Merge branch 'local' into develop

# Conflicts: # app/core/config.py
2024-06-23 16:12:08 +08:00
parent 9291e350e0 6c7c6b47af
commit 9adabc3f36
20 changed files with 455 additions and 406 deletions
--- a/app/api/api_attribute_retrieve.py
+++ b/app/api/api_attribute_retrieve.py
@@ -2,9 +2,10 @@ import json
 import logging
 from fastapi import APIRouter, HTTPException

+from app.core.config import DEBUG
 from app.schemas.attribute_retrieve import *
 from app.schemas.response_template import ResponseModel
-from app.service.attribute.config import const
+from app.service.attribute.config import const, local_debug_const
 from app.service.attribute.service_att_recognition import AttributeRecognition
 from app.service.attribute.service_category_recognition import CategoryRecognition

@@ -17,13 +18,16 @@ logger = logging.getLogger()
 def attribute_recognition(request_item: list[AttributeRecognitionModel]):
    try:
        logger.info(f"attribute_recognition request item is : @@@@@@:{request_item}")
-        service = AttributeRecognition(const=const, request_data=request_item)
+        if 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.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))
-    return ResponseModel(data=data)
+    return ResponseModel(data={"list": data})


 # 类别识别
--- a/app/api/api_design.py
+++ b/app/api/api_design.py
@@ -4,9 +4,10 @@ import time

 from fastapi import APIRouter, HTTPException

-from app.schemas.design import DesignModel
+from app.schemas.design import DesignModel, DesignProgressModel
 from app.schemas.response_template import ResponseModel
 from app.service.design.service import generate
+from app.service.design.utils.redis_utils import Redis

 router = APIRouter()
 logger = logging.getLogger()
@@ -22,3 +23,19 @@ def design(request_data: DesignModel):
        logger.warning(f"design Run Exception @@@@@@:{e}")
        raise HTTPException(status_code=404, detail=str(e))
    return ResponseModel(data=data)
+
+
+@router.post('/get_progress')
+def get_progress(request_data: DesignProgressModel):
+    try:
+        logger.info(f"get_progress request item is : @@@@@@:{request_data}")
+        process_id = request_data.process_id
+        r = Redis()
+        data = r.read(key=process_id)
+        if data is None:
+            raise ValueError("The progress must be numbers ")
+        logging.info(f"get_progress process_id @@@@@@ : {process_id} , progress : {data}")
+    except Exception as e:
+        logger.warning(f"get_progress Run Exception @@@@@@:{e}")
+        raise HTTPException(status_code=404, detail=str(e))
+    return ResponseModel(data=data)
--- a/app/api/api_test.py
+++ b/app/api/api_test.py
@@ -1,6 +1,6 @@
 import logging
 from fastapi import APIRouter
-from app.core.config import SR_RABBITMQ_QUEUES, GI_RABBITMQ_QUEUES, GPI_RABBITMQ_QUEUES
+from app.core.config import SR_RABBITMQ_QUEUES, GI_RABBITMQ_QUEUES, GPI_RABBITMQ_QUEUES, GRI_RABBITMQ_QUEUES, OSS
 from fastapi import FastAPI, HTTPException

 from app.schemas.response_template import ResponseModel
@@ -15,6 +15,8 @@ def test(id: int):
        "SR_RABBITMQ_QUEUES message": SR_RABBITMQ_QUEUES,
        "GI_RABBITMQ_QUEUES": GI_RABBITMQ_QUEUES,
        "GPI_RABBITMQ_QUEUES": GPI_RABBITMQ_QUEUES,
+        "GRI_RABBITMQ_QUEUES": GRI_RABBITMQ_QUEUES,
+        "local_oss_server": OSS
    }
    logger.info(data)
    if id == 1:
--- a/app/core/config.py
+++ b/app/core/config.py
@@ -19,15 +19,16 @@ class Settings(BaseSettings):
    LOGGING_CONFIG_FILE = 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"
-    FACE_CLASSIFIER = "service/generate_image/utils/haarcascade_frontalface_alt.xml"
+    # FACE_CLASSIFIER = "service/generate_image/utils/haarcascade_frontalface_alt.xml"
 else:
    LOGS_PATH = "app/logs/"
    CATEGORY_PATH = "app/service/attribute/config/descriptor/category/category_dis.csv"
-    FACE_CLASSIFIER = 'app/service/generate_image/utils/haarcascade_frontalface_alt.xml'
+    # FACE_CLASSIFIER = 'app/service/generate_image/utils/haarcascade_frontalface_alt.xml'

