') -def get_progress(request_data: DesignProgressModel): +@router.post("/seg_anything") +async def seg_anything(request_data: SAMRequestModel): """ - 获取design 进度 - 创建一个具有以下参数的请求体: - - **process_id**: 进度id + **Segment Anything 交互式分割接口** - 示例参数： - { - "process_id": "6878547032381675" - } + 通过传入图片路径和点击的点坐标，返回分割后的掩码数据。 + + ### 参数说明: + - **user_id**:用户id 用于存储分割图 + - **image_path**: 图片在服务器或云端的相对路径。 + - **type**: 推理类型 + - **box**: 框选矩形点位信息 + - **points**: 交互点的坐标列表。每个点为 [x, y] 像素格式。 + - **labels**: 坐标点的属性标签，必须与 points 长度一致： + - 1: **前景点** (代表想要分割出的区域) + - 0: **背景点** (代表想要排除的区域) + + ### 请求体示例: + ```json + point + { + "user_id": 1, + "image_path": "aida-users/89/sketch/4e8fe37d-7068-400a-ac94-c01647fa5f6f.png", + "type":"point", + "points": [[310, 403], [493, 375], [261, 266], [404, 484]], + "labels": [1, 1, 0, 1] + } + + box + { + "user_id": 1, + "image_path": "aida-users/89/sketch/4e8fe37d-7068-400a-ac94-c01647fa5f6f.png", + "type":"box", + "box": [350, 286, 544, 520] + } + ``` """ try: - logger.info(f"get_progress request item is : @@@@@@:{json.dumps(request_data.dict())}") - process_id = request_data.process_id - r = Redis() - data = r.read(key=process_id) - if data is None: - raise ValueError(f"No progress ID: {process_id}") - logging.info(f"get_progress process_id @@@@@@ : {process_id} , progress : {json.dumps(data)}") + logger.info(f"seg_anything request item is : @@@@@@:{json.dumps(request_data.dict(), indent=4)}") + data = requests.post(f"http://{settings.A6000_SERVICE_HOST}:10075/predict", json=request_data.dict()) + logger.info(f"seg_anything response @@@@@@:{json.dumps(json.loads(data.content), indent=4)}") + return ResponseModel(data=json.loads(data.content)) except Exception as e: - logger.warning(f"get_progress Run Exception @@@@@@:{e}") - raise HTTPException(status_code=404, detail=str(e)) - return ResponseModel(data=data) + logger.warning(f"seg_anything Run Exception @@@@@@:{e}") + + +# @router.post('/get_progress') +# def get_progress(request_data: DesignProgressModel): +# """ +# 获取design 进度 +# 创建一个具有以下参数的请求体: +# - **process_id**: 进度id +# +# 示例参数： +# { +# "process_id": "6878547032381675" +# } +# """ +# try: +# logger.info(f"get_progress request item is : @@@@@@:{json.dumps(request_data.dict(), indent=4)}") +# process_id = request_data.process_id +# r = Redis() +# data = r.read(key=process_id) +# if data is None: +# raise ValueError(f"No progress ID: {process_id}") +# logging.info(f"get_progress process_id @@@@@@ : {process_id} , progress : {json.dumps(data, indent=4)}") +# except Exception as e: +# logger.warning(f"get_progress Run Exception @@@@@@:{e}") +# raise HTTPException(status_code=404, detail=str(e)) +# return ResponseModel(data=data) @router.post('/model_process') @@ -419,44 +472,42 @@ def model_process(request_data: ModelProgressModel): } """ try: - logger.info(f"model_process request item is : @@@@@@:{json.dumps(request_data.dict())}") + logger.info(f"model_process request item is : @@@@@@:{json.dumps(request_data.dict(), indent=4)}") data = model_transpose(image_path=request_data.model_path) - logger.info(f"model_process response @@@@@@:{json.dumps(data)}") + logger.info(f"model_process response @@@@@@:{json.dumps(data, indent=4)}") except Exception as e: logger.warning(f"model_process Run Exception @@@@@@:{e}") raise HTTPException(status_code=404, detail=str(e)) return ResponseModel(data=data) -# ############################################################## - - -@router.post("/design_batch_generate") -async def design_batch(file: UploadFile = File(...), - tasks_id: str = Form(...), - user_id: str = Form(...), - file_name: str = Form(...), - total: int = Form(...) - ): - dbg_config = DBGConfigModel( - tasks_id=tasks_id, - user_id=user_id, - file_name=file_name, - total=total - ) - contents = await file.read() - file_name = file.filename - await save_request_file(contents, file_name) - return await start_design_batch_generate(dbg_config, contents) - - -async def save_request_file(contents, file_name): - # 创建保存文件的目录（如果不存在） - save_dir = os.path.join(os.getcwd(), "service/design_batch", "request_data") - if not os.path.exists(save_dir): - os.makedirs(save_dir) - # 处理文件 - file_path = os.path.join(save_dir, file_name) - with open(file_path, "wb") as f: - f.write(contents) +"""design 批量处理 停用""" +# @router.post("/design_batch_generate") +# async def design_batch(file: UploadFile = File(...), +# tasks_id: str = Form(...), +# user_id: str = Form(...), +# file_name: str = Form(...), +# total: int = Form(...) +# ): +# dbg_config = DBGConfigModel( +# tasks_id=tasks_id, +# user_id=user_id, +# file_name=file_name, +# total=total +# ) +# contents = await file.read() +# file_name = file.filename +# await save_request_file(contents, file_name) +# return await start_design_batch_generate(dbg_config, contents) +# +# +# async def save_request_file(contents, file_name): +# # 创建保存文件的目录（如果不存在） +# save_dir = os.path.join(os.getcwd(), "service/design_batch", "request_data") +# if not os.path.exists(save_dir): +# os.makedirs(save_dir) +# # 处理文件 +# file_path = os.path.join(save_dir, file_name) +# with open(file_path, "wb") as f: +# f.write(contents) diff --git a/app/api/api_design_pre_processing.py b/app/api/api_design_pre_processing.py index f260e22..46f9e5e 100644 --- a/app/api/api_design_pre_processing.py +++ b/app/api/api_design_pre_processing.py @@ -30,10 +30,10 @@ def design_pre_processing(request_data: DesignPreProcessingModel): } """ try: - logger.info(f"design_pre_processing request item is : @@@@@@:{json.dumps(request_data.dict())}") + logger.info(f"design_pre_processing request item is : @@@@@@:{json.dumps(request_data.dict(), indent=4)}") server = DesignPreprocessing() data = server.pipeline(image_list=request_data.sketches) - logger.info(f"design response @@@@@@:{json.dumps(data)}") + logger.info(f"design response @@@@@@:{json.dumps(data, indent=4)}") except Exception as e: logger.warning(f"design Run Exception @@@@@@:{e}") raise HTTPException(status_code=404, detail=str(e)) diff --git a/app/api/api_generate_image.py b/app/api/api_generate_image.py index 5bd5404..14271c6 100644 --- a/app/api/api_generate_image.py +++ b/app/api/api_generate_image.py @@ -33,18 +33,30 @@ def generate_image(request_item: GenerateImageModel, background_tasks: Backgroun - **version**: 使用模型版本 fast 或者 high 示例参数： + 1. txt 2 img { - "tasks_id": "123-89", - "prompt": "skeleton sitting by the side of a river looking soulful, concert poster, 4k, artistic", - "image_url": "aida-collection-element/87/Printboard/842c09cf-7297-42d9-9e6e-9c17d4a13cb5.jpg", - "mode": "img2img", - "category": "sketch", - "gender": "male", - "version": "fast" + "tasks_id": "bd2cf809-24bc-49a6-91c9-193c6272a52e-2-89", + "prompt": "a single item of sketch of dress, 4k, white background", + "image_url": "", + "mode": "txt2img", + "category": "sketch", + "gender": "Female", + "version": "fast" } + 2. img 2 img + { + "tasks_id": "b861d4fa-5ae3-4a30-9c7a-7ba6bb9aa37b-1-89", + "prompt": "a single item of sketch of dress, 4k, white background", + "image_url": "aida-collection-element/89/Sketchboard/548da3a2-834f-49a7-b52c-e729c5ab5062.png", + "mode": "img2img", + "category": "sketch", + "gender": "Female", + "version": "fast" + } + """ try: - logger.info(f"generate_image request item is : @@@@@@:{json.dumps(request_item.dict())}") + logger.info(f"generate_image request item is : @@@@@@:{json.dumps(request_item.dict(), indent=4)}") service = GenerateImage(request_item) background_tasks.add_task(service.get_result) except Exception as e: @@ -65,42 +77,41 @@ def generate_image(tasks_id: str): return ResponseModel(data=data['data']) -'''multi view''' +'''multi view 停用''' + +# @router.post("/generate_multi_view") +# def generate_multi_view(request_item: GenerateMultiViewModel, background_tasks: BackgroundTasks): +# """ +# 创建一个具有以下参数的请求体: +# - **tasks_id**: 任务id 用于取消生成任务和获取生成结果 +# - **image_url**: 前视角图的输入，minio或S3 url 地址 +# +# 示例参数： +# { +# "tasks_id": "123-89", +# "image_url": "aida-collection-element/87/Printboard/842c09cf-7297-42d9-9e6e-9c17d4a13cb5.jpg" +# } +# """ +# try: +# logger.info(f"generate_multi_view request item is : @@@@@@:{json.dumps(request_item.dict(),indent=4)}") +# service = GenerateMultiView(request_item) +# background_tasks.add_task(service.get_result) +# except Exception as e: +# logger.warning(f"generate_multi_view Run Exception @@@@@@:{e}") +# raise HTTPException(status_code=404, detail=str(e)) +# return ResponseModel() -@router.post("/generate_multi_view") -def generate_multi_view(request_item: GenerateMultiViewModel, background_tasks: BackgroundTasks): - """ - 创建一个具有以下参数的请求体: - - **tasks_id**: 任务id 用于取消生成任务和获取生成结果 - - **image_url**: 前视角图的输入，minio或S3 url 地址 - - 示例参数： - { - "tasks_id": "123-89", - "image_url": "aida-collection-element/87/Printboard/842c09cf-7297-42d9-9e6e-9c17d4a13cb5.jpg" - } - """ - try: - logger.info(f"generate_multi_view request item is : @@@@@@:{json.dumps(request_item.dict())}") - service = GenerateMultiView(request_item) - background_tasks.add_task(service.get_result) - except Exception as e: - logger.warning(f"generate_multi_view Run Exception @@@@@@:{e}") - raise HTTPException(status_code=404, detail=str(e)) - return ResponseModel() - - -@router.get("/generate_multi_view_cancel/{tasks_id}") -def generate_multi_view(tasks_id: str): - try: - logger.info(f"generate_cancel request item is : @@@@@@:{tasks_id}") - data = generate_multi_view_cancel(tasks_id) - logger.info(f"generate_cancel response @@@@@@:{data}") - except Exception as e: - logger.warning(f"generate_cancel Run Exception @@@@@@:{e}") - raise HTTPException(status_code=404, detail=str(e)) - return ResponseModel(data=data['data']) +# @router.get("/generate_multi_view_cancel/{tasks_id}") +# def generate_multi_view(tasks_id: str): +# try: +# logger.info(f"generate_cancel request item is : @@@@@@:{tasks_id}") +# data = generate_multi_view_cancel(tasks_id) +# logger.info(f"generate_cancel response @@@@@@:{data}") +# except Exception as e: +# logger.warning(f"generate_cancel Run Exception @@@@@@:{e}") +# raise HTTPException(status_code=404, detail=str(e)) +# return ResponseModel(data=data['data']) '''single logo''' @@ -122,7 +133,7 @@ def generate_single_logo(request_item: GenerateSingleLogoImageModel, background_ } """ try: - logger.info(f"generate_single_logo request item is : @@@@@@:{json.dumps(request_item.dict())}") + logger.info(f"generate_single_logo request item is : @@@@@@:{json.dumps(request_item.dict(), indent=4)}") service = GenerateSingleLogoImage(request_item) background_tasks.add_task(service.get_result) except Exception as e: @@ -167,7 +178,7 @@ def generate_product_image(request_item: GenerateProductImageModel, background_t } """ try: - logger.info(f"generate_product_image request item is : @@@@@@:{json.dumps(request_item.dict())}") + logger.info(f"generate_product_image request item is : @@@@@@:{json.dumps(request_item.dict(),indent=4)}") service = GenerateProductImage(request_item) background_tasks.add_task(service.get_result) except Exception as e: @@ -188,166 +199,164 @@ def generate_product_image(tasks_id: str): return ResponseModel(data=data['data']) -'''relight image''' +'''relight image 停用''' + +# @router.post("/generate_relight_image") +# def generate_relight_image(request_item: GenerateRelightImageModel, background_tasks: BackgroundTasks): +# """ +# 创建一个具有以下参数的请求体: +# - **tasks_id**: 任务id 用于取消生成任务和获取生成结果 +# - **prompt**: 想要生成图片的描述词 +# - **image_url**: 被生成图片的S3或minio url地址 +# - **direction**: 光源方向 Right Light Left Light Top Light Bottom Light +# - **product_type**: 输入single item 还是 overall item +# +# +# 示例参数： +# { +# "tasks_id": "123-89", +# "prompt": "beautiful woman, detailed face, sunshine, outdoor, warm atmosphere", +# "image_url": "aida-results/result_0000b606-1902-11ef-9424-0242ac180002.png", +# "direction": "Right Light", +# "product_type": "overall" +# } +# """ +# try: +# logger.info(f"generate_relight_image request item is : @@@@@@:{json.dumps(request_item.dict(),indent=4)}") +# service = GenerateRelightImage(request_item) +# background_tasks.add_task(service.get_result) +# except Exception as e: +# logger.warning(f"generate_relight_image Run Exception @@@@@@:{e}") +# raise HTTPException(status_code=404, detail=str(e)) +# return ResponseModel() +# +# +# @router.get("/generate_relight_image_cancel_cancel/{tasks_id}") +# def generate_relight_image(tasks_id: str): +# try: +# logger.info(f"generate_relight_image_cancel_cancel request item is : @@@@@@:{tasks_id}") +# data = generate_relight_image_cancel(tasks_id) +# logger.info(f"generate_relight_image_cancel_cancel response @@@@@@:{data}") +# except Exception as e: +# logger.warning(f"generate_relight_image_cancel_cancel Run Exception @@@@@@:{e}") +# raise HTTPException(status_code=404, detail=str(e)) +# return ResponseModel(data=data['data']) -@router.post("/generate_relight_image") -def generate_relight_image(request_item: GenerateRelightImageModel, background_tasks: BackgroundTasks): - """ - 创建一个具有以下参数的请求体: - - **tasks_id**: 任务id 用于取消生成任务和获取生成结果 - - **prompt**: 想要生成图片的描述词 - - **image_url**: 被生成图片的S3或minio url地址 - - **direction**: 光源方向 Right Light Left Light Top Light Bottom Light - - **product_type**: 输入single item 还是 overall item +"""batch generate img 停用""" + +# @router.post("/batch_generate_product_image") +# async def batch_generate_product(request_batch_item: BatchGenerateProductImageModel): +# """ +# 创建一个具有以下参数的请求体: +# - **tasks_id**: 任务id 用于获取生成结果 +# - **prompt**: 想要生成图片的描述词 +# - **image_url**: 被生成图片的S3或minio url地址 +# - **image_strength**: 生成强度，越低越接近原图 +# - **product_type**: 输入single item 还是 overall item +# - **batch_size**: 批生成数量 +# +# +# 示例参数： +# { +# "tasks_id": "123-89", +# "prompt": "the best quality, masterpiece. detailed, high-res, simple background, studio photography, extremely detailed, updo, detailed face, face, close-up, HDR, UHD, 8K realistic, Highly detailed, simple background, Studio lighting", +# "image_url": "aida-results/result_00097282-ebb2-11ee-a822-b48351119060.png", +# "image_strength": 0.8, +# "product_type": "overall", +# "batch_size": 1 +# } +# """ +# return await start_product_batch_generate(request_batch_item) +# +# +# @router.post("/batch_generate_relight_image") +# async def batch_generate_relight(request_batch_item: BatchGenerateRelightImageModel): +# """ +# 创建一个具有以下参数的请求体: +# - **tasks_id**: 任务id 用于获取生成结果 +# - **prompt**: 想要生成图片的描述词 +# - **image_url**: 被生成图片的S3或minio url地址 +# - **direction**: 光源方向 Right Light Left Light Top Light Bottom Light +# - **product_type**: 输入single item 还是 overall item +# - **batch_size**: 批生成数量 +# +# +# 示例参数： +# { +# "tasks_id": "123-89", +# "prompt": "beautiful woman, detailed face, sunshine, outdoor, warm atmosphere", +# "image_url": "aida-results/result_0000b606-1902-11ef-9424-0242ac180002.png", +# "direction": "Right Light", +# "product_type": "overall", +# "batch_size": 1 +# } +# """ +# return await start_relight_batch_generate(request_batch_item) - 示例参数： - { - "tasks_id": "123-89", - "prompt": "beautiful woman, detailed face, sunshine, outdoor, warm atmosphere", - "image_url": "aida-results/result_0000b606-1902-11ef-9424-0242ac180002.png", - "direction": "Right Light", - "product_type": "overall" - } - """ - try: - logger.info(f"generate_relight_image request item is : @@@@@@:{json.dumps(request_item.dict())}") - service = GenerateRelightImage(request_item) - background_tasks.add_task(service.get_result) - except Exception as e: - logger.warning(f"generate_relight_image Run Exception @@@@@@:{e}") - raise HTTPException(status_code=404, detail=str(e)) - return ResponseModel() - - -@router.get("/generate_relight_image_cancel_cancel/{tasks_id}") -def generate_relight_image(tasks_id: str): - try: - logger.info(f"generate_relight_image_cancel_cancel request item is : @@@@@@:{tasks_id}") - data = generate_relight_image_cancel(tasks_id) - logger.info(f"generate_relight_image_cancel_cancel response @@@@@@:{data}") - except Exception as e: - logger.warning(f"generate_relight_image_cancel_cancel Run Exception @@@@@@:{e}") - raise HTTPException(status_code=404, detail=str(e)) - return ResponseModel(data=data['data']) - - -"""batch generate img""" - - -@router.post("/batch_generate_product_image") -async def batch_generate_product(request_batch_item: BatchGenerateProductImageModel): - """ - 创建一个具有以下参数的请求体: - - **tasks_id**: 任务id 用于获取生成结果 - - **prompt**: 想要生成图片的描述词 - - **image_url**: 被生成图片的S3或minio url地址 - - **image_strength**: 生成强度，越低越接近原图 - - **product_type**: 输入single item 还是 overall item - - **batch_size**: 批生成数量 - - - 示例参数： - { - "tasks_id": "123-89", - "prompt": "the best quality, masterpiece. detailed, high-res, simple background, studio photography, extremely detailed, updo, detailed face, face, close-up, HDR, UHD, 8K realistic, Highly detailed, simple background, Studio lighting", - "image_url": "aida-results/result_00097282-ebb2-11ee-a822-b48351119060.png", - "image_strength": 0.8, - "product_type": "overall", - "batch_size": 1 - } - """ - return await start_product_batch_generate(request_batch_item) - - -@router.post("/batch_generate_relight_image") -async def batch_generate_relight(request_batch_item: BatchGenerateRelightImageModel): - """ - 创建一个具有以下参数的请求体: - - **tasks_id**: 任务id 用于获取生成结果 - - **prompt**: 想要生成图片的描述词 - - **image_url**: 被生成图片的S3或minio url地址 - - **direction**: 光源方向 Right Light Left Light Top Light Bottom Light - - **product_type**: 输入single item 还是 overall item - - **batch_size**: 批生成数量 - - - 示例参数： - { - "tasks_id": "123-89", - "prompt": "beautiful woman, detailed face, sunshine, outdoor, warm atmosphere", - "image_url": "aida-results/result_0000b606-1902-11ef-9424-0242ac180002.png", - "direction": "Right Light", - "product_type": "overall", - "batch_size": 1 - } - """ - return await start_relight_batch_generate(request_batch_item) - - -@router.post("/batch_generate_pose_transform_image") -async def batch_generate_pose_transform(request_batch_item: BatchPoseTransformModel): - """ - 创建一个具有以下参数的请求体: - - **tasks_id**: 任务id 用于取消生成任务和获取生成结果 - - **image_url**: 被生成图片的S3或minio url地址 - - **pose_id**: 1 - - **batch_size**: 批生成数量 - - - 示例参数： - { - "tasks_id": "123-89", - "image_url": "aida-results/result_0000b606-1902-11ef-9424-0242ac180002.png", - "pose_id": "1", - "batch_size": 1 - } - """ - return await start_pose_transform_batch_generate(request_batch_item) - - -"""agent tool""" - - -@router.post("/agent_tool_generate_image") -def agent_tool_generate_image(request_item: AgentTollGenerateImageModel, background_tasks: BackgroundTasks): - """ - 创建一个具有以下参数的请求体: - - **prompt**: 想要生成图片的描述词 - - **category**: 生成图片的类别，sketch print 等等 - - **gender**: 生成sketch专用，服装类别 - - **version**: 使用模型版本 fast 或者 high - - **size**: 生成数量 - - **version**: 使用模型版本 fast 或者 high - - - 示例参数： - { - "prompt": "a single item of sketch of Wabi-sabi, skirt, tiered, 4k, white background", - "category": "sketch", - "gender": "male", - "size":2, - "version":"high" - } - """ - try: - logger.info(f"agent_tool_generate_image request item is : @@@@@@:{request_item.dict()}") - request_data = request_item.dict() - service = AgentToolGenerateImage(request_data['version']) - image_url_list, clothing_category_list = service.get_result( - prompt=request_data['prompt'], - size=request_data['size'], - version=request_data['version'], - category=request_data['category'], - gender=request_data['gender'] - ) - data = { - "image_url_list": image_url_list, - "clothing_category_list": clothing_category_list - } - logger.info(f"agent_tool_generate_image response item is : @@@@@@:{data}") - except Exception as e: - logger.warning(f"agent_tool_generate_image Run Exception @@@@@@:{e}") - raise HTTPException(status_code=404, detail=str(e)) - return ResponseModel(data=data) +# @router.post("/batch_generate_pose_transform_image") +# async def batch_generate_pose_transform(request_batch_item: BatchPoseTransformModel): +# """ +# 创建一个具有以下参数的请求体: +# - **tasks_id**: 任务id 用于取消生成任务和获取生成结果 +# - **image_url**: 被生成图片的S3或minio url地址 +# - **pose_id**: 1 +# - **batch_size**: 批生成数量 +# +# +# 示例参数： +# { +# "tasks_id": "123-89", +# "image_url": "aida-results/result_0000b606-1902-11ef-9424-0242ac180002.png", +# "pose_id": "1", +# "batch_size": 1 +# } +# """ +# return await start_pose_transform_batch_generate(request_batch_item) +# +# +# """agent tool""" +# +# +# @router.post("/agent_tool_generate_image") +# def agent_tool_generate_image(request_item: AgentTollGenerateImageModel): +# """ +# 创建一个具有以下参数的请求体: +# - **prompt**: 想要生成图片的描述词 +# - **category**: 生成图片的类别，sketch print 等等 +# - **gender**: 生成sketch专用，服装类别 +# - **version**: 使用模型版本 fast 或者 high +# - **size**: 生成数量 +# - **version**: 使用模型版本 fast 或者 high +# +# +# 示例参数： +# { +# "prompt": "a single item of sketch of Wabi-sabi, skirt, tiered, 4k, white background", +# "category": "sketch", +# "gender": "male", +# "size":2, +# "version":"high" +# } +# """ +# try: +# logger.info(f"agent_tool_generate_image request item is : @@@@@@:{request_item.dict()}") +# request_data = request_item.dict() +# service = AgentToolGenerateImage(request_data['version']) +# image_url_list, clothing_category_list = service.get_result( +# prompt=request_data['prompt'], +# size=request_data['size'], +# version=request_data['version'], +# category=request_data['category'], +# gender=request_data['gender'] +# ) +# data = { +# "image_url_list": image_url_list, +# "clothing_category_list": clothing_category_list +# } +# logger.info(f"agent_tool_generate_image response item is : @@@@@@:{data}") +# except Exception as e: +# logger.warning(f"agent_tool_generate_image Run Exception @@@@@@:{e}") +# raise HTTPException(status_code=404, detail=str(e)) +# return ResponseModel(data=data) diff --git a/app/api/api_image2sketch.py b/app/api/api_image2sketch.py index cac7652..085fc72 100644 --- a/app/api/api_image2sketch.py +++ b/app/api/api_image2sketch.py @@ -14,22 +14,22 @@ logger = logging.getLogger() @router.post("/image2sketch") def image2sketch(request_item: Image2SketchModel): """ - 创建一个具有以下参数的请求体: - - **image_url**: 提取图片url - - **default_style**: 原始、 1、2、3、4、5 - - **sketch_bucket**: sketch保存的bucket - - **sketch_name**: sketch保存的object name + 创建一个具有以下参数的请求体: + - **image_url**: 提取图片url + - **default_style**: 原始、 1、2、3、4、5 + - **sketch_bucket**: sketch保存的bucket + - **sketch_name**: sketch保存的object name - 示例参数： - { - "image_url": "test/image2sketch/real_Dress_3200fecdc83d0c556c2bd96aedbd7fbf.jpg_Img.jpg", - "default_style": 0, - "sketch_bucket": "test", - "sketch_name": "image2sketch/area_fill_img.png" - } - """ + 示例参数： + { + "image_url": "test/image2sketch/real_Dress_3200fecdc83d0c556c2bd96aedbd7fbf.jpg_Img.jpg", + "default_style": 0, + "sketch_bucket": "test", + "sketch_name": "image2sketch/area_fill_img.png" + } + """ try: - logger.info(f"image2sketch request item is : @@@@@@:{json.dumps(request_item.dict())}") + logger.info(f"image2sketch request item is : @@@@@@:{json.dumps(request_item.dict(),indent=4)}") service = LineArtService(request_item) result_url = service.get_result() except Exception as e: diff --git a/app/api/api_import_sys_sketch.py b/app/api/api_import_sys_sketch.py new file mode 100644 index 0000000..3654124 --- /dev/null +++ b/app/api/api_import_sys_sketch.py @@ -0,0 +1,116 @@ +import logging +import sys +from typing import Optional +from fastapi import APIRouter, HTTPException, Query +from concurrent.futures import ThreadPoolExecutor +import threading + +from app.schemas.response_template import ResponseModel +from app.service.recommendation_system.import_sys_sketch_to_milvus import main as import_main + +logger = logging.getLogger() +router = APIRouter() + +# 使用线程池执行器来运行长时间任务 +executor = ThreadPoolExecutor(max_workers=1) +# 用于跟踪任务状态 +task_status = {"running": False} + + +def run_import_task(batch_size: int, retry_times: int, limit: Optional[int], offset: int, skip_create_collection: bool): + """在后台线程中运行导入任务""" + original_argv = None + try: + task_status["running"] = True + # 保存原始 sys.argv + original_argv = sys.argv.copy() + + # 模拟命令行参数 + sys.argv = [ + "import_sys_sketch_to_milvus.py", + "--batch-size", str(batch_size), + "--retry-times", str(retry_times), + ] + if limit is not None: + sys.argv.extend(["--limit", str(limit)]) + if offset > 0: + sys.argv.extend(["--offset", str(offset)]) + if skip_create_collection: + sys.argv.append("--skip-create-collection") + + import_main() + task_status["running"] = False + logger.info("导入任务完成") + except Exception as e: + task_status["running"] = False + logger.error(f"导入任务失败: {e}", exc_info=True) + raise + finally: + # 恢复原始 sys.argv + if original_argv is not None: + sys.argv = original_argv + + +@router.post("/import-sys-sketch", response_model=ResponseModel) +async def import_sys_sketch( + batch_size: int = Query(1000, description="批量处理大小（默认：1000）"), + retry_times: int = Query(3, description="失败重试次数（默认：3）"), + limit: Optional[int] = Query(None, description="限制处理数量（用于测试，默认：不限制）"), + offset: int = Query(0, description="起始偏移量（默认：0）"), + skip_create_collection: bool = Query(False, description="跳过创建集合（如果集合已存在）"), +): + """ + 从 t_sys_file 导入系统图向量到 Milvus + + 该接口会异步执行导入任务，任务在后台运行。 + """ + try: + # 检查是否有任务正在运行 + if task_status["running"]: + raise HTTPException( + status_code=409, + detail="已有导入任务正在运行，请等待完成后再试" + ) + + # 在后台线程中执行任务 + executor.submit( + run_import_task, + batch_size, + retry_times, + limit, + offset, + skip_create_collection + ) + + return ResponseModel( + code=200, + msg="导入任务已启动，正在后台执行", + data={ + "status": "started", + "batch_size": batch_size, + "retry_times": retry_times, + "limit": limit, + "offset": offset, + "skip_create_collection": skip_create_collection + } + ) + except HTTPException: + raise + except Exception as e: + logger.error(f"启动导入任务失败: {e}", exc_info=True) + raise HTTPException(status_code=500, detail=f"启动导入任务失败: {str(e)}") + + +@router.get("/import-sys-sketch/status", response_model=ResponseModel) +async def get_import_status(): + """ + 获取导入任务状态 + """ + return ResponseModel( + code=200, + msg="OK", + data={ + "running": task_status["running"] + } + ) + diff --git a/app/api/api_mannequins_edit.py b/app/api/api_mannequins_edit.py index 6ff34d4..7a5a56f 100644 --- a/app/api/api_mannequins_edit.py +++ b/app/api/api_mannequins_edit.py @@ -35,10 +35,10 @@ def mannequins_edit(request_data: MannequinModel): }** """ try: - logger.info(f"mannequins_edit request item is : @@@@@@:{json.dumps(request_data.dict())}") + logger.info(f"mannequins_edit request item is : @@@@@@:{json.dumps(request_data.dict(),indent=4)}") service = MannequinEditService(request_data) data = service() - logger.info(f"mannequins_edit response @@@@@@:{json.dumps(data)}") + logger.info(f"mannequins_edit response @@@@@@:{json.dumps(data, indent=4)}") except Exception as e: logger.warning(f"mannequins_edit Run Exception @@@@@@:{e}") raise HTTPException(status_code=404, detail=str(e)) diff --git a/app/api/api_pose_transform.py b/app/api/api_pose_transform.py index 534e296..b915ad7 100644 --- a/app/api/api_pose_transform.py +++ b/app/api/api_pose_transform.py @@ -4,55 +4,55 @@ import logging import requests from fastapi import APIRouter, BackgroundTasks, HTTPException -from app.core.config import COMFYUI_SERVER_ADDRESS +from app.core.config import settings from app.schemas.comfyui_i2v import ComfyuiI2VModel, ComfyuiFLF2VModel from app.schemas.pose_transform import PoseTransformModel from app.schemas.response_template import ResponseModel from app.service.comfyui_I2V.flf2v_server import ComfyUIServerFLF2V from app.service.comfyui_I2V.i2v_server import ComfyUIServerI2V from app.service.comfyui_I2V.pose2v_server import ComfyUIServerPose2V -from app.service.generate_image.service_pose_transform import PoseTransformService, infer_cancel as pose_transform_infer_cancel router = APIRouter() logger = logging.getLogger() +"""停用""" -@router.post("/pose_transform") -def pose_transform(request_item: PoseTransformModel, background_tasks: BackgroundTasks): - """ - 创建一个具有以下参数的请求体: - - **tasks_id**: 任务id 用于取消生成任务和获取生成结果 - - **image_url**: 被生成图片的S3或minio url地址 - - **pose_id**: 1 +# @router.post("/pose_transform") +# def pose_transform(request_item: PoseTransformModel, background_tasks: BackgroundTasks): +# """ +# 创建一个具有以下参数的请求体: +# - **tasks_id**: 任务id 用于取消生成任务和获取生成结果 +# - **image_url**: 被生成图片的S3或minio url地址 +# - **pose_id**: 1 +# +# +# 示例参数： +# { +# "tasks_id": "123-89", +# "image_url": "aida-results/result_0000b606-1902-11ef-9424-0242ac180002.png", +# "pose_id": "1" +# } +# """ +# try: +# logger.info(f"pose_transform request item is : @@@@@@:{json.dumps(request_item.dict(),indent=4)}") +# service = PoseTransformService(request_item) +# background_tasks.add_task(service.get_result) +# except Exception as e: +# logger.warning(f"pose_transform Run Exception @@@@@@:{e}") +# raise HTTPException(status_code=404, detail=str(e)) +# return ResponseModel() - 示例参数： - { - "tasks_id": "123-89", - "image_url": "aida-results/result_0000b606-1902-11ef-9424-0242ac180002.png", - "pose_id": "1" - } - """ - try: - logger.info(f"pose_transform request item is : @@@@@@:{json.dumps(request_item.dict())}") - service = PoseTransformService(request_item) - background_tasks.add_task(service.get_result) - except Exception as e: - logger.warning(f"pose_transform Run Exception @@@@@@:{e}") - raise HTTPException(status_code=404, detail=str(e)) - return ResponseModel() - - -@router.get("/pose_transform_cancel/{tasks_id}") -def pose_transform_cancel(tasks_id: str): - try: - logger.info(f"pose_transform_cancel request item is : @@@@@@:{tasks_id}") - data = pose_transform_infer_cancel(tasks_id) - logger.info(f"pose_transform_cancel response @@@@@@:{data}") - except Exception as e: - logger.warning(f"pose_transform_cancel Run Exception @@@@@@:{e}") - raise HTTPException(status_code=404, detail=str(e)) - return ResponseModel(data=data['data']) +# @router.get("/pose_transform_cancel/{tasks_id}") +# def pose_transform_cancel(tasks_id: str): +# try: +# logger.info(f"pose_transform_cancel request item is : @@@@@@:{tasks_id}") +# data = pose_transform_infer_cancel(tasks_id) +# logger.info(f"pose_transform_cancel response @@@@@@:{data}") +# except Exception as e: +# logger.warning(f"pose_transform_cancel Run Exception @@@@@@:{e}") +# raise HTTPException(status_code=404, detail=str(e)) +# return ResponseModel(data=data['data']) """ @@ -77,7 +77,7 @@ def comfyui_image_pose_2_video(request_item: PoseTransformModel, background_task } """ try: - logger.info(f"image_pose_2_video request item is : @@@@@@:{json.dumps(request_item.dict())}") + logger.info(f"image_pose_2_video request item is : @@@@@@:{json.dumps(request_item.dict(),indent=4)}") service = ComfyUIServerPose2V(request_item) background_tasks.add_task(service.get_result) except Exception as e: @@ -107,7 +107,7 @@ def comfyui_image_2_video(request_item: ComfyuiI2VModel, background_tasks: Backg } """ try: - logger.info(f"image_2_video request item is : @@@@@@:{json.dumps(request_item.dict())}") + logger.info(f"image_2_video request item is : @@@@@@:{json.dumps(request_item.dict(),indent=4)}") service = ComfyUIServerI2V(request_item) background_tasks.add_task(service.get_result) except Exception as e: @@ -139,7 +139,7 @@ def comfyui_flf_2_video(request_item: ComfyuiFLF2VModel, background_tasks: Backg } """ try: - logger.info(f"flf_2_video request item is : @@@@@@:{json.dumps(request_item.dict())}") + logger.info(f"flf_2_video request item is : @@@@@@:{json.dumps(request_item.dict(),indent=4)}") service = ComfyUIServerFLF2V(request_item) background_tasks.add_task(service.get_result) except Exception as e: @@ -153,7 +153,7 @@ def comfyui_i_2_video_cancel(tasks_id: str): try: logger.info(f"comfyui_i_2_video_cancel request item is : @@@@@@:{tasks_id}") response = requests.post( - f"http://{COMFYUI_SERVER_ADDRESS}/interrupt", + f"http://{settings.COMFYUI_SERVER_ADDRESS}/interrupt", json={"prompt_id": tasks_id} ) data = {} diff --git a/app/api/api_precompute.py b/app/api/api_precompute.py new file mode 100644 index 0000000..afebac7 --- /dev/null +++ b/app/api/api_precompute.py @@ -0,0 +1,85 @@ +import logging +from fastapi import APIRouter, HTTPException +from concurrent.futures import ThreadPoolExecutor + +from app.schemas.response_template import ResponseModel +from app.service.recommendation_system.precompute import run_precompute + +logger = logging.getLogger() +router = APIRouter() + +# 使用线程池执行器来运行长时间任务 +executor = ThreadPoolExecutor(max_workers=1) +# 用于跟踪任务状态 +task_status = {"running": False} + + +def run_precompute_task(): + """在后台线程中运行预计算任务""" + try: + task_status["running"] = True + logger.info("开始执行预计算任务...") + run_precompute() + task_status["running"] = False + logger.info("预计算任务完成") + except Exception as e: + task_status["running"] = False + logger.error(f"预计算任务失败: {e}", exc_info=True) + raise + + +@router.post("/precompute", response_model=ResponseModel) +async def precompute(): + """ + 运行预计算任务 + + 该接口会异步执行预计算任务，包括： + 1. 优化数据库表结构 + 2. 历史数据迁移 + 3. 初始用户偏好向量生成 + + 任务在后台运行。 + """ + try: + # 检查是否有任务正在运行 + if task_status["running"]: + raise HTTPException( + status_code=409, + detail="已有预计算任务正在运行，请等待完成后再试" + ) + + # 在后台线程中执行任务 + executor.submit(run_precompute_task) + + return ResponseModel( + code=200, + msg="预计算任务已启动，正在后台执行", + data={ + "status": "started", + "tasks": [ + "优化数据库表结构", + "历史数据迁移", + "初始用户偏好向量生成" + ] + } + ) + except HTTPException: + raise + except Exception as e: + logger.error(f"启动预计算任务失败: {e}", exc_info=True) + raise HTTPException(status_code=500, detail=f"启动预计算任务失败: {str(e)}") + + +@router.get("/precompute/status", response_model=ResponseModel) +async def get_precompute_status(): + """ + 获取预计算任务状态 + """ + return ResponseModel( + code=200, + msg="OK", + data={ + "running": task_status["running"] + } + ) + diff --git a/app/api/api_prompt_generation.py b/app/api/api_prompt_generation.py index 4df957f..b4a42b8 100644 --- a/app/api/api_prompt_generation.py +++ b/app/api/api_prompt_generation.py @@ -1,13 +1,10 @@ -import json import logging -import time from fastapi import APIRouter, HTTPException from app.schemas.prompt_generation import PromptGenerationImageModel, ImageRequest from app.schemas.response_template import ResponseModel -from app.service.prompt_generation.chatgpt_for_translation import get_translation_from_llama3, \ - get_prompt_from_image +from app.service.prompt_generation.chatgpt_for_translation import get_translation_from_llama3, get_prompt_from_image router = APIRouter() logger = logging.getLogger() @@ -34,19 +31,19 @@ def prompt_generation(request_data: PromptGenerationImageModel): raise HTTPException(status_code=404, detail=str(e)) return ResponseModel(data=data) - -@router.post("/img2prompt") -def get_prompt_from_img(img: ImageRequest): - """ - 自动识别图片并输出为prompt - - :param img: 图片的minio地址 - :return: 图片的文字描述 - """ - text = ("Please describe the clothing in the image and provide a line art description of the outfit. " - "The description should allow for the reconstruction of the corresponding line art based on the details " - "given.") - logger.info(f"get_prompt_from_img request item is : @@@@@@:{img}") - description = get_prompt_from_image(img, text) - logger.info(f"生成的图片描述 response @@@@@@:{description}") - return description +# 停用 +# @router.post("/img2prompt") +# def get_prompt_from_img(img: ImageRequest): +# """ +# 自动识别图片并输出为prompt +# +# :param img: 图片的minio地址 +# :return: 图片的文字描述 +# """ +# text = ("Please describe the clothing in the image and provide a line art description of the outfit. " +# "The description should allow for the reconstruction of the corresponding line art based on the details " +# "given.") +# logger.info(f"get_prompt_from_img request item is : @@@@@@:{img}") +# description = get_prompt_from_image(img, text) +# logger.info(f"生成的图片描述 response @@@@@@:{description}") +# return description diff --git a/app/api/api_query_image.py b/app/api/api_query_image.py index ca0dbe6..1a32c30 100644 --- a/app/api/api_query_image.py +++ b/app/api/api_query_image.py @@ -26,9 +26,9 @@ def query_image(request_data: QueryImageModel): } """ try: - logger.info(f"query_image request item is : @@@@@@:{json.dumps(request_data.dict())}") + logger.info(f"query_image request item is : @@@@@@:{json.dumps(request_data.dict(), indent=4)}") data = query(request_data.gender, request_data.content) - logger.info(f"query_image response @@@@@@:{json.dumps(data)}") + logger.info(f"query_image response @@@@@@:{json.dumps(data, indent=4)}") except Exception as e: logger.warning(f"query_image Run Exception @@@@@@:{e}") raise HTTPException(status_code=404, detail=str(e)) diff --git a/app/api/api_recommendation.py b/app/api/api_recommendation.py index 477ab08..ac1e9ec 100644 --- a/app/api/api_recommendation.py +++ b/app/api/api_recommendation.py @@ -1,207 +1,206 @@ import io import logging import sys -import time -from typing import List -import os -import json -import math -import random -import numpy as np +from typing import List, Optional +from fastapi import HTTPException, APIRouter, Query from apscheduler.schedulers.background import BackgroundScheduler -from apscheduler.triggers.cron import CronTrigger -from fastapi import HTTPException, APIRouter -from app.service.recommend.service import load_resources, matrix_data +from app.service.recommendation_system.recommendation_api import get_recommendations as get_new_recommendations +from app.service.recommendation_system.incremental_listener import start_background_listener +from app.service.recommendation_system.milvus_client import create_collection sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8') logger = logging.getLogger() router = APIRouter() +# ========== 旧版推荐接口（基于 npy 矩阵，已废弃）========== +# @router.get("/recommend/{user_id}/{category}/{num_recommendations}/{brand_id}/{brand_scale}", response_model=List[str]) +# async def get_recommendations(user_id: int, category: str, brand_id: int, brand_scale: float, num_recommendations: int = 10): +# """ +# :param user_id: 4 +# :param category: female_skirt +# :param num_recommendations: 1 +# :return: +# [ +# "aida-sys-image/images/female/skirt/903000017.jpg" +# ] +# """ +# try: +# start_time = time.time() +# cache_key = (user_id, category) +# # === 新增：用户存在性检查 === +# user_exists_inter = user_id in matrix_data["user_index_interaction"] +# user_exists_feat = user_id in matrix_data["user_index_feature"] +# +# # 任一矩阵不存在用户则返回随机推荐 +# if not (user_exists_inter and user_exists_feat): +# logger.info(f"用户 {user_id} 数据不完整，触发随机推荐") +# return get_random_recommendations(category, num_recommendations) +# +# # 检查缓存 +# if cache_key in matrix_data["cached_scores"]: +# processed_inter, processed_feat = matrix_data["cached_scores"][cache_key] +# valid_sketch_idxs_inter = matrix_data["cached_valid_idxs"][cache_key] +# else: +# # 实时计算逻辑（同原代码） +# user_idx_inter = matrix_data["user_index_interaction"].get(user_id) +# user_idx_feature = matrix_data["user_index_feature"].get(user_id) +# +# category_iids = matrix_data["category_to_iids"].get(category, []) +# valid_sketch_idxs_inter = [ +# idx for iid, idx in matrix_data["sketch_index_interaction"].items() +# if iid in category_iids +# ] +# +# # 处理交互分数 +# raw_inter_scores = [] +# if user_idx_inter is not None and valid_sketch_idxs_inter: +# raw_inter_scores = matrix_data["interaction_matrix"][user_idx_inter, valid_sketch_idxs_inter] +# processed_inter = raw_inter_scores * 0.7 +# +# # 处理特征分数 +# valid_sketch_idxs_feature = [ +# idx for iid, idx in matrix_data["sketch_index_feature"].items() +# if iid in category_iids +# ] +# raw_feat_scores = [] +# if user_idx_feature is not None and valid_sketch_idxs_feature: +# raw_feat_scores = matrix_data["feature_matrix"][user_idx_feature, valid_sketch_idxs_feature] +# raw_feat_scores = (raw_feat_scores - np.min(raw_feat_scores)) / ( +# np.max(raw_feat_scores) - np.min(raw_feat_scores) + 1e-8) +# processed_feat = raw_feat_scores +# else: +# processed_feat = np.array([]) +# +# # 更新缓存 +# matrix_data["cached_scores"][cache_key] = (processed_inter, processed_feat) +# matrix_data["cached_valid_idxs"][cache_key] = valid_sketch_idxs_inter +# +# # 合并分数 +# if brand_id is not None: +# brand_idx_feature = matrix_data["brand_index_map"].get(brand_id) +# +# brand_feat_valid = ( +# matrix_data["brand_feature_matrix"].size > 0 and # 矩阵非空 +# brand_idx_feature is not None and +# valid_sketch_idxs_feature # 有可用索引 +# ) +# +# if brand_feat_valid: +# raw_brand_feat_scores = matrix_data["brand_feature_matrix"][ +# brand_idx_feature, valid_sketch_idxs_feature +# ] +# raw_brand_feat_scores = (raw_brand_feat_scores - np.min(raw_brand_feat_scores)) / ( +# np.max(raw_brand_feat_scores) - np.min(raw_brand_feat_scores) + 1e-8 +# ) +# processed_brand_feat = raw_brand_feat_scores +# +# # 如果 processed_feat 是空的，替换为全 0，避免 shape 不一致 +# if processed_feat.size == 0: +# processed_feat = np.zeros_like(processed_brand_feat) +# +# final_scores = processed_inter + 0.3 * ( +# (1 - brand_scale) * processed_feat + brand_scale * processed_brand_feat +# ) +# else: +# # brand 信息不可用 +# final_scores = processed_inter + 0.3 * processed_feat if processed_feat.size > 0 else processed_inter +# else: +# final_scores = processed_inter + 0.3 * processed_feat if processed_feat.size > 0 else processed_inter +# +# valid_sketch_idxs = matrix_data["cached_valid_idxs"][cache_key] +# +# # 概率采样 +# scores = np.array(final_scores) +# +# # 调整后的概率转换（带温度控制的softmax） +# def calibrated_softmax(scores, temperature=1.0): +# scores = scores / temperature +# scale = scores - max(scores) +# exps = np.exp(scale) +# return exps / np.sum(exps) +# +# probs = calibrated_softmax(scores, 0.09) +# +# chosen_indices = np.random.choice( +# len(valid_sketch_idxs), +# size=min(num_recommendations, len(valid_sketch_idxs)), +# p=probs, +# replace=False +# ) +# recommendations = [matrix_data["iid_to_sketch"][valid_sketch_idxs[idx]] for idx in chosen_indices] +# +# logger.info(f"推荐生成完成，耗时: {time.time() - start_time:.2f}秒") +# return recommendations +# except Exception as e: +# logger.error(f"推荐失败: {str(e)}", exc_info=True) +# raise HTTPException(status_code=500, detail=str(e)) + @router.on_event("startup") async def startup_event(): - # 初始加载 - load_resources() + """启动时初始化增量监听任务""" + try: + # 屏蔽 apscheduler 的 INFO 日志 + logging.getLogger("apscheduler").setLevel(logging.WARNING) - # 配置定时任务 - scheduler = BackgroundScheduler() - scheduler.add_job( - load_resources, - trigger=CronTrigger(hour=0, minute=30), - name="每日资源刷新" - ) - scheduler.start() - logger.info("定时任务已启动") + # 确保 Milvus 集合已创建（若已存在则直接返回） + try: + create_collection() + except Exception as exc: + logger.error("Milvus 集合创建/检查失败，不影响服务继续启动: %s", exc, exc_info=True) + + # 配置定时任务 + scheduler = BackgroundScheduler() + start_background_listener(scheduler) + scheduler.start() + logger.info("增量监听定时任务已启动") + except Exception as e: + logger.error(f"启动增量监听任务失败: {e}", exc_info=True) -def softmax(scores): - max_score = max(scores) - exp_scores = [math.exp(s - max_score) for s in scores] - sum_exp = sum(exp_scores) - return [s / sum_exp for s in exp_scores] +@router.get("/recommend/{user_id}/{category}", response_model=List[str]) +async def recommend( + user_id: int, + category: str, + style: Optional[str] = Query( + None, + description="风格样式（可选）：若传入，则在利用分支对同 style 的候选进行加分", + ), +): + """新版推荐接口（Milvus + Redis 偏好向量）。""" + try: + results = get_new_recommendations(user_id, category, style) + path = results[0] if results else "" + return [path] + except Exception as e: + logger.error("新版推荐接口失败 [user=%s, category=%s]: %s", user_id, category, e, exc_info=True) + raise HTTPException(status_code=500, detail=str(e)) -# def get_random_recommendations(category: str, num: int) -> List[str]: -# """根据预加载热度向量推荐（冷启动）""" -# try: -# heat_data = matrix_data.get("heat_data", {}) -# -# if category not in heat_data: -# raise ValueError(f"热度数据缺少类别 {category}，使用随机推荐") -# -# heat_dict = heat_data[category] # {url: score} -# urls = list(heat_dict.keys()) -# scores = list(heat_dict.values()) -# -# if not urls: -# raise ValueError("该类别下无热度记录，使用随机推荐") -# -# probs = softmax(scores) -# sample_size = min(num, len(urls)) -# sampled_urls = random.choices(urls, weights=probs, k=sample_size) -# -# return sampled_urls -# -# except Exception as e: -# # 回退：完全随机推荐 -# all_iids = list(matrix_data["iid_to_sketch"].keys()) -# category_iids = matrix_data["category_to_iids"].get(category, all_iids) -# sample_size = min(num, len(category_iids)) -# sampled = np.random.choice(category_iids, size=sample_size, replace=False) -# return [matrix_data["iid_to_sketch"][iid] for iid in sampled] - -def get_random_recommendations(category: str, num: int) -> List[str]: - """全品类随机推荐""" - all_iids = list(matrix_data["iid_to_sketch"].keys()) - # 优先从当前品类选择 - category_iids = matrix_data["category_to_iids"].get(category, all_iids) - # 确保不超出实际数量 - sample_size = min(num, len(category_iids)) - sampled = np.random.choice(category_iids, size=sample_size, replace=False) - return [matrix_data["iid_to_sketch"][iid] for iid in sampled] - - -@router.get("/recommend/{user_id}/{category}/{num_recommendations}/{brand_id}/{brand_scale}", response_model=List[str]) -async def get_recommendations(user_id: int, category: str, brand_id: int, brand_scale: float, num_recommendations: int = 10): +@router.get("/redis/user_pref") +async def get_all_user_preferences(): """ - :param user_id: 4 - :param category: female_skirt - :param num_recommendations: 1 - :return: - [ - "aida-sys-image/images/female/skirt/903000017.jpg" - ] + 获取所有以 user_pref 为前缀的 Redis key 信息 """ try: - logger.info(f"user_id:{user_id}-----category:{category}-----brand_id:{brand_id}-----brand_scale:{brand_scale}-----num_recommendations:{num_recommendations}") - start_time = time.time() - cache_key = (user_id, category) - # === 新增：用户存在性检查 === - user_exists_inter = user_id in matrix_data["user_index_interaction"] - user_exists_feat = user_id in matrix_data["user_index_feature"] - - # 任一矩阵不存在用户则返回随机推荐 - if not (user_exists_inter and user_exists_feat): - logger.info(f"用户 {user_id} 数据不完整，触发随机推荐") - return get_random_recommendations(category, num_recommendations) - - # 检查缓存 - if cache_key in matrix_data["cached_scores"]: - processed_inter, processed_feat = matrix_data["cached_scores"][cache_key] - valid_sketch_idxs_inter = matrix_data["cached_valid_idxs"][cache_key] - else: - # 实时计算逻辑（同原代码） - user_idx_inter = matrix_data["user_index_interaction"].get(user_id) - user_idx_feature = matrix_data["user_index_feature"].get(user_id) - - category_iids = matrix_data["category_to_iids"].get(category, []) - valid_sketch_idxs_inter = [ - idx for iid, idx in matrix_data["sketch_index_interaction"].items() - if iid in category_iids - ] - - # 处理交互分数 - raw_inter_scores = [] - if user_idx_inter is not None and valid_sketch_idxs_inter: - raw_inter_scores = matrix_data["interaction_matrix"][user_idx_inter, valid_sketch_idxs_inter] - processed_inter = raw_inter_scores * 0.7 - - # 处理特征分数 - valid_sketch_idxs_feature = [ - idx for iid, idx in matrix_data["sketch_index_feature"].items() - if iid in category_iids - ] - raw_feat_scores = [] - if user_idx_feature is not None and valid_sketch_idxs_feature: - raw_feat_scores = matrix_data["feature_matrix"][user_idx_feature, valid_sketch_idxs_feature] - raw_feat_scores = (raw_feat_scores - np.min(raw_feat_scores)) / ( - np.max(raw_feat_scores) - np.min(raw_feat_scores) + 1e-8) - processed_feat = raw_feat_scores - else: - processed_feat = np.array([]) - - # 更新缓存 - matrix_data["cached_scores"][cache_key] = (processed_inter, processed_feat) - matrix_data["cached_valid_idxs"][cache_key] = valid_sketch_idxs_inter - - # 合并分数 - if brand_id is not None: - brand_idx_feature = matrix_data["brand_index_map"].get(brand_id) - - brand_feat_valid = ( - matrix_data["brand_feature_matrix"].size > 0 and # 矩阵非空 - brand_idx_feature is not None and - valid_sketch_idxs_feature # 有可用索引 - ) - - if brand_feat_valid: - raw_brand_feat_scores = matrix_data["brand_feature_matrix"][ - brand_idx_feature, valid_sketch_idxs_feature - ] - raw_brand_feat_scores = (raw_brand_feat_scores - np.min(raw_brand_feat_scores)) / ( - np.max(raw_brand_feat_scores) - np.min(raw_brand_feat_scores) + 1e-8 - ) - processed_brand_feat = raw_brand_feat_scores - - # 如果 processed_feat 是空的，替换为全 0，避免 shape 不一致 - if processed_feat.size == 0: - processed_feat = np.zeros_like(processed_brand_feat) - - final_scores = processed_inter + 0.3 * ( - (1 - brand_scale) * processed_feat + brand_scale * processed_brand_feat - ) - else: - # brand 信息不可用 - final_scores = processed_inter + 0.3 * processed_feat if processed_feat.size > 0 else processed_inter - else: - final_scores = processed_inter + 0.3 * processed_feat if processed_feat.size > 0 else processed_inter - - valid_sketch_idxs = matrix_data["cached_valid_idxs"][cache_key] - - # 概率采样 - scores = np.array(final_scores) - - # 调整后的概率转换（带温度控制的softmax） - def calibrated_softmax(scores, temperature=1.0): - scores = scores / temperature - scale = scores - max(scores) - exps = np.exp(scale) - return exps / np.sum(exps) - - probs = calibrated_softmax(scores, 0.09) - - chosen_indices = np.random.choice( - len(valid_sketch_idxs), - size=min(num_recommendations, len(valid_sketch_idxs)), - p=probs, - replace=False - ) - recommendations = [matrix_data["iid_to_sketch"][valid_sketch_idxs[idx]] for idx in chosen_indices] - - logger.info(f"推荐生成完成，耗时: {time.time() - start_time:.2f}秒") - return recommendations - + from app.service.utils.redis_utils import Redis + from app.service.recommendation_system.config import REDIS_KEY_USER_PREF_PREFIX + + # 扫描所有匹配 user_pref:* 的 key + pattern = f"{REDIS_KEY_USER_PREF_PREFIX}:*" + keys = Redis.scan_keys(pattern) + + # 直接返回所有 key 和原始 value + result = {} + for key in keys: + # 读取对应的值 + value = Redis.read(key) + if value: + result[key] = value + + return result + except Exception as e: - logger.error(f"推荐失败: {str(e)}", exc_info=True) - raise HTTPException(status_code=500, detail=str(e)) + logger.error("获取用户偏好数据失败: %s", e, exc_info=True) + raise HTTPException(status_code=500, detail=str(e)) \ No newline at end of file diff --git a/app/api/api_route.py b/app/api/api_route.py index eedb6fb..1af7b9f 100644 --- a/app/api/api_route.py +++ b/app/api/api_route.py @@ -1,38 +1,42 @@ from fastapi import APIRouter - -from app.api import api_attribute_retrieve, api_query_image +from app.api import api_attribute_retrieve from app.api import api_brand_dna -from app.api import api_brighten -from app.api import api_chat_robot from app.api import api_clothing_seg from app.api import api_design from app.api import api_design_pre_processing -from app.api import api_extraction_project_info from app.api import api_generate_image -from app.api import api_image2sketch from app.api import api_mannequins_edit from app.api import api_pose_transform +from app.api import api_precompute from app.api import api_prompt_generation from app.api import api_recommendation -from app.api import api_super_resolution from app.api import api_test router = APIRouter() router.include_router(api_test.router, tags=["test"], prefix="/test") -router.include_router(api_super_resolution.router, tags=["super_resolution"], prefix="/api") router.include_router(api_generate_image.router, tags=["generate_image"], prefix="/api") router.include_router(api_attribute_retrieve.router, tags=["attribute_retrieve"], prefix="/api") router.include_router(api_design.router, tags=['design'], prefix="/api") -router.include_router(api_chat_robot.router, tags=['chat_robot'], prefix="/api") router.include_router(api_prompt_generation.router, tags=['prompt_generation'], prefix="/api") router.include_router(api_design_pre_processing.router, tags=['design_pre_processing'], prefix="/api") -router.include_router(api_image2sketch.router, tags=['api_image2sketch'], prefix="/api") -router.include_router(api_brighten.router, tags=['api_brighten'], prefix="/api") -router.include_router(api_query_image.router, tags=['api_query_image'], prefix="/api") router.include_router(api_brand_dna.router, tags=['api_brand_dna'], prefix="/api") router.include_router(api_recommendation.router, tags=['api_recommendation'], prefix="/api") +router.include_router(api_precompute.router, tags=['api_precompute'], prefix="/api") router.include_router(api_mannequins_edit.router, tags=['api_mannequins_edit'], prefix="/api") router.include_router(api_pose_transform.router, tags=['api_pose_transform'], prefix="/api") router.include_router(api_clothing_seg.router, tags=['api_clothing_seg'], prefix="/api") -router.include_router(api_extraction_project_info.router, tags=['api_extraction_project_info'], prefix="/api") + +"""停用""" +# from app.api import api_chat_robot +# from app.api import api_query_image +# from app.api import api_brighten +# from app.api import api_extraction_project_info +# from app.api import api_image2sketch +# from app.api import api_super_resolution +# router.include_router(api_image2sketch.router, tags=['api_image2sketch'], prefix="/api") +# router.include_router(api_query_image.router, tags=['api_query_image'], prefix="/api") +# router.include_router(api_chat_robot.router, tags=['chat_robot'], prefix="/api") +# router.include_router(api_super_resolution.router, tags=["super_resolution"], prefix="/api") +# router.include_router(api_brighten.router, tags=['api_brighten'], prefix="/api") +# router.include_router(api_extraction_project_info.router, tags=['api_extraction_project_info'], prefix="/api") diff --git a/app/api/api_super_resolution.py b/app/api/api_super_resolution.py index ce853fd..1cbb938 100644 --- a/app/api/api_super_resolution.py +++ b/app/api/api_super_resolution.py @@ -27,7 +27,7 @@ def super_resolution(request_item: SuperResolutionModel, background_tasks: Backg } """ try: - logger.info(f"super_resolution request item is : @@@@@@:{json.dumps(request_item.dict())}") + logger.info(f"super_resolution request item is : @@@@@@:{json.dumps(request_item.dict(),indent=4)}") service = SuperResolution(request_item) background_tasks.add_task(service.sr_result) except Exception as e: diff --git a/app/api/api_test.py b/app/api/api_test.py index c7bdb62..7e34afa 100644 --- a/app/api/api_test.py +++ b/app/api/api_test.py @@ -4,8 +4,7 @@ import logging from fastapi import APIRouter from fastapi import HTTPException -from app.core.config import SR_RABBITMQ_QUEUES, GI_RABBITMQ_QUEUES, GPI_RABBITMQ_QUEUES, GRI_RABBITMQ_QUEUES, OSS, JAVA_STREAM_API_URL, GMV_RABBITMQ_QUEUES, SLOGAN_RABBITMQ_QUEUES, GEN_SINGLE_LOGO_RABBITMQ_QUEUES, PS_RABBITMQ_QUEUES, BATCH_GPI_RABBITMQ_QUEUES, BATCH_GRI_RABBITMQ_QUEUES, \ - BATCH_PS_RABBITMQ_QUEUES, RABBITMQ_ENV +from app.core.config import settings, SR_RABBITMQ_QUEUES, GMV_RABBITMQ_QUEUES, PS_RABBITMQ_QUEUES, SLOGAN_RABBITMQ_QUEUES, GI_RABBITMQ_QUEUES, GPI_RABBITMQ_QUEUES, GRI_RABBITMQ_QUEUES, BATCH_GPI_RABBITMQ_QUEUES, BATCH_GRI_RABBITMQ_QUEUES, BATCH_PS_RABBITMQ_QUEUES from app.schemas.response_template import ResponseModel logger = logging.getLogger() @@ -15,9 +14,9 @@ router = APIRouter() @router.get("{id}") def test(id: int): data = { - "RABBITMQ_ENV":RABBITMQ_ENV, - "超分 SR_RABBITMQ_QUEUES": SR_RABBITMQ_QUEUES, - "多视角 GMV_RABBITMQ_QUEUES": GMV_RABBITMQ_QUEUES, + "RABBITMQ_ENV": settings.SERVE_ENV, + # "超分 SR_RABBITMQ_QUEUES": SR_RABBITMQ_QUEUES, + # "多视角 GMV_RABBITMQ_QUEUES": GMV_RABBITMQ_QUEUES, "pose transform PS_RABBITMQ_QUEUES": PS_RABBITMQ_QUEUES, "logan SLOGAN_RABBITMQ_QUEUES": SLOGAN_RABBITMQ_QUEUES, "image and single logo GI_RABBITMQ_QUEUES": GI_RABBITMQ_QUEUES, @@ -29,10 +28,9 @@ def test(id: int): "batch relight BATCH_GRI_RABBITMQ_QUEUES": BATCH_GRI_RABBITMQ_QUEUES, "batch pose transform BATCH_PS_RABBITMQ_QUEUES": BATCH_PS_RABBITMQ_QUEUES, - "JAVA_STREAM_API_URL": JAVA_STREAM_API_URL, - "local_oss_server": OSS + "JAVA_STREAM_API_URL": settings.JAVA_STREAM_API_URL, } - logger.info(json.dumps(data)) + logger.info(json.dumps(data, ensure_ascii=False, indent=4)) if id == 1: raise HTTPException(status_code=404, detail="Item not found") diff --git a/app/core/config.backup.py b/app/core/config.backup.py new file mode 100644 index 0000000..e6c7283 --- /dev/null +++ b/app/core/config.backup.py @@ -0,0 +1,235 @@ +import os + +import pika +from dotenv import load_dotenv +from pydantic import BaseSettings + +BASE_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../')) +load_dotenv(os.path.join(BASE_DIR, '.env')) + + +class Settings(BaseSettings): + PROJECT_NAME: str = 'FASTAPI BASE' + SECRET_KEY: str = '' + API_PREFIX: str = '' + BACKEND_CORS_ORIGINS: list[str] = ['*'] + DATABASE_URL: str = '' + ACCESS_TOKEN_EXPIRE_SECONDS: int = 60 * 60 * 24 * 7 # Token expired after 7 days + SECURITY_ALGORITHM: str = 'HS256' + LOGGING_CONFIG_FILE: str = os.path.join(BASE_DIR, 'logging_env.py') + + +OSS = "minio" +DEBUG = False +if DEBUG: + LOGS_PATH = "logs/" + CATEGORY_PATH = "service/attribute/config/descriptor/category/category_dis.csv" + SEG_CACHE_PATH = "../seg_cache/" + POSE_TRANSFORM_VIDEO_PATH = "../pose_transform_video/" + RECOMMEND_PATH_PREFIX = "service/recommend/" + CHROMADB_PATH = "./chromadb/" +else: + LOGS_PATH = "app/logs/" + CATEGORY_PATH = "app/service/attribute/config/descriptor/category/category_dis.csv" + SEG_CACHE_PATH = "/seg_cache/" + POSE_TRANSFORM_VIDEO_PATH = "/pose_transform_video/" + RECOMMEND_PATH_PREFIX = "app/service/recommend/" + CHROMADB_PATH = "/chromadb/" + +# RABBITMQ_ENV = "" # 生产环境 +RABBITMQ_ENV = os.getenv("RABBITMQ_ENV", "-dev") +# RABBITMQ_ENV = "-local" # 本地测试环境 + +if RABBITMQ_ENV == "-dev": + JAVA_STREAM_API_URL = f"https://develop.api.aida.com.hk/api/third/party/receiveDesignResults" +elif RABBITMQ_ENV == "-prod": + JAVA_STREAM_API_URL = f"https://api.aida.com.hk/api/third/party/receiveDesignResults" + +settings = Settings() + +# minio 配置 +MINIO_URL = "www.minio-api.aida.com.hk" +MINIO_ACCESS = 'vXKFLSJkYeEq2DrSZvkB' +MINIO_SECRET = 'uKTZT3x7C43WvPN9QTc99DiRkwddWZrG9Uh3JVlR' +MINIO_SECURE = True + +# S3 配置 +S3_ACCESS_KEY = "AKIAVD3OJIMF6UJFLSHZ" +S3_AWS_SECRET_ACCESS_KEY = "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" +S3_REGION_NAME = "ap-east-1" + +# redis 配置 +REDIS_HOST = "10.1.1.240" +REDIS_PORT = "6379" +REDIS_DB = "2" + +# rabbitmq config +RABBITMQ_PARAMS = { + "host": "18.167.251.121", + "port": 5672, + "credentials": pika.credentials.PlainCredentials(username='rabbit', password='123456'), + "virtual_host": "/" +} + +# milvus 配置 +MILVUS_URL = "http://10.1.1.240:19530" +MILVUS_TOKEN = "root:Milvus" +MILVUS_ALIAS = "default" +MILVUS_TABLE_KEYPOINT = "keypoint_cache_2" +MILVUS_TABLE_SEG = "seg_cache" + +# Mysql 配置 +DB_HOST = '18.167.251.121' # 数据库主机地址 +# DB_PORT = int( 33006) +DB_PORT = 33008 # 数据库端口 +DB_USERNAME = 'aida_con_python' # 数据库用户名 +DB_PASSWORD = '123456' # 数据库密码 +DB_NAME = 'aida' # 数据库库名 + +# openai +os.environ['SERPAPI_API_KEY'] = "a793513017b0718db7966207c31703d280d12435c982f1e67bbcbffa52e7632c" +OPENAI_STREAM = True +BUFFER_THRESHOLD = 6 # must be even number +SINGLE_TOKEN_THRESHOLD = 200 +TOKEN_THRESHOLD = 600 +OPENAI_TEMPERATURE = 0 + +# OPENAI_API_KEY = "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" +OPENAI_API_KEY = "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" +OPENAI_MODEL = "gpt-3.5-turbo-0613" +OPENAI_MODEL_LIST = {"gpt-3.5-turbo-0613", + "gpt-3.5-turbo-16k-0613", + "gpt-4-0314", + "gpt-4-32k-0314", + "gpt-4-0613", + "gpt-4-32k-0613", } + +# SR service config +SR_MODEL_NAME = "super_resolution" +SR_TRITON_URL = "10.1.1.240:10031" +SR_MINIO_BUCKET = "aida-users" +SR_RABBITMQ_QUEUES = f"SuperResolution{RABBITMQ_ENV}" + +# GenerateImage service config +FAST_GI_MODEL_URL = '10.1.1.243:10011' +FAST_GI_MODEL_NAME = 'stable_diffusion_xl' + +GI_MODEL_URL = '10.1.1.240:10061' +GI_MODEL_NAME = 'flux' + +GMV_MODEL_URL = '10.1.1.243:10081' +GMV_MODEL_NAME = 'multi_view' + +GMV_RABBITMQ_QUEUES = f"GenerateMultiView{RABBITMQ_ENV}" + +GI_MINIO_BUCKET = "aida-users" +GI_RABBITMQ_QUEUES = f"GenerateImage{RABBITMQ_ENV}" +GI_SYS_IMAGE_URL = "aida-sys-image/generate_image/white_image.jpg" + +# SLOGAN service config +SLOGAN_RABBITMQ_QUEUES = f"Slogan{RABBITMQ_ENV}" + +# Generate Single Logo service config +GSL_MODEL_URL = '10.1.1.243:10041' +GSL_MINIO_BUCKET = "aida-users" +GSL_MODEL_NAME = 'stable_diffusion_xl_transparent' +GEN_SINGLE_LOGO_RABBITMQ_QUEUES = f"GenSingleLogo{RABBITMQ_ENV}" + +# Generate Product service config +# GPI_RABBITMQ_QUEUES = os.getenv("GEN_PRODUCT_IMAGE_RABBITMQ_QUEUES", f"ToProductImage{RABBITMQ_ENV}") +# GPI_MODEL_NAME_OVERALL = 'sdxl_ensemble_all' +# GPI_MODEL_URL = '10.1.1.243:10051' + +# Generate Product service config 旧版product img 模型 +GPI_RABBITMQ_QUEUES = f"ToProductImage{RABBITMQ_ENV}" +BATCH_GPI_RABBITMQ_QUEUES = f"BatchToProductImage{RABBITMQ_ENV}" +GPI_MODEL_NAME_OVERALL = 'diffusion_ensemble_all' +GPI_MODEL_NAME_SINGLE = 'stable_diffusion_1_5_cnet' +GPI_MODEL_URL = '10.1.1.243:10051' + +# Generate Single Logo service config +GRI_RABBITMQ_QUEUES = f"Relight{RABBITMQ_ENV}" +BATCH_GRI_RABBITMQ_QUEUES = f"BatchRelight{RABBITMQ_ENV}" +GRI_MODEL_NAME_OVERALL = 'diffusion_relight_ensemble' +GRI_MODEL_NAME_SINGLE = 'stable_diffusion_1_5_relight' +GRI_MODEL_URL = '10.1.1.240:10051' + +# Pose Transform service config + +PS_RABBITMQ_QUEUES = f"PoseTransform{RABBITMQ_ENV}" +BATCH_PS_RABBITMQ_QUEUES = f"BatchPoseTransform{RABBITMQ_ENV}" +PT_MODEL_URL = '10.1.1.243:10061' + +# SEG service config +SEGMENTATION = { + "new_model_name": "seg_knet", + "name": "seg_ocrnet_hr18", + "input": "seg_input__0", + "output": "seg_output__0", +} +# ollama config +OLLAMA_URL = "http://10.1.1.240:11434/api/embeddings" + +# design batch +BATCH_DESIGN_RABBITMQ_QUEUES = f"DesignBatch{RABBITMQ_ENV}" + +# DESIGN config +DESIGN_MODEL_URL = '10.1.1.240:10000' +AIDA_CLOTHING = "aida-clothing" +KEYPOINT_RESULT_TABLE_FIELD_SET = ('neckline_left', 'neckline_right', 'shoulder_left', 'shoulder_right', 'armpit_left', 'armpit_right', + 'cuff_left_in', 'cuff_left_out', 'cuff_right_in', 'cuff_right_out', 'waistband_left', 'waistband_right') + +# DESIGN 预处理 +IF_DEBUG_SHOW = False + +# 优先级 +PRIORITY_DICT = { + 'earring_front': 99, + 'bag_front': 98, + 'hairstyle_front': 97, + 'outwear_front': 20, + 'tops_front': 19, + 'dress_front': 18, + 'blouse_front': 17, + 'skirt_front': 16, + 'trousers_front': 15, + 'bottoms_front': 14, + 'shoes_right': 1, + 'shoes_left': 1, + 'body': 0, + 'bottoms_back': -14, + 'trousers_back': -15, + 'skirt_back': -16, + 'blouse_back': -17, + 'dress_back': -18, + 'tops_back': -19, + 'outwear_back': -20, + 'hairstyle_back': -97, + 'bag_back': -98, + 'earring_back': -99, +} + +QWEN_API_KEY = "sk-f31c29e61ac2498ba5e307aaa6dc10e0" + +DB_CONFIG = { + "host": "18.167.251.121", + "port": 3306, + "user": "root", + "password": "QWa998345", + "database": "aida", + "charset": "utf8mb4" +} + +TABLE_CATEGORIES = { + "female_dress": "female/dress", + "female_outwear": "female/outwear", + "female_trousers": "female/trousers", + "female_skirt": "female/skirt", + "female_blouse": "female/blouse", + "male_tops": "male/tops", + "male_bottoms": "male/bottoms", + "male_outwear": "male/outwear" +} + +# --- ComfyUI 配置信息 --- +COMFYUI_SERVER_ADDRESS = "10.1.2.227:8080" # 替换为您的 ComfyUI 服务器地址 diff --git a/app/core/config.py b/app/core/config.py index 30ca5ef..77d4509 100644 --- a/app/core/config.py +++ b/app/core/config.py @@ -1,188 +1,95 @@ -import os - -import pika -from dotenv import load_dotenv -from pydantic import BaseSettings - -BASE_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../')) -load_dotenv(os.path.join(BASE_DIR, '.env')) +from pydantic import Field +from pydantic_settings import BaseSettings, SettingsConfigDict class Settings(BaseSettings): - PROJECT_NAME: str = 'FASTAPI BASE' - SECRET_KEY: str = '' - API_PREFIX: str = '' - BACKEND_CORS_ORIGINS: list[str] = ['*'] - DATABASE_URL: str = '' - ACCESS_TOKEN_EXPIRE_SECONDS: int = 60 * 60 * 24 * 7 # Token expired after 7 days - SECURITY_ALGORITHM: str = 'HS256' - LOGGING_CONFIG_FILE: str = os.path.join(BASE_DIR, 'logging_env.py') + """ + 应用配置类。Pydantic Settings 会自动从环境变量和 .env 文件中加载这些值。 + """ + model_config = SettingsConfigDict( + env_file='.env', + env_file_encoding='utf-8', + # extra='ignore' # 忽略环境变量中多余的键 + ) + # --- 服务端口配置信息 --- + PORT: int = Field(default=8001, description="") + # --- 服务环境 配置信息 --- + SERVE_ENV: str = Field(default='', description="") + # --- 开发状态 配置信息 --- + DEBUG: bool = Field(default=False, description="") + # --- 千问api 配置信息 --- + QWEN_API_KEY: str = Field(default="", description="") + # --- ComfyUI 配置信息 --- + COMFYUI_SERVER_ADDRESS: str = Field(default='', description="") -OSS = "minio" -DEBUG = False -if DEBUG: - LOGS_PATH = "logs/" - CATEGORY_PATH = "service/attribute/config/descriptor/category/category_dis.csv" - SEG_CACHE_PATH = "../seg_cache/" - POSE_TRANSFORM_VIDEO_PATH = "../pose_transform_video/" - RECOMMEND_PATH_PREFIX = "service/recommend/" - CHROMADB_PATH = "./chromadb/" -else: - LOGS_PATH = "app/logs/" - CATEGORY_PATH = "app/service/attribute/config/descriptor/category/category_dis.csv" - SEG_CACHE_PATH = "/seg_cache/" - POSE_TRANSFORM_VIDEO_PATH = "/pose_transform_video/" - RECOMMEND_PATH_PREFIX = "app/service/recommend/" - CHROMADB_PATH = "/chromadb/" + # --- minio 配置信息 --- + MINIO_URL: str = Field(default='', description="") + MINIO_ACCESS: str = Field(default='', description="") + MINIO_SECRET: str = Field(default='', description="") + MINIO_SECURE: bool = Field(default=True, description="") -# RABBITMQ_ENV = "" # 生产环境 -RABBITMQ_ENV = os.getenv("RABBITMQ_ENV", "-prod") -# RABBITMQ_ENV = "-local" # 本地测试环境 + # --- redis 配置信息 --- + REDIS_HOST: str = Field(default='', description="") + REDIS_PORT: str = Field(default='', description="") + REDIS_DB: int = Field(default=0, description="") + + # --- mysql 配置信息 --- + MYSQL_HOST: str = Field(default='', description="") + MYSQL_PORT: int = Field(default='', description="") + MYSQL_USER: str = Field(default='', description="") + MYSQL_PASSWORD: str = Field(default='', description="") + MYSQL_DB: str = Field(default='', description="") + MYSQL_CHARSET: str = Field(default='utf8mb4', description="") + + # --- rabbit-mq 配置信息 --- + MQ_HOST: str = Field(default='', description="") + MQ_PORT: str = Field(default='', description="") + MQ_USERNAME: str = Field(default='', description="") + MQ_PASSWORD: str = Field(default='', description="") + MQ_VIRTUAL_HOST: str = Field(default='/', description="") + MQ_ENV: str = Field(default='', description="") + + # --- milvus 配置信息 --- + MILVUS_URL: str = Field(default='', description="") + MILVUS_TOKEN: str = Field(default='', description="") + MILVUS_ALIAS: str = Field(default='', description="") + + # --- ollama 配置信息 --- + CHROMADB_PATH: str = Field(default='', description="") + + # --- ollama 配置信息 --- + OLLAMA_URL: str = Field(default='', description="") + + # --- Design Callback Java 接口 --- + JAVA_STREAM_API_URL: str = Field(default='', description="") + + # --- 服务器IP --- + A6000_SERVICE_HOST: str = Field(default='', description="") + B_4_X_4090_SERVICE_HOST: str = Field(default='', description="") + + # --- 其他配置信息 以下均为Docker容器内配置--- + LOGS_PATH: str = Field(default="/logs/", description="") + CATEGORY_PATH: str = Field(default="/app/service/attribute/config/descriptor/category/category_dis.csv", description="") + SEG_CACHE_PATH: str = Field(default="/seg_cache/", description="") + RECOMMEND_PATH_PREFIX: str = Field(default="/app/service/recommend/", description="") -if RABBITMQ_ENV == "-dev": - JAVA_STREAM_API_URL = f"https://develop.api.aida.com.hk/api/third/party/receiveDesignResults" -elif RABBITMQ_ENV == "-prod": - JAVA_STREAM_API_URL = f"https://api.aida.com.hk/api/third/party/receiveDesignResults" settings = Settings() -# minio 配置 -MINIO_URL = "www.minio-api.aida.com.hk" -MINIO_ACCESS = 'vXKFLSJkYeEq2DrSZvkB' -MINIO_SECRET = 'uKTZT3x7C43WvPN9QTc99DiRkwddWZrG9Uh3JVlR' -MINIO_SECURE = True - -# S3 配置 -S3_ACCESS_KEY = "AKIAVD3OJIMF6UJFLSHZ" -S3_AWS_SECRET_ACCESS_KEY = "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" -S3_REGION_NAME = "ap-east-1" - -# redis 配置 -REDIS_HOST = "10.1.1.240" -REDIS_PORT = "6379" -REDIS_DB = "2" - -# rabbitmq config -RABBITMQ_PARAMS = { - "host": "18.167.251.121", - "port": 5672, - "credentials": pika.credentials.PlainCredentials(username='rabbit', password='123456'), - "virtual_host": "/" +"""Design 服务""" +# 推荐服装类别映射 +TABLE_CATEGORIES = { + "female_dress": "female/dress", + "female_outwear": "female/outwear", + "female_trousers": "female/trousers", + "female_skirt": "female/skirt", + "female_blouse": "female/blouse", + "male_tops": "male/tops", + "male_bottoms": "male/bottoms", + "male_outwear": "male/outwear" } - -# milvus 配置 -MILVUS_URL = "http://10.1.1.240:19530" -MILVUS_TOKEN = "root:Milvus" -MILVUS_ALIAS = "default" -MILVUS_TABLE_KEYPOINT = "keypoint_cache_2" -MILVUS_TABLE_SEG = "seg_cache" - -# Mysql 配置 -DB_HOST = '18.167.251.121' # 数据库主机地址 -# DB_PORT = int( 33006) -DB_PORT = 33008 # 数据库端口 -DB_USERNAME = 'aida_con_python' # 数据库用户名 -DB_PASSWORD = '123456' # 数据库密码 -DB_NAME = 'aida' # 数据库库名 - -# openai -os.environ['SERPAPI_API_KEY'] = "a793513017b0718db7966207c31703d280d12435c982f1e67bbcbffa52e7632c" -OPENAI_STREAM = True -BUFFER_THRESHOLD = 6 # must be even number -SINGLE_TOKEN_THRESHOLD = 200 -TOKEN_THRESHOLD = 600 -OPENAI_TEMPERATURE = 0 - -# OPENAI_API_KEY = "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" -OPENAI_API_KEY = "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" -OPENAI_MODEL = "gpt-3.5-turbo-0613" -OPENAI_MODEL_LIST = {"gpt-3.5-turbo-0613", - "gpt-3.5-turbo-16k-0613", - "gpt-4-0314", - "gpt-4-32k-0314", - "gpt-4-0613", - "gpt-4-32k-0613", } - -# SR service config -SR_MODEL_NAME = "super_resolution" -SR_TRITON_URL = "10.1.1.240:10031" -SR_MINIO_BUCKET = "aida-users" -SR_RABBITMQ_QUEUES = f"SuperResolution{RABBITMQ_ENV}" - -# GenerateImage service config -FAST_GI_MODEL_URL = '10.1.1.243:10011' -FAST_GI_MODEL_NAME = 'stable_diffusion_xl' - -GI_MODEL_URL = '10.1.1.240:10061' -GI_MODEL_NAME = 'flux' - -GMV_MODEL_URL = '10.1.1.243:10081' -GMV_MODEL_NAME = 'multi_view' - -GMV_RABBITMQ_QUEUES = f"GenerateMultiView{RABBITMQ_ENV}" - -GI_MINIO_BUCKET = "aida-users" -GI_RABBITMQ_QUEUES = f"GenerateImage{RABBITMQ_ENV}" -GI_SYS_IMAGE_URL = "aida-sys-image/generate_image/white_image.jpg" - -# SLOGAN service config -SLOGAN_RABBITMQ_QUEUES = f"Slogan{RABBITMQ_ENV}" - -# Generate Single Logo service config -GSL_MODEL_URL = '10.1.1.243:10041' -GSL_MINIO_BUCKET = "aida-users" -GSL_MODEL_NAME = 'stable_diffusion_xl_transparent' -GEN_SINGLE_LOGO_RABBITMQ_QUEUES = f"GenSingleLogo{RABBITMQ_ENV}" - -# Generate Product service config -# GPI_RABBITMQ_QUEUES = os.getenv("GEN_PRODUCT_IMAGE_RABBITMQ_QUEUES", f"ToProductImage{RABBITMQ_ENV}") -# GPI_MODEL_NAME_OVERALL = 'sdxl_ensemble_all' -# GPI_MODEL_URL = '10.1.1.243:10051' - -# Generate Product service config 旧版product img 模型 -GPI_RABBITMQ_QUEUES = f"ToProductImage{RABBITMQ_ENV}" -BATCH_GPI_RABBITMQ_QUEUES = f"BatchToProductImage{RABBITMQ_ENV}" -GPI_MODEL_NAME_OVERALL = 'diffusion_ensemble_all' -GPI_MODEL_NAME_SINGLE = 'stable_diffusion_1_5_cnet' -GPI_MODEL_URL = '10.1.1.243:10051' - -# Generate Single Logo service config -GRI_RABBITMQ_QUEUES = f"Relight{RABBITMQ_ENV}" -BATCH_GRI_RABBITMQ_QUEUES = f"BatchRelight{RABBITMQ_ENV}" -GRI_MODEL_NAME_OVERALL = 'diffusion_relight_ensemble' -GRI_MODEL_NAME_SINGLE = 'stable_diffusion_1_5_relight' -GRI_MODEL_URL = '10.1.1.240:10051' - -# Pose Transform service config - -PS_RABBITMQ_QUEUES = f"PoseTransform{RABBITMQ_ENV}" -BATCH_PS_RABBITMQ_QUEUES = f"BatchPoseTransform{RABBITMQ_ENV}" -PT_MODEL_URL = '10.1.1.243:10061' - -# SEG service config -SEGMENTATION = { - "new_model_name": "seg_knet", - "name": "seg_ocrnet_hr18", - "input": "seg_input__0", - "output": "seg_output__0", -} -# ollama config -OLLAMA_URL = "http://10.1.1.240:11434/api/embeddings" - -# design batch -BATCH_DESIGN_RABBITMQ_QUEUES = f"DesignBatch{RABBITMQ_ENV}" - -# DESIGN config -DESIGN_MODEL_URL = '10.1.1.240:10000' -AIDA_CLOTHING = "aida-clothing" -KEYPOINT_RESULT_TABLE_FIELD_SET = ('neckline_left', 'neckline_right', 'shoulder_left', 'shoulder_right', 'armpit_left', 'armpit_right', - 'cuff_left_in', 'cuff_left_out', 'cuff_right_in', 'cuff_right_out', 'waistband_left', 'waistband_right') - -# DESIGN 预处理 -IF_DEBUG_SHOW = False - -# 优先级 +# Design前后排优先级 PRIORITY_DICT = { 'earring_front': 99, 'bag_front': 98, @@ -208,28 +115,71 @@ PRIORITY_DICT = { 'bag_back': -98, 'earring_back': -99, } +# Design 关键点字段 +KEYPOINT_RESULT_TABLE_FIELD_SET = ('neckline_left', 'neckline_right', 'shoulder_left', 'shoulder_right', 'armpit_left', 'armpit_right', 'cuff_left_in', 'cuff_left_out', 'cuff_right_in', 'cuff_right_out', 'waistband_left', 'waistband_right') +# milvus配置信息 +MILVUS_TABLE_KEYPOINT = "keypoint_cache_2" -QWEN_API_KEY = "sk-f31c29e61ac2498ba5e307aaa6dc10e0" +# ollama 地址 +OLLAMA_URL = f"http://{settings.A6000_SERVICE_HOST}:11434/api/embeddings" -DB_CONFIG = { - "host": "18.167.251.121", - "port": 3306, - "user": "root", - "password": "QWa998345", - "database": "aida", - "charset": "utf8mb4" -} +"""Triton Server Config""" +# Design +DESIGN_MODEL_URL = f'{settings.A6000_SERVICE_HOST}:10000' +DESIGN_MODEL_NAME = 'seg_knet' +# Generate Image +GI_MODEL_URL = f'{settings.A6000_SERVICE_HOST}:10061' +GI_MODEL_NAME = 'flux' +# Generate Single Logo +GSL_MODEL_URL = f'{settings.B_4_X_4090_SERVICE_HOST}:10041' +GSL_MODEL_NAME = 'stable_diffusion_xl_transparent' +# Generate Product （整套和单品） +GPI_MODEL_URL = f'{settings.B_4_X_4090_SERVICE_HOST}:10051' +GPI_MODEL_NAME_OVERALL = 'diffusion_ensemble_all' +GPI_MODEL_NAME_SINGLE = 'stable_diffusion_1_5_cnet' -TABLE_CATEGORIES = { - "female_dress": "female/dress", - "female_outwear": "female/outwear", - "female_trousers": "female/trousers", - "female_skirt": "female/skirt", - "female_blouse": "female/blouse", - "male_tops": "male/tops", - "male_bottoms": "male/bottoms", - "male_outwear": "male/outwear" -} +# 以下停用中...