Merge remote-tracking branch 'origin/develop' into develop

app/api/api_agent_generate_image.py

@@ -0,0 +1,19 @@
+import io
+import logging
+
+from fastapi import APIRouter, HTTPException
+from starlette.responses import StreamingResponse
+
+from app.schemas.response_template import ResponseModel
+from app.service.generate_image.agent_generate import GenerateImage
+
+router = APIRouter()
+logger = logging.getLogger()
+
+
+@router.get("/agent_generate_image")
+def generate_image(prompt: str):
+    server = GenerateImage()
+    byte_stream = server.get_result(prompt)
+    # 返回流式响应
+    return StreamingResponse(byte_stream, media_type="image/png")

app/api/api_mannequins_edit.py

@@ -0,0 +1,40 @@
+import json
+import logging
+
+from fastapi import APIRouter, HTTPException
+
+from app.schemas.mannequin_edit import MannequinModel
+from app.schemas.response_template import ResponseModel
+from app.service.mannequins_edit.service import MannequinEditService
+
+router = APIRouter()
+logger = logging.getLogger()
+
+
+@router.post("/mannequins_edit")
+def mannequins_edit(request_data: MannequinModel):
+    """
+        模特腿长调整
+        创建一个具有以下参数的请求体:
+        - **mannequins**: mannequins url等信息
+        - **scale**: 大腿小腿比例
+        - **bucket_name**: bucket name
+        - **mannequin_name**: 模特名称
+
+        示例参数：
+        - **{
+          "mannequins": "aida-sys-image/models/male/dc36ce58-46c3-4b6f-8787-5ca7d6fc26e6.png",
+          "scale": [0.75, 0.75],
+          "bucket_name": "test",
+          "mannequin_name": "mannequin_name"
+        }**
+    """
+    try:
+        logger.info(f"mannequins_edit request item is : @@@@@@:{json.dumps(request_data.dict())}")
+        service = MannequinEditService(request_data)
+        data = service()
+        logger.info(f"mannequins_edit response @@@@@@:{json.dumps(data)}")
+    except Exception as e:
+        logger.warning(f"mannequins_edit Run Exception @@@@@@:{e}")
+        raise HTTPException(status_code=404, detail=str(e))
+    return ResponseModel(data=data)

app/api/api_pose_transform.py

@@ -0,0 +1,49 @@
+import json
+import logging
+
+from fastapi import APIRouter, BackgroundTasks, HTTPException
+
+from app.schemas.pose_transform import PoseTransformModel
+from app.schemas.response_template import ResponseModel
+from app.service.generate_image.service_pose_transform import PoseTransformService, infer_cancel as pose_transform_infer_cancel
+
+router = APIRouter()
+logger = logging.getLogger()
+
+
+@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())}")
+        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'])

app/api/api_route.py

@@ -1,5 +1,6 @@
 from fastapi import APIRouter
 
+from app.api import api_agent_generate_image, api_recommendation
 from app.api import api_attribute_retrieve, api_query_image
 from app.api import api_brand_dna
 from app.api import api_brighten
@@ -8,9 +9,10 @@ from app.api import api_design
 from app.api import api_design_pre_processing
 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_prompt_generation
 from app.api import api_super_resolution
-from app.api import api_recommendation
 from app.api import api_test
 
 router = APIRouter()
@@ -28,3 +30,6 @@ 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_mannequins_edit.router, tags=['api_mannequins_edit'], prefix="/api")
+router.include_router(api_agent_generate_image.router, tags=['api_agent_generate_image'], prefix="/api")
+router.include_router(api_pose_transform.router, tags=['api_pose_transform'], prefix="/api")

app/core/config.py

@@ -138,7 +138,7 @@ GEN_SINGLE_LOGO_RABBITMQ_QUEUES = os.getenv("GEN_SINGLE_LOGO_RABBITMQ_QUEUES", f
 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 = '10.1.1.243:15551'
+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}")

app/schemas/mannequin_edit.py

@@ -0,0 +1,8 @@
+from pydantic import BaseModel
+
+
+class MannequinModel(BaseModel):
+    mannequins: str
+    scale: list[float, float]
+    bucket_name: str
+    mannequin_name: str