 # RABBITMQ_ENV = ""  # 生产环境
 # RABBITMQ_ENV = "-dev"  # 开发环境
@@ -47,7 +48,7 @@ S3_AWS_SECRET_ACCESS_KEY = "LNIwFFB27/QedtZ+Q/viVUoX9F5x1DbuM8N0DkD8"
 S3_REGION_NAME = "ap-east-1"

 # redis 配置
-REDIS_HOST = "10.1.1.240"
+REDIS_HOST = "10.1.1.150"
 REDIS_PORT = "6379"
 REDIS_DB = "2"

@@ -60,9 +61,9 @@ RABBITMQ_PARAMS = {
 }

 # milvus 配置
-MILVUS_DB_HOST = "10.1.1.240"
+MILVUS_URL = "http://10.1.1.240:19530"
+MILVUS_TOKEN = "root:Milvus"
 MILVUS_ALIAS = "default"
-MILVUS_PORT = "19530"
 MILVUS_TABLE_KEYPOINT = "keypoint_cache"
 MILVUS_TABLE_SEG = "seg_cache"

@@ -123,8 +124,8 @@ GPI_MODEL_NAME = 'diffusion_ensemble_all'
 GPI_MODEL_URL = '10.1.1.240:10041'

 # Generate Single Logo service config
-GRI_RABBITMQ_QUEUES = os.getenv("GEN_PRODUCT_IMAGE_RABBITMQ_QUEUES", f"Relight{RABBITMQ_ENV}")
-GRI_MODEL_NAME = 'stable_diffusion_1_5'
+GRI_RABBITMQ_QUEUES = os.getenv("GEN_RELIGHT_IMAGE_RABBITMQ_QUEUES", f"Relight{RABBITMQ_ENV}")
+GRI_MODEL_NAME = 'diffusion_relight_ensemble'
 GRI_MODEL_URL = '10.1.1.240:10051'

 # SEG service config
--- a/app/schemas/design.py
+++ b/app/schemas/design.py
@@ -48,3 +48,7 @@ from pydantic import BaseModel
 class DesignModel(BaseModel):
    objects: list[dict]
    process_id: str
+
+
+class DesignProgressModel(BaseModel):
+    process_id: str
--- a/app/service/attribute/config/const.py
+++ b/app/service/attribute/config/const.py
@@ -1,13 +1,13 @@
-top_description_list = ['service/attribute/config/descriptor/top/length.csv',
-                        'service/attribute/config/descriptor/top/type.csv',
-                        'service/attribute/config/descriptor/top/sleeve_length.csv',
-                        'service/attribute/config/descriptor/top/sleeve_shape.csv',
-                        'service/attribute/config/descriptor/top/sleeve_shoulder.csv',
-                        'service/attribute/config/descriptor/top/neckline.csv',
-                        'service/attribute/config/descriptor/top/design.csv',
-                        'service/attribute/config/descriptor/top/opening_type.csv',
-                        'service/attribute/config/descriptor/top/silhouette.csv',
-                        'service/attribute/config/descriptor/top/collar.csv']
+top_description_list = ['app/service/attribute/config/descriptor/top/length.csv',
+                        'app/service/attribute/config/descriptor/top/type.csv',
+                        'app/service/attribute/config/descriptor/top/sleeve_length.csv',
+                        'app/service/attribute/config/descriptor/top/sleeve_shape.csv',
+                        'app/service/attribute/config/descriptor/top/sleeve_shoulder.csv',
+                        'app/service/attribute/config/descriptor/top/neckline.csv',
+                        'app/service/attribute/config/descriptor/top/design.csv',
+                        'app/service/attribute/config/descriptor/top/opening_type.csv',
+                        'app/service/attribute/config/descriptor/top/silhouette.csv',
+                        'app/service/attribute/config/descriptor/top/collar.csv']

 top_model_list = ['attr_retrieve_T_length',
                  'attr_retrieve_T_type',
@@ -22,11 +22,11 @@ top_model_list = ['attr_retrieve_T_length',
                  ]

 bottom_description_list = [
-    'service/attribute/config/descriptor/bottom/subtype.csv',
-    'service/attribute/config/descriptor/bottom/length.csv',
-    'service/attribute/config/descriptor/bottom/silhouette.csv',
-    'service/attribute/config/descriptor/bottom/opening_type.csv',
-    'service/attribute/config/descriptor/bottom/design.csv']
+    'app/service/attribute/config/descriptor/bottom/subtype.csv',
+    'app/service/attribute/config/descriptor/bottom/length.csv',
+    'app/service/attribute/config/descriptor/bottom/silhouette.csv',
+    'app/service/attribute/config/descriptor/bottom/opening_type.csv',
+    'app/service/attribute/config/descriptor/bottom/design.csv']

 bottom_model_list = [
    'attr_retrieve_B_subtype',
@@ -35,14 +35,14 @@ bottom_model_list = [
    'attr_recong_B_optype',
    'attr_retrieve_B_design']