************* +# 多视角生成 +GMV_MODEL_URL = f'{settings.B_4_X_4090_SERVICE_HOST}:10081' +GMV_MODEL_NAME = 'multi_view' +# 超分 +SR_MODEL_NAME = "super_resolution" +SR_TRITON_URL = f"{settings.A6000_SERVICE_HOST}:10031" +# 打光 +GRI_MODEL_URL = f'{settings.A6000_SERVICE_HOST}:10051' +GRI_MODEL_NAME_OVERALL = 'diffusion_relight_ensemble' +GRI_MODEL_NAME_SINGLE = 'stable_diffusion_1_5_relight' +# agent 图片生成 +FAST_GI_MODEL_URL = f'{settings.B_4_X_4090_SERVICE_HOST}:10011' +FAST_GI_MODEL_NAME = 'stable_diffusion_xl' +# 图转视频 triton版 +PT_MODEL_URL = f'{settings.B_4_X_4090_SERVICE_HOST}:10061' -# --- ComfyUI 配置信息 --- -COMFYUI_SERVER_ADDRESS = "10.1.2.227:8080" # 替换为您的 ComfyUI 服务器地址 +# ************* + +"""MQ 队列信息""" +# 生成图片 moodboard printboard sketchboard +GI_RABBITMQ_QUEUES = f"GenerateImage-{settings.SERVE_ENV}" +# 生成slogan +SLOGAN_RABBITMQ_QUEUES = f"Slogan-{settings.SERVE_ENV}" +# 转产品图 +GPI_RABBITMQ_QUEUES = f"ToProductImage-{settings.SERVE_ENV}" +# 产品图转视频 +PS_RABBITMQ_QUEUES = f"PoseTransform-{settings.SERVE_ENV}" + +# 以下停用中...************* +# 产品图打光 +GRI_RABBITMQ_QUEUES = f"Relight-{settings.SERVE_ENV}" +# 超分 +SR_RABBITMQ_QUEUES = f"SuperResolution-{settings.SERVE_ENV}" +# 生成多视图 +GMV_RABBITMQ_QUEUES = f"GenerateMultiView-{settings.SERVE_ENV}" +# 批量转产品图 +BATCH_GPI_RABBITMQ_QUEUES = f"BatchToProductImage-{settings.SERVE_ENV}" +# 批量打光 +BATCH_GRI_RABBITMQ_QUEUES = f"BatchRelight-{settings.SERVE_ENV}" +# 批量图片转视频 +BATCH_PS_RABBITMQ_QUEUES = f"BatchPoseTransform-{settings.SERVE_ENV}" +# 批量design +BATCH_DESIGN_RABBITMQ_QUEUES = f"DesignBatch-{settings.SERVE_ENV}" +# ************* diff --git a/app/core/mysql_config.py b/app/core/mysql_config.py new file mode 100644 index 0000000..1dd846a --- /dev/null +++ b/app/core/mysql_config.py @@ -0,0 +1,10 @@ +from app.core.config import settings + +DB_CONFIG = { + "host": settings.MYSQL_HOST, + "port": settings.MYSQL_PORT, + "user": settings.MYSQL_USER, + "password": settings.MYSQL_PASSWORD, + "database": settings.MYSQL_DB, + "charset": settings.MYSQL_CHARSET, +} diff --git a/app/core/rabbit_mq_config.py b/app/core/rabbit_mq_config.py new file mode 100644 index 0000000..e4634b0 --- /dev/null +++ b/app/core/rabbit_mq_config.py @@ -0,0 +1,10 @@ +# rabbitmq config +import pika +from app.core.config import settings + +RABBITMQ_PARAMS = { + "host": settings.MQ_HOST, + "port": settings.MQ_PORT, + "credentials": pika.credentials.PlainCredentials(username=settings.MQ_USERNAME, password=settings.MQ_PASSWORD), + "virtual_host": settings.MQ_VIRTUAL_HOST, +} diff --git a/app/design_batch/request_data/requests_data.json b/app/design_batch/request_data/requests_data.json index 1dba8d1..32e7c52 100644 --- a/app/design_batch/request_data/requests_data.json +++ b/app/design_batch/request_data/requests_data.json @@ -79,12 +79,8 @@ } ] } - ], "process_id": "87", - "tasks_id": , + "tasks_id": "" } - -//用 openai jsonl -// \ No newline at end of file diff --git a/app/main.py b/app/main.py index cbdce4a..e1c6f95 100644 --- a/app/main.py +++ b/app/main.py @@ -1,31 +1,40 @@ +# 1. 这里的顺序至关重要！必须在最顶端 +import sys + +try: + import asyncore +except ImportError: + import pyasyncore + + sys.modules['asyncore'] = pyasyncore import logging.config import uvicorn -from apscheduler.schedulers.background import BackgroundScheduler -from apscheduler.triggers.cron import CronTrigger from fastapi import FastAPI -from fastapi import HTTPException, Request +from fastapi import HTTPException from fastapi.responses import JSONResponse from app.api.api_route import router from app.core.config import settings from app.core.record_api_count import count_api_calls from app.schemas.response_template import ResponseModel -from app.service.recommend.service import load_resources from logging_env import LOGGER_CONFIG_DICT +from dotenv import load_dotenv +from starlette.middleware.cors import CORSMiddleware logging.config.dictConfig(LOGGER_CONFIG_DICT) logging.getLogger("pika").setLevel(logging.WARNING) -from starlette.middleware.cors import CORSMiddleware - logger = logging.getLogger(__name__) +load_dotenv() + def get_application() -> FastAPI: application = FastAPI( - title=settings.PROJECT_NAME, docs_url="/docs", redoc_url='/re-docs', - openapi_url=f"{settings.API_PREFIX}/openapi.json", + docs_url="/docs", + redoc_url='/re-docs', + openapi_url=f"/openapi.json", description=''' Base frame with FastAPI - Super Resolution API @@ -34,13 +43,13 @@ def get_application() -> FastAPI: ) application.add_middleware( CORSMiddleware, - allow_origins=[str(origin) for origin in settings.BACKEND_CORS_ORIGINS], + allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) application.middleware("http")(count_api_calls) - application.include_router(router=router, prefix=settings.API_PREFIX) + application.include_router(router=router) return application @@ -48,14 +57,12 @@ app = get_application() @app.exception_handler(HTTPException) -async def http_exception_handler(request: Request, exc: HTTPException): +async def http_exception_handler(exc: HTTPException): return JSONResponse( status_code=exc.status_code, content=ResponseModel(code=exc.status_code, msg=exc.detail, data=exc.detail).dict() ) - - if __name__ == '__main__': - uvicorn.run(app, host="0.0.0.0", port=8000) + uvicorn.run(app, host="0.0.0.0", port=settings.PORT) diff --git a/app/schemas/design.py b/app/schemas/design.py index 3fb6de7..6f0a633 100644 --- a/app/schemas/design.py +++ b/app/schemas/design.py @@ -1,4 +1,15 @@ -from pydantic import BaseModel +from typing import List, Optional + +from pydantic import BaseModel, Field + + +class SAMRequestModel(BaseModel): + user_id: int = Field(..., description="用户id, 必填字段") + image_path: str = Field(..., description="图片路径，必填字段") + type: str = Field(..., description="推理类型，必填字段") + points: Optional[List[List[float]]] = None + labels: Optional[List[int]] = None + box: Optional[List[int]] = None class DesignModel(BaseModel): diff --git a/app/service/attribute/service_att_recognition.py b/app/service/attribute/service_att_recognition.py index f93146e..c007184 100644 --- a/app/service/attribute/service_att_recognition.py +++ b/app/service/attribute/service_att_recognition.py @@ -1,22 +1,24 @@ #!/usr/bin/env python # -*- coding: UTF-8 -*- -import logging from pprint import pprint -import torch + import cv2 import mmcv import numpy as np import pandas as pd -from minio import Minio +import torch import tritonclient.http as httpclient -from app.core.config import * +from minio import Minio + +from app.core.config import settings, DESIGN_MODEL_URL from app.schemas.attribute_retrieve import AttributeRecognitionModel -from app.service.utils.oss_client import oss_get_image +from app.service.utils.new_oss_client import oss_get_image + +minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE) class AttributeRecognition: def __init__(self, const, request_data): - # self.minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE) self.request_data = [] for i, sketch in enumerate(request_data): self.request_data.append( @@ -96,11 +98,12 @@ class AttributeRecognition: res = {**dict1, **dict2} return res - def get_image(self, url): + @staticmethod + def get_image(url): # response = self.minio_client.get_object(url.split("/", 1)[0], url.split("/", 1)[1]) # img = np.frombuffer(response.data, np.uint8) # 转成8位无符号整型 # img = cv2.imdecode(img, cv2.IMREAD_COLOR) # - img = oss_get_image(bucket=url.split("/", 1)[0], object_name=url.split("/", 1)[1], data_type="cv2") + img = oss_get_image(oss_client=minio_client, bucket=url.split("/", 1)[0], object_name=url.split("/", 1)[1], data_type="cv2") img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) return img diff --git a/app/service/attribute/service_category_recognition.py b/app/service/attribute/service_category_recognition.py index 7c277c9..5a04ba2 100644 --- a/app/service/attribute/service_category_recognition.py +++ b/app/service/attribute/service_category_recognition.py @@ -7,24 +7,25 @@ @Date ：2023/9/16 18:31:08 @detail ： """ +from minio import Minio from skimage import transform import cv2 import mmcv import numpy as np import pandas as pd -from minio import Minio import tritonclient.http as httpclient import torch -from app.core.config import * +from app.core.config import settings, DESIGN_MODEL_URL from app.schemas.attribute_retrieve import CategoryRecognitionModel -from app.service.utils.oss_client import oss_get_image +from app.service.utils.new_oss_client import oss_get_image + +minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE) class CategoryRecognition: def __init__(self, request_data): - self.attr_type = pd.read_csv(CATEGORY_PATH) - # self.minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE) + self.attr_type = pd.read_csv(settings.CATEGORY_PATH) self.request_data = [] self.triton_client = httpclient.InferenceServerClient(url=DESIGN_MODEL_URL) for sketch in request_data: @@ -46,13 +47,14 @@ class CategoryRecognition: preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0) return preprocessed_img - def get_image(self, url): + @staticmethod + def get_image(url): # Get data of an object. # Read data from response. # response = self.minio_client.get_object(url.split("/", 1)[0], url.split("/", 1)[1]) # img = np.frombuffer(response.data, np.uint8) # 转成8位无符号整型 # img = cv2.imdecode(img, cv2.IMREAD_COLOR) # 解码 - img = oss_get_image(bucket=url.split("/", 1)[0], object_name=url.split("/", 1)[1], data_type="cv2") + img = oss_get_image(oss_client=minio_client, bucket=url.split("/", 1)[0], object_name=url.split("/", 1)[1], data_type="cv2") img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) return img @@ -68,7 +70,7 @@ class CategoryRecognition: colattr = list(self.attr_type['labelName']) - task = self.attr_type['taskName'][0] + # self.attr_type['taskName'][0] maxsc = np.max(scores[0][:5]) indexs = np.argwhere(scores == maxsc)[:, 1] diff --git a/app/service/brand_dna/service.py b/app/service/brand_dna/service.py index 393d75a..148e1e9 100644 --- a/app/service/brand_dna/service.py +++ b/app/service/brand_dna/service.py @@ -9,15 +9,16 @@ import torch.nn.functional as F import tritonclient.http as httpclient from minio import Minio -from app.core.config import MINIO_URL, MINIO_ACCESS, MINIO_SECRET, MINIO_SECURE, DESIGN_MODEL_URL, CATEGORY_PATH +from app.core.config import DESIGN_MODEL_URL +from app.core.config import settings from app.schemas.brand_dna import BrandDnaModel -from app.service.attribute.config import local_debug_const, const +from app.service.attribute.config import const from app.service.utils.generate_uuid import generate_uuid from app.service.utils.new_oss_client import oss_upload_image, oss_get_image -logger = logging.getLogger() +minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE) -minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE) +logger = logging.getLogger() class BrandDna: @@ -25,7 +26,7 @@ class BrandDna: self.sketch_bucket = "test" self.image_url = request_item.image_url self.is_brand_dna = request_item.is_brand_dna - self.attr_type = pd.read_csv(CATEGORY_PATH) + self.attr_type = pd.read_csv(settings.CATEGORY_PATH) # self.attr_type = pd.read_csv(r"E:\workspace\trinity_client_aida\app\service\attribute\config\descriptor\category\category_dis.csv") self.att_client = httpclient.InferenceServerClient(url=DESIGN_MODEL_URL) self.seg_client = httpclient.InferenceServerClient(url='10.1.1.243:30000') diff --git a/app/service/brand_dna/service_generate_brand_info.py b/app/service/brand_dna/service_generate_brand_info.py index 367a4d6..fa8d2e5 100644 --- a/app/service/brand_dna/service_generate_brand_info.py +++ b/app/service/brand_dna/service_generate_brand_info.py @@ -3,23 +3,25 @@ import logging import cv2 import numpy as np import tritonclient.grpc as grpcclient -from langchain.output_parsers import ResponseSchema, StructuredOutputParser +from langchain_classic.output_parsers import ResponseSchema, StructuredOutputParser from langchain_community.chat_models import ChatTongyi from langchain_core.prompts import PromptTemplate -# from langchain_openai import ChatOpenAI from minio import Minio from tritonclient.utils import np_to_triton_dtype -from app.core.config import GI_MODEL_URL, MINIO_URL, MINIO_ACCESS, MINIO_SECRET, MINIO_SECURE, GI_MODEL_NAME +from app.core.config import GI_MODEL_URL, GI_MODEL_NAME from app.schemas.brand_dna import GenerateBrandModel from app.service.utils.generate_uuid import generate_uuid from app.service.utils.new_oss_client import oss_upload_image +from app.core.config import settings + class GenerateBrandInfo: def __init__(self, request_data): # minio client init - self.minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE) + self.generate_logo_prompt = None + self.minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE) # user info init self.user_id = request_data.user_id @@ -55,7 +57,7 @@ class GenerateBrandInfo: return self.result_data def llm_generate_brand_info(self): - output = self.model(self._input.to_messages()) + output = self.model.invoke(self._input.to_messages()) brand_data = self.output_parser.parse(output.content) self.result_data = brand_data self.generate_logo_prompt = brand_data['brand_logo_prompt'] @@ -87,8 +89,8 @@ class GenerateBrandInfo: def upload_logo_image(self, image, object_name): try: _, img_byte_array = cv2.imencode('.jpg', image) - object_name = f'{self.user_id}/{self.category}/{object_name}' - req = oss_upload_image(oss_client=self.minio_client, bucket="aida-users", object_name=object_name, image_bytes=img_byte_array) + object_name = f'{self.user_id}/{self.category}/{object_name}.jpg' + oss_upload_image(oss_client=self.minio_client, bucket="aida-users", object_name=object_name, image_bytes=img_byte_array) image_url = f"aida-users/{object_name}" return image_url except Exception as e: diff --git a/app/service/brand_dna/test.py b/app/service/brand_dna/test.py deleted file mode 100644 index 966f76e..0000000 --- a/app/service/brand_dna/test.py +++ /dev/null @@ -1,32 +0,0 @@ -from dotenv import load_dotenv -from langchain.output_parsers import StructuredOutputParser, ResponseSchema -from langchain_core.prompts import PromptTemplate -from langchain_openai import ChatOpenAI - -# 加载.env文件的环境变量 -load_dotenv() - -# 创建一个大语言模型，model指定了大语言模型的种类 -model = ChatOpenAI(model="qwen2.5-14b-instruct") - -# 想要接收的响应模式 -response_schemas = [ - ResponseSchema(name="brand_name", description="Brand name."), - ResponseSchema(name="brand_slogan", description="Brand slogan."), - ResponseSchema(name="brand_logo_prompt", description="prompt required for brand logo generation.") -] -output_parser = StructuredOutputParser.from_response_schemas(response_schemas) -format_instructions = output_parser.get_format_instructions() -prompt = PromptTemplate( - template="你是一个时装品牌的设计师。根据用户输入提取出brand name，brand slogan,brand logo 描述。如果没有以上内容，需要你根据用户输入随意发挥。随后根据brand logo 描述生成一个prompt，这个prompt用于生成模型.\n{format_instructions}\n{question}", - input_variables=["question"], - partial_variables={"format_instructions": format_instructions} -) -_input = prompt.format_prompt(question="brand name: cat home") - -output = model(_input.to_messages()) -brand_data = output_parser.parse(output.content) - - -def generate_logo(bucket_name, object_name, prompt): - pass diff --git a/app/service/chat_robot/script/agents/agent_executor.py b/app/service/chat_robot/script/agents/agent_executor.py index cc69936..68460d9 100644 --- a/app/service/chat_robot/script/agents/agent_executor.py +++ b/app/service/chat_robot/script/agents/agent_executor.py @@ -3,27 +3,20 @@ import json import logging from typing import Any, Dict, List, Optional, Union, Tuple -from langchain.agents import AgentExecutor -from langchain.callbacks.manager import Callbacks, CallbackManager -from langchain.load.dump import dumpd -from langchain.schema import RUN_KEY, RunInfo +from langchain_classic.agents import AgentExecutor +from langchain_classic.schema import RUN_KEY from langchain_core.agents import AgentAction, AgentFinish +from langchain_core.callbacks import Callbacks, CallbackManager +from langchain_core.load import dumpd +from langchain_core.outputs import RunInfo class CustomAgentExecutor(AgentExecutor): - def __call__( - self, - inputs: Union[Dict[str, Any], Any], - return_only_outputs: bool = False, - callbacks: Callbacks = None, - session_key: str = "", - *, - tags: Optional[List[str]] = None, - include_run_info: bool = False, - ) -> Dict[str, Any]: + def __call__(self, inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Callbacks = None, session_key: str = "", *, tags: Optional[List[str]] = None, include_run_info: bool = False, **kwargs) -> Dict[str, Any]: """Run the logic of this chain and add to output if desired. Args: + **kwargs: inputs: Dictionary of inputs, or single input if chain expects only one param. return_only_outputs: boolean for whether to return only outputs in the @@ -72,7 +65,7 @@ class CustomAgentExecutor(AgentExecutor): """Validate and prep outputs.""" self._validate_outputs(outputs) if self.memory is not None and outputs['need_record']: - self.memory.save_context(inputs, outputs, session_key) + self.memory.save_context(inputs, outputs) if return_only_outputs: return outputs else: @@ -95,7 +88,7 @@ class CustomAgentExecutor(AgentExecutor): ) inputs = {list(_input_keys)[0]: inputs} if self.memory is not None: - external_context = self.memory.load_memory_variables(inputs, session_key) + external_context = self.memory.load_memory_variables(inputs) inputs = dict(inputs, **external_context) self._validate_inputs(inputs) return inputs @@ -119,7 +112,8 @@ class CustomAgentExecutor(AgentExecutor): {return_value_key: observation}, "", ) - except: + except Exception as e: + print(e) pass # Invalid tools won't be in the map, so we return False. diff --git a/app/service/chat_robot/script/agents/conversational_functions_agent.py b/app/service/chat_robot/script/agents/conversational_functions_agent.py index eb362a7..6a42307 100644 --- a/app/service/chat_robot/script/agents/conversational_functions_agent.py +++ b/app/service/chat_robot/script/agents/conversational_functions_agent.py @@ -1,26 +1,15 @@ import json -import re +from dataclasses import dataclass from json import JSONDecodeError from typing import List, Tuple, Any, Union -from dataclasses import dataclass -from langchain.callbacks.manager import Callbacks -from langchain.agents import ( - OpenAIFunctionsAgent, -) -from langchain.schema import ( - AgentAction, - AgentFinish, - BaseMessage, - OutputParserException -) -from langchain.schema.messages import ( - AIMessage, - FunctionMessage -) -from langchain.tools import BaseTool, StructuredTool -# from langchain.tools.convert_to_openai import FunctionDescription -from langchain.utils.openai_functions import FunctionDescription +from langchain_classic.agents import OpenAIFunctionsAgent +from langchain_community.utils.ernie_functions import FunctionDescription +from langchain_core.agents import AgentAction, AgentFinish +from langchain_core.callbacks import Callbacks +from langchain_core.exceptions import OutputParserException +from langchain_core.messages import BaseMessage, AIMessage, FunctionMessage +from langchain_core.tools import BaseTool @dataclass @@ -76,7 +65,6 @@ def _create_function_message( content = observation return FunctionMessage( name=agent_action.tool, - content=content, ) @@ -177,6 +165,7 @@ class ConversationalFunctionsAgent(OpenAIFunctionsAgent): into it. Args: + callbacks: intermediate_steps: Steps the LLM has taken to date, along with observations **kwargs: User inputs. **kwargs: Including user's input string diff --git a/app/service/chat_robot/script/callbacks/openai_token_record_callback.py b/app/service/chat_robot/script/callbacks/openai_token_record_callback.py index 83d22a1..198fb14 100644 --- a/app/service/chat_robot/script/callbacks/openai_token_record_callback.py +++ b/app/service/chat_robot/script/callbacks/openai_token_record_callback.py @@ -2,18 +2,16 @@ from typing import Any, Dict from langchain_community.callbacks.openai_info import OpenAICallbackHandler -from langchain.schema import LLMResult from langchain_community.callbacks.openai_info import standardize_model_name, MODEL_COST_PER_1K_TOKENS, \ get_openai_token_cost_for_model - - -# from langchain.callbacks.openai_info import standardize_model_name, MODEL_COST_PER_1K_TOKENS, get_openai_token_cost_for_model +from langchain_core.outputs import LLMResult class OpenAITokenRecordCallbackHandler(OpenAICallbackHandler): need_record: bool = True response_type: str = "string" """Callback Handler that tracks OpenAI info and write to redis after agent finish""" + def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None: """Collect token usage.""" if response.llm_output is None: @@ -22,7 +20,7 @@ class OpenAITokenRecordCallbackHandler(OpenAICallbackHandler): if "token_usage" not in response.llm_output: return None if "function_call" in response.generations[0][0].message.additional_kwargs: - if response.generations[0][0].message.additional_kwargs["function_call"]["name"] in ["sql_db_query", "sql_db_schema","tutorial_tool"]: + if response.generations[0][0].message.additional_kwargs["function_call"]["name"] in ["sql_db_query", "sql_db_schema", "tutorial_tool"]: self.need_record = False if response.generations[0][0].message.additional_kwargs["function_call"]["name"] == "sql_db_query": self.response_type = "image" @@ -39,6 +37,7 @@ class OpenAITokenRecordCallbackHandler(OpenAICallbackHandler): self.total_tokens += token_usage.get("total_tokens", 0) self.prompt_tokens += prompt_tokens self.completion_tokens += completion_tokens + return None def on_chain_end(self, outputs: Dict, **kwargs: Any) -> None: """Write token usage to redis.""" diff --git a/app/service/chat_robot/script/database.py b/app/service/chat_robot/script/database.py index bcc0b2c..934c82c 100644 --- a/app/service/chat_robot/script/database.py +++ b/app/service/chat_robot/script/database.py @@ -44,12 +44,17 @@ class CustomDatabase(SQLDatabase): final_str = "\n\n".join(tables) return final_str - def run(self, command: str, fetch: str = "all") -> str: + def run(self, command: str, fetch: str = "all", **kwargs) -> str: """Execute a SQL command and return a string representing the results. If the statement returns rows, a string of the results is returned. If the statement returns no rows, an empty string is returned. + Args: + command: + fetch: + **kwargs: + """ with self._engine.begin() as connection: if self._schema is not None: diff --git a/app/service/chat_robot/script/main.py b/app/service/chat_robot/script/main.py index 573fae5..1bfe322 100644 --- a/app/service/chat_robot/script/main.py +++ b/app/service/chat_robot/script/main.py @@ -1,15 +1,15 @@ import json import logging -from langchain.agents import Tool -from langchain.callbacks import FileCallbackHandler -from langchain.prompts.chat import ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder -from langchain.schema import SystemMessage, AIMessage -from langchain.utilities import SerpAPIWrapper +from langchain_community.utilities import SerpAPIWrapper +from langchain_core.callbacks import FileCallbackHandler +from langchain_core.messages import SystemMessage, AIMessage +from langchain_core.prompts import MessagesPlaceholder, HumanMessagePromptTemplate, ChatPromptTemplate +from langchain_core.tools import Tool from langchain_community.chat_models import ChatTongyi from loguru import logger -from app.core.config import * +from app.core.config import settings from app.service.chat_robot.script.agents import CustomAgentExecutor, ConversationalFunctionsAgent from app.service.chat_robot.script.database import CustomDatabase from app.service.chat_robot.script.memory import UserConversationBufferWindowMemory @@ -30,10 +30,10 @@ log_handler = FileCallbackHandler(logfile) # # callbacks=[OpenAICallbackHandler()] # ) -llm = ChatTongyi(api_key=QWEN_API_KEY) +llm = ChatTongyi(api_key=settings.QWEN_API_KEY) search = SerpAPIWrapper() -db = CustomDatabase.from_uri(f'mysql+pymysql://{DB_USERNAME}:{DB_PASSWORD}@{DB_HOST}:{DB_PORT}/attribute_retrieval_V3', +db = CustomDatabase.from_uri(f'mysql+pymysql://{settings.DB_USERNAME}:{settings.DB_PASSWORD}@{settings.DB_HOST}:{settings.DB_PORT}/attribute_retrieval_V3', include_tables=['female_top', 'female_skirt', 'female_pants', 'female_dress', 'female_outwear', 'male_bottom', 'male_top', 'male_outwear'], engine_args={"pool_recycle": 7200}) @@ -43,11 +43,11 @@ tools = [ description="Can be used to perform Internet searches", func=search.run ), - QuerySQLDataBaseTool(db=db, return_direct=False), + QuerySQLDataBaseTool(db=db), InfoSQLDatabaseTool(db=db), ListSQLDatabaseTool(db=db), # QuerySQLCheckerTool(db=db, llm=OpenAI(temperature=0, openai_api_key=OPENAI_API_KEY)), - QuerySQLCheckerTool(db=db, llm=ChatTongyi(temperature=0, api_key=QWEN_API_KEY)), + QuerySQLCheckerTool(db=db, llm=ChatTongyi(api_key=settings.QWEN_API_KEY)), # Tool( # name="tutorial_tool", # description="Utilize this tool to retrieve specific statements related to user guidance tutorials." @@ -133,5 +133,5 @@ def chat(post_data): 'completion_tokens': final_outputs['completion_tokens'], 'response_type': final_outputs["response_type"] } - logging.info(json.dumps(api_response)) + logging.info(json.dumps(api_response, indent=4)) return api_response diff --git a/app/service/chat_robot/script/memory/user_buffer_window.py b/app/service/chat_robot/script/memory/user_buffer_window.py index 1c70f02..d6315e0 100644 --- a/app/service/chat_robot/script/memory/user_buffer_window.py +++ b/app/service/chat_robot/script/memory/user_buffer_window.py @@ -3,13 +3,12 @@ from typing import Any, Dict, List, Tuple import json import redis +from langchain_classic.memory.chat_memory import BaseChatMemory +from langchain_classic.memory.utils import get_prompt_input_key +from langchain_core.messages import messages_from_dict, get_buffer_string, BaseMessage, HumanMessage, AIMessage, message_to_dict from redis import Redis -from langchain.memory.chat_memory import BaseChatMemory -from langchain.schema.messages import BaseMessage, get_buffer_string, HumanMessage, AIMessage -from langchain.schema.messages import _message_to_dict, messages_from_dict -from langchain.memory.utils import get_prompt_input_key -from app.core.config import * +from app.core.config import settings class UserConversationBufferWindowMemory(BaseChatMemory): @@ -24,8 +23,8 @@ class UserConversationBufferWindowMemory(BaseChatMemory): @classmethod def from_redis( cls, - host: str = REDIS_HOST, - port: int = REDIS_PORT, + host: str = settings.REDIS_HOST, + port: int = settings.REDIS_PORT, db: int = 3, **kwargs ): @@ -79,7 +78,7 @@ class UserConversationBufferWindowMemory(BaseChatMemory): return inputs[prompt_input_key], outputs[output_key] def add_message(self, key: str, message: BaseMessage) -> None: - self.redis_client.lpush(key, json.dumps(_message_to_dict(message))) + self.redis_client.lpush(key, json.dumps(message_to_dict(message))) def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str], key: str = "") -> None: """Save context from this conversation to buffer.""" diff --git a/app/service/chat_robot/script/service/CallQWen.py b/app/service/chat_robot/script/service/CallQWen.py index 5ab74ba..ea0c75b 100644 --- a/app/service/chat_robot/script/service/CallQWen.py +++ b/app/service/chat_robot/script/service/CallQWen.py @@ -5,10 +5,10 @@ from dashscope import Generation from retry import retry from urllib3.exceptions import NewConnectionError -from app.core.config import * +from app.core.config import settings from app.service.chat_robot.script.callbacks.qwen_callback_handler import QWenCallbackHandler from app.service.chat_robot.script.database import CustomDatabase -from app.service.chat_robot.script.prompt import FASHION_CHAT_BOT_PREFIX, TOOLS_FUNCTIONS_SUFFIX, TUTORIAL_TOOL_RETURN, \ +from app.service.chat_robot.script.prompt import TOOLS_FUNCTIONS_SUFFIX, TUTORIAL_TOOL_RETURN, \ GET_LANGUAGE_PREFIX, FASHION_CHAT_BOT_PREFIX_TEMP from app.service.search_image_with_text.service import query @@ -149,7 +149,7 @@ tools = [ } ] -db = CustomDatabase.from_uri(f'mysql+pymysql://{DB_USERNAME}:{DB_PASSWORD}@{DB_HOST}:{DB_PORT}/attribute_retrieval_V3', +db = CustomDatabase.from_uri(f'mysql+pymysql://{settings.MYSQL_USER}:{settings.MYSQL_PASSWORD}@{settings.MYSQL_HOST}:{settings.MYSQL_PORT}/attribute_retrieval_V3', include_tables=['female_top', 'female_skirt', 'female_pants', 'female_dress', 'female_outwear', 'male_bottom', 'male_top', 'male_outwear'], engine_args={"pool_recycle": 7200}) @@ -159,7 +159,7 @@ qwen = QWenCallbackHandler() def search_from_internet(message): response = Generation.call( model='qwen-turbo', - api_key=QWEN_API_KEY, + api_key=settings.QWEN_API_KEY, messages=message, prompt='The output must be in English.Keep the final result under 200 words.' # tools=tools, @@ -190,7 +190,7 @@ def get_image_from_vector_db(gender, content): def get_response(messages): response = Generation.call( model='qwen-max', - api_key=QWEN_API_KEY, + api_key=settings.QWEN_API_KEY, messages=messages, tools=tools, # seed=random.randint(1, 10000), # 设置随机数种子seed，如果没有设置，则随机数种子默认为1234 @@ -203,7 +203,7 @@ def get_response(messages): def get_assistant_response(messages): response = Generation.call( model='qwen-max', - api_key=QWEN_API_KEY, + api_key=settings.QWEN_API_KEY, messages=messages, # seed=random.randint(1, 10000), # 设置随机数种子seed，如果没有设置，则随机数种子默认为1234 result_format='message', # 将输出设置为message形式 @@ -212,8 +212,10 @@ def get_assistant_response(messages): return response +global tool_info + + def call_with_messages(message): - global tool_info user_input = message print('\n') @@ -241,7 +243,7 @@ def call_with_messages(message): response_type = "chat" while flag and count <= 3: - first_response = get_response(messages) + first_response = get_response assistant_output = first_response.output.choices[0].message QWenCallbackHandler.on_llm_end(qwen, first_response.usage) print(f"\n大模型第 {count} 轮输出信息：{first_response}\n") @@ -260,7 +262,7 @@ def call_with_messages(message): ] tool_info['content'] = search_from_internet(message) flag = False - result_content = tool_info['content'].output.text + result_content = tool_info['content'] # 如果模型选择的工具是get_database_table # elif assistant_output.tool_calls[0]['function']['name'] == 'get_database_table': # tool_info = {"name": "get_database_table", "role": "tool", 'content': get_database_table()} diff --git a/app/service/chat_robot/script/tools/sql_tools.py b/app/service/chat_robot/script/tools/sql_tools.py index 89576d3..1f82616 100644 --- a/app/service/chat_robot/script/tools/sql_tools.py +++ b/app/service/chat_robot/script/tools/sql_tools.py @@ -2,21 +2,15 @@ """Tools for interacting with a SQL database.""" from typing import Any, Dict, Optional, Type -from pydantic import BaseModel, Extra, Field, root_validator - -from langchain.base_language import BaseLanguageModel -from langchain.callbacks.manager import ( - AsyncCallbackManagerForToolRun, - CallbackManagerForToolRun, -) -from langchain.chains.llm import LLMChain -from langchain.prompts import PromptTemplate +from langchain_community.tools.sql_database.prompt import QUERY_CHECKER +from langchain_community.tools.sql_database.tool import _QuerySQLCheckerToolInput # from langchain.sql_database import SQLDatabase from langchain_community.utilities import SQLDatabase -from langchain.tools.base import BaseTool -from langchain_community.tools.sql_database.prompt import QUERY_CHECKER - -from langchain_community.tools.sql_database.tool import QuerySQLCheckerTool, _QuerySQLCheckerToolInput +from langchain_core.callbacks import CallbackManagerForToolRun, AsyncCallbackManagerForToolRun +from langchain_core.language_models import BaseLanguageModel +from langchain_core.prompts import PromptTemplate +from langchain_core.tools import BaseTool +from pydantic import BaseModel, Extra, Field, root_validator class BaseSQLDatabaseTool(BaseModel): @@ -62,7 +56,7 @@ class QuerySQLDataBaseTool(BaseSQLDatabaseTool, BaseTool): "LIMIT 1'" "Example Input 2: 'SELECT img_name FROM top WHERE sleeve_length = 'Long' AND type = 'Blouse' " "order by rand() LIMIT 2'" - ) + ) def _run( self, @@ -95,9 +89,9 @@ class InfoSQLDatabaseTool(BaseSQLDatabaseTool, BaseTool): "Input to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables." "There are eight tables covering eight fashion categories: female_top, female_pants, female_dress," "female_skirt, female_outwear, male_bottom, male_top, and male_outwear." - + "Example Input: 'female_outwear, male_top'" - ) + ) def _run( self, @@ -183,11 +177,11 @@ class QuerySQLCheckerTool(BaseSQLDatabaseTool, BaseTool): args_schema: Type[BaseModel] = _QuerySQLCheckerToolInput @root_validator(pre=True) - def initialize_llm_chain(cls, values: Dict[str, Any]) -> Dict[str, Any]: + def initialize_llm_chain(self, values: Dict[str, Any]) -> Dict[str, Any]: if "llm_chain" not in values: # from langchain.chains.llm import LLMChain - llm = values.get("llm") # type: ignore[arg-type] + llm = values.get("llm") # type: ignore[arg-type] prompt = PromptTemplate( template=QUERY_CHECKER, input_variables=["dialect", "query"] ) diff --git a/app/service/chat_robot/script/tools/tutorial_tool.py b/app/service/chat_robot/script/tools/tutorial_tool.py index c08eb9d..64c70ba 100644 --- a/app/service/chat_robot/script/tools/tutorial_tool.py +++ b/app/service/chat_robot/script/tools/tutorial_tool.py @@ -1,6 +1,6 @@ from typing import Any -from langchain.tools.base import BaseTool +from langchain_core.tools import BaseTool from app.service.chat_robot.script.prompt import TUTORIAL_TOOL_RETURN diff --git a/app/service/clothing_seg/service.py b/app/service/clothing_seg/service.py index 34ea7ee..e3cf83c 100644 --- a/app/service/clothing_seg/service.py +++ b/app/service/clothing_seg/service.py @@ -9,14 +9,14 @@ from PIL import Image from minio import Minio from tritonclient.utils import np_to_triton_dtype -from app.core.config import * +from app.core.config import settings from app.schemas.clothing_seg import ClothingSegModel from app.service.design_fast.utils.design_ensemble import get_seg_result from app.service.utils.decorator import RunTime from app.service.utils.generate_uuid import generate_uuid from app.service.utils.new_oss_client import oss_get_image, oss_upload_image -minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE) +minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE) class ClothingSeg: @@ -64,9 +64,9 @@ class ClothingSeg: if image_type == "sketch": if len(image.shape) == 2: image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB) - seg_mask = get_seg_result(1, image[:, :, :3]) + seg_mask = get_seg_result(image[:, :, :3]) else: - seg_mask = get_seg_result(1, image[:, :, :3]) + seg_mask = get_seg_result(image[:, :, :3]) temp = seg_mask != 0.0 mask = (255 * (temp + 0).astype(np.uint8)) x_min, y_min, x_max, y_max = get_bounding_box(mask) diff --git a/app/service/comfyui_I2V/flf2v_server.py b/app/service/comfyui_I2V/flf2v_server.py index 448f9ad..6a14550 100644 --- a/app/service/comfyui_I2V/flf2v_server.py +++ b/app/service/comfyui_I2V/flf2v_server.py @@ -12,7 +12,8 @@ from PIL import Image from minio import Minio, S3Error from moviepy.video.io.VideoFileClip import VideoFileClip -from app.core.config import MINIO_URL, MINIO_ACCESS, MINIO_SECRET, MINIO_SECURE, COMFYUI_SERVER_ADDRESS, PS_RABBITMQ_QUEUES, DEBUG +from app.core.config import PS_RABBITMQ_QUEUES +from app.core.config import settings from app.schemas.comfyui_i2v import ComfyuiFLF2VModel from app.service.generate_image.utils.mq import publish_status @@ -305,13 +306,14 @@ workflow_json = { class ComfyUIServerFLF2V: def __init__(self, request_data): + self.pose_transform_data = None self.start_image_url = request_data.start_image_url self.end_image_url = request_data.end_image_url self.prompt = request_data.prompt self.tasks_id = request_data.tasks_id self.user_id = self.tasks_id[self.tasks_id.rfind('-') + 1:] self.server_status_data = {'tasks_id': self.tasks_id, 'status': 'PENDING', 'message': "pending", 'gif_url': '', 'video_url': '', 'image_url': ''} - self.minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE) + self.minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE) def get_result(self): workflow_json['6']['inputs']['text'] = self.prompt @@ -341,7 +343,7 @@ class ComfyUIServerFLF2V: # 1. 提交任务 prompt_response = self.queue_prompt(workflow_json, self.tasks_id) if not prompt_response: - return + return None prompt_id = prompt_response.get("prompt_id") logger.info(f" 任务已提交，Prompt ID: {prompt_id}") @@ -361,6 +363,7 @@ class ComfyUIServerFLF2V: } logger.info(file_list) return self.process_and_upload_comfyui_video(filename=file_list['filename'], subfolder=file_list['subfolder'], prompt_id=prompt_response['prompt_id']), prompt_id + return None def download_from_minio_in_memory(self, image_url): bucket = image_url.split('/')[0] @@ -391,8 +394,9 @@ class ComfyUIServerFLF2V: logger.error(f"❌ MinIO 下载过程中发生未知错误: {e}") return None, None - def upload_in_memory_file_to_comfyui(self, in_memory_file, filename): - upload_url = f"http://{COMFYUI_SERVER_ADDRESS}/upload/image" + @staticmethod + def upload_in_memory_file_to_comfyui(in_memory_file, filename): + upload_url = f"http://{settings.COMFYUI_SERVER_ADDRESS}/upload/image" data = { "overwrite": "true", @@ -430,7 +434,7 @@ class ComfyUIServerFLF2V: # 1. 从 ComfyUI 获取视频二进制数据 mp4_bytes = self.get_comfyui_video_bytes(filename, subfolder) if not mp4_bytes: - return + return None # 2. 准备进行视频处理 # moviepy 不支持直接使用 bytes，需要将 bytes 写入一个 BytesIO 或临时文件 @@ -518,7 +522,7 @@ class ComfyUIServerFLF2V: self.pose_transform_data = {'tasks_id': self.tasks_id, 'status': 'SUCCESS', 'message': "success", 'gif_url': f'aida-users/{GIF_OBJECT}', 'video_url': f'aida-users/{MP4_OBJECT}', 'image_url': f'aida-users/{FRAME_OBJECT}'} # 推送消息 - if not DEBUG: + if not settings.DEBUG: publish_status(json.dumps(self.pose_transform_data), PS_RABBITMQ_QUEUES) logger.info( f" [x] Sent to： {PS_RABBITMQ_QUEUES} data：@@@@ {json.dumps(self.pose_transform_data, indent=4)}") @@ -530,13 +534,14 @@ class ComfyUIServerFLF2V: return None # --- 辅助函数：提交任务到队列 --- - def queue_prompt(self, prompt, client_id): + @staticmethod + def queue_prompt(prompt, client_id): """向 ComfyUI 提交工作流提示。""" p = {"prompt": prompt, "client_id": client_id, "prompt_id": client_id} data = json.dumps(p).encode('utf-8') # 提交任务到 /prompt 端点 - response = requests.post(f"http://{COMFYUI_SERVER_ADDRESS}/prompt", data=data) + response = requests.post(f"http://{settings.COMFYUI_SERVER_ADDRESS}/prompt", data=data) # print(f"-------------{response.text}") # print(f"------------{client_id}") @@ -547,9 +552,10 @@ class ComfyUIServerFLF2V: logger.warning(response.text) return None - def poll_history(self, prompt_id, interval_seconds=5): + @staticmethod + def poll_history(prompt_id, interval_seconds=5): """步骤 2: 轮询 /history/{prompt_id} 检查任务是否完成""" - url = f"http://{COMFYUI_SERVER_ADDRESS}/history/{prompt_id}" + url = f"http://{settings.COMFYUI_SERVER_ADDRESS}/history/{prompt_id}" logger.info(f"⏳ 开始轮询状态 (间隔 {interval_seconds} 秒)...") @@ -574,7 +580,8 @@ class ComfyUIServerFLF2V: logger.info(f"⚠️ 轮询时发生错误: {e}") pass - def get_comfyui_video_bytes(self, filename: str, subfolder: str, file_type: str = "output"): + @staticmethod + def get_comfyui_video_bytes(filename: str, subfolder: str, file_type: str = "output"): """ 从 ComfyUI 的 /view 端点获取视频文件的二进制数据。 @@ -586,7 +593,7 @@ class ComfyUIServerFLF2V: 返回: - 视频文件的二进制内容 (bytes) 或 None。 """ - url = f"http://{COMFYUI_SERVER_ADDRESS}/view" + url = f"http://{settings.COMFYUI_SERVER_ADDRESS}/view" params = { "filename": filename, "subfolder": subfolder, diff --git a/app/service/comfyui_I2V/i2v_server.py b/app/service/comfyui_I2V/i2v_server.py index bcb34c4..0a48eda 100644 --- a/app/service/comfyui_I2V/i2v_server.py +++ b/app/service/comfyui_I2V/i2v_server.py @@ -12,8 +12,8 @@ from PIL import Image from minio import Minio, S3Error from moviepy.video.io.VideoFileClip import VideoFileClip -from app.core.config import MINIO_URL, MINIO_ACCESS, MINIO_SECRET, MINIO_SECURE, COMFYUI_SERVER_ADDRESS, PS_RABBITMQ_QUEUES, DEBUG -from app.schemas.comfyui_i2v import ComfyuiPose2VModel, ComfyuiI2VModel +from app.core.config import PS_RABBITMQ_QUEUES, settings +from app.schemas.comfyui_i2v import ComfyuiI2VModel from app.service.generate_image.utils.mq import publish_status logger = logging.getLogger() @@ -293,13 +293,14 @@ workflow_json = { class ComfyUIServerI2V: def __init__(self, request_data): + self.pose_transform_data = None self.image_url = request_data.image_url self.prompt = request_data.prompt self.tasks_id = request_data.tasks_id self.user_id = self.tasks_id[self.tasks_id.rfind('-') + 1:] self.server_status_data = {'tasks_id': self.tasks_id, 'status': 'PENDING', 'message': "pending", 'gif_url': '', 'video_url': '', 'image_url': ''} - self.minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE) + self.minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE) def get_result(self): workflow_json['93']['inputs']['text'] = self.prompt @@ -319,7 +320,7 @@ class ComfyUIServerI2V: # 1. 提交任务 prompt_response = self.queue_prompt(workflow_json, self.tasks_id) if not prompt_response: - return + return None prompt_id = prompt_response.get("prompt_id") logger.info(f" 任务已提交，Prompt ID: {prompt_id}") outputs = self.poll_history(prompt_id) @@ -339,6 +340,7 @@ class ComfyUIServerI2V: } logger.info(file_list) return self.process_and_upload_comfyui_video(filename=file_list['filename'], subfolder=file_list['subfolder'], prompt_id=prompt_response['prompt_id']), prompt_id + return None def download_from_minio_in_memory(self, image_url): bucket = image_url.split('/')[0] @@ -369,8 +371,9 @@ class ComfyUIServerI2V: logger.error(f"❌ MinIO 下载过程中发生未知错误: {e}") return None, None - def upload_in_memory_file_to_comfyui(self, in_memory_file, filename): - upload_url = f"http://{COMFYUI_SERVER_ADDRESS}/upload/image" + @staticmethod + def upload_in_memory_file_to_comfyui(in_memory_file, filename): + upload_url = f"http://{settings.COMFYUI_SERVER_ADDRESS}/upload/image" data = { "overwrite": "true", @@ -408,7 +411,7 @@ class ComfyUIServerI2V: # 1. 从 ComfyUI 获取视频二进制数据 mp4_bytes = self.get_comfyui_video_bytes(filename, subfolder) if not mp4_bytes: - return + return None # 2. 准备进行视频处理 # moviepy 不支持直接使用 bytes，需要将 bytes 写入一个 BytesIO 或临时文件 @@ -496,7 +499,7 @@ class ComfyUIServerI2V: self.pose_transform_data = {'tasks_id': self.tasks_id, 'status': 'SUCCESS', 'message': "success", 'gif_url': f'aida-users/{GIF_OBJECT}', 'video_url': f'aida-users/{MP4_OBJECT}', 'image_url': f'aida-users/{FRAME_OBJECT}'} # 推送消息 - if not DEBUG: + if not settings.DEBUG: publish_status(json.dumps(self.pose_transform_data), PS_RABBITMQ_QUEUES) logger.info( f" [x] Sent to： {PS_RABBITMQ_QUEUES} data：@@@@ {json.dumps(self.pose_transform_data, indent=4)}") @@ -508,13 +511,14 @@ class ComfyUIServerI2V: return None # --- 辅助函数：提交任务到队列 --- - def queue_prompt(self, prompt, client_id): + @staticmethod + def queue_prompt(prompt, client_id): """向 ComfyUI 提交工作流提示。""" p = {"prompt": prompt, "client_id": client_id, "prompt_id": client_id} data = json.dumps(p).encode('utf-8') # 提交任务到 /prompt 端点 - response = requests.post(f"http://{COMFYUI_SERVER_ADDRESS}/prompt", data=data) + response = requests.post(f"http://{settings.COMFYUI_SERVER_ADDRESS}/prompt", data=data) # print(f"-------------{response.text}") # print(f"------------{client_id}") @@ -525,9 +529,10 @@ class ComfyUIServerI2V: logger.warning(response.text) return None - def poll_history(self, prompt_id, interval_seconds=5): + @staticmethod + def poll_history(prompt_id, interval_seconds=5): """步骤 2: 轮询 /history/{prompt_id} 检查任务是否完成""" - url = f"http://{COMFYUI_SERVER_ADDRESS}/history/{prompt_id}" + url = f"http://{settings.COMFYUI_SERVER_ADDRESS}/history/{prompt_id}" logger.info(f"⏳ 开始轮询状态 (间隔 {interval_seconds} 秒)...") @@ -552,7 +557,8 @@ class ComfyUIServerI2V: logger.info(f"⚠️ 轮询时发生错误: {e}") pass - def get_comfyui_video_bytes(self, filename: str, subfolder: str, file_type: str = "output"): + @staticmethod + def get_comfyui_video_bytes(filename: str, subfolder: str, file_type: str = "output"): """ 从 ComfyUI 的 /view 端点获取视频文件的二进制数据。 @@ -564,7 +570,7 @@ class ComfyUIServerI2V: 返回: - 视频文件的二进制内容 (bytes) 或 None。 """ - url = f"http://{COMFYUI_SERVER_ADDRESS}/view" + url = f"http://{settings.COMFYUI_SERVER_ADDRESS}/view" params = { "filename": filename, "subfolder": subfolder, diff --git a/app/service/comfyui_I2V/pose2v_server.py b/app/service/comfyui_I2V/pose2v_server.py index d1db7bc..17e3fc5 100644 --- a/app/service/comfyui_I2V/pose2v_server.py +++ b/app/service/comfyui_I2V/pose2v_server.py @@ -13,7 +13,7 @@ from PIL import Image from minio import Minio, S3Error from moviepy.video.io.VideoFileClip import VideoFileClip -from app.core.config import REDIS_HOST, REDIS_PORT, REDIS_DB, MINIO_URL, MINIO_ACCESS, MINIO_SECRET, MINIO_SECURE, COMFYUI_SERVER_ADDRESS, PS_RABBITMQ_QUEUES, DEBUG +from app.core.config import settings from app.schemas.comfyui_i2v import ComfyuiPose2VModel from app.service.generate_image.utils.mq import publish_status @@ -371,11 +371,11 @@ class ComfyUIServerPose2V: self.pose_num = request_data.pose_id self.tasks_id = request_data.tasks_id self.user_id = self.tasks_id[self.tasks_id.rfind('-') + 1:] - self.redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True) + self.redis_client = redis.StrictRedis(host=settings.REDIS_HOST, port=settings.REDIS_PORT, db=settings.REDIS_DB, decode_responses=True) self.pose_transform_data = {'tasks_id': self.tasks_id, 'status': 'PENDING', 'message': "pending", 'gif_url': '', 'video_url': '', 'image_url': ''} self.redis_client.set(self.tasks_id, json.dumps(self.pose_transform_data)) self.redis_client.expire(self.tasks_id, 600) - self.minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE) + self.minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE) def get_result(self): workflow_json['174']['inputs']['file'] = video_map[self.pose_num] @@ -389,7 +389,7 @@ class ComfyUIServerPose2V: # 1. 提交任务 prompt_response = self.queue_prompt(workflow_json, self.tasks_id) if not prompt_response: - return + return None prompt_id = prompt_response.get("prompt_id") logger.info(f" 任务已提交，Prompt ID: {prompt_id}") @@ -411,6 +411,7 @@ class ComfyUIServerPose2V: } logger.info(file_list) return self.process_and_upload_comfyui_video(filename=file_list['filename'], subfolder=file_list['subfolder'], prompt_id=prompt_response['prompt_id']), prompt_id + return None def read_tasks_status(self): status_data = self.redis_client.get(self.tasks_id) @@ -492,8 +493,9 @@ class ComfyUIServerPose2V: except Exception as e: logger.error(f"❌ 发生未知错误: {e}") - def upload_in_memory_file_to_comfyui(self, in_memory_file, filename): - upload_url = f"http://{COMFYUI_SERVER_ADDRESS}/upload/image" + @staticmethod + def upload_in_memory_file_to_comfyui(in_memory_file, filename): + upload_url = f"http://{settings.COMFYUI_SERVER_ADDRESS}/upload/image" data = { "overwrite": "true", @@ -531,7 +533,7 @@ class ComfyUIServerPose2V: # 1. 从 ComfyUI 获取视频二进制数据 mp4_bytes = self.get_comfyui_video_bytes(filename, subfolder) if not mp4_bytes: - return + return None # 2. 准备进行视频处理 # moviepy 不支持直接使用 bytes，需要将 bytes 写入一个 BytesIO 或临时文件 @@ -619,10 +621,10 @@ class ComfyUIServerPose2V: self.pose_transform_data = {'tasks_id': self.tasks_id, 'status': 'SUCCESS', 'message': "success", 'gif_url': f'aida-users/{GIF_OBJECT}', 'video_url': f'aida-users/{MP4_OBJECT}', 'image_url': f'aida-users/{FRAME_OBJECT}'} # 推送消息 - if not DEBUG: - publish_status(json.dumps(self.pose_transform_data), PS_RABBITMQ_QUEUES) + if not settings.DEBUG: + publish_status(json.dumps(self.pose_transform_data), settings.COMFYUI_SERVER_ADDRESS) logger.info( - f" [x] Sent to： {PS_RABBITMQ_QUEUES} data：@@@@ {json.dumps(self.pose_transform_data, indent=4)}") + f" [x] Sent to： {settings.COMFYUI_SERVER_ADDRESS} data：@@@@ {json.dumps(self.pose_transform_data, indent=4)}") return "\n🎉 所有任务完成！" @@ -631,13 +633,15 @@ class ComfyUIServerPose2V: return None # --- 辅助函数：提交任务到队列 --- - def queue_prompt(self, prompt, client_id): + @staticmethod + def queue_prompt(prompt, client_id): """向 ComfyUI 提交工作流提示。""" p = {"prompt": prompt, "client_id": client_id, "prompt_id": client_id} data = json.dumps(p).encode('utf-8') # 提交任务到 /prompt 端点 - response = requests.post(f"http://{COMFYUI_SERVER_ADDRESS}/prompt", data=data) + # noinspection HttpUrlsUsage + response = requests.post(f"http://{settings.COMFYUI_SERVER_ADDRESS}/prompt", data=data) # print(f"-------------{response.text}") # print(f"------------{client_id}") @@ -648,9 +652,10 @@ class ComfyUIServerPose2V: logger.warning(response.text) return None - def poll_history(self, prompt_id, interval_seconds=5): + @staticmethod + def poll_history(prompt_id, interval_seconds=5): """步骤 2: 轮询 /history/{prompt_id} 检查任务是否完成""" - url = f"http://{COMFYUI_SERVER_ADDRESS}/history/{prompt_id}" + url = f"http://{settings.COMFYUI_SERVER_ADDRESS}/history/{prompt_id}" logger.info(f"⏳ 开始轮询状态 (间隔 {interval_seconds} 秒)...") @@ -675,7 +680,8 @@ class ComfyUIServerPose2V: logger.info(f"⚠️ 轮询时发生错误: {e}") pass - def get_comfyui_video_bytes(self, filename: str, subfolder: str, file_type: str = "output"): + @staticmethod + def get_comfyui_video_bytes(filename: str, subfolder: str, file_type: str = "output"): """ 从 ComfyUI 的 /view 端点获取视频文件的二进制数据。 @@ -687,7 +693,7 @@ class ComfyUIServerPose2V: 返回: - 视频文件的二进制内容 (bytes) 或 None。 """ - url = f"http://{COMFYUI_SERVER_ADDRESS}/view" + url = f"http://{settings.COMFYUI_SERVER_ADDRESS}/view" params = { "filename": filename, "subfolder": subfolder, diff --git a/app/service/design/core/layer.py b/app/service/design/core/layer.py deleted file mode 100644 index 0628851..0000000 --- a/app/service/design/core/layer.py +++ /dev/null @@ -1,116 +0,0 @@ -import logging - -import numpy as np -import cv2 -from matplotlib import pyplot as plt - -from PIL import Image - - -def show(img, win_name="temp"): - cv2.imshow(win_name, img) - cv2.waitKey(0) - - -def crop(img): - mid_point_h, mid_point_w = int(img.shape[0] / 2 + 30), int(img.shape[1] / 2) - img_roi = img[mid_point_h - 520: mid_point_h + 520, mid_point_w - 340: mid_point_w + 340] - return img_roi - - -class Layer(object): - def __init__(self): - self._layer = [] - - @property - def layer(self): - return self._layer - - def insert(self, layer_instance): - if layer_instance['name'] == 'body': - self._body = layer_instance - self._layer.append(layer_instance) - - def sort(self, priority): - self._layer.sort(key=lambda x: priority[x['name']]) - - # def merge(self, cfg): - # """ - # opencv shape order (height, width, channel) - # image coordinate system: - # |------------->x (width) - # | - # | - # | - # y (height) - # Returns: - # - # - # """ - # base_image = Image.new('RGBA', self._layer[1]['image'].size, (0, 0, 0, 0)) - # for layer in self._layer: - # y, x = layer['position'] - # base_image.paste(layer['image'], (x, y), layer['image']) - # # base_image.show() - # - # for x in self._layer: - # if np.all(x['mask'] == 0): - # continue - # # obtain region of interest about roi(roi) and item-image(roi_image, roi_mask) - # roi, roi_mask, roi_image, signal = self.get_roi(dst=dst, image=x) - # temp_bg = np.expand_dims(cv2.bitwise_not(roi_mask), axis=2).repeat(3, axis=2) - # tmp1 = (roi * (temp_bg / 255)).astype(np.uint8) - # temp_fg = np.expand_dims(roi_mask, axis=2).repeat(3, axis=2) - # tmp2 = (roi_image * (temp_fg / 255)).astype(np.uint8) - # - # roi[:] = cv2.add(tmp1, tmp2) - # # show(cv2.resize(dst, (int(dst.shape[1] * 0.5), int(dst.shape[0] * 0.5)), interpolation=cv2.INTER_AREA), - # # win_name=x.get('name')) - # # crop image and get the central part - # if cfg.get('basic')['self_template'] == False: - # dst_roi = crop(dst) - # else: - # dst_roi = dst - # return dst_roi, signal - # - # @staticmethod - # def get_roi(dst, image): - # signal = False - # dst_y, dst_x = dst.shape[:2] - # roi_height, roi_width = image['mask'].shape - # roi_y0, roi_x0 = image['position'] - # - # if roi_y0 < 0: - # roi_yin = 0 - # mask_yin = -roi_y0 - # signal = True - # else: - # roi_yin = roi_y0 - # mask_yin = 0 - # if roi_y0 + roi_height > dst_y: - # roi_yout = dst_y - # mask_yout = dst_y - roi_y0 - # signal = True - # else: - # roi_yout = roi_height + roi_y0 - # mask_yout = roi_height - # # x part - # if roi_x0 < 0: - # roi_xin = 0 - # mask_xin = -roi_x0 - # signal = True - # else: - # roi_xin = roi_x0 - # mask_xin = 0 - # if roi_x0 + roi_width > dst_x: - # roi_xout = dst_x - # mask_xout = dst_x - roi_x0 - # signal = True - # else: - # roi_xout = roi_width + roi_x0 - # mask_xout = roi_width - # - # roi = dst[roi_yin: roi_yout, roi_xin: roi_xout] - # roi_mask = image['mask'][mask_yin: mask_yout, mask_xin: mask_xout] - # roi_image = image['image'][mask_yin: mask_yout, mask_xin: mask_xout] - # return roi, roi_mask, roi_image, signal diff --git a/app/service/design/core/priority.py b/app/service/design/core/priority.py deleted file mode 100644 index dc111ea..0000000 --- a/app/service/design/core/priority.py +++ /dev/null @@ -1,45 +0,0 @@ -class Priority(object): - """Item layer priority levels. - """ - - def __init__(self, item_list): - self._priority = dict( - earring_front=99, - bag_front=98, - hairstyle_front=97, - outwear_front=20, - bottoms_front=19, - dress_front=18, - blouse_front=17, - skirt_front=16, - trousers_front=15, - tops_front=14, - shoes_right=1, - shoes_left=1, - body=0, - tops_back=-14, - trousers_back=-15, - skirt_back=-16, - blouse_back=-17, - dress_back=-18, - bottoms_back=-19, - outwear_back=-20, - hairstyle_back=-97, - bag_back=-98, - earring_back=-99, - ) - self.clothing_start_num = 10 - if not isinstance(item_list, list): - raise ValueError('item_list must be a list!') - for cate in item_list: - cate = cate.lower() - if cate not in ('outwear', 'dress', 'blouse', 'skirt', 'trousers', 'tops', 'bottoms'): - raise ValueError(f'Item type error. Cannot recognize {cate}') - for i, cate in enumerate(item_list): - cate = cate.lower() - self._priority[f'{cate}_front'] = self.clothing_start_num - i - self._priority[f'{cate}_back'] = -(self.clothing_start_num - i) - - @property - def priority(self): - return self._priority diff --git a/app/service/design/items/__init__.py b/app/service/design/items/__init__.py deleted file mode 100644 index e3e6bd5..0000000 --- a/app/service/design/items/__init__.py +++ /dev/null @@ -1,16 +0,0 @@ -from .builder import ITEMS, build_item -from .clothing import Clothing # 4.0 sec -from .body import Body -from .top import Top, Blouse, Outwear, Dress -from .bottom import Bottom, Trousers, Skirt -from .shoes import Shoes -from .bag import Bag -from .others import Hairstyle, Earring - -__all__ = [ - 'ITEMS', 'build_item', - 'Clothing', 'Body', - 'Top', 'Blouse', 'Outwear', 'Dress', - 'Bottom', 'Trousers', 'Skirt', - 'Shoes', 'Bag', 'Hairstyle', 'Earring' -] diff --git a/app/service/design/items/bag.py b/app/service/design/items/bag.py deleted file mode 100644 index 12b4c68..0000000 --- a/app/service/design/items/bag.py +++ /dev/null @@ -1,45 +0,0 @@ -import random - -from .builder import ITEMS -from .clothing import Clothing - - -@ITEMS.register_module() -class Bag(Clothing): - def __init__(self, **kwargs): - pipeline = [ - dict(type='LoadImageFromFile', path=kwargs['path'], color=kwargs['color']), - dict(type='KeypointDetection'), - dict(type='ContourDetection'), - dict(type='Painting'), - dict(type='Scaling'), - dict(type='Split'), - # dict(type='ImageShow', key=['image', 'mask', 'pattern_image']), - ] - kwargs.update(pipeline=pipeline) - super(Bag, self).__init__(**kwargs) - - @staticmethod - def calculate_start_point(keypoint_type, scale, clothes_point, body_point): - """ - align left - Args: - keypoint_type: string, "hand_point" - scale: float - clothes_point: dict{'left': [x1, y1, z1], 'right': [x2, y2, z2]} - body_point: dict, containing keypoint data of body figure - - Returns: - start_point: tuple (y', x') - x' = y_body - y1 * scale - y' = x_body - x1 * scale - """ - location = random.choice(seq=['left', 'right']) - if location == 'left': - side_indicator = f'{keypoint_type}_left' - else: - side_indicator = f'{keypoint_type}_right' - # clothes_point = {k: tuple(map(lambda x: int(scale * x), v[0: 2])) for k, v in clothes_point.items()} - start_point = (body_point[side_indicator][1] - int(int(clothes_point[keypoint_type].split("_")[1]) * scale), - body_point[side_indicator][0] - int(int(clothes_point[keypoint_type].split("_")[0]) * scale)) - return start_point diff --git a/app/service/design/items/body.py b/app/service/design/items/body.py deleted file mode 100644 index c336ae9..0000000 --- a/app/service/design/items/body.py +++ /dev/null @@ -1,36 +0,0 @@ -import cv2 - -from .builder import ITEMS -from .pipelines import Compose - - -@ITEMS.register_module() -class Body(object): - def __init__(self, **kwargs): - pipeline = [ - dict(type='LoadBodyImageFromFile', body_path=kwargs['body_path']), - # dict(type='ImageShow', key=['body_image', "body_mask"]) - ] - self.pipeline = Compose(pipeline) - self.result = dict() - - def process(self): - self.pipeline(self.result) - pass - - def organize(self, layer): - body_layer = dict(priority=0, - name=type(self).__name__.lower(), - image=self.result['body_image'], - image_url=self.result['image_url'], - mask_image=None, - mask_url=None, - sacle=1, - # mask=self.result['body_mask'], - position=(0, 0)) - layer.insert(body_layer) - - @staticmethod - def show(img): - cv2.imshow('', img) - cv2.waitKey(0) diff --git a/app/service/design/items/bottom.py b/app/service/design/items/bottom.py deleted file mode 100644 index e01ec02..0000000 --- a/app/service/design/items/bottom.py +++ /dev/null @@ -1,39 +0,0 @@ -from .builder import ITEMS -from .clothing import Clothing - - -@ITEMS.register_module() -class Bottom(Clothing): - def __init__(self, pipeline, **kwargs): - if pipeline is None: - pipeline = [ - dict(type='LoadImageFromFile', path=kwargs['path'], color=kwargs['color'], print_dict=kwargs['print']), - dict(type='KeypointDetection'), - dict(type='ContourDetection'), - # dict(type='Segmentation'), - dict(type='Painting', painting_flag=True), - dict(type='PrintPainting', print_flag=True), - dict(type='Scaling'), - dict(type='Split'), - # dict(type='ImageShow', key=['image', 'mask', 'pattern_image', 'print_image']), - ] - kwargs.update(pipeline=pipeline) - super(Bottom, self).__init__(**kwargs) - - -@ITEMS.register_module() -class Trousers(Bottom): - def __init__(self, pipeline=None, **kwargs): - super(Trousers, self).__init__(pipeline, **kwargs) - - -@ITEMS.register_module() -class Skirt(Bottom): - def __init__(self, pipeline=None, **kwargs): - super(Skirt, self).__init__(pipeline, **kwargs) - - -@ITEMS.register_module() -class Bottoms(Bottom): - def __init__(self, pipeline=None, **kwargs): - super(Bottoms, self).__init__(pipeline, **kwargs) diff --git a/app/service/design/items/builder.py b/app/service/design/items/builder.py deleted file mode 100644 index 26e04f1..0000000 --- a/app/service/design/items/builder.py +++ /dev/null @@ -1,9 +0,0 @@ -from mmcv.utils import Registry, build_from_cfg - -ITEMS = Registry('item') -PIPELINES = Registry('pipeline') - - -def build_item(cfg, default_args=None): - item = build_from_cfg(cfg, ITEMS, default_args) - return item diff --git a/app/service/design/items/clothing.py b/app/service/design/items/clothing.py deleted file mode 100644 index 953cecf..0000000 --- a/app/service/design/items/clothing.py +++ /dev/null @@ -1,100 +0,0 @@ -import cv2 - -from app.core.config import PRIORITY_DICT -from .builder import ITEMS -from .pipelines import Compose - - -@ITEMS.register_module() -class Clothing(object): - def __init__(self, pipeline, **kwargs): - self.pipeline = Compose(pipeline) - self.result = dict(name=type(self).__name__.lower(), **kwargs) - - def process(self): - self.pipeline(self.result) - - def apply_scale(self, img): - scale = self.result['scale'] - height, width = img.shape[0: 2] - if len(img.shape) > 2: - height, width = img.shape[0: 2] - scaled_img = cv2.resize(img, (int(width * scale), int(height * scale)), interpolation=cv2.INTER_AREA) - return scaled_img - - def organize(self, layer): - start_point = self.calculate_start_point(self.result['keypoint'], self.result['scale'], self.result['clothes_keypoint'], self.result['body_point_test'], self.result["offset"], self.result["resize_scale"]) - - front_layer = dict(priority=self.result.get("priority", None) if self.result.get("layer_order", False) else PRIORITY_DICT.get(f'{type(self).__name__.lower()}_front', None), - name=f'{type(self).__name__.lower()}_front', - image=self.result["front_image"], - # mask_image=self.result['front_mask_image'], - image_url=self.result['front_image_url'], - mask_url=self.result['mask_url'], - sacle=self.result['scale'], - clothes_keypoint=self.result['clothes_keypoint'], - position=start_point, - resize_scale=self.result["resize_scale"], - mask=cv2.resize(self.result['mask'], self.result["front_image"].size), - gradient_string=self.result['gradient_string'] if 'gradient_string' in self.result.keys() else "", - pattern_image_url=self.result['pattern_image_url'], - pattern_image=self.result['pattern_image'] - - ) - layer.insert(front_layer) - - back_layer = dict(priority=-self.result.get("priority", 0) if self.result.get("layer_order", False) else PRIORITY_DICT.get(f'{type(self).__name__.lower()}_back', None), - name=f'{type(self).__name__.lower()}_back', - image=self.result["back_image"], - # mask_image=self.result['back_mask_image'], - image_url=self.result['back_image_url'], - mask_url=self.result['mask_url'], - sacle=self.result['scale'], - clothes_keypoint=self.result['clothes_keypoint'], - position=start_point, - resize_scale=self.result["resize_scale"], - mask=cv2.resize(self.result['mask'], self.result["front_image"].size), - gradient_string=self.result['gradient_string'] if 'gradient_string' in self.result.keys() else "", - pattern_image_url=self.result['pattern_image_url'], - ) - layer.insert(back_layer) - - @staticmethod - def calculate_start_point(keypoint_type, scale, clothes_point, body_point, offset, resize_scale): - """ - Align left - Args: - keypoint_type: string, "waistband" | "shoulder" | "ear_point" - scale: float - clothes_point: dict{'left': [x1, y1, z1], 'right': [x2, y2, z2]} - body_point: dict, containing keypoint data of body figure - - Returns: - start_point: tuple (x', y') - x' = y_body - y1 * scale + offset - y' = x_body - x1 * scale + offset - - """ - - side_indicator = f'{keypoint_type}_left' - - # if keypoint_type == "ear_point": - # start_point = (body_point[side_indicator][1] - int(int(clothes_point[side_indicator].split("_")[1]) * scale), - # body_point[side_indicator][0] - int(int(clothes_point[side_indicator].split("_")[0]) * scale)) - # else: - # start_point = ( - # int(body_point[side_indicator][1] + offset[1] - int(clothes_point[side_indicator].split("_")[0]) * scale), # y - # int(body_point[side_indicator][0] + offset[0] - int(clothes_point[side_indicator].split("_")[1]) * scale) # x - # ) - - # milvus_DB_keypoint_cache: - start_point = ( - int(body_point[side_indicator][1] + offset[1] - int(clothes_point[side_indicator][0]) * scale), # y - int(body_point[side_indicator][0] + offset[0] - int(clothes_point[side_indicator][1]) * scale) # x - ) - # start_point = ( - # int(body_point[side_indicator][1] + offset[1] - int(clothes_point[side_indicator].split("_")[0]) * scale), # y - # int(body_point[side_indicator][0] + offset[0] - int(clothes_point[side_indicator].split("_")[1]) * scale) # x - # ) - - return start_point diff --git a/app/service/design/items/others.py b/app/service/design/items/others.py deleted file mode 100644 index 5cb5796..0000000 --- a/app/service/design/items/others.py +++ /dev/null @@ -1,59 +0,0 @@ -from .builder import ITEMS -from .clothing import Clothing - - -@ITEMS.register_module() -class Hairstyle(Clothing): - def __init__(self, **kwargs): - pipeline = [ - dict(type='LoadImageFromFile', path=kwargs['path']), - dict(type='KeypointDetection'), - dict(type='ContourDetection'), - dict(type='Painting'), - dict(type='Scaling'), - dict(type='Split'), - # dict(type='ImageShow', key=['image', 'mask', 'pattern_image']), - ] - kwargs.update(pipeline=pipeline) - super(Hairstyle, self).__init__(**kwargs) - - @staticmethod - def calculate_start_point(keypoint_type, scale, clothes_point, body_point): - """ - align up - Args: - keypoint_type: string, "head_point" - scale: float - clothes_point: dict{'left': [x1, y1, z1], 'right': [x2, y2, z2]} - body_point: dict, containing keypoint data of body figure - - Returns: - start_point: tuple (x', y') - x' = y_body - y1 * scale - y' = x_body - x1 * scale - """ - side_indicator = f'{keypoint_type}_up' - # clothes_point = {k: tuple(map(lambda x: int(scale * x), v[0: 2])) for k, v in clothes_point.items()} - # logging.info(clothes_point[side_indicator]) - - start_point = ( - int(body_point[side_indicator][1] - int(clothes_point[side_indicator].split("_")[1] * scale)), - int(body_point[side_indicator][0] - int(clothes_point[side_indicator].split("_")[0] * scale)) - ) - return start_point - - -@ITEMS.register_module() -class Earring(Clothing): - def __init__(self, **kwargs): - pipeline = [ - dict(type='LoadImageFromFile', path=kwargs['path']), - dict(type='KeypointDetection'), - dict(type='ContourDetection'), - dict(type='Painting'), - dict(type='Scaling'), - dict(type='Split'), - # dict(type='ImageShow', key=['image', 'mask', 'pattern_image']), - ] - kwargs.update(pipeline=pipeline) - super(Earring, self).__init__(**kwargs) diff --git a/app/service/design/items/pipelines/__init__.py b/app/service/design/items/pipelines/__init__.py deleted file mode 100644 index 9abb09c..0000000 --- a/app/service/design/items/pipelines/__init__.py +++ /dev/null @@ -1,19 +0,0 @@ -from .compose import Compose -from .loading import LoadImageFromFile, LoadBodyImageFromFile, ImageShow -from .keypoints import KeypointDetection -from .segmentation import Segmentation -from .painting import Painting, PrintPainting -from .scale import Scaling -from .contour_detection import ContourDetection -from .split import Split - -__all__ = [ - 'Compose', - 'LoadImageFromFile', 'LoadBodyImageFromFile', 'ImageShow', - 'KeypointDetection', - 'Segmentation', - 'Painting', 'PrintPainting', - 'Scaling', - 'ContourDetection', - 'split', -] diff --git a/app/service/design/items/pipelines/compose.py b/app/service/design/items/pipelines/compose.py deleted file mode 100644 index daf6977..0000000 --- a/app/service/design/items/pipelines/compose.py +++ /dev/null @@ -1,36 +0,0 @@ -import collections - -from mmcv.utils import build_from_cfg - -from ..builder import PIPELINES - - -@PIPELINES.register_module() -class Compose(object): - def __init__(self, transforms): - assert isinstance(transforms, collections.abc.Sequence) - self.transforms = [] - for transform in transforms: - if isinstance(transform, dict): - transform = build_from_cfg(transform, PIPELINES) - self.transforms.append(transform) - elif callable(transform): - self.transforms.append(transform) - else: - raise TypeError('transform must be callable or a dict') - - def __call__(self, data): - """Call function to apply transforms sequentially. - - Args: - data (dict): A result dict contains the data to transform. - - Returns: - dict: Transformed data. - """ - - for t in self.transforms: - data = t(data) - if data is None: - return None - return data diff --git a/app/service/design/items/pipelines/contour_detection.py b/app/service/design/items/pipelines/contour_detection.py deleted file mode 100644 index 487d2d6..0000000 --- a/app/service/design/items/pipelines/contour_detection.py +++ /dev/null @@ -1,59 +0,0 @@ -import cv2 -import numpy as np - -from ..builder import PIPELINES - - -@PIPELINES.register_module() -class ContourDetection(object): - def __init__(self): - # logging.info("ContourDetection run ") - pass - - # @ RunTime - def __call__(self, result): - # shoe diff - if result['name'] == 'shoes': - Contour = self.get_contours(result['image']) - Mask = np.zeros(result['image'].shape[:2], np.uint8) - for i in range(2): - Max_contour = Contour[i] - Epsilon = 0.001 * cv2.arcLength(Max_contour, True) - Approx = cv2.approxPolyDP(Max_contour, Epsilon, True) - cv2.drawContours(Mask, [Approx], -1, 255, -1) - if result['pre_mask'] is None: - result['mask'] = Mask - else: - result['mask'] = cv2.bitwise_and(Mask, result['pre_mask']) - else: - Contour = self.get_contours(result['image']) - Mask = np.zeros(result['image'].shape[:2], np.uint8) - if len(Contour): - Max_contour = Contour[0] - Epsilon = 0.001 * cv2.arcLength(Max_contour, True) - Approx = cv2.approxPolyDP(Max_contour, Epsilon, True) - cv2.drawContours(Mask, [Approx], -1, 255, -1) - else: - Mask = np.ones(result['image'].shape[:2], np.uint8) * 255 - # TODO 修复部分图片出现透明的情况 下版本上线 - # img2gray = cv2.cvtColor(result['image'], cv2.COLOR_BGR2GRAY) - # ret, Mask = cv2.threshold(img2gray, 126, 255, cv2.THRESH_BINARY) - # Mask = cv2.bitwise_not(Mask) - if result['pre_mask'] is None: - result['mask'] = Mask - else: - result['mask'] = cv2.bitwise_and(Mask, result['pre_mask']) - result['front_mask'] = result['mask'] - result['back_mask'] = result['mask'] - return result - - @staticmethod - def get_contours(image): - gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) - Edge = cv2.Canny(gray, 10, 150) - kernel = np.ones((5, 5), np.uint8) - Edge = cv2.dilate(Edge, kernel=kernel, iterations=1) - Edge = cv2.erode(Edge, kernel=kernel, iterations=1) - Contour, _ = cv2.findContours(Edge, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) - Contour = sorted(Contour, key=cv2.contourArea, reverse=True) - return Contour diff --git a/app/service/design/items/pipelines/keypoints.py b/app/service/design/items/pipelines/keypoints.py deleted file mode 100644 index fded7de..0000000 --- a/app/service/design/items/pipelines/keypoints.py +++ /dev/null @@ -1,140 +0,0 @@ -import logging -import time - -import numpy as np -from pymilvus import MilvusClient - -from app.core.config import * -from app.service.utils.decorator import RunTime, ClassCallRunTime -from ..builder import PIPELINES -from ...utils.design_ensemble import get_keypoint_result - - -@PIPELINES.register_module() -class KeypointDetection(object): - """ - path here: abstract path - """ - - # def __init__(self): - # self.client = MilvusClient( - # uri="http://10.1.1.240:19530", - # token="root:Milvus", - # db_name=MILVUS_ALIAS - # ) - - # def __del__(self): - # start_time = time.time() - # self.client.close() - # print(f"client close time : {time.time() - start_time}") - - # @ClassCallRunTime - def __call__(self, result): - # logging.info("KeypointDetection run ") - if result['name'] in ['blouse', 'skirt', 'dress', 'outwear', 'trousers', 'tops', 'bottoms']: # 查询是否有数据 且类别相同 相同则直接读 不同则推理后更新 - # result['clothes_keypoint'] = self.infer_keypoint_result(result) - site = 'up' if result['name'] in ['blouse', 'outwear', 'dress', 'tops'] else 'down' - # keypoint_cache = search_keypoint_cache(result["image_id"], site) - - keypoint_cache = self.keypoint_cache(result, site) - # 取消向量查询 直接过模型推理 - # keypoint_cache = False - - if keypoint_cache is False: - keypoint_infer_result, site = self.infer_keypoint_result(result) - result['clothes_keypoint'] = self.save_keypoint_cache(result["image_id"], keypoint_infer_result, site) - else: - result['clothes_keypoint'] = keypoint_cache - return result - - @staticmethod - def infer_keypoint_result(result): - site = 'up' if result['name'] in ['blouse', 'outwear', 'dress', 'tops'] else 'down' - start_time = time.time() - keypoint_infer_result = get_keypoint_result(result["image"], site) # 推理结果 - # logging.info(f"infer keypoint time : {time.time() - start_time}") - return keypoint_infer_result, site - - @staticmethod - # @ RunTime - def save_keypoint_cache(keypoint_id, cache, site): - if site == "down": - zeros = np.zeros(20, dtype=int) - result = np.concatenate([zeros, cache.flatten()]) - else: - zeros = np.zeros(4, dtype=int) - result = np.concatenate([cache.flatten(), zeros]) - # 取消向量保存 直接拿结果 - data = [ - {"keypoint_id": keypoint_id, - "keypoint_site": site, - "keypoint_vector": result.tolist() - } - ] - try: - client = MilvusClient(uri=MILVUS_URL, token=MILVUS_TOKEN, db_name=MILVUS_ALIAS) - # start_time = time.time() - res = client.upsert(collection_name=MILVUS_TABLE_KEYPOINT, data=data) - # logging.info(f"save keypoint time : {time.time() - start_time}") - client.close() - return dict(zip(KEYPOINT_RESULT_TABLE_FIELD_SET, result.reshape(12, 2).astype(int).tolist())) - except Exception as e: - logging.info(f"save keypoint cache milvus error : {e}") - return dict(zip(KEYPOINT_RESULT_TABLE_FIELD_SET, result.reshape(12, 2).astype(int).tolist())) - - @staticmethod - def update_keypoint_cache(keypoint_id, infer_result, search_result, site): - if site == "up": - # 需要的是up 即推理出来的是up 那么查询的就是down - result = np.concatenate([infer_result.flatten(), search_result[-4:]]) - else: - # 需要的是down 即推理出来的是down 那么查询的就是up - result = np.concatenate([search_result[:20], infer_result.flatten()]) - data = [ - {"keypoint_id": keypoint_id, - "keypoint_site": "all", - "keypoint_vector": result.tolist() - } - ] - - try: - client = MilvusClient(uri=MILVUS_URL, token=MILVUS_TOKEN, db_name=MILVUS_ALIAS) - # connections.connect(alias=MILVUS_ALIAS, host=MILVUS_DB_HOST, port=MILVUS_PORT) - start_time = time.time() - # collection = Collection(MILVUS_TABLE_KEYPOINT) # Get an existing collection. - # mr = collection.upsert(data) - client.upsert( - collection_name=MILVUS_TABLE_KEYPOINT, - data=data - ) - # logging.info(f"save keypoint time : {time.time() - start_time}") - return dict(zip(KEYPOINT_RESULT_TABLE_FIELD_SET, result.reshape(12, 2).astype(int).tolist())) - except Exception as e: - logging.info(f"save keypoint cache milvus error : {e}") - return dict(zip(KEYPOINT_RESULT_TABLE_FIELD_SET, result.reshape(12, 2).astype(int).tolist())) - - # @ RunTime - def keypoint_cache(self, result, site): - try: - client = MilvusClient(uri=MILVUS_URL, token=MILVUS_TOKEN, db_name=MILVUS_ALIAS) - keypoint_id = result['image_id'] - res = client.query( - collection_name=MILVUS_TABLE_KEYPOINT, - # ids=[keypoint_id], - filter=f"keypoint_id == {keypoint_id}", - output_fields=['keypoint_vector', 'keypoint_site'] - ) - if len(res) == 0: - # 没有结果 直接推理拿结果 并保存 - keypoint_infer_result, site = self.infer_keypoint_result(result) - return self.save_keypoint_cache(result['image_id'], keypoint_infer_result, site) - elif res[0]["keypoint_site"] == "all" or res[0]["keypoint_site"] == site: - # 需要的类型和查询的类型一致，或者查询的类型为all 则直接返回查询的结果 - return dict(zip(KEYPOINT_RESULT_TABLE_FIELD_SET, np.array(res[0]['keypoint_vector']).astype(int).reshape(12, 2).tolist())) - elif res[0]["keypoint_site"] != site: - # 需要的类型和查询到的不一致，则更新类型为all - keypoint_infer_result, site = self.infer_keypoint_result(result) - return self.update_keypoint_cache(result["image_id"], keypoint_infer_result, res[0]['keypoint_vector'], site) - except Exception as e: - logging.info(f"search keypoint cache milvus error {e}") - return False diff --git a/app/service/design/items/pipelines/loading.py b/app/service/design/items/pipelines/loading.py deleted file mode 100644 index 04dc4d8..0000000 --- a/app/service/design/items/pipelines/loading.py +++ /dev/null @@ -1,134 +0,0 @@ -import cv2 - -from app.service.utils.oss_client import oss_get_image -from ..builder import PIPELINES - - -@PIPELINES.register_module() -class LoadImageFromFile(object): - def __init__(self, path, color=None, print_dict=None): - self.path = path - self.color = color - self.print_dict = print_dict - # self.minio_client = Minio(f"{MINIO_URL}", access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE) - - # @ClassCallRunTime - def __call__(self, result): - result['image'], result['pre_mask'] = self.read_image(self.path) - result['gray'] = cv2.cvtColor(result['image'], cv2.COLOR_BGR2GRAY) - result['keypoint'] = self.get_keypoint(result['name']) - result['path'] = self.path - result['img_shape'] = result['image'].shape - result['ori_shape'] = result['image'].shape - result['color'] = self.color if self.color is not None else None - result['print_dict'] = self.print_dict - return result - - @staticmethod - def get_keypoint(name): - if name == 'blouse' or name == 'outwear' or name == 'dress' or name == 'tops': - keypoint = 'shoulder' - elif name == 'trousers' or name == 'skirt' or name == 'bottoms': - keypoint = 'waistband' - elif name == 'bag': - keypoint = 'hand_point' - elif name == 'shoes': - keypoint = 'toe' - elif name == 'hairstyle': - keypoint = 'head_point' - elif name == 'earring': - keypoint = 'ear_point' - else: - raise KeyError(f"{name} does not belong to item category list: blouse, outwear, dress, trousers, skirt, " - f"bag, shoes, hairstyle, earring.") - return keypoint - - @staticmethod - def read_image(image_path): - image_mask = None - image = oss_get_image(bucket=image_path.split("/", 1)[0], object_name=image_path.split("/", 1)[1], data_type="cv2") - if len(image.shape) == 2: - image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB) - if image.shape[2] == 4: # 如果是四通道 mask - image_mask = image[:, :, 3] - image = image[:, :, :3] - - if image.shape[:2] <= (50, 50): - # 计算新尺寸 - new_size = (image.shape[1] * 2, image.shape[0] * 2) - # 调整大小 - image = cv2.resize(image, new_size, interpolation=cv2.INTER_LINEAR) - return image, image_mask - - -@PIPELINES.register_module() -class LoadBodyImageFromFile(object): - def __init__(self, body_path): - self.body_path = body_path - # self.minioClient = Minio(f"{MINIO_URL}", access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE) - - # response = self.minioClient.get_object("aida-mannequins", "model_1693218345.2714431.png") - - # @ RunTime - def __call__(self, result): - result["image_url"] = result['body_path'] = self.body_path - result["name"] = "mannequin" - # if not result['image_url'].lower().endswith(".png"): - # bucket = self.body_path.split("/", 1)[0] - # object_name = self.body_path.split("/", 1)[1] - # new_object_name = f'{object_name[:object_name.rfind(".")]