app/schemas/pose_transform.py

@@ -0,0 +1,7 @@
+from pydantic import BaseModel
+
+
+class PoseTransformModel(BaseModel):
+    image_url: str
+    tasks_id: str
+    pose_id: str

app/service/design_fast/pipeline/color.py

@@ -29,6 +29,24 @@ class Color:
         else:
             pattern = self.get_pattern(result['color'])
             resize_pattern = cv2.resize(pattern, (dim_image_w, dim_image_h), interpolation=cv2.INTER_AREA)
+
+        if "partial_color" in result.keys() and result['partial_color'] != "":
+            bucket_name = result['partial_color'].split('/')[0]
+            object_name = result['partial_color'][result['partial_color'].find('/') + 1:]
+            partial_color = oss_get_image(oss_client=self.minio_client, bucket=bucket_name, object_name=object_name, data_type="cv2")
+            h, w = partial_color.shape[0:2]
+            resize_pattern = cv2.resize(resize_pattern, (w, h), interpolation=cv2.INTER_AREA)
+            # 分离出 png 图的 alpha 通道
+            alpha_channel = partial_color[:, :, 3]
+            # 提取 png 图的 RGB 通道
+            png_rgb = partial_color[:, :, :3]
+            # 创建一个与 cv 图大小相同的掩码，用于指示哪些像素需要替换
+            mask = alpha_channel > 0
+            # 将掩码扩展为 3 通道，以便与 cv 图进行逐元素操作
+            mask_3ch = np.stack([mask] * 3, axis=-1)
+            # 根据掩码将 png 图的颜色覆盖到 cv 图上
+            resize_pattern[mask_3ch] = png_rgb[mask_3ch]
+            resize_pattern = cv2.resize(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)

app/service/generate_image/agent_generate.py

@@ -0,0 +1,72 @@
+#!/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 io
+import logging
+from datetime import timedelta
+
+import cv2
+import numpy as np
+import tritonclient.grpc as grpcclient
+from minio import Minio
+from tritonclient.utils import np_to_triton_dtype
+
+from app.core.config import *
+from app.service.utils.oss_client import oss_upload_image
+
+logger = logging.getLogger()
+
+
+class GenerateImage:
+    def __init__(self):
+        self.grpc_client = grpcclient.InferenceServerClient(url=GI_MODEL_URL)
+        self.image = np.random.randint(0, 256, (1024, 1024, 3), dtype=np.uint8)
+        self.batch_size = 1
+        self.mode = 'txt2img'
+        self.minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE)
+
+    def get_result(self, prompt):
+        prompts = [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, 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]
+        result = self.grpc_client.infer(model_name=GI_MODEL_NAME, inputs=inputs)
+        image = result.as_numpy("generated_image")
+        image_result = cv2.cvtColor(np.squeeze(image.astype(np.uint8)), cv2.COLOR_RGB2BGR)
+        _, img_byte_array = cv2.imencode('.jpg', image_result)
+        byte_stream = io.BytesIO(img_byte_array)
+        byte_stream.seek(0)
+
+        # object_name = f'test.jpg'
+        # req = oss_upload_image(bucket='test', object_name=object_name, image_bytes=img_byte_array)
+        # url = self.minio_client.get_presigned_url(
+        #     "GET",
+        #     "test",
+        #     object_name,
+        #     expires=timedelta(hours=2),
+        # )
+        return byte_stream
+
+
+if __name__ == '__main__':
+    server = GenerateImage()
+    print(server.get_result("rabbit"))

