diff --git a/app/api/api_design.py b/app/api/api_design.py index aa9fe43..665d544 100644 --- a/app/api/api_design.py +++ b/app/api/api_design.py @@ -2,13 +2,13 @@ import json import logging import os -from fastapi import APIRouter, HTTPException, UploadFile, File, Form +from fastapi import APIRouter, HTTPException, UploadFile, File, Form, BackgroundTasks from app.schemas.design import DesignModel, DesignProgressModel, ModelProgressModel, DBGConfigModel 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 +from app.service.design_fast.design_generate import design_generate, design_generate_v2 from app.service.design_fast.utils.redis_utils import Redis router = APIRouter() @@ -16,7 +16,7 @@ logger = logging.getLogger() @router.post("/design") -def design(request_data: DesignModel): +def design(request_data: DesignModel, background_tasks: BackgroundTasks): """ 创建一个具有以下参数的请求体: 示例参数： @@ -67,7 +67,6 @@ def design(request_data: DesignModel): 0 ], "path": "aida-sys-image/images/female/trousers/0825000630.jpg", - "seg_mask_url": "test/result.png", "print": { "element": { "element_angle_list": [], @@ -104,7 +103,6 @@ def design(request_data: DesignModel): 0 ], "path": "aida-sys-image/images/female/blouse/0902003811.jpg", - "seg_mask_url": "test/result.png", "print": { "element": { "element_angle_list": [], @@ -141,7 +139,6 @@ def design(request_data: DesignModel): 0 ], "path": "aida-sys-image/images/female/outwear/0825000410.jpg", - "seg_mask_url": "test/result.png", "print": { "element": { "element_angle_list": [], @@ -167,6 +164,10 @@ def design(request_data: DesignModel): 1.0, 1.0 ], + "transparent":{ + "mask_url":"test/transparent_test/transparent_mask.png", + "scale":0.1 + }, "type": "Outwear" }, { @@ -195,6 +196,182 @@ def design(request_data: DesignModel): return ResponseModel(data=data) +@router.post("/design_v2") +async def design_v2(request_data: DesignModel, background_tasks: BackgroundTasks): + """ + 创建一个具有以下参数的请求体: + 示例参数： + { + "objects": [ + { + "basic": { + "body_point_test": { + "waistband_right": [ + 200, + 241 + ], + "hand_point_right": [ + 223, + 297 + ], + "waistband_left": [ + 112, + 241 + ], + "hand_point_left": [ + 92, + 305 + ], + "shoulder_left": [ + 99, + 116 + ], + "shoulder_right": [ + 215, + 116 + ] + }, + "layer_order": true, + "scale_bag": 0.7, + "scale_earrings": 0.16, + "self_template": true, + "single_overall": "overall", + "switch_category": "" + }, + "items": [ + { + "businessId": 270372, + "color": "30 28 28", + "image_id": 69780, + "offset": [ + 0, + 0 + ], + "path": "aida-sys-image/images/female/trousers/0825000630.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": 10, + "resize_scale": [ + 1.0, + 1.0 + ], + "type": "Trousers" + }, + { + "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, + "type": "Body" + } + ] + } + ], + "process_id": "83" + } + """ + try: + # 异步 + logger.info(f"generate_image request item is : @@@@@@:{json.dumps(request_data.dict())}") + background_tasks.add_task(design_generate_v2, request_data) + except Exception as e: + logger.warning(f"design Run Exception @@@@@@:{e}") + raise HTTPException(status_code=404, detail=str(e)) + return ResponseModel() + + @router.post('/get_progress') def get_progress(request_data: DesignProgressModel): """ diff --git a/app/api/api_generate_image.py b/app/api/api_generate_image.py index 3dee667..53790a3 100644 --- a/app/api/api_generate_image.py +++ b/app/api/api_generate_image.py @@ -26,6 +26,7 @@ def generate_image(request_item: GenerateImageModel, background_tasks: Backgroun - **mode**: 生成模式，img2img或者txt2img - **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 示例参数： { @@ -34,7 +35,8 @@ def generate_image(request_item: GenerateImageModel, background_tasks: Backgroun "image_url": "aida-collection-element/87/Printboard/842c09cf-7297-42d9-9e6e-9c17d4a13cb5.jpg", "mode": "img2img", "category": "sketch", - "gender": "male" + "gender": "male", + "version": "fast" } """ try: diff --git a/app/core/config.py b/app/core/config.py index 35c12b7..d369ff2 100644 --- a/app/core/config.py +++ b/app/core/config.py @@ -93,9 +93,6 @@ OPENAI_MODEL_LIST = {"gpt-3.5-turbo-0613", "gpt-4-0613", "gpt-4-32k-0613", } -# attribute service config -ATT_TRITON_URL = "10.1.1.240:10000" - # SR service config SR_MODEL_NAME = "super_resolution" SR_TRITON_URL = "10.1.1.240:10031" @@ -103,8 +100,12 @@ SR_MINIO_BUCKET = "aida-users" SR_RABBITMQ_QUEUES = os.getenv("SR_RABBITMQ_QUEUES", f"SuperResolution{RABBITMQ_ENV}") # GenerateImage service config -GI_MODEL_NAME = 'stable_diffusion_xl' -GI_MODEL_URL = '10.1.1.240:10041' +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' + GI_MINIO_BUCKET = "aida-users" GI_RABBITMQ_QUEUES = os.getenv("GI_RABBITMQ_QUEUES", f"GenerateImage{RABBITMQ_ENV}") GI_SYS_IMAGE_URL = "aida-sys-image/generate_image/white_image.jpg" @@ -113,17 +114,15 @@ GI_SYS_IMAGE_URL = "aida-sys-image/generate_image/white_image.jpg" SLOGAN_RABBITMQ_QUEUES = os.getenv("SLOGAN_RABBITMQ_QUEUES", f"Slogan{RABBITMQ_ENV}") # Generate Single Logo service config -GSL_MODEL_URL = '10.1.1.240:10041' +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 = os.getenv("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 = 'diffusion_ensemble_all' -GPI_MODEL_NAME_SINGLE = 'stable_diffusion_1_5_cnet' - -GPI_MODEL_URL = '10.1.1.240:10041' +GPI_MODEL_NAME_OVERALL = 'sdxl_ensemble_all' +GPI_MODEL_URL = '10.1.1.243:10051' # Generate Single Logo service config GRI_RABBITMQ_QUEUES = os.getenv("GEN_RELIGHT_IMAGE_RABBITMQ_QUEUES", f"Relight{RABBITMQ_ENV}") @@ -132,14 +131,14 @@ GRI_MODEL_NAME_SINGLE = 'stable_diffusion_1_5_relight' GRI_MODEL_URL = '10.1.1.240:10051' # SEG service config -SEG_MODEL_URL = '10.1.1.240:10000' SEGMENTATION = { "new_model_name": "seg_knet", "name": "seg_ocrnet_hr18", "input": "seg_input__0", "output": "seg_output__0", } - +# ollama config +OLLAMA_URL = "http://10.1.1.243:11434/api/embeddings" # DESIGN config DESIGN_MODEL_URL = '10.1.1.240:10000' AIDA_CLOTHING = "aida-clothing" diff --git a/app/schemas/generate_image.py b/app/schemas/generate_image.py index 3dd7cf8..11e295f 100644 --- a/app/schemas/generate_image.py +++ b/app/schemas/generate_image.py @@ -8,6 +8,7 @@ class GenerateImageModel(BaseModel): mode: str category: str gender: str + version: str class GenerateSingleLogoImageModel(BaseModel): diff --git a/app/service/attribute/service_att_recognition.py b/app/service/attribute/service_att_recognition.py index 1251891..f93146e 100644 --- a/app/service/attribute/service_att_recognition.py +++ b/app/service/attribute/service_att_recognition.py @@ -28,7 +28,7 @@ class AttributeRecognition: } ) self.const = const - self.triton_client = httpclient.InferenceServerClient(url=f"{ATT_TRITON_URL}") + self.triton_client = httpclient.InferenceServerClient(url=f"{DESIGN_MODEL_URL}") def get_result(self): for sketch in self.request_data: diff --git a/app/service/attribute/service_category_recognition.py b/app/service/attribute/service_category_recognition.py index f917af2..7c277c9 100644 --- a/app/service/attribute/service_category_recognition.py +++ b/app/service/attribute/service_category_recognition.py @@ -26,7 +26,7 @@ class CategoryRecognition: 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.request_data = [] - self.triton_client = httpclient.InferenceServerClient(url=ATT_TRITON_URL) + self.triton_client = httpclient.InferenceServerClient(url=DESIGN_MODEL_URL) for sketch in request_data: self.request_data.append( { diff --git a/app/service/design_fast/design_generate.py b/app/service/design_fast/design_generate.py index 582de4c..f4012cf 100644 --- a/app/service/design_fast/design_generate.py +++ b/app/service/design_fast/design_generate.py @@ -2,11 +2,12 @@ import logging import threading import time +import requests from minio import Minio from app.core.config import * -from app.service.design_fast.item import BodyItem, TopItem, BottomItem -from app.service.design_fast.utils.organize import organize_body, organize_clothing +from app.service.design_fast.item import BodyItem, TopItem, BottomItem, AccessoriesItem +from app.service.design_fast.utils.organize import organize_body, organize_clothing, organize_accessories 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.utils.decorator import RunTime @@ -26,9 +27,14 @@ def process_item(item, basic): 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() - else: + 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 ['accessories']: + bottom_server = AccessoriesItem(data=item, basic=basic, minio_client=minio_client) + item_data = bottom_server.process() + else: + raise NotImplementedError(f"Item type {item['type']} not implemented") return item_data @@ -38,6 +44,10 @@ def process_layer(item, layers): body_layer = organize_body(item) layers.append(body_layer) return item['body_image'].size + elif item['name'] == 'accessories': + front_layer, back_layer = organize_accessories(item) + layers.append(front_layer) + layers.append(back_layer) else: front_layer, back_layer = organize_clothing(item) layers.append(front_layer) @@ -57,7 +67,7 @@ def design_generate(request_data): def process_object(step, object): nonlocal active_threads basic = object['basic'] - items_response = {'layers': []} + items_response = {'layers': [], 'objectSign': object['objectSign'] if 'objectSign' in object.keys() else ""} if basic['single_overall'] == "overall": item_results = [] for item in object['items']: @@ -126,6 +136,117 @@ def design_generate(request_data): return object_response +@RunTime +def design_generate_v2(request_data): + objects_data = request_data.dict()['objects'] + threads = [] + + def process_object(step, object): + basic = object['basic'] + items_response = { + 'layers': [], + 'objectSign': object['objectSign'] if 'objectSign' in object.keys() else "", + 'requestId': object['requestId'] if 'requestId' in object.keys() else "" + } + if basic['single_overall'] == "overall": + item_results = [] + 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) + layers = sorted(layers, key=lambda s: s.get("priority", float('inf'))) + + layers, new_size = update_base_size_priority(layers, body_size) + + for lay in layers: + items_response['layers'].append({ + 'image_category': "body" if lay['name'] == 'mannequin' else 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, + # '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) + else: + item_result = process_item(object['items'][0], basic) + 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']) + + # 发送结果给java端 + url = "https://3998-117-143-125-51.ngrok-free.app/api/third/party/receiveDesignResults" + headers = { + 'Accept': "*/*", + 'Accept-Encoding': "gzip, deflate, br", + 'User-Agent': "PostmanRuntime-ApipostRuntime/1.1.0", + 'Connection': "keep-alive", + 'Content-Type': "application/json" + } + response = post_request(url, json_data=items_response, headers=headers) + if response: + # 打印结果 + logger.info(response.text) + logger.info(items_response) + + for step, object in enumerate(objects_data): + t = threading.Thread(target=process_object, args=(step, object)) + threads.append(t) + t.start() + + +def post_request(url, data=None, json_data=None, headers=None, auth=None, timeout=5): + """ + 发送POST请求的封装函数 + + :param url: 接口的URL地址 + :param data: 要发送的数据（字典形式，用于表单数据等，会自动编码） + :param json_data: 要发送的JSON数据（字典形式，会自动转换为JSON字符串） + :param headers: 请求头字典 + :param auth: 认证信息（如 ('username', 'password') 形式用于基本认证） + :param timeout: 超时时间，单位为秒 + :return: 返回接口的响应对象 + """ + try: + response = requests.post( + url, + data=data, + json=json_data, + headers=headers, + auth=auth, + timeout=timeout + ) + response.raise_for_status() # 如果请求失败，抛出异常 + return response + except requests.RequestException as e: + print(f"POST请求出错: {e}") + return None + + if __name__ == '__main__': object_data = { "objects": [ diff --git a/app/service/design_fast/item.py b/app/service/design_fast/item.py index f7af700..ec18b17 100644 --- a/app/service/design_fast/item.py +++ b/app/service/design_fast/item.py @@ -1,4 +1,4 @@ -from app.service.design_fast.pipeline import LoadImage, KeyPoint, Segmentation, Color, PrintPainting, Scaling, Split, LoadBodyImage, ContourDetection, BackPerspective +from app.service.design_fast.pipeline import LoadImage, KeyPoint, Segmentation, Color, PrintPainting, Scaling, Split, LoadBodyImage, ContourDetection class BaseItem: @@ -9,6 +9,27 @@ class BaseItem: self.result.update(basic) +class AccessoriesItem(BaseItem): + def __init__(self, data, basic, minio_client): + super().__init__(data, basic) + self.Accessories_pipeline = [ + LoadImage(minio_client), + # KeyPoint(), + ContourDetection(), + # Segmentation(minio_client), + # BackPerspective(minio_client), + Color(minio_client), + PrintPainting(minio_client), + Scaling(), + Split(minio_client) + ] + + def process(self): + for item in self.Accessories_pipeline: + self.result = item(self.result) + return self.result + + class TopItem(BaseItem): def __init__(self, data, basic, minio_client): super().__init__(data, basic) diff --git a/app/service/design_fast/pipeline/loading.py b/app/service/design_fast/pipeline/loading.py index 0ce0dfa..5a55d9d 100644 --- a/app/service/design_fast/pipeline/loading.py +++ b/app/service/design_fast/pipeline/loading.py @@ -74,6 +74,8 @@ class LoadImage: keypoint = 'head_point' elif name == 'earring': keypoint = 'ear_point' + elif name == 'accessories': + keypoint = "accessories" else: raise KeyError(f"{name} does not belong to item category list: blouse, outwear, dress, trousers, skirt, " f"bag, shoes, hairstyle, earring.") diff --git a/app/service/design_fast/pipeline/scale.py b/app/service/design_fast/pipeline/scale.py index 732fcd8..d1c7a36 100644 --- a/app/service/design_fast/pipeline/scale.py +++ b/app/service/design_fast/pipeline/scale.py @@ -18,7 +18,7 @@ class Scaling: - int(result['body_point_test'][result['keypoint'] + '_right'][0])) ** 2 + 1 ) - + if distance_clo == 0: result['scale'] = 1 else: @@ -46,4 +46,16 @@ class Scaling: result['scale'] = result['scale_bag'] elif result['keypoint'] == 'ear_point': result['scale'] = result['scale_earrings'] + elif result['keypoint'] == 'accessories': + # 由于没有识别配饰keypoint的模型 所以统一将配饰的两个关键点设定为 (0,0) (0,img.width) + # 模特的关键点设定为(0,0) (0,320/2) 距离比例简写为 160 / img.width + distance_clo = result['img_shape'][1] + distance_bdy = 320 / 2 + + if distance_clo == 0: + result['scale'] = 1 + else: + result['scale'] = distance_bdy / distance_clo + else: + result['scale'] = 1 return result diff --git a/app/service/design_fast/pipeline/split.py b/app/service/design_fast/pipeline/split.py index 737b50e..344c5c5 100644 --- a/app/service/design_fast/pipeline/split.py +++ b/app/service/design_fast/pipeline/split.py @@ -8,9 +8,10 @@ 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 from app.service.utils.generate_uuid import generate_uuid -from app.service.utils.new_oss_client import oss_upload_image +from app.service.utils.new_oss_client import oss_upload_image, oss_get_image class Split(object): @@ -20,7 +21,7 @@ class Split(object): def __call__(self, result): try: - if result['name'] in ('outwear', 'dress', 'blouse', 'skirt', 'trousers', 'tops', 'bottoms'): + if result['name'] in ('outwear', 'dress', 'blouse', 'skirt', 'trousers', 'tops', 'bottoms','accessories'): front_mask = result['front_mask'] back_mask = result['back_mask'] rgba_image = rgb_to_rgba(result['final_image'], front_mask + back_mask) @@ -30,6 +31,24 @@ class Split(object): 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)) + 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_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) + 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 = front_mask.shape diff --git a/app/service/design_fast/utils/organize.py b/app/service/design_fast/utils/organize.py index 92be044..33edc4f 100644 --- a/app/service/design_fast/utils/organize.py +++ b/app/service/design_fast/utils/organize.py @@ -55,6 +55,45 @@ def organize_clothing(layer): return front_layer, back_layer +def organize_accessories(layer): + # 起始坐标 + start_point = (0, 0) + # 前片数据 + 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'], + image_url=layer['front_image_url'], + mask_url=layer['mask_url'], + 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_image_url=layer['pattern_image_url'], + pattern_image=layer['pattern_image'], + # 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'], + image_url=layer['back_image_url'], + mask_url=layer['mask_url'], + 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_image_url=layer['pattern_image_url'], + # back_perspective_url=layer['back_perspective_url'] if 'back_perspective_url' in layer.keys() else "" + ) + return front_layer, back_layer + + def calculate_start_point(keypoint_type, scale, clothes_point, body_point, offset, resize_scale): """ Align left diff --git a/app/service/design_fast/utils/synthesis_item.py b/app/service/design_fast/utils/synthesis_item.py index f5d505f..d7711f3 100644 --- a/app/service/design_fast/utils/synthesis_item.py +++ b/app/service/design_fast/utils/synthesis_item.py @@ -79,9 +79,11 @@ def synthesis(data, size, basic_info): _, 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) + accessories_outer_mask = np.array(binary_body_mask) top = True bottom = True + accessories = True i = len(data) while i: i -= 1 @@ -109,10 +111,23 @@ def synthesis(data, size, basic_info): 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 accessories and data[i]['name'] in ['accessories_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] + accessories_outer_mask = background + accessories_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, accessories_outer_mask) for layer in data: if layer['image'] is not None: diff --git a/app/service/design_fast/utils/transparent.py b/app/service/design_fast/utils/transparent.py new file mode 100644 index 0000000..3f73807 --- /dev/null +++ b/app/service/design_fast/utils/transparent.py @@ -0,0 +1,26 @@ +from PIL import Image + + +def sketch_to_transparent(image, mask, transparency): + # 打开原始图片 + image = image.convert("RGBA") + # 打开mask图片，假设mask图片是灰度图，白色区域为要处理的区域，黑色区域为保留的区域 + mask = Image.fromarray(mask) + + # 根据透明度调整因子，将透明度转换为0-255之间的值 + alpha_value = int((1 - transparency) * 255.0) + + # 获取图片的像素数据 + image_pixels = image.load() + mask_pixels = mask.load() + + width, height = image.size + + for y in range(height): + for x in range(width): + # 如果mask区域对应的像素为白色（值大于128，这里假设白色为要处理的区域，可根据实际情况调整） + if mask_pixels[x, y] > 128: + r, g, b, a = image_pixels[x, y] + image_pixels[x, y] = (r, g, b, alpha_value) + + return image diff --git a/app/service/generate_image/service_generate_image.py b/app/service/generate_image/service_generate_image.py index dac211c..86912f8 100644 --- a/app/service/generate_image/service_generate_image.py +++ b/app/service/generate_image/service_generate_image.py @@ -35,7 +35,12 @@ class GenerateImage: # 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.version = request_data.version + if request_data.version == "fast": + self.grpc_client = grpcclient.InferenceServerClient(url=FAST_GI_MODEL_URL) + 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) if