}.png' - # image = self.minioClient.get_object(bucket, object_name) - # image = Image.open(io.BytesIO(image.data)) - # image = image.convert("RGBA") - # data = image.getdata() - # # - # new_data = [] - # for item in data: - # if item[0] >= 230 and item[1] >= 230 and item[2] >= 230: - # new_data.append((255, 255, 255, 0)) - # else: - # new_data.append(item) - # image.putdata(new_data) - # image_data = io.BytesIO() - # image.save(image_data, format='PNG') - # image_data.seek(0) - # image_bytes = image_data.read() - # image_path = f"{bucket}/{self.minioClient.put_object(bucket, new_object_name, io.BytesIO(image_bytes), len(image_bytes), content_type='image/png').object_name}" - # self.body_path = image_path - # result["image_url"] = result['body_path'] = self.body_path - # response = self.minioClient.get_object(self.body_path.split("/", 1)[0], self.body_path.split("/", 1)[1]) - # put_image_time = time.time() - # result['body_image'] = Image.open(io.BytesIO(response.read())) - result['body_image'] = oss_get_image(bucket=self.body_path.split("/", 1)[0], object_name=self.body_path.split("/", 1)[1], data_type="PIL") - # logging.info(f"Image.open time is : {time.time() - put_image_time}") - return result - - -@PIPELINES.register_module() -class ImageShow(object): - def __init__(self, key): - self.key = key - - # @ RunTime - def __call__(self, result): - import matplotlib.pyplot as plt - if isinstance(self.key, list): - for key in self.key: - plt.imshow(result[key]) - plt.title(key) - plt.show() - elif isinstance(self.key, str): - img = self._resize_img(result[self.key]) - cv2.imshow(self.key, img) - cv2.waitKey(0) - else: - raise TypeError(f'key should be string but got type {type(self.key)}.') - return result - - @staticmethod - def _resize_img(img): - shape = img.shape - if shape[0] > 400 or shape[1] > 400: - ratio = min(400 / shape[0], 400 / shape[1]) - img = cv2.resize(img, (int(ratio * shape[1]), int(ratio * shape[0]))) - return img diff --git a/app/service/design/items/pipelines/painting.py b/app/service/design/items/pipelines/painting.py deleted file mode 100644 index 993697c..0000000 --- a/app/service/design/items/pipelines/painting.py +++ /dev/null @@ -1,605 +0,0 @@ -import logging -import random - -import cv2 -import numpy as np -from PIL import Image - -from app.service.utils.oss_client import oss_get_image -from ..builder import PIPELINES - -logger = logging.getLogger() - - -@PIPELINES.register_module() -class Painting(object): - def __init__(self, painting_flag=True): - self.painting_flag = painting_flag - - # @ClassCallRunTime - def __call__(self, result): - if result['name'] not in ['hairstyle', 'earring'] and self.painting_flag and result['color'] != 'none': - dim_image_h, dim_image_w = result['image'].shape[0:2] - if "gradient" in result.keys() and result['gradient'] != "": - bucket_name = result['gradient'].split('/')[0] - object_name = result['gradient'][result['gradient'].find('/') + 1:] - pattern = self.get_gradient(bucket_name=bucket_name, object_name=object_name) - resize_pattern = cv2.resize(pattern, (dim_image_w, dim_image_h), interpolation=cv2.INTER_AREA) - else: - pattern = self.get_pattern(result['color']) - resize_pattern = cv2.resize(pattern, (dim_image_w, dim_image_h), interpolation=cv2.INTER_AREA) - closed_mo = np.expand_dims(result['mask'], axis=2).repeat(3, axis=2) - gray_mo = np.expand_dims(result['gray'], axis=2).repeat(3, axis=2) - get_image_fir = resize_pattern * (closed_mo / 255) * (gray_mo / 255) - result['pattern_image'] = get_image_fir.astype(np.uint8) - result['final_image'] = result['pattern_image'] - canvas = np.full_like(result['final_image'], 255) - temp_bg = np.expand_dims(cv2.bitwise_not(result['mask']), axis=2).repeat(3, axis=2) - tmp1 = (canvas * (temp_bg / 255)).astype(np.uint8) - temp_fg = np.expand_dims(result['mask'], axis=2).repeat(3, axis=2) - tmp2 = (result['final_image'] * (temp_fg / 255)).astype(np.uint8) - result['single_image'] = cv2.add(tmp1, tmp2) - result['alpha'] = 100 / 255.0 - else: - closed_mo = np.expand_dims(result['mask'], axis=2).repeat(3, axis=2) - get_image_fir = result['image'] * (closed_mo / 255) - result['pattern_image'] = get_image_fir.astype(np.uint8) - result['final_image'] = result['pattern_image'] - return result - - @staticmethod - def get_gradient(bucket_name, object_name): - # image_data = minio_client.get_object(bucket_name, object_name) - # image_data = s3.get_object(Bucket=bucket_name, Key=object_name)['Body'] - - # 从数据流中读取图像 - # image_bytes = image_data.read() - - # 将图像数据转换为numpy数组 - # image_array = np.asarray(bytearray(image_bytes), dtype=np.uint8) - - # 使用OpenCV解码图像数组 - # image = cv2.imdecode(image_array, cv2.IMREAD_COLOR) - image = oss_get_image(bucket=bucket_name, object_name=object_name, data_type="cv2") - if image.shape[2] == 4: - image = cv2.cvtColor(image, cv2.COLOR_BGRA2BGR) - return image - - @staticmethod - def crop_image(image, image_size_h, image_size_w): - x_offset = np.random.randint(low=0, high=int(image_size_h / 5) - 6) - y_offset = np.random.randint(low=0, high=int(image_size_w / 5) - 6) - image = image[x_offset: x_offset + image_size_h, y_offset: y_offset + image_size_w, :] - return image - - @staticmethod - def get_pattern(single_color): - if single_color is None: - raise False - R, G, B = single_color.split(' ') - pattern = np.zeros([1, 1, 3], np.uint8) - pattern[0, 0, 0] = int(B) - pattern[0, 0, 1] = int(G) - pattern[0, 0, 2] = int(R) - return pattern - - -@PIPELINES.register_module() -class PrintPainting(object): - def __init__(self, print_flag=True): - self.print_flag = print_flag - - # @ClassCallRunTime - def __call__(self, result): - single_print = result['print']['single'] - overall_print = result['print']['overall'] - element_print = result['print']['element'] - result['single_image'] = None - result['print_image'] = None - if overall_print['print_path_list']: - painting_dict = {'dim_image_h': result['pattern_image'].shape[0], 'dim_image_w': result['pattern_image'].shape[1]} - result['print_image'] = result['pattern_image'] - if "print_angle_list" in overall_print.keys() and overall_print['print_angle_list'][0] != 0: - painting_dict = self.painting_collection(painting_dict, overall_print, print_trigger=True) - painting_dict['tile_print'] = self.rotate_crop_image(img=painting_dict['tile_print'], angle=-overall_print['print_angle_list'][0], crop=True) - painting_dict['mask_inv_print'] = self.rotate_crop_image(img=painting_dict['mask_inv_print'], angle=-overall_print['print_angle_list'][0], crop=True) - - # resize 到sketch大小 - painting_dict['tile_print'] = self.resize_and_crop(img=painting_dict['tile_print'], target_width=painting_dict['dim_image_w'], target_height=painting_dict['dim_image_h']) - painting_dict['mask_inv_print'] = self.resize_and_crop(img=painting_dict['mask_inv_print'], target_width=painting_dict['dim_image_w'], target_height=painting_dict['dim_image_h']) - else: - painting_dict = self.painting_collection(painting_dict, overall_print, print_trigger=True, is_single=False) - result['print_image'] = self.printpaint(result, painting_dict, print_=True) - result['single_image'] = result['final_image'] = result['pattern_image'] = result['print_image'] - - if single_print['print_path_list']: - print_background = np.zeros((result['pattern_image'].shape[0], result['pattern_image'].shape[1], 3), dtype=np.uint8) - mask_background = np.zeros((result['pattern_image'].shape[0], result['pattern_image'].shape[1], 3), dtype=np.uint8) - for i in range(len(single_print['print_path_list'])): - image, image_mode = self.read_image(single_print['print_path_list'][i]) - if image_mode == "RGBA": - new_size = (int(image.width * single_print['print_scale_list'][i]), int(image.height * single_print['print_scale_list'][i])) - - mask = image.split()[3] - resized_source = image.resize(new_size) - resized_source_mask = mask.resize(new_size) - - rotated_resized_source = resized_source.rotate(-single_print['print_angle_list'][i]) - rotated_resized_source_mask = resized_source_mask.rotate(-single_print['print_angle_list'][i]) - - source_image_pil = Image.fromarray(cv2.cvtColor(print_background, cv2.COLOR_BGR2RGB)) - source_image_pil_mask = Image.fromarray(cv2.cvtColor(mask_background, cv2.COLOR_BGR2RGB)) - - source_image_pil.paste(rotated_resized_source, (int(single_print['location'][i][0]), int(single_print['location'][i][1])), rotated_resized_source) - source_image_pil_mask.paste(rotated_resized_source_mask, (int(single_print['location'][i][0]), int(single_print['location'][i][1])), rotated_resized_source_mask) - - print_background = cv2.cvtColor(np.array(source_image_pil), cv2.COLOR_RGBA2BGR) - mask_background = cv2.cvtColor(np.array(source_image_pil_mask), cv2.COLOR_RGBA2BGR) - ret, mask_background = cv2.threshold(mask_background, 124, 255, cv2.THRESH_BINARY) - else: - mask = self.get_mask_inv(image) - mask = np.expand_dims(mask, axis=2) - mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR) - mask = cv2.bitwise_not(mask) - # 旋转后的坐标需要重新算 - rotate_mask, _ = self.img_rotate(mask, single_print['print_angle_list'][i], single_print['print_scale_list'][i]) - rotate_image, rotated_new_size = self.img_rotate(image, single_print['print_angle_list'][i], single_print['print_scale_list'][i]) - # x, y = int(result['print']['location'][i][0] - rotated_new_size[0] - (rotate_mask.shape[0] - image.shape[0]) / 2), int(result['print']['location'][i][1] - rotated_new_size[1] - (rotate_mask.shape[1] - image.shape[1]) / 2) - x, y = int(single_print['location'][i][0] - rotated_new_size[0]), int(single_print['location'][i][1] - rotated_new_size[1]) - - image_x = print_background.shape[1] - image_y = print_background.shape[0] - print_x = rotate_image.shape[1] - print_y = rotate_image.shape[0] - - # 有bug - # if x + print_x > image_x: - # rotate_image = rotate_image[:, :x + print_x - image_x] - # rotate_mask = rotate_mask[:, :x + print_x - image_x] - # # - # if y + print_y > image_y: - # rotate_image = rotate_image[:y + print_y - image_y] - # rotate_mask = rotate_mask[:y + print_y - image_y] - - # 不能是并行 - # 当前第一轮的if （108以及115）是判断有没有过下界和右界。第二轮的是判断左上有没有超出。 如果这个样子的话，先裁了右边，再左移，region就会有问题 - # 先挪 再判断 最后裁剪 - - # 如果print旋转了 或者 print贴边了 则需要判断 判断左界和上界是否小于0 - if x <= 0: - rotate_image = rotate_image[:, -x:] - rotate_mask = rotate_mask[:, -x:] - start_x = x = 0 - else: - start_x = x - - if y <= 0: - rotate_image = rotate_image[-y:, :] - rotate_mask = rotate_mask[-y:, :] - start_y = y = 0 - else: - start_y = y - - # ------------------ - # 如果print-size大于image-size 则需要裁剪print - - if x + print_x > image_x: - rotate_image = rotate_image[:, :image_x - x] - rotate_mask = rotate_mask[:, :image_x - x] - - if y + print_y > image_y: - rotate_image = rotate_image[:image_y - y, :] - rotate_mask = rotate_mask[:image_y - y, :] - - # mask_background[start_y:y + rotate_mask.shape[0], start_x:x + rotate_mask.shape[1]] = cv2.bitwise_xor(mask_background[start_y:y + rotate_mask.shape[0], start_x:x + rotate_mask.shape[1]], rotate_mask) - # print_background[start_y:y + rotate_image.shape[0], start_x:x + rotate_image.shape[1]] = cv2.add(print_background[start_y:y + rotate_image.shape[0], start_x:x + rotate_image.shape[1]], rotate_image) - - # mask_background[start_y:y + rotate_mask.shape[0], start_x:x + rotate_mask.shape[1]] = rotate_mask - # print_background[start_y:y + rotate_image.shape[0], start_x:x + rotate_image.shape[1]] = rotate_image - mask_background = self.stack_prin(mask_background, result['pattern_image'], rotate_mask, start_y, y, start_x, x) - print_background = self.stack_prin(print_background, result['pattern_image'], rotate_image, start_y, y, start_x, x) - - # gray_image = cv2.cvtColor(mask_background, cv2.COLOR_BGR2GRAY) - # print_background = cv2.bitwise_and(print_background, print_background, mask=gray_image) - - print_mask = cv2.bitwise_and(result['mask'], cv2.cvtColor(mask_background, cv2.COLOR_BGR2GRAY)) - img_fg = cv2.bitwise_or(print_background, print_background, mask=print_mask) - img_bg = cv2.bitwise_and(result['pattern_image'], result['pattern_image'], mask=cv2.bitwise_not(print_mask)) - mask_mo = np.expand_dims(print_mask, axis=2).repeat(3, axis=2) - gray_mo = np.expand_dims(result['gray'], axis=2).repeat(3, axis=2) - img_fg = (img_fg * (mask_mo / 255) * (gray_mo / 255)).astype(np.uint8) - result['final_image'] = cv2.add(img_bg, img_fg) - canvas = np.full_like(result['final_image'], 255) - temp_bg = np.expand_dims(cv2.bitwise_not(result['mask']), axis=2).repeat(3, axis=2) - tmp1 = (canvas * (temp_bg / 255)).astype(np.uint8) - temp_fg = np.expand_dims(result['mask'], axis=2).repeat(3, axis=2) - tmp2 = (result['final_image'] * (temp_fg / 255)).astype(np.uint8) - result['single_image'] = cv2.add(tmp1, tmp2) - - if element_print['element_path_list']: - print_background = np.zeros((result['final_image'].shape[0], result['final_image'].shape[1], 3), dtype=np.uint8) - mask_background = np.zeros((result['final_image'].shape[0], result['final_image'].shape[1], 3), dtype=np.uint8) - for i in range(len(element_print['element_path_list'])): - image, image_mode = self.read_image(element_print['element_path_list'][i]) - if image_mode == "RGBA": - new_size = (int(image.width * element_print['element_scale_list'][i]), int(image.height * element_print['element_scale_list'][i])) - - mask = image.split()[3] - resized_source = image.resize(new_size) - resized_source_mask = mask.resize(new_size) - - rotated_resized_source = resized_source.rotate(-element_print['element_angle_list'][i]) - rotated_resized_source_mask = resized_source_mask.rotate(-element_print['element_angle_list'][i]) - - source_image_pil = Image.fromarray(cv2.cvtColor(print_background, cv2.COLOR_BGR2RGB)) - source_image_pil_mask = Image.fromarray(cv2.cvtColor(mask_background, cv2.COLOR_BGR2RGB)) - - source_image_pil.paste(rotated_resized_source, (int(element_print['location'][i][0]), int(element_print['location'][i][1])), rotated_resized_source) - source_image_pil_mask.paste(rotated_resized_source_mask, (int(element_print['location'][i][0]), int(element_print['location'][i][1])), rotated_resized_source_mask) - - print_background = cv2.cvtColor(np.array(source_image_pil), cv2.COLOR_RGBA2BGR) - mask_background = cv2.cvtColor(np.array(source_image_pil_mask), cv2.COLOR_RGBA2BGR) - else: - mask = self.get_mask_inv(image) - mask = np.expand_dims(mask, axis=2) - mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR) - mask = cv2.bitwise_not(mask) - # 旋转后的坐标需要重新算 - rotate_mask, _ = self.img_rotate(mask, element_print['element_angle_list'][i], element_print['element_scale_list'][i]) - rotate_image, rotated_new_size = self.img_rotate(image, element_print['element_angle_list'][i], element_print['element_scale_list'][i]) - # x, y = int(result['print']['location'][i][0] - rotated_new_size[0] - (rotate_mask.shape[0] - image.shape[0]) / 2), int(result['print']['location'][i][1] - rotated_new_size[1] - (rotate_mask.shape[1] - image.shape[1]) / 2) - x, y = int(element_print['location'][i][0] - rotated_new_size[0]), int(element_print['location'][i][1] - rotated_new_size[1]) - - image_x = print_background.shape[1] - image_y = print_background.shape[0] - print_x = rotate_image.shape[1] - print_y = rotate_image.shape[0] - - # 有bug - # if x + print_x > image_x: - # rotate_image = rotate_image[:, :x + print_x - image_x] - # rotate_mask = rotate_mask[:, :x + print_x - image_x] - # # - # if y + print_y > image_y: - # rotate_image = rotate_image[:y + print_y - image_y] - # rotate_mask = rotate_mask[:y + print_y - image_y] - - # 不能是并行 - # 当前第一轮的if （108以及115）是判断有没有过下界和右界。第二轮的是判断左上有没有超出。 如果这个样子的话，先裁了右边，再左移，region就会有问题 - # 先挪 再判断 最后裁剪 - - # 如果print旋转了 或者 print贴边了 则需要判断 判断左界和上界是否小于0 - if x <= 0: - rotate_image = rotate_image[:, -x:] - rotate_mask = rotate_mask[:, -x:] - start_x = x = 0 - else: - start_x = x - - if y <= 0: - rotate_image = rotate_image[-y:, :] - rotate_mask = rotate_mask[-y:, :] - start_y = y = 0 - else: - start_y = y - - # ------------------ - # 如果print-size大于image-size 则需要裁剪print - - if x + print_x > image_x: - rotate_image = rotate_image[:, :image_x - x] - rotate_mask = rotate_mask[:, :image_x - x] - - if y + print_y > image_y: - rotate_image = rotate_image[:image_y - y, :] - rotate_mask = rotate_mask[:image_y - y, :] - - # mask_background[start_y:y + rotate_mask.shape[0], start_x:x + rotate_mask.shape[1]] = cv2.bitwise_xor(mask_background[start_y:y + rotate_mask.shape[0], start_x:x + rotate_mask.shape[1]], rotate_mask) - # print_background[start_y:y + rotate_image.shape[0], start_x:x + rotate_image.shape[1]] = cv2.add(print_background[start_y:y + rotate_image.shape[0], start_x:x + rotate_image.shape[1]], rotate_image) - - # mask_background[start_y:y + rotate_mask.shape[0], start_x:x + rotate_mask.shape[1]] = rotate_mask - # print_background[start_y:y + rotate_image.shape[0], start_x:x + rotate_image.shape[1]] = rotate_image - mask_background = self.stack_prin(mask_background, result['pattern_image'], rotate_mask, start_y, y, start_x, x) - print_background = self.stack_prin(print_background, result['pattern_image'], rotate_image, start_y, y, start_x, x) - - # gray_image = cv2.cvtColor(mask_background, cv2.COLOR_BGR2GRAY) - # print_background = cv2.bitwise_and(print_background, print_background, mask=gray_image) - - print_mask = cv2.bitwise_and(result['mask'], cv2.cvtColor(mask_background, cv2.COLOR_BGR2GRAY)) - img_fg = cv2.bitwise_or(print_background, print_background, mask=print_mask) - # TODO element 丢失信息 - three_channel_image = cv2.merge([cv2.bitwise_not(print_mask), cv2.bitwise_not(print_mask), cv2.bitwise_not(print_mask)]) - img_bg = cv2.bitwise_and(result['final_image'], three_channel_image) - # mask_mo = np.expand_dims(print_mask, axis=2).repeat(3, axis=2) - # gray_mo = np.expand_dims(result['gray'], axis=2).repeat(3, axis=2) - # img_fg = (img_fg * (mask_mo / 255) * (gray_mo / 255)).astype(np.uint8) - result['final_image'] = cv2.add(img_bg, img_fg) - canvas = np.full_like(result['final_image'], 255) - temp_bg = np.expand_dims(cv2.bitwise_not(result['mask']), axis=2).repeat(3, axis=2) - tmp1 = (canvas * (temp_bg / 255)).astype(np.uint8) - temp_fg = np.expand_dims(result['mask'], axis=2).repeat(3, axis=2) - tmp2 = (result['final_image'] * (temp_fg / 255)).astype(np.uint8) - result['single_image'] = cv2.add(tmp1, tmp2) - return result - - @staticmethod - def stack_prin(print_background, pattern_image, rotate_image, start_y, y, start_x, x): - temp_print = np.zeros((pattern_image.shape[0], pattern_image.shape[1], 3), dtype=np.uint8) - temp_print[start_y:y + rotate_image.shape[0], start_x:x + rotate_image.shape[1]] = rotate_image - img2gray = cv2.cvtColor(temp_print, cv2.COLOR_BGR2GRAY) - ret, mask_ = cv2.threshold(img2gray, 1, 255, cv2.THRESH_BINARY) - mask_inv = cv2.bitwise_not(mask_) - img1_bg = cv2.bitwise_and(print_background, print_background, mask=mask_inv) - img2_fg = cv2.bitwise_and(temp_print, temp_print, mask=mask_) - print_background = img1_bg + img2_fg - return print_background - - def painting_collection(self, painting_dict, print_dict, print_trigger=False, is_single=False): - if print_trigger: - print_ = self.get_print(print_dict) - painting_dict['Trigger'] = not is_single - painting_dict['location'] = print_['location'] - single_mask_inv_print = self.get_mask_inv(print_['image']) - dim_max = max(painting_dict['dim_image_h'], painting_dict['dim_image_w']) - dim_pattern = (int(dim_max * print_['scale'] / 5), int(dim_max * print_['scale'] / 5)) - if not is_single: - self.random_seed = random.randint(0, 1000) - # 如果print 模式为overall 且 有角度的话 ， 组合的print为正方形，方便裁剪 - if "print_angle_list" in print_dict.keys() and print_dict['print_angle_list'][0] != 0: - painting_dict['mask_inv_print'] = self.tile_image(single_mask_inv_print, dim_pattern, print_['scale'], dim_max, dim_max, painting_dict['location'], trigger=True) - painting_dict['tile_print'] = self.tile_image(print_['image'], dim_pattern, print_['scale'], dim_max, dim_max, painting_dict['location'], trigger=True) - else: - painting_dict['mask_inv_print'] = self.tile_image(single_mask_inv_print, dim_pattern, print_['scale'], painting_dict['dim_image_h'], painting_dict['dim_image_w'], painting_dict['location'], trigger=True) - painting_dict['tile_print'] = self.tile_image(print_['image'], dim_pattern, print_['scale'], painting_dict['dim_image_h'], painting_dict['dim_image_w'], painting_dict['location'], trigger=True) - else: - painting_dict['mask_inv_print'] = self.tile_image(single_mask_inv_print, dim_pattern, print_['scale'], painting_dict['dim_image_h'], painting_dict['dim_image_w'], painting_dict['location']) - painting_dict['tile_print'] = self.tile_image(print_['image'], dim_pattern, print_['scale'], painting_dict['dim_image_h'], painting_dict['dim_image_w'], painting_dict['location']) - painting_dict['dim_print_h'], painting_dict['dim_print_w'] = dim_pattern - return painting_dict - - def tile_image(self, pattern, dim, scale, dim_image_h, dim_image_w, location, trigger=False): - tile = None - if not trigger: - tile = cv2.resize(pattern, dim, interpolation=cv2.INTER_AREA) - else: - resize_pattern = cv2.resize(pattern, dim, interpolation=cv2.INTER_AREA) - if len(pattern.shape) == 2: - tile = np.tile(resize_pattern, (int((5 + 1) / scale) + 4, int((5 + 1) / scale) + 4)) - if len(pattern.shape) == 3: - tile = np.tile(resize_pattern, (int((5 + 1) / scale) + 4, int((5 + 1) / scale) + 4, 1)) - tile = self.crop_image(tile, dim_image_h, dim_image_w, location, resize_pattern.shape) - return tile - - def get_mask_inv(self, print_): - if print_[0][0][0] == 255 and print_[0][0][1] == 255 and print_[0][0][2] == 255: - bg_color = cv2.cvtColor(print_, cv2.COLOR_BGR2LAB)[0][0] - print_tile = cv2.cvtColor(print_, cv2.COLOR_BGR2LAB) - bg_l, bg_a, bg_b = bg_color[0], bg_color[1], bg_color[2] - bg_L_high, bg_L_low = self.get_low_high_lab(bg_l, L=True) - bg_a_high, bg_a_low = self.get_low_high_lab(bg_a) - bg_b_high, bg_b_low = self.get_low_high_lab(bg_b) - lower = np.array([bg_L_low, bg_a_low, bg_b_low]) - upper = np.array([bg_L_high, bg_a_high, bg_b_high]) - mask_inv = cv2.inRange(print_tile, lower, upper) - return mask_inv - else: - # bg_color = cv2.cvtColor(print_, cv2.COLOR_BGR2LAB)[0][0] - # print_tile = cv2.cvtColor(print_, cv2.COLOR_BGR2LAB) - # bg_l, bg_a, bg_b = bg_color[0], bg_color[1], bg_color[2] - # bg_L_high, bg_L_low = self.get_low_high_lab(bg_l, L=True) - # bg_a_high, bg_a_low = self.get_low_high_lab(bg_a) - # bg_b_high, bg_b_low = self.get_low_high_lab(bg_b) - # lower = np.array([bg_L_low, bg_a_low, bg_b_low]) - # upper = np.array([bg_L_high, bg_a_high, bg_b_high]) - - # print_tile = cv2.cvtColor(print_, cv2.COLOR_BGR2LAB) - # mask_inv = cv2.cvtColor(print_tile, cv2.COLOR_BGR2GRAY) - - # mask_inv = cv2.cvtColor(print_, cv2.COLOR_BGR2GRAY) - mask_inv = np.zeros(print_.shape[:2], dtype=np.uint8) - return mask_inv - - @staticmethod - def printpaint(result, painting_dict, print_=False): - - if print_ and painting_dict['Trigger']: - print_mask = cv2.bitwise_and(result['mask'], cv2.bitwise_not(painting_dict['mask_inv_print'])) - img_fg = cv2.bitwise_and(painting_dict['tile_print'], painting_dict['tile_print'], mask=print_mask) - else: - print_mask = result['mask'] - img_fg = result['final_image'] - if print_ and not painting_dict['Trigger']: - index_ = None - try: - index_ = len(painting_dict['location']) - except: - assert f'there must be parameter of location if choose IfSingle' - - for i in range(index_): - start_h, start_w = int(painting_dict['location'][i][1]), int(painting_dict['location'][i][0]) - - length_h = min(start_h + painting_dict['dim_print_h'], img_fg.shape[0]) - length_w = min(start_w + painting_dict['dim_print_w'], img_fg.shape[1]) - - change_region = img_fg[start_h: length_h, start_w: length_w, :] - # problem in change_mask - change_mask = print_mask[start_h: length_h, start_w: length_w] - # get real part into change mask - _, change_mask = cv2.threshold(change_mask, 220, 255, cv2.THRESH_BINARY) - mask = cv2.bitwise_not(painting_dict['mask_inv_print']) - img_fg[start_h:start_h + painting_dict['dim_print_h'], start_w:start_w + painting_dict['dim_print_w'], :] = change_region - - clothes_mask_print = cv2.bitwise_not(print_mask) - - img_bg = cv2.bitwise_and(result['pattern_image'], result['pattern_image'], mask=clothes_mask_print) - mask_mo = np.expand_dims(print_mask, axis=2).repeat(3, axis=2) - gray_mo = np.expand_dims(result['gray'], axis=2).repeat(3, axis=2) - img_fg = (img_fg * (mask_mo / 255) * (gray_mo / 255)).astype(np.uint8) - print_image = cv2.add(img_bg, img_fg) - return print_image - - @staticmethod - def get_print(print_dict): - if 'print_scale_list' not in print_dict.keys() or print_dict['print_scale_list'][0] < 0.3: - print_dict['scale'] = 0.3 - else: - print_dict['scale'] = print_dict['print_scale_list'][0] - - bucket_name = print_dict['print_path_list'][0].split("/", 1)[0] - object_name = print_dict['print_path_list'][0].split("/", 1)[1] - image = oss_get_image(bucket=bucket_name, object_name=object_name, data_type="PIL") - # 判断图片格式，如果是RGBA 则贴在一张纯白图片上 防止透明转黑 - if image.mode == "RGBA": - new_background = Image.new('RGB', image.size, (255, 255, 255)) - new_background.paste(image, mask=image.split()[3]) - image = new_background - print_dict['image'] = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR) - return print_dict - - def crop_image(self, image, image_size_h, image_size_w, location, print_shape): - print_w = print_shape[1] - print_h = print_shape[0] - - random.seed(self.random_seed) - # logging.info(f'overall print location : {location}') - # x_offset = random.randint(0, image.shape[0] - image_size_h) - # y_offset = random.randint(0, image.shape[1] - image_size_w) - - # 1.拿到偏移量后和resize后的print宽高取余 得到真正偏移量 - x_offset = print_w - int(location[0][1] % print_w) - y_offset = print_w - int(location[0][0] % print_h) - - # y_offset = int(location[0][0]) - # x_offset = int(location[0][1]) - - if len(image.shape) == 2: - image = image[x_offset: x_offset + image_size_h, y_offset: y_offset + image_size_w] - elif len(image.shape) == 3: - image = image[x_offset: x_offset + image_size_h, y_offset: y_offset + image_size_w, :] - return image - - @staticmethod - def get_low_high_lab(Lab_value, L=False): - if L: - high = Lab_value + 30 if Lab_value + 30 < 255 else 255 - low = Lab_value - 30 if Lab_value - 30 > 0 else 0 - else: - high = Lab_value + 30 if Lab_value + 30 < 255 else 255 - low = Lab_value - 30 if Lab_value - 30 > 0 else 0 - return high, low - - @staticmethod - def img_rotate(image, angel, scale): - """顺时针旋转图像任意角度 - - Args: - image (np.array): [原始图像] - angel (float): [逆时针旋转的角度] - - Returns: - [array]: [旋转后的图像] - """ - - h, w = image.shape[:2] - center = (w // 2, h // 2) - # if type(angel) is not int: - # angel = 0 - M = cv2.getRotationMatrix2D(center, -angel, scale) - # 调整旋转后的图像长宽 - rotated_h = int((w * np.abs(M[0, 1]) + (h * np.abs(M[0, 0])))) - rotated_w = int((h * np.abs(M[0, 1]) + (w * np.abs(M[0, 0])))) - M[0, 2] += (rotated_w - w) // 2 - M[1, 2] += (rotated_h - h) // 2 - # 旋转图像 - rotated_img = cv2.warpAffine(image, M, (rotated_w, rotated_h)) - - return rotated_img, ((rotated_img.shape[1] - image.shape[1] * scale) // 2, (rotated_img.shape[0] - image.shape[0] * scale) // 2) - # return rotated_img, (0, 0) - - @staticmethod - def rotate_crop_image(img, angle, crop): - """ - angle: 旋转的角度 - crop: 是否需要进行裁剪，布尔向量 - """ - crop_image = lambda img, x0, y0, w, h: img[y0:y0 + h, x0:x0 + w] - w, h = img.shape[:2] - # 旋转角度的周期是360° - angle %= 360 - # 计算仿射变换矩阵 - M_rotation = cv2.getRotationMatrix2D((w / 2, h / 2), angle, 1) - # 得到旋转后的图像 - img_rotated = cv2.warpAffine(img, M_rotation, (w, h)) - - # 如果需要去除黑边 - if crop: - # 裁剪角度的等效周期是180° - angle_crop = angle % 180 - if angle > 90: - angle_crop = 180 - angle_crop - # 转化角度为弧度 - theta = angle_crop * np.pi / 180 - # 计算高宽比 - hw_ratio = float(h) / float(w) - # 计算裁剪边长系数的分子项 - tan_theta = np.tan(theta) - numerator = np.cos(theta) + np.sin(theta) * np.tan(theta) - - # 计算分母中和高宽比相关的项 - r = hw_ratio if h > w else 1 / hw_ratio - # 计算分母项 - denominator = r * tan_theta + 1 - # 最终的边长系数 - crop_mult = numerator / denominator - - # 得到裁剪区域 - w_crop = int(crop_mult * w) - h_crop = int(crop_mult * h) - x0 = int((w - w_crop) / 2) - y0 = int((h - h_crop) / 2) - - img_rotated = crop_image(img_rotated, x0, y0, w_crop, h_crop) - - return img_rotated - - @staticmethod - def read_image(image_url): - image = oss_get_image(bucket=image_url.split("/", 1)[0], object_name=image_url.split("/", 1)[1], data_type="cv2") - if image.shape[2] == 4: - image_rgb = cv2.cvtColor(image, cv2.COLOR_BGRA2RGBA) - image = Image.fromarray(image_rgb) - image_mode = "RGBA" - else: - image_mode = "RGB" - return image, image_mode - - @staticmethod - def resize_and_crop(img, target_width, target_height): - # 获取原始图像的尺寸 - original_height, original_width = img.shape[:2] - - # 计算目标尺寸的宽高比 - target_ratio = target_width / target_height - - # 计算原始图像的宽高比 - original_ratio = original_width / original_height - - # 调整尺寸 - if original_ratio > target_ratio: - # 原始图像更宽，按高度resize，然后裁剪宽度 - new_height = target_height - new_width = int(original_width * (target_height / original_height)) - resized_img = cv2.resize(img, (new_width, new_height)) - # 裁剪宽度 - start_x = (new_width - target_width) // 2 - cropped_img = resized_img[:, start_x:start_x + target_width] - else: - # 原始图像更高，按宽度resize，然后裁剪高度 - new_width = target_width - new_height = int(original_height * (target_width / original_width)) - resized_img = cv2.resize(img, (new_width, new_height)) - # 裁剪高度 - start_y = (new_height - target_height) // 2 - cropped_img = resized_img[start_y:start_y + target_height, :] - - return cropped_img diff --git a/app/service/design/items/pipelines/scale.py b/app/service/design/items/pipelines/scale.py deleted file mode 100644 index edd98c9..0000000 --- a/app/service/design/items/pipelines/scale.py +++ /dev/null @@ -1,57 +0,0 @@ -import math - -import cv2 - -from app.service.utils.decorator import ClassCallRunTime -from ..builder import PIPELINES - - -@PIPELINES.register_module() -class Scaling(object): - def __init__(self): - pass - - # @ClassCallRunTime - def __call__(self, result): - if result['keypoint'] in ['waistband', 'shoulder', 'head_point']: - # milvus_db_keypoint_cache - distance_clo = math.sqrt( - (int(result['clothes_keypoint'][result['keypoint'] + '_left'][0]) - int(result['clothes_keypoint'][result['keypoint'] + '_right'][0])) ** 2 - + - (int(result['clothes_keypoint'][result['keypoint'] + '_left'][1]) - int(result['clothes_keypoint'][result['keypoint'] + '_right'][1])) ** 2) - - distance_bdy = math.sqrt((int(result['body_point_test'][result['keypoint'] + '_left'][0]) - int(result['body_point_test'][result['keypoint'] + '_right'][0])) ** 2 + 1) - # distance_clo = math.sqrt( - # (int(result['clothes_keypoint'][result['keypoint'] + '_left'].split("_")[0]) - int(result['clothes_keypoint'][result['keypoint'] + '_right'].split("_")[0])) ** 2 - # + - # (int(result['clothes_keypoint'][result['keypoint'] + '_left'].split("_")[1]) - int(result['clothes_keypoint'][result['keypoint'] + '_right'].split("_")[1])) ** 2) - # - # distance_bdy = math.sqrt((int(result['body_point_test'][result['keypoint'] + '_left'][0]) - int(result['body_point_test'][result['keypoint'] + '_right'][0])) ** 2 + 1) - if distance_clo == 0: - result['scale'] = 1 - else: - result['scale'] = distance_bdy / distance_clo - elif result['keypoint'] == 'toe': - distance_bdy = math.sqrt( - (int(result['body_point_test']['foot_length'][0]) - int(result['body_point_test']['foot_length'][2])) ** 2 - + - (int(result['body_point_test']['foot_length'][1]) - int(result['body_point_test']['foot_length'][3])) ** 2 - ) - - Blur = cv2.GaussianBlur(result['gray'], (3, 3), 0) - Edge = cv2.Canny(Blur, 10, 200) - Edge = cv2.dilate(Edge, None) - Edge = cv2.erode(Edge, None) - Contour, _ = cv2.findContours(Edge, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) - Contours = sorted(Contour, key=cv2.contourArea, reverse=True) - - Max_contour = Contours[0] - x, y, w, h = cv2.boundingRect(Max_contour) - width = w - distance_clo = width - result['scale'] = distance_bdy / distance_clo - elif result['keypoint'] == 'hand_point': - result['scale'] = result['scale_bag'] - elif result['keypoint'] == 'ear_point': - result['scale'] = result['scale_earrings'] - return result diff --git a/app/service/design/items/pipelines/segmentation.py b/app/service/design/items/pipelines/segmentation.py deleted file mode 100644 index 7ed43e5..0000000 --- a/app/service/design/items/pipelines/segmentation.py +++ /dev/null @@ -1,71 +0,0 @@ -import logging -import os - -import cv2 -import numpy as np - -from app.core.config import SEG_CACHE_PATH -from app.service.utils.decorator import ClassCallRunTime -from app.service.utils.oss_client import oss_get_image -from ..builder import PIPELINES -from ...utils.design_ensemble import get_seg_result - -logger = logging.getLogger() - - -@PIPELINES.register_module() -class Segmentation(object): - - @ClassCallRunTime - def __call__(self, result): - if "seg_mask_url" in result.keys() and result['seg_mask_url'] != "": - seg_mask = oss_get_image(bucket=result['seg_mask_url'].split('/')[0], object_name=result['seg_mask_url'][result['seg_mask_url'].find('/') + 1:], data_type="cv2") - seg_mask = cv2.resize(seg_mask, (result['img_shape'][1], result['img_shape'][0]), interpolation=cv2.INTER_NEAREST) - # 转换颜色空间为 RGB（OpenCV 默认是 BGR） - image_rgb = cv2.cvtColor(seg_mask, cv2.COLOR_BGR2RGB) - - r, g, b = cv2.split(image_rgb) - red_mask = r > g - green_mask = g > r - - # 创建红色和绿色掩码 - result['front_mask'] = np.array(red_mask, dtype=np.uint8) * 255 - result['back_mask'] = np.array(green_mask, dtype=np.uint8) * 255 - result['mask'] = result['front_mask'] + result['back_mask'] - else: - # 本地查询seg 缓存是否存在 - _, seg_result = self.load_seg_result(result["image_id"]) - result['seg_result'] = seg_result - if not _: - # 推理获得seg 结果 - seg_result = get_seg_result(result["image_id"], result['image'])[0] - self.save_seg_result(seg_result, result['image_id']) - # 处理前片后片 - temp_front = seg_result == 1.0 - result['front_mask'] = (255 * (temp_front + 0).astype(np.uint8)) - temp_back = seg_result == 2.0 - result['back_mask'] = (255 * (temp_back + 0).astype(np.uint8)) - result['mask'] = result['front_mask'] + result['back_mask'] - return result - - @staticmethod - def save_seg_result(seg_result, image_id): - file_path = f"{SEG_CACHE_PATH}{image_id}.npy" - try: - np.save(file_path, seg_result) - logger.debug(f"保存成功 ：{os.path.abspath(file_path)}") - except Exception as e: - logger.error(f"保存失败: {e}") - - @staticmethod - def load_seg_result(image_id): - file_path = f"{SEG_CACHE_PATH}{image_id}.npy" - try: - seg_result = np.load(file_path) - return True, seg_result - except FileNotFoundError: - # logger.warning("文件不存在") - return False, None - except Exception as e: - logger.error(f"加载失败: {e}") - return False, None diff --git a/app/service/design/items/pipelines/split.py b/app/service/design/items/pipelines/split.py deleted file mode 100644 index 3485453..0000000 --- a/app/service/design/items/pipelines/split.py +++ /dev/null @@ -1,79 +0,0 @@ -import io -import logging - -import cv2 -import numpy as np -from PIL import Image -from cv2 import cvtColor, COLOR_BGR2RGBA - -from app.core.config import AIDA_CLOTHING -from app.service.utils.generate_uuid import generate_uuid -from app.service.utils.oss_client import oss_upload_image -from ..builder import PIPELINES -from ...utils.conversion_image import rgb_to_rgba -from ...utils.upload_image import upload_png_mask - - -@PIPELINES.register_module() -class Split(object): - """ - Split image into front and back layer according to the segmentation result - """ - - # @ClassCallRunTime - # KNet - def __call__(self, result): - try: - - if result['name'] in ('outwear', 'dress', 'blouse', 'skirt', 'trousers', 'tops', 'bottoms'): - front_mask = result['front_mask'] - back_mask = result['back_mask'] - rgba_image = rgb_to_rgba(result['final_image'], front_mask + back_mask) - new_size = (int(rgba_image.shape[1] * result["scale"] * result["resize_scale"][0]), int(rgba_image.shape[0] * result["scale"] * result["resize_scale"][1])) - rgba_image = cv2.resize(rgba_image, new_size) - result_front_image = np.zeros_like(rgba_image) - front_mask = cv2.resize(front_mask, new_size) - result_front_image[front_mask != 0] = rgba_image[front_mask != 0] - result_front_image_pil = Image.fromarray(cvtColor(result_front_image, COLOR_BGR2RGBA)) - result['front_image'], result["front_image_url"], _ = upload_png_mask(result_front_image_pil, f'{generate_uuid()}', mask=None) - - height, width = front_mask.shape - mask_image = np.zeros((height, width, 3)) - mask_image[front_mask != 0] = [0, 0, 255] - - if result["name"] in ('blouse', 'dress', 'outwear', 'tops'): - result_back_image = np.zeros_like(rgba_image) - back_mask = cv2.resize(back_mask, new_size) - result_back_image[back_mask != 0] = rgba_image[back_mask != 0] - result_back_image_pil = Image.fromarray(cvtColor(result_back_image, COLOR_BGR2RGBA)) - result['back_image'], result["back_image_url"], _ = upload_png_mask(result_back_image_pil, f'{generate_uuid()}', mask=None) - mask_image[back_mask != 0] = [0, 255, 0] - - rbga_mask = rgb_to_rgba(mask_image, front_mask + back_mask) - mask_pil = Image.fromarray(cvtColor(rbga_mask.astype(np.uint8), COLOR_BGR2RGBA)) - image_data = io.BytesIO() - mask_pil.save(image_data, format='PNG') - image_data.seek(0) - image_bytes = image_data.read() - req = oss_upload_image(bucket=AIDA_CLOTHING, object_name=f"mask/mask_{generate_uuid()}.png", image_bytes=image_bytes) - result['mask_url'] = req.bucket_name + "/" + req.object_name - else: - rbga_mask = rgb_to_rgba(mask_image, front_mask) - mask_pil = Image.fromarray(cvtColor(rbga_mask.astype(np.uint8), COLOR_BGR2RGBA)) - image_data = io.BytesIO() - mask_pil.save(image_data, format='PNG') - image_data.seek(0) - image_bytes = image_data.read() - req = oss_upload_image(bucket=AIDA_CLOTHING, object_name=f"mask/mask_{generate_uuid()}.png", image_bytes=image_bytes) - result['mask_url'] = req.bucket_name + "/" + req.object_name - result['back_image'] = None - result["back_image_url"] = None - # result["back_mask_url"] = None - # result['back_mask_image'] = None - # 创建中间图层 - result_pattern_image_rgba = rgb_to_rgba(result['pattern_image'], result['mask']) - result_pattern_image_pil = Image.fromarray(cvtColor(result_pattern_image_rgba, COLOR_BGR2RGBA)) - result['pattern_image'], result['pattern_image_url'], _ = upload_png_mask(result_pattern_image_pil, f'{generate_uuid()}') - return result - except Exception as e: - logging.warning(f"split runtime exception : {e} image_id : {result['image_id']}") diff --git a/app/service/design/items/shoes.py b/app/service/design/items/shoes.py deleted file mode 100644 index aa20d3c..0000000 --- a/app/service/design/items/shoes.py +++ /dev/null @@ -1,121 +0,0 @@ -import cv2 -import numpy as np -from PIL import Image - -from .builder import ITEMS -from .clothing import Clothing -from ..utils.conversion_image import rgb_to_rgba -from ..utils.upload_image import upload_png_mask -from ...utils.generate_uuid import generate_uuid - - -@ITEMS.register_module() -class Shoes(Clothing): - # TODO location of shoes has little mismatch - def __init__(self, **kwargs): - pipeline = [ - dict(type='LoadImageFromFile', path=kwargs['path'], color=kwargs['color']), - dict(type='KeypointDetection'), - dict(type='ContourDetection'), - dict(type='Painting'), - dict(type='Scaling'), - dict(type='Split'), - # dict(type='ImageShow', key=['image', 'mask', 'pattern_image']), - ] - kwargs.update(pipeline=pipeline) - super(Shoes, self).__init__(**kwargs) - - def organize(self, layer): - left_shoe_mask, right_shoe_mask = self.cut() - - left_layer = dict(name=f'{type(self).__name__.lower()}_left', - image=self.result['shoes_left'], - image_url=self.result['left_image_url'], - mask_url=self.result['left_mask_url'], - sacle=self.result['scale'], - clothes_keypoint=self.result['clothes_keypoint'], - position=self.calculate_start_point(self.result['keypoint'], - self.result['scale'], - self.result['clothes_keypoint'], - self.result['body_point'], - 'left')) - layer.insert(left_layer) - - right_layer = dict(name=f'{type(self).__name__.lower()}_right', - image=self.result['shoes_right'], - image_url=self.result['right_image_url'], - mask_url=self.result['right_mask_url'], - sacle=self.result['scale'], - clothes_keypoint=self.result['clothes_keypoint'], - position=self.calculate_start_point(self.result['keypoint'], - self.result['scale'], - self.result['clothes_keypoint'], - self.result['body_point'], - 'right')) - - layer.insert(right_layer) - - def cut(self): - """ - Cut shoes mask into two pieces - Returns: - """ - contour, _ = cv2.findContours(self.result['mask'], cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) - contours = sorted(contour, key=cv2.contourArea, reverse=True) - - bounding_boxes = [cv2.boundingRect(c) for c in contours[:2]] - (contours, bounding_boxes) = zip(*sorted(zip(contours[:2], bounding_boxes), key=lambda x: x[1][0], reverse=False)) - - epsilon_left = 0.001 * cv2.arcLength(contours[0], True) - - approx_left = cv2.approxPolyDP(contours[0], epsilon_left, True) - mask_left = np.zeros(self.result['final_image'].shape[:2], np.uint8) - cv2.drawContours(mask_left, [approx_left], -1, 255, -1) - item_mask_left = cv2.GaussianBlur(mask_left, (5, 5), 0) - - rgba_image = rgb_to_rgba((self.result['final_image'].shape[0], self.result['final_image'].shape[1]), self.result['final_image'], item_mask_left) - result_image = np.zeros_like(rgba_image) - result_image[self.result['front_mask'] != 0] = rgba_image[self.result['front_mask'] != 0] - result_left_image_pil = Image.fromarray(result_image, 'RGBA') - result_left_image_pil = result_left_image_pil.resize((int(result_left_image_pil.width * self.result["scale"]), int(result_left_image_pil.height * self.result["scale"])), Image.LANCZOS) - self.result['shoes_left'], self.result["left_image_url"], self.result["left_mask_url"] = upload_png_mask(result_left_image_pil, f"{generate_uuid()}") - - epsilon_right = 0.001 * cv2.arcLength(contours[1], True) - approx_right = cv2.approxPolyDP(contours[1], epsilon_right, True) - mask_right = np.zeros(self.result['final_image'].shape[:2], np.uint8) - cv2.drawContours(mask_right, [approx_right], -1, 255, -1) - item_mask_right = cv2.GaussianBlur(mask_right, (5, 5), 0) - - rgba_image = rgb_to_rgba((self.result['final_image'].shape[0], self.result['final_image'].shape[1]), self.result['final_image'], item_mask_right) - result_image = np.zeros_like(rgba_image) - result_image[self.result['front_mask'] != 0] = rgba_image[self.result['front_mask'] != 0] - result_right_image_pil = Image.fromarray(result_image, 'RGBA') - result_right_image_pil = result_right_image_pil.resize((int(result_right_image_pil.width * self.result["scale"]), int(result_right_image_pil.height * self.result["scale"])), Image.LANCZOS) - self.result['shoes_right'], self.result["right_image_url"], self.result["right_mask_url"] = upload_png_mask(result_right_image_pil, f"{generate_uuid()}") - - return item_mask_left, item_mask_right - - @staticmethod - def calculate_start_point(keypoint_type, scale, clothes_point, body_point, location): - """ - left shoes align left - right shoes align right - Args: - keypoint_type: string, "toe" - scale: float - clothes_point: dict{'left': [x1, y1, z1], 'right': [x2, y2, z2]} - body_point: dict, containing keypoint data of body figure - location: string, indicates whether the start point belongs to right or left shoe - - Returns: - start_point: tuple (x', y') - x' = y_body - y1 * scale - y' = x_body - x1 * scale - """ - if location not in ['left', 'right']: - raise KeyError(f'location value must be left or right but got {location}') - side_indicator = f'{keypoint_type}_{location}' - # clothes_point = {k: tuple(map(lambda x: int(scale * x), v[0: 2])) for k, v in clothes_point.items()} - start_point = (body_point[side_indicator][1] - int(int(clothes_point[side_indicator].split("_")[1]) * scale), - body_point[side_indicator][0] - int(int(clothes_point[side_indicator].split("_")[0]) * scale)) - return start_point diff --git a/app/service/design/items/top.py b/app/service/design/items/top.py deleted file mode 100644 index fc0d2a5..0000000 --- a/app/service/design/items/top.py +++ /dev/null @@ -1,46 +0,0 @@ -from .builder import ITEMS -from .clothing import Clothing - - -@ITEMS.register_module() -class Top(Clothing): - def __init__(self, pipeline, **kwargs): - if pipeline is None: - pipeline = [ - dict(type='LoadImageFromFile', path=kwargs['path'], color=kwargs['color'], print_dict=kwargs['print']), - dict(type='KeypointDetection'), - # dict(type='ContourDetection'), - dict(type='Segmentation'), - dict(type='Painting', painting_flag=True), - dict(type='PrintPainting', print_flag=True), - # dict(type='ImageShow', key=['image', 'mask', 'seg_visualize', 'pattern_image']), - dict(type='Scaling'), - dict(type='Split'), - ] - kwargs.update(pipeline=pipeline) - super(Top, self).__init__(**kwargs) - - -@ITEMS.register_module() -class Blouse(Top): - def __init__(self, pipeline=None, **kwargs): - super(Blouse, self).__init__(pipeline, **kwargs) - - -@ITEMS.register_module() -class Outwear(Top): - def __init__(self, pipeline=None, **kwargs): - super(Outwear, self).__init__(pipeline, **kwargs) - - -@ITEMS.register_module() -class Dress(Top): - def __init__(self, pipeline=None, **kwargs): - super(Dress, self).__init__(pipeline, **kwargs) - - -# Men's clothing -@ITEMS.register_module() -class Tops(Top): - def __init__(self, pipeline=None, **kwargs): - super(Tops, self).__init__(pipeline, **kwargs) diff --git a/app/service/design/service.py b/app/service/design/service.py deleted file mode 100644 index ba7e987..0000000 --- a/app/service/design/service.py +++ /dev/null @@ -1,197 +0,0 @@ -import concurrent.futures -import io - -import cv2 - -from app.core.config import PRIORITY_DICT -from app.service.design.core.layer import Layer -from app.service.design.items import build_item -from app.service.design.utils.redis_utils import Redis -from app.service.design.utils.synthesis_item import synthesis, synthesis_single -from app.service.utils.decorator import RunTime -from app.service.utils.oss_client import oss_upload_image - - -def process_item(item, layers): - # logging.info("process running.........") - item.process() - item.organize(layers) - if item.result['name'] == "mannequin": - return item.result['body_image'].size - - -def update_progress(process_id, total): - r = Redis() - progress = r.read(key=process_id) - if progress and total != 1: - if int(progress) <= 100: - r.write(key=process_id, value=int(progress) + int(100 / total)) - else: - r.write(key=process_id, value=99) - return progress - elif total == 1: - r.write(key=process_id, value=100) - return progress - else: - r.write(key=process_id, value=int(100 / total)) - return progress - - -def final_progress(process_id): - r = Redis() - progress = r.read(key=process_id) - r.write(key=process_id, value=100) - return progress - - -@RunTime -def generate(request_data): - return_response = {} - return_png_mask = [] - request_data = request_data.dict() - assert "process_id" in request_data.keys(), "Need process_id parameters" - - objects = request_data['objects'] - # insert_keypoint_cache(objects) - process_id = request_data['process_id'] - with concurrent.futures.ThreadPoolExecutor() as executor: - # 提交每个对象的处理任务 - futures = {executor.submit(process_object, cfg, process_id, len(objects)): obj for obj, cfg in enumerate(objects)} - # 获取处理结果 - for future in concurrent.futures.as_completed(futures): - obj = futures[future] - return_response[obj] = future.result()[0] - return_png_mask.extend(future.result()[1]) - # upload_results = process_images(return_png_mask) - final_progress(process_id) - return return_response - - -def process_object(cfg, process_id, total): - uploaded_images = [] - basic_info = cfg.get('basic') - items_response = { - 'layers': [] - } - if cfg.get('basic')['single_overall'] == 'overall': - basic_info['debug'] = False - items = [build_item(x, default_args=basic_info) for x in cfg.get('items')] - layers = Layer() - body_size = None - futures = [] - for item in items: - futures = [process_item(item, layers)] - for future in futures: - if future is not None: - body_size = future - # 是否自定义排序 - if basic_info.get('layer_order', False): - layers = sorted(layers.layer, key=lambda s: s.get("priority", float('inf'))) - else: - layers = sorted(layers.layer, key=lambda x: PRIORITY_DICT.get(x['name'], float('inf'))) - # 上传所有图片 - # for layer in layers: - # if 'image' in layer.keys() and layer['image'] is not None: - # uploaded_images.append({'image_obj': layer['image'], 'image_url': layer['image_url'], 'image_type': 'image'}) - # if 'pattern_image' in layer.keys() and layer['pattern_image'] is not None: - # uploaded_images.append({'image_obj': layer['pattern_image'], 'image_url': layer['pattern_image_url'], 'image_type': 'pattern_image'}) - # if 'mask' in layer.keys() and layer['mask'] is not None and layer['mask_url'] is not None: - # uploaded_images.append({'image_obj': layer['mask'], 'image_url': layer['mask_url'], 'image_type': 'mask'}) - layers, new_size = update_base_size_priority(layers, body_size) - # 合成 - items_response['synthesis_url'] = synthesis(layers, new_size, basic_info) - - for lay in layers: - items_response['layers'].append({ - 'image_category': lay['name'], - 'position': lay['position'], - 'priority': lay.get("priority", None), - 'resize_scale': lay['resize_scale'] if "resize_scale" in lay.keys() else None, - 'image_size': lay['image'] if lay['image'] is None else lay['image'].size, - 'gradient_string': lay['gradient_string'] if 'gradient_string' in lay.keys() else "", - 'mask_url': lay['mask_url'], - 'image_url': lay['image_url'] if 'image_url' in lay.keys() else None, - 'pattern_image_url': lay['pattern_image_url'] if 'pattern_image_url' in lay.keys() else None, - - # 'image': lay['image'], - # 'mask_image': lay['mask_image'], - }) - elif cfg.get('basic')['single_overall'] == 'single': - assert cfg.get('basic')['switch_category'] in [x['type'] for x in cfg.get('items')], "Lack of switch_category parameters " - basic_info['debug'] = False - for item in cfg.get('items'): - if item['type'] == cfg.get('basic')['switch_category']: - item = build_item(item, default_args=cfg.get('basic')) - item.process() - items_response['layers'].append({ - 'image_category': f"{item.result['name']}_front", - 'image_size': item.result['back_image'].size if item.result['back_image'] else None, - 'position': None, - 'priority': 0, - 'image_url': item.result['front_image_url'], - 'mask_url': item.result['mask_url'], - "gradient_string": item.result['gradient_string'] if 'gradient_string' in item.result.keys() else "", - 'pattern_image_url': item.result['pattern_image_url'] if 'pattern_image_url' in item.result.keys() else None, - - }) - items_response['layers'].append({ - 'image_category': f"{item.result['name']}_back", - 'image_size': item.result['front_image'].size if item.result['front_image'] else None, - 'position': None, - 'priority': 0, - 'image_url': item.result['back_image_url'], - 'mask_url': item.result['mask_url'], - "gradient_string": item.result['gradient_string'] if 'gradient_string' in item.result.keys() else "", - 'pattern_image_url': item.result['pattern_image_url'] if 'pattern_image_url' in item.result.keys() else None, - - }) - items_response['synthesis_url'] = synthesis_single(item.result['front_image'], item.result['back_image']) - break - update_progress(process_id, total) - return items_response, uploaded_images - - -@RunTime -def process_images(images): - with concurrent.futures.ThreadPoolExecutor() as executor: - results = list(executor.map(upload_images, images)) - # results = [] - # for image in images: - # results.append(upload_images(image)) - return results - - -# @RunTime -def upload_images(image_obj): - bucket_name = image_obj['image_url'].split("/", 1)[0] - object_name = image_obj['image_url'].split("/", 1)[1] - if image_obj['image_type'] == 'image' or image_obj['image_type'] == 'pattern_image': - image_data = io.BytesIO() - image_obj['image_obj'].save(image_data, format='PNG') - image_data.seek(0) - image_bytes = image_data.read() - req = oss_upload_image(bucket=bucket_name, object_name=object_name, image_bytes=image_bytes) - return image_obj['image_url'] - else: - mask_inverted = cv2.bitwise_not(image_obj['image_obj']) - # 将掩模的3通道转换为4通道，白色部分不透明，黑色部分透明 - rgba_image = cv2.cvtColor(mask_inverted, cv2.COLOR_BGR2BGRA) - rgba_image[rgba_image[:, :, 0] == 0] = [0, 0, 0, 0] - req = oss_upload_image(bucket=bucket_name, object_name=object_name, image_bytes=cv2.imencode('.png', rgba_image)[1]) - return image_obj['image_url'] - - -def update_base_size_priority(layers, size): - # 计算透明背景图片的宽度 - min_x = min(info['position'][1] for info in layers) - x_list = [] - for info in layers: - if info['image'] is not None: - x_list.append(info['position'][1] + info['image'].width) - max_x = max(x_list) - new_width = max_x - min_x - new_height = 700 - # 更新坐标 - for info in layers: - info['adaptive_position'] = (info['position'][0], info['position'][1] - min_x) - return layers, (new_width, new_height) diff --git a/app/service/design/utils/conversion_image.py b/app/service/design/utils/conversion_image.py deleted file mode 100644 index 11e39ae..0000000 --- a/app/service/design/utils/conversion_image.py +++ /dev/null @@ -1,31 +0,0 @@ -#!/usr/bin/env python -# -*- coding: UTF-8 -*- -""" -@Project ：trinity_client -@File ：conversion_image.py -@Author ：周成融 -@Date ：2023/8/21 10:40:29 -@detail ： -""" -import numpy as np - - -# def rgb_to_rgba(rgb_size, rgb_image, mask): -# alpha_channel = np.full(rgb_size, 255, dtype=np.uint8) -# # 创建四通道的结果图像 -# rgba_image = np.dstack((rgb_image, alpha_channel)) -# alpha_channel = np.where(mask > 0, 255, 0) -# # 更新RGBA图像的透明度通道 -# rgba_image[:, :, 3] = alpha_channel -# return rgba_image - -def rgb_to_rgba(rgb_image, mask): - # 创建全透明的alpha通道 - alpha_channel = np.where(mask > 0, 255, 0).astype(np.uint8) - # 合并RGB图像和alpha通道 - rgba_image = np.dstack((rgb_image, alpha_channel)) - return rgba_image - - -if __name__ == '__main__': - image = open("") diff --git a/app/service/design/utils/design_ensemble.py b/app/service/design/utils/design_ensemble.py deleted file mode 100644 index f4f6a34..0000000 --- a/app/service/design/utils/design_ensemble.py +++ /dev/null @@ -1,143 +0,0 @@ -#!/usr/bin/env python -# -*- coding: UTF-8 -*- -""" -@Project ：trinity_client -@File ：design_ensemble.py -@Author ：周成融 -@Date ：2023/8/16 19:36:21 -@detail ：发起请求 获取推理结果 -""" -import logging - -import cv2 -import mmcv -import numpy as np -import torch -import torch.nn.functional as F -import tritonclient.http as httpclient - -from app.core.config import * - -""" - keypoint - 预处理 推理 后处理 -""" - - -def keypoint_preprocess(img_path): - img = mmcv.imread(img_path) - img_scale = (256, 256) - h, w = img.shape[:2] - img = cv2.resize(img, img_scale) - w_scale = img_scale[0] / w - h_scale = img_scale[1] / h - img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True) - preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0) - return preprocessed_img, (w_scale, h_scale) - - -# @ RunTime -# 推理 -def get_keypoint_result(image, site): - keypoint_result = None - try: - image, scale_factor = keypoint_preprocess(image) - client = httpclient.InferenceServerClient(url=DESIGN_MODEL_URL) - transformed_img = image.astype(np.float32) - inputs = [httpclient.InferInput(f"input", transformed_img.shape, datatype="FP32")] - inputs[0].set_data_from_numpy(transformed_img, binary_data=True) - outputs = [httpclient.InferRequestedOutput(f"output", binary_data=True)] - results = client.infer(model_name=f"keypoint_{site}_ocrnet_hr18", inputs=inputs, outputs=outputs) - inference_output = torch.from_numpy(results.as_numpy(f'output')) - keypoint_result = keypoint_postprocess(inference_output, scale_factor) - except Exception as e: - logging.warning(f"get_keypoint_result : {e}") - return keypoint_result - - -def keypoint_postprocess(output, scale_factor): - max_indices = torch.argmax(output.view(output.size(0), output.size(1), -1), dim=2).unsqueeze(dim=2) - max_coords = torch.cat((max_indices / output.size(3), max_indices % output.size(3)), dim=2) - segment_result = max_coords.numpy() - scale_factor = [1 / x for x in scale_factor[::-1]] - scale_matrix = np.diag(scale_factor) - nan = np.isinf(scale_matrix) - scale_matrix[nan] = 0 - return np.ceil(np.dot(segment_result, scale_matrix) * 4) - - -""" - seg - 预处理 推理 后处理 -""" - - -# KNet -def seg_preprocess(img_path): - img = mmcv.imread(img_path) - ori_shape = img.shape[:2] - img_scale_w, img_scale_h = ori_shape - if ori_shape[0] > 1024: - img_scale_w = 1024 - if ori_shape[1] > 1024: - img_scale_h = 1024 - # 如果图片size任意一边 大于 1024， 则会resize 成1024 - if ori_shape != (img_scale_w, img_scale_h): - # mmcv.imresize(img, img_scale_h, img_scale_w) # 老代码 引以为戒！哈哈哈~ h和w写反了 - img = cv2.resize(img, (img_scale_h, img_scale_w)) - img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True) - preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0) - return preprocessed_img, ori_shape - - -# @ RunTime -def get_seg_result(image_id, image): - image, ori_shape = seg_preprocess(image) - client = httpclient.InferenceServerClient(url=f"{DESIGN_MODEL_URL}") - transformed_img = image.astype(np.float32) - # 输入集 - inputs = [ - httpclient.InferInput(SEGMENTATION['input'], transformed_img.shape, datatype="FP32") - ] - inputs[0].set_data_from_numpy(transformed_img, binary_data=True) - # 输出集 - outputs = [ - httpclient.InferRequestedOutput(SEGMENTATION['output'], binary_data=True), - ] - results = client.infer(model_name=SEGMENTATION['new_model_name'], inputs=inputs, outputs=outputs) - # 推理 - # 取结果 - inference_output1 = results.as_numpy(SEGMENTATION['output']) - seg_result = seg_postprocess(int(image_id), inference_output1, ori_shape) - return seg_result - - -# no cache -def seg_postprocess(image_id, output, ori_shape): - seg_logit = F.interpolate(torch.tensor(output).float(), size=ori_shape, scale_factor=None, mode='bilinear', align_corners=False) - seg_pred = seg_logit.cpu().numpy() - return seg_pred[0] - - -def key_point_show(image_path, key_point_result=None): - img = cv2.imread(image_path) - points_list = key_point_result - point_size = 1 - point_color = (0, 0, 255) # BGR - thickness = 4 # 可以为 0 、4、8 - for point in points_list: - cv2.circle(img, point[::-1], point_size, point_color, thickness) - cv2.imshow("0", img) - cv2.waitKey(0) - - -if __name__ == '__main__': - image = cv2.imread("9070101c-e5be-49b5-9602-4113a968969b.png") - a = get_keypoint_result(image, "up") - new_list = [] - print(list) - for i in a[0]: - new_list.append((int(i[0]), int(i[1]))) - key_point_show("9070101c-e5be-49b5-9602-4113a968969b.png", new_list) - # a = get_seg_result(1, image) - print(a) diff --git a/app/service/design/utils/redis_utils.py b/app/service/design/utils/redis_utils.py deleted file mode 100644 index 012fbe0..0000000 --- a/app/service/design/utils/redis_utils.py +++ /dev/null @@ -1,99 +0,0 @@ -import redis - -from app.core.config import REDIS_HOST, REDIS_PORT - - -class Redis(object): - """ - redis数据库操作 - """ - - @staticmethod - def _get_r(): - host = REDIS_HOST - port = REDIS_PORT - db = 0 - r = redis.StrictRedis(host, port, db) - return r - - @classmethod - def write(cls, key, value, expire=None): - """ - 写入键值对 - """ - # 判断是否有过期时间，没有就设置默认值 - if expire: - expire_in_seconds = expire - else: - expire_in_seconds = 100 - r = cls._get_r() - r.set(key, value, ex=expire_in_seconds) - - @classmethod - def read(cls, key): - """ - 读取键值对内容 - """ - r = cls._get_r() - value = r.get(key) - return value.decode('utf-8') if value else value - - @classmethod - def hset(cls, name, key, value): - """ - 写入hash表 - """ - r = cls._get_r() - r.hset(name, key, value) - - @classmethod - def hget(cls, name, key): - """ - 读取指定hash表的键值 - """ - r = cls._get_r() - value = r.hget(name, key) - return value.decode('utf-8') if value else value - - @classmethod - def hgetall(cls, name): - """ - 获取指定hash表所有的值 - """ - r = cls._get_r() - return r.hgetall(name) - - @classmethod - def delete(cls, *names): - """ - 删除一个或者多个 - """ - r = cls._get_r() - r.delete(*names) - - @classmethod - def hdel(cls, name, key): - """ - 删除指定hash表的键值 - """ - r = cls._get_r() - r.hdel(name, key) - - @classmethod - def expire(cls, name, expire=None): - """ - 设置过期时间 - """ - if expire: - expire_in_seconds = expire - else: - expire_in_seconds = 100 - r = cls._get_r() - r.expire(name, expire_in_seconds) - - -if __name__ == '__main__': - redis_client = Redis() - # print(redis_client.write(key="1230", value=0)) - redis_client.write(key="1230", value=10) - # print(redis_client.read(key="1230")) diff --git a/app/service/design/utils/synthesis_item.py b/app/service/design/utils/synthesis_item.py deleted file mode 100644 index 03df2d9..0000000 --- a/app/service/design/utils/synthesis_item.py +++ /dev/null @@ -1,181 +0,0 @@ -#!