app/service/generate_image/service_pose_transform.py

@@ -0,0 +1,117 @@
+#!/usr/bin/env python
+# -*- coding: UTF-8 -*-
+"""
+@Project ：trinity_client
+@File    ：service_pose_transform.py
+@Author  ：周成融
+@Date    ：2023/7/26 12:01:05
+@detail  ：
+"""
+import json
+import logging
+
+import cv2
+import numpy as np
+import redis
+import tritonclient.grpc as grpcclient
+from PIL import Image
+
+from app.core.config import *
+from app.schemas.pose_transform import PoseTransformModel
+from app.service.generate_image.utils.upload_sd_image import upload_SDXL_image
+from app.service.utils.oss_client import oss_get_image
+
+logger = logging.getLogger()
+
+
+class PoseTransformService:
+    def __init__(self, request_data):
+        if DEBUG is False:
+            self.connection = pika.BlockingConnection(pika.ConnectionParameters(**RABBITMQ_PARAMS))
+            self.channel = self.connection.channel()
+        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.category = "pose_transform"
+        self.batch_size = 1
+        self.seed = "1"
+        self.image_url = request_data.image_url
+        self.image = oss_get_image(bucket=self.image_url.split('/')[0], object_name=self.image_url[self.image_url.find('/') + 1:], data_type="cv2")
+        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': 'SUCCESS', 'message': "success", 'image_url': ''}
+        self.redis_client.set(self.tasks_id, json.dumps(self.gen_product_data))
+        self.redis_client.expire(self.tasks_id, 600)
+
+    def callback(self, result, error):
+        if error:
+            self.gen_product_data['status'] = "FAILURE"
+            self.gen_product_data['message'] = str(error)
+            self.redis_client.set(self.tasks_id, json.dumps(self.gen_product_data))
+        else:
+            image = result.as_numpy("generated_inpaint_image")
+            image_result = Image.fromarray(np.squeeze(image.astype(np.uint8)))
+            image_url = upload_SDXL_image(image_result, 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)
+            self.redis_client.set(self.tasks_id, json.dumps(self.gen_product_data))
+
+    def read_tasks_status(self):
+        status_data = self.redis_client.get(self.tasks_id)
+        return json.loads(status_data), status_data
+
+    def get_result(self):
+        try:
+            image = cv2.cvtColor(self.image, cv2.COLOR_BGR2RGB)
+            image = cv2.resize(image, (512, 768))
+            images = [image.astype(np.uint8)] * self.batch_size
+
+            image_obj = np.array(images, dtype=np.uint8).reshape((768, 512, 3))
+
+            input_image = grpcclient.InferInput("input_image", image_obj.shape, "UINT8")
+
+            input_image.set_data_from_numpy(image_obj)
+
+            inputs = [input_image]
+            # ctx = self.grpc_client.async_infer(model_name=GRI_MODEL_NAME_OVERALL, inputs=inputs, callback=self.callback)
+
+            # time_out = 600
+            # while time_out > 0:
+            #     gen_product_data, _ = self.read_tasks_status()
+            #     if gen_product_data['status'] in ["REVOKED", "FAILURE", "NO_FACE"]:
+            #         ctx.cancel()
+            #         break
+            #     elif gen_product_data['status'] == "SUCCESS":
+            #         break
+            #     time_out -= 1
+            #     time.sleep(0.1)
+            gen_product_data, _ = self.read_tasks_status()
+            return gen_product_data
+        except Exception as e:
+            self.gen_product_data['status'] = "FAILURE"
+            self.gen_product_data['message'] = str(e)
+            self.redis_client.set(self.tasks_id, json.dumps(self.gen_product_data))
+            raise Exception(str(e))
+        finally:
+            dict_gen_product_data, str_gen_product_data = self.read_tasks_status()
+            if DEBUG is False:
+                self.channel.basic_publish(exchange='', routing_key=GRI_RABBITMQ_QUEUES, body=str_gen_product_data)
+            logger.info(f" [x] Sent to： {GRI_RABBITMQ_QUEUES} data：@@@@  {json.dumps(dict_gen_product_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'}
+    gen_product_data = json.dumps(data)
+    redis_client.set(tasks_id, gen_product_data)
+    return data
+
+
+if __name__ == '__main__':
+    rd = PoseTransformModel(
+        tasks_id="123-89",
+        image_url='aida-results/result_0000b606-1902-11ef-9424-0242ac180002.png',
+        pose_id="1"
+    )
+    server = PoseTransformService(rd)
+    print(server.get_result())