-outwear_description_list = ['service/attribute/config/descriptor/outwear/length.csv',
-                            'service/attribute/config/descriptor/outwear/sleeve_length.csv',
-                            'service/attribute/config/descriptor/outwear/sleeve_shape.csv',
-                            'service/attribute/config/descriptor/outwear/sleeve_shoulder.csv',
-                            'service/attribute/config/descriptor/outwear/collar.csv',
-                            'service/attribute/config/descriptor/outwear/design.csv',
-                            'service/attribute/config/descriptor/outwear/opening_type.csv',
-                            'service/attribute/config/descriptor/outwear/silhouette.csv', ]
+outwear_description_list = ['app/service/attribute/config/descriptor/outwear/length.csv',
+                            'app/service/attribute/config/descriptor/outwear/sleeve_length.csv',
+                            'app/service/attribute/config/descriptor/outwear/sleeve_shape.csv',
+                            'app/service/attribute/config/descriptor/outwear/sleeve_shoulder.csv',
+                            'app/service/attribute/config/descriptor/outwear/collar.csv',
+                            'app/service/attribute/config/descriptor/outwear/design.csv',
+                            'app/service/attribute/config/descriptor/outwear/opening_type.csv',
+                            'app/service/attribute/config/descriptor/outwear/silhouette.csv', ]

 outwear_model_list = ['attr_recong_O_length',
                      'attr_retrieve_O_sleeve_length',
@@ -53,15 +53,15 @@ outwear_model_list = ['attr_recong_O_length',
                      'attr_recong_O_optype',
                      'attr_retrieve_O_silhouette']

-dress_description_list = [  # 'service/attribute/config/descriptor/dress/D_length.csv',
-    'service/attribute/config/descriptor/dress/sleeve_length.csv',
-    'service/attribute/config/descriptor/dress/sleeve_shape.csv',
-    #  'service/attribute/config/descriptor/dress/D_sleeve_shoulder.csv',
-    'service/attribute/config/descriptor/dress/neckline.csv',
-    'service/attribute/config/descriptor/dress/collar.csv',
-    'service/attribute/config/descriptor/dress/design.csv',
-    'service/attribute/config/descriptor/dress/silhouette.csv',
-    'service/attribute/config/descriptor/dress/type.csv']
+dress_description_list = [  # 'app/service/attribute/config/descriptor/dress/D_length.csv',
+    'app/service/attribute/config/descriptor/dress/sleeve_length.csv',
+    'app/service/attribute/config/descriptor/dress/sleeve_shape.csv',
+    #  'app/service/attribute/config/descriptor/dress/D_sleeve_shoulder.csv',
+    'app/service/attribute/config/descriptor/dress/neckline.csv',
+    'app/service/attribute/config/descriptor/dress/collar.csv',
+    'app/service/attribute/config/descriptor/dress/design.csv',
+    'app/service/attribute/config/descriptor/dress/silhouette.csv',
+    'app/service/attribute/config/descriptor/dress/type.csv']

 dress_model_list = [  # 'attr_recong_D_length',
    'attr_retrieve_D_sleeve_length',
--- a/app/service/attribute/service_att_recognition.py
+++ b/app/service/attribute/service_att_recognition.py
@@ -11,12 +11,12 @@ from minio import Minio
 import tritonclient.http as httpclient
 from app.core.config import *
 from app.schemas.attribute_retrieve import AttributeRecognitionModel
+from app.service.utils.oss_client import oss_get_image


 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)
-        logging.info("实例化完成")
+        # 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(
@@ -97,9 +97,10 @@ class AttributeRecognition:
        return res

    def get_image(self, 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)  # 解码
+        # 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 = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        return img

--- a/app/service/attribute/service_category_recognition.py
+++ b/app/service/attribute/service_category_recognition.py
@@ -18,12 +18,13 @@ import torch

 from app.core.config import *
 from app.schemas.attribute_retrieve import CategoryRecognitionModel
+from app.service.utils.oss_client import oss_get_image