/usr/bin/env python -# -*- coding: UTF-8 -*- -""" -@Project ：trinity_client -@File ：synthesis_item.py -@Author ：周成融 -@Date ：2023/8/26 14:13:04 -@detail ： -""" -import io -import logging - -import cv2 -import numpy as np -from PIL import Image - -from app.service.utils.generate_uuid import generate_uuid -from app.service.utils.oss_client import oss_upload_image - - -def positioning(all_mask_shape, mask_shape, offset): - all_start = 0 - all_end = 0 - mask_start = 0 - mask_end = 0 - if offset == 0: - all_start = 0 - all_end = min(all_mask_shape, mask_shape) - - mask_start = 0 - mask_end = min(all_mask_shape, mask_shape) - elif offset > 0: - all_start = min(offset, all_mask_shape) - all_end = min(offset + mask_shape, all_mask_shape) - - mask_start = 0 - mask_end = 0 if offset > all_mask_shape else min(all_mask_shape - offset, mask_shape) - elif offset < 0: - if abs(offset) > mask_shape: - all_start = 0 - all_end = 0 - else: - all_start = 0 - if mask_shape - abs(offset) > all_mask_shape: - all_end = min(mask_shape - abs(offset), all_mask_shape) - else: - all_end = mask_shape - abs(offset) - - if abs(offset) > mask_shape: - mask_start = mask_shape - mask_end = mask_shape - else: - mask_start = abs(offset) - if mask_shape - abs(offset) >= all_mask_shape: - mask_end = all_mask_shape + abs(offset) - else: - mask_end = mask_shape - return all_start, all_end, mask_start, mask_end - - -# @RunTime -def synthesis(data, size, basic_info): - # 创建底图 - base_image = Image.new('RGBA', size, (0, 0, 0, 0)) - try: - all_mask_shape = (size[1], size[0]) - body_mask = None - for d in data: - if d['name'] == 'body': - # 创建一个新的宽高透明图像， 把模特贴上去获取mask - transparent_image = Image.new("RGBA", size, (0, 0, 0, 0)) - transparent_image.paste(d['image'], (d['adaptive_position'][1], d['adaptive_position'][0]), d['image']) # 此处可变数组会被paste篡改值，所以使用下标获取position - body_mask = np.array(transparent_image.split()[3]) - - # 根据新的坐标获取新的肩点 - left_shoulder = [x + y for x, y in zip(basic_info['body_point_test']['shoulder_left'], [d['adaptive_position'][1], d['adaptive_position'][0]])] - right_shoulder = [x + y for x, y in zip(basic_info['body_point_test']['shoulder_right'], [d['adaptive_position'][1], d['adaptive_position'][0]])] - body_mask[:min(left_shoulder[1], right_shoulder[1]), left_shoulder[0]:right_shoulder[0]] = 255 - _, binary_body_mask = cv2.threshold(body_mask, 127, 255, cv2.THRESH_BINARY) - top_outer_mask = np.array(binary_body_mask) - bottom_outer_mask = np.array(binary_body_mask) - - top = True - bottom = True - i = len(data) - while i: - i -= 1 - if top and data[i]['name'] in ["blouse_front", "outwear_front", "dress_front", "tops_front"]: - top = False - mask_shape = data[i]['mask'].shape - y_offset, x_offset = data[i]['adaptive_position'] - # 初始化叠加区域的起始和结束位置 - all_y_start, all_y_end, mask_y_start, mask_y_end = positioning(all_mask_shape=all_mask_shape[0], mask_shape=mask_shape[0], offset=y_offset) - all_x_start, all_x_end, mask_x_start, mask_x_end = positioning(all_mask_shape=all_mask_shape[1], mask_shape=mask_shape[1], offset=x_offset) - # 将叠加区域赋值为相应的像素值 - _, sketch_mask = cv2.threshold(data[i]['mask'], 127, 255, cv2.THRESH_BINARY) - background = np.zeros_like(top_outer_mask) - background[all_y_start:all_y_end, all_x_start:all_x_end] = sketch_mask[mask_y_start:mask_y_end, mask_x_start:mask_x_end] - top_outer_mask = background + top_outer_mask - elif bottom and data[i]['name'] in ["trousers_front", "skirt_front", "bottoms_front", "dress_front"]: - bottom = False - mask_shape = data[i]['mask'].shape - y_offset, x_offset = data[i]['adaptive_position'] - # 初始化叠加区域的起始和结束位置 - all_y_start, all_y_end, mask_y_start, mask_y_end = positioning(all_mask_shape=all_mask_shape[0], mask_shape=mask_shape[0], offset=y_offset) - all_x_start, all_x_end, mask_x_start, mask_x_end = positioning(all_mask_shape=all_mask_shape[1], mask_shape=mask_shape[1], offset=x_offset) - # 将叠加区域赋值为相应的像素值 - _, sketch_mask = cv2.threshold(data[i]['mask'], 127, 255, cv2.THRESH_BINARY) - background = np.zeros_like(top_outer_mask) - background[all_y_start:all_y_end, all_x_start:all_x_end] = sketch_mask[mask_y_start:mask_y_end, mask_x_start:mask_x_end] - bottom_outer_mask = background + bottom_outer_mask - elif bottom is False and top is False: - break - - all_mask = cv2.bitwise_or(top_outer_mask, bottom_outer_mask) - - for layer in data: - if layer['image'] is not None: - if layer['name'] != "body": - test_image = Image.new('RGBA', size, (0, 0, 0, 0)) - test_image.paste(layer['image'], (layer['adaptive_position'][1], layer['adaptive_position'][0]), layer['image']) - mask_data = np.where(all_mask > 0, 255, 0).astype(np.uint8) - mask_alpha = Image.fromarray(mask_data) - cropped_image = Image.composite(test_image, Image.new("RGBA", test_image.size, (255, 255, 255, 0)), mask_alpha) - base_image.paste(test_image, (0, 0), cropped_image) # test_image 已经按照坐标贴到最大宽值的图片上 坐着这里坐标为00 - else: - base_image.paste(layer['image'], (layer['adaptive_position'][1], layer['adaptive_position'][0]), layer['image']) - - result_image = base_image - - image_data = io.BytesIO() - result_image.save(image_data, format='PNG') - image_data.seek(0) - - # oss upload - image_bytes = image_data.read() - bucket_name = "aida-results" - object_name = f'result_{generate_uuid()}.png' - req = oss_upload_image(bucket=bucket_name, object_name=object_name, image_bytes=image_bytes) - return f"{bucket_name}/{object_name}" - # return f"aida-results/{minio_client.put_object('aida-results', f'result_{generate_uuid()}.png', io.BytesIO(image_bytes), len(image_bytes), content_type='image/png').object_name}" - - # object_name = f'result_{generate_uuid()}.png' - # response = s3.put_object(Bucket="aida-results", Key=object_name, Body=data, ContentType='image/png') - # object_url = f"aida-results/{object_name}" - # if response['ResponseMetadata']['HTTPStatusCode'] == 200: - # return object_url - # else: - # return "" - - except Exception as e: - logging.warning(f"synthesis runtime exception : {e}") - - -def synthesis_single(front_image, back_image): - result_image = None - if front_image: - result_image = front_image - if back_image: - result_image.paste(back_image, (0, 0), back_image) - - # with io.BytesIO() as output: - # result_image.save(output, format='PNG') - # data = output.getvalue() - # object_name = f'result_{generate_uuid()}.png' - # response = s3.put_object(Bucket="aida-results", Key=object_name, Body=data, ContentType='image/png') - # object_url = f"aida-results/{object_name}" - # if response['ResponseMetadata']['HTTPStatusCode'] == 200: - # return object_url - # else: - # return "" - image_data = io.BytesIO() - result_image.save(image_data, format='PNG') - image_data.seek(0) - image_bytes = image_data.read() - # return f"aida-results/{minio_client.put_object('aida-results', f'result_{generate_uuid()}.png', io.BytesIO(image_bytes), len(image_bytes), content_type='image/png').object_name}" - # oss upload - bucket_name = 'aida-results' - object_name = f'result_{generate_uuid()}.png' - req = oss_upload_image(bucket=bucket_name, object_name=object_name, image_bytes=image_bytes) - return f"{bucket_name}/{object_name}" diff --git a/app/service/design_batch/design_batch_celery.py b/app/service/design_batch/design_batch_celery.py index f7be62e..2eff696 100644 --- a/app/service/design_batch/design_batch_celery.py +++ b/app/service/design_batch/design_batch_celery.py @@ -4,7 +4,7 @@ import threading from celery import Celery from minio import Minio -from app.core.config import * +from app.core.config import settings from app.service.design_batch.item import BodyItem, TopItem, BottomItem, OthersItem from app.service.design_batch.utils.MQ import publish_status from app.service.design_batch.utils.organize import organize_body, organize_clothing, organize_others @@ -12,12 +12,12 @@ from app.service.design_batch.utils.save_json import oss_upload_json from app.service.design_batch.utils.synthesis_item import update_base_size_priority, synthesis, synthesis_single id_lock = threading.Lock() -celery_app = Celery('tasks', broker=f'amqp://rabbit:123456@18.167.251.121:5672//', backend='rpc://', BROKER_CONNECTION_RETRY_ON_STARTUP=True) +celery_app = Celery('tasks', broker=f'amqp://{settings.MQ_USERNAME}:{settings.MQ_PASSWORD}@{settings.MQ_HOST}:{settings.MQ_PORT}//', backend='rpc://') celery_app.conf.worker_log_format = '%(asctime)s %(filename)s [line:%(lineno)d] %(levelname)s %(message)s' celery_app.conf.worker_hijack_root_logger = False logging.getLogger('pika').setLevel(logging.WARNING) logger = logging.getLogger() -minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE) +minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE) print("start") @@ -51,10 +51,12 @@ def process_layer(item, layers): front_layer, back_layer = organize_others(item) layers.append(front_layer) layers.append(back_layer) + return None else: front_layer, back_layer = organize_clothing(item) layers.append(front_layer) layers.append(back_layer) + return None @celery_app.task @@ -76,12 +78,11 @@ def batch_design(objects_data, tasks_id, json_name): for item in object['items']: item_results.append(process_item(item, basic)) layers = [] - body_size = None for item in item_results: - body_size = process_layer(item, layers) + process_layer(item, layers) layers = sorted(layers, key=lambda s: s.get("priority", float('inf'))) - layers, new_size = update_base_size_priority(layers, body_size) + layers, new_size = update_base_size_priority(layers) for lay in layers: items_response['layers'].append({ diff --git a/app/service/design_batch/pipeline/back_perspective.py b/app/service/design_batch/pipeline/back_perspective.py index 5ddd37c..825a3cb 100644 --- a/app/service/design_batch/pipeline/back_perspective.py +++ b/app/service/design_batch/pipeline/back_perspective.py @@ -18,11 +18,11 @@ class BackPerspective: result['back_perspective_url'] = file_path return result else: - seg_result = get_seg_result("1", result['image'])[0] + seg_result = get_seg_result(result['image'])[0] elif result['name'] in ['blouse', 'outwear', 'dress', 'tops']: seg_result = result['seg_result'] else: - seg_result = get_seg_result("1", result['image'])[0] + seg_result = get_seg_result(result['image'])[0] m = self.thicken_contours_and_display(seg_result, thickness=10, color=(0, 0, 0)) back_sketch = result['image'].copy() @@ -34,7 +34,8 @@ class BackPerspective: result['back_perspective_url'] = f"{resp.bucket_name}/{resp.object_name}" return result - def thicken_contours_and_display(self, mask, thickness=10, color=(0, 0, 0)): + @staticmethod + def thicken_contours_and_display(mask, thickness=10, color=(0, 0, 0)): mask = mask.astype(np.uint8) * 255 # 查找轮廓 contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) @@ -48,9 +49,9 @@ class BackPerspective: # 在空白图像上绘制白色的轮廓 cv2.drawContours(blank, [contour], -1, 255, thickness=thick) # 找到轮廓的中心（可以用重心等方法近似） - M = cv2.moments(contour) - cx = int(M['m10'] / M['m00']) - cy = int(M['m01'] / M['m00']) + m = cv2.moments(contour) + cx = int(m['m10'] / m['m00']) + cy = int(m['m01'] / m['m00']) # 进行距离变换，离中心越近的值越小 dist_transform = cv2.distanceTransform(255 - blank, cv2.DIST_L2, 5) # 根据距离变换的值来决定是否保留像素，离中心近的像素更容易被保留 diff --git a/app/service/design_batch/pipeline/color.py b/app/service/design_batch/pipeline/color.py index d6c84e4..a7928db 100644 --- a/app/service/design_batch/pipeline/color.py +++ b/app/service/design_batch/pipeline/color.py @@ -79,9 +79,9 @@ class Color: def get_pattern(single_color): if single_color is None: raise False - R, G, B = single_color.split(' ') + r, g, b = single_color.split(' ') pattern = np.zeros([1, 1, 3], np.uint8) - pattern[0, 0, 0] = int(B) - pattern[0, 0, 1] = int(G) - pattern[0, 0, 2] = int(R) + pattern[0, 0, 0] = int(b) + pattern[0, 0, 1] = int(g) + pattern[0, 0, 2] = int(r) return pattern diff --git a/app/service/design_batch/pipeline/keypoint.py b/app/service/design_batch/pipeline/keypoint.py index 73d7586..2b2607a 100644 --- a/app/service/design_batch/pipeline/keypoint.py +++ b/app/service/design_batch/pipeline/keypoint.py @@ -3,7 +3,7 @@ import logging import numpy as np from pymilvus import MilvusClient -from app.core.config import * +from app.core.config import KEYPOINT_RESULT_TABLE_FIELD_SET, MILVUS_TABLE_KEYPOINT, settings from app.service.design_fast.utils.design_ensemble import get_keypoint_result from app.service.utils.decorator import ClassCallRunTime, RunTime @@ -21,12 +21,12 @@ class KeyPoint: def __call__(self, result): if result['name'] in ['blouse', 'skirt', 'dress', 'outwear', 'trousers', 'tops', 'bottoms']: # 查询是否有数据 且类别相同 相同则直接读 不同则推理后更新 # result['clothes_keypoint'] = self.infer_keypoint_result(result) - site = 'up' if result['name'] in ['blouse', 'outwear', 'dress', 'tops'] else 'down' + # 'up' if result['name'] in ['blouse', 'outwear', 'dress', 'tops'] else 'down' # keypoint_cache = search_keypoint_cache(result["image_id"], site) # keypoint_cache = self.keypoint_cache(result, site) keypoint_cache = False # 取消向量查询 直接过模型推理 - if keypoint_cache is False: + if not keypoint_cache: keypoint_infer_result, site = self.infer_keypoint_result(result) result['clothes_keypoint'] = self.save_keypoint_cache(result["image_id"], keypoint_infer_result, site) else: @@ -55,8 +55,8 @@ class KeyPoint: } ] try: - client = MilvusClient(uri=MILVUS_URL, token=MILVUS_TOKEN, db_name=MILVUS_ALIAS) - res = client.upsert(collection_name=MILVUS_TABLE_KEYPOINT, data=data) + client = MilvusClient(uri=settings.MILVUS_URL, token=settings.MILVUS_TOKEN, db_name=settings.MILVUS_ALIAS) + client.upsert(collection_name=MILVUS_TABLE_KEYPOINT, data=data) client.close() return dict(zip(KEYPOINT_RESULT_TABLE_FIELD_SET, result.reshape(12, 2).astype(int).tolist())) except Exception as e: @@ -79,7 +79,7 @@ class KeyPoint: ] try: - client = MilvusClient(uri=MILVUS_URL, token=MILVUS_TOKEN, db_name=MILVUS_ALIAS) + client = MilvusClient(uri=settings.MILVUS_URL, token=settings.MILVUS_TOKEN, db_name=settings.MILVUS_ALIAS) client.upsert( collection_name=MILVUS_TABLE_KEYPOINT, data=data @@ -92,7 +92,7 @@ class KeyPoint: @RunTime def keypoint_cache(self, result, site): try: - client = MilvusClient(uri=MILVUS_URL, token=MILVUS_TOKEN, db_name=MILVUS_ALIAS) + client = MilvusClient(uri=settings.MILVUS_URL, token=settings.MILVUS_TOKEN, db_name=settings.MILVUS_ALIAS) keypoint_id = result['image_id'] res = client.query( collection_name=MILVUS_TABLE_KEYPOINT, diff --git a/app/service/design_batch/pipeline/loading.py b/app/service/design_batch/pipeline/loading.py index 332f29a..90166d1 100644 --- a/app/service/design_batch/pipeline/loading.py +++ b/app/service/design_batch/pipeline/loading.py @@ -1,9 +1,6 @@ -import io import logging import cv2 -import numpy as np -from PIL import Image from app.service.utils.new_oss_client import oss_get_image diff --git a/app/service/design_batch/pipeline/print_painting.py b/app/service/design_batch/pipeline/print_painting.py index 1534f9c..fde05fb 100644 --- a/app/service/design_batch/pipeline/print_painting.py +++ b/app/service/design_batch/pipeline/print_painting.py @@ -9,6 +9,7 @@ from app.service.utils.new_oss_client import oss_get_image class PrintPainting: def __init__(self, minio_client): + self.random_seed = None self.minio_client = minio_client def __call__(self, result): @@ -408,7 +409,7 @@ class PrintPainting: change_mask = print_mask[start_h: length_h, start_w: length_w] # get real part into change mask _, change_mask = cv2.threshold(change_mask, 220, 255, cv2.THRESH_BINARY) - mask = cv2.bitwise_not(painting_dict['mask_inv_print']) + cv2.bitwise_not(painting_dict['mask_inv_print']) img_fg[start_h:start_h + painting_dict['dim_print_h'], start_w:start_w + painting_dict['dim_print_w'], :] = change_region clothes_mask_print = cv2.bitwise_not(print_mask) diff --git a/app/service/design_batch/pipeline/segmentation.py b/app/service/design_batch/pipeline/segmentation.py index 0c9c51e..9d619d7 100644 --- a/app/service/design_batch/pipeline/segmentation.py +++ b/app/service/design_batch/pipeline/segmentation.py @@ -4,7 +4,7 @@ import os import cv2 import numpy as np -from app.core.config import SEG_CACHE_PATH +from app.core.config import settings from app.service.design_fast.utils.design_ensemble import get_seg_result from app.service.utils.decorator import ClassCallRunTime from app.service.utils.new_oss_client import oss_get_image @@ -36,11 +36,11 @@ class Segmentation: # preview 过模型 不缓存 if "preview_submit" in result.keys() and result['preview_submit'] == "preview": # 推理获得seg 结果 - seg_result = get_seg_result(result["image_id"], result['image']) + seg_result = get_seg_result(result['image']) # submit 过模型 缓存 elif "preview_submit" in result.keys() and result['preview_submit'] == "submit": # 推理获得seg 结果 - seg_result = get_seg_result(result["image_id"], result['image']) + seg_result = get_seg_result(result['image']) self.save_seg_result(seg_result, result['image_id']) # null 正常流程 加载本地缓存 无缓存则过模型 else: @@ -49,7 +49,7 @@ class Segmentation: # 判断缓存和实际图片size是否相同 if not _ or result["image"].shape[:2] != seg_result.shape: # 推理获得seg 结果 - seg_result = get_seg_result(result["image_id"], result['image']) + seg_result = get_seg_result(result['image']) self.save_seg_result(seg_result, result['image_id']) result['seg_result'] = seg_result @@ -63,7 +63,7 @@ class Segmentation: @staticmethod def save_seg_result(seg_result, image_id): - file_path = f"{SEG_CACHE_PATH}{image_id}.npy" + file_path = f"{settings.SEG_CACHE_PATH}{image_id}.npy" try: np.save(file_path, seg_result) logger.debug(f"保存成功 ：{os.path.abspath(file_path)}") @@ -72,7 +72,7 @@ class Segmentation: @staticmethod def load_seg_result(image_id): - file_path = f"{SEG_CACHE_PATH}{image_id}.npy" + file_path = f"{settings.SEG_CACHE_PATH}{image_id}.npy" # logger.info(f"load seg file name is :{SEG_CACHE_PATH}{image_id}.npy") try: seg_result = np.load(file_path) diff --git a/app/service/design_batch/pipeline/split.py b/app/service/design_batch/pipeline/split.py index 288381a..cc32e05 100644 --- a/app/service/design_batch/pipeline/split.py +++ b/app/service/design_batch/pipeline/split.py @@ -4,9 +4,7 @@ import logging import cv2 import numpy as np from PIL import Image -from cv2 import cvtColor, COLOR_BGR2RGBA -from app.core.config import AIDA_CLOTHING from app.service.design_fast.utils.conversion_image import rgb_to_rgba from app.service.design_fast.utils.transparent import sketch_to_transparent from app.service.design_fast.utils.upload_image import upload_png_mask @@ -40,7 +38,7 @@ class Split(object): result_front_image = np.zeros_like(rgba_image) front_mask = cv2.resize(front_mask, new_size) result_front_image[front_mask != 0] = rgba_image[front_mask != 0] - result_front_image_pil = Image.fromarray(cvtColor(result_front_image, COLOR_BGR2RGBA)) + result_front_image_pil = Image.fromarray(cv2.cvtColor(result_front_image, cv2.COLOR_BGR2RGBA)) if 'transparent' in result.keys(): # 用户自选区域transparent transparent = result['transparent'] @@ -98,21 +96,21 @@ class Split(object): result_back_image = np.zeros_like(rgba_image) back_mask = cv2.resize(back_mask, new_size) result_back_image[back_mask != 0] = rgba_image[back_mask != 0] - result_back_image_pil = Image.fromarray(cvtColor(result_back_image, COLOR_BGR2RGBA)) + result_back_image_pil = Image.fromarray(cv2.cvtColor(result_back_image, cv2.COLOR_BGR2RGBA)) result['back_image'], result["back_image_url"], _ = upload_png_mask(self.minio_client, result_back_image_pil, f'{generate_uuid()}', mask=None) mask_image[back_mask != 0] = [0, 255, 0] rbga_mask = rgb_to_rgba(mask_image, front_mask + back_mask) - mask_pil = Image.fromarray(cvtColor(rbga_mask.astype(np.uint8), COLOR_BGR2RGBA)) + mask_pil = Image.fromarray(cv2.cvtColor(rbga_mask.astype(np.uint8), cv2.COLOR_BGR2RGBA)) image_data = io.BytesIO() mask_pil.save(image_data, format='PNG') image_data.seek(0) image_bytes = image_data.read() - req = oss_upload_image(oss_client=self.minio_client, bucket=AIDA_CLOTHING, object_name=f"mask/mask_{generate_uuid()}.png", image_bytes=image_bytes) + req = oss_upload_image(oss_client=self.minio_client, bucket="aida-clothing", object_name=f"mask/mask_{generate_uuid()}.png", image_bytes=image_bytes) result['mask_url'] = req.bucket_name + "/" + req.object_name # 创建中间图层 result_pattern_image_rgba = rgb_to_rgba(result['pattern_image'], result['mask']) - result_pattern_image_pil = Image.fromarray(cvtColor(result_pattern_image_rgba, COLOR_BGR2RGBA)) + result_pattern_image_pil = Image.fromarray(cv2.cvtColor(result_pattern_image_rgba, cv2.COLOR_BGR2RGBA)) result['pattern_image'], result['pattern_image_url'], _ = upload_png_mask(self.minio_client, result_pattern_image_pil, f'{generate_uuid()}') return result except Exception as e: diff --git a/app/service/design_batch/utils/MQ.py b/app/service/design_batch/utils/MQ.py index 4fc839b..42cea41 100644 --- a/app/service/design_batch/utils/MQ.py +++ b/app/service/design_batch/utils/MQ.py @@ -2,16 +2,17 @@ import json import pika -from app.core.config import RABBITMQ_PARAMS, BATCH_DESIGN_RABBITMQ_QUEUES +from app.core.config import settings +from app.core.rabbit_mq_config import RABBITMQ_PARAMS def publish_status(task_id, progress, result): connection = pika.BlockingConnection(pika.ConnectionParameters(**RABBITMQ_PARAMS)) channel = connection.channel() - channel.queue_declare(queue=BATCH_DESIGN_RABBITMQ_QUEUES, durable=True) + channel.queue_declare(queue=settings.BATCH_DESIGN_RABBITMQ_QUEUES, durable=True) message = {'task_id': task_id, 'progress': progress, "result": result} channel.basic_publish(exchange='', - routing_key=BATCH_DESIGN_RABBITMQ_QUEUES, + routing_key=settings.BATCH_DESIGN_RABBITMQ_QUEUES, body=json.dumps(message), properties=pika.BasicProperties( delivery_mode=2, diff --git a/app/service/design_batch/utils/design_ensemble.py b/app/service/design_batch/utils/design_ensemble.py index f4f6a34..193da0e 100644 --- a/app/service/design_batch/utils/design_ensemble.py +++ b/app/service/design_batch/utils/design_ensemble.py @@ -16,7 +16,7 @@ import torch import torch.nn.functional as F import tritonclient.http as httpclient -from app.core.config import * +from app.core.config import DESIGN_MODEL_URL, DESIGN_MODEL_NAME """ keypoint @@ -91,29 +91,29 @@ def seg_preprocess(img_path): # @ RunTime -def get_seg_result(image_id, image): +def get_seg_result(image): image, ori_shape = seg_preprocess(image) client = httpclient.InferenceServerClient(url=f"{DESIGN_MODEL_URL}") transformed_img = image.astype(np.float32) # 输入集 inputs = [ - httpclient.InferInput(SEGMENTATION['input'], transformed_img.shape, datatype="FP32") + httpclient.InferInput(DESIGN_MODEL_NAME, transformed_img.shape, datatype="FP32") ] inputs[0].set_data_from_numpy(transformed_img, binary_data=True) # 输出集 outputs = [ - httpclient.InferRequestedOutput(SEGMENTATION['output'], binary_data=True), + httpclient.InferRequestedOutput("seg_input__0", binary_data=True), ] - results = client.infer(model_name=SEGMENTATION['new_model_name'], inputs=inputs, outputs=outputs) + results = client.infer(model_name=DESIGN_MODEL_NAME, inputs=inputs, outputs=outputs) # 推理 # 取结果 - inference_output1 = results.as_numpy(SEGMENTATION['output']) - seg_result = seg_postprocess(int(image_id), inference_output1, ori_shape) + inference_output1 = results.as_numpy("seg_input__0") + seg_result = seg_postprocess(inference_output1, ori_shape) return seg_result # no cache -def seg_postprocess(image_id, output, ori_shape): +def seg_postprocess(output, ori_shape): seg_logit = F.interpolate(torch.tensor(output).float(), size=ori_shape, scale_factor=None, mode='bilinear', align_corners=False) seg_pred = seg_logit.cpu().numpy() return seg_pred[0] diff --git a/app/service/design_batch/utils/organize.py b/app/service/design_batch/utils/organize.py index 0550419..877b4a5 100644 --- a/app/service/design_batch/utils/organize.py +++ b/app/service/design_batch/utils/organize.py @@ -98,6 +98,8 @@ def calculate_start_point(keypoint_type, scale, clothes_point, body_point, offse """ Align left Args: + offset: + resize_scale: keypoint_type: string, "waistband" | "shoulder" | "ear_point" scale: float clothes_point: dict{'left': [x1, y1, z1], 'right': [x2, y2, z2]} diff --git a/app/service/design_batch/utils/progress.py b/app/service/design_batch/utils/progress.py index 0f2c9cf..e970639 100644 --- a/app/service/design_batch/utils/progress.py +++ b/app/service/design_batch/utils/progress.py @@ -1,6 +1,6 @@ import logging -from app.service.design_fast.utils.redis_utils import Redis +from app.service.utils.redis_utils import Redis logger = logging.getLogger(__name__) diff --git a/app/service/design_batch/utils/redis_utils.py b/app/service/design_batch/utils/redis_utils.py deleted file mode 100644 index 012fbe0..0000000 --- a/app/service/design_batch/utils/redis_utils.py +++ /dev/null @@ -1,99 +0,0 @@ -import redis - -from app.core.config import REDIS_HOST, REDIS_PORT - - -class Redis(object): - """ - redis数据库操作 - """ - - @staticmethod - def _get_r(): - host = REDIS_HOST - port = REDIS_PORT - db = 0 - r = redis.StrictRedis(host, port, db) - return r - - @classmethod - def write(cls, key, value, expire=None): - """ - 写入键值对 - """ - # 判断是否有过期时间，没有就设置默认值 - if expire: - expire_in_seconds = expire - else: - expire_in_seconds = 100 - r = cls._get_r() - r.set(key, value, ex=expire_in_seconds) - - @classmethod - def read(cls, key): - """ - 读取键值对内容 - """ - r = cls._get_r() - value = r.get(key) - return value.decode('utf-8') if value else value - - @classmethod - def hset(cls, name, key, value): - """ - 写入hash表 - """ - r = cls._get_r() - r.hset(name, key, value) - - @classmethod - def hget(cls, name, key): - """ - 读取指定hash表的键值 - """ - r = cls._get_r() - value = r.hget(name, key) - return value.decode('utf-8') if value else value - - @classmethod - def hgetall(cls, name): - """ - 获取指定hash表所有的值 - """ - r = cls._get_r() - return r.hgetall(name) - - @classmethod - def delete(cls, *names): - """ - 删除一个或者多个 - """ - r = cls._get_r() - r.delete(*names) - - @classmethod - def hdel(cls, name, key): - """ - 删除指定hash表的键值 - """ - r = cls._get_r() - r.hdel(name, key) - - @classmethod - def expire(cls, name, expire=None): - """ - 设置过期时间 - """ - if expire: - expire_in_seconds = expire - else: - expire_in_seconds = 100 - r = cls._get_r() - r.expire(name, expire_in_seconds) - - -if __name__ == '__main__': - redis_client = Redis() - # print(redis_client.write(key="1230", value=0)) - redis_client.write(key="1230", value=10) - # print(redis_client.read(key="1230")) diff --git a/app/service/design_batch/utils/synthesis_item.py b/app/service/design_batch/utils/synthesis_item.py index 272ab23..2b03077 100644 --- a/app/service/design_batch/utils/synthesis_item.py +++ b/app/service/design_batch/utils/synthesis_item.py @@ -13,9 +13,12 @@ import logging import cv2 import numpy as np from PIL import Image - +from minio import Minio +from app.core.config import settings from app.service.utils.generate_uuid import generate_uuid -from app.service.utils.oss_client import oss_upload_image +from app.service.utils.new_oss_client import oss_upload_image + +minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE) def positioning(all_mask_shape, mask_shape, offset): @@ -136,7 +139,7 @@ def synthesis(data, size, basic_info): image_bytes = image_data.read() bucket_name = "aida-results" object_name = f'result_{generate_uuid()}.png' - req = oss_upload_image(bucket=bucket_name, object_name=object_name, image_bytes=image_bytes) + oss_upload_image(oss_client=minio_client, bucket=bucket_name, object_name=object_name, image_bytes=image_bytes) return f"{bucket_name}/{object_name}" # return f"aida-results/{minio_client.put_object('aida-results', f'result_{generate_uuid()}.png', io.BytesIO(image_bytes), len(image_bytes), content_type='image/png').object_name}" @@ -177,11 +180,11 @@ def synthesis_single(front_image, back_image): # oss upload bucket_name = 'aida-results' object_name = f'result_{generate_uuid()}.png' - req = oss_upload_image(bucket=bucket_name, object_name=object_name, image_bytes=image_bytes) + oss_upload_image(oss_client=minio_client, bucket=bucket_name, object_name=object_name, image_bytes=image_bytes) return f"{bucket_name}/{object_name}" -def update_base_size_priority(layers, size): +def update_base_size_priority(layers): # 计算透明背景图片的宽度 min_x = min(info['position'][1] for info in layers) x_list = [] diff --git a/app/service/design_batch/utils/upload_image.py b/app/service/design_batch/utils/upload_image.py index 2c79f9f..3e3dd2c 100644 --- a/app/service/design_batch/utils/upload_image.py +++ b/app/service/design_batch/utils/upload_image.py @@ -12,7 +12,6 @@ import logging import cv2 -from app.core.config import * from app.service.utils.new_oss_client import oss_upload_image @@ -25,15 +24,15 @@ def upload_png_mask(minio_client, front_image, object_name, mask=None): # 将掩模的3通道转换为4通道，白色部分不透明，黑色部分透明 rgba_image = cv2.cvtColor(mask_inverted, cv2.COLOR_BGR2BGRA) rgba_image[rgba_image[:, :, 0] == 0] = [0, 0, 0, 0] - req = oss_upload_image(oss_client=minio_client, bucket=AIDA_CLOTHING, object_name=f"mask/mask_{object_name}.png", image_bytes=cv2.imencode('.png', rgba_image)[1]) - mask_url = f"{AIDA_CLOTHING}/mask/mask_{object_name}.png" + req = oss_upload_image(oss_client=minio_client, bucket="aida-clothing", object_name=f"mask/mask_{object_name}.png", image_bytes=cv2.imencode('.png', rgba_image)[1]) + mask_url = f"aida-clothing/mask/mask_{object_name}.png" image_data = io.BytesIO() front_image.save(image_data, format='PNG') image_data.seek(0) image_bytes = image_data.read() - req = oss_upload_image(oss_client=minio_client, bucket=AIDA_CLOTHING, object_name=f"image/image_{object_name}.png", image_bytes=image_bytes) - image_url = f"{AIDA_CLOTHING}/image/image_{object_name}.png" + req = oss_upload_image(oss_client=minio_client, bucket="aida-clothing", object_name=f"image/image_{object_name}.png", image_bytes=image_bytes) + image_url = f"aida-clothing/image/image_{object_name}.png" return front_image, image_url, mask_url except Exception as e: logging.warning(f"upload_png_mask runtime exception : {e}") diff --git a/app/service/design_fast/design_generate.py b/app/service/design_fast/design_generate.py index eb0b8f9..43a21da 100644 --- a/app/service/design_fast/design_generate.py +++ b/app/service/design_fast/design_generate.py @@ -5,36 +5,60 @@ import time import requests from minio import Minio -from app.core.config import * -from app.service.design_fast.item import BodyItem, TopItem, BottomItem, OthersItem +from app.core.config import settings +from app.service.design_fast.item import BodyItem, TopItem, BottomItem, OthersItem, TopMergeItem, BottomMergeItem, OthersMergeItem from app.service.design_fast.utils.organize import organize_body, organize_clothing, organize_others from app.service.design_fast.utils.progress import final_progress, update_progress -from app.service.design_fast.utils.synthesis_item import synthesis, synthesis_single, update_base_size_priority +from app.service.design_fast.utils.synthesis_item import synthesis, synthesis_single, update_base_size_priority, merge from app.service.utils.decorator import RunTime id_lock = threading.Lock() logger = logging.getLogger() -minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE) +minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE) -def process_item(item, basic): - # 处理project中单个item - if item['type'] == "Body": - body_server = BodyItem(data=item, basic=basic, minio_client=minio_client) - item_data = body_server.process() - elif item['type'].lower() in ['blouse', 'outwear', 'dress', 'tops']: - top_server = TopItem(data=item, basic=basic, minio_client=minio_client) - item_data = top_server.process() - elif item['type'].lower() in ['skirt', 'trousers', 'bottoms']: - bottom_server = BottomItem(data=item, basic=basic, minio_client=minio_client) - item_data = bottom_server.process() - elif item['type'].lower() in ['others']: - bottom_server = OthersItem(data=item, basic=basic, minio_client=minio_client) - item_data = bottom_server.process() +def process_item(item, basic, design_type): + # 1. 定义映射配置 + # key 为 item_type 的小写，value 为对应的处理类 + DESIGN_MAP = { + 'body': BodyItem, + 'blouse': TopItem, 'outwear': TopItem, + 'dress': TopItem, 'tops': TopItem, + 'skirt': BottomItem, 'trousers': BottomItem, + 'bottoms': BottomItem, + 'others': OthersItem + } + + MERGE_MAP = { + 'body_merge': BodyItem, + 'blouse_merge': TopMergeItem, 'outwear_merge': TopMergeItem, + 'dress_merge': TopMergeItem, 'tops_merge': TopMergeItem, + 'skirt_merge': BottomMergeItem, 'trousers_merge': BottomMergeItem, + 'bottoms_merge': BottomMergeItem, + 'others_merge': OthersMergeItem + } + + # 2. 根据 design_type 选择映射表 + mapping = MERGE_MAP if design_type == 'merge' else DESIGN_MAP + + if design_type == 'merge': + item_type_key = f"{item['type'].lower()}_merge" + elif design_type == 'default': + item_type_key = item['type'].lower() else: - raise NotImplementedError(f"Item type {item['type']} not implemented") + item_type_key = item['type'].lower() + + handler_class = mapping.get(item_type_key) + + if not handler_class: + raise NotImplementedError(f"Item type {item['type']} not implemented for design_type={design_type}") + + # 4. 统一实例化并执行 + # 注意：这里假设所有 Item 类构造函数签名一致 + server = handler_class(data=item, basic=basic, minio_client=minio_client) + item_data = server.process() return item_data @@ -44,14 +68,16 @@ def process_layer(item, layers): body_layer = organize_body(item) layers.append(body_layer) return item['body_image'].size - elif item['name'] == 'others': + elif item['name'] in ['others', 'others_merge']: front_layer, back_layer = organize_others(item) layers.append(front_layer) layers.append(back_layer) + return None else: front_layer, back_layer = organize_clothing(item) layers.append(front_layer) layers.append(back_layer) + return None @RunTime @@ -68,17 +94,17 @@ def design_generate(request_data): nonlocal active_threads basic = object['basic'] items_response = {'layers': [], 'objectSign': object['objectSign'] if 'objectSign' in object.keys() else ""} + design_type = basic.get('design_type', "default") if basic['single_overall'] == "overall": item_results = [] for item in object['items']: - item_results.append(process_item(item, basic)) + item_results.append(process_item(item, basic, design_type)) layers = [] - body_size = None for item in item_results: - body_size = process_layer(item, layers) + process_layer(item, layers) layers = sorted(layers, key=lambda s: s.get("priority", float('inf'))) - layers, new_size = update_base_size_priority(layers, body_size) + layers, new_size = update_base_size_priority(layers) # pattern_overall_image_url 、 pattern_print_image_url for lay in layers: items_response['layers'].append({ @@ -92,12 +118,19 @@ def design_generate(request_data): 'image_url': lay['image_url'] if 'image_url' in lay.keys() else None, 'pattern_overall_image_url': lay['pattern_overall_image_url'] if 'pattern_overall_image_url' in lay.keys() else None, 'pattern_print_image_url': lay['pattern_print_image_url'] if 'pattern_print_image_url' in lay.keys() else None, - + 'transpose': lay.get('transpose', None), + 'rotate': lay.get('rotate', None), # 'back_perspective_url': lay['back_perspective_url'] if 'back_perspective_url' in lay.keys() else None, }) - items_response['synthesis_url'] = synthesis(layers, new_size, basic) + if basic.get('design_type') == 'default': + items_response['synthesis_url'] = synthesis(layers, new_size, basic) + elif basic.get('design_type') == 'merge': + items_response['synthesis_url'] = merge(layers, new_size, basic) + else: + items_response['synthesis_url'] = synthesis(layers, new_size, basic) + else: - item_result = process_item(object['items'][0], basic) + item_result = process_item(object['items'][0], basic, design_type) items_response['layers'].append({ 'image_category': f"{item_result['name']}_front", 'image_size': item_result['back_image'].size if item_result['back_image'] else None, @@ -149,8 +182,9 @@ def design_generate_v2(request_data): request_id = request_data.requestId threads = [] - def process_object(step, object, callback_url): + def process_object(object, callback_url): basic = object['basic'] + design_type = basic.get('design_type', "default") items_response = { 'layers': [], 'objectSign': object['objectSign'] if 'objectSign' in object.keys() else "", @@ -159,14 +193,13 @@ def design_generate_v2(request_data): if basic['single_overall'] == "overall": item_results = [] for item in object['items']: - item_results.append(process_item(item, basic)) + item_results.append(process_item(item, basic, design_type)) layers = [] - body_size = None for item in item_results: - body_size = process_layer(item, layers) + process_layer(item, layers) layers = sorted(layers, key=lambda s: s.get("priority", float('inf'))) - layers, new_size = update_base_size_priority(layers, body_size) + layers, new_size = update_base_size_priority(layers) for lay in layers: items_response['layers'].append({ @@ -185,7 +218,7 @@ def design_generate_v2(request_data): }) items_response['synthesis_url'] = synthesis(layers, new_size, basic) else: - item_result = process_item(object['items'][0], basic) + item_result = process_item(object['items'][0], basic, design_type) items_response['layers'].append({ 'image_category': f"{item_result['name']}_front", 'image_size': item_result['back_image'].size if item_result['back_image'] else None, @@ -229,7 +262,7 @@ def design_generate_v2(request_data): logger.info(response.text) for step, object in enumerate(objects_data): - t = threading.Thread(target=process_object, args=(step, object, callback_url)) + t = threading.Thread(target=process_object, args=(object, callback_url)) threads.append(t) t.start() diff --git a/app/service/design_fast/item.py b/app/service/design_fast/item.py index dcad5f0..b629698 100644 --- a/app/service/design_fast/item.py +++ b/app/service/design_fast/item.py @@ -7,6 +7,7 @@ class BaseItem: self.result['name'] = data['type'].lower() self.result.pop("type") self.result.update(basic) + self.result['design_type'] = basic.get('design_type', None) class OthersItem(BaseItem): @@ -14,13 +15,7 @@ class OthersItem(BaseItem): super().__init__(data, basic) self.Others_pipeline = [ LoadImage(minio_client), - # KeyPoint(), - # ContourDetection(), Segmentation(minio_client), - # BackPerspective(minio_client), - Color(minio_client), - NoSegPrintPainting(minio_client), - PrintPainting(minio_client), Scaling(), Split(minio_client) ] @@ -74,6 +69,65 @@ class BottomItem(BaseItem): return self.result +"""merge""" + + +class OthersMergeItem(BaseItem): + def __init__(self, data, basic, minio_client): + super().__init__(data, basic) + self.Others_pipeline = [ + LoadImage(minio_client), + # KeyPoint(), + # ContourDetection(), + Segmentation(minio_client), + # BackPerspective(minio_client), + Color(minio_client), + # NoSegPrintPainting(minio_client), + # PrintPainting(minio_client), + Scaling(), + Split(minio_client) + ] + + def process(self): + for item in self.Others_pipeline: + self.result = item(self.result) + return self.result + + +class TopMergeItem(BaseItem): + def __init__(self, data, basic, minio_client): + super().__init__(data, basic) + self.top_pipeline = [ + LoadImage(minio_client), + KeyPoint(), + Segmentation(minio_client), + Scaling(), + Split(minio_client) + ] + + def process(self): + for item in self.top_pipeline: + self.result = item(self.result) + return self.result + + +class BottomMergeItem(BaseItem): + def __init__(self, data, basic, minio_client): + super().__init__(data, basic) + self.bottom_pipeline = [ + LoadImage(minio_client), + KeyPoint(), + Segmentation(minio_client), + Scaling(), + Split(minio_client) + ] + + def process(self): + for item in self.bottom_pipeline: + self.result = item(self.result) + return self.result + + class BodyItem(BaseItem): def __init__(self, data, basic, minio_client): super().__init__(data, basic) diff --git a/app/service/design/model_process_service.py b/app/service/design_fast/model_process_service.py similarity index 58% rename from app/service/design/model_process_service.py rename to app/service/design_fast/model_process_service.py index 076e04d..e97cb4b 100644 --- a/app/service/design/model_process_service.py +++ b/app/service/design_fast/model_process_service.py @@ -1,13 +1,18 @@ import io -from app.service.utils.oss_client import oss_get_image, oss_upload_image +from minio import Minio +from app.core.config import settings + +from app.service.utils.new_oss_client import oss_get_image, oss_upload_image + +minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE) def model_transpose(image_path): bucket = image_path.split("/", 1)[0] object_name = image_path.split("/", 1)[1] new_object_name = f'{object_name[:object_name.rfind(".")]}.png' - image = oss_get_image(bucket=bucket, object_name=object_name, data_type="PIL") + image = oss_get_image(oss_client=minio_client, bucket=bucket, object_name=object_name, data_type="PIL") image = image.convert("RGBA") data = image.getdata() # @@ -23,6 +28,6 @@ def model_transpose(image_path): image.save(image_data, format='PNG') image_data.seek(0) image_bytes = image_data.read() - oss_upload_image(bucket=bucket, object_name=new_object_name, image_bytes=image_bytes) + oss_upload_image(oss_client=minio_client, bucket=bucket, object_name=new_object_name, image_bytes=image_bytes) image_path = f"{bucket}/{new_object_name}" return image_path diff --git a/app/service/design_fast/pipeline/back_perspective.py b/app/service/design_fast/pipeline/back_perspective.py index 5ddd37c..a1022d4 100644 --- a/app/service/design_fast/pipeline/back_perspective.py +++ b/app/service/design_fast/pipeline/back_perspective.py @@ -18,11 +18,11 @@ class BackPerspective: result['back_perspective_url'] = file_path return result else: - seg_result = get_seg_result("1", result['image'])[0] + seg_result = get_seg_result(result['image'])[0] elif result['name'] in ['blouse', 'outwear', 'dress', 'tops']: seg_result = result['seg_result'] else: - seg_result = get_seg_result("1", result['image'])[0] + seg_result = get_seg_result(result['image'])[0] m = self.thicken_contours_and_display(seg_result, thickness=10, color=(0, 0, 0)) back_sketch = result['image'].copy() @@ -34,7 +34,8 @@ class BackPerspective: result['back_perspective_url'] = f"{resp.bucket_name}/{resp.object_name}" return result - def thicken_contours_and_display(self, mask, thickness=10, color=(0, 0, 0)): + @staticmethod + def thicken_contours_and_display(mask, thickness=10, color=(0, 0, 0)): mask = mask.astype(np.uint8) * 255 # 查找轮廓 contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) @@ -48,9 +49,9 @@ class BackPerspective: # 在空白图像上绘制白色的轮廓 cv2.drawContours(blank, [contour], -1, 255, thickness=thick) # 找到轮廓的中心（可以用重心等方法近似） - M = cv2.moments(contour) - cx = int(M['m10'] / M['m00']) - cy = int(M['m01'] / M['m00']) + m = cv2.moments(contour) + # cx = int(m['m10'] / m['m00']) + # cy = int(m['m01'] / m['m00']) # 进行距离变换，离中心越近的值越小 dist_transform = cv2.distanceTransform(255 - blank, cv2.DIST_L2, 5) # 根据距离变换的值来决定是否保留像素，离中心近的像素更容易被保留 diff --git a/app/service/design_fast/pipeline/color.py b/app/service/design_fast/pipeline/color.py index 99ba8eb..a184125 100644 --- a/app/service/design_fast/pipeline/color.py +++ b/app/service/design_fast/pipeline/color.py @@ -81,9 +81,9 @@ class Color: def get_pattern(single_color): if single_color is None: raise False - R, G, B = single_color.split(' ') + r, g, b = single_color.split(' ') pattern = np.zeros([1, 1, 3], np.uint8) - pattern[0, 0, 0] = int(B) - pattern[0, 0, 1] = int(G) - pattern[0, 0, 2] = int(R) + pattern[0, 0, 0] = int(b) + pattern[0, 0, 1] = int(g) + pattern[0, 0, 2] = int(r) return pattern diff --git a/app/service/design_fast/pipeline/keypoint.py b/app/service/design_fast/pipeline/keypoint.py index 73d7586..51f1fbc 100644 --- a/app/service/design_fast/pipeline/keypoint.py +++ b/app/service/design_fast/pipeline/keypoint.py @@ -1,9 +1,9 @@ import logging import numpy as np -from pymilvus import MilvusClient +# from pymilvus import MilvusClient -from app.core.config import * +from app.core.config import KEYPOINT_RESULT_TABLE_FIELD_SET, MILVUS_TABLE_KEYPOINT, settings from app.service.design_fast.utils.design_ensemble import get_keypoint_result from app.service.utils.decorator import ClassCallRunTime, RunTime @@ -21,12 +21,12 @@ class KeyPoint: def __call__(self, result): if result['name'] in ['blouse', 'skirt', 'dress', 'outwear', 'trousers', 'tops', 'bottoms']: # 查询是否有数据 且类别相同 相同则直接读 不同则推理后更新 # result['clothes_keypoint'] = self.infer_keypoint_result(result) - site = 'up' if result['name'] in ['blouse', 'outwear', 'dress', 'tops'] else 'down' + # 'up' if result['name'] in ['blouse', 'outwear', 'dress', 'tops'] else 'down' # keypoint_cache = search_keypoint_cache(result["image_id"], site) # keypoint_cache = self.keypoint_cache(result, site) keypoint_cache = False # 取消向量查询 直接过模型推理 - if keypoint_cache is False: + if not keypoint_cache: keypoint_infer_result, site = self.infer_keypoint_result(result) result['clothes_keypoint'] = self.save_keypoint_cache(result["image_id"], keypoint_infer_result, site) else: @@ -54,63 +54,64 @@ class KeyPoint: "keypoint_vector": result.tolist() } ] - try: - client = MilvusClient(uri=MILVUS_URL, token=MILVUS_TOKEN, db_name=MILVUS_ALIAS) - res = client.upsert(collection_name=MILVUS_TABLE_KEYPOINT, data=data) - client.close() - return dict(zip(KEYPOINT_RESULT_TABLE_FIELD_SET, result.reshape(12, 2).astype(int).tolist())) - except Exception as e: - logger.info(f"save keypoint cache milvus error : {e}") - return dict(zip(KEYPOINT_RESULT_TABLE_FIELD_SET, result.reshape(12, 2).astype(int).tolist())) + return dict(zip(KEYPOINT_RESULT_TABLE_FIELD_SET, result.reshape(12, 2).astype(int).tolist())) - @staticmethod - def update_keypoint_cache(keypoint_id, infer_result, search_result, site): - if site == "up": - # 需要的是up 即推理出来的是up 那么查询的就是down - result = np.concatenate([infer_result.flatten(), search_result[-4:]]) - else: - # 需要的是down 即推理出来的是down 那么查询的就是up - result = np.concatenate([search_result[:20], infer_result.flatten()]) - data = [ - {"keypoint_id": keypoint_id, - "keypoint_site": "all", - "keypoint_vector": result.tolist() - } - ] + # try: + # client = MilvusClient(uri=settings.MILVUS_URL, token=settings.MILVUS_TOKEN, db_name=settings.MILVUS_ALIAS) + # client.upsert(collection_name=MILVUS_TABLE_KEYPOINT, data=data) + # client.close() + # except Exception as e: + # logger.info(f"save keypoint cache milvus error : {e}") + # return dict(zip(KEYPOINT_RESULT_TABLE_FIELD_SET, result.reshape(12, 2).astype(int).tolist())) - try: - client = MilvusClient(uri=MILVUS_URL, token=MILVUS_TOKEN, db_name=MILVUS_ALIAS) - client.upsert( - collection_name=MILVUS_TABLE_KEYPOINT, - data=data - ) - return dict(zip(KEYPOINT_RESULT_TABLE_FIELD_SET, result.reshape(12, 2).astype(int).tolist())) - except Exception as e: - logger.info(f"save keypoint cache milvus error : {e}") - return dict(zip(KEYPOINT_RESULT_TABLE_FIELD_SET, result.reshape(12, 2).astype(int).tolist())) + # @staticmethod + # def update_keypoint_cache(keypoint_id, infer_result, search_result, site): + # if site == "up": + # # 需要的是up 即推理出来的是up 那么查询的就是down + # result = np.concatenate([infer_result.flatten(), search_result[-4:]]) + # else: + # # 需要的是down 即推理出来的是down 那么查询的就是up + # result = np.concatenate([search_result[:20], infer_result.flatten()]) + # data = [ + # {"keypoint_id": keypoint_id, + # "keypoint_site": "all", + # "keypoint_vector": result.tolist() + # } + # ] + # + # try: + # client = MilvusClient(uri=settings.MILVUS_URL, token=settings.MILVUS_TOKEN, db_name=settings.MILVUS_ALIAS) + # client.upsert( + # collection_name=MILVUS_TABLE_KEYPOINT, + # data=data + # ) + # return dict(zip(KEYPOINT_RESULT_TABLE_FIELD_SET, result.reshape(12, 2).astype(int).tolist())) + # except Exception as e: + # logger.info(f"save keypoint cache milvus error : {e}") + # return dict(zip(KEYPOINT_RESULT_TABLE_FIELD_SET, result.reshape(12, 2).astype(int).tolist())) - @RunTime - def keypoint_cache(self, result, site): - try: - client = MilvusClient(uri=MILVUS_URL, token=MILVUS_TOKEN, db_name=MILVUS_ALIAS) - keypoint_id = result['image_id'] - res = client.query( - collection_name=MILVUS_TABLE_KEYPOINT, - # ids=[keypoint_id], - filter=f"keypoint_id == {keypoint_id}", - output_fields=['keypoint_vector', 'keypoint_site'] - ) - if len(res) == 0: - # 没有结果 直接推理拿结果 并保存 - keypoint_infer_result, site = self.infer_keypoint_result(result) - return self.save_keypoint_cache(result['image_id'], keypoint_infer_result, site) - elif res[0]["keypoint_site"] == "all" or res[0]["keypoint_site"] == site: - # 需要的类型和查询的类型一致，或者查询的类型为all 则直接返回查询的结果 - return dict(zip(KEYPOINT_RESULT_TABLE_FIELD_SET, np.array(res[0]['keypoint_vector']).astype(int).reshape(12, 2).tolist())) - elif res[0]["keypoint_site"] != site: - # 需要的类型和查询到的不一致，则更新类型为all - keypoint_infer_result, site = self.infer_keypoint_result(result) - return self.update_keypoint_cache(result["image_id"], keypoint_infer_result, res[0]['keypoint_vector'], site) - except Exception as e: - logger.info(f"search keypoint cache milvus error {e}") - return False + # @RunTime + # def keypoint_cache(self, result, site): + # try: + # client = MilvusClient(uri=settings.MILVUS_URL, token=settings.MILVUS_TOKEN, db_name=settings.MILVUS_ALIAS) + # keypoint_id = result['image_id'] + # res = client.query( + # collection_name=MILVUS_TABLE_KEYPOINT, + # # ids=[keypoint_id], + # filter=f"keypoint_id == {keypoint_id}", + # output_fields=['keypoint_vector', 'keypoint_site'] + # ) + # if len(res) == 0: + # # 没有结果 直接推理拿结果 并保存 + # keypoint_infer_result, site = self.infer_keypoint_result(result) + # return self.save_keypoint_cache(result['image_id'], keypoint_infer_result, site) + # elif res[0]["keypoint_site"] == "all" or res[0]["keypoint_site"] == site: + # # 需要的类型和查询的类型一致，或者查询的类型为all 则直接返回查询的结果 + # return dict(zip(KEYPOINT_RESULT_TABLE_FIELD_SET, np.array(res[0]['keypoint_vector']).astype(int).reshape(12, 2).tolist())) + # elif res[0]["keypoint_site"] != site: + # # 需要的类型和查询到的不一致，则更新类型为all + # keypoint_infer_result, site = self.infer_keypoint_result(result) + # return self.update_keypoint_cache(result["image_id"], keypoint_infer_result, res[0]['keypoint_vector'], site) + # except Exception as e: + # logger.info(f"search keypoint cache milvus error {e}") + # return False diff --git a/app/service/design_fast/pipeline/loading.py b/app/service/design_fast/pipeline/loading.py index 7cc4296..88662fa 100644 --- a/app/service/design_fast/pipeline/loading.py +++ b/app/service/design_fast/pipeline/loading.py @@ -1,10 +1,7 @@ -import io import logging -import os - +from skimage.morphology import skeletonize import cv2 import numpy as np -from PIL import Image from app.service.utils.new_oss_client import oss_get_image @@ -38,41 +35,26 @@ class LoadImage: return cls.name def __call__(self, result): + if result.get("merge_image_path"): + result['merge_image'], _ = self.read_image(result['merge_image_path']) result['image'], result['pre_mask'] = self.read_image(result['path']) - # if 'extract_lines' in result.keys(): - # if result['extract_lines']: - # result['gray'] = self.get_lines(cv2.cvtColor(result['image'], cv2.COLOR_BGR2GRAY), result['path']) - # else: - # result['gray'] = cv2.cvtColor(result['image'], cv2.COLOR_BGR2GRAY) - # else: - # result['gray'] = cv2.cvtColor(result['image'], cv2.COLOR_BGR2GRAY) - - result['gray'] = self.get_lines(cv2.cvtColor(result['image'], cv2.COLOR_BGR2GRAY), result['path']) + result['gray'] = self.get_lines(cv2.cvtColor(result['image'], cv2.COLOR_BGR2GRAY)) result['keypoint'] = self.get_keypoint(result['name']) result['img_shape'] = result['image'].shape result['ori_shape'] = result['image'].shape return result - def get_lines(self, img, path): + @staticmethod + def get_lines(img): binary = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 25, 10) - - # 步骤2：细化边缘（可选，让线条更干净） - # kernel = np.ones((1, 1), np.uint8) - # clean = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel) - - thinned = cv2.ximgproc.thinning(binary, thinningType=cv2.ximgproc.THINNING_ZHANGSUEN) # thinning算法细化线条 - mask = thinned > 0 + binary_bool = binary > 0 + skeleton = skeletonize(binary_bool, method='zhang') + mask = skeleton result = np.ones_like(img) * 255 result[mask] = img[mask] - - # 步骤3：反转回 白底黑线 - # lines = cv2.bitwise_not(thinned) - # cv2.imwrite(os.path.join('/home/user/PycharmProjects/trinity_client_aida/test/lines_original_result_5', f"Original_{path.replace('/', '-')}.png"), img) - # cv2.imwrite(os.path.join('/home/user/PycharmProjects/trinity_client_aida/test/lines_original_result_5', f"Line_{path.replace('/', '-')}.png"), result) - return result def read_image(self, image_path): @@ -93,19 +75,19 @@ class LoadImage: @staticmethod def get_keypoint(name): - if name == 'blouse' or name == 'outwear' or name == 'dress' or name == 'tops': + if name in ['blouse', 'outwear', 'dress', 'tops', 'blouse_merge', 'outwear_merge', 'dress_merge', 'tops_merge']: keypoint = 'shoulder' - elif name == 'trousers' or name == 'skirt' or name == 'bottoms': + elif name in ['trousers', 'skirt', 'bottoms', 'trousers_merge', 'skirt_merge', 'bottoms_merge']: keypoint = 'waistband' - elif name == 'bag': + elif name in ['bag', 'bag_merge']: keypoint = 'hand_point' - elif name == 'shoes': + elif name in ['shoes', 'shoes_merge']: keypoint = 'toe' - elif name == 'hairstyle': + elif name in ['hairstyle', 'hairstyle_merge']: keypoint = 'head_point' - elif name == 'earring': + elif name in ['earring', 'earring_merge']: keypoint = 'ear_point' - elif name == 'others': + elif name in ['others', 'others_merge']: keypoint = "others" else: raise KeyError(f"{name} does not belong to item category list: blouse, outwear, dress, trousers, skirt, " diff --git a/app/service/design_fast/pipeline/no_seg_print_painting.py b/app/service/design_fast/pipeline/no_seg_print_painting.py index dcceaba..b6449db 100644 --- a/app/service/design_fast/pipeline/no_seg_print_painting.py +++ b/app/service/design_fast/pipeline/no_seg_print_painting.py @@ -20,16 +20,8 @@ class NoSegPrintPainting: if overall_print['print_path_list']: painting_dict = {'dim_image_h': result['pattern_image'].shape[0], 'dim_image_w': result['pattern_image'].shape[1]} - if "print_angle_list" in overall_print.keys() and overall_print['print_angle_list'][0] != 0: - painting_dict = self.painting_collection(painting_dict, overall_print, print_trigger=True) - painting_dict['tile_print'] = self.rotate_crop_image(img=painting_dict['tile_print'], angle=-overall_print['print_angle_list'][0], crop=True) - painting_dict['mask_inv_print'] = self.rotate_crop_image(img=painting_dict['mask_inv_print'], angle=-overall_print['print_angle_list'][0], crop=True) - - # resize 到sketch大小 - painting_dict['tile_print'] = self.resize_and_crop(img=painting_dict['tile_print'], target_width=painting_dict['dim_image_w'], target_height=painting_dict['dim_image_h']) - painting_dict['mask_inv_print'] = self.resize_and_crop(img=painting_dict['mask_inv_print'], target_width=painting_dict['dim_image_w'], target_height=painting_dict['dim_image_h']) - else: - painting_dict = self.painting_collection(painting_dict, overall_print, print_trigger=True, is_single=False) + # 获取平铺 + 旋转 的overall print + painting_dict = self.painting_collection(painting_dict, overall_print) result['no_seg_sketch_overall'] = result['no_seg_sketch_print'] = self.printpaint(result, painting_dict, print_=True) result['pattern_image'] = result['no_seg_sketch_overall'] @@ -150,7 +142,6 @@ class NoSegPrintPainting: temp_fg = np.expand_dims(result['mask'], axis=2).repeat(3, axis=2) tmp2 = (result['final_image'] * (temp_fg / 255)).astype(np.uint8) result['no_seg_sketch_print'] = cv2.add(tmp1, tmp2) - return result @staticmethod @@ -165,27 +156,21 @@ class NoSegPrintPainting: print_background = img1_bg + img2_fg return print_background - def painting_collection(self, painting_dict, print_dict, print_trigger=False, is_single=False): - if print_trigger: - print_ = self.get_print(print_dict) - painting_dict['Trigger'] = not is_single - painting_dict['location'] = print_['location'] - single_mask_inv_print = self.get_mask_inv(print_['image']) - dim_max = max(painting_dict['dim_image_h'], painting_dict['dim_image_w']) - dim_pattern = (int(dim_max * print_['scale'] / 5), int(dim_max * print_['scale'] / 5)) - if not is_single: - self.random_seed = random.randint(0, 1000) - # 如果print 模式为overall 且 有角度的话 ， 组合的print为正方形，方便裁剪 - if "print_angle_list" in print_dict.keys() and print_dict['print_angle_list'][0] != 0: - painting_dict['mask_inv_print'] = self.tile_image(single_mask_inv_print, dim_pattern, print_['scale'], dim_max, dim_max, painting_dict['location'], trigger=True) - painting_dict['tile_print'] = self.tile_image(print_['image'], dim_pattern, print_['scale'], dim_max, dim_max, painting_dict['location'], trigger=True) - else: - painting_dict['mask_inv_print'] = self.tile_image(single_mask_inv_print, dim_pattern, print_['scale'], painting_dict['dim_image_h'], painting_dict['dim_image_w'], painting_dict['location'], trigger=True) - painting_dict['tile_print'] = self.tile_image(print_['image'], dim_pattern, print_['scale'], painting_dict['dim_image_h'], painting_dict['dim_image_w'], painting_dict['location'], trigger=True) - else: - painting_dict['mask_inv_print'] = self.tile_image(single_mask_inv_print, dim_pattern, print_['scale'], painting_dict['dim_image_h'], painting_dict['dim_image_w'], painting_dict['location']) - painting_dict['tile_print'] = self.tile_image(print_['image'], dim_pattern, print_['scale'], painting_dict['dim_image_h'], painting_dict['dim_image_w'], painting_dict['location']) - painting_dict['dim_print_h'], painting_dict['dim_print_w'] = dim_pattern + def painting_collection(self, painting_dict, print_dict): + print_ = self.get_print(print_dict) + painting_dict['location'] = print_['location'] + dim_max = max(painting_dict['dim_image_h'], painting_dict['dim_image_w']) + dim_pattern = (int(dim_max * print_['scale'] / 5), int(dim_max * print_['scale'] / 5)) + gap = print_dict.get('gap', [[0, 0]])[0] + painting_dict['tile_print'] = tile_image(pattern=print_['image'], + dim=dim_pattern, + gap_x=gap[0], + gap_y=gap[1], + canvas_h=painting_dict['dim_image_h'], + canvas_w=painting_dict['dim_image_w'], + location=painting_dict['location'], + angle=45) + painting_dict['mask_inv_print'] = np.zeros(painting_dict['tile_print'].shape[:2], dtype=np.uint8) return painting_dict def tile_image(self, pattern, dim, scale, dim_image_h, dim_image_w, location, trigger=False): @@ -219,33 +204,32 @@ class NoSegPrintPainting: @staticmethod def printpaint(result, painting_dict, print_=False): - - if print_ and painting_dict['Trigger']: + if print_: print_mask = cv2.bitwise_and(result['mask'], cv2.bitwise_not(painting_dict['mask_inv_print'])) img_fg = cv2.bitwise_and(painting_dict['tile_print'], painting_dict['tile_print'], mask=print_mask) else: print_mask = result['mask'] img_fg = result['final_image'] - if print_ and not painting_dict['Trigger']: - index_ = None - try: - index_ = len(painting_dict['location']) - except: - assert f'there must be parameter of location if choose IfSingle' - - for i in range(index_): - start_h, start_w = int(painting_dict['location'][i][1]), int(painting_dict['location'][i][0]) - - length_h = min(start_h + painting_dict['dim_print_h'], img_fg.shape[0]) - length_w = min(start_w + painting_dict['dim_print_w'], img_fg.shape[1]) - - change_region = img_fg[start_h: length_h, start_w: length_w, :] - # problem in change_mask - change_mask = print_mask[start_h: length_h, start_w: length_w] - # get real part into change mask - _, change_mask = cv2.threshold(change_mask, 220, 255, cv2.THRESH_BINARY) - mask = cv2.bitwise_not(painting_dict['mask_inv_print']) - img_fg[start_h:start_h + painting_dict['dim_print_h'], start_w:start_w + painting_dict['dim_print_w'], :] = change_region + # if print_ and not painting_dict['Trigger']: + # index_ = None + # try: + # index_ = len(painting_dict['location']) + # except: + # assert f'there must be parameter of location if choose IfSingle' + # + # for i in range(index_): + # start_h, start_w = int(painting_dict['location'][i][1]), int(painting_dict['location'][i][0]) + # + # length_h = min(start_h + painting_dict['dim_print_h'], img_fg.shape[0]) + # length_w = min(start_w + painting_dict['dim_print_w'], img_fg.shape[1]) + # + # change_region = img_fg[start_h: length_h, start_w: length_w, :] + # # problem in change_mask + # change_mask = print_mask[start_h: length_h, start_w: length_w] + # # get real part into change mask + # _, change_mask = cv2.threshold(change_mask, 220, 255, cv2.THRESH_BINARY) + # cv2.bitwise_not(painting_dict['mask_inv_print']) + # img_fg[start_h:start_h + painting_dict['dim_print_h'], start_w:start_w + painting_dict['dim_print_w'], :] = change_region clothes_mask_print = cv2.bitwise_not(print_mask) @@ -277,8 +261,6 @@ class NoSegPrintPainting: print_w = print_shape[1] print_h = print_shape[0] - random.seed(self.random_seed) - # 1.拿到偏移量后和resize后的print宽高取余 得到真正偏移量 # 偏移量增加2分之print.w 使坐标位于图中间 如果要位于左上角删除+ print_w // 2 即可 x_offset = print_w - int(location[0][1] % print_w) + print_w // 2 @@ -420,3 +402,96 @@ class NoSegPrintPainting: cropped_img = resized_img[start_y:start_y + target_height, :] return cropped_img + + +def tile_image(pattern, dim, gap_x, gap_y, canvas_h, canvas_w, location, angle=0): + """ + 按照指定的 X/Y 间距平铺印花，并支持旋转 + :param angle: 旋转角度 (度数, 逆时针) + """ + # 1. 确保输入是 RGBA + if pattern.shape[2] == 3: + pattern = cv2.cvtColor(pattern, cv2.COLOR_BGR2BGRA) + + # 2. 缩放与旋转印花 + resized_p = cv2.resize(pattern, dim, interpolation=cv2.INTER_AREA) + rotated_p = rotate_image(resized_p, angle) + p_h, p_w = rotated_p.shape[:2] + + # 3. 创建透明单元格 + cell_h, cell_w = p_h + gap_y, p_w + gap_x + unit_cell = np.zeros((cell_h, cell_w, 4), dtype=np.uint8) + unit_cell[:p_h, :p_w, :] = rotated_p + + # 4. 执行平铺 + tiles_y = (canvas_h // cell_h) + 2 + tiles_x = (canvas_w // cell_w) + 2 + full_tiled = np.tile(unit_cell, (tiles_y, tiles_x, 1)) + + # 5. 裁剪平铺层 + offset_x = int(location[0][1] % cell_w) + offset_y = int(location[0][0] % cell_h) + tiled_layer = full_tiled[offset_y: offset_y + canvas_h, + offset_x: offset_x + canvas_w] + + # 6. 创建纯白色背景并合成 + # 创建一个纯白色的 BGR 画布 + white_background = np.full((canvas_h, canvas_w, 3), 255, dtype=np.uint8) + + # 分离平铺层的颜色通道和 Alpha 通道 + tiled_bgr = tiled_layer[:, :, :3] + alpha_mask = tiled_layer[:, :, 3] / 255.0 # 归一化到 0-1 + alpha_mask = cv2.merge([alpha_mask, alpha_mask, alpha_mask]) # 扩展到 3 通道 + + # 执行 Alpha 混合：结果 = 平铺层 * alpha + 背景 * (1 - alpha) + result = (tiled_bgr * alpha_mask + white_background * (1 - alpha_mask)).astype(np.uint8) + + return result + + +def rotate_image(image, angle): + """ + 旋转图片并保持完整内容（自动扩大画布） + """ + if angle == 0: + return image + + (h, w) = image.shape[:2] + (cX, cY) = (w // 2, h // 2) + + # 获取旋转矩阵 + M = cv2.getRotationMatrix2D((cX, cY), angle, 1.0) + + # 计算旋转后新边界的 sine 和 cosine + cos = np.abs(M[0, 0]) + sin = np.abs(M[0, 1]) + + # 计算新的画布尺寸 + nW = int((h * sin) + (w * cos)) + nH = int((h * cos) + (w * sin)) + + # 调整旋转矩阵以考虑平移 + M[0, 2] += (nW / 2) - cX + M[1, 2] += (nH / 2) - cY + + # 执行旋转 + return cv2.warpAffine(image, M, (nW, nH)) + + +def crop_image(image, image_size_h, image_size_w, location, print_shape): + print_w = print_shape[1] + print_h = print_shape[0] + + # 1.拿到偏移量后和resize后的print宽高取余 得到真正偏移量 + # 偏移量增加2分之print.w 使坐标位于图中间 如果要位于左上角删除+ print_w // 2 即可 + x_offset = print_w - int(location[0][1] % print_w) + print_w // 2 + y_offset = print_h - int(location[0][0] % print_h) + print_h // 2 + + # y_offset = int(location[0][0]) + # x_offset = int(location[0][1]) + + if len(image.shape) == 2: + image = image[x_offset: x_offset + image_size_h, y_offset: y_offset + image_size_w] + elif len(image.shape) == 3: + image = image[x_offset: x_offset + image_size_h, y_offset: y_offset + image_size_w, :] + return image diff --git a/app/service/design_fast/pipeline/print_painting.py b/app/service/design_fast/pipeline/print_painting.py index 2dff103..7b1b2ed 100644 --- a/app/service/design_fast/pipeline/print_painting.py +++ b/app/service/design_fast/pipeline/print_painting.py @@ -38,23 +38,14 @@ class PrintPainting: overall_print['location'][0] = [x * y for x, y in zip(overall_print['location'][0], result['resize_scale'])] painting_dict = {'dim_image_h': result['pattern_image'].shape[0], 'dim_image_w': result['pattern_image'].shape[1]} result['print_image'] = result['pattern_image'] - if "print_angle_list" in overall_print.keys() and overall_print['print_angle_list'][0] != 0: - painting_dict = self.painting_collection(painting_dict, overall_print, print_trigger=True) - painting_dict['tile_print'] = self.rotate_crop_image(img=painting_dict['tile_print'], angle=-overall_print['print_angle_list'][0], crop=True) - painting_dict['mask_inv_print'] = self.rotate_crop_image(img=painting_dict['mask_inv_print'], angle=-overall_print['print_angle_list'][0], crop=True) - - # resize 到sketch大小 - painting_dict['tile_print'] = self.resize_and_crop(img=painting_dict['tile_print'], target_width=painting_dict['dim_image_w'], target_height=painting_dict['dim_image_h']) - painting_dict['mask_inv_print'] = self.resize_and_crop(img=painting_dict['mask_inv_print'], target_width=painting_dict['dim_image_w'], target_height=painting_dict['dim_image_h']) - else: - painting_dict = self.painting_collection(painting_dict, overall_print, print_trigger=True, is_single=False) + # 获取平铺 + 旋转 的overall print + painting_dict = self.painting_collection(painting_dict, overall_print) result['print_image'] = self.printpaint(result, painting_dict, print_=True) result['single_image'] = result['final_image'] = result['pattern_image'] = result['print_image'] if single_print['print_path_list']: # 2025-9-19 印花调整 印花坐标按照sketch的缩放比调整 sketch_resize_scale = result['resize_scale'] - print_background = np.zeros((result['pattern_image'].shape[0], result['pattern_image'].shape[1], 3), dtype=np.uint8) mask_background = np.zeros((result['pattern_image'].shape[0], result['pattern_image'].shape[1], 3), dtype=np.uint8) for i in range(len(single_print['print_path_list'])): @@ -77,75 +68,6 @@ class PrintPainting: print_background = cv2.cvtColor(np.array(source_image_pil), cv2.COLOR_RGBA2BGR) mask_background = cv2.cvtColor(np.array(source_image_pil_mask), cv2.COLOR_RGBA2BGR) ret, mask_background = cv2.threshold(mask_background, 124, 255, cv2.THRESH_BINARY) - # else: - # mask = self.get_mask_inv(image) - # mask = np.expand_dims(mask, axis=2) - # mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR) - # mask = cv2.bitwise_not(mask) - # - # mask = cv2.resize(mask, (int(result['final_image'].shape[1] * single_print['print_scale_list'][i][0]), int(result['final_image'].shape[0] * single_print['print_scale_list'][i][1]))) - # image = cv2.resize(image, (int(result['final_image'].shape[1] * single_print['print_scale_list'][i][0]), int(result['final_image'].shape[0] * single_print['print_scale_list'][i][1]))) - # # 旋转后的坐标需要重新算 - # rotate_mask, _ = self.img_rotate(mask, single_print['print_angle_list'][i]) - # rotate_image, rotated_new_size = self.img_rotate(image, single_print['print_angle_list'][i]) - # # x, y = int(result['print']['location'][i][0] - rotated_new_size[0] - (rotate_mask.shape[0] - image.shape[0]) / 2), int(result['print']['location'][i][1] - rotated_new_size[1] - (rotate_mask.shape[1] - image.shape[1]) / 2) - # x, y = int(single_print['location'][i][0] - rotated_new_size[0]), int(single_print['location'][i][1] - rotated_new_size[1]) - # - # image_x = print_background.shape[1] # 底图宽 - # image_y = print_background.shape[0] # 底图高 - # print_x = rotate_image.shape[1] #印花宽 - # print_y = rotate_image.shape[0] #印花高 - # - # # 有bug - # # if x + print_x > image_x: - # # rotate_image = rotate_image[:, :x + print_x - image_x] - # # rotate_mask = rotate_mask[:, :x + print_x - image_x] - # # # - # # if y + print_y > image_y: - # # rotate_image = rotate_image[:y + print_y - image_y] - # # rotate_mask = rotate_mask[:y + print_y - image_y] - # - # # 不能是并行 - # # 当前第一轮的if （108以及115）是判断有没有过下界和右界。第二轮的是判断左上有没有超出。 如果这个样子的话，先裁了右边，再左移，region就会有问题 - # # 先挪 再判断 最后裁剪 - # - # # 如果print旋转了 或者 print贴边了 则需要判断 判断左界和上界是否小于0 - # if x <= 0: # 如果X轴偏移量小于0，说明印花需要被裁剪至合适大小 或当X轴偏移量大于印花宽度时，裁剪后的印花宽度为0 - # rotate_image = rotate_image[:, abs(x):] - # rotate_mask = rotate_mask[:, abs(x):] - # start_x = x = 0 - # else: - # start_x = x - # - # if y <= 0: # 如果X轴偏移量大于0，说明印花需要被裁剪至合适大小 或当Y轴偏移量大于印花宽度时，裁剪后的印花宽度为0 - # rotate_image = rotate_image[abs(y):, :] - # rotate_mask = rotate_mask[abs(y):, :] - # start_y = y = 0 - # else: - # start_y = y - # - # # ------------------ - # # 如果print-size大于image-size 则需要裁剪print - # - # if x + print_x > image_x: - # rotate_image = rotate_image[:, :image_x - x] - # rotate_mask = rotate_mask[:, :image_x - x] - # - # if y + print_y > image_y: - # rotate_image = rotate_image[:image_y - y, :] - # rotate_mask = rotate_mask[:image_y - y, :] - # - # # mask_background[start_y:y + rotate_mask.shape[0], start_x:x + rotate_mask.shape[1]] = cv2.bitwise_xor(mask_background[start_y:y + rotate_mask.shape[0], start_x:x + rotate_mask.shape[1]], rotate_mask) - # # print_background[start_y:y + rotate_image.shape[0], start_x:x + rotate_image.shape[1]] = cv2.add(print_background[start_y:y + rotate_image.shape[0], start_x:x + rotate_image.shape[1]], rotate_image) - # - # # mask_background[start_y:y + rotate_mask.shape[0], start_x:x + rotate_mask.shape[1]] = rotate_mask - # # print_background[start_y:y + rotate_image.shape[0], start_x:x + rotate_image.shape[1]] = rotate_image - # mask_background = self.stack_prin(mask_background, result['pattern_image'], rotate_mask, start_y, y, start_x, x) - # print_background = self.stack_prin(print_background, result['pattern_image'], rotate_image, start_y, y, start_x, x) - - # gray_image = cv2.cvtColor(mask_background, cv2.COLOR_BGR2GRAY) - # print_background = cv2.bitwise_and(print_background, print_background, mask=gray_image) - print_mask = cv2.bitwise_and(result['mask'], cv2.cvtColor(mask_background, cv2.COLOR_BGR2GRAY)) img_fg = cv2.bitwise_or(print_background, print_background, mask=print_mask) img_bg = cv2.bitwise_and(result['pattern_image'], result['pattern_image'], mask=cv2.bitwise_not(print_mask)) @@ -165,7 +87,6 @@ class PrintPainting: if element_print['element_path_list']: # 2025-9-19 印花调整 印花坐标按照sketch的缩放比调整 sketch_resize_scale = result['resize_scale'] - print_background = np.zeros((result['final_image'].shape[0], result['final_image'].shape[1], 3), dtype=np.uint8) mask_background = np.zeros((result['final_image'].shape[0], result['final_image'].shape[1], 3), dtype=np.uint8) for i in range(len(element_print['element_path_list'])): @@ -206,20 +127,6 @@ class PrintPainting: print_x = rotate_image.shape[1] print_y = rotate_image.shape[0] - # 有bug - # if x + print_x > image_x: - # rotate_image = rotate_image[:, :x + print_x - image_x] - # rotate_mask = rotate_mask[:, :x + print_x - image_x] - # # - # if y + print_y > image_y: - # rotate_image = rotate_image[:y + print_y - image_y] - # rotate_mask = rotate_mask[:y + print_y - image_y] - - # 不能是并行 - # 当前第一轮的if （108以及115）是判断有没有过下界和右界。第二轮的是判断左上有没有超出。 如果这个样子的话，先裁了右边，再左移，region就会有问题 - # 先挪 再判断 最后裁剪 - - # 如果print旋转了 或者 print贴边了 则需要判断 判断左界和上界是否小于0 if x <= 0: rotate_image = rotate_image[:, -x:] rotate_mask = rotate_mask[:, -x:] @@ -234,9 +141,6 @@ class PrintPainting: else: start_y = y - # ------------------ - # 如果print-size大于image-size 则需要裁剪print - if x + print_x > image_x: rotate_image = rotate_image[:, :image_x - x] rotate_mask = rotate_mask[:, :image_x - x] @@ -245,11 +149,6 @@ class PrintPainting: rotate_image = rotate_image[:image_y - y, :] rotate_mask = rotate_mask[:image_y - y, :] - # mask_background[start_y:y + rotate_mask.shape[0], start_x:x + rotate_mask.shape[1]] = cv2.bitwise_xor(mask_background[start_y:y + rotate_mask.shape[0], start_x:x + rotate_mask.shape[1]], rotate_mask) - # print_background[start_y:y + rotate_image.shape[0], start_x:x + rotate_image.shape[1]] = cv2.add(print_background[start_y:y + rotate_image.shape[0], start_x:x + rotate_image.shape[1]], rotate_image) - - # mask_background[start_y:y + rotate_mask.shape[0], start_x:x + rotate_mask.shape[1]] = rotate_mask - # print_background[start_y:y + rotate_image.shape[0], start_x:x + rotate_image.shape[1]] = rotate_image mask_background = self.stack_prin(mask_background, result['pattern_image'], rotate_mask, start_y, y, start_x, x) print_background = self.stack_prin(print_background, result['pattern_image'], rotate_image, start_y, y, start_x, x) @@ -297,12 +196,8 @@ class PrintPainting: ret, mask_background = cv2.threshold(mask_background, 124, 255, cv2.THRESH_BINARY) print_mask = cv2.bitwise_and(result['mask'], cv2.cvtColor(mask_background, cv2.COLOR_BGR2GRAY)) img_fg = cv2.bitwise_or(print_background, print_background, mask=print_mask) - # TODO element 丢失信息 three_channel_image = cv2.merge([cv2.bitwise_not(print_mask), cv2.bitwise_not(print_mask), cv2.bitwise_not(print_mask)]) img_bg = cv2.bitwise_and(result['final_image'], three_channel_image) - # mask_mo = np.expand_dims(print_mask, axis=2).repeat(3, axis=2) - # gray_mo = np.expand_dims(result['gray'], axis=2).repeat(3, axis=2) - # img_fg = (img_fg * (mask_mo / 255) * (gray_mo / 255)).astype(np.uint8) result['final_image'] = cv2.add(img_bg, img_fg) canvas = np.full_like(result['final_image'], 255) temp_bg = np.expand_dims(cv2.bitwise_not(result['mask']), axis=2).repeat(3, axis=2) @@ -324,27 +219,21 @@ class PrintPainting: print_background = img1_bg + img2_fg return print_background - def painting_collection(self, painting_dict, print_dict, print_trigger=False, is_single=False): - if print_trigger: - print_ = self.get_print(print_dict) - painting_dict['Trigger'] = not is_single - painting_dict['location'] = print_['location'] - single_mask_inv_print = self.get_mask_inv(print_['image']) - dim_max = max(painting_dict['dim_image_h'], painting_dict['dim_image_w']) - dim_pattern = (int(dim_max * print_['scale'] / 5), int(dim_max * print_['scale'] / 5)) - if not is_single: - self.random_seed = random.randint(0, 1000) - # 如果print 模式为overall 且 有角度的话 ， 组合的print为正方形，方便裁剪 - if "print_angle_list" in print_dict.keys() and print_dict['print_angle_list'][0] != 0: - painting_dict['mask_inv_print'] = self.tile_image(single_mask_inv_print, dim_pattern, print_['scale'], dim_max, dim_max, painting_dict['location'], trigger=True) - painting_dict['tile_print'] = self.tile_image(print_['image'], dim_pattern, print_['scale'], dim_max, dim_max, painting_dict['location'], trigger=True) - else: - painting_dict['mask_inv_print'] = self.tile_image(single_mask_inv_print, dim_pattern, print_['scale'], painting_dict['dim_image_h'], painting_dict['dim_image_w'], painting_dict['location'], trigger=True) - painting_dict['tile_print'] = self.tile_image(print_['image'], dim_pattern, print_['scale'], painting_dict['dim_image_h'], painting_dict['dim_image_w'], painting_dict['location'], trigger=True) - else: - painting_dict['mask_inv_print'] = self.tile_image(single_mask_inv_print, dim_pattern, print_['scale'], painting_dict['dim_image_h'], painting_dict['dim_image_w'], painting_dict['location']) - painting_dict['tile_print'] = self.tile_image(print_['image'], dim_pattern, print_['scale'], painting_dict['dim_image_h'], painting_dict['dim_image_w'], painting_dict['location']) - painting_dict['dim_print_h'], painting_dict['dim_print_w'] = dim_pattern + def painting_collection(self, painting_dict, print_dict): + print_ = self.get_print(print_dict) + painting_dict['location'] = print_['location'] + dim_max = max(painting_dict['dim_image_h'], painting_dict['dim_image_w']) + dim_pattern = (int(dim_max * print_['scale'] / 5), int(dim_max * print_['scale'] / 5)) + gap = print_dict.get('gap', [[0, 0]])[0] + painting_dict['tile_print'] = tile_image(pattern=print_['image'], + dim=dim_pattern, + gap_x=gap[0], + gap_y=gap[1], + canvas_h=painting_dict['dim_image_h'], + canvas_w=painting_dict['dim_image_w'], + location=painting_dict['location'], + angle=45) + painting_dict['mask_inv_print'] = np.zeros(painting_dict['tile_print'].shape[:2], dtype=np.uint8) return painting_dict def tile_image(self, pattern, dim, scale, dim_image_h, dim_image_w, location, trigger=False): @@ -373,51 +262,37 @@ class PrintPainting: mask_inv = cv2.inRange(print_tile, lower, upper) return mask_inv else: - # bg_color = cv2.cvtColor(print_, cv2.COLOR_BGR2LAB)[0][0] - # print_tile = cv2.cvtColor(print_, cv2.COLOR_BGR2LAB) - # bg_l, bg_a, bg_b = bg_color[0], bg_color[1], bg_color[2] - # bg_L_high, bg_L_low = self.get_low_high_lab(bg_l, L=True) - # bg_a_high, bg_a_low = self.get_low_high_lab(bg_a) - # bg_b_high, bg_b_low = self.get_low_high_lab(bg_b) - # lower = np.array([bg_L_low, bg_a_low, bg_b_low]) - # upper = np.array([bg_L_high, bg_a_high, bg_b_high]) - - # print_tile = cv2.cvtColor(print_, cv2.COLOR_BGR2LAB) - # mask_inv = cv2.cvtColor(print_tile, cv2.COLOR_BGR2GRAY) - - # mask_inv = cv2.cvtColor(print_, cv2.COLOR_BGR2GRAY) mask_inv = np.zeros(print_.shape[:2], dtype=np.uint8) return mask_inv @staticmethod def printpaint(result, painting_dict, print_=False): - - if print_ and painting_dict['Trigger']: + if print_: print_mask = cv2.bitwise_and(result['mask'], cv2.bitwise_not(painting_dict['mask_inv_print'])) img_fg = cv2.bitwise_and(painting_dict['tile_print'], painting_dict['tile_print'], mask=print_mask) else: print_mask = result['mask'] img_fg = result['final_image'] - if print_ and not painting_dict['Trigger']: - index_ = None - try: - index_ = len(painting_dict['location']) - except: - assert f'there must be parameter of location if choose IfSingle' - - for i in range(index_): - start_h, start_w = int(painting_dict['location'][i][1]), int(painting_dict['location'][i][0]) - - length_h = min(start_h + painting_dict['dim_print_h'], img_fg.shape[0]) - length_w = min(start_w + painting_dict['dim_print_w'], img_fg.shape[1]) - - change_region = img_fg[start_h: length_h, start_w: length_w, :] - # problem in change_mask - change_mask = print_mask[start_h: length_h, start_w: length_w] - # get real part into change mask - _, change_mask = cv2.threshold(change_mask, 220, 255, cv2.THRESH_BINARY) - mask = cv2.bitwise_not(painting_dict['mask_inv_print']) - img_fg[start_h:start_h + painting_dict['dim_print_h'], start_w:start_w + painting_dict['dim_print_w'], :] = change_region + # if print_ and not painting_dict['Trigger']: + # index_ = None + # try: + # index_ = len(painting_dict['location']) + # except: + # assert f'there must be parameter of location if choose IfSingle' + # + # for i in range(index_): + # start_h, start_w = int(painting_dict['location'][i][1]), int(painting_dict['location'][i][0]) + # + # length_h = min(start_h + painting_dict['dim_print_h'], img_fg.shape[0]) + # length_w = min(start_w + painting_dict['dim_print_w'], img_fg.shape[1]) + # + # change_region = img_fg[start_h: length_h, start_w: length_w, :] + # # problem in change_mask + # change_mask = print_mask[start_h: length_h, start_w: length_w] + # # get real part into change mask + # _, change_mask = cv2.threshold(change_mask, 220, 255, cv2.THRESH_BINARY) + # cv2.bitwise_not(painting_dict['mask_inv_print']) + # img_fg[start_h:start_h + painting_dict['dim_print_h'], start_w:start_w + painting_dict['dim_print_w'], :] = change_region clothes_mask_print = cv2.bitwise_not(print_mask) @@ -449,11 +324,6 @@ class PrintPainting: print_w = print_shape[1] print_h = print_shape[0] - random.seed(self.random_seed) - # logging.info(f'overall print location : {location}') - # x_offset = random.randint(0, image.shape[0] - image_size_h) - # y_offset = random.randint(0, image.shape[1] - image_size_w) - # 1.