request_data.mode == "img2img": # cv2 读图片是BGR PIL读图片是RGB @@ -87,23 +92,28 @@ class GenerateImage: image_result = cv2.cvtColor(np.squeeze(image.astype(np.uint8)), cv2.COLOR_RGB2BGR) is_smudge = True if self.category == "sketch": - # 色阶调整 - cutoff = 1 - levels_img = autoLevels(image_result, cutoff) - # 亮度调整 - luminance = luminance_adjust(0.3, levels_img) - # 去背景 - remove_bg_image = remove_background(luminance) - # 人脸检测 - # if face_detect_pic(remove_bg_image, self.user_id, self.category, self.tasks_id) > 0: - # is_smudge = False - # else: - # 污点/ - is_smudge, not_smudge_image = stain_detection(remove_bg_image, self.user_id, self.category, self.tasks_id) - # 类型识别 - category, scores, not_smudge_image = generate_category_recognition(image=remove_bg_image, gender=self.gender) - self.generate_data['category'] = str(category) - image_result = not_smudge_image + if self.version == "fast": + # 色阶调整 + cutoff = 1 + levels_img = autoLevels(image_result, cutoff) + # 亮度调整 + luminance = luminance_adjust(0.3, levels_img) + # 去背景 + remove_bg_image = remove_background(luminance) + # 人脸检测 + # if face_detect_pic(remove_bg_image, self.user_id, self.category, self.tasks_id) > 0: + # is_smudge = False + # else: + # 污点/ + is_smudge, not_smudge_image = stain_detection(remove_bg_image, self.user_id, self.category, self.tasks_id) + # 类型识别 + category, scores, not_smudge_image = generate_category_recognition(image=remove_bg_image, gender=self.gender) + self.generate_data['category'] = str(category) + image_result = not_smudge_image + else: + category, scores, not_smudge_image = generate_category_recognition(image=image_result, gender=self.gender) + self.generate_data['category'] = str(category) + 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}", file_name=f"{self.tasks_id}.png") @@ -134,15 +144,19 @@ class GenerateImage: 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_image = grpcclient.InferInput("input_image", image_obj.shape, np_to_triton_dtype(image_obj.dtype)) + input_mode = grpcclient.InferInput("mode", mode_obj.shape, np_to_triton_dtype(mode_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.grpc_client.async_infer(model_name=GI_MODEL_NAME, inputs=inputs, callback=self.callback) + if self.version == "fast": + ctx = self.grpc_client.async_infer(model_name=FAST_GI_MODEL_NAME, inputs=inputs, callback=self.callback) + else: + ctx = self.grpc_client.async_infer(model_name=GI_MODEL_NAME, inputs=inputs, callback=self.callback) + time_out = 600 generate_data = None while time_out > 0: @@ -181,11 +195,12 @@ def infer_cancel(tasks_id): if __name__ == '__main__': rd = GenerateImageModel( tasks_id="123-89", - prompt='skeleton sitting by the side of a river looking soulful, concert poster, 4k, artistic', + prompt='a single item of sketch of Wabi-sabi, skirt, tiered, 4k, white background', image_url="aida-collection-element/87/Printboard/842c09cf-7297-42d9-9e6e-9c17d4a13cb5.jpg", mode='txt2img', category="test", - gender="male" + gender="male", + version="high" ) server = GenerateImage(rd) print(server.get_result()) diff --git a/app/service/generate_image/service_generate_product_image.py b/app/service/generate_image/service_generate_product_image.py index 5ea6f83..1c20a13 100644 --- a/app/service/generate_image/service_generate_product_image.py +++ b/app/service/generate_image/service_generate_product_image.py @@ -15,7 +15,7 @@ import cv2 import numpy as np import redis import tritonclient.grpc as grpcclient -from PIL import Image, ImageOps +from PIL import Image from tritonclient.utils import np_to_triton_dtype from app.core.config import * @@ -41,7 +41,7 @@ class GenerateProductImage: self.batch_size = 1 self.product_type = request_data.product_type self.prompt = request_data.prompt - self.image, self.image_size = pre_processing_image(request_data.image_url) + self.image, self.image_size, self.left, self.top = pre_processing_image(request_data.image_url) self.tasks_id = request_data.tasks_id self.user_id = self.tasks_id[self.tasks_id.rfind('-') + 1:] self.gen_product_data = {'tasks_id': self.tasks_id, 'status': 'PENDING', 'message': "pending", 'image_url': ''} @@ -55,12 +55,10 @@ class GenerateProductImage: self.redis_client.set(self.tasks_id, json.dumps(self.gen_product_data)) else: # pil图像转成numpy数组 - if self.product_type == "single": - image = result.as_numpy("generated_cnet_image") - else: - image = result.as_numpy("generated_inpaint_image") + image = result.as_numpy("generated_inpaint_image") image_result = Image.fromarray(np.squeeze(image.astype(np.uint8))).resize(self.image_size) - image_url = upload_SDXL_image(image_result, user_id=self.user_id, category=f"{self.category}", file_name=f"{self.tasks_id}.png") + cropped_image = post_processing_image(image_result, self.left, self.top) + image_url = upload_SDXL_image(cropped_image, user_id=self.user_id, category=f"{self.category}", file_name=f"{self.tasks_id}.png") self.gen_product_data['status'] = "SUCCESS" self.gen_product_data['message'] = "success" self.gen_product_data['image_url'] = str(image_url) @@ -74,16 +72,16 @@ class GenerateProductImage: try: prompts = [self.prompt] * self.batch_size self.image = cv2.cvtColor(self.image, cv2.COLOR_BGR2RGB) - self.image = cv2.resize(self.image, (512, 768)) + self.image = cv2.resize(self.image, (1024, 1024)) images = [self.image.astype(np.uint8)] * self.batch_size if self.product_type == "single": text_obj = np.array(prompts, dtype="object").reshape(-1, 1) - image_obj = np.array(images, dtype=np.uint8).reshape((-1, 768, 512, 3)) + image_obj = np.array(images, dtype=np.uint8).reshape((-1, 1024, 1024, 3)) image_strength_obj = np.array(self.image_strength, dtype=np.float32).reshape(-1, 1) else: - text_obj = np.array(prompts, dtype="object").reshape(1) - image_obj = np.array(images, dtype=np.uint8).reshape((768, 512, 3)) + text_obj = np.array(prompts, dtype="object").reshape((1)) + image_obj = np.array(images, dtype=np.uint8).reshape((1024, 1024, 3)) image_strength_obj = np.array(self.image_strength, dtype=np.float32).reshape((1)) # 假设 prompts、images 和 self.image_strength 已经定义 @@ -94,11 +92,12 @@ class GenerateProductImage: input_text.set_data_from_numpy(text_obj) input_image.set_data_from_numpy(image_obj) - inputs = [input_text, input_image, input_image_strength] input_image_strength.set_data_from_numpy(image_strength_obj) + inputs = [input_text, input_image, input_image_strength] + if self.product_type == "single": - ctx = self.grpc_client.async_infer(model_name=GPI_MODEL_NAME_SINGLE, inputs=inputs, callback=self.callback) + ctx = self.grpc_client.async_infer(model_name="stable_diffusion_xl_cnet_inpaint", inputs=inputs, callback=self.callback) else: ctx = self.grpc_client.async_infer(model_name=GPI_MODEL_NAME_OVERALL, inputs=inputs, callback=self.callback) @@ -136,22 +135,13 @@ 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") + # resize 原图至1024*1024 + image = image.resize((int(1024 / image.height * image.width), 1024)) + # 原始图片的尺寸 width, height = image.size - # 计算长宽比为 3:2 的新尺寸 - desired_ratio = 2 / 3 - current_ratio = width / height - - if current_ratio > desired_ratio: - # 原始图片更宽，需要在上下添加 padding - new_width = width - new_height = int(width / desired_ratio) - else: - # 原始图片更高或者长宽比已经为 3:2 - new_height = height - new_width = int(height * desired_ratio) - + new_height, new_width = 1024, 1024 # 创建一个新的画布，大小为添加 padding 后的尺寸，并设置为白色背景 pad_image = Image.new('RGBA', (new_width, new_height), (0, 0, 0, 0)) @@ -160,9 +150,9 @@ def pre_processing_image(image_url): top = (new_height - height) // 2 pad_image.paste(image, (left, top)) - # 将画布 resize 成宽度 500，长度 750 - resized_image = pad_image.resize((500, 750)) - image_size = (512, 768) + # 将画布 resize 成宽度 1024，长度 1024 + resized_image = pad_image.resize((1024, 1024)) + image_size = (1024, 1024) if resized_image.mode in ('RGBA', 'LA') or (resized_image.mode == 'P' and 'transparency' in resized_image.info): # 创建白色背景 @@ -171,16 +161,29 @@ def pre_processing_image(image_url): background.paste(resized_image, mask=resized_image.split()[3]) image = np.array(background) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) - return image, image_size + return image, image_size, left, top + + +def post_processing_image(image, left, top): + resized_image = image.resize((int(image.width * (768 / image.height)), 768)) + # 计算裁剪的坐标 + left = (resized_image.width - 512) // 2 + upper = 0 + right = left + 512 + lower = 768 + + # 进行裁剪 + cropped_image = resized_image.crop((left, upper, right, lower)) + return cropped_image if __name__ == '__main__': rd = GenerateProductImageModel( tasks_id="123-89", # prompt="", - image_strength=0.9, - 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=0.7, + prompt="The best quality, masterpiece,outwear, 8K realistic, HUD", + image_url="aida-results/result_53381ada-ac64-11ef-ae9d-0242ac150002.png", product_type="overall" ) server = GenerateProductImage(rd) diff --git a/app/service/generate_image/utils/image_processing.py b/app/service/generate_image/utils/image_processing.py index af36188..02d8bee 100644 --- a/app/service/generate_image/utils/image_processing.py +++ b/app/service/generate_image/utils/image_processing.py @@ -81,7 +81,7 @@ def get_contours(image): def seg_infer_image(image_obj): image, ori_shape = seg_preprocess(image_obj) - client = httpclient.InferenceServerClient(url=f"{SEG_MODEL_URL}") + client = httpclient.InferenceServerClient(url=f"{DESIGN_MODEL_URL}") transformed_img = image.astype(np.float32) # 输入集 inputs = [ @@ -250,7 +250,7 @@ def generate_category_recognition(image, gender): return preprocessed_img preprocessed_img = preprocess(image) - triton_client = httpclient.InferenceServerClient(url=ATT_TRITON_URL) + triton_client = httpclient.InferenceServerClient(url=DESIGN_MODEL_URL) inputs = [ httpclient.InferInput("input__0", preprocessed_img.shape, datatype="FP32") diff --git a/app/service/search_image_with_text/service.py b/app/service/search_image_with_text/service.py index 2274c51..edd4d93 100644 --- a/app/service/search_image_with_text/service.py +++ b/app/service/search_image_with_text/service.py @@ -6,6 +6,8 @@ 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 + # 读取 csv 文件 # csv_file_path = r'D:/Files/csv/output/output.csv' # image_path = r'D:/images-clean' @@ -18,7 +20,7 @@ client = chromadb.Client(Settings(is_persistent=True, persist_directory="/vector # client = chromadb.Client(Settings(is_persistent=True, persist_directory="D:/workspace/AiDLab/vector_db")) # 创建集合 # embedding_fn = OllamaEmbeddingFunction(url="http://localhost:11434/api/embeddings", model_name="mxbai-embed-large") -embedding_fn = OllamaEmbeddingFunction(url="http://10.1.1.240:11434/api/embeddings", model_name="mxbai-embed-large") +embedding_fn = OllamaEmbeddingFunction(url=OLLAMA_URL, model_name="mxbai-embed-large") # def create_collection(): diff --git a/app/service/utils/new_oss_client.py b/app/service/utils/new_oss_client.py index 6d644a5..4b3cbb1 100644 --- a/app/service/utils/new_oss_client.py +++ b/app/service/utils/new_oss_client.py @@ -82,9 +82,10 @@ 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_e961eed6-9278-11ef-a957-0826ae3ad6b3.png" + url = "aida-users/89/test/123-89.png" + # url = "aida-collection-element/12148/Sketchboard/95ea577b-305b-4a62-b30a-39c0dd3ddb3f.png" - read_type = "cv2" + read_type = "2" if read_type == "cv2": img = oss_get_image(oss_client=minio_client, bucket=url.split('/')[0], object_name=url[url.find('/') + 1:], data_type=read_type) cv2.imshow("", img)