app/service/mannequins_edit/service.py

@@ -0,0 +1,101 @@
+import cv2
+import mediapipe as mp
+import numpy as np
+from minio import Minio
+
+from app.core.config import MINIO_URL, MINIO_ACCESS, MINIO_SECRET, MINIO_SECURE
+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)
+
+
+class MannequinEditService():
+    def __init__(self, request_data):
+        self.scale = request_data.scale
+        self.image = oss_get_image(oss_client=minio_client, bucket=request_data.mannequins.split('/')[0], object_name=request_data.mannequins[request_data.mannequins.find('/') + 1:], data_type="cv2")
+        self.mannequin_name = request_data.mannequin_name
+        self.bucket_name = request_data.bucket_name
+        if self.image.shape[2] == 4:
+            self.bgr = self.image[:, :, :3]
+            self.alpha = self.image[:, :, 3]
+            self.bgr = cv2.bitwise_and(self.bgr, self.bgr, mask=cv2.normalize(self.alpha, None, 0, 1, cv2.NORM_MINMAX))
+            self.h, self.w, _ = self.bgr.shape
+        else:
+            self.bgr = self.image
+            self.h, self.w, _ = self.bgr.shape
+            self.alpha = None
+
+    def __call__(self, *args, **kwargs):
+        leg_top, leg_bottom = self.attitude_detection()
+        if leg_top and leg_bottom:
+            new_mannequin = self.resize_leg(leg_top, leg_bottom)
+            _, encoded_image = cv2.imencode('.png', new_mannequin)
+            image_bytes = encoded_image.tobytes()
+            req = oss_upload_image(oss_client=minio_client, bucket=self.bucket_name, object_name=f"{self.mannequin_name}.png", image_bytes=image_bytes)
+            return req.bucket_name + "/" + req.object_name
+        else:
+            return "No leg detected"
+
+    def attitude_detection(self):
+        mp_pose = mp.solutions.pose
+        pose = mp_pose.Pose()
+
+        # 将 BGR 图像转换为 RGB 格式
+        image_rgb = cv2.cvtColor(self.bgr, cv2.COLOR_BGR2RGB)
+        leg_top, leg_bottom = None, None
+        # 进行姿态检测
+        results = pose.process(image_rgb)
+        if results.pose_landmarks:
+            # 获取腿部关键点
+            landmarks = results.pose_landmarks.landmark
+
+            # 找到腿部上边界和下边界
+            leg_top = int(landmarks[mp_pose.PoseLandmark.LEFT_HIP].y * self.h)
+            leg_bottom = int(max(landmarks[mp_pose.PoseLandmark.LEFT_ANKLE].y,
+                                 landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE].y) * self.h)
+
+        return leg_top, leg_bottom
+
+    def resize_leg(self, leg_top, leg_bottom):
+        # 上半身
+        top_part_bgr = self.bgr[:leg_top, :]
+        top_part_bgr_alpha = self.alpha[:leg_top, :]
+
+        # 小腿
+        part_thigh = self.bgr[leg_top:leg_bottom, :]
+        part_thigh_alpha = self.alpha[leg_top:leg_bottom, :]
+
+        # 大腿
+        part_calf = self.bgr[leg_bottom:, :]
+        part_calf_alpha = self.alpha[leg_bottom:, :]
+
+        new_thigh_height = int((leg_bottom - leg_top) * self.scale[0])
+        new_calf_height = int((self.h - leg_bottom) * self.scale[1])
+
+        resized_thigh = cv2.resize(part_thigh, (self.w, new_thigh_height), interpolation=cv2.INTER_LINEAR)
+        resized_thigh_alpha = cv2.resize(part_thigh_alpha, (self.w, new_thigh_height), interpolation=cv2.INTER_LINEAR)
+        resized_calf = cv2.resize(part_calf, (self.w, new_calf_height), interpolation=cv2.INTER_LINEAR)
+        resized_calf_alpha = cv2.resize(part_calf_alpha, (self.w, new_calf_height), interpolation=cv2.INTER_LINEAR)
+
+        new_bgr = np.vstack((top_part_bgr, resized_thigh, resized_calf))
+        new_bgr_alpha = np.vstack((top_part_bgr_alpha, resized_thigh_alpha, resized_calf_alpha))
+
+        if self.alpha is not None:
+            # 拼接 alpha 通道
+            # 合并 BGR 通道和 alpha 通道
+            new_image = np.dstack((new_bgr, new_bgr_alpha))
+        else:
+            new_image = new_bgr
+        return new_image
+
+
+if __name__ == '__main__':
+    request_data = MannequinModel(
+        mannequins="aida-sys-image/models/male/dc36ce58-46c3-4b6f-8787-5ca7d6fc26e6.png",
+        scale=[0.75, 0.75],
+        bucket_name="test",
+        mannequin_name="mannequin_name"
+    )
+    service = MannequinEditService(request_data)
+    print(service())