拿到偏移量后和resize后的print宽高取余 得到真正偏移量 # 偏移量增加2分之print.w 使坐标位于图中间 如果要位于左上角删除+ print_w // 2 即可 x_offset = print_w - int(location[0][1] % print_w) + print_w // 2 @@ -595,3 +465,96 @@ class PrintPainting: cropped_img = resized_img[start_y:start_y + target_height, :] return cropped_img + + +def tile_image(pattern, dim, gap_x, gap_y, canvas_h, canvas_w, location, angle=0): + """ + 按照指定的 X/Y 间距平铺印花，并支持旋转 + :param angle: 旋转角度 (度数, 逆时针) + """ + # 1. 确保输入是 RGBA + if pattern.shape[2] == 3: + pattern = cv2.cvtColor(pattern, cv2.COLOR_BGR2BGRA) + + # 2. 缩放与旋转印花 + resized_p = cv2.resize(pattern, dim, interpolation=cv2.INTER_AREA) + rotated_p = rotate_image(resized_p, angle) + p_h, p_w = rotated_p.shape[:2] + + # 3. 创建透明单元格 + cell_h, cell_w = p_h + gap_y, p_w + gap_x + unit_cell = np.zeros((cell_h, cell_w, 4), dtype=np.uint8) + unit_cell[:p_h, :p_w, :] = rotated_p + + # 4. 执行平铺 + tiles_y = (canvas_h // cell_h) + 2 + tiles_x = (canvas_w // cell_w) + 2 + full_tiled = np.tile(unit_cell, (tiles_y, tiles_x, 1)) + + # 5. 裁剪平铺层 + offset_x = int(location[0][1] % cell_w) + offset_y = int(location[0][0] % cell_h) + tiled_layer = full_tiled[offset_y: offset_y + canvas_h, + offset_x: offset_x + canvas_w] + + # 6. 创建纯白色背景并合成 + # 创建一个纯白色的 BGR 画布 + white_background = np.full((canvas_h, canvas_w, 3), 255, dtype=np.uint8) + + # 分离平铺层的颜色通道和 Alpha 通道 + tiled_bgr = tiled_layer[:, :, :3] + alpha_mask = tiled_layer[:, :, 3] / 255.0 # 归一化到 0-1 + alpha_mask = cv2.merge([alpha_mask, alpha_mask, alpha_mask]) # 扩展到 3 通道 + + # 执行 Alpha 混合：结果 = 平铺层 * alpha + 背景 * (1 - alpha) + result = (tiled_bgr * alpha_mask + white_background * (1 - alpha_mask)).astype(np.uint8) + + return result + + +def rotate_image(image, angle): + """ + 旋转图片并保持完整内容（自动扩大画布） + """ + if angle == 0: + return image + + (h, w) = image.shape[:2] + (cX, cY) = (w // 2, h // 2) + + # 获取旋转矩阵 + M = cv2.getRotationMatrix2D((cX, cY), angle, 1.0) + + # 计算旋转后新边界的 sine 和 cosine + cos = np.abs(M[0, 0]) + sin = np.abs(M[0, 1]) + + # 计算新的画布尺寸 + nW = int((h * sin) + (w * cos)) + nH = int((h * cos) + (w * sin)) + + # 调整旋转矩阵以考虑平移 + M[0, 2] += (nW / 2) - cX + M[1, 2] += (nH / 2) - cY + + # 执行旋转 + return cv2.warpAffine(image, M, (nW, nH)) + + +def crop_image(image, image_size_h, image_size_w, location, print_shape): + print_w = print_shape[1] + print_h = print_shape[0] + + # 1.拿到偏移量后和resize后的print宽高取余 得到真正偏移量 + # 偏移量增加2分之print.w 使坐标位于图中间 如果要位于左上角删除+ print_w // 2 即可 + x_offset = print_w - int(location[0][1] % print_w) + print_w // 2 + y_offset = print_h - int(location[0][0] % print_h) + print_h // 2 + + # y_offset = int(location[0][0]) + # x_offset = int(location[0][1]) + + if len(image.shape) == 2: + image = image[x_offset: x_offset + image_size_h, y_offset: y_offset + image_size_w] + elif len(image.shape) == 3: + image = image[x_offset: x_offset + image_size_h, y_offset: y_offset + image_size_w, :] + return image diff --git a/app/service/design_fast/pipeline/segmentation.py b/app/service/design_fast/pipeline/segmentation.py index 0c9c51e..bdf75a0 100644 --- a/app/service/design_fast/pipeline/segmentation.py +++ b/app/service/design_fast/pipeline/segmentation.py @@ -4,7 +4,7 @@ import os import cv2 import numpy as np -from app.core.config import SEG_CACHE_PATH +from app.core.config import settings from app.service.design_fast.utils.design_ensemble import get_seg_result from app.service.utils.decorator import ClassCallRunTime from app.service.utils.new_oss_client import oss_get_image @@ -34,22 +34,22 @@ class Segmentation: result['mask'] = result['front_mask'] + result['back_mask'] else: # preview 过模型 不缓存 - if "preview_submit" in result.keys() and result['preview_submit'] == "preview": - # 推理获得seg 结果 - seg_result = get_seg_result(result["image_id"], result['image']) - # submit 过模型 缓存 - elif "preview_submit" in result.keys() and result['preview_submit'] == "submit": - # 推理获得seg 结果 - seg_result = get_seg_result(result["image_id"], result['image']) - self.save_seg_result(seg_result, result['image_id']) - # null 正常流程 加载本地缓存 无缓存则过模型 + if result.get("design_type", None) == "merge": + seg_result = get_seg_result(result['image']) + # 默认design 模式 - 过模型 缓存 + # elif result.get("design_type", None) == "submit": + # 推理获得seg 结果 + # seg_result = get_seg_result(result['image']) + # self.save_seg_result(seg_result, result['image_id']) + + # 默认模式- 加载模型,找不到则过模型推理,推理后保存到本地 else: # 本地查询seg 缓存是否存在 _, seg_result = self.load_seg_result(result["image_id"]) # 判断缓存和实际图片size是否相同 if not _ or result["image"].shape[:2] != seg_result.shape: # 推理获得seg 结果 - seg_result = get_seg_result(result["image_id"], result['image']) + seg_result = get_seg_result(result['image']) self.save_seg_result(seg_result, result['image_id']) result['seg_result'] = seg_result @@ -63,7 +63,7 @@ class Segmentation: @staticmethod def save_seg_result(seg_result, image_id): - file_path = f"{SEG_CACHE_PATH}{image_id}.npy" + file_path = f"{settings.SEG_CACHE_PATH}{image_id}.npy" try: np.save(file_path, seg_result) logger.debug(f"保存成功 ：{os.path.abspath(file_path)}") @@ -72,7 +72,7 @@ class Segmentation: @staticmethod def load_seg_result(image_id): - file_path = f"{SEG_CACHE_PATH}{image_id}.npy" + file_path = f"{settings.SEG_CACHE_PATH}{image_id}.npy" # logger.info(f"load seg file name is :{SEG_CACHE_PATH}{image_id}.npy") try: seg_result = np.load(file_path) diff --git a/app/service/design_fast/pipeline/split.py b/app/service/design_fast/pipeline/split.py index 906fc93..192529e 100644 --- a/app/service/design_fast/pipeline/split.py +++ b/app/service/design_fast/pipeline/split.py @@ -4,9 +4,8 @@ import logging import cv2 import numpy as np from PIL import Image -from cv2 import cvtColor, COLOR_BGR2RGBA +from celery.bin.result import result -from app.core.config import AIDA_CLOTHING from app.service.design_fast.utils.conversion_image import rgb_to_rgba from app.service.design_fast.utils.transparent import sketch_to_transparent from app.service.design_fast.utils.upload_image import upload_png_mask @@ -21,112 +20,131 @@ class Split(object): def __call__(self, result): try: if result['name'] in ('outwear', 'dress', 'blouse', 'skirt', 'trousers', 'tops', 'bottoms', 'others'): - ori_front_mask = result['front_mask'].copy() - ori_back_mask = result['back_mask'].copy() + if result.get('design_type', None) == 'merge': + ori_front_mask = result['front_mask'].copy() + ori_back_mask = result['back_mask'].copy() - if result['resize_scale'][0] == 1.0 and result['resize_scale'][1] == 1.0: - front_mask = result['front_mask'] - back_mask = result['back_mask'] - else: - height, width = result['front_mask'].shape[:2] - new_width = int(width * result['resize_scale'][0]) - new_height = int(height * result['resize_scale'][1]) - - front_mask = cv2.resize(result['front_mask'], (new_width, new_height), interpolation=cv2.INTER_AREA) - back_mask = cv2.resize(result['back_mask'], (new_width, new_height), interpolation=cv2.INTER_AREA) - - rgba_image = rgb_to_rgba(result['final_image'], front_mask + back_mask) - new_size = (int(rgba_image.shape[1] * result["scale"]), int(rgba_image.shape[0] * result["scale"])) - rgba_image = cv2.resize(rgba_image, new_size, interpolation=cv2.INTER_AREA) - result_front_image = np.zeros_like(rgba_image) - front_mask = cv2.resize(front_mask, new_size, interpolation=cv2.INTER_AREA) - result_front_image[front_mask != 0] = rgba_image[front_mask != 0] - result_front_image_pil = Image.fromarray(cvtColor(result_front_image, COLOR_BGR2RGBA)) - if 'transparent' in result.keys(): - # 用户自选区域transparent - transparent = result['transparent'] - if transparent['mask_url'] is not None and transparent['mask_url'] != "": - # 预处理用户自选区mask - seg_mask = oss_get_image(oss_client=self.minio_client, bucket=transparent['mask_url'].split('/')[0], object_name=transparent['mask_url'][transparent['mask_url'].find('/') + 1:], data_type="cv2") - seg_mask = cv2.resize(seg_mask, new_size, interpolation=cv2.INTER_AREA) - # 转换颜色空间为 RGB（OpenCV 默认是 BGR） - image_rgb = cv2.cvtColor(seg_mask, cv2.COLOR_BGR2RGB) - - r, g, b = cv2.split(image_rgb) - blue_mask = b > r - - # 创建红色和绿色掩码 - transparent_mask = np.array(blue_mask, dtype=np.uint8) * 255 - result_front_image_pil = sketch_to_transparent(result_front_image_pil, transparent_mask, transparent["scale"]) + if result['resize_scale'][0] == 1.0 and result['resize_scale'][1] == 1.0: + front_mask = result['front_mask'] + back_mask = result['back_mask'] else: - result_front_image_pil = sketch_to_transparent(result_front_image_pil, front_mask, transparent["scale"]) - result['front_image'], result["front_image_url"], _ = upload_png_mask(self.minio_client, result_front_image_pil, f'{generate_uuid()}', mask=None) + height, width = result['front_mask'].shape[:2] + new_width = int(width * result['resize_scale'][0]) + new_height = int(height * result['resize_scale'][1]) - # 前片部分 (红图部分) - # height, width = front_mask.shape - # mask_image = np.zeros((height, width, 3)) - # mask_image[front_mask != 0] = [0, 0, 255] + front_mask = cv2.resize(result['front_mask'], (new_width, new_height), interpolation=cv2.INTER_AREA) + back_mask = cv2.resize(result['back_mask'], (new_width, new_height), interpolation=cv2.INTER_AREA) + result['merge_image'] = cv2.resize(result['merge_image'], (new_width, new_height), interpolation=cv2.INTER_AREA) - # 切换为原始图片尺寸------------------------------- - height, width = ori_front_mask.shape - mask_image = np.zeros((height, width, 3)) - mask_image[ori_front_mask != 0] = [0, 0, 255] - # ----------------------------------------------- + rgba_image = rgb_to_rgba(result['merge_image'], front_mask + back_mask) + new_size = (int(rgba_image.shape[1] * result["scale"]), int(rgba_image.shape[0] * result["scale"])) + rgba_image = cv2.resize(rgba_image, new_size, interpolation=cv2.INTER_AREA) + result_front_image = np.zeros_like(rgba_image) + front_mask = cv2.resize(front_mask, new_size, interpolation=cv2.INTER_AREA) + result_front_image[front_mask != 0] = rgba_image[front_mask != 0] + result_front_image_pil = Image.fromarray(cv2.cvtColor(result_front_image, cv2.COLOR_BGR2RGBA)) + result['front_image'], result["front_image_url"], _ = upload_png_mask(self.minio_client, result_front_image_pil, f'{generate_uuid()}', mask=None) - # if result["name"] in ('blouse', 'dress', 'outwear', 'tops'): - # result_back_image = np.zeros_like(rgba_image) - # back_mask = cv2.resize(back_mask, new_size, interpolation=cv2.INTER_AREA) - # result_back_image[back_mask != 0] = rgba_image[back_mask != 0] - # result_back_image_pil = Image.fromarray(cvtColor(result_back_image, COLOR_BGR2RGBA)) - # result['back_image'], result["back_image_url"], _ = upload_png_mask(self.minio_client, result_back_image_pil, f'{generate_uuid()}', mask=None) - # mask_image[back_mask != 0] = [0, 255, 0] - # - # rbga_mask = rgb_to_rgba(mask_image, front_mask + back_mask) - # mask_pil = Image.fromarray(cvtColor(rbga_mask.astype(np.uint8), COLOR_BGR2RGBA)) - # image_data = io.BytesIO() - # mask_pil.save(image_data, format='PNG') - # image_data.seek(0) - # image_bytes = image_data.read() - # req = oss_upload_image(oss_client=self.minio_client, bucket=AIDA_CLOTHING, object_name=f"mask/mask_{generate_uuid()}.png", image_bytes=image_bytes) - # result['mask_url'] = req.bucket_name + "/" + req.object_name - # else: - # rbga_mask = rgb_to_rgba(mask_image, front_mask) - # mask_pil = Image.fromarray(cvtColor(rbga_mask.astype(np.uint8), COLOR_BGR2RGBA)) - # image_data = io.BytesIO() - # mask_pil.save(image_data, format='PNG') - # image_data.seek(0) - # image_bytes = image_data.read() - # req = oss_upload_image(oss_client=self.minio_client, bucket=AIDA_CLOTHING, object_name=f"mask/mask_{generate_uuid()}.png", image_bytes=image_bytes) - # result['mask_url'] = req.bucket_name + "/" + req.object_name - # result['back_image'] = None - # result["back_image_url"] = None - # # result["back_mask_url"] = None - # # result['back_mask_image'] = None + height, width = ori_front_mask.shape + mask_image = np.zeros((height, width, 3)) + mask_image[ori_front_mask != 0] = [0, 0, 255] + mask_image[ori_back_mask != 0] = [0, 255, 0] + rbga_mask = rgb_to_rgba(mask_image, ori_front_mask + ori_back_mask) + mask_pil = Image.fromarray(cv2.cvtColor(rbga_mask.astype(np.uint8), cv2.COLOR_BGR2RGBA)) + image_data = io.BytesIO() + mask_pil.save(image_data, format='PNG') + image_data.seek(0) + image_bytes = image_data.read() + req = oss_upload_image(oss_client=self.minio_client, bucket="aida-clothing", object_name=f"mask/mask_{generate_uuid()}.png", image_bytes=image_bytes) + result['mask_url'] = req.bucket_name + "/" + req.object_name - result_back_image = np.zeros_like(rgba_image) - back_mask = cv2.resize(back_mask, new_size, interpolation=cv2.INTER_AREA) - result_back_image[back_mask != 0] = rgba_image[back_mask != 0] - result_back_image_pil = Image.fromarray(cvtColor(result_back_image, COLOR_BGR2RGBA)) - result['back_image'], result["back_image_url"], _ = upload_png_mask(self.minio_client, result_back_image_pil, f'{generate_uuid()}', mask=None) - # mask_image[back_mask != 0] = [0, 255, 0] - mask_image[ori_back_mask != 0] = [0, 255, 0] + result_back_image = np.zeros_like(rgba_image) + back_mask = cv2.resize(back_mask, new_size, interpolation=cv2.INTER_AREA) + result_back_image[back_mask != 0] = rgba_image[back_mask != 0] + result_back_image_pil = Image.fromarray(cv2.cvtColor(result_back_image, cv2.COLOR_BGR2RGBA)) + result['back_image'], result["back_image_url"], _ = upload_png_mask(self.minio_client, result_back_image_pil, f'{generate_uuid()}', mask=None) + return result + else: + ori_front_mask = result['front_mask'].copy() + ori_back_mask = result['back_mask'].copy() - rbga_mask = rgb_to_rgba(mask_image, ori_front_mask + ori_back_mask) - mask_pil = Image.fromarray(cvtColor(rbga_mask.astype(np.uint8), COLOR_BGR2RGBA)) - image_data = io.BytesIO() - mask_pil.save(image_data, format='PNG') - image_data.seek(0) - image_bytes = image_data.read() - req = oss_upload_image(oss_client=self.minio_client, bucket=AIDA_CLOTHING, object_name=f"mask/mask_{generate_uuid()}.png", image_bytes=image_bytes) - result['mask_url'] = req.bucket_name + "/" + req.object_name + if result['resize_scale'][0] == 1.0 and result['resize_scale'][1] == 1.0: + front_mask = result['front_mask'] + back_mask = result['back_mask'] + else: + height, width = result['front_mask'].shape[:2] + new_width = int(width * result['resize_scale'][0]) + new_height = int(height * result['resize_scale'][1]) + front_mask = cv2.resize(result['front_mask'], (new_width, new_height), interpolation=cv2.INTER_AREA) + back_mask = cv2.resize(result['back_mask'], (new_width, new_height), interpolation=cv2.INTER_AREA) + + rgba_image = rgb_to_rgba(result['final_image'], front_mask + back_mask) + new_size = (int(rgba_image.shape[1] * result["scale"]), int(rgba_image.shape[0] * result["scale"])) + rgba_image = cv2.resize(rgba_image, new_size, interpolation=cv2.INTER_AREA) + result_front_image = np.zeros_like(rgba_image) + front_mask = cv2.resize(front_mask, new_size, interpolation=cv2.INTER_AREA) + result_front_image[front_mask != 0] = rgba_image[front_mask != 0] + result_front_image_pil = Image.fromarray(cv2.cvtColor(result_front_image, cv2.COLOR_BGR2RGBA)) + if 'transparent' in result.keys(): + # 用户自选区域transparent + transparent = result['transparent'] + if transparent['mask_url'] is not None and transparent['mask_url'] != "": + # 预处理用户自选区mask + seg_mask = oss_get_image(oss_client=self.minio_client, bucket=transparent['mask_url'].split('/')[0], object_name=transparent['mask_url'][transparent['mask_url'].find('/') + 1:], data_type="cv2") + seg_mask = cv2.resize(seg_mask, new_size, interpolation=cv2.INTER_AREA) + # 转换颜色空间为 RGB（OpenCV 默认是 BGR） + image_rgb = cv2.cvtColor(seg_mask, cv2.COLOR_BGR2RGB) + + r, g, b = cv2.split(image_rgb) + blue_mask = b > r + + # 创建红色和绿色掩码 + transparent_mask = np.array(blue_mask, dtype=np.uint8) * 255 + result_front_image_pil = sketch_to_transparent(result_front_image_pil, transparent_mask, transparent["scale"]) + else: + result_front_image_pil = sketch_to_transparent(result_front_image_pil, front_mask, transparent["scale"]) + result['front_image'], result["front_image_url"], _ = upload_png_mask(self.minio_client, result_front_image_pil, f'{generate_uuid()}', mask=None) + + height, width = ori_front_mask.shape + mask_image = np.zeros((height, width, 3)) + mask_image[ori_front_mask != 0] = [0, 0, 255] + + result_back_image = np.zeros_like(rgba_image) + back_mask = cv2.resize(back_mask, new_size, interpolation=cv2.INTER_AREA) + result_back_image[back_mask != 0] = rgba_image[back_mask != 0] + result_back_image_pil = Image.fromarray(cv2.cvtColor(result_back_image, cv2.COLOR_BGR2RGBA)) + result['back_image'], result["back_image_url"], _ = upload_png_mask(self.minio_client, result_back_image_pil, f'{generate_uuid()}', mask=None) + + # mask_image[back_mask != 0] = [0, 255, 0] + mask_image[ori_back_mask != 0] = [0, 255, 0] + + rbga_mask = rgb_to_rgba(mask_image, ori_front_mask + ori_back_mask) + mask_pil = Image.fromarray(cv2.cvtColor(rbga_mask.astype(np.uint8), cv2.COLOR_BGR2RGBA)) + image_data = io.BytesIO() + mask_pil.save(image_data, format='PNG') + image_data.seek(0) + image_bytes = image_data.read() + req = oss_upload_image(oss_client=self.minio_client, bucket="aida-clothing", object_name=f"mask/mask_{generate_uuid()}.png", image_bytes=image_bytes) + result['mask_url'] = req.bucket_name + "/" + req.object_name + + # 创建中间图层(未分割图层) 1.color + overall_print 2.color + overall_print + print + result_pattern_overall_image_pil = Image.fromarray(cv2.cvtColor(rgb_to_rgba(result['no_seg_sketch_overall'], ori_front_mask + ori_back_mask), cv2.COLOR_BGR2RGBA)) + result['pattern_overall_image'], result['pattern_overall_image_url'], _ = upload_png_mask(self.minio_client, result_pattern_overall_image_pil, f'{generate_uuid()}') + + result_pattern_print_image_pil = Image.fromarray(cv2.cvtColor(rgb_to_rgba(result['no_seg_sketch_print'], ori_front_mask + ori_back_mask), cv2.COLOR_BGR2RGBA)) + result['pattern_print_image'], result['pattern_print_image_url'], _ = upload_png_mask(self.minio_client, result_pattern_print_image_pil, f'{generate_uuid()}') + return result + else: + ori_front_mask, ori_back_mask = None, None # 创建中间图层(未分割图层) 1.color + overall_print 2.color + overall_print + print - result_pattern_overall_image_pil = Image.fromarray(cvtColor(rgb_to_rgba(result['no_seg_sketch_overall'], ori_front_mask + ori_back_mask), COLOR_BGR2RGBA)) + result_pattern_overall_image_pil = Image.fromarray(cv2.cvtColor(rgb_to_rgba(result['no_seg_sketch_overall'], ori_front_mask + ori_back_mask), cv2.COLOR_BGR2RGBA)) result['pattern_overall_image'], result['pattern_overall_image_url'], _ = upload_png_mask(self.minio_client, result_pattern_overall_image_pil, f'{generate_uuid()}') - result_pattern_print_image_pil = Image.fromarray(cvtColor(rgb_to_rgba(result['no_seg_sketch_print'], ori_front_mask + ori_back_mask), COLOR_BGR2RGBA)) + result_pattern_print_image_pil = Image.fromarray(cv2.cvtColor(rgb_to_rgba(result['no_seg_sketch_print'], ori_front_mask + ori_back_mask), cv2.COLOR_BGR2RGBA)) result['pattern_print_image'], result['pattern_print_image_url'], _ = upload_png_mask(self.minio_client, result_pattern_print_image_pil, f'{generate_uuid()}') return result + except Exception as e: logging.warning(f"split runtime exception : {e} image_id : {result['image_id']}") diff --git a/app/service/design_fast/utils/design_ensemble.py b/app/service/design_fast/utils/design_ensemble.py index 8eef4f2..9aa674c 100644 --- a/app/service/design_fast/utils/design_ensemble.py +++ b/app/service/design_fast/utils/design_ensemble.py @@ -15,7 +15,7 @@ import numpy as np import torch import tritonclient.http as httpclient -from app.core.config import * +from app.core.config import DESIGN_MODEL_URL, DESIGN_MODEL_NAME """ keypoint @@ -98,29 +98,29 @@ def seg_preprocess(img_path): # @ RunTime -def get_seg_result(image_id, image): +def get_seg_result(image): image, ori_shape = seg_preprocess(image) - client = httpclient.InferenceServerClient(url=f"{DESIGN_MODEL_URL}") + client = httpclient.InferenceServerClient(url=DESIGN_MODEL_URL) transformed_img = image.astype(np.float32) # 输入集 inputs = [ - httpclient.InferInput(SEGMENTATION['input'], transformed_img.shape, datatype="FP32") + httpclient.InferInput("seg_input__0", transformed_img.shape, datatype="FP32") ] inputs[0].set_data_from_numpy(transformed_img, binary_data=True) # 输出集 outputs = [ - httpclient.InferRequestedOutput(SEGMENTATION['output'], binary_data=True), + httpclient.InferRequestedOutput("seg_output__0", binary_data=True), ] - results = client.infer(model_name=SEGMENTATION['new_model_name'], inputs=inputs, outputs=outputs) + results = client.infer(model_name=DESIGN_MODEL_NAME, inputs=inputs, outputs=outputs) # 推理 # 取结果 - inference_output1 = results.as_numpy(SEGMENTATION['output']) - seg_result = seg_postprocess(int(image_id), inference_output1, ori_shape) + inference_output1 = results.as_numpy("seg_output__0") + seg_result = seg_postprocess(inference_output1, ori_shape) return seg_result # no cache -def seg_postprocess(image_id, output, ori_shape): +def seg_postprocess(output, ori_shape): seg_logit = cv2.resize(output[0][0].astype(np.uint8), (ori_shape[1] + 50, ori_shape[0] + 50)) seg_logit = seg_logit[25: - 25, 25: - 25] return seg_logit diff --git a/app/service/design_fast/utils/organize.py b/app/service/design_fast/utils/organize.py index ce4a961..82e8026 100644 --- a/app/service/design_fast/utils/organize.py +++ b/app/service/design_fast/utils/organize.py @@ -23,20 +23,23 @@ def organize_clothing(layer): front_layer = dict(priority=layer['priority'] if layer.get("layer_order", False) else PRIORITY_DICT.get(f'{layer["name"].lower()}_front', None), name=f'{layer["name"].lower()}_front', image=layer["front_image"], + merge_image=layer["front_image"], # mask_image=layer['front_mask_image'], image_url=layer['front_image_url'], - mask_url=layer['mask_url'], + mask_url=layer.get("mask_url", None), sacle=layer['scale'], clothes_keypoint=layer['clothes_keypoint'], position=start_point, resize_scale=layer["resize_scale"], mask=cv2.resize(layer['mask'], layer["front_image"].size), gradient_string=layer['gradient_string'] if 'gradient_string' in layer.keys() else "", - pattern_overall_image_url=layer['pattern_overall_image_url'], - pattern_print_image_url=layer['pattern_print_image_url'], + pattern_overall_image_url=layer.get('pattern_overall_image_url', None), + pattern_print_image_url=layer.get('pattern_print_image_url', None), - pattern_image=layer['pattern_image'], + pattern_image=layer.get('pattern_image', None), # back_perspective_url=layer['back_perspective_url'] if 'back_perspective_url' in layer.keys() else "" + transpose=layer.get("transpose", [1, 1]), # 默认为1, 1代表不镜像 + rotate=layer.get('rotate', 0), ) # 后片数据 back_layer = dict(priority=-layer.get("priority", 0) if layer.get("layer_order", False) else PRIORITY_DICT.get(f'{layer["name"].lower()}_back', None), @@ -44,16 +47,18 @@ def organize_clothing(layer): image=layer["back_image"], # mask_image=layer['back_mask_image'], image_url=layer['back_image_url'], - mask_url=layer['mask_url'], + mask_url=layer.get('mask_url', None), sacle=layer['scale'], clothes_keypoint=layer['clothes_keypoint'], position=start_point, resize_scale=layer["resize_scale"], mask=cv2.resize(layer['mask'], layer["front_image"].size), gradient_string=layer['gradient_string'] if 'gradient_string' in layer.keys() else "", - pattern_overall_image_url=layer['pattern_overall_image_url'], - pattern_print_image_url=layer['pattern_print_image_url'], + pattern_overall_image_url=layer.get('pattern_overall_image_url', None), + pattern_print_image_url=layer.get('pattern_print_image_url', None), # back_perspective_url=layer['back_perspective_url'] if 'back_perspective_url' in layer.keys() else "" + transpose=layer.get("transpose", [1, 1]), # 默认为1, 1代表不镜像 + rotate=layer.get('rotate', 0), ) return front_layer, back_layer @@ -74,35 +79,35 @@ def organize_others(layer): front_layer = dict(priority=layer['priority'] if layer.get("layer_order", False) else PRIORITY_DICT.get(f'{layer["name"].lower()}_front', None), name=f'{layer["name"].lower()}_front', image=layer["front_image"], - # mask_image=layer['front_mask_image'], + mask_image=layer['front_mask_image'], image_url=layer['front_image_url'], - mask_url=layer['mask_url'], + mask_url=layer.get('mask_url', None), sacle=layer['scale'], clothes_keypoint=(0, 0), position=start_point, resize_scale=layer["resize_scale"], mask=cv2.resize(layer['mask'], layer["front_image"].size), gradient_string=layer['gradient_string'] if 'gradient_string' in layer.keys() else "", - pattern_overall_image_url=layer['pattern_overall_image_url'], - pattern_print_image_url=layer['pattern_print_image_url'], - pattern_image=layer['pattern_image'], + pattern_overall_image_url=layer.get('pattern_overall_image_url', None), + pattern_print_image_url=layer.get('pattern_print_image_url', None), + pattern_image=layer.get('pattern_image', None), # back_perspective_url=layer['back_perspective_url'] if 'back_perspective_url' in layer.keys() else "" ) # 后片数据 back_layer = dict(priority=-layer.get("priority", 0) if layer.get("layer_order", False) else PRIORITY_DICT.get(f'{layer["name"].lower()}_back', None), name=f'{layer["name"].lower()}_back', image=layer["back_image"], - # mask_image=layer['back_mask_image'], + mask_image=layer['back_mask_image'], image_url=layer['back_image_url'], - mask_url=layer['mask_url'], + mask_url=layer.get('mask_url', None), sacle=layer['scale'], clothes_keypoint=(0, 0), position=start_point, resize_scale=layer["resize_scale"], mask=cv2.resize(layer['mask'], layer["front_image"].size), gradient_string=layer['gradient_string'] if 'gradient_string' in layer.keys() else "", - pattern_overall_image_url=layer['pattern_overall_image_url'], - pattern_print_image_url=layer['pattern_print_image_url'], + pattern_overall_image_url=layer.get('pattern_overall_image_url', None), + pattern_print_image_url=layer.get('pattern_print_image_url', None), # back_perspective_url=layer['back_perspective_url'] if 'back_perspective_url' in layer.keys() else "" ) return front_layer, back_layer @@ -112,6 +117,8 @@ def calculate_start_point(keypoint_type, scale, clothes_point, body_point, offse """ Align left Args: + offset: + resize_scale: keypoint_type: string, "waistband" | "shoulder" | "ear_point" scale: float clothes_point: dict{'left': [x1, y1, z1], 'right': [x2, y2, z2]} diff --git a/app/service/design_fast/utils/progress.py b/app/service/design_fast/utils/progress.py index 0f2c9cf..e970639 100644 --- a/app/service/design_fast/utils/progress.py +++ b/app/service/design_fast/utils/progress.py @@ -1,6 +1,6 @@ import logging -from app.service.design_fast.utils.redis_utils import Redis +from app.service.utils.redis_utils import Redis logger = logging.getLogger(__name__) diff --git a/app/service/design_fast/utils/redis_utils.py b/app/service/design_fast/utils/redis_utils.py deleted file mode 100644 index 012fbe0..0000000 --- a/app/service/design_fast/utils/redis_utils.py +++ /dev/null @@ -1,99 +0,0 @@ -import redis - -from app.core.config import REDIS_HOST, REDIS_PORT - - -class Redis(object): - """ - redis数据库操作 - """ - - @staticmethod - def _get_r(): - host = REDIS_HOST - port = REDIS_PORT - db = 0 - r = redis.StrictRedis(host, port, db) - return r - - @classmethod - def write(cls, key, value, expire=None): - """ - 写入键值对 - """ - # 判断是否有过期时间，没有就设置默认值 - if expire: - expire_in_seconds = expire - else: - expire_in_seconds = 100 - r = cls._get_r() - r.set(key, value, ex=expire_in_seconds) - - @classmethod - def read(cls, key): - """ - 读取键值对内容 - """ - r = cls._get_r() - value = r.get(key) - return value.decode('utf-8') if value else value - - @classmethod - def hset(cls, name, key, value): - """ - 写入hash表 - """ - r = cls._get_r() - r.hset(name, key, value) - - @classmethod - def hget(cls, name, key): - """ - 读取指定hash表的键值 - """ - r = cls._get_r() - value = r.hget(name, key) - return value.decode('utf-8') if value else value - - @classmethod - def hgetall(cls, name): - """ - 获取指定hash表所有的值 - """ - r = cls._get_r() - return r.hgetall(name) - - @classmethod - def delete(cls, *names): - """ - 删除一个或者多个 - """ - r = cls._get_r() - r.delete(*names) - - @classmethod - def hdel(cls, name, key): - """ - 删除指定hash表的键值 - """ - r = cls._get_r() - r.hdel(name, key) - - @classmethod - def expire(cls, name, expire=None): - """ - 设置过期时间 - """ - if expire: - expire_in_seconds = expire - else: - expire_in_seconds = 100 - r = cls._get_r() - r.expire(name, expire_in_seconds) - - -if __name__ == '__main__': - redis_client = Redis() - # print(redis_client.write(key="1230", value=0)) - redis_client.write(key="1230", value=10) - # print(redis_client.read(key="1230")) diff --git a/app/service/design_fast/utils/synthesis_item.py b/app/service/design_fast/utils/synthesis_item.py index 606b1b5..ff44157 100644 --- a/app/service/design_fast/utils/synthesis_item.py +++ b/app/service/design_fast/utils/synthesis_item.py @@ -13,9 +13,12 @@ import logging import cv2 import numpy as np from PIL import Image - +from minio import Minio +from app.core.config import settings from app.service.utils.generate_uuid import generate_uuid -from app.service.utils.oss_client import oss_upload_image +from app.service.utils.new_oss_client import oss_upload_image + +minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE) def positioning(all_mask_shape, mask_shape, offset): @@ -148,9 +151,11 @@ def synthesis(data, size, basic_info): if layer['image'] is not None: if layer['name'] != "body": test_image = Image.new('RGBA', size, (0, 0, 0, 0)) - test_image.paste(layer['image'], (layer['adaptive_position'][1], layer['adaptive_position'][0]), layer['image']) + paste_img, position = transpose_rotate(layer, layer['image']) + test_image.paste(paste_img, position, paste_img) mask_data = np.where(all_mask > 0, 255, 0).astype(np.uint8) mask_alpha = Image.fromarray(mask_data) + mask_alpha.paste(paste_img.getchannel('A'), position, paste_img.getchannel('A')) cropped_image = Image.composite(test_image, Image.new("RGBA", test_image.size, (255, 255, 255, 0)), mask_alpha) base_image.paste(test_image, (0, 0), cropped_image) # test_image 已经按照坐标贴到最大宽值的图片上 坐着这里坐标为00 else: @@ -166,7 +171,7 @@ def synthesis(data, size, basic_info): image_bytes = image_data.read() bucket_name = "aida-results" object_name = f'result_{generate_uuid()}.png' - req = oss_upload_image(bucket=bucket_name, object_name=object_name, image_bytes=image_bytes) + oss_upload_image(oss_client=minio_client, bucket=bucket_name, object_name=object_name, image_bytes=image_bytes) return f"{bucket_name}/{object_name}" # return f"aida-results/{minio_client.put_object('aida-results', f'result_{generate_uuid()}.png', io.BytesIO(image_bytes), len(image_bytes), content_type='image/png').object_name}" @@ -182,6 +187,111 @@ def synthesis(data, size, basic_info): logging.warning(f"synthesis runtime exception : {e}") +def merge(data, size, basic_info): + # out_of_bounds_control: 是否允许服装越界 True 允许 False 不允许 默认情况允许 + out_of_bounds_control = basic_info.get('out_of_bounds_control', True) + # 创建底图 + base_image = Image.new('RGBA', size, (0, 0, 0, 0)) + try: + all_mask_shape = (size[1], size[0]) + body_mask = None + for d in data: + if d['name'] == 'body' or d['name'] == 'mannequin': + # 创建一个新的宽高透明图像， 把模特贴上去获取mask + transparent_image = Image.new("RGBA", size, (0, 0, 0, 0)) + transparent_image.paste(d['image'], (d['adaptive_position'][1], d['adaptive_position'][0]), d['image']) # 此处可变数组会被paste篡改值，所以使用下标获取position + body_mask = np.array(transparent_image.split()[3]) + + # 根据新的坐标获取新的肩点 + left_shoulder = [x + y for x, y in zip(basic_info['body_point_test']['shoulder_left'], [d['adaptive_position'][1], d['adaptive_position'][0]])] + right_shoulder = [x + y for x, y in zip(basic_info['body_point_test']['shoulder_right'], [d['adaptive_position'][1], d['adaptive_position'][0]])] + body_mask[:min(left_shoulder[1], right_shoulder[1]), left_shoulder[0]:right_shoulder[0]] = 255 + _, binary_body_mask = cv2.threshold(body_mask, 127, 255, cv2.THRESH_BINARY) + top_outer_mask = np.array(binary_body_mask) + bottom_outer_mask = np.array(binary_body_mask) + others_outer_mask = np.array(binary_body_mask) + + top = True + bottom = True + others = True + i = len(data) + while i: + i -= 1 + if top and data[i]['name'] in ["blouse_front", "outwear_front", "dress_front", "tops_front"]: + if out_of_bounds_control: + top = True + else: + top = False + mask_shape = data[i]['mask'].shape + y_offset, x_offset = data[i]['adaptive_position'] + # 初始化叠加区域的起始和结束位置 + all_y_start, all_y_end, mask_y_start, mask_y_end = positioning(all_mask_shape=all_mask_shape[0], mask_shape=mask_shape[0], offset=y_offset) + all_x_start, all_x_end, mask_x_start, mask_x_end = positioning(all_mask_shape=all_mask_shape[1], mask_shape=mask_shape[1], offset=x_offset) + # 将叠加区域赋值为相应的像素值 + _, sketch_mask = cv2.threshold(data[i]['mask'], 127, 255, cv2.THRESH_BINARY) + background = np.zeros_like(top_outer_mask) + background[all_y_start:all_y_end, all_x_start:all_x_end] = sketch_mask[mask_y_start:mask_y_end, mask_x_start:mask_x_end] + top_outer_mask = background + top_outer_mask + elif bottom and data[i]['name'] in ["trousers_front", "skirt_front", "bottoms_front", "dress_front"]: + # bottom = False + mask_shape = data[i]['mask'].shape + y_offset, x_offset = data[i]['adaptive_position'] + # 初始化叠加区域的起始和结束位置 + all_y_start, all_y_end, mask_y_start, mask_y_end = positioning(all_mask_shape=all_mask_shape[0], mask_shape=mask_shape[0], offset=y_offset) + all_x_start, all_x_end, mask_x_start, mask_x_end = positioning(all_mask_shape=all_mask_shape[1], mask_shape=mask_shape[1], offset=x_offset) + # 将叠加区域赋值为相应的像素值 + _, sketch_mask = cv2.threshold(data[i]['mask'], 127, 255, cv2.THRESH_BINARY) + background = np.zeros_like(top_outer_mask) + background[all_y_start:all_y_end, all_x_start:all_x_end] = sketch_mask[mask_y_start:mask_y_end, mask_x_start:mask_x_end] + bottom_outer_mask = background + bottom_outer_mask + elif others and data[i]['name'] in ['others_front']: + mask_shape = data[i]['mask'].shape + y_offset, x_offset = data[i]['adaptive_position'] + # 初始化叠加区域的起始和结束位置 + all_y_start, all_y_end, mask_y_start, mask_y_end = positioning(all_mask_shape=all_mask_shape[0], mask_shape=mask_shape[0], offset=y_offset) + all_x_start, all_x_end, mask_x_start, mask_x_end = positioning(all_mask_shape=all_mask_shape[1], mask_shape=mask_shape[1], offset=x_offset) + # 将叠加区域赋值为相应的像素值 + _, sketch_mask = cv2.threshold(data[i]['mask'], 127, 255, cv2.THRESH_BINARY) + background = np.zeros_like(top_outer_mask) + background[all_y_start:all_y_end, all_x_start:all_x_end] = sketch_mask[mask_y_start:mask_y_end, mask_x_start:mask_x_end] + others_outer_mask = background + others_outer_mask + pass + elif bottom is False and top is False: + break + + all_mask = cv2.bitwise_or(top_outer_mask, bottom_outer_mask) + all_mask = cv2.bitwise_or(all_mask, others_outer_mask) + + for layer in data: + if layer['image'] is not None: + if layer['name'] != "body": + test_image = Image.new('RGBA', size, (0, 0, 0, 0)) + paste_img, position = transpose_rotate(layer, layer['image']) + test_image.paste(paste_img, position, paste_img) + mask_data = np.where(all_mask > 0, 255, 0).astype(np.uint8) + mask_alpha = Image.fromarray(mask_data) + mask_alpha.paste(paste_img.getchannel('A'), position, paste_img.getchannel('A')) + cropped_image = Image.composite(test_image, Image.new("RGBA", test_image.size, (255, 255, 255, 0)), mask_alpha) + base_image.paste(test_image, (0, 0), cropped_image) # test_image 已经按照坐标贴到最大宽值的图片上 坐着这里坐标为00 + else: + base_image.paste(layer['merge_image'], (layer['adaptive_position'][1], layer['adaptive_position'][0]), layer['merge_image']) + + result_image = base_image + + image_data = io.BytesIO() + result_image.save(image_data, format='PNG') + image_data.seek(0) + + # oss upload + image_bytes = image_data.read() + bucket_name = "aida-results" + object_name = f'result_{generate_uuid()}.png' + oss_upload_image(oss_client=minio_client, bucket=bucket_name, object_name=object_name, image_bytes=image_bytes) + return f"{bucket_name}/{object_name}" + except Exception as e: + logging.warning(f"synthesis runtime exception : {e}") + + def synthesis_single(front_image, back_image): result_image = None if front_image: @@ -207,11 +317,11 @@ def synthesis_single(front_image, back_image): # oss upload bucket_name = 'aida-results' object_name = f'result_{generate_uuid()}.png' - req = oss_upload_image(bucket=bucket_name, object_name=object_name, image_bytes=image_bytes) + oss_upload_image(oss_client=minio_client, bucket=bucket_name, object_name=object_name, image_bytes=image_bytes) return f"{bucket_name}/{object_name}" -def update_base_size_priority(layers, size): +def update_base_size_priority(layers): # 计算透明背景图片的宽度 min_x = min(info['position'][1] for info in layers) x_list = [] @@ -229,3 +339,35 @@ def update_base_size_priority(layers, size): for info in layers: info['adaptive_position'] = (info['position'][0], info['position'][1] - min_x) return layers, (new_width, new_height) + + +def transpose_rotate(layer, image): + # transpose[0]是左右 transpose[1]是上下 + transpose = layer.get('transpose', [1, 1]) # 默认为1, 1代表不镜像 + + rotate = layer.get('rotate', 0) + paste_x, paste_y = layer['adaptive_position'][1], layer['adaptive_position'][0] + + # transpose左右是1 上下是-1 + if transpose[0] != 1: + # 左右 + image = image.transpose(0) + + if transpose[1] != 1: + # 上下 + image = image.transpose(1) + + if rotate: + image = image.rotate(-rotate, expand=True) + # 4. 计算粘贴位置以保持视觉中心一致 + # 原本 (15, 36) 是 288*288 的左上角，我们计算其中心点 + target_center_x = 15 + 288 // 2 + target_center_y = 36 + 288 // 2 + + # 获取旋转后图像的新尺寸 + new_w, new_h = image.size + + # 计算新的左上角坐标，使得旋转后的图像中心依然在原定的中心位置 + paste_x = target_center_x - new_w // 2 + paste_y = target_center_y - new_h // 2 + return image, (paste_x, paste_y) diff --git a/app/service/design_fast/utils/upload_image.py b/app/service/design_fast/utils/upload_image.py index 2c79f9f..3e3dd2c 100644 --- a/app/service/design_fast/utils/upload_image.py +++ b/app/service/design_fast/utils/upload_image.py @@ -12,7 +12,6 @@ import logging import cv2 -from app.core.config import * from app.service.utils.new_oss_client import oss_upload_image @@ -25,15 +24,15 @@ def upload_png_mask(minio_client, front_image, object_name, mask=None): # 将掩模的3通道转换为4通道，白色部分不透明，黑色部分透明 rgba_image = cv2.cvtColor(mask_inverted, cv2.COLOR_BGR2BGRA) rgba_image[rgba_image[:, :, 0] == 0] = [0, 0, 0, 0] - req = oss_upload_image(oss_client=minio_client, bucket=AIDA_CLOTHING, object_name=f"mask/mask_{object_name}.png", image_bytes=cv2.imencode('.png', rgba_image)[1]) - mask_url = f"{AIDA_CLOTHING}/mask/mask_{object_name}.png" + req = oss_upload_image(oss_client=minio_client, bucket="aida-clothing", object_name=f"mask/mask_{object_name}.png", image_bytes=cv2.imencode('.png', rgba_image)[1]) + mask_url = f"aida-clothing/mask/mask_{object_name}.png" image_data = io.BytesIO() front_image.save(image_data, format='PNG') image_data.seek(0) image_bytes = image_data.read() - req = oss_upload_image(oss_client=minio_client, bucket=AIDA_CLOTHING, object_name=f"image/image_{object_name}.png", image_bytes=image_bytes) - image_url = f"{AIDA_CLOTHING}/image/image_{object_name}.png" + req = oss_upload_image(oss_client=minio_client, bucket="aida-clothing", object_name=f"image/image_{object_name}.png", image_bytes=image_bytes) + image_url = f"aida-clothing/image/image_{object_name}.png" return front_image, image_url, mask_url except Exception as e: logging.warning(f"upload_png_mask runtime exception : {e}") diff --git a/app/service/design_pre_processing/service.py b/app/service/design_pre_processing/service.py index 636360c..9c9dd66 100644 --- a/app/service/design_pre_processing/service.py +++ b/app/service/design_pre_processing/service.py @@ -1,19 +1,22 @@ import logging +import os import time import cv2 import numpy as np import torch import tritonclient.grpc as grpcclient -from pymilvus import MilvusClient +from minio import Minio +# from pymilvus import MilvusClient from urllib3.exceptions import ResponseError -from app.core.config import * +from app.core.config import settings, SR_MODEL_NAME, SR_TRITON_URL, MILVUS_TABLE_KEYPOINT, KEYPOINT_RESULT_TABLE_FIELD_SET from app.schemas.pre_processing import DesignPreProcessingModel from app.service.design_fast.utils.design_ensemble import get_seg_result, get_keypoint_result -from app.service.utils.oss_client import oss_get_image, oss_upload_image +from app.service.utils.new_oss_client import oss_get_image, oss_upload_image logger = logging.getLogger() +minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE) class DesignPreprocessing: @@ -46,20 +49,36 @@ class DesignPreprocessing: del d['keypoint_result'] return result - def read_image(self, image_list): + @staticmethod + def read_image(image_list): for obj in image_list: # file = self.minio_client.get_object(obj['image_url'].split("/", 1)[0], obj['image_url'].split("/", 1)[1]).data # image = cv2.imdecode(np.frombuffer(file, np.uint8), 1) - image = oss_get_image(bucket=obj['image_url'].split("/", 1)[0], object_name=obj['image_url'].split("/", 1)[1], data_type="cv2") + image = oss_get_image(oss_client=minio_client, bucket=obj['image_url'].split("/", 1)[0], object_name=obj['image_url'].split("/", 1)[1], data_type="cv2") if len(image.shape) == 2: image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB) elif image.shape[2] == 4: # 如果是四通道 mask - image = image[:, :, :3] + # 分离RGB和Alpha通道 + bgr = image[:, :, :3] + alpha = image[:, :, 3] + + # 创建白色背景（也可改为其他颜色，如(255,255,255)就是白色） + background_color = (255, 255, 255) + background = np.full_like(bgr, background_color) + + # 将Alpha通道转换为掩码（0=透明，255=不透明） + alpha_mask = alpha / 255.0 # 归一化到0-1 + alpha_mask = np.expand_dims(alpha_mask, axis=-1) # 扩展维度，方便广播计算 + + # 混合背景和原图：透明区域显示背景色，不透明区域显示原图 + image = (bgr * alpha_mask + background * (1 - alpha_mask)).astype(np.uint8) + # 此时image已经是3通道RGB，无需再执行image = image[:, :, :3] obj["image_obj"] = image return image_list # @ RunTime - def bounding_box(self, image_list): + @staticmethod + def bounding_box(image_list): for item in image_list: image = item['image_obj'] height, width = image.shape[:2] @@ -77,11 +96,6 @@ class DesignPreprocessing: x_max = max(x_max, x + w) y_max = max(y_max, y + h) - if IF_DEBUG_SHOW: - image_with_big_rect = cv2.rectangle(image.copy(), (x_min, y_min), (x_max, y_max), (0, 255, 0), 2) - cv2.imshow("bounding_box image", image_with_big_rect) - cv2.waitKey(0) - # 根据大矩形的坐标来裁剪原始图像 if len(contours) > 0: cropped_image = image[y_min:y_max, x_min:x_max] @@ -107,7 +121,8 @@ class DesignPreprocessing: item['obj'] = padded_image return image_list - def super_resolution(self, image_list): + @staticmethod + def super_resolution(image_list): for item in image_list: # 判断 两边是否同时都小于512 因为此处做四倍超分 if item['obj'].shape[0] <= 512 and item['obj'].shape[1] <= 512: @@ -136,7 +151,7 @@ class DesignPreprocessing: # self.minio_client.put_object(item['image_url'].split("/", 1)[0], item['image_url'].split("/", 1)[1], io.BytesIO(image_bytes), len(image_bytes), content_type="image/jpeg", ) bucket_name = item['image_url'].split("/", 1)[0] object_name = item['image_url'].split("/", 1)[1] - oss_upload_image(bucket=bucket_name, object_name=object_name, image_bytes=image_bytes) + oss_upload_image(oss_client=minio_client, bucket=bucket_name, object_name=object_name, image_bytes=image_bytes) logging.info(f"Object '{item['image_url'].split('/', 1)[1]}' overwritten successfully.") except ResponseError as err: logging.warning(f"Error: {err}") @@ -144,7 +159,6 @@ class DesignPreprocessing: # @ RunTime def infer_image(self, image_list): - seg_result = None for sketch in image_list: # 小写 image_category = sketch['image_category'].lower() @@ -156,36 +170,17 @@ class DesignPreprocessing: _, seg_cache = self.load_seg_result(sketch['image_id']) if not _: # 推理获得seg 结果 - seg_result = get_seg_result(sketch["image_id"], sketch['obj'])[0] + seg_result = get_seg_result(sketch['obj'])[0] self.save_seg_result(seg_result, sketch['image_id']) logger.info(f"{sketch['image_id']} image size is :{sketch['obj'].shape} , seg cache size is :{seg_result.shape}") else: logger.info(f"{sketch['image_id']} image size is :{sketch['obj'].shape} , seg cache size is :{seg_cache.shape}") - if IF_DEBUG_SHOW: - debug_show_image = sketch['obj'].copy() - points_list = [] - point_size = 1 - point_color = (0, 0, 255) # BGR - thickness = 4 # 可以为 0 、4、8 - for i in sketch['keypoint_result'].values(): - points_list.append((int(i[1]), int(i[0]))) - for point in points_list: - cv2.circle(debug_show_image, point, point_size, point_color, thickness) - cv2.imshow("seg_result", seg_result) - cv2.imshow("", debug_show_image) - cv2.waitKey(0) - # # 关键点在上部则推理seg - # if sketch["site"] == "up": - # # 判断seg缓存是否存在,是否与当前图片shape一致 - # seg_result = self.search_seg_result(sketch["image_id"], sketch["obj"].shape) - # if seg_result is False: - # # 推理seg + 保存 - # seg_result = get_seg_result(sketch['image_id'], sketch['obj']) return image_list # @ RunTime - def composing_image(self, image_list): + @staticmethod + def composing_image(image_list): for image in image_list: ''' 比例相同 整合上下装代码''' image_width = image['obj'].shape[1] @@ -193,22 +188,20 @@ class DesignPreprocessing: scale = 0.4 if waist_width / scale >= image_width: add_width = int((waist_width / scale - image_width) / 2) - ret = cv2.copyMakeBorder(image['obj'], 0, 0, add_width, add_width, cv2.BORDER_CONSTANT, value=(256, 256, 256)) - if IF_DEBUG_SHOW: - cv2.imshow("composing_image", ret) - cv2.waitKey(0) - image_bytes = cv2.imencode(".jpg", ret)[1].tobytes() + ret = cv2.copyMakeBorder(image['obj'], 0, 0, add_width, add_width, cv2.BORDER_CONSTANT, value=(255, 255, 255)) + img_rgba = cv2.cvtColor(ret, cv2.COLOR_RGB2RGBA) + image_bytes = cv2.imencode(".png", img_rgba)[1].tobytes() # image['show_image_url'] = f"{image['image_url'].split('/', 1)[0]}/{self.minio_client.put_object(image['image_url'].split('/', 1)[0], image['image_url'].split('/', 1)[1].replace('.', '-show.'), io.BytesIO(image_bytes), len(image_bytes), content_type='image/jpeg').object_name}" bucket_name = image['image_url'].split('/', 1)[0] object_name = image['image_url'].split('/', 1)[1].replace('.', '-show.') - oss_upload_image(bucket=bucket_name, object_name=object_name, image_bytes=image_bytes) + oss_upload_image(oss_client=minio_client, bucket=bucket_name, object_name=object_name, image_bytes=image_bytes) image['show_image_url'] = f"{bucket_name}/{object_name}" else: image_bytes = cv2.imencode(".jpg", image['obj'])[1].tobytes() # image['show_image_url'] = f"{image['image_url'].split('/', 1)[0]}/{self.minio_client.put_object(image['image_url'].split('/', 1)[0], image['image_url'].split('/', 1)[1].replace('.', '-show.'), io.BytesIO(image_bytes), len(image_bytes), content_type='image/jpeg').object_name}" bucket_name = image['image_url'].split('/', 1)[0] object_name = image['image_url'].split('/', 1)[1].replace('.', '-show.') - oss_upload_image(bucket=bucket_name, object_name=object_name, image_bytes=image_bytes) + oss_upload_image(oss_client=minio_client, bucket=bucket_name, object_name=object_name, image_bytes=image_bytes) image['show_image_url'] = f"{bucket_name}/{object_name}" # if image['site'] == 'down': @@ -261,7 +254,7 @@ class DesignPreprocessing: @staticmethod def load_seg_result(image_id): - file_path = f"{SEG_CACHE_PATH}{image_id}.npy" + file_path = f"{settings.SEG_CACHE_PATH}{image_id}.npy" try: seg_result = np.load(file_path) return True, seg_result @@ -274,7 +267,7 @@ class DesignPreprocessing: @staticmethod def save_seg_result(seg_result, image_id): - file_path = f"{SEG_CACHE_PATH}{image_id}.npy" + file_path = f"{settings.SEG_CACHE_PATH}{image_id}.npy" try: np.save(file_path, seg_result) logging.debug(f"保存成功,{os.path.abspath(file_path)}") @@ -283,14 +276,15 @@ class DesignPreprocessing: def keypoint_cache(self, sketch): try: - client = MilvusClient(uri=MILVUS_URL, token=MILVUS_TOKEN, db_name=MILVUS_ALIAS) + # client = MilvusClient(uri=settings.MILVUS_URL, token=settings.MILVUS_TOKEN, db_name=settings.MILVUS_ALIAS) keypoint_id = sketch['image_id'] - res = client.query( - collection_name=MILVUS_TABLE_KEYPOINT, - # ids=[keypoint_id], - filter=f"keypoint_id == {keypoint_id}", - output_fields=['keypoint_vector', 'keypoint_site'] - ) + # res = client.query( + # collection_name=MILVUS_TABLE_KEYPOINT, + # # ids=[keypoint_id], + # filter=f"keypoint_id == {keypoint_id}", + # output_fields=['keypoint_vector', 'keypoint_site'] + # ) + res = [] if len(res) == 0: # 没有结果 直接推理拿结果 并保存 keypoint_infer_result = self.infer_keypoint_result(sketch) @@ -307,7 +301,8 @@ class DesignPreprocessing: return False # @ RunTime - def infer_keypoint_result(self, sketch): + @staticmethod + def infer_keypoint_result(sketch): keypoint_infer_result = get_keypoint_result(sketch["obj"], sketch['site']) # 推理结果 return keypoint_infer_result @@ -320,14 +315,14 @@ class DesignPreprocessing: else: zeros = np.zeros(4, dtype=int) result = np.concatenate([keypoint_infer_result.flatten(), zeros]) - data = [ - [int(sketch['image_id'])], - [sketch['site']], - [result.tolist()] - ] + # [ + # [int(sketch['image_id'])], + # [sketch['site']], + # [result.tolist()] + # ] try: # connections.connect(alias=MILVUS_ALIAS, host=MILVUS_DB_HOST, port=MILVUS_PORT) - start_time = time.time() + time.time() # collection = Collection(MILVUS_TABLE_KEYPOINT) # Get an existing collection. # mr = collection.insert(data) # logging.info(f"save keypoint time : {time.time() - start_time}") @@ -344,11 +339,11 @@ class DesignPreprocessing: else: # 需要的是down 即推理出来的是down 那么查询的就是up result = np.concatenate([search_result[:20], infer_result.flatten()]) - data = [ - [int(sketch['image_id'])], - ["all"], - [result.tolist()] - ] + # [ + # [int(sketch['image_id'])], + # ["all"], + # [result.tolist()] + # ] try: # connections.connect(alias=MILVUS_ALIAS, host=MILVUS_DB_HOST, port=MILVUS_PORT) # start_time = time.time() diff --git a/app/service/generate_batch_image/service_batch_generate_product_image.py b/app/service/generate_batch_image/service_batch_generate_product_image.py index 570354a..4707cbf 100644 --- a/app/service/generate_batch_image/service_batch_generate_product_image.py +++ b/app/service/generate_batch_image/service_batch_generate_product_image.py @@ -13,17 +13,19 @@ import logging import cv2 import numpy as np +import pika import tritonclient.grpc as grpcclient from PIL import Image from celery import Celery from tritonclient.utils import np_to_triton_dtype -from app.core.config import * +from app.core.config import settings, GPI_MODEL_URL, GPI_MODEL_NAME_SINGLE, GPI_MODEL_NAME_OVERALL, BATCH_GPI_RABBITMQ_QUEUES +from app.core.rabbit_mq_config import RABBITMQ_PARAMS from app.schemas.generate_image import BatchGenerateProductImageModel, ProductItemModel from app.service.generate_image.utils.upload_sd_image import upload_SDXL_image -from app.service.utils.oss_client import oss_get_image +from app.service.utils.new_oss_client import oss_get_image -celery_app = Celery('product_tasks', broker=f'amqp://rabbit:123456@18.167.251.121:5672//', backend='rpc://', BROKER_CONNECTION_RETRY_ON_STARTUP=True) +celery_app = Celery('product_tasks', broker=f'amqp://rabbit:123456@18.167.251.121:5672//', backend='rpc://') celery_app.conf.task_default_queue = 'queue_product' celery_app.conf.worker_log_format = '%(asctime)s %(filename)s [line:%(lineno)d] %(levelname)s %(message)s' celery_app.conf.worker_hijack_root_logger = False @@ -104,7 +106,7 @@ def batch_generate_product(batch_request_data): result_data_list.append(data) # 发送每条结果 - if DEBUG: + if settings.DEBUG: logger.info(f" [x]Queue : {BATCH_GPI_RABBITMQ_QUEUES} | tasks_id：{tasks_id} | progress：{i + 1}/{batch_size} | result_data：{data}") print(f" [x]Queue : {BATCH_GPI_RABBITMQ_QUEUES} | tasks_id：{tasks_id} | progress：{i + 1}/{batch_size} | result_data：{data}") else: @@ -112,7 +114,7 @@ def batch_generate_product(batch_request_data): logger.info(f" [x]Queue : {BATCH_GPI_RABBITMQ_QUEUES} | tasks_id：{tasks_id} | progress：{i + 1}/{batch_size} | result_data：{data}") # 任务完成，发送所有数据结果 - if DEBUG: + if settings.DEBUG: print(result_data_list) logger.info(f" [x]Queue : {BATCH_GPI_RABBITMQ_QUEUES} | batch_tasks_id：{batch_tasks_id} | progress：OK | result_data_list：{result_data_list}") print(f" [x]Queue : {BATCH_GPI_RABBITMQ_QUEUES} | batch_tasks_id：{batch_tasks_id} | progress：OK | result_data_list：{result_data_list}") diff --git a/app/service/generate_batch_image/service_batch_generate_relight_image.py b/app/service/generate_batch_image/service_batch_generate_relight_image.py index 0a039d5..9b4e5e1 100644 --- a/app/service/generate_batch_image/service_batch_generate_relight_image.py +++ b/app/service/generate_batch_image/service_batch_generate_relight_image.py @@ -12,18 +12,20 @@ import logging import cv2 import numpy as np +import pika import tritonclient.grpc as grpcclient from PIL import Image from celery import Celery from tritonclient.utils import np_to_triton_dtype -from app.core.config import * +from app.core.config import settings, GRI_MODEL_URL, BATCH_GRI_RABBITMQ_QUEUES, GRI_MODEL_NAME_SINGLE, GRI_MODEL_NAME_OVERALL +from app.core.rabbit_mq_config import RABBITMQ_PARAMS from app.schemas.generate_image import BatchGenerateRelightImageModel, RelightItemModel from app.service.generate_image.utils.upload_sd_image import upload_SDXL_image -from app.service.utils.oss_client import oss_get_image +from app.service.utils.new_oss_client import oss_get_image logger = logging.getLogger() -celery_app = Celery('relight_tasks', broker=f'amqp://rabbit:123456@18.167.251.121:5672//', backend='rpc://', BROKER_CONNECTION_RETRY_ON_STARTUP=True) +celery_app = Celery('relight_tasks', broker=f'amqp://rabbit:123456@18.167.251.121:5672//', backend='rpc://') celery_app.conf.task_default_queue = 'queue_relight' celery_app.conf.worker_log_format = '%(asctime)s %(filename)s [line:%(lineno)d] %(levelname)s %(message)s' celery_app.conf.worker_hijack_root_logger = False @@ -133,14 +135,14 @@ def batch_generate_relight(batch_request_data): result_data_list.append(data) # 发送每条结果 - if DEBUG: + if settings.DEBUG: logger.info(f" [x]Queue : {BATCH_GRI_RABBITMQ_QUEUES} | tasks_id：{tasks_id} | progress：{i + 1}/{batch_size} | result_data：{data}") print(f" [x]Queue : {BATCH_GRI_RABBITMQ_QUEUES} | tasks_id：{tasks_id} | progress：{i + 1}/{batch_size} | result_data：{data}") else: publish_status(tasks_id, f"{i + 1}/{batch_size}", data) logger.info(f" [x]Queue : {BATCH_GRI_RABBITMQ_QUEUES} | tasks_id：{tasks_id} | progress：{i + 1}/{batch_size} | result_data：{data}") # 任务完成，发送所有数据结果 - if DEBUG: + if settings.DEBUG: print(result_data_list) logger.info(f" [x]Queue : {BATCH_GRI_RABBITMQ_QUEUES} | batch_tasks_id：{batch_tasks_id} | progress：OK | result_data_list：{result_data_list}") print(f" [x]Queue : {BATCH_GRI_RABBITMQ_QUEUES} | batch_tasks_id：{batch_tasks_id} | progress：OK | result_data_list：{result_data_list}") diff --git a/app/service/generate_batch_image/service_batch_pose_transform.py b/app/service/generate_batch_image/service_batch_pose_transform.py index 0114ee5..e2215ff 100644 --- a/app/service/generate_batch_image/service_batch_pose_transform.py +++ b/app/service/generate_batch_image/service_batch_pose_transform.py @@ -14,22 +14,24 @@ from io import BytesIO import imageio import numpy as np +import pika import tritonclient.grpc as grpcclient from PIL import Image from celery import Celery from minio import Minio from tritonclient.utils import np_to_triton_dtype -from app.core.config import * +from app.core.config import settings, BATCH_PS_RABBITMQ_QUEUES, PT_MODEL_URL +from app.core.rabbit_mq_config import RABBITMQ_PARAMS from app.schemas.pose_transform import BatchPoseTransformModel from app.service.generate_image.utils.pose_transform_upload import upload_gif, upload_video from app.service.utils.new_oss_client import oss_upload_image -from app.service.utils.oss_client import oss_get_image +from app.service.utils.new_oss_client import oss_get_image -minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE) +minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE) logger = logging.getLogger() -celery_app = Celery('post_transform_tasks', broker=f'amqp://rabbit:123456@18.167.251.121:5672//', backend='rpc://', BROKER_CONNECTION_RETRY_ON_STARTUP=True) +celery_app = Celery('post_transform_tasks', broker=f'amqp://rabbit:123456@18.167.251.121:5672//', backend='rpc://') celery_app.conf.task_default_queue = 'queue_post_transform' celery_app.conf.worker_log_format = '%(asctime)s %(filename)s [line:%(lineno)d] %(levelname)s %(message)s' celery_app.conf.worker_hijack_root_logger = False @@ -45,7 +47,7 @@ def upload_first_image(image, user_id, category, file_name): image_data.seek(0) image_bytes = image_data.read() object_name = f'{user_id}/{category}/{file_name}' - req = oss_upload_image(oss_client=minio_client, bucket=GI_MINIO_BUCKET, object_name=object_name, image_bytes=image_bytes) + req = oss_upload_image(oss_client=minio_client, bucket="aida-users", object_name=object_name, image_bytes=image_bytes) image_url = f"aida-users/{object_name}" return image_url except Exception as e: @@ -141,7 +143,7 @@ def batch_generate_pose_transform(batch_request_data): print(e) data = {} result_url_list.append(data) - if DEBUG is False: + if settings.DEBUG is False: if i + 1 < batch_size: publish_status(tasks_id, f"{i + 1}/{batch_size}", data) logger.info(f" [x]Queue : {BATCH_PS_RABBITMQ_QUEUES} | tasks_id：{tasks_id} | progress：{i + 1}/{batch_size} | image_url：{image_url}") diff --git a/app/service/generate_batch_image/test.py b/app/service/generate_batch_image/test.py index ece4b39..cea4190 100644 --- a/app/service/generate_batch_image/test.py +++ b/app/service/generate_batch_image/test.py @@ -1,16 +1,11 @@ -from app.schemas.generate_image import BatchGenerateRelightImageModel, BatchGenerateProductImageModel +from app.schemas.generate_image import BatchGenerateProductImageModel from app.service.generate_batch_image.service_batch_generate_product_image import batch_generate_product -from app.service.generate_batch_image.service_batch_generate_relight_image import batch_generate_relight - if __name__ == '__main__': rd = BatchGenerateProductImageModel( - tasks_id="test1-89", - image_strength=0.7, - prompt=" The best quality, masterpiece, real image.Outwear,high quality clothing details,8K realistic,HDR", - image_url="aida-results/result_40b1a2fe-e220-11ef-9bfa-0242ac150003.png", - product_type="single", - batch_size=2 + batch_tasks_id="", + batch_data_list="", + user_id="" ) x = batch_generate_product.delay(rd.dict()) print(x) diff --git a/app/service/generate_image/service_agent_tool_generate_image.py b/app/service/generate_image/service_agent_tool_generate_image.py index a5c295c..76f5de8 100644 --- a/app/service/generate_image/service_agent_tool_generate_image.py +++ b/app/service/generate_image/service_agent_tool_generate_image.py @@ -8,25 +8,24 @@ @detail ： """ import logging -import time import uuid + import cv2 import mmcv import numpy as np import pandas as pd import torch -import tritonclient.http as httpclient -import cv2 -import numpy as np import tritonclient.grpc as grpcclient +import tritonclient.http as httpclient from minio import Minio from tritonclient.utils import np_to_triton_dtype -from app.core.config import * + +from app.core.config import settings, FAST_GI_MODEL_URL, GI_MODEL_URL, DESIGN_MODEL_URL, FAST_GI_MODEL_NAME, GI_MODEL_NAME from app.service.utils.new_oss_client import oss_upload_image logger = logging.getLogger() -minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE) +minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE) class AgentToolGenerateImage: @@ -85,7 +84,8 @@ class AgentToolGenerateImage: self.grpc_client.close() self.triton_client.close() - def preprocess(self, img): + @staticmethod + def preprocess(img): img = mmcv.imread(img) img_scale = (224, 224) img = cv2.resize(img, img_scale) @@ -126,7 +126,7 @@ class AgentToolGenerateImage: return category_list -attr_type = pd.read_csv(CATEGORY_PATH) +attr_type = pd.read_csv(settings.CATEGORY_PATH) if __name__ == '__main__': request_data = { diff --git a/app/service/generate_image/service_generate_image.py b/app/service/generate_image/service_generate_image.py index 7d00b87..d9772b5 100644 --- a/app/service/generate_image/service_generate_image.py +++ b/app/service/generate_image/service_generate_image.py @@ -16,16 +16,18 @@ import minio import numpy as np import redis import tritonclient.grpc as grpcclient +from minio import Minio from tritonclient.utils import np_to_triton_dtype -from app.core.config import * +from app.core.config import settings, FAST_GI_MODEL_URL, GI_MODEL_URL, FAST_GI_MODEL_NAME, GI_MODEL_NAME, GI_RABBITMQ_QUEUES from app.schemas.generate_image import GenerateImageModel from app.service.generate_image.utils.image_processing import remove_background, stain_detection, generate_category_recognition, autoLevels, luminance_adjust from app.service.generate_image.utils.mq import publish_status from app.service.generate_image.utils.upload_sd_image import upload_png_sd -from app.service.utils.oss_client import oss_get_image +from app.service.utils.new_oss_client import oss_get_image logger = logging.getLogger() +minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE) class GenerateImage: @@ -36,7 +38,7 @@ class GenerateImage: else: self.grpc_client = grpcclient.InferenceServerClient(url=GI_MODEL_URL) - self.redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True) + self.redis_client = redis.StrictRedis(host=settings.REDIS_HOST, port=settings.REDIS_PORT, db=settings.REDIS_DB, decode_responses=True) if request_data.mode == "img2img": # cv2 读图片是BGR PIL读图片是RGB self.image = self.get_image(request_data.image_url) @@ -67,8 +69,7 @@ class GenerateImage: # image_array = np.asarray(bytearray(image_file.read()), dtype=np.uint8) # image_cv2 = cv2.imdecode(image_array, cv2.IMREAD_COLOR) # image_rbg = cv2.cvtColor(image_cv2, cv2.COLOR_BGR2RGB) - - image_cv2 = oss_get_image(bucket=image_url.split('/')[0], object_name=image_url[image_url.find('/') + 1:], data_type="cv2") + image_cv2 = oss_get_image(oss_client=minio_client, bucket=image_url.split('/')[0], object_name=image_url[image_url.find('/') + 1:], data_type="cv2") image_rbg = cv2.cvtColor(image_cv2, cv2.COLOR_BGR2RGB) image = cv2.resize(image_rbg, (1024, 1024)) except minio.error.S3Error: @@ -120,7 +121,7 @@ class GenerateImage: else: # 有污点 保存图片到本地 测试用 self.generate_data['status'] = "SUCCESS" self.generate_data['message'] = "success" - self.generate_data['image_url'] = str(GI_SYS_IMAGE_URL) + self.generate_data['image_url'] = "aida-sys-image/generate_image/white_image.jpg" self.redis_client.set(self.tasks_id, json.dumps(self.generate_data)) # logger.info(f"stain_detection result : {self.generate_data}") @@ -171,12 +172,12 @@ class GenerateImage: raise Exception(str(e)) finally: dict_generate_data, str_generate_data = self.read_tasks_status() - if not DEBUG: + if not settings.DEBUG: publish_status(str_generate_data, GI_RABBITMQ_QUEUES) def infer_cancel(tasks_id): - redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True) + redis_client = redis.StrictRedis(host=settings.REDIS_HOST, port=settings.REDIS_PORT, db=settings.REDIS_DB, decode_responses=True) data = {'tasks_id': tasks_id, 'status': 'REVOKED', 'message': "revoked", 'data': 'revoked'} generate_data = json.dumps(data) redis_client.set(tasks_id, generate_data) @@ -186,12 +187,12 @@ def infer_cancel(tasks_id): if __name__ == '__main__': rd = GenerateImageModel( tasks_id="123-89", - prompt="Women's clothing ,dress,technical drawing style, clean line art, no shading, no texture, flat sketch, no human body, no face, centered composition, pure white background, single garmentsingle garment only, front flat view", - image_url="aida-collection-element/87/Printboard/842c09cf-7297-42d9-9e6e-9c17d4a13cb5.jpg", - mode='txt2img', - category="test", - gender="male", - version="high" + prompt="a single item of sketch of dress, 4k, white background", + image_url="aida-collection-element/89/Sketchboard/95f20cdc-e059-435c-b8b1-d04cc9e80c3d.png", + mode='img2img', + category="sketch", + gender="Female", + version="fast" ) server = GenerateImage(rd) print(server.get_result()) diff --git a/app/service/generate_image/service_generate_multi_view.py b/app/service/generate_image/service_generate_multi_view.py index 5ac7819..6f12da5 100644 --- a/app/service/generate_image/service_generate_multi_view.py +++ b/app/service/generate_image/service_generate_multi_view.py @@ -15,11 +15,11 @@ import numpy as np import redis import tritonclient.grpc as grpcclient -from app.core.config import * +from app.core.config import settings, GMV_MODEL_URL, GMV_MODEL_NAME, GMV_RABBITMQ_QUEUES from app.schemas.generate_image import GenerateMultiViewModel from app.service.generate_image.utils.mq import publish_status from app.service.generate_image.utils.upload_sd_image import upload_png_sd -from app.service.utils.oss_client import oss_get_image +from app.service.utils.new_oss_client import oss_get_image logger = logging.getLogger() @@ -27,7 +27,7 @@ logger = logging.getLogger() class GenerateMultiView: def __init__(self, request_data): self.grpc_client = grpcclient.InferenceServerClient(url=GMV_MODEL_URL) - self.redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True) + self.redis_client = redis.StrictRedis(host=settings.REDIS_HOST, port=settings.REDIS_PORT, db=settings.REDIS_DB, decode_responses=True) self.image = self.get_image(request_data.image_url) self.tasks_id = request_data.tasks_id self.user_id = self.tasks_id[self.tasks_id.rfind('-') + 1:] @@ -35,7 +35,8 @@ class GenerateMultiView: self.redis_client.set(self.tasks_id, json.dumps(self.generate_data)) self.redis_client.expire(self.tasks_id, 600) - def get_image(self, image_url): + @staticmethod + def get_image(image_url): try: image = oss_get_image(bucket=image_url.split('/')[0], object_name=image_url[image_url.find('/') + 1:], data_type="PIL") return image @@ -92,12 +93,12 @@ class GenerateMultiView: raise Exception(str(e)) finally: dict_generate_data, str_generate_data = self.read_tasks_status() - if not DEBUG: + if not settings.DEBUG: publish_status(str_generate_data, GMV_RABBITMQ_QUEUES) def infer_cancel(tasks_id): - redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True) + redis_client = redis.StrictRedis(host=settings.REDIS_HOST, port=settings.REDIS_PORT, db=settings.REDIS_DB, decode_responses=True) data = {'tasks_id': tasks_id, 'status': 'REVOKED', 'message': "revoked", 'data': 'revoked'} generate_data = json.dumps(data) redis_client.set(tasks_id, generate_data) diff --git a/app/service/generate_image/service_generate_product_image.py b/app/service/generate_image/service_generate_product_image.py index 1191352..828da67 100644 --- a/app/service/generate_image/service_generate_product_image.py +++ b/app/service/generate_image/service_generate_product_image.py @@ -35,7 +35,7 @@ # # self.channel = self.connection.channel() # # self.minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE) # self.grpc_client = grpcclient.InferenceServerClient(url=GPI_MODEL_URL) -# self.redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True) +# self.redis_client = redis.StrictRedis(host=settings.REDIS_HOST, port=settings.REDIS_PORT, db=settings.REDIS_DB, decode_responses=True) # self.category = "product_image" # self.image_strength = request_data.image_strength # self.batch_size = 1 @@ -126,7 +126,7 @@ # # # def infer_cancel(tasks_id): -# redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True) +# redis_client = redis.StrictRedis(host=settings.REDIS_HOST, port=settings.REDIS_PORT, db=settings.REDIS_DB, decode_responses=True) # data = {'tasks_id': tasks_id, 'status': 'REVOKED', 'message': "revoked", 'data': 'revoked'} # gen_product_data = json.dumps(data) # redis_client.set(tasks_id, gen_product_data) @@ -208,21 +208,23 @@ import numpy as np import redis import tritonclient.grpc as grpcclient from PIL import Image +from minio import Minio from tritonclient.utils import np_to_triton_dtype -from app.core.config import * +from app.core.config import settings, GPI_MODEL_URL, GPI_MODEL_NAME_SINGLE, GPI_MODEL_NAME_OVERALL, GPI_RABBITMQ_QUEUES from app.schemas.generate_image import GenerateProductImageModel from app.service.generate_image.utils.mq import publish_status from app.service.generate_image.utils.upload_sd_image import upload_SDXL_image -from app.service.utils.oss_client import oss_get_image +from app.service.utils.new_oss_client import oss_get_image logger = logging.getLogger() +minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE) class GenerateProductImage: def __init__(self, request_data): self.grpc_client = grpcclient.InferenceServerClient(url=GPI_MODEL_URL) - self.redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True) + self.redis_client = redis.StrictRedis(host=settings.REDIS_HOST, port=settings.REDIS_PORT, db=settings.REDIS_DB, decode_responses=True) self.category = "product_image" self.image_strength = request_data.image_strength self.batch_size = 1 @@ -313,12 +315,12 @@ class GenerateProductImage: raise Exception(str(e)) finally: dict_gen_product_data, str_gen_product_data = self.read_tasks_status() - if not DEBUG: + if not settings.DEBUG: publish_status(str_gen_product_data, GPI_RABBITMQ_QUEUES) def infer_cancel(tasks_id): - redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True) + redis_client = redis.StrictRedis(host=settings.REDIS_HOST, port=settings.REDIS_PORT, db=settings.REDIS_DB, decode_responses=True) data = {'tasks_id': tasks_id, 'status': 'REVOKED', 'message': "revoked", 'data': 'revoked'} gen_product_data = json.dumps(data) redis_client.set(tasks_id, gen_product_data) @@ -326,7 +328,7 @@ def infer_cancel(tasks_id): def pre_processing_image(image_url): - image = oss_get_image(bucket=image_url.split('/')[0], object_name=image_url[image_url.find('/') + 1:], data_type="PIL") + image = oss_get_image(oss_client=minio_client, bucket=image_url.split('/')[0], object_name=image_url[image_url.find('/') + 1:], data_type="PIL") # 目标图片的尺寸 target_width = 512 target_height = 768 diff --git a/app/service/generate_image/service_generate_relight_image.py b/app/service/generate_image/service_generate_relight_image.py index 7db12c2..c67ca3a 100644 --- a/app/service/generate_image/service_generate_relight_image.py +++ b/app/service/generate_image/service_generate_relight_image.py @@ -18,11 +18,11 @@ import tritonclient.grpc as grpcclient from PIL import Image from tritonclient.utils import np_to_triton_dtype -from app.core.config import * +from app.core.config import settings, GRI_MODEL_URL, GRI_MODEL_NAME_SINGLE, GRI_MODEL_NAME_OVERALL, GRI_RABBITMQ_QUEUES from app.schemas.generate_image import GenerateRelightImageModel from app.service.generate_image.utils.mq import publish_status from app.service.generate_image.utils.upload_sd_image import upload_SDXL_image -from app.service.utils.oss_client import oss_get_image +from app.service.utils.new_oss_client import oss_get_image logger = logging.getLogger() @@ -30,7 +30,7 @@ logger = logging.getLogger() class GenerateRelightImage: def __init__(self, request_data): self.grpc_client = grpcclient.InferenceServerClient(url=GRI_MODEL_URL) - self.redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True) + self.redis_client = redis.StrictRedis(host=settings.REDIS_HOST, port=settings.REDIS_PORT, db=settings.REDIS_DB, decode_responses=True) self.category = "relight_image" self.batch_size = 1 self.prompt = request_data.prompt @@ -134,9 +134,10 @@ class GenerateRelightImage: raise Exception(str(e)) finally: dict_gen_product_data, str_gen_product_data = self.read_tasks_status() - if not DEBUG: + if not settings.DEBUG: publish_status(str_gen_product_data, GRI_RABBITMQ_QUEUES) + def pre_processing_image(image_url): image = oss_get_image(bucket=image_url.split('/')[0], object_name=image_url[image_url.find('/') + 1:], data_type="PIL") # 目标图片的尺寸 @@ -178,8 +179,9 @@ def pre_processing_image(image_url): # image = cv2.cvtColor(image, cv2.COLOR_BGRA2RGBA) return image + def infer_cancel(tasks_id): - redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True) + redis_client = redis.StrictRedis(host=settings.REDIS_HOST, port=settings.REDIS_PORT, db=settings.REDIS_DB, decode_responses=True) data = {'tasks_id': tasks_id, 'status': 'REVOKED', 'message': "revoked", 'data': 'revoked'} gen_product_data = json.dumps(data) redis_client.set(tasks_id, gen_product_data) diff --git a/app/service/generate_image/service_generate_single_logo.py b/app/service/generate_image/service_generate_single_logo.py index 1e6b0d2..3fe1ee3 100644 --- a/app/service/generate_image/service_generate_single_logo.py +++ b/app/service/generate_image/service_generate_single_logo.py @@ -11,18 +11,16 @@ import json import logging import time -import cv2 import numpy as np import redis +import tritonclient.grpc as grpcclient from PIL import Image -from minio import Minio from tritonclient.utils import np_to_triton_dtype -from app.core.config import * -import tritonclient.grpc as grpcclient +from app.core.config import settings, GI_RABBITMQ_QUEUES, GSL_MODEL_NAME, GSL_MODEL_URL from app.schemas.generate_image import GenerateSingleLogoImageModel from app.service.generate_image.utils.mq import publish_status -from app.service.generate_image.utils.upload_sd_image import upload_png_sd, upload_SDXL_image +from app.service.generate_image.utils.upload_sd_image import upload_SDXL_image logger = logging.getLogger() @@ -30,7 +28,7 @@ logger = logging.getLogger() class GenerateSingleLogoImage: def __init__(self, request_data): self.grpc_client = grpcclient.InferenceServerClient(url=GSL_MODEL_URL) - self.redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True) + self.redis_client = redis.StrictRedis(host=settings.REDIS_HOST, port=settings.REDIS_PORT, db=settings.REDIS_DB, decode_responses=True) self.batch_size = 1 self.category = "single_logo" self.negative_prompts = "bad, ugly" @@ -93,12 +91,12 @@ class GenerateSingleLogoImage: raise Exception(str(e)) finally: dict_generate_data, str_generate_data = self.read_tasks_status() - if not DEBUG: + if not settings.DEBUG: publish_status(str_generate_data, GI_RABBITMQ_QUEUES) def infer_cancel(tasks_id): - redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True) + redis_client = redis.StrictRedis(host=settings.REDIS_HOST, port=settings.REDIS_PORT, db=settings.REDIS_DB, decode_responses=True) data = {'tasks_id': tasks_id, 'status': 'REVOKED', 'message': "revoked", 'data': 'revoked'} generate_data = json.dumps(data) redis_client.set(tasks_id, generate_data) diff --git a/app/service/generate_image/service_pose_transform.py b/app/service/generate_image/service_pose_transform.py index 1616d76..d43952b 100644 --- a/app/service/generate_image/service_pose_transform.py +++ b/app/service/generate_image/service_pose_transform.py @@ -17,21 +17,23 @@ import numpy as np import redis import tritonclient.grpc as grpcclient from PIL import Image +from minio import Minio from tritonclient.utils import np_to_triton_dtype -from app.core.config import * +from app.core.config import settings, PS_RABBITMQ_QUEUES, PT_MODEL_URL from app.schemas.pose_transform import PoseTransformModel from app.service.generate_image.utils.mq import publish_status from app.service.generate_image.utils.pose_transform_upload import upload_gif, upload_video, upload_first_image -from app.service.utils.oss_client import oss_get_image +from app.service.utils.new_oss_client import oss_get_image logger = logging.getLogger() +minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE) class PoseTransformService: def __init__(self, request_data): self.grpc_client = grpcclient.InferenceServerClient(url=PT_MODEL_URL) - self.redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True) + self.redis_client = redis.StrictRedis(host=settings.REDIS_HOST, port=settings.REDIS_PORT, db=settings.REDIS_DB, decode_responses=True) self.category = "pose_transform" self.image_url = request_data.image_url self.pose_num = request_data.pose_id @@ -115,16 +117,14 @@ class PoseTransformService: raise Exception(str(e)) finally: dict_pose_transform_data, str_pose_transform_data = self.read_tasks_status() - if not DEBUG: + if not settings.DEBUG: publish_status(json.dumps(str_pose_transform_data), PS_RABBITMQ_QUEUES) logger.info( f" [x] Sent to： {PS_RABBITMQ_QUEUES} data：@@@@ {json.dumps(dict_pose_transform_data, indent=4)}") - - def infer_cancel(tasks_id): - redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True) + redis_client = redis.StrictRedis(host=settings.REDIS_HOST, port=settings.REDIS_PORT, db=settings.REDIS_DB, decode_responses=True) data = {'tasks_id': tasks_id, 'status': 'REVOKED', 'message': "revoked", 'data': 'revoked'} pose_transform_data = json.dumps(data) redis_client.set(tasks_id, pose_transform_data) @@ -132,8 +132,7 @@ def infer_cancel(tasks_id): def pre_processing_image(image_url): - image = oss_get_image(bucket=image_url.split('/')[0], object_name=image_url[image_url.find('/') + 1:], - data_type="PIL") + image = oss_get_image(oss_client=minio_client, bucket=image_url.split('/')[0], object_name=image_url[image_url.find('/') + 1:], data_type="PIL") # 目标图片的尺寸 target_width = 512 target_height = 768 diff --git a/app/service/generate_image/test.py b/app/service/generate_image/test.py deleted file mode 100644 index 2c7277c..0000000 --- a/app/service/generate_image/test.py +++ /dev/null @@ -1,177 +0,0 @@ -#!