 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.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)
        for sketch in request_data:
@@ -51,9 +52,10 @@ class CategoryRecognition:
    def get_image(self, 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
+        # 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 = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        return img

--- a/app/service/design/items/pipelines/keypoints.py
+++ b/app/service/design/items/pipelines/keypoints.py
@@ -1,5 +1,6 @@
 import logging
 import time
+
 import numpy as np
 from pymilvus import MilvusClient

@@ -14,17 +15,17 @@ 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 __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}")
+    # def __del__(self):
+    # start_time = time.time()
+    # self.client.close()
+    # print(f"client close time : {time.time() - start_time}")

    # @ RunTime
    def __call__(self, result):
@@ -55,7 +56,7 @@ class KeypointDetection(object):

    @staticmethod
    # @ RunTime
-    def save_keypoint_cache(keypoint_id, cache, site, KEYPOINT_RESULT_TABLE_FIELD_SET=None):
+    def save_keypoint_cache(keypoint_id, cache, site):
        if site == "down":
            zeros = np.zeros(20, dtype=int)
            result = np.concatenate([zeros, cache.flatten()])
@@ -69,24 +70,16 @@ class KeypointDetection(object):
             "keypoint_vector": result.tolist()
             }
        ]
-        client = MilvusClient(
-            uri="http://10.1.1.240:19530",
-            token="root:Milvus",
-            db_name=MILVUS_ALIAS
-        )
        try:
-            start_time = time.time()
-            res = client.upsert(
-                collection_name=MILVUS_TABLE_KEYPOINT,
-                data=data,
-            )
+            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()))
-        finally:
-            client.close()

    @staticmethod
    def update_keypoint_cache(keypoint_id, infer_result, search_result, site):
@@ -102,12 +95,9 @@ class KeypointDetection(object):
             "keypoint_vector": result.tolist()
             }
        ]
-        client = MilvusClient(
-            uri="http://10.1.1.240:19530",
-            token="root:Milvus",
-            db_name=MILVUS_ALIAS
-        )
+
        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.
@@ -125,8 +115,9 @@ class KeypointDetection(object):
    # @ 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 = self.client.query(
+            res = client.query(
                collection_name=MILVUS_TABLE_KEYPOINT,
                # ids=[keypoint_id],
                filter=f"keypoint_id == {keypoint_id}",
--- a/app/service/design/items/pipelines/loading.py
+++ b/app/service/design/items/pipelines/loading.py
@@ -1,6 +1,5 @@
 import io
 import logging
-import time

 import cv2
 import numpy as np
@@ -8,6 +7,7 @@ from PIL import Image
 from minio import Minio

 from app.core.config import *
+from app.service.utils.oss_client import oss_get_image
 from ..builder import PIPELINES


@@ -70,11 +70,7 @@ class LoadImageFromFile(object):
 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)
+        # 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")

@@ -82,33 +78,33 @@ class LoadBodyImageFromFile(object):
    def __call__(self, result):
        result["image_url"] = result['body_path'] = self.body_path
        result["name"] = "mannequin"
-        if not result['image_url'].lower().endswith(".png"):
-            logging.info(1)
-            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])
+        # 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'] = 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

--- a/app/service/design/items/pipelines/painting.py
+++ b/app/service/design/items/pipelines/painting.py
@@ -1,19 +1,16 @@
 import random
-from io import BytesIO
+
 # import boto3
 import cv2
 import numpy as np
 from PIL import Image
-from minio import Minio

-from app.core.config import *
+from app.service.utils.oss_client import oss_get_image
 from ..builder import PIPELINES

-minio_client = Minio(
-    MINIO_URL,
-    access_key=MINIO_ACCESS,
-    secret_key=MINIO_SECRET,
-    secure=MINIO_SECURE)
+
+# minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=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)

@@ -56,17 +53,18 @@ class Painting(object):

    @staticmethod
    def get_gradient(bucket_name, object_name):
-        image_data = minio_client.get_object(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()
+        # 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)
+        # 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 = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
+        image = oss_get_image(bucket=bucket_name, object_name=object_name, data_type="cv2")
        return image

    @staticmethod
@@ -494,16 +492,20 @@ class PrintPainting(object):
        if not 'IfSingle' in print_dict.keys():
            print_dict['IfSingle'] = False