/usr/bin/env python -# -*- coding: UTF-8 -*- -""" -@Project ：trinity_client -@File ：service_att_recognition.py -@Author ：周成融 -@Date ：2023/7/26 12:01:05 -@detail ： -""" -import json -import logging -import time -from io import BytesIO - -import cv2 -import minio -import redis -import tritonclient.grpc as grpcclient -import numpy as np -from minio import Minio -from tritonclient.utils import np_to_triton_dtype - -from app.core.config import * -from app.schemas.generate_image import GenerateImageModel -from app.service.generate_image.utils.adjust_contrast import adjust_contrast -from app.service.generate_image.utils.image_processing import remove_background, stain_detection -from app.service.generate_image.utils.upload_sd_image import upload_png_sd - -logger = logging.getLogger() - - -class GenerateImage: - def __init__(self, request_data): - if DEBUG is False: - self.connection = pika.BlockingConnection(pika.ConnectionParameters(**RABBITMQ_PARAMS)) - self.channel = self.connection.channel() - # self.connection = pika.BlockingConnection(pika.ConnectionParameters(**RABBITMQ_PARAMS)) - # self.channel = self.connection.channel() - self.minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE) - self.grpc_client = grpcclient.InferenceServerClient(url=GI_MODEL_URL) - self.redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True) - if request_data.mode == "img2img": - self.image = self.get_image(request_data.image_url) - self.prompt = request_data.prompt - else: - self.image = np.random.randint(0, 256, (1024, 1024, 3), dtype=np.uint8) - self.prompt = request_data.prompt - - self.tasks_id = request_data.tasks_id - self.user_id = self.tasks_id[self.tasks_id.rfind('-') + 1:] - self.mode = request_data.mode - self.batch_size = 1 - self.category = request_data.category - self.index = 0 - self.generate_data = {'tasks_id': self.tasks_id, 'status': 'PENDING', 'message': "pending", 'data': ''} - self.redis_client.set(self.tasks_id, json.dumps(self.generate_data)) - self.redis_client.expire(self.tasks_id, 600) - - def get_image(self, image_url): - # Get data of an object. - # Read data from response. - try: - response = self.minio_client.get_object(image_url.split('/')[0], image_url[image_url.find('/') + 1:]) - image_file = BytesIO(response.data) - image_array = np.asarray(bytearray(image_file.read()), dtype=np.uint8) - image_cv2 = cv2.imdecode(image_array, cv2.IMREAD_COLOR) - image = cv2.resize(image_cv2, (1024, 1024)) - except minio.error.S3Error: - image = np.random.randint(0, 256, (1024, 1024, 3), dtype=np.uint8) - return image - - def callback(self, result, error): - if error: - self.generate_data['status'] = "FAILURE" - self.generate_data['message'] = str(error) - self.generate_data['data'] = str(error) - self.redis_client.set(self.tasks_id, json.dumps(self.generate_data)) - else: - image_result = result.as_numpy("generated_image")[0] - is_smudge = True - if self.category == "sketch": - # 去背景 - remove_bg_image = remove_background(np.asarray(image_result)) - # 污点检测 - is_smudge, not_smudge_image = stain_detection(remove_bg_image) - image_result = not_smudge_image - if is_smudge: # 无污点 - image_result = adjust_contrast(image_result) - image_url = upload_png_sd(image_result, user_id=self.user_id, category=f"{self.category}", object_name=f"{self.tasks_id}.png") - # logger.info(f"upload image SUCCESS ： {image_url}") - self.generate_data['status'] = "SUCCESS" - self.generate_data['message'] = "success" - self.generate_data['data'] = str(image_url) - self.redis_client.set(self.tasks_id, json.dumps(self.generate_data)) - else: # 有污点 - self.generate_data['status'] = "SUCCESS" - self.generate_data['message'] = "success" - self.generate_data['data'] = str(GI_SYS_IMAGE_URL) - self.redis_client.set(self.tasks_id, json.dumps(self.generate_data)) - # logger.info(f"stain_detection result : {self.generate_data}") - - def read_tasks_status(self): - status_data = self.redis_client.get(self.tasks_id) - return json.loads(status_data), status_data - - def infer(self, inputs): - return self.grpc_client.infer( - model_name=GI_MODEL_NAME, - inputs=inputs, - # callback=self.callback - ) - - def get_result(self): - try: - prompts = [self.prompt] * self.batch_size - modes = [self.mode] * self.batch_size - images = [self.image.astype(np.float16)] * self.batch_size - - text_obj = np.array(prompts, dtype="object").reshape((-1, 1)) - mode_obj = np.array(modes, dtype="object").reshape((-1, 1)) - image_obj = np.array(images, dtype=np.float16).reshape((-1, 1024, 1024, 3)) - - input_text = grpcclient.InferInput("prompt", text_obj.shape, np_to_triton_dtype(text_obj.dtype)) - input_image = grpcclient.InferInput("input_image", image_obj.shape, "FP16") - input_mode = grpcclient.InferInput("mode", mode_obj.shape, np_to_triton_dtype(text_obj.dtype)) - - input_text.set_data_from_numpy(text_obj) - input_image.set_data_from_numpy(image_obj) - input_mode.set_data_from_numpy(mode_obj) - - inputs = [input_text, input_image, input_mode] - ctx = self.infer(inputs) - time_out = 600 - generate_data = None - while time_out > 0: - generate_data, _ = self.read_tasks_status() - # logger.info(generate_data) - if generate_data['status'] in ["REVOKED", "FAILURE"]: - ctx.cancel() - break - elif generate_data['status'] == "SUCCESS": - break - time_out -= 1 - time.sleep(0.1) - # logger.info(time_out, generate_data) - return generate_data - except Exception as e: - # self.generate_data['status'] = "FAILURE" - # self.generate_data['message'] = "failure" - # self.generate_data['data'] = str(e) - # self.redis_client.set(self.tasks_id, json.dumps(self.generate_data)) - raise Exception(str(e)) - # finally: - # dict_generate_data, str_generate_data = self.read_tasks_status() - # if DEBUG is False: - # self.channel.basic_publish(exchange='', routing_key=GI_RABBITMQ_QUEUES, body=str_generate_data) - # logger.info(f" [x] Sent {json.dumps(dict_generate_data, indent=4)}") - - -def infer_cancel(tasks_id): - redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True) - data = {'tasks_id': tasks_id, 'status': 'REVOKED', 'message': "revoked", 'data': 'revoked'} - generate_data = json.dumps(data) - redis_client.set(tasks_id, generate_data) - return data - - -if __name__ == '__main__': - rd = GenerateImageModel( - tasks_id="123-89", - prompt='skeleton sitting by the side of a river looking soulful, concert poster, 4k, artistic', - image_url="", - mode='txt2img', - category="test" - ) - server = GenerateImage(rd) - print(server.get_result()) diff --git a/app/service/generate_image/utils/image_processing.py b/app/service/generate_image/utils/image_processing.py index 02d8bee..692ffc9 100644 --- a/app/service/generate_image/utils/image_processing.py +++ b/app/service/generate_image/utils/image_processing.py @@ -7,7 +7,7 @@ import numpy as np import torch import tritonclient.http as httpclient -from app.core.config import * +from app.core.config import settings, DESIGN_MODEL_URL, DESIGN_MODEL_NAME from app.service.generate_image.utils.upload_sd_image import upload_stain_png_sd, upload_face_png_sd logger = logging.getLogger() @@ -65,40 +65,40 @@ def get_contours(image): # transformed_img = image.astype(np.float32) # # 输入集 # inputs = [ -# httpclient.InferInput(SEGMENTATION['input'], transformed_img.shape, datatype="FP32") +# httpclient.InferInput(DESIGN_MODEL_NAME, transformed_img.shape, datatype="FP32") # ] # inputs[0].set_data_from_numpy(transformed_img, binary_data=True) # # 输出集 # outputs = [ -# httpclient.InferRequestedOutput(SEGMENTATION['output'], binary_data=True), +# httpclient.InferRequestedOutput("seg_input__0", binary_data=True), # ] # results = client.infer(model_name=SEGMENTATION['name'], inputs=inputs, outputs=outputs) # # 推理 # # 取结果 -# inference_output1 = torch.from_numpy(results.as_numpy(SEGMENTATION['output'])) +# inference_output1 = torch.from_numpy(results.as_numpy("seg_input__0")) # seg_result = seg_postprocess(inference_output1, ori_shape) # return seg_result def seg_infer_image(image_obj): image, ori_shape = seg_preprocess(image_obj) - client = httpclient.InferenceServerClient(url=f"{DESIGN_MODEL_URL}") + client = httpclient.InferenceServerClient(url=DESIGN_MODEL_URL) transformed_img = image.astype(np.float32) # 输入集 inputs = [ - httpclient.InferInput(SEGMENTATION['input'], transformed_img.shape, datatype="FP32") + httpclient.InferInput("seg_input__0", transformed_img.shape, datatype="FP32") ] inputs[0].set_data_from_numpy(transformed_img, binary_data=True) # 输出集 outputs = [ - httpclient.InferRequestedOutput(SEGMENTATION['output'], binary_data=True), + httpclient.InferRequestedOutput("seg_output__0", binary_data=True), ] start_time = time.time() - results = client.infer(model_name=SEGMENTATION['new_model_name'], inputs=inputs, outputs=outputs) + results = client.infer(model_name=DESIGN_MODEL_NAME, inputs=inputs, outputs=outputs) print(f"KNet infer time is :{time.time() - start_time}") # 推理 # 取结果 - inference_output1 = results.as_numpy(SEGMENTATION['output']) - seg_result = seg_postprocess(inference_output1, ori_shape) + inference_output1 = results.as_numpy("seg_output__0") + seg_result = seg_postprocess(inference_output1) return seg_result @@ -110,7 +110,7 @@ def seg_infer_image(image_obj): # return seg_pred # KNet -def seg_postprocess(output, ori_shape): +def seg_postprocess(output): # seg_logit = F.interpolate(torch.tensor(output).float(), size=ori_shape, scale_factor=None, mode='bilinear', align_corners=False) # seg_logit = F.softmax(seg_logit, dim=1) # seg_pred = seg_logit.argmax(dim=1) @@ -201,7 +201,7 @@ def stain_detection(image, user_id, category, tasks_id, spot_size=100): # 如果有连续的纯白区域存在 if filtered_contours: # 将纯白区域替换为灰色 - if DEBUG: + if settings.DEBUG: for cnt in filtered_contours: x, y, w, h = cv2.boundingRect(cnt) # 在原始图像上进行替换 @@ -216,7 +216,7 @@ def stain_detection(image, user_id, category, tasks_id, spot_size=100): if is_pure_white: return False, None - if DEBUG: + if settings.DEBUG: for corner_coords in [ (0, 0), # (0, width - spot_size), @@ -236,7 +236,7 @@ def stain_detection(image, user_id, category, tasks_id, spot_size=100): ]: cv2.rectangle(dst, corner_coords, (corner_coords[0] + spot_size, corner_coords[1] + spot_size), (0, 0, 255), 2) cv2.rectangle(dst, (center_x - spot_size // 2, center_y - spot_size // 2), (center_x + spot_size // 2, center_y + spot_size // 2), (0, 255, 0), 2) # 在原始图像上绘制矩形框 - image_url = upload_stain_png_sd(dst, user_id=user_id, category=f"{category}", object_name=f"{tasks_id}.png") + upload_stain_png_sd(dst, user_id=user_id, category=f"{category}", object_name=f"{tasks_id}.png") return True, image @@ -262,10 +262,10 @@ def generate_category_recognition(image, gender): scores = inference_output.detach().numpy() import pandas as pd - attr_type = pd.read_csv(CATEGORY_PATH) + attr_type = pd.read_csv(settings.CATEGORY_PATH) colattr = list(attr_type['labelName']) - task = attr_type['taskName'][0] + # attr_type['taskName'][0] maxsc = np.max(scores[0][:5]) indexs = np.argwhere(scores == maxsc)[:, 1] @@ -321,12 +321,13 @@ def face_detect_pic(image, user_id, category, tasks_id): # cv2.imshow("gray", gray) # 2、训练一组人脸 + FACE_CLASSIFIER = "" face_detector = cv2.CascadeClassifier(FACE_CLASSIFIER) # 3、检测人脸（用灰度图检测，返回人脸矩形坐标(4个角)） faces_rect = face_detector.detectMultiScale(gray, 1.05, 3) - if DEBUG: + if settings.DEBUG: dst = image.copy() for x, y, w, h in faces_rect: cv2.rectangle(dst, (x, y), (x + w, y + h), (0, 0, 255), 3) # 画出矩形框 @@ -336,7 +337,7 @@ def face_detect_pic(image, user_id, category, tasks_id): dst = image.copy() for x, y, w, h in faces_rect: cv2.rectangle(dst, (x, y), (x + w, y + h), (0, 0, 255), 3) # 画出矩形框 - image_url = upload_face_png_sd(dst, user_id=user_id, category=f"{category}", object_name=f"{tasks_id}.png") + upload_face_png_sd(dst, user_id=user_id, category=f"{category}", object_name=f"{tasks_id}.png") return len(faces_rect) diff --git a/app/service/generate_image/utils/mq.py b/app/service/generate_image/utils/mq.py index 86e1df6..1e812f8 100644 --- a/app/service/generate_image/utils/mq.py +++ b/app/service/generate_image/utils/mq.py @@ -3,7 +3,7 @@ import json import pika import logging -from app.core.config import RABBITMQ_PARAMS +from app.core.rabbit_mq_config import RABBITMQ_PARAMS logger = logging.getLogger(__name__) diff --git a/app/service/generate_image/utils/pose_transform_upload.py b/app/service/generate_image/utils/pose_transform_upload.py index f5e5318..956955a 100644 --- a/app/service/generate_image/utils/pose_transform_upload.py +++ b/app/service/generate_image/utils/pose_transform_upload.py @@ -3,19 +3,13 @@ import logging import os.path import numpy as np -# import boto3 from minio import Minio from moviepy.video.io.ImageSequenceClip import ImageSequenceClip -from app.core.config import * +from app.core.config import settings from app.service.utils.new_oss_client import oss_upload_image -# minio 配置 -MINIO_URL = "www.minio-api.aida.com.hk" -MINIO_ACCESS = 'vXKFLSJkYeEq2DrSZvkB' -MINIO_SECRET = 'uKTZT3x7C43WvPN9QTc99DiRkwddWZrG9Uh3JVlR' -MINIO_SECURE = True -minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE) +minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE) def upload_first_image(image, user_id, category, file_name): @@ -25,7 +19,7 @@ def upload_first_image(image, user_id, category, file_name): image_data.seek(0) image_bytes = image_data.read() object_name = f'{user_id}/{category}/{file_name}' - req = oss_upload_image(oss_client=minio_client, bucket=GI_MINIO_BUCKET, object_name=object_name, image_bytes=image_bytes) + oss_upload_image(oss_client=minio_client, bucket="aida-users", object_name=object_name, image_bytes=image_bytes) image_url = f"aida-users/{object_name}" return image_url except Exception as e: @@ -35,7 +29,7 @@ def upload_first_image(image, user_id, category, file_name): def upload_gif(gif_buffer, user_id, category, file_name): try: object_name = f'{user_id}/{category}/{file_name}' - req = minio_client.put_object( + minio_client.put_object( "aida-users", object_name, gif_buffer, @@ -62,8 +56,8 @@ def upload_video(frames, user_id, category, file_name): logging.warning(f"upload_video runtime exception : {e}") -def ndarray_to_video(images, output_path, frame_size=(512, 768), fps=9): - save_path = os.path.join(POSE_TRANSFORM_VIDEO_PATH, output_path) +def ndarray_to_video(images, output_path, fps=9): + save_path = os.path.join("../pose_transform_video/", output_path) clip = ImageSequenceClip([frame for frame in images], fps=fps) clip.write_videofile(save_path, codec='libx264') diff --git a/app/service/generate_image/utils/upload_sd_image.py b/app/service/generate_image/utils/upload_sd_image.py index a63488c..0693c1d 100644 --- a/app/service/generate_image/utils/upload_sd_image.py +++ b/app/service/generate_image/utils/upload_sd_image.py @@ -9,16 +9,13 @@ """ import io import logging - -# import boto3 import cv2 -from PIL import Image from minio import Minio -from app.core.config import * -from app.service.utils.oss_client import oss_upload_image +from app.core.config import settings +from app.service.utils.new_oss_client import oss_upload_image -minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE) +minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE) # s3 = boto3.client('s3', aws_access_key_id=S3_ACCESS_KEY, aws_secret_access_key=S3_AWS_SECRET_ACCESS_KEY, region_name=S3_REGION_NAME) @@ -52,7 +49,7 @@ def upload_SDXL_image(image, user_id, category, file_name): # content_type='image/jpeg' # ) object_name = f'{user_id}/{category}/{file_name}' - req = oss_upload_image(bucket=GI_MINIO_BUCKET, object_name=object_name, image_bytes=image_bytes) + oss_upload_image(oss_client=minio_client, bucket="aida-users", object_name=object_name, image_bytes=image_bytes) image_url = f"aida-users/{object_name}" return image_url except Exception as e: @@ -63,7 +60,7 @@ def upload_png_sd(image, user_id, category, file_name): try: _, img_byte_array = cv2.imencode('.jpg', image) object_name = f'{user_id}/{category}/{file_name}' - req = oss_upload_image(bucket=GI_MINIO_BUCKET, object_name=object_name, image_bytes=img_byte_array) + oss_upload_image(oss_client=minio_client, bucket="aida-users", object_name=object_name, image_bytes=img_byte_array) image_url = f"aida-users/{object_name}" return image_url except Exception as e: diff --git a/app/service/image2sketch/checkpoints/download_checkpoints.py b/app/service/image2sketch/checkpoints/download_checkpoints.py deleted file mode 100644 index 03cc2c6..0000000 --- a/app/service/image2sketch/checkpoints/download_checkpoints.py +++ /dev/null @@ -1,45 +0,0 @@ -import os - -from minio import Minio -from minio.error import S3Error - -MINIO_URL = "www.minio.aida.com.hk:12024" -MINIO_ACCESS = 'vXKFLSJkYeEq2DrSZvkB' -MINIO_SECRET = 'uKTZT3x7C43WvPN9QTc99DiRkwddWZrG9Uh3JVlR' -MINIO_SECURE = True -# 配置MinIO客户端 -minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE) - - -# 下载函数 -def download_folder(bucket_name, folder_name, local_dir): - try: - # 确保本地目录存在 - if not os.path.exists(local_dir): - os.makedirs(local_dir) - - # 遍历MinIO中的文件 - objects = minio_client.list_objects(bucket_name, prefix=folder_name, recursive=True) - for obj in objects: - # 构造本地文件路径 - local_file_path = os.path.join(local_dir, obj.object_name[len(folder_name):]) - local_file_dir = os.path.dirname(local_file_path) - - # 确保本地目录存在 - if not os.path.exists(local_file_dir): - os.makedirs(local_file_dir) - - # 下载文件 - minio_client.fget_object(bucket_name, obj.object_name, local_file_path) - print(f"Downloaded {obj.object_name} to {local_file_path}") - - except S3Error as e: - print(f"Error occurred: {e}") - - -# 使用示例 -bucket_name = "test" # 替换成你的bucket名称 -folder_name = "checkpoints/" # 权重文件夹的路径 -local_dir = "app/service/image2sketch/checkpoints" # 替换成你希望保存到的本地目录 - -download_folder(bucket_name, folder_name, local_dir) diff --git a/app/service/image2sketch/datasets/ref_unpair/testC/style_1.jpg b/app/service/image2sketch/datasets/ref_unpair/testC/style_1.jpg deleted file mode 100644 index 3a66b7f..0000000 Binary files a/app/service/image2sketch/datasets/ref_unpair/testC/style_1.jpg and /dev/null differ diff --git a/app/service/image2sketch/datasets/ref_unpair/testC/style_2.jpeg b/app/service/image2sketch/datasets/ref_unpair/testC/style_2.jpeg deleted file mode 100644 index 0347322..0000000 Binary files a/app/service/image2sketch/datasets/ref_unpair/testC/style_2.jpeg and /dev/null differ diff --git a/app/service/image2sketch/datasets/ref_unpair/testC/style_3.png b/app/service/image2sketch/datasets/ref_unpair/testC/style_3.png deleted file mode 100644 index 8d8bcf4..0000000 Binary files a/app/service/image2sketch/datasets/ref_unpair/testC/style_3.png and /dev/null differ diff --git a/app/service/image2sketch/infer.py b/app/service/image2sketch/infer.py deleted file mode 100644 index 8ec241f..0000000 --- a/app/service/image2sketch/infer.py +++ /dev/null @@ -1,89 +0,0 @@ -import os - -import numpy as np -import torch -import torchvision.transforms as transforms -from PIL import Image - -from .models import create_model - - -def tensor2im(input_image, imtype=np.uint8): - if not isinstance(input_image, np.ndarray): - if isinstance(input_image, torch.Tensor): # get the data from a variable - image_tensor = input_image.data - else: - return input_image - image_numpy = image_tensor[0].cpu().float().numpy() # convert it into a numpy array - if image_numpy.shape[0] == 1: # grayscale to RGB - image_numpy = np.tile(image_numpy, (3, 1, 1)) - image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 # post-processing: tranpose and scaling - else: # if it is a numpy array, do nothing - image_numpy = input_image - return image_numpy.astype(imtype) - - -def save_image(image_numpy, image_path, w, h, aspect_ratio=1.0): - """Save a numpy image to the disk - - Parameters: - image_numpy (numpy array) -- input numpy array - image_path (str) -- the path of the image - """ - - image_pil = Image.fromarray(image_numpy) - image_pil = image_pil.resize((w, h)) - image_pil.save(image_path) - - -def save_img(image_tensor, w, h, filename): - image_pil = tensor2im(image_tensor) - - save_image(image_pil, filename, w, h, aspect_ratio=1.0) - print("Image saved as {}".format(filename)) - - -def load_img(filepath): - img = Image.open(filepath).convert('L') - # print(img.size) - width = img.size[0] - height = img.size[1] - # img = img.resize((512, 512), Image.BICUBIC) - return img, width, height - - -if __name__ == '__main__': - img_A = "/workspace/Semi_ref2sketch_code/datasets/ref_unpair/testA/real_Dress_732caedc416a0cbfedd0e6528040eac7.jpg_Img.jpg" - img_B = "/workspace/Semi_ref2sketch_code/datasets/ref_unpair/testC/style_3.png" - from opt import Config - - opt = Config() # get test options - # hard-code some parameters for test - opt.num_threads = 0 # test code only supports num_threads = 0 - opt.batch_size = 1 # test code only supports batch_size = 1 - opt.serial_batches = True # disable data shuffling; comment this line if results on randomly chosen images are needed. - opt.no_flip = True # no flip; comment this line if results on flipped images are needed. - opt.display_id = -1 # no visdom display; the test code saves the results to a HTML file. - device = torch.device("cuda:0") - model = create_model(opt) # create a model given opt.model and other options - model.setup(opt) - transform_list = [transforms.ToTensor(), transforms.Normalize([0.5], [0.5])] - transform = transforms.Compose(transform_list) - if opt.eval: - model.eval() - data = {} - print(os.getcwd()) - B = reference, _, _ = load_img(r"/app/service/image2sketch/datasets/ref_unpair/testC/style_3.png") - style_img = transform(reference) - data['B'] = style_img - data['B'] = data['B'].unsqueeze(0).to(device) - A = Image.open(r"E:\workspace\trinity_client_aida\app\service\image2sketch\datasets\ref_unpair\testA\real_Dress_3200fecdc83d0c556c2bd96aedbd7fbf.jpg_Img.jpg") - width = A.size[0] - height = A.size[1] - # data['A'] = A.resize((512, 512)) - data['A'] = transform(A) - data['A'] = data['A'].unsqueeze(0).to(device) - model.set_input(data) - model.test() # run inference - visuals = model.get_current_visuals() # get image results - save_img(visuals['content_output'].cpu(), width, height, "result/result.jpg") diff --git a/app/service/image2sketch/models/__init__.py b/app/service/image2sketch/models/__init__.py deleted file mode 100644 index 809105c..0000000 --- a/app/service/image2sketch/models/__init__.py +++ /dev/null @@ -1,49 +0,0 @@ -import importlib - -from app.service.image2sketch.models import unpaired_model as modellib -from .base_model import BaseModel - - -def find_model_using_name(model_name): - """Import the module "models/[model_name]_model.py". - - In the file, the class called DatasetNameModel() will - be instantiated. It has to be a subclass of BaseModel, - and it is case-insensitive. - """ - # model_filename = "." + model_name + "_model" - # modellib = importlib.import_module(model_filename) - model = None - target_model_name = model_name.replace('_', '') + 'model' - for name, cls in modellib.__dict__.items(): - if name.lower() == target_model_name.lower() \ - and issubclass(cls, BaseModel): - model = cls - - if model is None: - print("In %s.py, there should be a subclass of BaseModel with class name that matches %s in lowercase." % (model_filename, target_model_name)) - exit(0) - - return model - - -def get_option_setter(model_name): - """Return the static method of the model class.""" - model_class = find_model_using_name(model_name) - return model_class.modify_commandline_options - - -def create_model(opt): - """Create a model given the option. - - This function warps the class CustomDatasetDataLoader. - This is the main interface between this package and 'train.py'/'test.py' - - Example: - >>> from .models import create_model - >>> model = create_model(opt) - """ - model = find_model_using_name(opt.model) - instance = model(opt) - print("model [%s] was created" % type(instance).__name__) - return instance diff --git a/app/service/image2sketch/models/base_model.py b/app/service/image2sketch/models/base_model.py deleted file mode 100644 index 6de961b..0000000 --- a/app/service/image2sketch/models/base_model.py +++ /dev/null @@ -1,230 +0,0 @@ -import os -import torch -from collections import OrderedDict -from abc import ABC, abstractmethod -from . import networks - - -class BaseModel(ABC): - """This class is an abstract base class (ABC) for models. - To create a subclass, you need to implement the following five functions: - -- <__init__>: initialize the class; first call BaseModel.__init__(self, opt). - -- : unpack data from dataset and apply preprocessing. - -- : produce intermediate results. - -- : calculate losses, gradients, and update network weights. - -- : (optionally) add model-specific options and set default options. - """ - - def __init__(self, opt): - """Initialize the BaseModel class. - - Parameters: - opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions - - When creating your custom class, you need to implement your own initialization. - In this function, you should first call - Then, you need to define four lists: - -- self.loss_names (str list): specify the training losses that you want to plot and save. - -- self.model_names (str list): define networks used in our training. - -- self.visual_names (str list): specify the images that you want to display and save. - -- self.optimizers (optimizer list): define and initialize optimizers. You can define one optimizer for each network. If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an example. - """ - self.opt = opt - self.gpu_ids = opt.gpu_ids - self.isTrain = opt.isTrain - self.device = torch.device('cuda:{}'.format(self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu') # get device name: CPU or GPU - self.save_dir = os.path.join(opt.checkpoints_dir, opt.name) # save all the checkpoints to save_dir - if opt.preprocess != 'scale_width': # with [scale_width], input images might have different sizes, which hurts the performance of cudnn.benchmark. - torch.backends.cudnn.benchmark = True - self.loss_names = [] - self.model_names = [] - self.visual_names = [] - self.optimizers = [] - self.image_paths = [] - self.metric = 0 # used for learning rate policy 'plateau' - - @staticmethod - def modify_commandline_options(parser, is_train): - """Add new model-specific options, and rewrite default values for existing options. - - Parameters: - parser -- original option parser - is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options. - - Returns: - the modified parser. - """ - return parser - - @abstractmethod - def set_input(self, input): - """Unpack input data from the dataloader and perform necessary pre-processing steps. - - Parameters: - input (dict): includes the data itself and its metadata information. - """ - pass - - @abstractmethod - def forward(self): - """Run forward pass; called by both functions and .""" - pass - - @abstractmethod - def optimize_parameters(self): - """Calculate losses, gradients, and update network weights; called in every training iteration""" - pass - - def setup(self, opt): - """Load and print networks; create schedulers - - Parameters: - opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions - """ - if self.isTrain: - self.schedulers = [networks.get_scheduler(optimizer, opt) for optimizer in self.optimizers] - if not self.isTrain or opt.continue_train: - load_suffix = 'iter_%d' % opt.load_iter if opt.load_iter > 0 else opt.epoch - self.load_networks(load_suffix) - self.print_networks(opt.verbose) - - def eval(self): - """Make models eval mode during test time""" - for name in self.model_names: - if isinstance(name, str): - net = getattr(self, 'net' + name) - net.eval() - - def test(self): - """Forward function used in test time. - - This function wraps function in no_grad() so we don't save intermediate steps for backprop - It also calls to produce additional visualization results - """ - with torch.no_grad(): - self.forward() - self.compute_visuals() - - def compute_visuals(self): - """Calculate additional output images for visdom and HTML visualization""" - pass - - def get_image_paths(self): - """ Return image paths that are used to load current data""" - return self.image_paths - - def update_learning_rate(self): - """Update learning rates for all the networks; called at the end of every epoch""" - old_lr = self.optimizers[0].param_groups[0]['lr'] - for scheduler in self.schedulers: - if self.opt.lr_policy == 'plateau': - scheduler.step(self.metric) - else: - scheduler.step() - - lr = self.optimizers[0].param_groups[0]['lr'] - print('learning rate %.7f -> %.7f' % (old_lr, lr)) - - def get_current_visuals(self): - """Return visualization images. train.py will display these images with visdom, and save the images to a HTML""" - visual_ret = OrderedDict() - for name in self.visual_names: - if isinstance(name, str): - visual_ret[name] = getattr(self, name) - return visual_ret - - def get_current_losses(self): - """Return traning losses / errors. train.py will print out these errors on console, and save them to a file""" - errors_ret = OrderedDict() - for name in self.loss_names: - if isinstance(name, str): - errors_ret[name] = float(getattr(self, 'loss_' + name)) # float(...) works for both scalar tensor and float number - return errors_ret - - def save_networks(self, epoch): - """Save all the networks to the disk. - - Parameters: - epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name) - """ - for name in self.model_names: - if isinstance(name, str): - save_filename = '%s_net_%s.pth' % (epoch, name) - save_path = os.path.join(self.save_dir, save_filename) - net = getattr(self, 'net' + name) - - if len(self.gpu_ids) > 0 and torch.cuda.is_available(): - torch.save(net.module.cpu().state_dict(), save_path) - net.cuda(self.gpu_ids[0]) - else: - torch.save(net.cpu().state_dict(), save_path) - - def __patch_instance_norm_state_dict(self, state_dict, module, keys, i=0): - """Fix InstanceNorm checkpoints incompatibility (prior to 0.4)""" - key = keys[i] - if i + 1 == len(keys): # at the end, pointing to a parameter/buffer - if module.__class__.__name__.startswith('InstanceNorm') and \ - (key == 'running_mean' or key == 'running_var'): - if getattr(module, key) is None: - state_dict.pop('.'.join(keys)) - if module.__class__.__name__.startswith('InstanceNorm') and \ - (key == 'num_batches_tracked'): - state_dict.pop('.'.join(keys)) - else: - self.__patch_instance_norm_state_dict(state_dict, getattr(module, key), keys, i + 1) - - def load_networks(self, epoch): - """Load all the networks from the disk. - - Parameters: - epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name) - """ - for name in self.model_names: - if isinstance(name, str): - load_filename = '%s_net_%s.pth' % (epoch, name) - load_path = os.path.join(self.save_dir, load_filename) - net = getattr(self, 'net' + name) - if isinstance(net, torch.nn.DataParallel): - net = net.module - print('loading the model from %s' % load_path) - # if you are using PyTorch newer than 0.4 (e.g., built from - # GitHub source), you can remove str() on self.device - state_dict = torch.load(load_path, map_location=str(self.device)) - if hasattr(state_dict, '_metadata'): - del state_dict._metadata - - # patch InstanceNorm checkpoints prior to 0.4 - for key in list(state_dict.keys()): # need to copy keys here because we mutate in loop - self.__patch_instance_norm_state_dict(state_dict, net, key.split('.')) - net.load_state_dict(state_dict) - - def print_networks(self, verbose): - """Print the total number of parameters in the network and (if verbose) network architecture - - Parameters: - verbose (bool) -- if verbose: print the network architecture - """ - print('---------- Networks initialized -------------') - for name in self.model_names: - if isinstance(name, str): - net = getattr(self, 'net' + name) - num_params = 0 - for param in net.parameters(): - num_params += param.numel() - if verbose: - print(net) - print('[Network %s] Total number of parameters : %.3f M' % (name, num_params / 1e6)) - print('-----------------------------------------------') - - def set_requires_grad(self, nets, requires_grad=False): - """Set requies_grad=Fasle for all the networks to avoid unnecessary computations - Parameters: - nets (network list) -- a list of networks - requires_grad (bool) -- whether the networks require gradients or not - """ - if not isinstance(nets, list): - nets = [nets] - for net in nets: - if net is not None: - for param in net.parameters(): - param.requires_grad = requires_grad diff --git a/app/service/image2sketch/models/layer.py b/app/service/image2sketch/models/layer.py deleted file mode 100644 index df96a35..0000000 --- a/app/service/image2sketch/models/layer.py +++ /dev/null @@ -1,354 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - - -class CNR2d(nn.Module): - def __init__(self, nch_in, nch_out, kernel_size=4, stride=1, padding=1, norm='bnorm', relu=0.0, drop=[], bias=[]): - super().__init__() - - if bias == []: - if norm == 'bnorm': - bias = False - else: - bias = True - - layers = [] - layers += [Conv2d(nch_in, nch_out, kernel_size=kernel_size, stride=stride, padding=padding, bias=bias)] - - if norm != []: - layers += [Norm2d(nch_out, norm)] - - if relu != []: - layers += [ReLU(relu)] - - if drop != []: - layers += [nn.Dropout2d(drop)] - - self.cbr = nn.Sequential(*layers) - - def forward(self, x): - return self.cbr(x) - - -class DECNR2d(nn.Module): - def __init__(self, nch_in, nch_out, kernel_size=4, stride=1, padding=1, output_padding=0, norm='bnorm', relu=0.0, drop=[], bias=[]): - super().__init__() - - if bias == []: - if norm == 'bnorm': - bias = False - else: - bias = True - - layers = [] - layers += [Deconv2d(nch_in, nch_out, kernel_size=kernel_size, stride=stride, padding=padding, output_padding=output_padding, bias=bias)] - - if norm != []: - layers += [Norm2d(nch_out, norm)] - - if relu != []: - layers += [ReLU(relu)] - - if drop != []: - layers += [nn.Dropout2d(drop)] - - self.decbr = nn.Sequential(*layers) - - def forward(self, x): - return self.decbr(x) - - -class ResBlock(nn.Module): - def __init__(self, nch_in, nch_out, kernel_size=3, stride=1, padding=1, padding_mode='reflection', norm='inorm', relu=0.0, drop=[], bias=[]): - super().__init__() - - if bias == []: - if norm == 'bnorm': - bias = False - else: - bias = True - - layers = [] - - # 1st conv - layers += [Padding(padding, padding_mode=padding_mode)] - layers += [CNR2d(nch_in, nch_out, kernel_size=kernel_size, stride=stride, padding=0, norm=norm, relu=relu)] - - if drop != []: - layers += [nn.Dropout2d(drop)] - - # 2nd conv - layers += [Padding(padding, padding_mode=padding_mode)] - layers += [CNR2d(nch_in, nch_out, kernel_size=kernel_size, stride=stride, padding=0, norm=norm, relu=[])] - - self.resblk = nn.Sequential(*layers) - - def forward(self, x): - return x + self.resblk(x) - - -class ResBlock_cat(nn.Module): - def __init__(self, nch_in, nch_out, kernel_size=3, stride=1, padding=1, padding_mode='reflection', norm='inorm', relu=0.0, drop=[], bias=[]): - super().__init__() - - if bias == []: - if norm == 'bnorm': - bias = False - else: - bias = True - - layers = [] - - # 1st conv - layers += [Padding(padding, padding_mode=padding_mode)] - layers += [CNR2d(nch_in*2, nch_out, kernel_size=kernel_size, stride=stride, padding=0, norm=norm, relu=relu)] - - if drop != []: - layers += [nn.Dropout2d(drop)] - - # 2nd conv - layers += [Padding(padding, padding_mode=padding_mode)] - layers += [CNR2d(nch_in, nch_out, kernel_size=kernel_size, stride=stride, padding=0, norm=norm, relu=[])] - - self.resblk = nn.Sequential(*layers) - - def forward(self,x,y): - output = x + self.resblk(torch.cat([x,y],dim=1)) - return output - -class LinearBlock(nn.Module): - def __init__(self, input_dim, output_dim, norm='none', activation='relu'): - super(LinearBlock, self).__init__() - use_bias = True - # initialize fully connected layer - if norm == 'sn': - self.fc = SpectralNorm(nn.Linear(input_dim, output_dim, bias=use_bias)) - else: - self.fc = nn.Linear(input_dim, output_dim, bias=use_bias) - - # initialize normalization - norm_dim = output_dim - if norm == 'bn': - self.norm = nn.BatchNorm1d(norm_dim) - elif norm == 'in': - self.norm = nn.InstanceNorm1d(norm_dim) - elif norm == 'ln': - self.norm = LayerNorm(norm_dim) - elif norm == 'none' or norm == 'sn': - self.norm = None - else: - assert 0, "Unsupported normalization: {}".format(norm) - - # initialize activation - if activation == 'relu': - self.activation = nn.ReLU(inplace=True) - elif activation == 'lrelu': - self.activation = nn.LeakyReLU(0.2, inplace=True) - elif activation == 'prelu': - self.activation = nn.PReLU() - elif activation == 'selu': - self.activation = nn.SELU(inplace=True) - elif activation == 'tanh': - self.activation = nn.Tanh() - elif activation == 'none': - self.activation = None - else: - assert 0, "Unsupported activation: {}".format(activation) - - def forward(self, x): - out = self.fc(x) - if self.norm: - out = self.norm(out) - if self.activation: - out = self.activation(out) - return out - -class MLP(nn.Module): - def __init__(self, input_dim, output_dim, dim, n_blk, norm='none', activ='relu'): - - super(MLP, self).__init__() - self.model = [] - self.model += [LinearBlock(input_dim, dim, norm=norm, activation=activ)] - for i in range(n_blk - 2): - self.model += [LinearBlock(dim, dim, norm=norm, activation=activ)] - self.model += [LinearBlock(dim, output_dim, norm='none', activation='none')] # no output activations - self.model = nn.Sequential(*self.model) - - def forward(self, x): - return self.model(x.view(x.size(0), -1)) - -class CNR1d(nn.Module): - def __init__(self, nch_in, nch_out, norm='bnorm', relu=0.0, drop=[]): - super().__init__() - - if norm == 'bnorm': - bias = False - else: - bias = True - - layers = [] - layers += [nn.Linear(nch_in, nch_out, bias=bias)] - - if norm != []: - layers += [Norm2d(nch_out, norm)] - - if relu != []: - layers += [ReLU(relu)] - - if drop != []: - layers += [nn.Dropout2d(drop)] - - self.cbr = nn.Sequential(*layers) - - def forward(self, x): - return self.cbr(x) - - -class Conv2d(nn.Module): - def __init__(self, nch_in, nch_out, kernel_size=4, stride=1, padding=1, bias=True): - super(Conv2d, self).__init__() - self.conv = nn.Conv2d(nch_in, nch_out, kernel_size=kernel_size, stride=stride, padding=padding, bias=bias) - - def forward(self, x): - return self.conv(x) - - -class Deconv2d(nn.Module): - def __init__(self, nch_in, nch_out, kernel_size=4, stride=1, padding=1, output_padding=0, bias=True): - super(Deconv2d, self).__init__() - self.deconv = nn.ConvTranspose2d(nch_in, nch_out, kernel_size=kernel_size, stride=stride, padding=padding, output_padding=output_padding, bias=bias) - - # layers = [nn.Upsample(scale_factor=2, mode='bilinear'), - # nn.ReflectionPad2d(1), - # nn.Conv2d(nch_in , nch_out, kernel_size=3, stride=1, padding=0)] - # - # self.deconv = nn.Sequential(*layers) - - def forward(self, x): - return self.deconv(x) - - -class Linear(nn.Module): - def __init__(self, nch_in, nch_out): - super(Linear, self).__init__() - self.linear = nn.Linear(nch_in, nch_out) - - def forward(self, x): - return self.linear(x) - - -class Norm2d(nn.Module): - def __init__(self, nch, norm_mode): - super(Norm2d, self).__init__() - if norm_mode == 'bnorm': - self.norm = nn.BatchNorm2d(nch) - elif norm_mode == 'inorm': - self.norm = nn.InstanceNorm2d(nch) - - def forward(self, x): - return self.norm(x) - - -class ReLU(nn.Module): - def __init__(self, relu): - super(ReLU, self).__init__() - if relu > 0: - self.relu = nn.LeakyReLU(relu, True) - elif relu == 0: - self.relu = nn.ReLU(True) - - def forward(self, x): - return self.relu(x) - - -class Padding(nn.Module): - def __init__(self, padding, padding_mode='zeros', value=0): - super(Padding, self).__init__() - if padding_mode == 'reflection': - self. padding = nn.ReflectionPad2d(padding) - elif padding_mode == 'replication': - self.padding = nn.ReplicationPad2d(padding) - elif padding_mode == 'constant': - self.padding = nn.ConstantPad2d(padding, value) - elif padding_mode == 'zeros': - self.padding = nn.ZeroPad2d(padding) - - def forward(self, x): - return self.padding(x) - - -class Pooling2d(nn.Module): - def __init__(self, nch=[], pool=2, type='avg'): - super().__init__() - - if type == 'avg': - self.pooling = nn.AvgPool2d(pool) - elif type == 'max': - self.pooling = nn.MaxPool2d(pool) - elif type == 'conv': - self.pooling = nn.Conv2d(nch, nch, kernel_size=pool, stride=pool) - - def forward(self, x): - return self.pooling(x) - - -class UnPooling2d(nn.Module): - def __init__(self, nch=[], pool=2, type='nearest'): - super().__init__() - - if type == 'nearest': - self.unpooling = nn.Upsample(scale_factor=pool, mode='nearest', align_corners=True) - elif type == 'bilinear': - self.unpooling = nn.Upsample(scale_factor=pool, mode='bilinear', align_corners=True) - elif type == 'conv': - self.unpooling = nn.ConvTranspose2d(nch, nch, kernel_size=pool, stride=pool) - - def forward(self, x): - return self.unpooling(x) - - -class Concat(nn.Module): - def __init__(self): - super().__init__() - - def forward(self, x1, x2): - diffy = x2.size()[2] - x1.size()[2] - diffx = x2.size()[3] - x1.size()[3] - - x1 = F.pad(x1, [diffx // 2, diffx - diffx // 2, - diffy // 2, diffy - diffy // 2]) - - return torch.cat([x2, x1], dim=1) - - -class TV1dLoss(nn.Module): - def __init__(self): - super(TV1dLoss, self).__init__() - - def forward(self, input): - # loss = torch.mean(torch.abs(input[:, :, :, :-1] - input[:, :, :, 1:])) + \ - # torch.mean(torch.abs(input[:, :, :-1, :] - input[:, :, 1:, :])) - loss = torch.mean(torch.abs(input[:, :-1] - input[:, 1:])) - - return loss - - -class TV2dLoss(nn.Module): - def __init__(self): - super(TV2dLoss, self).__init__() - - def forward(self, input): - loss = torch.mean(torch.abs(input[:, :, :, :-1] - input[:, :, :, 1:])) + \ - torch.mean(torch.abs(input[:, :, :-1, :] - input[:, :, 1:, :])) - return loss - - -class SSIM2dLoss(nn.Module): - def __init__(self): - super(SSIM2dLoss, self).__init__() - - def forward(self, input, targer): - loss = 0 - return loss - diff --git a/app/service/image2sketch/models/networks.py b/app/service/image2sketch/models/networks.py deleted file mode 100644 index fc341c2..0000000 --- a/app/service/image2sketch/models/networks.py +++ /dev/null @@ -1,734 +0,0 @@ -import functools - -from torch.nn import init -from torch.optim import lr_scheduler - -from .layer import * - - -############################################################################### -# Helper Functions -############################################################################### - - -class Identity(nn.Module): - def forward(self, x): - return x - - -def get_norm_layer(norm_type='instance'): - """Return a normalization layer - - Parameters: - norm_type (str) -- the name of the normalization layer: batch | instance | none - - For BatchNorm, we use learnable affine parameters and track running statistics (mean/stddev). - For InstanceNorm, we do not use learnable affine parameters. We do not track running statistics. - """ - if norm_type == 'batch': - norm_layer = functools.partial(nn.BatchNorm2d, affine=True, track_running_stats=True) - elif norm_type == 'instance': - norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False) - elif norm_type == 'none': - def norm_layer(x): - return Identity() - else: - raise NotImplementedError('normalization layer [%s] is not found' % norm_type) - return norm_layer - - -def get_scheduler(optimizer, opt): - """Return a learning rate scheduler - - Parameters: - optimizer -- the optimizer of the network - opt (option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions．　 - opt.lr_policy is the name of learning rate policy: linear | step | plateau | cosine - - For 'linear', we keep the same learning rate for the first epochs - and linearly decay the rate to zero over the next epochs. - For other schedulers (step, plateau, and cosine), we use the default PyTorch schedulers. - See https://pytorch.org/docs/stable/optim.html for more details. - """ - if opt.lr_policy == 'linear': - def lambda_rule(epoch): - lr_l = 1.0 - max(0, epoch + opt.epoch_count - opt.n_epochs) / float(opt.n_epochs_decay + 1) - return lr_l - - scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule) - elif opt.lr_policy == 'step': - scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_decay_iters, gamma=0.1) - elif opt.lr_policy == 'plateau': - scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.2, threshold=0.01, patience=5) - elif opt.lr_policy == 'cosine': - scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=opt.n_epochs, eta_min=0) - else: - return NotImplementedError('learning rate policy [%s] is not implemented', opt.lr_policy) - return scheduler - - -def init_weights(net, init_type='normal', init_gain=0.02): - """Initialize network weights. - - Parameters: - net (network) -- network to be initialized - init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal - init_gain (float) -- scaling factor for normal, xavier and orthogonal. - - We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might - work better for some applications. Feel free to try yourself. - """ - - def init_func(m): # define the initialization function - classname = m.__class__.__name__ - if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1): - if init_type == 'normal': - init.normal_(m.weight.data, 0.0, init_gain) - elif init_type == 'xavier': - init.xavier_normal_(m.weight.data, gain=init_gain) - elif init_type == 'kaiming': - init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') - elif init_type == 'orthogonal': - init.orthogonal_(m.weight.data, gain=init_gain) - else: - raise NotImplementedError('initialization method [%s] is not implemented' % init_type) - if hasattr(m, 'bias') and m.bias is not None: - init.constant_(m.bias.data, 0.0) - elif classname.find('BatchNorm2d') != -1: # BatchNorm Layer's weight is not a matrix; only normal distribution applies. - init.normal_(m.weight.data, 1.0, init_gain) - init.constant_(m.bias.data, 0.0) - - print('initialize network with %s' % init_type) - net.apply(init_func) # apply the initialization function - - -def init_net(net, init_type='normal', init_gain=0.02, gpu_ids=[]): - """Initialize a network: 1. register CPU/GPU device (with multi-GPU support); 2. initialize the network weights - Parameters: - net (network) -- the network to be initialized - init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal - gain (float) -- scaling factor for normal, xavier and orthogonal. - gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2 - - Return an initialized network. - """ - if len(gpu_ids) > 0: - assert (torch.cuda.is_available()) - net.to(gpu_ids[0]) - net = torch.nn.DataParallel(net, gpu_ids) # multi-GPUs - init_weights(net, init_type, init_gain=init_gain) - return net - - -def define_G(input_nc, output_nc, ngf, netG, norm='batch', use_dropout=False, init_type='normal', init_gain=0.02, gpu_ids=[]): - net = None - norm_layer = get_norm_layer(norm_type=norm) - - if netG == 'ref_unpair_cbam_cat': - net = ref_unpair(input_nc, output_nc, ngf, norm='inorm', status='ref_unpair_cbam_cat') - elif netG == 'ref_unpair_recon': - net = ref_unpair(input_nc, output_nc, ngf, norm='inorm', status='ref_unpair_recon') - elif netG == 'triplet': - net = triplet(input_nc, output_nc, ngf, norm='inorm') - - else: - raise NotImplementedError('Generator model name [%s] is not recognized' % netG) - return init_net(net, init_type, init_gain, gpu_ids) - - -class AdaIN(nn.Module): - def __init__(self): - super().__init__() - - def forward(self, x, y): - eps = 1e-5 - mean_x = torch.mean(x, dim=[2, 3]) - mean_y = torch.mean(y, dim=[2, 3]) - - std_x = torch.std(x, dim=[2, 3]) - std_y = torch.std(y, dim=[2, 3]) - - mean_x = mean_x.unsqueeze(-1).unsqueeze(-1) - mean_y = mean_y.unsqueeze(-1).unsqueeze(-1) - - std_x = std_x.unsqueeze(-1).unsqueeze(-1) + eps - std_y = std_y.unsqueeze(-1).unsqueeze(-1) + eps - - out = (x - mean_x) / std_x * std_y + mean_y - - return out - - -class HED(nn.Module): - def __init__(self): - super(HED, self).__init__() - - self.moduleVggOne = nn.Sequential( - nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=1), - nn.ReLU(inplace=False), - nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1), - nn.ReLU(inplace=False) - ) - - self.moduleVggTwo = nn.Sequential( - nn.MaxPool2d(kernel_size=2, stride=2), - nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1), - nn.ReLU(inplace=False), - nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1), - nn.ReLU(inplace=False) - ) - - self.moduleVggThr = nn.Sequential( - nn.MaxPool2d(kernel_size=2, stride=2), - nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1), - nn.ReLU(inplace=False), - nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1), - nn.ReLU(inplace=False), - nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1), - nn.ReLU(inplace=False) - ) - - self.moduleVggFou = nn.Sequential( - nn.MaxPool2d(kernel_size=2, stride=2), - nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=1, padding=1), - nn.ReLU(inplace=False), - nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1), - nn.ReLU(inplace=False), - nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1), - nn.ReLU(inplace=False) - ) - - self.moduleVggFiv = nn.Sequential( - nn.MaxPool2d(kernel_size=2, stride=2), - nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1), - nn.ReLU(inplace=False), - nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1), - nn.ReLU(inplace=False), - nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1), - nn.ReLU(inplace=False) - ) - - self.moduleScoreOne = nn.Conv2d(in_channels=64, out_channels=1, kernel_size=1, stride=1, padding=0) - self.moduleScoreTwo = nn.Conv2d(in_channels=128, out_channels=1, kernel_size=1, stride=1, padding=0) - self.moduleScoreThr = nn.Conv2d(in_channels=256, out_channels=1, kernel_size=1, stride=1, padding=0) - self.moduleScoreFou = nn.Conv2d(in_channels=512, out_channels=1, kernel_size=1, stride=1, padding=0) - self.moduleScoreFiv = nn.Conv2d(in_channels=512, out_channels=1, kernel_size=1, stride=1, padding=0) - - self.moduleCombine = nn.Sequential( - nn.Conv2d(in_channels=5, out_channels=1, kernel_size=1, stride=1, padding=0), - nn.Sigmoid() - ) - - def forward(self, tensorInput): - tensorBlue = (tensorInput[:, 2:3, :, :] * 255.0) - 104.00698793 - tensorGreen = (tensorInput[:, 1:2, :, :] * 255.0) - 116.66876762 - tensorRed = (tensorInput[:, 0:1, :, :] * 255.0) - 122.67891434 - tensorInput = torch.cat([tensorBlue, tensorGreen, tensorRed], 1) - - tensorVggOne = self.moduleVggOne(tensorInput) - tensorVggTwo = self.moduleVggTwo(tensorVggOne) - tensorVggThr = self.moduleVggThr(tensorVggTwo) - tensorVggFou = self.moduleVggFou(tensorVggThr) - tensorVggFiv = self.moduleVggFiv(tensorVggFou) - - tensorScoreOne = self.moduleScoreOne(tensorVggOne) - tensorScoreTwo = self.moduleScoreTwo(tensorVggTwo) - tensorScoreThr = self.moduleScoreThr(tensorVggThr) - tensorScoreFou = self.moduleScoreFou(tensorVggFou) - tensorScoreFiv = self.moduleScoreFiv(tensorVggFiv) - - tensorScoreOne = nn.functional.interpolate(input=tensorScoreOne, size=(tensorInput.size(2), tensorInput.size(3)), mode='bilinear', align_corners=False) - tensorScoreTwo = nn.functional.interpolate(input=tensorScoreTwo, size=(tensorInput.size(2), tensorInput.size(3)), mode='bilinear', align_corners=False) - tensorScoreThr = nn.functional.interpolate(input=tensorScoreThr, size=(tensorInput.size(2), tensorInput.size(3)), mode='bilinear', align_corners=False) - tensorScoreFou = nn.functional.interpolate(input=tensorScoreFou, size=(tensorInput.size(2), tensorInput.size(3)), mode='bilinear', align_corners=False) - tensorScoreFiv = nn.functional.interpolate(input=tensorScoreFiv, size=(tensorInput.size(2), tensorInput.size(3)), mode='bilinear', align_corners=False) - - return self.moduleCombine(torch.cat([tensorScoreOne, tensorScoreTwo, tensorScoreThr, tensorScoreFou, tensorScoreFiv], 1)) - # return self.moduleCombine(torch.cat([ tensorScoreOne, tensorScoreTwo, tensorScoreThr, tensorScoreOne, tensorScoreTwo ], 1)) - - # return torch.sigmoid(tensorScoreOne),torch.sigmoid(tensorScoreTwo),torch.sigmoid(tensorScoreThr),torch.sigmoid(tensorScoreFou),torch.sigmoid(tensorScoreFiv),self.moduleCombine(torch.cat([ tensorScoreOne, tensorScoreTwo, tensorScoreThr, tensorScoreFou, tensorScoreFiv ], 1)) - # return torch.sigmoid(tensorScoreTwo) - - -def define_HED(init_weights_, gpu_ids_=[]): - net = HED() - - if len(gpu_ids_) > 0: - assert (torch.cuda.is_available()) - net.to(gpu_ids_[0]) - net = torch.nn.DataParallel(net, gpu_ids_) # multi-GPUs - - if not init_weights_ == None: - device = torch.device('cuda:{}'.format(gpu_ids_[0])) if gpu_ids_ else torch.device('cpu') - print('Loading model from: %s' % init_weights_) - state_dict = torch.load(init_weights_, map_location=str(device)) - if isinstance(net, torch.nn.DataParallel): - net.module.load_state_dict(state_dict) - else: - net.load_state_dict(state_dict) - print('load the weights successfully') - - return net - - -def define_styletps(init_weights_, gpu_ids_=[], shape=False): - net = None - if shape == False: - net = triplet() - if len(gpu_ids_) > 0: - assert (torch.cuda.is_available()) - net.to(gpu_ids_[0]) - net = torch.nn.DataParallel(net, gpu_ids_) # multi-GPUs - - if not init_weights_ == None: - device = torch.device('cuda:{}'.format(gpu_ids_[0])) if gpu_ids_ else torch.device('cpu') - print('Loading model from: %s' % init_weights_) - state_dict = torch.load(init_weights_, map_location=str(device)) - if isinstance(net, torch.nn.DataParallel): - net.module.load_state_dict(state_dict) - else: - net.load_state_dict(state_dict) - print('load the weights successfully') - - return net - - -class triplet(nn.Module): - def __init__(self): # mnblk=4 - super(triplet, self).__init__() - - # self.channels = nch_in - self.nch_in = 1 - self.nch_out = 1 - self.nch_ker = 64 - self.norm = 'bnorm' - # self.nblk = nblk - - if self.norm == 'bnorm': - self.bias = False - else: - self.bias = True - - self.conv0 = CNR2d(self.nch_in, self.nch_ker, kernel_size=7, stride=1, padding=3, norm=self.norm, relu=0.0) - self.conv1 = CNR2d(self.nch_ker, 2 * self.nch_ker, kernel_size=4, stride=2, padding=1, norm=self.norm, relu=0.0) - self.conv2 = CNR2d(2 * self.nch_ker, 4 * self.nch_ker, kernel_size=4, stride=2, padding=1, norm=self.norm, relu=0.0) - - self.final_pool = nn.AdaptiveAvgPool2d((1, 1)) - self.linear = nn.Linear(256, 128) - - def forward(self, x, y, z): - - x = self.conv0(x) - x = self.conv1(x) - x = self.conv2(x) - x = self.final_pool(x) - x = torch.flatten(x, 1) - x = self.linear(x) - - y = self.conv0(y) - y = self.conv1(y) - y = self.conv2(y) - y = self.final_pool(y) - y = torch.flatten(y, 1) - y = self.linear(y) - - z = self.conv0(z) - z = self.conv1(z) - z = self.conv2(z) - z = self.final_pool(z) - z = torch.flatten(z, 1) - z = self.linear(z) - - return x, y, z - - -class MLP(nn.Module): - def __init__(self, input_dim, output_dim, dim, n_blk, norm='none', activ='relu'): - super(MLP, self).__init__() - self.model = [] - self.model += [LinearBlock(input_dim, dim, norm=norm, activation=activ)] - for i in range(n_blk - 2): - self.model += [LinearBlock(dim, dim, norm=norm, activation=activ)] - self.model += [LinearBlock(dim, output_dim, norm='none', activation='none')] # no output activations - self.model = nn.Sequential(*self.model) - - def forward(self, x): - return self.model(x.view(x.size(0), -1)) - - -class ref_unpair(nn.Module): - def __init__(self, nch_in, nch_out, nch_ker=64, norm='bnorm', nblk=4, status='ref_unpair'): - super(ref_unpair, self).__init__() - - nch_ker = 64 - # self.channels = nch_in - self.nch_in = nch_in - self.nchs_in = 1 - self.status = status - - if self.status == 'ref_unpair_recon': - self.nch_out = 3 - self.nch_in = 1 - else: - self.nch_out = 1 - - self.nch_ker = nch_ker - self.norm = norm - self.nblk = nblk - self.dec0 = [] - - if status == 'ref_unpair_cbam_cat': - self.cbam_c = CBAM(nch_ker * 8, 16, 3, cbam_status="channel") - self.cbam_s = CBAM(nch_ker * 8, 16, 3, cbam_status="spatial") - - self.enc1_s = CNR2d(self.nchs_in, self.nch_ker, kernel_size=7, stride=1, padding=3, norm=self.norm, relu=0.0) - self.enc2_s = CNR2d(self.nch_ker, 2 * self.nch_ker, kernel_size=4, stride=2, padding=1, norm=self.norm, relu=0.0) - self.enc3_s = CNR2d(2 * self.nch_ker, 4 * self.nch_ker, kernel_size=4, stride=2, padding=1, norm=self.norm, relu=0.0) - self.enc4_s = CNR2d(4 * self.nch_ker, 8 * self.nch_ker, kernel_size=4, stride=2, padding=1, norm=self.norm, relu=0.0) - - if norm == 'bnorm': - self.bias = False - else: - self.bias = True - - self.enc1_c = CNR2d(self.nch_in, self.nch_ker, kernel_size=7, stride=1, padding=3, norm=self.norm, relu=0.0) - self.enc2_c = CNR2d(self.nch_ker, 2 * self.nch_ker, kernel_size=4, stride=2, padding=1, norm=self.norm, relu=0.0) - self.enc3_c = CNR2d(2 * self.nch_ker, 4 * self.nch_ker, kernel_size=4, stride=2, padding=1, norm=self.norm, relu=0.0) - self.enc4_c = CNR2d(4 * self.nch_ker, 8 * self.nch_ker, kernel_size=4, stride=2, padding=1, norm=self.norm, relu=0.0) - - if status == 'ref_unpair_cbam_cat': - self.res_cat1 = ResBlock_cat(8 * self.nch_ker, 8 * self.nch_ker, kernel_size=3, stride=1, padding=1, norm=self.norm, relu=0.0, padding_mode='reflection') - self.res_cat2 = ResBlock_cat(8 * self.nch_ker, 8 * self.nch_ker, kernel_size=3, stride=1, padding=1, norm=self.norm, relu=0.0, padding_mode='reflection') - self.res_cat3 = ResBlock_cat(8 * self.nch_ker, 8 * self.nch_ker, kernel_size=3, stride=1, padding=1, norm=self.norm, relu=0.0, padding_mode='reflection') - self.res_cat4 = ResBlock_cat(8 * self.nch_ker, 8 * self.nch_ker, kernel_size=3, stride=1, padding=1, norm=self.norm, relu=0.0, padding_mode='reflection') - - if self.nblk and status != 'ref_unpair_cbam_cat': - res = [] - for i in range(self.nblk): - res += [ResBlock(8 * self.nch_ker, 8 * self.nch_ker, kernel_size=3, stride=1, padding=1, norm=self.norm, relu=0.0, padding_mode='reflection')] - self.res1 = nn.Sequential(*res) - - # self.dec0 += [DECNR2d(16 * self.nch_ker, 8 * self.nch_ker, kernel_size=4, stride=2, padding=1, norm=self.norm, relu=0.0)] - self.dec0 += [DECNR2d(8 * self.nch_ker, 4 * self.nch_ker, kernel_size=4, stride=2, padding=1, norm=self.norm, relu=0.0)] - self.dec0 += [DECNR2d(4 * self.nch_ker, 2 * self.nch_ker, kernel_size=4, stride=2, padding=1, norm=self.norm, relu=0.0)] - self.dec0 += [DECNR2d(2 * self.nch_ker, 1 * self.nch_ker, kernel_size=4, stride=2, padding=1, norm=self.norm, relu=0.0)] - self.dec0 += [DECNR2d(1 * self.nch_ker, 1 * self.nch_ker, kernel_size=7, stride=1, padding=3, norm=self.norm, relu=0.0)] - self.dec0 += [nn.Conv2d(1 * self.nch_ker, self.nch_out, kernel_size=3, stride=1, padding=1)] - - self.dec = nn.Sequential(*self.dec0) - - def forward(self, content, style): - - content_cs = self.enc1_c(content) - content_cs = self.enc2_c(content_cs) - content_cs = self.enc3_c(content_cs) - content_cs = self.enc4_c(content_cs) - # content_cs = self.enc5_c(content_cs) - - if self.status == 'ref_unpair_cbam_cat': - cbam_content_cs = self.cbam_s(content_cs) - sp_content_cs = content_cs + cbam_content_cs - - style_cs = self.enc1_s(style) - style_cs = self.enc2_s(style_cs) - style_cs = self.enc3_s(style_cs) - style_cs = self.enc4_s(style_cs) - - cbam_style_cs = self.cbam_c(style_cs) - ch_style_cs = style_cs + cbam_style_cs - - content_output = self.adaptive_instance_normalization(content_cs, style_cs) - cbam_content_output = self.adaptive_instance_normalization(sp_content_cs, ch_style_cs) - - content_output = self.res_cat1(content_output, cbam_content_output) - content_output = self.res_cat2(content_output, cbam_content_output) - content_output = self.res_cat3(content_output, cbam_content_output) - content_output = self.res_cat4(content_output, cbam_content_output) - - - else: - content_output = content_cs - - if self.nblk and self.status != 'ref_unpair_cbam_cat': - content_cs = self.res1(content_output) - - content_output = self.dec(content_output) - - content_output = torch.tanh(content_output) - - return content_output - - def calc_mean_std(self, feat, eps=1e-5): - # eps is a small value added to the variance to avoid divide-by-zero. - size = feat.size() - assert (len(size) == 4) - N, C = size[:2] - feat_var = feat.view(N, C, -1).var(dim=2) + eps - feat_std = feat_var.sqrt().view(N, C, 1, 1) - feat_mean = feat.view(N, C, -1).mean(dim=2).view(N, C, 1, 1) - return feat_mean, feat_std - - def adaptive_instance_normalization(self, content_feat, style_feat): - assert (content_feat.size()[:2] == style_feat.size()[:2]) - size = content_feat.size() - style_mean, style_std = self.calc_mean_std(style_feat) - content_mean, content_std = self.calc_mean_std(content_feat) - - normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size) - return normalized_feat * style_std.expand(size) + style_mean.expand(size) - - -def define_D(input_nc, ndf, netD, n_layers_D=3, norm='batch', init_type='normal', init_gain=0.02, gpu_ids=[]): - net = None - norm_layer = get_norm_layer(norm_type=norm) - - if netD == 'basic': # default PatchGAN classifier - net = NLayerDiscriminator(input_nc, ndf, n_layers=3, norm_layer=norm_layer) - elif netD == 'n_layers': # more options - net = NLayerDiscriminator(input_nc, ndf, n_layers_D, norm_layer=norm_layer) - elif netD == 'pixel': # classify if each pixel is real or fake - net = PixelDiscriminator(input_nc, ndf, norm_layer=norm_layer) - else: - raise NotImplementedError('Discriminator model name [%s] is not recognized' % netD) - return init_net(net, init_type, init_gain, gpu_ids) - - -############################################################################## -# Classes -############################################################################## -class GANLoss(nn.Module): - """Define different GAN objectives. - - The GANLoss class abstracts away the need to create the target label tensor - that has the same size as the input. - """ - - def __init__(self, gan_mode, target_real_label=1.0, target_fake_label=0.0): - """ Initialize the GANLoss class. - - Parameters: - gan_mode (str) - - the type of GAN objective. It currently supports vanilla, lsgan, and wgangp. - target_real_label (bool) - - label for a real image - target_fake_label (bool) - - label of a fake image - - Note: Do not use sigmoid as the last layer of Discriminator. - LSGAN needs no sigmoid. vanilla GANs will handle it with BCEWithLogitsLoss. - """ - super(GANLoss, self).__init__() - self.register_buffer('real_label', torch.tensor(target_real_label)) - self.register_buffer('fake_label', torch.tensor(target_fake_label)) - self.gan_mode = gan_mode - if gan_mode == 'lsgan': - self.loss = nn.MSELoss() - elif gan_mode == 'vanilla': - self.loss = nn.BCEWithLogitsLoss() - elif gan_mode in ['wgangp']: - self.loss = None - else: - raise NotImplementedError('gan mode %s not implemented' % gan_mode) - - def get_target_tensor(self, prediction, target_is_real): - if target_is_real: - target_tensor = self.real_label - else: - target_tensor = self.fake_label - return target_tensor.expand_as(prediction) - - def __call__(self, prediction, target_is_real): - if self.gan_mode in ['lsgan', 'vanilla']: - target_tensor = self.get_target_tensor(prediction, target_is_real) - loss = self.loss(prediction, target_tensor) - elif self.gan_mode == 'wgangp': - if target_is_real: - loss = -prediction.mean() - else: - loss = prediction.mean() - return loss - - -def cal_gradient_penalty(netD, real_data, fake_data, device, type='mixed', constant=1.0, lambda_gp=10.0): - if lambda_gp > 0.0: - if type == 'real': # either use real images, fake images, or a linear interpolation of two. - interpolatesv = real_data - elif type == 'fake': - interpolatesv = fake_data - elif type == 'mixed': - alpha = torch.rand(real_data.shape[0], 1, device=device) - alpha = alpha.expand(real_data.shape[0], real_data.nelement() // real_data.shape[0]).contiguous().view(*real_data.shape) - interpolatesv = alpha * real_data + ((1 - alpha) * fake_data) - else: - raise NotImplementedError('{} not implemented'.format(type)) - interpolatesv.requires_grad_(True) - disc_interpolates = netD(interpolatesv) - gradients = torch.autograd.grad(outputs=disc_interpolates, inputs=interpolatesv, - grad_outputs=torch.ones(disc_interpolates.size()).to(device), - create_graph=True, retain_graph=True, only_inputs=True) - gradients = gradients[0].view(real_data.size(0), -1) # flat the data - gradient_penalty = (((gradients + 1e-16).norm(2, dim=1) - constant) ** 2).mean() * lambda_gp # added eps - return gradient_penalty, gradients - else: - return 0.0, None - - -class NLayerDiscriminator(nn.Module): - """Defines a PatchGAN discriminator""" - - def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d): - """Construct a PatchGAN discriminator - - Parameters: - input_nc (int) -- the number of channels in input images - ndf (int) -- the number of filters in the last conv layer - n_layers (int) -- the number of conv layers in the discriminator - norm_layer -- normalization layer - """ - super(NLayerDiscriminator, self).__init__() - if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm2d has affine parameters - use_bias = norm_layer.func == nn.InstanceNorm2d - else: - use_bias = norm_layer == nn.InstanceNorm2d - kw = 4 - padw = 1 - sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)] - nf_mult = 1 - nf_mult_prev = 1 - for n in range(1, n_layers): # gradually increase the number of filters - nf_mult_prev = nf_mult - nf_mult = min(2 ** n, 8) - sequence += [ - nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), - norm_layer(ndf * nf_mult), - nn.LeakyReLU(0.2, True) - ] - - nf_mult_prev = nf_mult - nf_mult = min(2 ** n_layers, 8) - sequence += [ - nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), - norm_layer(ndf * nf_mult), - nn.LeakyReLU(0.2, True) - ] - - sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] # output 1 channel prediction map - self.model = nn.Sequential(*sequence) - - def forward(self, input): - """Standard forward.""" - return self.model(input) - - -class PixelDiscriminator(nn.Module): - """Defines a 1x1 PatchGAN discriminator (pixelGAN)""" - - def __init__(self, input_nc, ndf=64, norm_layer=nn.BatchNorm2d): - """Construct a 1x1 PatchGAN discriminator - - Parameters: - input_nc (int) -- the number of channels in input images - ndf (int) -- the number of filters in the last conv layer - norm_layer -- normalization layer - """ - super(PixelDiscriminator, self).__init__() - if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm2d has affine parameters - use_bias = norm_layer.func == nn.InstanceNorm2d - else: - use_bias = norm_layer == nn.InstanceNorm2d - - self.net = [ - nn.Conv2d(input_nc, ndf, kernel_size=1, stride=1, padding=0), - nn.LeakyReLU(0.2, True), - nn.Conv2d(ndf, ndf * 2, kernel_size=1, stride=1, padding=0, bias=use_bias), - norm_layer(ndf * 2), - nn.LeakyReLU(0.2, True), - nn.Conv2d(ndf * 2, 1, kernel_size=1, stride=1, padding=0, bias=use_bias)] - - self.net = nn.Sequential(*self.net) - - def forward(self, input): - """Standard forward.""" - return self.net(input) - - -class CBAM(nn.Module): - def __init__(self, n_channels_in, reduction_ratio, kernel_size, cbam_status): - super(CBAM, self).__init__() - self.n_channels_in = n_channels_in - self.reduction_ratio = reduction_ratio - self.kernel_size = kernel_size - self.channel_attention = ChannelAttention_nopara(n_channels_in, reduction_ratio) - self.spatial_attention = SpatialAttention_nopara(kernel_size) - self.status = cbam_status - - def forward(self, x): - ## We don't use cbam in this version - if self.status == "cbam": - chan_att = self.channel_attention(x) - fp = chan_att * x - spat_att = self.spatial_attention(fp) - fpp = spat_att * fp - - if self.status == "spatial": - spat_att = self.spatial_attention(x) # * s_para_1d - fpp = spat_att * x - if self.status == "channel": - chan_att = self.channel_attention(x) # * c_para_1d - fpp = chan_att * x - - return fpp # ,c_wgt,s_wgt - - -class SpatialAttention_nopara(nn.Module): - def __init__(self, kernel_size): - super(SpatialAttention_nopara, self).__init__() - self.kernel_size = kernel_size - assert kernel_size % 2 == 1, "Odd kernel size required" - self.conv = nn.Conv2d(in_channels=2, out_channels=1, kernel_size=kernel_size, padding=int((kernel_size - 1) / 2)) - - def forward(self, x): - max_pool = self.agg_channel(x, "max") - avg_pool = self.agg_channel(x, "avg") - pool = torch.cat([max_pool, avg_pool], dim=1) - conv = self.conv(pool) - conv = conv.repeat(1, x.size()[1], 1, 1) - att = torch.sigmoid(conv) - return att - - def agg_channel(self, x, pool="max"): - b, c, h, w = x.size() - x = x.view(b, c, h * w) - x = x.permute(0, 2, 1) - if pool == "max": - x = F.max_pool1d(x, c) - elif pool == "avg": - x = F.avg_pool1d(x, c) - x = x.permute(0, 2, 1) - x = x.view(b, 1, h, w) - return x - - -class ChannelAttention_nopara(nn.Module): - def __init__(self, n_channels_in, reduction_ratio): - super(ChannelAttention_nopara, self).__init__() - self.n_channels_in = n_channels_in - self.reduction_ratio = reduction_ratio - self.middle_layer_size = int(self.n_channels_in / float(self.reduction_ratio)) - self.bottleneck = nn.Sequential( - nn.Linear(self.n_channels_in, self.middle_layer_size), - nn.ReLU(), - nn.Linear(self.middle_layer_size, self.n_channels_in) - ) - - def forward(self, x): - kernel = (x.size()[2], x.size()[3]) - avg_pool = F.avg_pool2d(x, kernel) - max_pool = F.max_pool2d(x, kernel) - avg_pool = avg_pool.view(avg_pool.size()[0], -1) - max_pool = max_pool.view(max_pool.size()[0], -1) - avg_pool_bck = self.bottleneck(avg_pool) - max_pool_bck = self.bottleneck(max_pool) - pool_sum = avg_pool_bck + max_pool_bck - sig_pool = torch.sigmoid(pool_sum) - sig_pool = sig_pool.unsqueeze(2).unsqueeze(3) - # out = sig_pool.repeat(1,1,kernel[0], kernel[1]) - - return sig_pool diff --git a/app/service/image2sketch/models/perceptual.py b/app/service/image2sketch/models/perceptual.py deleted file mode 100644 index 666fab8..0000000 --- a/app/service/image2sketch/models/perceptual.py +++ /dev/null @@ -1,86 +0,0 @@ -import torch -import torchvision - -class VGGPerceptualLoss(torch.nn.Module): - def __init__(self, resize=True): - super(VGGPerceptualLoss, self).__init__() - blocks = [] - blocks.append(torchvision.models.vgg16(pretrained=True).features[:4].eval()) - blocks.append(torchvision.models.vgg16(pretrained=True).features[4:9].eval()) - blocks.append(torchvision.models.vgg16(pretrained=True).features[9:16].eval()) - blocks.append(torchvision.models.vgg16(pretrained=True).features[16:23].eval()) - for bl in blocks: - for p in bl: - p.requires_grad = False - self.blocks = torch.nn.ModuleList(blocks) - self.transform = torch.nn.functional.interpolate - self.mean = torch.nn.Parameter(torch.tensor([0.485, 0.456, 0.406]).view(1,3,1,1)) - self.std = torch.nn.Parameter(torch.tensor([0.229, 0.224, 0.225]).view(1,3,1,1)) - self.resize = resize - - def forward(self, input, target, feature_layers=[0, 1, 2, 3], style_layers=[]): - if input.shape[1] != 3: - input = input.repeat(1, 3, 1, 1) - target = target.repeat(1, 3, 1, 1) - input = (input-self.mean) / self.std - target = (target-self.mean) / self.std - if self.resize: - input = self.transform(input, mode='bilinear', size=(224, 224), align_corners=False) - target = self.transform(target, mode='bilinear', size=(224, 224), align_corners=False) - loss = 0.0 - x = input - y = target - for i, block in enumerate(self.blocks): - x = block(x) - y = block(y) - if i in feature_layers: - loss += torch.nn.functional.l1_loss(x, y) - if i in style_layers: - act_x = x.reshape(x.shape[0], x.shape[1], -1) - act_y = y.reshape(y.shape[0], y.shape[1], -1) - gram_x = act_x @ act_x.permute(0, 2, 1) - gram_y = act_y @ act_y.permute(0, 2, 1) - loss += torch.nn.functional.l1_loss(gram_x, gram_y) - return loss - -class VGGstyleLoss(torch.nn.Module): - def __init__(self, resize=True): - super(VGGstyleLoss, self).__init__() - blocks = [] - blocks.append(torchvision.models.vgg16(pretrained=True).features[:4].eval()) - blocks.append(torchvision.models.vgg16(pretrained=True).features[4:9].eval()) - blocks.append(torchvision.models.vgg16(pretrained=True).features[9:16].eval()) - blocks.append(torchvision.models.vgg16(pretrained=True).features[16:23].eval()) - for bl in blocks: - for p in bl: - p.requires_grad = False - self.blocks = torch.nn.ModuleList(blocks) - self.transform = torch.nn.functional.interpolate - self.mean = torch.nn.Parameter(torch.tensor([0.485, 0.456, 0.406]).view(1,3,1,1)) - self.std = torch.nn.Parameter(torch.tensor([0.229, 0.224, 0.225]).view(1,3,1,1)) - self.resize = resize - - def forward(self, input, target, feature_layers=[0,1,2,3], style_layers=[]): - if input.shape[1] != 3: - input = input.repeat(1, 3, 1, 1) - target = target.repeat(1, 3, 1, 1) - input = (input-self.mean) / self.std - target = (target-self.mean) / self.std - if self.resize: - input = self.transform(input, mode='bilinear', size=(224, 224), align_corners=False) - target = self.transform(target, mode='bilinear', size=(224, 224), align_corners=False) - loss = 0.0 - x = input - y = target - for i, block in enumerate(self.blocks): - x = block(x) - y = block(y) - if i in feature_layers: - loss += torch.nn.functional.l1_loss(x, y) - if i in style_layers: - act_x = x.reshape(x.shape[0], x.shape[1], -1) - act_y = y.reshape(y.shape[0], y.shape[1], -1) - gram_x = act_x @ act_x.permute(0, 2, 1) - gram_y = act_y @ act_y.permute(0, 2, 1) - loss += torch.nn.functional.l1_loss(gram_x, gram_y) - return loss diff --git a/app/service/image2sketch/models/template_model.py b/app/service/image2sketch/models/template_model.py deleted file mode 100644 index 45c68b2..0000000 --- a/app/service/image2sketch/models/template_model.py +++ /dev/null @@ -1,82 +0,0 @@ -import torch -from .base_model import BaseModel -from . import networks - - -class TemplateModel(BaseModel): - @staticmethod - def modify_commandline_options(parser, is_train=True): - """Add new model-specific options and rewrite default values for existing options. - - Parameters: - parser -- the option parser - is_train -- if it is training phase or test phase. You can use this flag to add training-specific or test-specific options. - - Returns: - the modified parser. - """ - parser.set_defaults(dataset_mode='aligned') # You can rewrite default values for this model. For example, this model usually uses aligned dataset as its dataset. - if is_train: - parser.add_argument('--lambda_regression', type=float, default=1.0, help='weight for the regression loss') # You can define new arguments for this model. - - return parser - - def __init__(self, opt): - """Initialize this model class. - - Parameters: - opt -- training/test options - - A few things can be done here. - - (required) call the initialization function of BaseModel - - define loss function, visualization images, model names, and optimizers - """ - BaseModel.__init__(self, opt) # call the initialization method of BaseModel - # specify the training losses you want to print out. The program will call base_model.get_current_losses to plot the losses to the console and save them to the disk. - self.loss_names = ['loss_G'] - # specify the images you want to save and display. The program will call base_model.get_current_visuals to save and display these images. - self.visual_names = ['data_A', 'data_B', 'output'] - # specify the models you want to save to the disk. The program will call base_model.save_networks and base_model.load_networks to save and load networks. - # you can use opt.isTrain to specify different behaviors for training and test. For example, some networks will not be used during test, and you don't need to load them. - self.model_names = ['G'] - # define networks; you can use opt.isTrain to specify different behaviors for training and test. - self.netG = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG, gpu_ids=self.gpu_ids) - if self.isTrain: # only defined during training time - # define your loss functions. You can use losses provided by torch.nn such as torch.nn.L1Loss. - # We also provide a GANLoss class "networks.GANLoss". self.criterionGAN = networks.GANLoss().to(self.device) - self.criterionLoss = torch.nn.L1Loss() - # define and initialize optimizers. You can define one optimizer for each network. - # If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an example. - self.optimizer = torch.optim.Adam(self.netG.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999)) - self.optimizers = [self.optimizer] - - # Our program will automatically call to define schedulers, load networks, and print networks - - def set_input(self, input): - """Unpack input data from the dataloader and perform necessary pre-processing steps. - - Parameters: - input: a dictionary that contains the data itself and its metadata information. - """ - AtoB = self.opt.direction == 'AtoB' # use to swap data_A and data_B - self.data_A = input['A' if AtoB else 'B'].to(self.device) # get image data A - self.data_B = input['B' if AtoB else 'A'].to(self.device) # get image data B - self.image_paths = input['A_paths' if AtoB else 'B_paths'] # get image paths - - def forward(self): - """Run forward pass. This will be called by both functions and .""" - self.output = self.netG(self.data_A) # generate output image given the input data_A - - def backward(self): - """Calculate losses, gradients, and update network weights; called in every training iteration""" - # caculate the intermediate results if necessary; here self.output has been computed during function - # calculate loss given the input and intermediate results - self.loss_G = self.criterionLoss(self.output, self.data_B) * self.opt.lambda_regression - self.loss_G.backward() # calculate gradients of network G w.r.t. loss_G - - def optimize_parameters(self): - """Update network weights; it will be called in every training iteration.""" - self.forward() # first call forward to calculate intermediate results - self.optimizer.zero_grad() # clear network G's existing gradients - self.backward() # calculate gradients for network G - self.optimizer.step() # update gradients for network G diff --git a/app/service/image2sketch/models/test_model.py b/app/service/image2sketch/models/test_model.py deleted file mode 100644 index 2f70821..0000000 --- a/app/service/image2sketch/models/test_model.py +++ /dev/null @@ -1,45 +0,0 @@ -from .base_model import BaseModel -from . import networks - - -class TestModel(BaseModel): - """ This TesteModel can be used to generate CycleGAN results for only one direction. - This model will automatically set '--dataset_mode single', which only loads the images from one collection. - - See the test instruction for more details. - """ - @staticmethod - def modify_commandline_options(parser, is_train=True): - assert not is_train, 'TestModel cannot be used during training time' - parser.set_defaults(dataset_mode='single') - parser.add_argument('--model_suffix', type=str, default='', help='In checkpoints_dir, [epoch]_net_G[model_suffix].pth will be loaded as the generator.') - - return parser - - def __init__(self, opt): - assert(not opt.isTrain) - BaseModel.__init__(self, opt) - # specify the training losses you want to print out. The training/test scripts will call - self.loss_names = [] - # specify the images you want to save/display. The training/test scripts will call - self.visual_names = ['real', 'fake'] - # specify the models you want to save to the disk. The training/test scripts will call and - self.model_names = ['G' + opt.model_suffix] # only generator is needed. - self.netG = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG, - opt.norm, not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids) - - # assigns the model to self.netG_[suffix] so that it can be loaded - # please see - setattr(self, 'netG' + opt.model_suffix, self.netG) # store netG in self. - - def set_input(self, input): - self.real = input['A'].to(self.device) - self.image_paths = input['A_paths'] - - def forward(self): - """Run forward pass.""" - self.fake = self.netG(self.real) # G(real) - - def optimize_parameters(self): - """No optimization for test model.""" - pass diff --git a/app/service/image2sketch/models/triplet_model.py b/app/service/image2sketch/models/triplet_model.py deleted file mode 100644 index a667d49..0000000 --- a/app/service/image2sketch/models/triplet_model.py +++ /dev/null @@ -1,68 +0,0 @@ -import torch -from .base_model import BaseModel -from . import networks -from util.image_pool import ImagePool - - -class TripletModel(BaseModel): - - @staticmethod - def modify_commandline_options(parser, is_train=True): - parser.set_defaults(norm='batch', netG='triplet', dataset_mode='triplet') - if is_train: - parser.set_defaults(pool_size=0, gan_mode='vanilla') - parser.add_argument('--lambda_L1', type=float, default=100.0, help='weight for L1 loss') - - return parser - - def __init__(self, opt): - - BaseModel.__init__(self, opt) - - self.loss_names = ['G_triplet'] - self.visual_names = ['x','y'] - - if self.isTrain: - self.model_names = ['G'] - else: - self.model_names = ['G'] - self.netG = networks.define_G(1, 1, opt.ngf, opt.netG, opt.norm, - not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids) - - - if self.isTrain: - self.fake_A_pool = ImagePool(opt.pool_size) # create image buffer to store previously generated images - self.fake_B_pool = ImagePool(opt.pool_size) # create image buffer to store previously generated images - - self.criterionGAN = networks.GANLoss(opt.gan_mode).to(self.device) - self.criterionL1 = torch.nn.L1Loss() - - self.triplet = torch.nn.TripletMarginLoss(margin=3.0) - self.optimizer_G = torch.optim.Adam(self.netG.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999)) - self.optimizers.append(self.optimizer_G) - - def set_input(self, input): - AtoB = self.opt.direction == 'AtoB' - self.real_A = input['A' if AtoB else 'B'].to(self.device) - self.real_B = input['B' if AtoB else 'A'].to(self.device) - self.real_C = input['C'].to(self.device) - - self.image_paths = input['A_paths' if AtoB else 'B_paths'] - - - - def forward(self): - self.x,self.y,self.z = self.netG(self.real_A,self.real_B,self.real_C) - - - def backward_G(self): - self.loss_G_triplet_1 = self.triplet(self.x,self.y,self.z) - self.loss_G_triplet = self.loss_G_triplet_1 - - self.loss_G = self.loss_G_triplet - self.loss_G.backward() - - def optimize_parameters(self): - self.optimizer_G.zero_grad() - self.backward_G() - self.optimizer_G.step() diff --git a/app/service/image2sketch/models/unpaired_model.py b/app/service/image2sketch/models/unpaired_model.py deleted file mode 100644 index 9c043ca..0000000 --- a/app/service/image2sketch/models/unpaired_model.py +++ /dev/null @@ -1,144 +0,0 @@ -import torch - -from . import networks -from .base_model import BaseModel -from .perceptual import VGGPerceptualLoss -from ..util.image_pool import ImagePool - - -class UnpairedModel(BaseModel): - - @staticmethod - def modify_commandline_options(parser, is_train=True): - parser.set_defaults(norm='batch', netG='ref_unpair_cbam_cat', netG2='ref_unpair_recon', dataset_mode='unaligned') - if is_train: - parser.set_defaults(pool_size=0, gan_mode='vanilla') - parser.add_argument('--lambda_L1', type=float, default=100.0, help='weight for L1 loss') - - return parser - - def __init__(self, opt): - BaseModel.__init__(self, opt) - # specify the training losses you want to print out. The training/test scripts will call - self.loss_names = ['G_GAN', 'G_L1_1', 'G_Rec', 'G_line', 'D_real', 'D_fake'] - self.visual_names = ['real_A', 'content_output', 'real_B'] - - if self.isTrain: - self.model_names = ['G_A', 'G_B', 'D'] - else: # during test time, only load G - self.model_names = ['G_A', 'G_B'] - # define networks (both generator and discriminator) - self.netG_A = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG, opt.norm, - not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids) - self.netG_B = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG2, opt.norm, - not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids) - - if self.isTrain: # define a discriminator; conditional GANs need to take both input and output images; Therefore, #channels for D is input_nc + output_nc - self.netD = networks.define_D(1, opt.ndf, opt.netD, - opt.n_layers_D, opt.norm, opt.init_type, opt.init_gain, self.gpu_ids) - self.styletps = networks.define_styletps(init_weights_='./checkpoints/contrastive_pretrained.pth', gpu_ids_=self.gpu_ids, shape=False) - self.HED = networks.define_HED(init_weights_='./checkpoints/network-bsds500.pytorch', gpu_ids_=self.gpu_ids) - - if self.isTrain: # define discriminators - self.netD_A = networks.define_D(opt.output_nc, opt.ndf, opt.netD, - opt.n_layers_D, opt.norm, opt.init_type, opt.init_gain, self.gpu_ids) - self.netD_B = networks.define_D(opt.input_nc, opt.ndf, opt.netD, - opt.n_layers_D, opt.norm, opt.init_type, opt.init_gain, self.gpu_ids) - - if self.isTrain: - self.fake_A_pool = ImagePool(opt.pool_size) # create image buffer to store previously generated images - self.fake_B_pool = ImagePool(opt.pool_size) # create image buffer to store previously generated images - # define loss functions - self.criterionGAN = networks.GANLoss(opt.gan_mode).to(self.device) - self.criterionL1_1 = torch.nn.L1Loss() - self.criterionL1_2 = torch.nn.L1Loss() - self.criterionL1_3 = torch.nn.L1Loss() - self.per_loss_1 = VGGPerceptualLoss().to(self.device) - self.per_loss_2 = VGGPerceptualLoss().to(self.device) - self.per_loss_3 = VGGPerceptualLoss().to(self.device) - - self.optimizer_GA = torch.optim.Adam(self.netG_A.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999)) - self.optimizer_GB = torch.optim.Adam(self.netG_B.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999)) - - self.optimizer_D = torch.optim.Adam(self.netD.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999)) - self.optimizers.append(self.optimizer_GA) - self.optimizers.append(self.optimizer_GB) - - self.optimizers.append(self.optimizer_D) - - def set_input(self, input): - """Unpack input data from the dataloader and perform necessary pre-processing steps. - - Parameters: - input (dict): include the data itself and its metadata information. - - The option 'direction' can be used to swap images in domain A and domain B. - """ - AtoB = self.opt.direction == 'AtoB' - self.real_A = input['A' if AtoB else 'B'].to(self.device) - self.real_B = input['B' if AtoB else 'A'].to(self.device) - # self.image_paths = input['A_paths' if AtoB else 'B_paths'] - - def forward(self): - """Run forward pass; called by both functions and .""" - self.content_output = self.netG_A(self.real_A, self.real_B) - self.rec_output = self.netG_B(self.content_output, self.content_output) - - def update_process(self, epoch, total_epoch): - self.epoch_count = epoch - self.epoch_count_total = total_epoch - - def backward_D(self): - """Calculate GAN loss for the discriminator - - Parameters: - netD (network) -- the discriminator D - real (tensor array) -- real images - fake (tensor array) -- images generated by a generator - - Return the discriminator loss. - We also call loss_D.backward() to calculate the gradients. - """ - # Real - pred_real = self.netD(self.real_B) - self.loss_D_real = self.criterionGAN(pred_real, True) - # Fake - pred_fake = self.netD(self.content_output.detach()) - self.loss_D_fake = self.criterionGAN(pred_fake, False) - # Combined loss and calculate gradients - loss_D = (self.loss_D_real + self.loss_D_fake) * 0.5 - loss_D.backward() - return loss_D - - def backward_G(self): - """Calculate GAN and L1 loss for the generator""" - - pred_fake = self.netD(self.content_output) - self.loss_G_GAN = self.criterionGAN(pred_fake, True) - - self.content_output_line = self.HED(self.real_A) - self.rec_output_line = self.HED(self.rec_output) - self.t1, self.t2, _ = self.styletps(self.content_output, self.real_B, self.real_B) - - decay_lambda = 5 - ((self.epoch_count * 4.5) / self.epoch_count_total) - self.loss_G_L1_1 = self.criterionL1_1(self.t1, self.t2) * 10 - self.loss_G_Rec = self.per_loss_2(self.real_A, self.rec_output) * decay_lambda - self.loss_G_line = self.per_loss_3(self.content_output_line, self.rec_output_line) * decay_lambda - - self.loss_G = self.loss_G_GAN + self.loss_G_L1_1 + self.loss_G_Rec + self.loss_G_line - self.loss_G.backward() - - def optimize_parameters(self): - self.forward() # compute fake images: G(A) - # update D - self.set_requires_grad(self.netD, True) # enable backprop for D - self.optimizer_D.zero_grad() # set D's gradients to zero - self.backward_D() # calculate gradients for backward_D_unsuper - self.optimizer_D.step() # update D's weights - # update G - self.set_requires_grad(self.netD, False) # D requires no gradients when optimizing G - self.optimizer_GA.zero_grad() # set G's gradients to zero - self.optimizer_GB.zero_grad() # set G's gradients to zero - self.backward_G() # calculate graidents for G - self.optimizer_GA.step() # udpate G's weights - self.optimizer_GB.step() # udpate G's weights diff --git a/app/service/image2sketch/opt.py b/app/service/image2sketch/opt.py deleted file mode 100644 index eb453fb..0000000 --- a/app/service/image2sketch/opt.py +++ /dev/null @@ -1,57 +0,0 @@ -from app.core.config import DEBUG - - -class Config: - def __init__(self): - # 基本参数 - self.dataroot = "app/service/image2sketch/datasets/ref_unpair" - self.name = 'semi_unpair' - self.gpu_ids = [0] - # 模型参数 - self.model = 'unpaired' - self.input_nc = 3 - self.output_nc = 3 - self.ngf = 64 - self.ndf = 64 - self.netD = 'basic' - self.netG = 'ref_unpair_cbam_cat' - self.netG2 = 'ref_unpair_recon' - self.n_layers_D = 3 - self.norm = 'instance' - self.init_type = 'normal' - self.init_gain = 0.02 - self.no_dropout = False # 对应 `--no_dropout` - # 数据集参数 - self.dataset_mode = 'single' - self.direction = 'AtoB' - self.serial_batches = True # 对应 `--serial_batches` - self.num_threads = 4 - self.batch_size = 4 - self.load_size = 512 - self.crop_size = 512 - self.max_dataset_size = float("inf") - self.preprocess = 'resize_and_crop' - self.no_flip = False # 对应 `--no_flip` - self.display_winsize = 256 - # 额外参数 - self.epoch = '100' - self.load_iter = 0 - self.verbose = False # 对应 `--verbose` - self.suffix = '' - self.isTrain = False - self.results_dir = 'service/image2sketch/results' - self.aspect_ratio = 1.0 - self.phase = 'test' - self.eval = False - self.num_test = 1000 - self.morm = 'batch' - if DEBUG: - self.style_image1 = "service/image2sketch/datasets/ref_unpair/testC/style_1.jpg" - self.style_image2 = "service/image2sketch/datasets/ref_unpair/testC/style_2.jpeg" - self.style_image3 = "service/image2sketch/datasets/ref_unpair/testC/style_3.png" - self.checkpoints_dir = 'service/image2sketch/checkpoints/' - else: - self.checkpoints_dir = 'app/service/image2sketch/checkpoints/' - self.style_image1 = "app/service/image2sketch/datasets/ref_unpair/testC/style_1.jpg" - self.style_image2 = "app/service/image2sketch/datasets/ref_unpair/testC/style_2.jpeg" - self.style_image3 = "app/service/image2sketch/datasets/ref_unpair/testC/style_3.png" diff --git a/app/service/image2sketch/server.py b/app/service/image2sketch/server.py deleted file mode 100644 index 3094eea..0000000 --- a/app/service/image2sketch/server.py +++ /dev/null @@ -1,88 +0,0 @@ -import logging - -import cv2 -import numpy as np -import torch -import torchvision.transforms as transforms -from PIL import Image - -from app.schemas.image2sketch import Image2SketchModel -from app.service.image2sketch.infer import tensor2im -from app.service.image2sketch.models import create_model -from app.service.image2sketch.opt import Config -from app.service.utils.oss_client import oss_get_image, oss_upload_image - -logger = logging.getLogger() - - -def tensor2im(input_image, imtype=np.uint8): - if not isinstance(input_image, np.ndarray): - if isinstance(input_image, torch.Tensor): # get the data from a variable - image_tensor = input_image.data - else: - return input_image - image_numpy = image_tensor[0].cpu().float().numpy() # convert it into a numpy array - if image_numpy.shape[0] == 1: # grayscale to RGB - image_numpy = np.tile(image_numpy, (3, 1, 1)) - image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 # post-processing: tranpose and scaling - else: # if it is a numpy array, do nothing - image_numpy = input_image - return image_numpy.astype(imtype) - - -class Image2SketchServer: - def __init__(self, request_data): - self.image_url = request_data.image_url - self.style_image_url = request_data.style_image_url - self.sketch_bucket = request_data.sketch_bucket - self.sketch_name = request_data.sketch_name - self.opt = Config() - self.opt.num_threads = 0 # test code only supports num_threads = 0 - self.opt.batch_size = 1 # test code only supports batch_size = 1 - self.opt.serial_batches = True # disable data shuffling; comment this line if results on randomly chosen images are needed. - self.opt.no_flip = True # no flip; comment this line if results on flipped images are needed. - self.opt.display_id = -1 # no visdom display; the test code saves the results to a HTML file. - self.data = {} - device = torch.device("cuda:0") - self.model = create_model(self.opt) - self.model.setup(self.opt) - transform_list = [transforms.ToTensor(), transforms.Normalize([0.5], [0.5])] - transform = transforms.Compose(transform_list) - if request_data.default_style == "1": - style_img = Image.open(self.opt.style_image1).convert('L') - elif request_data.default_style == "2": - style_img = Image.open(self.opt.style_image2).convert('L') - elif request_data.default_style == "3": - style_img = Image.open(self.opt.style_image3).convert('L') - else: - style_img = oss_get_image(bucket=self.style_image_url.split('/')[0], object_name=self.style_image_url[self.style_image_url.find('/') + 1:], data_type="PIL") - style_img = style_img.convert('L') - style_img = transform(style_img) - self.data['B'] = style_img - self.data['B'] = self.data['B'].unsqueeze(0).to(device) - A, self.width, self.height = self.get_image(self.image_url) - self.data['A'] = transform(A) - self.data['A'] = self.data['A'].unsqueeze(0).to(device) - - def get_result(self): - self.model.set_input(self.data) - self.model.test() # run inference - visuals = self.model.get_current_visuals() # get image results - image_numpy = tensor2im(visuals['content_output'].cpu()) - image_bytes = cv2.imencode(".jpg", image_numpy)[1].tobytes() - req = oss_upload_image(bucket=self.sketch_bucket, object_name=self.sketch_name, image_bytes=image_bytes) - return f"{req.bucket_name}/{req.object_name}" - - def get_image(self, image_url): - image = oss_get_image(bucket=image_url.split('/')[0], object_name=image_url[image_url.find('/') + 1:], data_type="PIL") - image = image.convert('RGB') - width = image.size[0] - height = image.size[1] - return image, width, height - - -if __name__ == '__main__': - data = Image2SketchModel(image_url="test/real_Dress_790b2c6e370644e134df7abdfe7e54d9.jpg_Img.jpg", sketch_bucket="test", sketch_name="test123.jpg") - server = Image2SketchServer(data) - sketch_url = server.get_result() - print(sketch_url) diff --git a/app/service/image2sketch/util/__init__.py b/app/service/image2sketch/util/__init__.py deleted file mode 100644 index ae36f63..0000000 --- a/app/service/image2sketch/util/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""This package includes a miscellaneous collection of useful helper functions.""" diff --git a/app/service/image2sketch/util/get_data.py b/app/service/image2sketch/util/get_data.py deleted file mode 100644 index 97edc3c..0000000 --- a/app/service/image2sketch/util/get_data.py +++ /dev/null @@ -1,110 +0,0 @@ -from __future__ import print_function -import os -import tarfile -import requests -from warnings import warn -from zipfile import ZipFile -from bs4 import BeautifulSoup -from os.path import abspath, isdir, join, basename - - -class GetData(object): - """A Python script for downloading CycleGAN or pix2pix datasets. - - Parameters: - technique (str) -- One of: 'cyclegan' or 'pix2pix'. - verbose (bool) -- If True, print additional information. - - Examples: - >>> from util.get_data import GetData - >>> gd = GetData(technique='cyclegan') - >>> new_data_path = gd.get(save_path='./datasets') # options will be displayed. - - Alternatively, You can use bash scripts: 'scripts/download_pix2pix_model.sh' - and 'scripts/download_cyclegan_model.sh'. - """ - - def __init__(self, technique='cyclegan', verbose=True): - url_dict = { - 'pix2pix': 'http://efrosgans.eecs.berkeley.edu/pix2pix/datasets/', - 'cyclegan': 'https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets' - } - self.url = url_dict.get(technique.lower()) - self._verbose = verbose - - def _print(self, text): - if self._verbose: - print(text) - - @staticmethod - def _get_options(r): - soup = BeautifulSoup(r.text, 'lxml') - options = [h.text for h in soup.find_all('a', href=True) - if h.text.endswith(('.zip', 'tar.gz'))] - return options - - def _present_options(self): - r = requests.get(self.url) - options = self._get_options(r) - print('Options:\n') - for i, o in enumerate(options): - print("{0}: {1}".format(i, o)) - choice = input("\nPlease enter the number of the " - "dataset above you wish to download:") - return options[int(choice)] - - def _download_data(self, dataset_url, save_path): - if not isdir(save_path): - os.makedirs(save_path) - - base = basename(dataset_url) - temp_save_path = join(save_path, base) - - with open(temp_save_path, "wb") as f: - r = requests.get(dataset_url) - f.write(r.content) - - if base.endswith('.tar.gz'): - obj = tarfile.open(temp_save_path) - elif base.endswith('.zip'): - obj = ZipFile(temp_save_path, 'r') - else: - raise ValueError("Unknown File Type: {0}.".format(base)) - - self._print("Unpacking Data...") - obj.extractall(save_path) - obj.close() - os.remove(temp_save_path) - - def get(self, save_path, dataset=None): - """ - - Download a dataset. - - Parameters: - save_path (str) -- A directory to save the data to. - dataset (str) -- (optional). A specific dataset to download. - Note: this must include the file extension. - If None, options will be presented for you - to choose from. - - Returns: - save_path_full (str) -- the absolute path to the downloaded data. - - """ - if dataset is None: - selected_dataset = self._present_options() - else: - selected_dataset = dataset - - save_path_full = join(save_path, selected_dataset.split('.')[0]) - - if isdir(save_path_full): - warn("\n'{0}' already exists. Voiding Download.".format( - save_path_full)) - else: - self._print('Downloading Data...') - url = "{0}/{1}".format(self.url, selected_dataset) - self._download_data(url, save_path=save_path) - - return abspath(save_path_full) diff --git a/app/service/image2sketch/util/html.py b/app/service/image2sketch/util/html.py deleted file mode 100644 index cc3262a..0000000 --- a/app/service/image2sketch/util/html.py +++ /dev/null @@ -1,86 +0,0 @@ -import dominate -from dominate.tags import meta, h3, table, tr, td, p, a, img, br -import os - - -class HTML: - """This HTML class allows us to save images and write texts into a single HTML file. - - It consists of functions such as (add a text header to the HTML file), - (add a row of images to the HTML file), and (save the HTML to the disk). - It is based on Python library 'dominate', a Python library for creating and manipulating HTML documents using a DOM API. - """ - - def __init__(self, web_dir, title, refresh=0): - """Initialize the HTML classes - - Parameters: - web_dir (str) -- a directory that stores the webpage. HTML file will be created at /index.html; images will be saved at 0: - with self.doc.head: - meta(http_equiv="refresh", content=str(refresh)) - - def get_image_dir(self): - """Return the directory that stores images""" - return self.img_dir - - def add_header(self, text): - """Insert a header to the HTML file - - Parameters: - text (str) -- the header text - """ - with self.doc: - h3(text) - - def add_images(self, ims, txts, links, width=400): - """add images to the HTML file - - Parameters: - ims (str list) -- a list of image paths - txts (str list) -- a list of image names shown on the website - links (str list) -- a list of hyperref links; when you click an image, it will redirect you to a new page - """ - self.t = table(border=1, style="table-layout: fixed;") # Insert a table - self.doc.add(self.t) - with self.t: - with tr(): - for im, txt, link in zip(ims, txts, links): - with td(style="word-wrap: break-word;", halign="center", valign="top"): - with p(): - with a(href=os.path.join('images', link)): - img(style="width:%dpx" % width, src=os.path.join('images', im)) - br() - p(txt) - - def save(self): - """save the current content to the HMTL file""" - html_file = '%s/index.html' % self.web_dir - f = open(html_file, 'wt') - f.write(self.doc.render()) - f.close() - - -if __name__ == '__main__': # we show an example usage here. - html = HTML('web/', 'test_html') - html.add_header('hello world') - - ims, txts, links = [], [], [] - for n in range(4): - ims.append('image_%d.png' % n) - txts.append('text_%d' % n) - links.append('image_%d.png' % n) - html.add_images(ims, txts, links) - html.save() diff --git a/app/service/image2sketch/util/image_pool.py b/app/service/image2sketch/util/image_pool.py deleted file mode 100644 index 6d086f8..0000000 --- a/app/service/image2sketch/util/image_pool.py +++ /dev/null @@ -1,54 +0,0 @@ -import random -import torch - - -class ImagePool(): - """This class implements an image buffer that stores previously generated images. - - This buffer enables us to update discriminators using a history of generated images - rather than the ones produced by the latest generators. - """ - - def __init__(self, pool_size): - """Initialize the ImagePool class - - Parameters: - pool_size (int) -- the size of image buffer, if pool_size=0, no buffer will be created - """ - self.pool_size = pool_size - if self.pool_size > 0: # create an empty pool - self.num_imgs = 0 - self.images = [] - - def query(self, images): - """Return an image from the pool. - - Parameters: - images: the latest generated images from the generator - - Returns images from the buffer. - - By 50/100, the buffer will return input images. - By 50/100, the buffer will return images previously stored in the buffer, - and insert the current images to the buffer. - """ - if self.pool_size == 0: # if the buffer size is 0, do nothing - return images - return_images = [] - for image in images: - image = torch.unsqueeze(image.data, 0) - if self.num_imgs < self.pool_size: # if the buffer is not full; keep inserting current images to the buffer - self.num_imgs = self.num_imgs + 1 - self.images.append(image) - return_images.append(image) - else: - p = random.uniform(0, 1) - if p > 0.5: # by 50% chance, the buffer will return a previously stored image, and insert the current image into the buffer - random_id = random.randint(0, self.pool_size - 1) # randint is inclusive - tmp = self.images[random_id].clone() - self.images[random_id] = image - return_images.append(tmp) - else: # by another 50% chance, the buffer will return the current image - return_images.append(image) - return_images = torch.cat(return_images, 0) # collect all the images and return - return return_images diff --git a/app/service/image2sketch/util/util.py b/app/service/image2sketch/util/util.py deleted file mode 100644 index b050c13..0000000 --- a/app/service/image2sketch/util/util.py +++ /dev/null @@ -1,103 +0,0 @@ -"""This module contains simple helper functions """ -from __future__ import print_function -import torch -import numpy as np -from PIL import Image -import os - - -def tensor2im(input_image, imtype=np.uint8): - """"Converts a Tensor array into a numpy image array. - - Parameters: - input_image (tensor) -- the input image tensor array - imtype (type) -- the desired type of the converted numpy array - """ - if not isinstance(input_image, np.ndarray): - if isinstance(input_image, torch.Tensor): # get the data from a variable - image_tensor = input_image.data - else: - return input_image - image_numpy = image_tensor[0].cpu().float().numpy() # convert it into a numpy array - if image_numpy.shape[0] == 1: # grayscale to RGB - image_numpy = np.tile(image_numpy, (3, 1, 1)) - image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 # post-processing: tranpose and scaling - else: # if it is a numpy array, do nothing - image_numpy = input_image - return image_numpy.astype(imtype) - - -def diagnose_network(net, name='network'): - """Calculate and print the mean of average absolute(gradients) - - Parameters: - net (torch network) -- Torch network - name (str) -- the name of the network - """ - mean = 0.0 - count = 0 - for param in net.parameters(): - if param.grad is not None: - mean += torch.mean(torch.abs(param.grad.data)) - count += 1 - if count > 0: - mean = mean / count - print(name) - print(mean) - - -def save_image(image_numpy, image_path, aspect_ratio=1.0): - """Save a numpy image to the disk - - Parameters: - image_numpy (numpy array) -- input numpy array - image_path (str) -- the path of the image - """ - - image_pil = Image.fromarray(image_numpy) - h, w, _ = image_numpy.shape - - if aspect_ratio > 1.0: - image_pil = image_pil.resize((h, int(w * aspect_ratio)), Image.BICUBIC) - if aspect_ratio < 1.0: - image_pil = image_pil.resize((int(h / aspect_ratio), w), Image.BICUBIC) - image_pil.save(image_path) - - -def print_numpy(x, val=True, shp=False): - """Print the mean, min, max, median, std, and size of a numpy array - - Parameters: - val (bool) -- if print the values of the numpy array - shp (bool) -- if print the shape of the numpy array - """ - x = x.astype(np.float64) - if shp: - print('shape,', x.shape) - if val: - x = x.flatten() - print('mean = %3.3f, min = %3.3f, max = %3.3f, median = %3.3f, std=%3.3f' % ( - np.mean(x), np.min(x), np.max(x), np.median(x), np.std(x))) - - -def mkdirs(paths): - """create empty directories if they don't exist - - Parameters: - paths (str list) -- a list of directory paths - """ - if isinstance(paths, list) and not isinstance(paths, str): - for path in paths: - mkdir(path) - else: - mkdir(paths) - - -def mkdir(path): - """create a single empty directory if it didn't exist - - Parameters: - path (str) -- a single directory path - """ - if not os.path.exists(path): - os.makedirs(path) diff --git a/app/service/image2sketch/util/visualizer.py b/app/service/image2sketch/util/visualizer.py deleted file mode 100644 index 239c5ee..0000000 --- a/app/service/image2sketch/util/visualizer.py +++ /dev/null @@ -1,223 +0,0 @@ -import numpy as np -import os -import sys -import ntpath -import time -from . import util, html -from subprocess import Popen, PIPE - - -if sys.version_info[0] == 2: - VisdomExceptionBase = Exception -else: - VisdomExceptionBase = ConnectionError - - -def save_images(webpage, visuals, image_path, aspect_ratio=1.0, width=256): - """Save images to the disk. - - Parameters: - webpage (the HTML class) -- the HTML webpage class that stores these imaegs (see html.py for more details) - visuals (OrderedDict) -- an ordered dictionary that stores (name, images (either tensor or numpy) ) pairs - image_path (str) -- the string is used to create image paths - aspect_ratio (float) -- the aspect ratio of saved images - width (int) -- the images will be resized to width x width - - This function will save images stored in 'visuals' to the HTML file specified by 'webpage'. - """ - image_dir = webpage.get_image_dir() - short_path = ntpath.basename(image_path[0]) - name = os.path.splitext(short_path)[0] - - webpage.add_header(name) - ims, txts, links = [], [], [] - - for label, im_data in visuals.items(): - im = util.tensor2im(im_data) - image_name = '%s_%s.png' % (name, label) - save_path = os.path.join(image_dir, image_name) - util.save_image(im, save_path, aspect_ratio=aspect_ratio) - ims.append(image_name) - txts.append(label) - links.append(image_name) - webpage.add_images(ims, txts, links, width=width) - - -class Visualizer(): - """This class includes several functions that can display/save images and print/save logging information. - - It uses a Python library 'visdom' for display, and a Python library 'dominate' (wrapped in 'HTML') for creating HTML files with images. - """ - - def __init__(self, opt): - """Initialize the Visualizer class - - Parameters: - opt -- stores all the experiment flags; needs to be a subclass of BaseOptions - Step 1: Cache the training/test options - Step 2: connect to a visdom server - Step 3: create an HTML object for saveing HTML filters - Step 4: create a logging file to store training losses - """ - self.opt = opt # cache the option - self.display_id = opt.display_id - self.use_html = opt.isTrain and not opt.no_html - self.win_size = opt.display_winsize - self.name = opt.name - self.port = opt.display_port - self.saved = False - ''' - if self.display_id > 0: # connect to a visdom server given and - import visdom - self.ncols = opt.display_ncols - self.vis = visdom.Visdom(server=opt.display_server, port=opt.display_port, env=opt.display_env) - if not self.vis.check_connection(): - self.create_visdom_connections() - ''' - if self.use_html: # create an HTML object at /web/; images will be saved under /web/images/ - self.web_dir = os.path.join(opt.checkpoints_dir, opt.name, 'web') - self.img_dir = os.path.join(self.web_dir, 'images') - print('create web directory %s...' % self.web_dir) - util.mkdirs([self.web_dir, self.img_dir]) - # create a logging file to store training losses - self.log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt') - with open(self.log_name, "a") as log_file: - now = time.strftime("%c") - log_file.write('================ Training Loss (%s) ================\n' % now) - - def reset(self): - """Reset the self.saved status""" - self.saved = False - ''' - def create_visdom_connections(self): - """If the program could not connect to Visdom server, this function will start a new server at port < self.port > """ - cmd = sys.executable + ' -m visdom.server -p %d &>/dev/null &' % self.port - print('\n\nCould not connect to Visdom server. \n Trying to start a server....') - print('Command: %s' % cmd) - Popen(cmd, shell=True, stdout=PIPE, stderr=PIPE) - - def display_current_results(self, visuals, epoch, save_result): - """Display current results on visdom; save current results to an HTML file. - - Parameters: - visuals (OrderedDict) - - dictionary of images to display or save - epoch (int) - - the current epoch - save_result (bool) - - if save the current results to an HTML file - """ - if self.display_id > 0: # show images in the browser using visdom - ncols = self.ncols - if ncols > 0: # show all the images in one visdom panel - ncols = min(ncols, len(visuals)) - h, w = next(iter(visuals.values())).shape[:2] - table_css = """""" % (w, h) # create a table css - # create a table of images. - title = self.name - label_html = '' - label_html_row = '' - images = [] - idx = 0 - for label, image in visuals.items(): - image_numpy = util.tensor2im(image) - label_html_row += '%s' % label - images.append(image_numpy.transpose([2, 0, 1])) - idx += 1 - if idx % ncols == 0: - label_html += '%s' % label_html_row - label_html_row = '' - white_image = np.ones_like(image_numpy.transpose([2, 0, 1])) * 255 - while idx % ncols != 0: - images.append(white_image) - label_html_row += '' - idx += 1 - if label_html_row != '': - label_html += '%s' % label_html_row - try: - self.vis.images(images, nrow=ncols, win=self.display_id + 1, - padding=2, opts=dict(title=title + ' images')) - label_html = '%s
' % label_html - self.vis.text(table_css + label_html, win=self.display_id + 2, - opts=dict(title=title + ' labels')) - except VisdomExceptionBase: - self.create_visdom_connections() - - else: # show each image in a separate visdom panel; - idx = 1 - try: - for label, image in visuals.items(): - image_numpy = util.tensor2im(image) - self.vis.image(image_numpy.transpose([2, 0, 1]), opts=dict(title=label), - win=self.display_id + idx) - idx += 1 - except VisdomExceptionBase: - self.create_visdom_connections() - - if self.use_html and (save_result or not self.saved): # save images to an HTML file if they haven't been saved. - self.saved = True - # save images to the disk - for label, image in visuals.items(): - image_numpy = util.tensor2im(image) - img_path = os.path.join(self.img_dir, 'epoch%.3d_%s.png' % (epoch, label)) - util.save_image(image_numpy, img_path) - - # update website - webpage = html.HTML(self.web_dir, 'Experiment name = %s' % self.name, refresh=1) - for n in range(epoch, 0, -1): - webpage.add_header('epoch [%d]' % n) - ims, txts, links = [], [], [] - - for label, image_numpy in visuals.items(): - image_numpy = util.tensor2im(image) - img_path = 'epoch%.3d_%s.png' % (n, label) - ims.append(img_path) - txts.append(label) - links.append(img_path) - webpage.add_images(ims, txts, links, width=self.win_size) - webpage.save() - ''' - def plot_current_losses(self, epoch, counter_ratio, losses): - """display the current losses on visdom display: dictionary of error labels and values - - Parameters: - epoch (int) -- current epoch - counter_ratio (float) -- progress (percentage) in the current epoch, between 0 to 1 - losses (OrderedDict) -- training losses stored in the format of (name, float) pairs - """ - if not hasattr(self, 'plot_data'): - self.plot_data = {'X': [], 'Y': [], 'legend': list(losses.keys())} - self.plot_data['X'].append(epoch + counter_ratio) - self.plot_data['Y'].append([losses[k] for k in self.plot_data['legend']]) - ''' - try: - self.vis.line( - X=np.stack([np.array(self.plot_data['X'])] * len(self.plot_data['legend']), 1), - Y=np.array(self.plot_data['Y']), - opts={ - 'title': self.name + ' loss over time', - 'legend': self.plot_data['legend'], - 'xlabel': 'epoch', - 'ylabel': 'loss'}, - win=self.display_id) - except VisdomExceptionBase: - self.create_visdom_connections() - ''' - # losses: same format as |losses| of plot_current_losses - def print_current_losses(self, epoch, iters, losses, t_comp, t_data): - """print current losses on console; also save the losses to the disk - - Parameters: - epoch (int) -- current epoch - iters (int) -- current training iteration during this epoch (reset to 0 at the end of every epoch) - losses (OrderedDict) -- training losses stored in the format of (name, float) pairs - t_comp (float) -- computational time per data point (normalized by batch_size) - t_data (float) -- data loading time per data point (normalized by batch_size) - """ - message = '(epoch: %d, iters: %d, time: %.3f, data: %.3f) ' % (epoch, iters, t_comp, t_data) - for k, v in losses.items(): - message += '%s: %.3f ' % (k, v) - - print(message) # print the message - with open(self.log_name, "a") as log_file: - log_file.write('%s\n' % message) # save the message diff --git a/app/service/image2sketch_2/download_checkpoints.py b/app/service/image2sketch_2/download_checkpoints.py deleted file mode 100644 index 9048c34..0000000 --- a/app/service/image2sketch_2/download_checkpoints.py +++ /dev/null @@ -1,45 +0,0 @@ -import os - -from minio import Minio -from minio.error import S3Error - -MINIO_URL = "www.minio.aida.com.hk:12024" -MINIO_ACCESS = 'vXKFLSJkYeEq2DrSZvkB' -MINIO_SECRET = 'uKTZT3x7C43WvPN9QTc99DiRkwddWZrG9Uh3JVlR' -MINIO_SECURE = True -# 配置MinIO客户端 -minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE) - - -# 下载函数 -def download_folder(bucket_name, folder_name, local_dir): - try: - # 确保本地目录存在 - if not os.path.exists(local_dir): - os.makedirs(local_dir) - - # 遍历MinIO中的文件 - objects = minio_client.list_objects(bucket_name, prefix=folder_name, recursive=True) - for obj in objects: - # 构造本地文件路径 - local_file_path = os.path.join(local_dir, obj.object_name[len(folder_name):]) - local_file_dir = os.path.dirname(local_file_path) - - # 确保本地目录存在 - if not os.path.exists(local_file_dir): - os.makedirs(local_file_dir) - - # 下载文件 - minio_client.fget_object(bucket_name, obj.object_name, local_file_path) - print(f"Downloaded {obj.object_name} to {local_file_path}") - - except S3Error as e: - print(f"Error occurred: {e}") - - -# 使用示例 -bucket_name = "test" # 替换成你的bucket名称 -folder_name = "checkpoints/lineart/" # 权重文件夹的路径 -local_dir = "app/service/image2sketch_2" # 替换成你希望保存到的本地目录 - -download_folder(bucket_name, folder_name, local_dir) diff --git a/app/service/image2sketch_2/server.py b/app/service/image2sketch_2/server.py deleted file mode 100644 index 41c0278..0000000 --- a/app/service/image2sketch_2/server.py +++ /dev/null @@ -1,142 +0,0 @@ -import cv2 -import numpy -import numpy as np -import torch -import torch.nn as nn -import torchvision.transforms as transforms -from PIL import Image - -from app.service.utils.oss_client import oss_get_image, oss_upload_image - -norm_layer = nn.InstanceNorm2d - -weights = [(0.7, 0.3), (0.5, 0.5), (0.3, 0.7), (0.1, 0.9), (0, 1)] -kernel = np.ones((3, 3), np.uint8) - - -class ResidualBlock(nn.Module): - def __init__(self, in_features): - super(ResidualBlock, self).__init__() - - conv_block = [nn.ReflectionPad2d(1), - nn.Conv2d(in_features, in_features, 3), - norm_layer(in_features), - nn.ReLU(inplace=True), - nn.ReflectionPad2d(1), - nn.Conv2d(in_features, in_features, 3), - norm_layer(in_features) - ] - - self.conv_block = nn.Sequential(*conv_block) - - def forward(self, x): - return x + self.conv_block(x) - - -class Generator(nn.Module): - def __init__(self, input_nc, output_nc, n_residual_blocks=9, sigmoid=True): - super(Generator, self).__init__() - - # Initial convolution block - model0 = [nn.ReflectionPad2d(3), - nn.Conv2d(input_nc, 64, 7), - norm_layer(64), - nn.ReLU(inplace=True)] - self.model0 = nn.Sequential(*model0) - - # Downsampling - model1 = [] - in_features = 64 - out_features = in_features * 2 - for _ in range(2): - model1 += [nn.Conv2d(in_features, out_features, 3, stride=2, padding=1), - norm_layer(out_features), - nn.ReLU(inplace=True)] - in_features = out_features - out_features = in_features * 2 - self.model1 = nn.Sequential(*model1) - - model2 = [] - # Residual blocks - for _ in range(n_residual_blocks): - model2 += [ResidualBlock(in_features)] - self.model2 = nn.Sequential(*model2) - - # Upsampling - model3 = [] - out_features = in_features // 2 - for _ in range(2): - model3 += [nn.ConvTranspose2d(in_features, out_features, 3, stride=2, padding=1, output_padding=1), - norm_layer(out_features), - nn.ReLU(inplace=True)] - in_features = out_features - out_features = in_features // 2 - self.model3 = nn.Sequential(*model3) - - # Output layer - model4 = [nn.ReflectionPad2d(3), - nn.Conv2d(64, output_nc, 7)] - if sigmoid: - model4 += [nn.Sigmoid()] - - self.model4 = nn.Sequential(*model4) - - def forward(self, x, cond=None): - out = self.model0(x) - out = self.model1(out) - out = self.model2(out) - out = self.model3(out) - out = self.model4(out) - - return out - - -model1 = Generator(3, 1, 3) -model1.load_state_dict(torch.load('app/service/image2sketch_2/model.pth', map_location=torch.device('cpu'))) -model1.eval() - - -def predict(input_img, width): - transform = transforms.Compose([transforms.Resize(width, Image.BICUBIC), transforms.ToTensor()]) - input_img = transform(input_img) - input_img = torch.unsqueeze(input_img, 0) - - with torch.no_grad(): - drawing = model1(input_img)[0].detach() - - drawing = transforms.ToPILImage()(drawing) - - # 转ndarray - drawing = numpy.array(drawing) - return drawing - - -def get_image(image_url): - image = oss_get_image(bucket=image_url.split('/')[0], object_name=image_url[image_url.find('/') + 1:], data_type="PIL") - image = image.convert('RGB') - width = image.size[0] - height = image.size[1] - return image, width, height - - -def processing_pipeline(image_url, thickness, sketch_bucket, sketch_name): - thickness = int(thickness) - # 提取sketch - image, width, height = get_image(image_url) - sketch_image = predict(image, width) - - # 设定线条粗细 - if thickness != 0: - dilated = cv2.erode(sketch_image, kernel, iterations=1) - # 将原图与膨胀后的图像进行混合，使用不同的权重 - sketch_image = cv2.addWeighted(sketch_image, weights[thickness][0], dilated, weights[thickness][1], 0) - - # 上传minio - image_bytes = cv2.imencode(".jpg", sketch_image)[1].tobytes() - req = oss_upload_image(bucket=sketch_bucket, object_name=sketch_name, image_bytes=image_bytes) - return f"{req.bucket_name}/{req.object_name}" - - -if __name__ == '__main__': - result_url = processing_pipeline("aida-users/89/relight_image/d5f0d967-f8e8-424d-98f9-a8ad8313deec-0-89.png", 1, "test", "test123.jpg") - print(result_url) diff --git a/app/service/lineart/service.py b/app/service/lineart/service.py index d822dfa..c459b3c 100644 --- a/app/service/lineart/service.py +++ b/app/service/lineart/service.py @@ -6,12 +6,14 @@ import numpy as np import torch import torch.nn.functional as F import tritonclient.http as httpclient - +from minio import Minio +from app.core.config import settings from app.core.config import DESIGN_MODEL_URL from app.schemas.image2sketch import Image2SketchModel -from app.service.utils.oss_client import oss_get_image, oss_upload_image +from app.service.utils.new_oss_client import oss_get_image, oss_upload_image logger = logging.getLogger() +minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE) class LineArtService: @@ -58,7 +60,7 @@ class LineArtService: def put_image(self, image): try: image_bytes = cv2.imencode('.jpg', image)[1].tobytes() - oss_upload_image(bucket=self.sketch_bucket, object_name=f"{self.sketch_name}.jpg", image_bytes=image_bytes) + oss_upload_image(oss_client=minio_client, bucket=self.sketch_bucket, object_name=f"{self.sketch_name}.jpg", image_bytes=image_bytes) return f"{self.sketch_bucket}/{self.sketch_name}.jpg" except Exception as e: logger.warning(e) diff --git a/app/service/mannequins_edit/service.py b/app/service/mannequins_edit/service.py index c0f0a44..5199c94 100644 --- a/app/service/mannequins_edit/service.py +++ b/app/service/mannequins_edit/service.py @@ -3,14 +3,14 @@ import numpy as np from PIL import Image from minio import Minio -from app.core.config import MINIO_URL, MINIO_ACCESS, MINIO_SECRET, MINIO_SECURE +from app.core.config import settings from app.schemas.mannequin_edit import MannequinModel from app.service.utils.new_oss_client import oss_get_image, oss_upload_image -minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE) +minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE) -class MannequinEditService(): +class MannequinEditService: def __init__(self, request_data): self.resize_pixel = request_data.resize_pixel self.top = request_data.top diff --git a/app/service/project_info_extraction/service.py b/app/service/project_info_extraction/service.py index cf9df6c..ba7d787 100644 --- a/app/service/project_info_extraction/service.py +++ b/app/service/project_info_extraction/service.py @@ -1,4 +1,4 @@ -from langchain.output_parsers import ResponseSchema, StructuredOutputParser +from langchain_classic.output_parsers import ResponseSchema, StructuredOutputParser from langchain_community.chat_models import ChatTongyi from langchain_core.prompts import PromptTemplate @@ -51,7 +51,7 @@ class ProjectInfoExtraction: return self.result_data def llm_extraction_project_info(self): - output = self.model(self._input.to_messages()) + output = self.model project_info = self.output_parser.parse(output.content) self.result_data = project_info diff --git a/app/service/prompt_generation/chatgpt_for_translation.py b/app/service/prompt_generation/chatgpt_for_translation.py index 4c50d0b..79d17b6 100644 --- a/app/service/prompt_generation/chatgpt_for_translation.py +++ b/app/service/prompt_generation/chatgpt_for_translation.py @@ -7,7 +7,7 @@ from dashscope import Generation from requests import RequestException from retry import retry -from app.core.config import QWEN_API_KEY +from app.core.config import settings from app.service.chat_robot.script.service.CallQWen import get_language from app.service.prompt_generation.util import minio_util @@ -51,19 +51,19 @@ def translate_to_en(text): # "The translation of \"Material suave\" into English would be \"Smooth material.\"". Instead, directly output "Smooth material". # """ # ) - messages = [ - # { - # Translate the entire text and ensure the output is a complete and coherent sentence in English. - # "content": template, # 系统message - # "role": "system" - # }, - { - # "content": input('请输入：'), # 用户message - "content": text, # 用户message - "role": "user" - } - ] - first_response = get_response(messages) + # messages = [ + # # { + # # Translate the entire text and ensure the output is a complete and coherent sentence in English. + # # "content": template, # 系统message + # # "role": "system" + # # }, + # { + # # "content": input('请输入：'), # 用户message + # "content": text, # 用户message + # "role": "user" + # } + # ] + first_response = get_response assistant_output = first_response.output.choices[0].message print("input : {}, translate result : {}".format(text, assistant_output.content)) return assistant_output.content @@ -79,7 +79,7 @@ def translate_to_en(text): def get_response(messages): response = Generation.call( model='qwen-turbo', - api_key=QWEN_API_KEY, + api_key=settings.QWEN_API_KEY, messages=messages, # seed=random.randint(1, 10000), # 设置随机数种子seed，如果没有设置，则随机数种子默认为1234 result_format='message', # 将输出设置为message形式 @@ -90,7 +90,7 @@ def get_response(messages): def get_translation_from_llama3(text): start_time = time.time() - url = "http://10.1.1.240:11434/api/generate" + url = f"http://{settings.A6000_SERVICE_HOST}:11434/api/generate" # url = "http://10.1.1.240:1143/api/generate" # prompt = f"System: {prefix_for_llama}\nUser:[{text}]" @@ -122,6 +122,7 @@ def get_translation_from_llama3(text): logger.info(f"translation server runtime is {time.time() - start_time} , response is {response.content}") print(f"Request failed with status code {response.status_code}") print(response.text) + return "" # 在llama3中创建一个翻译模型 @@ -174,6 +175,7 @@ def get_prompt_from_image(image_path, text): logger.info(f"sketch re-generate server runtime is {time.time() - start_time} , response is {response.content}") print(f"Request failed with status code {response.status_code}") print(response.text) + return "" def main(): diff --git a/app/service/prompt_generation/util/minio_util.py b/app/service/prompt_generation/util/minio_util.py index 8708ae9..c5a8c99 100644 --- a/app/service/prompt_generation/util/minio_util.py +++ b/app/service/prompt_generation/util/minio_util.py @@ -2,20 +2,19 @@ import base64 from minio import Minio -from app.core.config import MINIO_URL, MINIO_ACCESS, MINIO_SECRET, MINIO_SECURE +from app.core.config import settings -minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE) +minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE) def minio_url_to_base64(minio_url: str) -> str: bucket_name, object_name = minio_url.split("/", 1) - + response = minio_client.get_object(bucket_name, object_name) try: - response = minio_client.get_object(bucket_name, object_name) image_data = response.read() return base64.b64encode(image_data).decode('utf-8') except Exception as e: raise RuntimeError(f"Failed to get object: {e}") finally: if 'response' in locals(): - response.close() \ No newline at end of file + response.close() diff --git a/app/service/recommend/scheduled_task.py b/app/service/recommend/scheduled_task.py index d3174ed..b146ffe 100644 --- a/app/service/recommend/scheduled_task.py +++ b/app/service/recommend/scheduled_task.py @@ -18,7 +18,8 @@ import pandas as pd from datetime import datetime, timedelta import json -from app.core.config import DB_CONFIG, TABLE_CATEGORIES, RECOMMEND_PATH_PREFIX +from app.core.config import TABLE_CATEGORIES, settings +from app.core.mysql_config import DB_CONFIG # 自动选择可用字体 try: @@ -51,7 +52,7 @@ minio_client = Minio( ) # 预加载系统sketch特征向量 -SYSTEM_FEATURES = np.load(f'{RECOMMEND_PATH_PREFIX}sketch_feature_dict.npy', allow_pickle=True).item() +SYSTEM_FEATURES = np.load(f'{settings.RECOMMEND_PATH_PREFIX}sketch_feature_dict.npy', allow_pickle=True).item() # 行为权重和衰减系数 BEHAVIOR_CONFIG = { @@ -61,6 +62,7 @@ BEHAVIOR_CONFIG = { 'sketchLike': {'weight': 4, 'decay': 0} # 不衰减 } + # 保存sketch_to_iid到文件 def save_sketch_to_iid(): """保存sketch到iid的映射""" @@ -147,11 +149,11 @@ def update_user_matrices(): cursor = conn.cursor() # 修改后的查询语句（移除category过滤） - cursor.execute(""" - SELECT account_id, path, COUNT(*) as like_count - FROM user_preference_log_test - GROUP BY account_id, path - """) + cursor.execute(""" + SELECT account_id, path, COUNT(*) as like_count + FROM user_preference_log_test + GROUP BY account_id, path + """) user_data = cursor.fetchall() logging.info(f"成功读取{len(user_data)}条用户偏好记录") @@ -164,17 +166,17 @@ def update_user_matrices(): feature_matrix, user_index_feature_matrix, sketch_index_feature_matrix, iid_to_category_feature_matrix = calculate_feature_matrix(user_data) # visualize_sparse_matrix(feature_matrix, '系统sketch与用户category平均特征向量关联度矩阵', 'correlation_matrix.png') # 存储矩阵 - np.save(f"{RECOMMEND_PATH_PREFIX}interaction_matrix.npy", interaction_matrix) - np.save(f"{RECOMMEND_PATH_PREFIX}feature_matrix.npy", feature_matrix) + np.save(f"{settings.RECOMMEND_PATH_PREFIX}interaction_matrix.npy", interaction_matrix) + np.save(f"{settings.RECOMMEND_PATH_PREFIX}feature_matrix.npy", feature_matrix) # - np.save(f"{RECOMMEND_PATH_PREFIX}iid_to_category_interaction_matrix.npy", iid_to_category_interaction_matrix) - np.save(f"{RECOMMEND_PATH_PREFIX}user_index_interaction_matrix.npy", user_index_interaction_matrix) + np.save(f"{settings.RECOMMEND_PATH_PREFIX}iid_to_category_interaction_matrix.npy", iid_to_category_interaction_matrix) + np.save(f"{settings.RECOMMEND_PATH_PREFIX}user_index_interaction_matrix.npy", user_index_interaction_matrix) # - np.save(f"{RECOMMEND_PATH_PREFIX}iid_to_category_feature_matrix.npy", iid_to_category_feature_matrix) - np.save(f"{RECOMMEND_PATH_PREFIX}user_index_feature_matrix.npy", user_index_feature_matrix) + np.save(f"{settings.RECOMMEND_PATH_PREFIX}iid_to_category_feature_matrix.npy", iid_to_category_feature_matrix) + np.save(f"{settings.RECOMMEND_PATH_PREFIX}user_index_feature_matrix.npy", user_index_feature_matrix) # - np.save(f"{RECOMMEND_PATH_PREFIX}sketch_index_interaction_matrix.npy", sketch_index_interaction_matrix) - np.save(f"{RECOMMEND_PATH_PREFIX}sketch_index_feature_matrix.npy", sketch_index_feature_matrix) + np.save(f"{settings.RECOMMEND_PATH_PREFIX}sketch_index_interaction_matrix.npy", sketch_index_interaction_matrix) + np.save(f"{settings.RECOMMEND_PATH_PREFIX}sketch_index_feature_matrix.npy", sketch_index_feature_matrix) # logging.info("矩阵更新完成") except Exception as e: @@ -235,6 +237,7 @@ def plot_interaction_count_matrix(interaction_count_matrix): except Exception as e: logging.error(f"绘图失败: {str(e)}", exc_info=True) + def visualize_sparse_matrix(matrix, title='Non-zero Interactions (Scatter Plot)', filename="scatter_figure_interaction.png"): if not sparse.issparse(matrix): # 转换为稀疏矩阵 @@ -253,6 +256,7 @@ def visualize_sparse_matrix(matrix, title='Non-zero Interactions (Scatter Plot)' plt.ylabel('Item Index') plt.savefig(filename) + def calculate_interaction_matrix(user_data): """基于新表结构的交互次数矩阵计算（仅系统sketch）""" # 获取所有用户ID @@ -475,6 +479,7 @@ def calculate_heat(row, current_date): # 计算热度值 = 权重 * e^(-衰减系数 * 天数) return config['weight'] * np.exp(-config['decay'] * days_passed) + def load_heat_matrix_as_array(file_path): """ 直接加载为二维numpy数组 @@ -484,10 +489,11 @@ def load_heat_matrix_as_array(file_path): saved = json.load(f) return ( np.array(saved['data']), # 二维矩阵 - saved['row_labels'], # 行标签列表 - saved['col_labels'] # 列标签列表 + saved['row_labels'], # 行标签列表 + saved['col_labels'] # 列标签列表 ) + def update_heat_matrices(): """每日计算并存储热度矩阵（gender_category × path）""" current_date = datetime.now() diff --git a/app/service/recommend/service.py b/app/service/recommend/service.py index 0db64dd..6fcb464 100644 --- a/app/service/recommend/service.py +++ b/app/service/recommend/service.py @@ -1,240 +1,240 @@ -# 预加载资源 -import logging -import time -from collections import defaultdict -import os -import json -import numpy as np - -from app.core.config import DB_CONFIG, RECOMMEND_PATH_PREFIX - -logger = logging.getLogger() -import pymysql -from concurrent.futures import ThreadPoolExecutor - -HEAT_VECTOR_FILE = 'heat_vectors_data/heat_vectors.json' # 可动态加载或配置 - -matrix_data = { - "interaction_matrix": None, - "feature_matrix": None, - "user_index_interaction": None, - "sketch_index_interaction": None, - "user_index_feature": None, - "sketch_index_feature": None, - "iid_to_sketch": None, - "category_to_iids": None, - "cached_scores": {}, - "cached_valid_idxs": {}, - "category_sketch_idxs_inter": None, - "category_sketch_idxs_feature": None, - "user_inter_full": dict(), - "user_feat_full": dict(), - "brand_feature_matrix": None, - "brand_index_map": None, - "heat_data": {}, -} - - -def load_resources(): - """加载所有矩阵和映射关系，并触发预缓存""" - try: - start_time = time.time() - - # 清空缓存 - matrix_data["cached_scores"].clear() - matrix_data["cached_valid_idxs"].clear() - - # 加载数据 - sketch_to_iid = np.load(f'{RECOMMEND_PATH_PREFIX}sketch_to_iid.npy', allow_pickle=True).item() - matrix_data["iid_to_sketch"] = {v: k for k, v in sketch_to_iid.items()} - - matrix_data["interaction_matrix"] = np.load(f"{RECOMMEND_PATH_PREFIX}interaction_matrix.npy", allow_pickle=True) - matrix_data["user_index_interaction"] = np.load(f"{RECOMMEND_PATH_PREFIX}user_index_interaction_matrix.npy", allow_pickle=True).item() - matrix_data["sketch_index_interaction"] = np.load(f"{RECOMMEND_PATH_PREFIX}sketch_index_interaction_matrix.npy", - allow_pickle=True).item() - - matrix_data["feature_matrix"] = np.load(f"{RECOMMEND_PATH_PREFIX}feature_matrix.npy", allow_pickle=True) - - brand_feature_path = f"{RECOMMEND_PATH_PREFIX}brand_feature_matrix.npy" - if os.path.exists(brand_feature_path): - matrix_data["brand_feature_matrix"] = np.load(brand_feature_path, allow_pickle=True) - else: - logger.warning("brand_feature_matrix 文件不存在，使用空数组") - matrix_data["brand_feature_matrix"] = np.array([]) - - # brand_index_map - brand_index_path = f"{RECOMMEND_PATH_PREFIX}brand_index_map.npy" - if os.path.exists(brand_index_path): - matrix_data["brand_index_map"] = np.load(brand_index_path, allow_pickle=True).item() - else: - logger.warning("brand_index_map 文件不存在，使用空字典") - matrix_data["brand_index_map"] = {} - - matrix_data["user_index_feature"] = np.load(f"{RECOMMEND_PATH_PREFIX}user_index_feature_matrix.npy", allow_pickle=True).item() - - matrix_data["sketch_index_feature"] = np.load(f"{RECOMMEND_PATH_PREFIX}sketch_index_feature_matrix.npy", allow_pickle=True).item() - - category_to_iid_map = np.load(f"{RECOMMEND_PATH_PREFIX}iid_to_category_interaction_matrix.npy", allow_pickle=True).item() - matrix_data["category_to_iids"] = defaultdict(list) - for iid, cat in category_to_iid_map.items(): - matrix_data["category_to_iids"][cat].append(iid) - - logger.info(f"资源加载完成，耗时: {time.time() - start_time:.2f}秒") - - # 触发预缓存 - precache_user_category() - - if os.path.exists(HEAT_VECTOR_FILE): - with open(HEAT_VECTOR_FILE, 'r', encoding='utf-8') as f: - heat_json = json.load(f) - matrix_data["heat_data"] = heat_json.get("data", {}) - logger.info(f"热度向量数据加载完成，共加载 {len(matrix_data['heat_data'])} 个类别") - else: - matrix_data["heat_data"] = {} - - except Exception as e: - logger.error(f"资源加载失败: {str(e)}") - raise RuntimeError("初始化失败") - - -def precache_user_category(): - """优化后的用户分类预缓存（添加耗时统计）""" - if not all([ - matrix_data["interaction_matrix"] is not None, - matrix_data["feature_matrix"] is not None, - matrix_data["user_index_interaction"] is not None - ]): - logger.warning("资源未加载完成，跳过预缓存") - return - - start_time = time.perf_counter() - time_stats = { - "get_all_user_categories": 0, - "process_user_category": 0, - "thread_execution": 0, - "cache_update": 0, - "total": 0, - } - - # 统计用户类别获取时间 - t1 = time.perf_counter() - user_categories = get_all_user_categories() - time_stats["get_all_user_categories"] = time.perf_counter() - t1 - - precached_count = 0 - - def process_user_category(user_id, categories): - """单用户类别缓存计算（统计耗时）""" - local_cache = {} - local_valid_idxs = {} - t_start = time.perf_counter() - - for category in categories: - cache_key = (user_id, category) - if cache_key in matrix_data["cached_scores"]: - continue - - try: - user_idx_inter = matrix_data["user_index_interaction"].get(user_id) - user_idx_feature = matrix_data["user_index_feature"].get(user_id) - - # 统计获取类别 IID 耗时 - t_iid = time.perf_counter() - category_iids = matrix_data["category_to_iids"].get(category, []) - valid_sketch_idxs_inter = [matrix_data["sketch_index_interaction"][iid] - for iid in category_iids if iid in matrix_data["sketch_index_interaction"]] - valid_sketch_idxs_feature = [matrix_data["sketch_index_feature"][iid] - for iid in category_iids if iid in matrix_data["sketch_index_feature"]] - time_stats["process_user_category"] += time.perf_counter() - t_iid - - # 统计矩阵计算耗时 - t_matrix = time.perf_counter() - processed_inter = np.zeros(len(valid_sketch_idxs_inter)) - if user_idx_inter is not None and valid_sketch_idxs_inter: - raw_inter_scores = matrix_data["interaction_matrix"][user_idx_inter, valid_sketch_idxs_inter] - processed_inter = raw_inter_scores * 0.7 - - processed_feat = np.zeros(len(valid_sketch_idxs_feature)) - if user_idx_feature is not None and valid_sketch_idxs_feature: - raw_feat_scores = matrix_data["feature_matrix"][user_idx_feature, valid_sketch_idxs_feature] - raw_feat_scores = (raw_feat_scores - np.min(raw_feat_scores)) / ( - np.max(raw_feat_scores) - np.min(raw_feat_scores) + 1e-8) - processed_feat = raw_feat_scores * 0.3 - time_stats["process_user_category"] += time.perf_counter() - t_matrix - - if len(processed_inter) == len(processed_feat): - local_cache[cache_key] = (processed_inter, processed_feat) - local_valid_idxs[cache_key] = valid_sketch_idxs_inter - - except Exception as e: - logger.error(f"预缓存失败 (user={user_id}, category={category}): {str(e)}") - - return local_cache, local_valid_idxs - - # 统计线程执行时间 - t2 = time.perf_counter() - with ThreadPoolExecutor(max_workers=8) as executor: - futures = {executor.submit(process_user_category, user_id, categories): user_id for user_id, categories in user_categories.items()} - for future in futures: - try: - t_cache = time.perf_counter() - cache_part, valid_idxs_part = future.result() - matrix_data["cached_scores"].update(cache_part) - matrix_data["cached_valid_idxs"].update(valid_idxs_part) - time_stats["cache_update"] += time.perf_counter() - t_cache - precached_count += len(cache_part) - except Exception as e: - logger.error(f"线程执行错误: {str(e)}") - time_stats["thread_execution"] = time.perf_counter() - t2 - - time_stats["total"] = time.perf_counter() - start_time - - # 输出统计信息 - logger.info(f""" - 预缓存完成，共缓存 {precached_count} 组数据，耗时统计如下： - - 获取用户类别数据: {time_stats["get_all_user_categories"]:.2f}s - - 计算用户类别缓存: {time_stats["process_user_category"]:.2f}s - - 线程任务执行: {time_stats["thread_execution"]:.2f}s - - 更新缓存数据: {time_stats["cache_update"]:.2f}s - - 总耗时: {time_stats["total"]:.2f}s - """) - - -def get_all_user_categories(): - """获取所有用户及其对应的分类""" - conn = None - try: - conn = pymysql.connect(**DB_CONFIG) - cursor = conn.cursor() - - query = """ - SELECT DISTINCT account_id, path - FROM user_preference_log_prediction - """ - cursor.execute(query) - results = cursor.fetchall() - - user_categories = defaultdict(set) - for account_id, path in results: - category = get_category_from_path(path) - user_categories[account_id].add(category) - - return dict(user_categories) - - except Exception as e: - logger.error(f"数据库查询失败: {str(e)}") - return {} - finally: - if conn: - conn.close() - - -def get_category_from_path(path: str) -> str: - """从路径解析类别""" - try: - parts = path.split('/') - if len(parts) >= 4: - return f"{parts[2]}_{parts[3]}" - return "unknown" - except: - return "unknown" +# # 预加载资源 +# import logging +# import time +# from collections import defaultdict +# import os +# import json +# import numpy as np +# +# from app.core.config import DB_CONFIG, RECOMMEND_PATH_PREFIX +# +# logger = logging.getLogger() +# import pymysql +# from concurrent.futures import ThreadPoolExecutor +# +# HEAT_VECTOR_FILE = 'heat_vectors_data/heat_vectors.json' # 可动态加载或配置 +# +# matrix_data = { +# "interaction_matrix": None, +# "feature_matrix": None, +# "user_index_interaction": None, +# "sketch_index_interaction": None, +# "user_index_feature": None, +# "sketch_index_feature": None, +# "iid_to_sketch": None, +# "category_to_iids": None, +# "cached_scores": {}, +# "cached_valid_idxs": {}, +# "category_sketch_idxs_inter": None, +# "category_sketch_idxs_feature": None, +# "user_inter_full": dict(), +# "user_feat_full": dict(), +# "brand_feature_matrix": None, +# "brand_index_map": None, +# "heat_data": {}, +# } +# +# +# def