-        data = minio_client.get_object(print_dict['print_path_list'][0].split("/", 1)[0], print_dict['print_path_list'][0].split("/", 1)[1])
-        # data = s3.get_object(Bucket=print_dict['print_path_list'][0].split("/", 1)[0], Key=print_dict['print_path_list'][0].split("/", 1)[1])['Body']
+        # data = minio_client.get_object(print_dict['print_path_list'][0].split("/", 1)[0], print_dict['print_path_list'][0].split("/", 1)[1])
+        # data_bytes = BytesIO(data.read())
+        # image = Image.open(data_bytes)
+        # image_mode = image.mode

-        data_bytes = BytesIO(data.read())
-        image = Image.open(data_bytes)
-        image_mode = image.mode
+        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="cv2")
        # 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])
+        if image.shape[2] == 4:
+            image_rgb = cv2.cvtColor(image, cv2.COLOR_BGRA2RGBA)
+            image_pil = Image.fromarray(image_rgb)
+            new_background = Image.new('RGB', image_pil.size, (255, 255, 255))
+            new_background.paste(image_pil, mask=image.split()[3])
            image = new_background
        print_dict['image'] = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)

@@ -577,21 +579,30 @@ class PrintPainting(object):

    @staticmethod
    def read_image(image_url):
-        data = minio_client.get_object(image_url.split("/", 1)[0], image_url.split("/", 1)[1])
-        # data = s3.get_object(Bucket=image_url.split("/", 1)[0], Key=image_url.split("/", 1)[1])['Body']
-
-        data_bytes = BytesIO(data.read())
-        image = Image.open(data_bytes)
-        image_mode = image.mode
-        # 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
-            return image, image_mode
-        image = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)
+        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

+        # data = minio_client.get_object(image_url.split("/", 1)[0], image_url.split("/", 1)[1])
+        # # data = s3.get_object(Bucket=image_url.split("/", 1)[0], Key=image_url.split("/", 1)[1])['Body']
+        #
+        # data_bytes = BytesIO(data.read())
+        # image = Image.open(data_bytes)
+        # image_mode = image.mode
+        # # 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
+        #     return image, image_mode
+        # image = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)
+        # return image, "RGB"
+
    # @staticmethod
    # def read_image(image_url):
    #     response = requests.get(image_url)
--- a/app/service/design/items/pipelines/split.py
+++ b/app/service/design/items/pipelines/split.py
@@ -41,7 +41,7 @@ class Split(object):
                else:
                    back_mask = result['back_mask']

-                rgba_image = rgb_to_rgba((result['final_image'].shape[0], result['final_image'].shape[1]), result['final_image'], result['mask'])
+                rgba_image = rgb_to_rgba((result['final_image'].shape[0], result['final_image'].shape[1]), re4sult['final_image'], result['mask'])
                result_front_image = np.zeros_like(rgba_image)
                result_front_image[front_mask != 0] = rgba_image[front_mask != 0]

--- a/app/service/design_pre_processing/service.py
+++ b/app/service/design_pre_processing/service.py
@@ -13,15 +13,12 @@ import io

 from app.core.config import *
 from app.service.design.utils.design_ensemble import get_keypoint_result
+from app.service.utils.oss_client import oss_get_image, oss_upload_image


 class DesignPreprocessing:
-    def __init__(self):
-        self.minio_client = Minio(
-            MINIO_URL,
-            access_key=MINIO_ACCESS,
-            secret_key=MINIO_SECRET,
-            secure=MINIO_SECURE)
+    # def __init__(self):
+    # self.minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE)