load_resources(): +# """加载所有矩阵和映射关系，并触发预缓存""" +# try: +# start_time = time.time() +# +# # 清空缓存 +# matrix_data["cached_scores"].clear() +# matrix_data["cached_valid_idxs"].clear() +# +# # 加载数据 +# sketch_to_iid = np.load(f'{RECOMMEND_PATH_PREFIX}sketch_to_iid.npy', allow_pickle=True).item() +# matrix_data["iid_to_sketch"] = {v: k for k, v in sketch_to_iid.items()} +# +# matrix_data["interaction_matrix"] = np.load(f"{RECOMMEND_PATH_PREFIX}interaction_matrix.npy", allow_pickle=True) +# matrix_data["user_index_interaction"] = np.load(f"{RECOMMEND_PATH_PREFIX}user_index_interaction_matrix.npy", allow_pickle=True).item() +# matrix_data["sketch_index_interaction"] = np.load(f"{RECOMMEND_PATH_PREFIX}sketch_index_interaction_matrix.npy", +# allow_pickle=True).item() +# +# matrix_data["feature_matrix"] = np.load(f"{RECOMMEND_PATH_PREFIX}feature_matrix.npy", allow_pickle=True) +# +# brand_feature_path = f"{RECOMMEND_PATH_PREFIX}brand_feature_matrix.npy" +# if os.path.exists(brand_feature_path): +# matrix_data["brand_feature_matrix"] = np.load(brand_feature_path, allow_pickle=True) +# else: +# logger.warning("brand_feature_matrix 文件不存在，使用空数组") +# matrix_data["brand_feature_matrix"] = np.array([]) +# +# # brand_index_map +# brand_index_path = f"{RECOMMEND_PATH_PREFIX}brand_index_map.npy" +# if os.path.exists(brand_index_path): +# matrix_data["brand_index_map"] = np.load(brand_index_path, allow_pickle=True).item() +# else: +# logger.warning("brand_index_map 文件不存在，使用空字典") +# matrix_data["brand_index_map"] = {} +# +# matrix_data["user_index_feature"] = np.load(f"{RECOMMEND_PATH_PREFIX}user_index_feature_matrix.npy", allow_pickle=True).item() +# +# matrix_data["sketch_index_feature"] = np.load(f"{RECOMMEND_PATH_PREFIX}sketch_index_feature_matrix.npy", allow_pickle=True).item() +# +# category_to_iid_map = np.load(f"{RECOMMEND_PATH_PREFIX}iid_to_category_interaction_matrix.npy", allow_pickle=True).item() +# matrix_data["category_to_iids"] = defaultdict(list) +# for iid, cat in category_to_iid_map.items(): +# matrix_data["category_to_iids"][cat].append(iid) +# +# logger.info(f"资源加载完成，耗时: {time.time() - start_time:.2f}秒") +# +# # 触发预缓存 +# precache_user_category() +# +# if os.path.exists(HEAT_VECTOR_FILE): +# with open(HEAT_VECTOR_FILE, 'r', encoding='utf-8') as f: +# heat_json = json.load(f) +# matrix_data["heat_data"] = heat_json.get("data", {}) +# logger.info(f"热度向量数据加载完成，共加载 {len(matrix_data['heat_data'])} 个类别") +# else: +# matrix_data["heat_data"] = {} +# +# except Exception as e: +# logger.error(f"资源加载失败: {str(e)}") +# raise RuntimeError("初始化失败") +# +# +# def precache_user_category(): +# """优化后的用户分类预缓存（添加耗时统计）""" +# if not all([ +# matrix_data["interaction_matrix"] is not None, +# matrix_data["feature_matrix"] is not None, +# matrix_data["user_index_interaction"] is not None +# ]): +# logger.warning("资源未加载完成，跳过预缓存") +# return +# +# start_time = time.perf_counter() +# time_stats = { +# "get_all_user_categories": 0, +# "process_user_category": 0, +# "thread_execution": 0, +# "cache_update": 0, +# "total": 0, +# } +# +# # 统计用户类别获取时间 +# t1 = time.perf_counter() +# user_categories = get_all_user_categories() +# time_stats["get_all_user_categories"] = time.perf_counter() - t1 +# +# precached_count = 0 +# +# def process_user_category(user_id, categories): +# """单用户类别缓存计算（统计耗时）""" +# local_cache = {} +# local_valid_idxs = {} +# t_start = time.perf_counter() +# +# for category in categories: +# cache_key = (user_id, category) +# if cache_key in matrix_data["cached_scores"]: +# continue +# +# try: +# user_idx_inter = matrix_data["user_index_interaction"].get(user_id) +# user_idx_feature = matrix_data["user_index_feature"].get(user_id) +# +# # 统计获取类别 IID 耗时 +# t_iid = time.perf_counter() +# category_iids = matrix_data["category_to_iids"].get(category, []) +# valid_sketch_idxs_inter = [matrix_data["sketch_index_interaction"][iid] +# for iid in category_iids if iid in matrix_data["sketch_index_interaction"]] +# valid_sketch_idxs_feature = [matrix_data["sketch_index_feature"][iid] +# for iid in category_iids if iid in matrix_data["sketch_index_feature"]] +# time_stats["process_user_category"] += time.perf_counter() - t_iid +# +# # 统计矩阵计算耗时 +# t_matrix = time.perf_counter() +# processed_inter = np.zeros(len(valid_sketch_idxs_inter)) +# if user_idx_inter is not None and valid_sketch_idxs_inter: +# raw_inter_scores = matrix_data["interaction_matrix"][user_idx_inter, valid_sketch_idxs_inter] +# processed_inter = raw_inter_scores * 0.7 +# +# processed_feat = np.zeros(len(valid_sketch_idxs_feature)) +# if user_idx_feature is not None and valid_sketch_idxs_feature: +# raw_feat_scores = matrix_data["feature_matrix"][user_idx_feature, valid_sketch_idxs_feature] +# raw_feat_scores = (raw_feat_scores - np.min(raw_feat_scores)) / ( +# np.max(raw_feat_scores) - np.min(raw_feat_scores) + 1e-8) +# processed_feat = raw_feat_scores * 0.3 +# time_stats["process_user_category"] += time.perf_counter() - t_matrix +# +# if len(processed_inter) == len(processed_feat): +# local_cache[cache_key] = (processed_inter, processed_feat) +# local_valid_idxs[cache_key] = valid_sketch_idxs_inter +# +# except Exception as e: +# logger.error(f"预缓存失败 (user={user_id}, category={category}): {str(e)}") +# +# return local_cache, local_valid_idxs +# +# # 统计线程执行时间 +# t2 = time.perf_counter() +# with ThreadPoolExecutor(max_workers=8) as executor: +# futures = {executor.submit(process_user_category, user_id, categories): user_id for user_id, categories in user_categories.items()} +# for future in futures: +# try: +# t_cache = time.perf_counter() +# cache_part, valid_idxs_part = future.result() +# matrix_data["cached_scores"].update(cache_part) +# matrix_data["cached_valid_idxs"].update(valid_idxs_part) +# time_stats["cache_update"] += time.perf_counter() - t_cache +# precached_count += len(cache_part) +# except Exception as e: +# logger.error(f"线程执行错误: {str(e)}") +# time_stats["thread_execution"] = time.perf_counter() - t2 +# +# time_stats["total"] = time.perf_counter() - start_time +# +# # 输出统计信息 +# logger.info(f""" +# 预缓存完成，共缓存 {precached_count} 组数据，耗时统计如下： +# - 获取用户类别数据: {time_stats["get_all_user_categories"]:.2f}s +# - 计算用户类别缓存: {time_stats["process_user_category"]:.2f}s +# - 线程任务执行: {time_stats["thread_execution"]:.2f}s +# - 更新缓存数据: {time_stats["cache_update"]:.2f}s +# - 总耗时: {time_stats["total"]:.2f}s +# """) +# +# +# def get_all_user_categories(): +# """获取所有用户及其对应的分类""" +# conn = None +# try: +# conn = pymysql.connect(**DB_CONFIG) +# cursor = conn.cursor() +# +# query = """ +# SELECT DISTINCT account_id, path +# FROM user_preference_log_prediction +# """ +# cursor.execute(query) +# results = cursor.fetchall() +# +# user_categories = defaultdict(set) +# for account_id, path in results: +# category = get_category_from_path(path) +# user_categories[account_id].add(category) +# +# return dict(user_categories) +# +# except Exception as e: +# logger.error(f"数据库查询失败: {str(e)}") +# return {} +# finally: +# if conn: +# conn.close() +# +# +# def get_category_from_path(path: str) -> str: +# """从路径解析类别""" +# try: +# parts = path.split('/') +# if len(parts) >= 4: +# return f"{parts[2]}_{parts[3]}" +# return "unknown" +# except: +# return "unknown" diff --git a/app/service/recommendation_system/__init__.py b/app/service/recommendation_system/__init__.py new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/app/service/recommendation_system/__init__.py @@ -0,0 +1 @@ + diff --git a/app/service/recommendation_system/config.py b/app/service/recommendation_system/config.py new file mode 100644 index 0000000..bd48362 --- /dev/null +++ b/app/service/recommendation_system/config.py @@ -0,0 +1,67 @@ +""" +推荐系统配置 +""" +import os +from app.core.config import settings + +# Milvus 集合名称 +MILVUS_COLLECTION_SKETCH_VECTORS = "sketch_vectors_norm" + +# Redis key 前缀 +REDIS_KEY_USER_PREF_PREFIX = "user_pref" + +# 推荐系统配置参数 +RECOMMENDATION_CONFIG = { + # 时间衰减半衰期（用于计算时间衰减权重） + # 值越小，最近的行为权重越大 + "K_half": 10, + + # 探索与利用的比例 (0.0-1.0) + # - 值越大，使用探索分支（随机推荐）的几率越大，结果更随机 + # - 值越小，使用利用分支（基于用户偏好）的几率越大，结果更精准 + # - 建议范围: 0.3-0.7，要增加随机性可提高到 0.6-0.8 + "explore_ratio": 0.5, + + # 向量检索返回的候选数量 + # 值越大，候选池越大，但计算成本也越高 + # 建议范围: 100-1000 + "topk": 200, + + # Style 加分系数（同 style 的候选进行加分） + # 值越大，匹配 style 的候选被选中的概率越大 + # 要降低某个结果的重复率，可以降低此值（如 0.1 或 0.05） + "style_bonus": 0.2, + + # Softmax 抽样的温度参数 + # - 温度越高（>1.0），概率分布越均匀，结果更随机，重复率更低 + # - 温度越低（<1.0），高分项概率越大，结果更集中，重复率更高 + # - 温度=1.0 为标准 Softmax + # - 建议范围: 1.0-3.0，要增加随机性可提高到 2.0-3.0 + "softmax_temperature": 0.07, + + # 监听间隔（秒） + "listen_interval_sec": 30, + + # 批量处理大小 + "batch_size": 1000, + + # Redis 过期时间（秒，30天） + "redis_expire_seconds": 2592000, + + # 向量维度 + "vector_dim": 2048, +} + +# 数据库表名 +TABLE_USER_PREFERENCE_LOG = "user_preference" +TABLE_SYS_FILE = "t_sys_file" + +# MySQL 连接配置（用于推荐系统） +MYSQL_CONFIG = { + "host": settings.MYSQL_HOST, + "port": settings.MYSQL_PORT, + "user": settings.MYSQL_USER, + "password": settings.MYSQL_PASSWORD, + "database": settings.MYSQL_DB, + "charset": "utf8mb4" +} diff --git a/app/service/recommendation_system/import_sys_sketch_to_milvus.py b/app/service/recommendation_system/import_sys_sketch_to_milvus.py new file mode 100644 index 0000000..b055089 --- /dev/null +++ b/app/service/recommendation_system/import_sys_sketch_to_milvus.py @@ -0,0 +1,331 @@ +""" +独立脚本：从 t_sys_file 导入系统图向量到 Milvus +可以单独运行，不依赖整个项目启动 + +使用方法： + python -m app.service.recommendation_system.import_sys_sketch_to_milvus + 或 + python app/service/recommendation_system/import_sys_sketch_to_milvus.py +""" +import sys +import os +import logging +import argparse +from pathlib import Path + +# 添加项目根目录到 Python 路径 +project_root = Path(__file__).parent.parent.parent.parent +sys.path.insert(0, str(project_root)) + +import numpy as np +import pymysql +from tqdm import tqdm + +from app.service.recommendation_system.config import ( + MYSQL_CONFIG, TABLE_SYS_FILE, + RECOMMENDATION_CONFIG, MILVUS_COLLECTION_SKETCH_VECTORS +) +from app.service.recommendation_system.vector_utils import extract_feature_vector, normalize_vector +from app.service.recommendation_system.milvus_client import create_collection, insert_vectors + +# 配置日志 +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(levelname)s - %(message)s', + handlers=[ + logging.StreamHandler(), + logging.FileHandler('import_sys_sketch.log', encoding='utf-8') + ] +) +logger = logging.getLogger(__name__) + + +def get_sys_file_records(conn, limit=None, offset=0): + """ + 从 t_sys_file 表获取系统图记录 + + Args: + conn: 数据库连接 + limit: 限制数量（None 表示不限制） + offset: 偏移量 + + Returns: + 记录列表，每个元素为 (id, url, style, level3_type, level2_type, deprecated) + """ + cursor = conn.cursor() + + query = f""" + SELECT id, url, style, level3_type, level2_type, deprecated + FROM {TABLE_SYS_FILE} + WHERE level1_type = 'Images' + AND style IS NOT NULL + AND style != '' + AND deprecated != 1 + ORDER BY id + """ + + if limit: + query += f" LIMIT {limit} OFFSET {offset}" + + cursor.execute(query) + records = cursor.fetchall() + cursor.close() + + return records + + +def get_total_count(conn): + """获取总记录数""" + cursor = conn.cursor() + cursor.execute(f""" + SELECT COUNT(*) + FROM {TABLE_SYS_FILE} + WHERE level1_type = 'Images' + AND style IS NOT NULL + AND style != '' + AND deprecated != 1 + """) + count = cursor.fetchone()[0] + cursor.close() + return count + + +def process_and_insert_batch(records, batch_size=1000, retry_times=3): + """ + 处理并批量插入向量 + + Args: + records: 记录列表 + batch_size: 批量大小 + retry_times: 失败重试次数 + + Returns: + (成功数量, 失败数量) + """ + success_count = 0 + failed_count = 0 + failed_records = [] + batch_data = [] + + # 使用 tqdm 显示进度 + with tqdm(total=len(records), desc="处理记录", unit="条") as pbar: + for idx, (sys_file_id, url, style, level3_type, level2_type, deprecated) in enumerate(records): + try: + # 计算 category + category = f"{level3_type.lower()}_{level2_type.lower()}" + + # 提取特征向量 + feature_vector = extract_feature_vector(url) + # 归一化，便于 IP≈cosine 度量 + feature_vector = normalize_vector(feature_vector) + + # 检查向量是否有效 + if np.all(feature_vector == 0): + logger.warning(f"向量提取失败，跳过: {url} (id={sys_file_id})") + failed_count += 1 + failed_records.append((sys_file_id, url)) + pbar.update(1) + continue + + # 准备数据 + data_item = { + "path": url, + "sys_file_id": sys_file_id, + "style": style, + "category": category, + "is_system_sketch": 1, + "deprecated": deprecated if deprecated else 0, + "feature_vector": feature_vector.tolist() + } + + batch_data.append(data_item) + + # 批量写入 + if len(batch_data) >= batch_size: + try: + insert_vectors(batch_data) + success_count += len(batch_data) + batch_data = [] + logger.info(f"已成功插入 {success_count} 条记录") + except Exception as e: + logger.error(f"批量写入失败: {e}") + failed_count += len(batch_data) + failed_records.extend([(item["sys_file_id"], item["path"]) for item in batch_data]) + batch_data = [] + + pbar.update(1) + + except Exception as e: + logger.error(f"处理记录失败 [id={sys_file_id}, url={url}]: {e}") + failed_count += 1 + failed_records.append((sys_file_id, url)) + pbar.update(1) + + # 写入剩余数据 + if batch_data: + try: + insert_vectors(batch_data) + success_count += len(batch_data) + logger.info(f"写入剩余 {len(batch_data)} 条记录") + except Exception as e: + logger.error(f"写入剩余数据失败: {e}") + failed_count += len(batch_data) + failed_records.extend([(item["sys_file_id"], item["path"]) for item in batch_data]) + + # 重试失败记录 + if failed_records and retry_times > 0: + logger.info(f"开始重试 {len(failed_records)} 条失败记录，最多重试 {retry_times} 次...") + + for retry in range(retry_times): + if not failed_records: + break + + retry_failed = [] + with tqdm(total=len(failed_records), desc=f"重试第 {retry + 1} 次", unit="条") as pbar: + for sys_file_id, url in failed_records: + try: + # 重新查询记录信息 + conn = pymysql.connect(**MYSQL_CONFIG) + cursor = conn.cursor() + cursor.execute(f""" + SELECT id, url, style, level3_type, level2_type, deprecated + FROM {TABLE_SYS_FILE} + WHERE id = %s + """, (sys_file_id,)) + record = cursor.fetchone() + cursor.close() + conn.close() + + if not record: + retry_failed.append((sys_file_id, url)) + pbar.update(1) + continue + + sys_file_id, url, style, level3_type, level2_type, deprecated = record + category = f"{level3_type.lower()}_{level2_type.lower()}" + + feature_vector = extract_feature_vector(url) + feature_vector = normalize_vector(feature_vector) + if np.all(feature_vector == 0): + retry_failed.append((sys_file_id, url)) + pbar.update(1) + continue + + data_item = { + "path": url, + "sys_file_id": sys_file_id, + "style": style, + "category": category, + "is_system_sketch": 1, + "deprecated": deprecated if deprecated else 0, + "feature_vector": feature_vector.tolist() + } + + insert_vectors([data_item]) + success_count += 1 + failed_count -= 1 + pbar.update(1) + + except Exception as e: + logger.error(f"重试失败 [id={sys_file_id}, url={url}]: {e}") + retry_failed.append((sys_file_id, url)) + pbar.update(1) + + failed_records = retry_failed + if failed_records: + logger.warning(f"第 {retry + 1} 次重试后仍有 {len(failed_records)} 条记录失败") + + return success_count, failed_count, failed_records + + +def main(): + """主函数""" + parser = argparse.ArgumentParser(description='从 t_sys_file 导入系统图向量到 Milvus') + parser.add_argument('--batch-size', type=int, default=1000, help='批量处理大小（默认：1000）') + parser.add_argument('--retry-times', type=int, default=3, help='失败重试次数（默认：3）') + parser.add_argument('--limit', type=int, default=None, help='限制处理数量（用于测试，默认：不限制）') + parser.add_argument('--offset', type=int, default=0, help='起始偏移量（默认：0）') + parser.add_argument('--skip-create-collection', action='store_true', help='跳过创建集合（如果集合已存在）') + + args = parser.parse_args() + + logger.info("=" * 60) + logger.info("开始从 t_sys_file 导入系统图向量到 Milvus") + logger.info("=" * 60) + logger.info(f"配置参数：") + logger.info(f" - 批量大小: {args.batch_size}") + logger.info(f" - 重试次数: {args.retry_times}") + logger.info(f" - 限制数量: {args.limit if args.limit else '不限制'}") + logger.info(f" - 起始偏移: {args.offset}") + logger.info("=" * 60) + + # 1. 创建 Milvus 集合 + if not args.skip_create_collection: + logger.info("创建 Milvus 集合...") + try: + create_collection() + logger.info("Milvus 集合创建成功（或已存在）") + except Exception as e: + logger.error(f"创建 Milvus 集合失败: {e}") + return + else: + logger.info("跳过创建集合") + + # 2. 连接数据库 + logger.info("连接数据库...") + try: + conn = pymysql.connect(**MYSQL_CONFIG) + logger.info("数据库连接成功") + except Exception as e: + logger.error(f"数据库连接失败: {e}") + return + + try: + # 3. 获取总记录数 + total_count = get_total_count(conn) + logger.info(f"找到 {total_count} 条系统图记录") + + if total_count == 0: + logger.warning("没有找到系统图数据") + return + + # 4. 获取记录 + logger.info("获取记录...") + records = get_sys_file_records(conn, limit=args.limit, offset=args.offset) + logger.info(f"获取到 {len(records)} 条记录") + + if not records: + logger.warning("没有获取到记录") + return + + # 5. 处理并插入 + logger.info("开始处理记录...") + success_count, failed_count, failed_records = process_and_insert_batch( + records, + batch_size=args.batch_size, + retry_times=args.retry_times + ) + + # 6. 输出结果 + logger.info("=" * 60) + logger.info("导入完成！") + logger.info(f" - 成功: {success_count} 条") + logger.info(f" - 失败: {failed_count} 条") + if failed_records: + logger.warning(f" - 失败记录列表（前10条）:") + for sys_file_id, url in failed_records[:10]: + logger.warning(f" ID={sys_file_id}, URL={url}") + if len(failed_records) > 10: + logger.warning(f" ... 还有 {len(failed_records) - 10} 条失败记录") + logger.info("=" * 60) + + except Exception as e: + logger.error(f"处理过程中发生错误: {e}", exc_info=True) + finally: + conn.close() + logger.info("数据库连接已关闭") + + +if __name__ == "__main__": + main() + diff --git a/app/service/recommendation_system/incremental_listener.py b/app/service/recommendation_system/incremental_listener.py new file mode 100644 index 0000000..5e554eb --- /dev/null +++ b/app/service/recommendation_system/incremental_listener.py @@ -0,0 +1,347 @@ +""" +增量监听模块 +实时监听 user_preference 表的新增记录，更新用户偏好向量 +""" +import logging +import math +import pymysql +import numpy as np +from typing import List, Dict, Set, Tuple, Optional +from datetime import datetime +from collections import defaultdict + +from apscheduler.schedulers.background import BackgroundScheduler +from apscheduler.schedulers.blocking import BlockingScheduler + +from app.service.recommendation_system.config import ( + MYSQL_CONFIG, TABLE_USER_PREFERENCE_LOG, TABLE_SYS_FILE, + RECOMMENDATION_CONFIG, REDIS_KEY_USER_PREF_PREFIX +) +from app.service.recommendation_system.vector_utils import extract_feature_vector, compute_weighted_average, normalize_vector +from app.service.recommendation_system.milvus_client import query_vectors_by_paths, insert_vectors +from app.service.utils.redis_utils import Redis +import json + +logger = logging.getLogger(__name__) + + +class IncrementalListener: + """增量监听器""" + + def __init__(self): + self.last_process_time = None + self.processed_combinations: Set[Tuple[int, str]] = set() # 已处理的 (account_id, category) 组合 + self.listen_interval = RECOMMENDATION_CONFIG["listen_interval_sec"] + + def get_new_like_records(self) -> List[Tuple]: + """ + 获取新增点赞记录 + + Returns: + 记录列表，每个元素为 (id, account_id, path, category, style, data_time, is_system_sketch, sys_file_id) + """ + conn = None + try: + conn = pymysql.connect(**MYSQL_CONFIG) + cursor = conn.cursor() + + if self.last_process_time is None: + # 第一次运行，查询最近30分钟的数据 + cursor.execute(f""" + SELECT id, account_id, path, category, style, data_time + FROM {TABLE_USER_PREFERENCE_LOG} + WHERE data_time > DATE_SUB(NOW(), INTERVAL 30 MINUTE) + ORDER BY data_time + """) + else: + # 基于上次处理时间查询 + cursor.execute(f""" + SELECT id, account_id, path, category, style, data_time + FROM {TABLE_USER_PREFERENCE_LOG} + WHERE data_time > %s + ORDER BY data_time + """, (self.last_process_time,)) + + records = cursor.fetchall() + return records + + except Exception as e: + logger.error(f"获取新增点赞记录失败: {e}", exc_info=True) + return [] + finally: + if conn: + conn.close() + + def process_new_records(self, records: List[Tuple]): + """ + 处理新增记录 + + Args: + records: 记录列表 + """ + if not records: + return + + # 按用户+类别分组 + user_category_records = defaultdict(list) + for record in records: + account_id = record[1] + category = record[3] + if category: # 只处理有类别的记录 + user_category_records[(account_id, category)].append(record) + + # 去重：只处理一次每个 (account_id, category) 组合 + to_process = [] + for (account_id, category), recs in user_category_records.items(): + if (account_id, category) not in self.processed_combinations: + to_process.append((account_id, category, recs)) + self.processed_combinations.add((account_id, category)) + + logger.info(f"需要处理 {len(to_process)} 个用户-类别组合") + + # 处理每个组合 + for account_id, category, recs in to_process: + try: + self.update_user_preference_vector(account_id, category) + except Exception as e: + logger.error(f"更新用户偏好向量失败 [user={account_id}, category={category}]: {e}", exc_info=True) + + # 更新最后处理时间 + if records: + self.last_process_time = records[-1][5] # data_time + # 重置去重集合，确保下次周期不会跳过同一用户-类别 + self.processed_combinations.clear() + + def update_user_preference_vector(self, account_id: int, category: str): + """ + 更新用户偏好向量 + + Args: + account_id: 用户ID + category: 类别 + """ + conn = None + try: + conn = pymysql.connect(**MYSQL_CONFIG) + cursor = conn.cursor() + + # 1. 获取该用户该类别的所有点赞记录 + cursor.execute(f""" + SELECT path, data_time + FROM {TABLE_USER_PREFERENCE_LOG} + WHERE account_id = %s AND category = %s + ORDER BY data_time DESC + """, (account_id, category)) + + like_records = cursor.fetchall() + + if not like_records: + return + + # 2. 批量查询点赞次数 + paths = [r[0] for r in like_records] + placeholders = ','.join(['%s'] * len(paths)) + cursor.execute(f""" + SELECT path, COUNT(*) as like_count + FROM {TABLE_USER_PREFERENCE_LOG} + WHERE account_id = %s AND category = %s AND path IN ({placeholders}) + GROUP BY path + """, (account_id, category) + tuple(paths)) + + like_counts = {row[0]: row[1] for row in cursor.fetchall()} + + # 3. 批量获取向量 + vectors_dict = query_vectors_by_paths(paths) + + # 处理查询不到的 path（新用户图或异常情况） + missing_paths = [p for p in paths if p not in vectors_dict] + if missing_paths: + logger.info(f"用户 {account_id} 类别 {category} 有 {len(missing_paths)} 个 path 需要实时计算向量") + self._compute_and_insert_missing_vectors(missing_paths, conn) + # 重新查询 + vectors_dict = query_vectors_by_paths(paths) + + # 4. 计算权重并加权平均 + vectors = [] + weights = [] + K_half = RECOMMENDATION_CONFIG["K_half"] + + for k, (path, data_time) in enumerate(like_records, 1): + if path not in vectors_dict: + continue + + vector_data = vectors_dict[path] + feature_vector = np.array(vector_data["feature_vector"]) + + # 时间衰减权重 + d_k = 0.5 ** (k / K_half) + + # 点赞次数权重 + like_count = like_counts.get(path, 1) + p_i = 1 + math.log(1 + like_count) + + # 综合权重 + w_i = d_k * p_i + + vectors.append(feature_vector) + weights.append(w_i) + + if not vectors: + logger.warning(f"用户 {account_id} 类别 {category} 没有有效向量") + return + + # 5. 计算加权平均并做 L2 归一化，IP≈cosine + preference_vector = compute_weighted_average(vectors, weights) + preference_vector = normalize_vector(preference_vector) + + # 6. 写入 Redis + key = f"{REDIS_KEY_USER_PREF_PREFIX}:{account_id}:{category}" + vector_json = json.dumps(preference_vector.tolist()) + Redis.write( + key=key, + value=vector_json, + expire=RECOMMENDATION_CONFIG["redis_expire_seconds"] + ) + + logger.debug(f"用户偏好向量更新成功 [user={account_id}, category={category}]") + + except Exception as e: + logger.error(f"更新用户偏好向量失败 [user={account_id}, category={category}]: {e}", exc_info=True) + raise + finally: + if conn: + conn.close() + + def _compute_and_insert_missing_vectors(self, paths: List[str], conn: pymysql.connections.Connection): + """ + 计算并插入缺失的向量 + + Args: + paths: 缺失的 path 列表 + conn: 数据库连接 + """ + cursor = conn.cursor() + data_to_insert = [] + + for path in paths: + try: + # 判断数据来源（查询 t_sys_file 表） + cursor.execute(f""" + SELECT id, url, style, level3_type, level2_type, deprecated + FROM {TABLE_SYS_FILE} + WHERE url = %s + LIMIT 1 + """, (path,)) + + sys_file = cursor.fetchone() + + # 提取特征向量 + feature_vector = extract_feature_vector(path) + + if np.all(feature_vector == 0): + logger.warning(f"向量提取失败，跳过: {path}") + continue + + if sys_file: + # 系统图 + sys_file_id, url, style, level3_type, level2_type, deprecated = sys_file + category = f"{level3_type.lower()}_{level2_type.lower()}" + + data_item = { + "path": path, + "sys_file_id": sys_file_id, + "style": style, + "category": category, + "is_system_sketch": 1, + "deprecated": deprecated if deprecated else 0, + "feature_vector": feature_vector.tolist() + } + else: + # 用户图 + # 从 user_preference 获取 category（如果有） + cursor.execute(f""" + SELECT category + FROM {TABLE_USER_PREFERENCE_LOG} + WHERE path = %s AND category IS NOT NULL + LIMIT 1 + """, (path,)) + + category_result = cursor.fetchone() + category = category_result[0] if category_result else None + + data_item = { + "path": path, + "sys_file_id": None, + "style": None, + "category": category, + "is_system_sketch": 0, + "deprecated": 0, + "feature_vector": feature_vector.tolist() + } + + data_to_insert.append(data_item) + + except Exception as e: + logger.error(f"处理缺失向量失败 [{path}]: {e}") + + # 批量插入 + if data_to_insert: + try: + insert_vectors(data_to_insert) + logger.info(f"成功插入 {len(data_to_insert)} 个缺失向量") + except Exception as e: + logger.error(f"插入缺失向量失败: {e}") + + def process_once(self): + """单次轮询任务，供调度器调用""" + try: + records = self.get_new_like_records() + + if records: + logger.info(f"发现 {len(records)} 条新增记录") + self.process_new_records(records) + else: + logger.debug("没有新增记录") + except Exception as e: + logger.error(f"监听轮询异常: {e}", exc_info=True) + + +def start_background_listener(scheduler: BackgroundScheduler): + """将增量监听任务注册到后台调度器""" + # 降低 apscheduler 的日志级别，避免大量刷屏 + logging.getLogger('apscheduler.executors.default').setLevel(logging.WARNING) + logging.getLogger('apscheduler.scheduler').setLevel(logging.WARNING) + + listener = IncrementalListener() + scheduler.add_job( + listener.process_once, + "interval", + seconds=listener.listen_interval, + max_instances=1, + coalesce=True, + id="recommendation_incremental_listener", + replace_existing=True, + ) + logger.info("增量监听任务已注册到调度器") + + +def start_blocking_listener(): + """以阻塞方式启动调度器（用于独立脚本运行）""" + listener = IncrementalListener() + scheduler = BlockingScheduler() + scheduler.add_job( + listener.process_once, + "interval", + seconds=listener.listen_interval, + max_instances=1, + coalesce=True, + id="recommendation_incremental_listener", + replace_existing=True, + ) + logger.info("增量监听调度器已启动（BlockingScheduler）") + scheduler.start() + + +if __name__ == "__main__": + start_blocking_listener() + diff --git a/app/service/recommendation_system/milvus_client.py b/app/service/recommendation_system/milvus_client.py new file mode 100644 index 0000000..5fefa71 --- /dev/null +++ b/app/service/recommendation_system/milvus_client.py @@ -0,0 +1,332 @@ +""" +Milvus 客户端封装 +""" +import logging +from typing import List, Dict, Optional, Any +import numpy as np +from pymilvus import MilvusClient, FieldSchema, CollectionSchema, DataType, connections, Collection + +from app.core.config import settings +from app.service.recommendation_system.config import MILVUS_COLLECTION_SKETCH_VECTORS, RECOMMENDATION_CONFIG + +logger = logging.getLogger(__name__) + +# Milvus 客户端（单例） +_milvus_client = None + + +def get_milvus_client() -> MilvusClient: + """获取 Milvus 客户端（单例模式）""" + global _milvus_client + if _milvus_client is None: + try: + _milvus_client = MilvusClient( + uri=settings.MILVUS_URL, + token=settings.MILVUS_TOKEN, + db_name="", + ) + logger.info("Milvus 客户端连接成功") + except Exception as e: + logger.error(f"Milvus 客户端连接失败: {e}") + raise + return _milvus_client + + +def create_collection(): + """ + 创建 Milvus 集合 sketch_vectors + + 集合结构： + - path (PK, varchar(512)) - 主键，MinIO 逻辑 URL + - sys_file_id (int64, 可为NULL) - 系统文件ID + - style (varchar(50), 可为NULL) - 风格样式 + - category (varchar(100), 可为NULL) - 类别 + - is_system_sketch (int8, 默认 1) - 标记字段：1-系统图，0-用户图 + - deprecated (int8, 默认 0) - 是否废弃 + - feature_vector (FloatVector(2048)) - 2048维特征向量 + """ + client = get_milvus_client() + + # 检查集合是否已存在 + collections = client.list_collections() + if MILVUS_COLLECTION_SKETCH_VECTORS in collections: + logger.info(f"集合 {MILVUS_COLLECTION_SKETCH_VECTORS} 已存在") + return + + try: + # 解析 Milvus URL + # 处理 http://host.docker.internal:19530 格式 + url_clean = settings.MILVUS_URL.replace("http://", "").replace("https://", "") + if ":" in url_clean: + host, port_str = url_clean.split(":", 1) + port = int(port_str) + else: + host = url_clean + port = 19530 + + # 使用传统 API 创建集合（更可靠） + # 连接到 Milvus（如果未连接） + try: + connections.connect( + alias=settings.MILVUS_ALIAS, + host=host, + port=port, + token=settings.MILVUS_TOKEN if settings.MILVUS_TOKEN else None + ) + logger.info(f"已连接到 Milvus: {host}:{port}") + except Exception as conn_e: + # 如果连接已存在，忽略错误 + if "already exists" in str(conn_e).lower() or "Connection already exists" in str(conn_e): + logger.info("Milvus 连接已存在") + else: + logger.warning(f"连接 Milvus 时出现警告: {conn_e}") + + # 定义字段 + fields = [ + FieldSchema(name="path", dtype=DataType.VARCHAR, is_primary=True, max_length=512), + FieldSchema(name="sys_file_id", dtype=DataType.INT64), + FieldSchema(name="style", dtype=DataType.VARCHAR, max_length=50), + FieldSchema(name="category", dtype=DataType.VARCHAR, max_length=50), + FieldSchema(name="is_system_sketch", dtype=DataType.INT8), + FieldSchema(name="deprecated", dtype=DataType.INT8), + FieldSchema( + name="feature_vector", + dtype=DataType.FLOAT_VECTOR, + dim=RECOMMENDATION_CONFIG["vector_dim"] + ) + ] + + # 创建 schema + schema = CollectionSchema( + fields=fields, + description="Sketch vectors collection for recommendation system" + ) + + # 创建集合 + collection = Collection( + name=MILVUS_COLLECTION_SKETCH_VECTORS, + schema=schema, + using=settings.MILVUS_ALIAS + ) + + # 创建索引 + # 注意：使用 IP（内积）作为度量类型，与搜索时保持一致 + # 如果向量已归一化，IP 等价于 COSINE + index_params = { + "metric_type": "IP", # 内积（Inner Product） + "index_type": "IVF_FLAT", + "params": {"nlist": 1024} + } + + collection.create_index( + field_name="feature_vector", + index_params=index_params + ) + + logger.info(f"集合 {MILVUS_COLLECTION_SKETCH_VECTORS} 创建成功") + + except Exception as e: + logger.error(f"创建集合失败: {e}", exc_info=True) + raise + + +def insert_vectors(data: List[Dict[str, Any]]): + """ + 批量插入向量到 Milvus + + Args: + data: 数据列表，每个元素包含： + - path: str + - sys_file_id: int (可选) + - style: str (可选) + - category: str (可选) + - is_system_sketch: int (默认 1) + - deprecated: int (默认 0) + - feature_vector: List[float] (2048维) + """ + if not data: + return + + client = get_milvus_client() + + try: + client.insert( + collection_name=MILVUS_COLLECTION_SKETCH_VECTORS, + data=data + ) + logger.info(f"成功插入 {len(data)} 条向量数据") + except Exception as e: + logger.error(f"插入向量失败: {e}", exc_info=True) + raise + + +def query_vectors_by_paths(paths: List[str]) -> Dict[str, Dict]: + """ + 根据 path 列表批量查询向量 + + Args: + paths: path 列表 + + Returns: + {path: {feature_vector: [...], ...}} 字典 + """ + if not paths: + return {} + + client = get_milvus_client() + + try: + # 构建查询表达式 + # 使用 filter 参数而不是 expr（根据 pymilvus MilvusClient API） + # 对于字符串列表，使用单引号包裹每个值 + path_list = ", ".join([f"'{p}'" for p in paths]) + filter_expr = f"path in [{path_list}]" + + results = client.query( + collection_name=MILVUS_COLLECTION_SKETCH_VECTORS, + filter=filter_expr, + output_fields=["path", "feature_vector", "style", "category", "sys_file_id", "is_system_sketch", "deprecated"] + ) + + # 转换为字典 + result_dict = {} + for r in results: + result_dict[r["path"]] = r + + return result_dict + except Exception as e: + logger.error(f"查询向量失败: {e}", exc_info=True) + return {} + + +def search_similar_vectors( + query_vector: np.ndarray, + category: str, + topk: int = 500, + style: Optional[str] = None, + style_boost_ratio: float = 0.2 +) -> List[Dict]: + """ + 向量相似度检索 + + Args: + query_vector: 查询向量（2048维） + category: 类别过滤 + topk: 返回数量 + style: 风格过滤（可选）- 当提供时，会给对应style的结果加分 + style_boost_ratio: 风格加分比例（默认0.1，即10%） + + Returns: + 检索结果列表，每个元素包含 path, score, style, category 等字段 + """ + client = get_milvus_client() + + try: + # 如果没有指定style，使用原始逻辑 + if not style: + filter_expr = f"category == '{category}' && deprecated == 0" + results = client.search( + collection_name=MILVUS_COLLECTION_SKETCH_VECTORS, + data=[query_vector.tolist()], + anns_field="feature_vector", + search_params={"metric_type": "IP", "params": {"nprobe": 10}}, + limit=topk, + filter=filter_expr, + output_fields=["path", "style", "category", "sys_file_id"] + ) + else: + # 有style参数时，使用两阶段搜索策略 + + # 第一阶段：搜索匹配style的向量，使用boosted query vector + filter_expr_style = f"category == '{category}' && deprecated == 0 && style == '{style}'" + boosted_query = query_vector * (1 + style_boost_ratio) + results_style = client.search( + collection_name=MILVUS_COLLECTION_SKETCH_VECTORS, + data=[boosted_query.tolist()], + anns_field="feature_vector", + search_params={"metric_type": "IP", "params": {"nprobe": 10}}, + limit=topk, + filter=filter_expr_style, + output_fields=["path", "style", "category", "sys_file_id"] + ) + + # 第二阶段：搜索其他style的向量 + filter_expr_others = f"category == '{category}' && deprecated == 0 && style != '{style}'" + results_others = client.search( + collection_name=MILVUS_COLLECTION_SKETCH_VECTORS, + data=[query_vector.tolist()], + anns_field="feature_vector", + search_params={"metric_type": "IP", "params": {"nprobe": 10}}, + limit=topk, + filter=filter_expr_others, + output_fields=["path", "style", "category", "sys_file_id"] + ) + + # 合并结果 + results = [] + if results_style and len(results_style) > 0: + results.extend(results_style[0]) + if results_others and len(results_others) > 0: + results.extend(results_others[0]) + + # 转换为单个结果列表格式 + results = [results] if results else [] + + # 格式化结果 + formatted_results = [] + if results and len(results) > 0: + for hit in results[0]: + formatted_results.append({ + "path": hit.get("entity", {}).get("path", ""), + "score": hit.get("distance", 0.0), + "style": hit.get("entity", {}).get("style", ""), + "category": hit.get("entity", {}).get("category", ""), + "sys_file_id": hit.get("entity", {}).get("sys_file_id") + }) + + # 按分数排序并返回topk + formatted_results.sort(key=lambda x: x["score"], reverse=True) + return formatted_results[:topk] + + except Exception as e: + logger.error(f"向量检索失败: {e}", exc_info=True) + return [] + + +def query_random_candidates(category: str, style: Optional[str] = None, limit: int = 10) -> List[Dict]: + """ + 随机查询候选（用于探索分支） + + Args: + category: 类别 + style: 风格（可选） + limit: 返回数量 + + Returns: + 候选列表 + """ + client = get_milvus_client() + + try: + # 构建过滤表达式 + filter_expr = f"category == '{category}' && deprecated == 0" + if style: + filter_expr += f" && style == '{style}'" + + # 查询所有符合条件的记录 + results = client.query( + collection_name=MILVUS_COLLECTION_SKETCH_VECTORS, + filter=filter_expr, + output_fields=["path", "style", "category"], + limit=10000 + ) + + # 随机选择 + if len(results) > limit: + import random + results = random.sample(results, limit) + + return results + except Exception as e: + logger.error(f"随机查询候选失败: {e}", exc_info=True) + return [] diff --git a/app/service/recommendation_system/precompute.py b/app/service/recommendation_system/precompute.py new file mode 100644 index 0000000..235e80f --- /dev/null +++ b/app/service/recommendation_system/precompute.py @@ -0,0 +1,557 @@ +""" +预计算模块 +包含：数据库表结构优化、Milvus集合创建、系统图向量预计算、初始用户偏好向量生成 +""" +import logging +import math +import pymysql +import numpy as np +from datetime import datetime +from typing import List, Dict, Tuple, Optional +from collections import defaultdict + +from app.service.recommendation_system.config import ( + MYSQL_CONFIG, TABLE_USER_PREFERENCE_LOG, TABLE_SYS_FILE, + RECOMMENDATION_CONFIG, REDIS_KEY_USER_PREF_PREFIX +) +from app.service.recommendation_system.vector_utils import extract_feature_vector, normalize_vector, compute_weighted_average +from app.service.recommendation_system.milvus_client import ( + create_collection, insert_vectors, query_vectors_by_paths +) +from app.service.utils.redis_utils import Redis +import json + +logger = logging.getLogger(__name__) + + +def optimize_database_table(): + """ + 优化 user_preference 表结构 + 添加冗余字段和索引 + """ + conn = None + try: + conn = pymysql.connect(**MYSQL_CONFIG) + cursor = conn.cursor() + + # 1. 添加冗余字段 + logger.info("添加冗余字段...") + alter_sqls = [ + f"ALTER TABLE {TABLE_USER_PREFERENCE_LOG} ADD COLUMN category VARCHAR(100) COMMENT '类别：lower(level3_type + \"_\" + level2_type)'", + f"ALTER TABLE {TABLE_USER_PREFERENCE_LOG} ADD COLUMN style VARCHAR(50) COMMENT '风格样式'", + f"ALTER TABLE {TABLE_USER_PREFERENCE_LOG} ADD COLUMN is_system_sketch TINYINT(1) DEFAULT 1 COMMENT '是否为系统图（1-是，0-用户图）'", + f"ALTER TABLE {TABLE_USER_PREFERENCE_LOG} ADD COLUMN sys_file_id BIGINT NULL COMMENT '系统文件ID'", + ] + + for sql in alter_sqls: + try: + cursor.execute(sql) + logger.info(f"执行成功: {sql[:50]}...") + except Exception as e: + if "Duplicate column name" in str(e): + logger.info(f"字段已存在，跳过: {sql[:50]}...") + else: + logger.warning(f"执行失败: {sql[:50]}... 错误: {e}") + + # 2. 创建索引（MySQL 不支持 IF NOT EXISTS，需要先检查） + logger.info("创建索引...") + index_definitions = [ + ("idx_account_category_time", ["account_id", "category", "data_time"]), + ("idx_account_path", ["account_id", "path"]), + ] + + for index_name, columns in index_definitions: + try: + # 检查索引是否已存在 + cursor.execute(f""" + SELECT COUNT(*) + FROM information_schema.statistics + WHERE table_schema = DATABASE() + AND table_name = '{TABLE_USER_PREFERENCE_LOG}' + AND index_name = '{index_name}' + """) + exists = cursor.fetchone()[0] > 0 + + if exists: + logger.info(f"索引已存在，跳过: {index_name}") + else: + # 创建索引 + columns_str = ', '.join(columns) + create_sql = f"CREATE INDEX {index_name} ON {TABLE_USER_PREFERENCE_LOG}({columns_str})" + cursor.execute(create_sql) + logger.info(f"索引创建成功: {index_name}") + except Exception as e: + logger.warning(f"索引创建失败: {index_name} 错误: {e}") + + conn.commit() + logger.info("数据库表结构优化完成") + + except Exception as e: + logger.error(f"数据库表结构优化失败: {e}", exc_info=True) + if conn: + conn.rollback() + finally: + if conn: + conn.close() + + +def migrate_historical_data(batch_size: int = 1000): + """ + 历史数据迁移：批量更新冗余字段 + + Args: + batch_size: 每批处理数量 + """ + conn = None + try: + conn = pymysql.connect(**MYSQL_CONFIG) + cursor = conn.cursor() + + # 查询需要更新的记录数 + cursor.execute(f""" + SELECT COUNT(*) + FROM {TABLE_USER_PREFERENCE_LOG} u + WHERE u.category IS NULL + """) + total_count = cursor.fetchone()[0] + logger.info(f"需要迁移的记录数: {total_count}") + + if total_count == 0: + logger.info("无需迁移数据") + return + + # 分批处理 + offset = 0 + processed = 0 + + while offset < total_count: + # 查询一批记录 + cursor.execute(f""" + SELECT u.id, u.path + FROM {TABLE_USER_PREFERENCE_LOG} u + WHERE u.category IS NULL + LIMIT {batch_size} OFFSET {offset} + """) + records = cursor.fetchall() + + if not records: + break + + # 批量更新 + for record_id, path in records: + # 查询 t_sys_file 表 + cursor.execute(f""" + SELECT id, url, style, level3_type, level2_type, deprecated + FROM {TABLE_SYS_FILE} + WHERE url = %s + LIMIT 1 + """, (path,)) + + sys_file = cursor.fetchone() + + if sys_file: + # 系统图 + sys_file_id, url, style, level3_type, level2_type, deprecated = sys_file + category = f"{level3_type.lower()}_{level2_type.lower()}" + + cursor.execute(f""" + UPDATE {TABLE_USER_PREFERENCE_LOG} + SET category = %s, + style = %s, + is_system_sketch = 1, + sys_file_id = %s + WHERE id = %s + """, (category, style, sys_file_id, record_id)) + else: + # 用户图 + cursor.execute(f""" + UPDATE {TABLE_USER_PREFERENCE_LOG} + SET is_system_sketch = 0, + category = NULL, + style = NULL, + sys_file_id = NULL + WHERE id = %s + """, (record_id,)) + + conn.commit() + processed += len(records) + offset += batch_size + logger.info(f"已迁移 {processed}/{total_count} 条记录") + + logger.info("历史数据迁移完成") + + except Exception as e: + logger.error(f"历史数据迁移失败: {e}", exc_info=True) + if conn: + conn.rollback() + finally: + if conn: + conn.close() + + +def precompute_system_sketch_vectors(batch_size: int = 1000, retry_times: int = 3): + """ + 系统图向量预计算与导入 + + Args: + batch_size: 每批处理数量 + retry_times: 失败重试次数 + """ + conn = None + try: + conn = pymysql.connect(**MYSQL_CONFIG) + cursor = conn.cursor() + + # 1. 数据筛选 + logger.info("查询系统图数据...") + cursor.execute(f""" + SELECT id, url, style, level3_type, level2_type, deprecated + FROM {TABLE_SYS_FILE} + WHERE level1_type = 'Images' + AND style IS NOT NULL + AND style != '' + AND deprecated != 1 + """) + records = cursor.fetchall() + logger.info(f"找到 {len(records)} 条系统图记录") + + if not records: + logger.warning("没有找到系统图数据") + return + + # 2. 批量处理 + failed_records = [] + batch_data = [] + + for idx, (sys_file_id, url, style, level3_type, level2_type, deprecated) in enumerate(records, 1): + try: + # 计算 category + category = f"{level3_type.lower()}_{level2_type.lower()}" + + # 提取特征向量 + feature_vector = extract_feature_vector(url) + + # 检查向量是否有效 + if np.all(feature_vector == 0): + logger.warning(f"向量提取失败，跳过: {url}") + failed_records.append((sys_file_id, url)) + continue + + # 准备数据 + data_item = { + "path": url, + "sys_file_id": sys_file_id, + "style": style, + "category": category, + "is_system_sketch": 1, + "deprecated": deprecated if deprecated else 0, + "feature_vector": feature_vector.tolist() + } + + batch_data.append(data_item) + + # 批量写入 + if len(batch_data) >= batch_size: + try: + insert_vectors(batch_data) + batch_data = [] + logger.info(f"已处理 {idx}/{len(records)} 条记录") + except Exception as e: + logger.error(f"批量写入失败: {e}") + failed_records.extend([(item["sys_file_id"], item["path"]) for item in batch_data]) + batch_data = [] + + except Exception as e: + logger.error(f"处理记录失败 [{url}]: {e}") + failed_records.append((sys_file_id, url)) + + # 写入剩余数据 + if batch_data: + try: + insert_vectors(batch_data) + except Exception as e: + logger.error(f"写入剩余数据失败: {e}") + failed_records.extend([(item["sys_file_id"], item["path"]) for item in batch_data]) + + # 3. 重试失败记录 + if failed_records and retry_times > 0: + logger.info(f"重试 {len(failed_records)} 条失败记录...") + for retry in range(retry_times): + retry_failed = [] + for sys_file_id, url in failed_records: + try: + category = f"{level3_type.lower()}_{level2_type.lower()}" + feature_vector = extract_feature_vector(url) + if not np.all(feature_vector == 0): + data_item = { + "path": url, + "sys_file_id": sys_file_id, + "style": style, + "category": category, + "is_system_sketch": 1, + "deprecated": 0, + "feature_vector": feature_vector.tolist() + } + insert_vectors([data_item]) + else: + retry_failed.append((sys_file_id, url)) + except Exception as e: + logger.error(f"重试失败 [{url}]: {e}") + retry_failed.append((sys_file_id, url)) + + failed_records = retry_failed + if not failed_records: + break + + if failed_records: + logger.warning(f"仍有 {len(failed_records)} 条记录处理失败") + + logger.info("系统图向量预计算完成") + + except Exception as e: + logger.error(f"系统图向量预计算失败: {e}", exc_info=True) + finally: + if conn: + conn.close() + + +def compute_user_preference_vector( + account_id: int, + category: str, + conn: Optional[pymysql.connections.Connection] = None, + max_date: Optional[datetime] = None +) -> Optional[np.ndarray]: + """ + 计算用户偏好向量 + + Args: + account_id: 用户ID + category: 类别 + conn: 数据库连接（可选） + max_date: 最大日期（可选，用于评估时只使用训练集数据） + + Returns: + 用户偏好向量（2048维），失败返回 None + """ + from datetime import datetime + + should_close = False + if conn is None: + conn = pymysql.connect(**MYSQL_CONFIG) + should_close = True + + try: + cursor = conn.cursor() + + # 1. 获取点赞记录（如果指定了max_date，只查询该日期之前的数据） + if max_date: + cursor.execute(f""" + SELECT path, data_time + FROM {TABLE_USER_PREFERENCE_LOG} + WHERE account_id = %s AND category = %s AND style is not null + AND data_time < %s + ORDER BY data_time DESC + """, (account_id, category, max_date)) + else: + cursor.execute(f""" + SELECT path, data_time + FROM {TABLE_USER_PREFERENCE_LOG} + WHERE account_id = %s AND category = %s AND style is not null + ORDER BY data_time DESC + """, (account_id, category)) + + like_records = cursor.fetchall() + + if not like_records: + return None + + # 2. 批量查询点赞次数（如果指定了max_date，只统计该日期之前的点赞） + paths = [r[0] for r in like_records] + if not paths: + return None + + placeholders = ','.join(['%s'] * len(paths)) + if max_date: + cursor.execute(f""" + SELECT path, COUNT(*) as like_count + FROM {TABLE_USER_PREFERENCE_LOG} + WHERE account_id = %s AND category = %s AND path IN ({placeholders}) + AND data_time < %s + GROUP BY path + """, (account_id, category) + tuple(paths) + (max_date,)) + else: + cursor.execute(f""" + SELECT path, COUNT(*) as like_count + FROM {TABLE_USER_PREFERENCE_LOG} + WHERE account_id = %s AND category = %s AND path IN ({placeholders}) + GROUP BY path + """, (account_id, category) + tuple(paths)) + + like_counts = {row[0]: row[1] for row in cursor.fetchall()} + + # 3. 批量获取向量 + vectors_dict = query_vectors_by_paths(paths) + + # 处理查询不到的 path（用户图或异常情况） + missing_paths = [p for p in paths if p not in vectors_dict] + if missing_paths: + logger.info(f"用户 {account_id} 类别 {category} 有 {len(missing_paths)} 个 path 需要实时计算向量") + # 目前未有非系统图向量，跳过 + # 这里可以实时计算并写入 Milvus，但为了简化，先跳过 + # 实际实现中应该调用 vector_utils.extract_feature_vector 并写入 Milvus + + # 4. 计算权重并加权平均 + vectors = [] + weights = [] + K_half = RECOMMENDATION_CONFIG["K_half"] + + for k, (path, data_time) in enumerate(like_records, 1): + if path not in vectors_dict: + continue + + vector_data = vectors_dict[path] + feature_vector = np.array(vector_data["feature_vector"]) + + # 时间衰减权重 + d_k = 0.5 ** (k / K_half) + + # 点赞次数权重 + like_count = like_counts.get(path, 1) + p_i = 1 + math.log(1 + like_count) + + # 综合权重 + w_i = d_k * p_i + # w_i = p_i + + vectors.append(feature_vector) + weights.append(w_i) + + if not vectors: + return None + + # 5. 计算加权平均并做 L2 归一化，IP≈cosine + preference_vector = compute_weighted_average(vectors, weights) + preference_vector = normalize_vector(preference_vector) + + return preference_vector + + except Exception as e: + logger.error(f"计算用户偏好向量失败 [user={account_id}, category={category}]: {e}", exc_info=True) + return None + finally: + if should_close and conn: + conn.close() + + +def generate_initial_user_preference_vectors(batch_size: int = 100): + """ + 初始用户偏好向量生成 + + Args: + batch_size: 每批处理用户数 + """ + conn = None + try: + conn = pymysql.connect(**MYSQL_CONFIG) + cursor = conn.cursor() + + # 1. 扫描历史数据 + logger.info("扫描用户和类别组合...") + cursor.execute(f""" + SELECT DISTINCT account_id, category + FROM {TABLE_USER_PREFERENCE_LOG} + WHERE category IS NOT NULL + AND style IS NOT NULL + """) + + user_categories = cursor.fetchall() + logger.info(f"找到 {len(user_categories)} 个用户-类别组合") + + if not user_categories: + logger.warning("没有找到用户-类别组合") + return + + # 2. 批量处理 + processed = 0 + failed = 0 + + for account_id, category in user_categories: + try: + # 计算偏好向量 + preference_vector = compute_user_preference_vector(account_id, category, conn) + + if preference_vector is not None: + # 写入 Redis + key = f"{REDIS_KEY_USER_PREF_PREFIX}:{account_id}:{category}" + # 序列化向量（使用 JSON） + vector_json = json.dumps(preference_vector.tolist()) + Redis.write( + key=key, + value=vector_json, + expire=RECOMMENDATION_CONFIG["redis_expire_seconds"] + ) + processed += 1 + else: + failed += 1 + + if (processed + failed) % batch_size == 0: + logger.info(f"已处理 {processed + failed}/{len(user_categories)} 个组合，成功: {processed}, 失败: {failed}") + + except Exception as e: + logger.error(f"处理失败 [user={account_id}, category={category}]: {e}") + failed += 1 + + logger.info(f"初始用户偏好向量生成完成，成功: {processed}, 失败: {failed}") + + except Exception as e: + logger.error(f"初始用户偏好向量生成失败: {e}", exc_info=True) + finally: + if conn: + conn.close() + + +def run_precompute(): + """ + 运行所有预计算任务 + """ + logger.info("=" * 50) + logger.info("开始预计算任务") + logger.info("=" * 50) + + # 1. 优化数据库表结构 + # logger.info("\n[1/5] 优化数据库表结构...") + # optimize_database_table() + + # # 2. 创建 Milvus 集合 + # logger.info("\n[2/5] 创建 Milvus 集合...") + # create_collection() + + # 3. 历史数据迁移 + # logger.info("\n[3/5] 历史数据迁移...") + # migrate_historical_data() + + # # 4. 系统图向量预计算 + # logger.info("\n[4/5] 系统图向量预计算...") + # precompute_system_sketch_vectors() + + # 5. 初始用户偏好向量生成 + logger.info("\n[5/5] 初始用户偏好向量生成...") + generate_initial_user_preference_vectors() + + logger.info("=" * 50) + logger.info("预计算任务完成") + logger.info("=" * 50) + + +if __name__ == "__main__": + # # 1. 优化数据库表结构 + # logger.info("\n[1/5] 优化数据库表结构...") + # optimize_database_table() + # + # # 3. 历史数据迁移 + # logger.info("\n[3/5] 历史数据迁移...") + # migrate_historical_data() + + # 5. 初始用户偏好向量生成 + logger.info("\n[5/5] 初始用户偏好向量生成...") + generate_initial_user_preference_vectors() diff --git a/app/service/recommendation_system/recommendation_api.py b/app/service/recommendation_system/recommendation_api.py new file mode 100644 index 0000000..7a856b8 --- /dev/null +++ b/app/service/recommendation_system/recommendation_api.py @@ -0,0 +1,214 @@ +""" +推荐接口实现 +实现探索/利用分支、向量检索、Softmax抽样等功能 +""" +import logging +import math +import random +import numpy as np +from typing import List, Dict, Optional + +from app.service.recommendation_system.config import RECOMMENDATION_CONFIG, REDIS_KEY_USER_PREF_PREFIX +from app.service.recommendation_system.milvus_client import search_similar_vectors, query_random_candidates +from app.service.recommendation_system.precompute import compute_user_preference_vector +from app.service.recommendation_system.vector_utils import normalize_vector +from app.service.utils.redis_utils import Redis +import json + +logger = logging.getLogger(__name__) + + +def get_user_preference_vector(user_id: int, category: str) -> Optional[np.ndarray]: + """ + 获取用户偏好向量 + + Args: + user_id: 用户ID + category: 类别 + + Returns: + 用户偏好向量（2048维），失败返回 None + """ + # 1. 从 Redis 获取 + key = f"{REDIS_KEY_USER_PREF_PREFIX}:{user_id}:{category}" + vector_json = Redis.read(key) + + if vector_json: + try: + vector_list = json.loads(vector_json) + return np.array(vector_list, dtype=np.float32) + except Exception as e: + logger.warning(f"解析 Redis 向量失败 [user={user_id}, category={category}]: {e}") + + # 2. 如果不存在，实时计算 + logger.info(f"Redis 中不存在用户偏好向量，实时计算 [user={user_id}, category={category}]") + preference_vector = compute_user_preference_vector(user_id, category) + + if preference_vector is not None: + # 写入 Redis + vector_json = json.dumps(preference_vector.tolist()) + Redis.write( + key=key, + value=vector_json, + expire=RECOMMENDATION_CONFIG["redis_expire_seconds"] + ) + + return preference_vector + + +def explore_branch(category: str, style: Optional[str] = None) -> List[str]: + """ + 探索分支（随机推荐） + + Args: + category: 类别 + style: 风格（可选） + + Returns: + 推荐结果列表，每个元素包含 path, style, category 等字段 + """ + # 查询候选（随机池） + pool_size = 10 # 固定查询10个，然后随机选择 + + candidates = query_random_candidates(category, style, limit=pool_size) + + if not candidates: + logger.warning(f"探索分支：类别 {category} 没有候选数据") + return [] + + # 随机选择 + if len(candidates) > 1: + import random + candidates = random.sample(candidates, 1) + + # 格式化返回结果 + return [candidate.get("path", "") for candidate in candidates[:1]] + + +def exploit_branch( + user_id: int, + category: str, + style: Optional[str] = None +) -> List[str]: + """ + 利用分支（基于向量相似度推荐） + + Args: + user_id: 用户ID + category: 类别 + num_recommendations: 返回数量 + style: 风格（可选，用于加分） + + Returns: + 推荐结果列表，每个元素包含 path, style, category, similarity, sample_score 等字段 + """ + # 1. 获取用户偏好向量 + embedding = get_user_preference_vector(user_id, category) + + if embedding is None: + logger.warning(f"利用分支：无法获取用户偏好向量，回退到探索分支 [user={user_id}, category={category}]") + return explore_branch(category, style) + + # 2. Milvus 相似度检索（内积 IP） + topk = RECOMMENDATION_CONFIG["topk"] + results = search_similar_vectors(embedding, category, topk) + + if not results: + logger.warning(f"利用分支：向量检索无结果，回退到探索分支 [user={user_id}, category={category}]") + return explore_branch(category, style) + + # 3. Style 加分（可选，需传入 style 参数） + style_bonus = RECOMMENDATION_CONFIG["style_bonus"] + if style: + for result in results: + similarity = result["score"] + if result.get("style") == style: + # 加分：相似度 * (1 + style_bonus) + similarity = similarity * (1 + style_bonus) + result["final_score"] = similarity + else: + for result in results: + result["final_score"] = result["score"] + + # 4. Softmax 抽样 + scores = [r["final_score"] for r in results] + probabilities = softmax_with_temperature(scores, RECOMMENDATION_CONFIG["softmax_temperature"]) + + # 根据概率抽样 + if not results: + return [] + + selected_index = np.random.choice(len(results), size=1, p=probabilities, replace=False) + selected_results = [results[int(selected_index[0])]] + + # 5. 返回结果 + return [result.get("path", "") for result in selected_results] + + +def softmax_with_temperature(scores: List[float], temperature: float = 1.0) -> List[float]: + """ + Softmax 函数（带温度参数） + + Args: + scores: 分数列表 + temperature: 温度参数 + + Returns: + 概率列表 + """ + if not scores: + return [] + + # 除以温度 + scaled_scores = [s / temperature for s in scores] + + # 减去最大值（数值稳定性） + max_score = max(scaled_scores) + exp_scores = [math.exp(s - max_score) for s in scaled_scores] + + # 归一化 + sum_exp = sum(exp_scores) + if sum_exp == 0: + # 如果所有分数都是负无穷或非常小，返回均匀分布 + return [1.0 / len(scores)] * len(scores) + + probabilities = [exp_s / sum_exp for exp_s in exp_scores] + return probabilities + + +def get_recommendations( + user_id: int, + category: str, + style: Optional[str] = None +) -> List[str]: + """ + 获取推荐结果（主函数） + + Args: + user_id: 用户ID + category: 类别（如 female_skirt） + num_recommendations: 返回推荐数量（默认 1） + style: 风格（可选）：若传入，则在利用分支对同 style 的候选进行加分 + + Returns: + 推荐结果列表，每个元素包含 path 等字段 + """ + try: + # 1. 读取配置参数 + explore_ratio = RECOMMENDATION_CONFIG["explore_ratio"] + + # 2. 探索/利用决策 + r = random.random() # 生成随机数 (0-1) + + if r < explore_ratio: + logger.debug(f"探索分支 [user={user_id}, category={category}]") + return explore_branch(category, style) + + logger.debug(f"利用分支 [user={user_id}, category={category}]") + return exploit_branch(user_id, category, style) + + except Exception as e: + logger.error(f"获取推荐结果失败 [user={user_id}, category={category}]: {e}", exc_info=True) + # 容错：回退到探索分支 + return explore_branch(category, style) + diff --git a/app/service/recommendation_system/vector_utils.py b/app/service/recommendation_system/vector_utils.py new file mode 100644 index 0000000..f6a78ab --- /dev/null +++ b/app/service/recommendation_system/vector_utils.py @@ -0,0 +1,189 @@ +""" +向量计算工具类 +包含 ResNet50 特征提取、向量归一化等功能 +""" +import io +import logging +import numpy as np +import torch +from torchvision import models, transforms +from PIL import Image +from minio import Minio + +from app.core.config import settings +from app.service.recommendation_system.config import RECOMMENDATION_CONFIG + +logger = logging.getLogger(__name__) + +# 图像预处理（与ResNet训练时的预处理一致） +transform = transforms.Compose([ + transforms.Resize((224, 224)), # ResNet 要求 224x224 的输入 + transforms.ToTensor(), # 转换为 Tensor + transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), # 标准化 +]) + +# 加载预训练的 ResNet50 模型（去掉最后全连接层） +_resnet_model = None + + +def get_resnet_model(): + """获取 ResNet50 模型（单例模式）""" + global _resnet_model + if _resnet_model is None: + logger.info("加载 ResNet50 模型...") + _resnet_model = models.resnet50(pretrained=True) + modules = list(_resnet_model.children())[:-1] # 移除最后的全连接层 + _resnet_model = torch.nn.Sequential(*modules) + _resnet_model.eval() # 设置为评估模式 + logger.info("ResNet50 模型加载完成") + return _resnet_model + + +# MinIO 客户端（单例） +_minio_client = None + + +def get_minio_client(): + """获取 MinIO 客户端（单例模式）""" + global _minio_client + if _minio_client is None: + _minio_client = Minio( + settings.MINIO_URL, + access_key=settings.MINIO_ACCESS, + secret_key=settings.MINIO_SECRET, + secure=settings.MINIO_SECURE + ) + return _minio_client + + +def get_image_from_minio(path: str) -> Image.Image: + """ + 从 MinIO 获取图片 + + Args: + path: MinIO 逻辑 URL，格式如 "bucket_name/object_name" + + Returns: + PIL Image 对象，失败返回 None + """ + try: + # 分割路径，获取桶名和文件路径 + path_parts = path.split('/', 1) + if len(path_parts) != 2: + logger.error(f"路径格式错误: {path}") + return None + + bucket_name, file_name = path_parts + minio_client = get_minio_client() + + # 获取文件 + obj = minio_client.get_object(bucket_name, file_name) + img_data = obj.read() # 读取图像数据 + img = Image.open(io.BytesIO(img_data)) # 将数据转为图像对象 + + return img + except Exception as e: + logger.error(f"从 MinIO 获取图片失败 [{path}]: {e}") + return None + + +def extract_feature_vector(path: str) -> np.ndarray: + """ + 使用 ResNet50 提取图片特征向量（2048维） + + Args: + path: MinIO 逻辑 URL + + Returns: + 2048维特征向量（numpy array），失败返回零向量 + """ + try: + # 从 MinIO 获取图像 + img = get_image_from_minio(path) + if img is None: + logger.warning(f"无法获取图片，返回零向量: {path}") + return np.zeros(RECOMMENDATION_CONFIG["vector_dim"], dtype=np.float32) + + # 预处理 + # 部分 MinIO 图片可能是 RGBA/CMYK，转换成 RGB 以匹配 3 通道标准化参数 + if img.mode != "RGB": + try: + img = img.convert("RGB") + except Exception: + logger.warning(f"无法转换图片为RGB，返回零向量: {path}") + return np.zeros(RECOMMENDATION_CONFIG["vector_dim"], dtype=np.float32) + + img_tensor = transform(img).unsqueeze(0) # 扩展维度以适应批量处理 + + # 提取特征 + resnet_model = get_resnet_model() + with torch.no_grad(): # 在不需要计算梯度的情况下进行推断 + feature_vector = resnet_model(img_tensor) # 获取 ResNet 的输出 + feature_vector = feature_vector.squeeze().cpu().numpy() # 转换为 NumPy 数组并去掉 batch 维度 + + # 确保是 2048 维 + if feature_vector.ndim > 1: + feature_vector = feature_vector.flatten() + + # 确保维度正确 + if len(feature_vector) != RECOMMENDATION_CONFIG["vector_dim"]: + logger.warning(f"向量维度不正确: {len(feature_vector)}, 期望: {RECOMMENDATION_CONFIG['vector_dim']}") + # 如果维度不对，尝试调整 + if len(feature_vector) > RECOMMENDATION_CONFIG["vector_dim"]: + feature_vector = feature_vector[:RECOMMENDATION_CONFIG["vector_dim"]] + else: + padded = np.zeros(RECOMMENDATION_CONFIG["vector_dim"], dtype=np.float32) + padded[:len(feature_vector)] = feature_vector + feature_vector = padded + + return feature_vector.astype(np.float32) + except Exception as e: + logger.error(f"提取特征向量失败 [{path}]: {e}", exc_info=True) + return np.zeros(RECOMMENDATION_CONFIG["vector_dim"], dtype=np.float32) + + +def normalize_vector(vector: np.ndarray) -> np.ndarray: + """ + L2 归一化向量 + + Args: + vector: 输入向量 + + Returns: + 归一化后的向量 + """ + norm = np.linalg.norm(vector) + if norm == 0: + return vector + return vector / norm + + +def compute_weighted_average(vectors: list, weights: list) -> np.ndarray: + """ + 计算加权平均向量 + + Args: + vectors: 向量列表 + weights: 权重列表 + + Returns: + 加权平均向量（不做归一化，模长为加权平均后的尺度） + """ + if not vectors or not weights: + return np.zeros(RECOMMENDATION_CONFIG["vector_dim"], dtype=np.float32) + + # 确保所有向量都是 numpy array + vectors = [np.array(v) for v in vectors] + weights = np.array(weights) + + # 计算加权和 + weighted_sum = np.zeros_like(vectors[0]) + for v, w in zip(vectors, weights): + weighted_sum += v * w + + # 返回加权平均（除以权重和，不做 L2 归一化，模长不会随条数线性暴涨） + weight_total = weights.sum() + if weight_total == 0: + return weighted_sum + return weighted_sum / weight_total + diff --git a/app/service/search_image_with_text/service.py b/app/service/search_image_with_text/service.py index 6d4f490..54691a8 100644 --- a/app/service/search_image_with_text/service.py +++ b/app/service/search_image_with_text/service.py @@ -1,12 +1,8 @@ import chromadb -import hashlib - -import pandas as pd from chromadb.config import Settings from chromadb.utils.embedding_functions.ollama_embedding_function import OllamaEmbeddingFunction -from tqdm import tqdm -from app.core.config import OLLAMA_URL, CHROMADB_PATH +from app.core.config import OLLAMA_URL, settings # 读取 csv 文件 # csv_file_path = r'D:/Files/csv/output/output.csv' @@ -15,7 +11,7 @@ from app.core.config import OLLAMA_URL, CHROMADB_PATH # df = pd.read_csv(csv_file_path, encoding='Windows-1252') # 创建 Chroma 客户端 -client = chromadb.Client(Settings(is_persistent=True, persist_directory=CHROMADB_PATH)) +client = chromadb.Client(Settings(is_persistent=True, persist_directory=settings.CHROMADB_PATH)) # client = chromadb.Client(Settings(is_persistent=True, persist_directory="./service/search_image_with_text/vector_db")) # client = chromadb.Client(Settings(is_persistent=True, persist_directory="D:/workspace/AiDLab/vector_db")) # 创建集合 diff --git a/app/service/super_resolution/service.py b/app/service/super_resolution/service.py index c2cf39d..be90745 100644 --- a/app/service/super_resolution/service.py +++ b/app/service/super_resolution/service.py @@ -5,21 +5,26 @@ import time import cv2 import minio.error import numpy as np +import pika import redis import torch import tritonclient.grpc as grpcclient +from minio import Minio -from app.core.config import * +from app.core.config import settings, SR_TRITON_URL, SR_RABBITMQ_QUEUES, SR_MODEL_NAME +from app.core.rabbit_mq_config import RABBITMQ_PARAMS from app.schemas.super_resolution import SuperResolutionModel -from app.service.utils.oss_client import oss_get_image, oss_upload_image +from app.service.utils.new_oss_client import oss_get_image, oss_upload_image logger = logging.getLogger() +minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE) + class SuperResolution: def __init__(self, data): self.triton_client = grpcclient.InferenceServerClient(url=SR_TRITON_URL) - self.redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True) + self.redis_client = redis.StrictRedis(host=settings.REDIS_HOST, port=settings.REDIS_PORT, db=settings.REDIS_DB, decode_responses=True) self.tasks_id = data.sr_tasks_id self.user_id = self.tasks_id[self.tasks_id.rfind('-') + 1:] self.sr_image_url = data.sr_image_url @@ -97,8 +102,8 @@ class SuperResolution: image_bytes = cv2.imencode('.jpg', image)[1].tobytes() # res = self.minio_client.put_object(f'{SR_MINIO_BUCKET}', f'{self.user_id}/sr/output/{self.tasks_id}.jpg', io.BytesIO(image_bytes), len(image_bytes), content_type='image/png') object_name = f'{self.user_id}/sr/output/{self.tasks_id}.jpg' - oss_upload_image(bucket=SR_MINIO_BUCKET, object_name=object_name, image_bytes=image_bytes) - image_url = f"{SR_MINIO_BUCKET}/{object_name}" + oss_upload_image(oss_client=minio_client, bucket="aida-users", object_name=object_name, image_bytes=image_bytes) + image_url = f"aida-users/{object_name}" return image_url except Exception as e: logger.warning(f"upload_png_mask runtime exception : {e}") @@ -122,7 +127,7 @@ class SuperResolution: def infer_cancel(tasks_id): - redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True) + redis_client = redis.StrictRedis(host=settings.REDIS_HOST, port=settings.REDIS_PORT, db=settings.REDIS_DB, decode_responses=True) data = {'tasks': tasks_id, 'status': 'REVOKED', 'message': "revoked", 'data': 'revoked'} sr_data = json.dumps({'status': 'REVOKED', 'message': "revoked", 'data': 'revoked'}) redis_client.set(tasks_id, sr_data) diff --git a/app/service/utils/new_oss_client.py b/app/service/utils/new_oss_client.py index 2f292f6..ac91f47 100644 --- a/app/service/utils/new_oss_client.py +++ b/app/service/utils/new_oss_client.py @@ -8,10 +8,10 @@ import urllib3 from PIL import Image, ImageDraw from minio import Minio -from app.core.config import * +from app.core.config import settings from app.service.utils.decorator import RunTime -minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE) +minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE) # 自定义 Retry 类 @@ -56,17 +56,16 @@ def oss_get_image(oss_client, bucket, object_name, data_type): data_bytes = BytesIO(image_data.read()) image_object = Image.open(data_bytes) except Exception as e: - logger.warning(f"{OSS} | 获取图片出现异常 ######: {e}") + logger.warning(f" | 获取图片出现异常 ######: {e}") return image_object -@RunTime def oss_upload_image(oss_client, bucket, object_name, image_bytes): req = None try: req = oss_client.put_object(bucket_name=bucket, object_name=object_name, data=io.BytesIO(image_bytes), length=len(image_bytes), content_type='image/png') except Exception as e: - logger.warning(f"{OSS} | 上传图片出现异常 ######: {e}") + logger.warning(f" | 上传图片出现异常 ######: {e}") return req @@ -82,7 +81,7 @@ if __name__ == '__main__': # url = "aida-users/89/sketchboard/female/Dress/e6724ab7-8d3f-4677-abe0-c3e42ab7af85.jpeg" # url = "aida-users/87/print/956614a2-7e75-4fbe-9ed0-c1831e37a2c9-4-87.png" # url = "aida-users/89/single_logo/123-89.png" - url = "aida-results/result_d2577888-92d7-11f0-9cfd-e0d362103998.png" + url = "aida-results/result_a7adcbd8-ef8d-11f0-8c92-0966ede33ab5.png" # url = "aida-collection-element/12148/Sketchboard/95ea577b-305b-4a62-b30a-39c0dd3ddb3f.png" read_type = "2" diff --git a/app/service/utils/oss_client.py b/app/service/utils/oss_client.py deleted file mode 100644 index 0bd9853..0000000 --- a/app/service/utils/oss_client.py +++ /dev/null @@ -1,90 +0,0 @@ -import io -import logging -from io import BytesIO -import cv2 -import numpy as np -import urllib3 -from PIL import Image -from minio import Minio - -from app.core.config import * - - -# 自定义 Retry 类 -class CustomRetry(urllib3.Retry): - def increment(self, method=None, url=None, response=None, error=None, **kwargs): - # 调用父类的 increment 方法 - new_retry = super(CustomRetry, self).increment(method, url, response, error, **kwargs) - # 打印重试信息 - logger.info(f"重试连接: {method} {url}，错误: {error}，重试次数: {self.total - new_retry.total}") - return new_retry - - -logger = logging.getLogger() -timeout = urllib3.Timeout(connect=1, read=10.0) # 连接超时 5 秒，读取超时 10 秒 -http_client = urllib3.PoolManager( - num_pools=10, # 设置连接池大小 - maxsize=10, - timeout=timeout, - cert_reqs='CERT_REQUIRED', # 需要证书验证 - retries=CustomRetry( - total=5, - backoff_factor=0.2, - status_forcelist=[500, 502, 503, 504], - ), -) - - -# 获取图片 -def oss_get_image(bucket, object_name, data_type): - # cv2 默认全通道读取 - image_object = None - try: - oss_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE, http_client=http_client) - image_data = oss_client.get_object(bucket_name=bucket, object_name=object_name) - if data_type == "cv2": - image_bytes = image_data.read() - image_array = np.frombuffer(image_bytes, np.uint8) # 转成8位无符号整型 - image_object = cv2.imdecode(image_array, cv2.IMREAD_UNCHANGED) - if image_object.dtype == np.uint16: - image_object = (image_object / 256).astype('uint8') - else: - data_bytes = BytesIO(image_data.read()) - image_object = Image.open(data_bytes) - except Exception as e: - logger.warning(f"{OSS} | 获取图片出现异常 ######: {e}") - return image_object - - -def oss_upload_image(bucket, object_name, image_bytes): - req = None - try: - oss_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE) - req = oss_client.put_object(bucket_name=bucket, object_name=object_name, data=io.BytesIO(image_bytes), length=len(image_bytes), content_type='image/png') - except Exception as e: - logger.warning(f"{OSS} | 上传图片出现异常 ######: {e}") - return req - - -if __name__ == '__main__': - # url = "aida-results/result_0002186a-e631-11ee-86a6-b48351119060.png" - # url = "aida-collection-element/11523/Moodboard/f60af0d2-94c2-48f9-90ff-74b8e8a481b5.jpg" - # url = "aida-sys-image/images/female/outwear/0628000054.jpg" - # url = "aida-users/89/product_image/string-89.png" - # url = "test/845046c7-4f62-4f54-a4a9-c26d49c6969335b5b3a9-d335-4871-a46c-3cc3caf07da259629dfd1f1f555a2e2a9def7e719366.png" - # url = 'aida-users/89/relight_image/123-89.png' - # url = 'aida-users/89/relight_image/123-89.png' - # url = 'aida-users/89/relight_image/123-89.png' - # url = "aida-users/89/sketchboard/female/Dress/e6724ab7-8d3f-4677-abe0-c3e42ab7af85.jpeg" - # url = "aida-users/87/print/956614a2-7e75-4fbe-9ed0-c1831e37a2c9-4-87.png" - # url = "aida-users/89/single_logo/123-89.png" - url = "aida-results/result_e2673d92-8d25-11ef-be24-0826ae3ad6b3.png" - # url = "aida-collection-element/12148/Sketchboard/95ea577b-305b-4a62-b30a-39c0dd3ddb3f.png" - read_type = "cv2" - if read_type == "cv2": - img = oss_get_image(bucket=url.split('/')[0], object_name=url[url.find('/') + 1:], data_type=read_type) - cv2.imshow("", img) - cv2.waitKey(0) - else: - img = oss_get_image(bucket=url.split('/')[0], object_name=url[url.find('/') + 1:], data_type=read_type) - img.show() diff --git a/app/service/utils/redis_utils.py b/app/service/utils/redis_utils.py index 012fbe0..8761fde 100644 --- a/app/service/utils/redis_utils.py +++ b/app/service/utils/redis_utils.py @@ -1,6 +1,6 @@ import redis -from app.core.config import REDIS_HOST, REDIS_PORT +from app.core.config import settings class Redis(object): @@ -10,8 +10,8 @@ class Redis(object): @staticmethod def _get_r(): - host = REDIS_HOST - port = REDIS_PORT + host = settings.REDIS_HOST + port = settings.REDIS_PORT db = 0 r = redis.StrictRedis(host, port, db) return r @@ -91,6 +91,21 @@ class Redis(object): r = cls._get_r() r.expire(name, expire_in_seconds) + @classmethod + def scan_keys(cls, pattern="*"): + """ + 扫描匹配模式的key + """ + r = cls._get_r() + keys = [] + cursor = 0 + while True: + cursor, partial_keys = r.scan(cursor, match=pattern, count=1000) + keys.extend(partial_keys) + if cursor == 0: + break + return [key.decode('utf-8') if isinstance(key, bytes) else key for key in keys] + if __name__ == '__main__': redis_client = Redis() diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000..a0bd4de --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,25 @@ +services: + aida_server: + build: + context: . + dockerfile: Dockerfile + working_dir: /app + volumes: + - ./app:/app/app + - ./.env_prod:/app/.env + - /etc/localtime:/etc/localtime:ro + - ./seg_cache:/seg_cache + ports: + - "10200:80" + depends_on: + - redis + redis: + image: redis + container_name: aida_redis + restart: always + ports: + - "6400:6379" + volumes: + - ./redis/data:/data + - ./redis/conf/redis.conf:/etc/redis/redis.conf + command: redis-server /etc/redis/redis.conf --appendonly yes \ No newline at end of file diff --git a/logging_env.py b/logging_env.py index 08873b0..2285782 100644 --- a/logging_env.py +++ b/logging_env.py @@ -1,10 +1,15 @@ -from app.core.config import LOGS_PATH +import os + +from app.core.config import settings LOGGER_CONFIG_DICT = { 'version': 1, 'disable_existing_loggers': False, 'formatters': { - 'simple': {'format': '%(asctime)s %(filename)s [line:%(lineno)d] %(levelname)s %(message)s'} + 'simple': { + 'format': '%(asctime)s %(filename)s [line:%(lineno)d] %(levelname)s %(message)s', + 'datefmt': '%Y-%m-%d %H:%M:%S' # 补充日期格式，日志更易读 + } }, 'handlers': { 'console': { @@ -17,7 +22,7 @@ LOGGER_CONFIG_DICT = { 'class': 'logging.handlers.RotatingFileHandler', 'level': 'INFO', 'formatter': 'simple', - 'filename': f'{LOGS_PATH}info.log', + 'filename': os.path.join(settings.LOGS_PATH, 'info.log'), 'maxBytes': 10485760, 'backupCount': 50, 'encoding': 'utf8', @@ -26,7 +31,7 @@ LOGGER_CONFIG_DICT = { 'class': 'logging.handlers.RotatingFileHandler', 'level': 'ERROR', 'formatter': 'simple', - 'filename': f'{LOGS_PATH}error.log', + 'filename': os.path.join(settings.LOGS_PATH, 'error.log'), 'maxBytes': 10485760, 'backupCount': 20, 'encoding': 'utf8', @@ -35,7 +40,7 @@ LOGGER_CONFIG_DICT = { 'class': 'logging.handlers.RotatingFileHandler', 'level': 'DEBUG', 'formatter': 'simple', - 'filename': f'{LOGS_PATH}debug.log', + 'filename': os.path.join(settings.LOGS_PATH, 'debug.log'), 'maxBytes': 10485760, 'backupCount': 50, 'encoding': 'utf8', @@ -45,7 +50,7 @@ LOGGER_CONFIG_DICT = { 'my_module': {'level': 'INFO', 'handlers': ['console'], 'propagate': 'no'} }, 'root': { - 'level': 'INFO', + 'level': 'DEBUG', 'handlers': ['error_file_handler', 'info_file_handler', 'debug_file_handler', 'console'], }, } diff --git a/pyproject.toml b/pyproject.toml index aa10a24..e143cde 100755 --- a/pyproject.toml +++ b/pyproject.toml @@ -2,17 +2,53 @@ name = "trinity-client-aida" version = "0.1.0" description = "Add your description here" -requires-python = ">=3.12" +requires-python = ">=3.12,<3.13" dependencies = [ - "apscheduler>=3.11.0", - "celery>=5.5.3", - "geventhttpclient>=2.3.4", - "google-search-results>=2.4.2", - "moviepy>=2.2.1", - "numpy==1.26.4", - "pandas-stubs==2.2.3.250527", - "pika-stubs==0.1.3", - "python-multipart>=0.0.20", - "tritonclient[all]>=2.58.0", - "types-urllib3==1.26.25.14", + "agentaction", + "aio-pika>=9.5.8", + "apscheduler>=3.11.1", + "bs4>=0.0.2", + "callbacks>=0.3.0", + "celery>=5.6.0", + "celery-types>=0.23.0", + "chromadb>=1.3.7", + "dashscope>=1.25.5", + "dominate>=2.9.1", + "dotenv>=0.9.9", + "fastapi[standard]>=0.125.0", + "image>=1.5.33", + "langchain>=1.2.0", + "langchain-community>=0.4.1", + "load>=1.0.14", + "load-dotenv>=0.1.0", + "loguru>=0.7.3", + "minio>=7.2.20", + "mmcv>=2.2.0", + "moviepy==1.0.3", + "numpy<2", + "ollama>=0.6.1", + "opencv-python>=4.11.0.86", + "pandas>=2.3.3", + "pandas-stubs~=2.3.3", + "pika>=1.3.2", + "pillow>=12.0.0", + "pyasyncore>=1.0.4", + "pydantic>=2.12.5", + 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