    # @ RunTime
    def pipeline(self, image_list):
@@ -51,8 +48,9 @@ class DesignPreprocessing:

    def read_image(self, 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)
+            # 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")
            if len(image.shape) == 2:
                image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
            elif image.shape[2] == 4:  # 如果是四通道 mask
@@ -125,7 +123,10 @@ class DesignPreprocessing:
            try:
                # 覆盖到minio
                image_bytes = cv2.imencode(".jpg", item['obj'])[1].tobytes()
-                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", )
+                # 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)
                print(f"Object '{item['image_url'].split('/', 1)[1]}' overwritten successfully.")
            except ResponseError as err:
                print(f"Error: {err}")
@@ -165,36 +166,76 @@ class DesignPreprocessing:
    # @ RunTime
    def composing_image(self, image_list):
        for image in image_list:
-            if image['site'] == 'down':
-                image_width = image['obj'].shape[1]
-                waist_width = image['keypoint_result']['waistband_right'][1] - image['keypoint_result']['waistband_left'][1]
-                scale = 0.4
-                if waist_width / scale >= image['obj'].shape[1]:
-                    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()
-                    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}"
-                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}"
+            ''' 比例相同 整合上下装代码'''
+            image_width = image['obj'].shape[1]
+            waist_width = image['keypoint_result']['waistband_right'][1] - image['keypoint_result']['waistband_left'][1]
+            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()
+                # 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]
+                oss_upload_image(bucket=bucket_name, object_name=object_name, image_bytes=image_bytes)
+                image['show_image_url'] = f"{bucket_name}/{object_name}"
            else:
-                scale = 0.4
-                image_width = image['obj'].shape[1]
-                waist_width = image['keypoint_result']['armpit_right'][1] - image['keypoint_result']['armpit_left'][1]
-                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()
-                    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}"
-                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}"
+                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]
+                oss_upload_image(bucket=bucket_name, object_name=object_name, image_bytes=image_bytes)
+                image['show_image_url'] = f"{bucket_name}/{object_name}"
+
+            # if image['site'] == 'down':
+            #     image_width = image['obj'].shape[1]
+            #     waist_width = image['keypoint_result']['waistband_right'][1] - image['keypoint_result']['waistband_left'][1]
+            #     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()
+            #         # 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]
+            #         oss_upload_image(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]
+            #         oss_upload_image(bucket=bucket_name, object_name=object_name, image_bytes=image_bytes)
+            #         image['show_image_url'] = f"{bucket_name}/{object_name}"
+            # else:
+            #     image_width = image['obj'].shape[1]
+            #     waist_width = image['keypoint_result']['waistband_right'][1] - image['keypoint_result']['waistband_left'][1]
+            #     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()
+            #         # 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]
+            #         oss_upload_image(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]
+            #         oss_upload_image(bucket=bucket_name, object_name=object_name, image_bytes=image_bytes)
+            #         image['show_image_url'] = f"{bucket_name}/{object_name}"
        return image_list

    @staticmethod
--- a/app/service/generate_image/service_generate_image.py
+++ b/app/service/generate_image/service_generate_image.py
@@ -10,21 +10,17 @@
 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, generate_category_recognition, autoLevels, luminance_adjust, face_detect_pic
-from app.service.generate_image.utils.upload_sd_image import upload_png_sd, upload_stain_png_sd
+from app.service.generate_image.utils.upload_sd_image import upload_png_sd
+from app.service.utils.oss_client import oss_get_image

 logger = logging.getLogger()

@@ -36,7 +32,7 @@ class GenerateImage:
            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.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":
@@ -63,10 +59,13 @@ class GenerateImage:
        # Read data from response.
        # read image use cv2
        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)
+            # 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_rbg = cv2.cvtColor(image_cv2, cv2.COLOR_BGR2RGB)
+
+            image_cv2 = oss_get_image(bucket=image_url.split('/')[0], object_name=image_url, data_type="cv2")
            image_rbg = cv2.cvtColor(image_cv2, cv2.COLOR_BGR2RGB)
            image = cv2.resize(image_rbg, (1024, 1024))
        except minio.error.S3Error:
@@ -189,7 +188,8 @@ if __name__ == '__main__':
        prompt='skeleton sitting by the side of a river looking soulful, concert poster, 4k, artistic',
        image_url="",
        mode='txt2img',
-        category="test"
+        category="test",
+        gender="male"
    )
    server = GenerateImage(rd)
-    print(server.get_result())
+    print(server.get_result())
--- a/app/service/generate_image/service_generate_product_image.py
+++ b/app/service/generate_image/service_generate_product_image.py
@@ -18,10 +18,10 @@ import numpy as np
 from PIL import Image, ImageOps
 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.schemas.generate_image import GenerateProductImageModel
 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()

@@ -33,69 +33,29 @@ class GenerateProductImage:
            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.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.category = "product_image"
        self.batch_size = 1
        self.prompt = request_data.prompt
-        # TODO aida design 结果图背景改为白色
-        self.image, self.image_size = self.get_image(request_data.image_url)
-        # TODO image 填充并resize成512*768
-
+        self.image, self.image_size = 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': ''}
        self.redis_client.set(self.tasks_id, json.dumps(self.gen_product_data))
        self.redis_client.expire(self.tasks_id, 600)

-    def get_image(self, image_url):
-        response = self.minio_client.get_object(image_url.split('/')[0], image_url[image_url.find('/') + 1:])
-        image_bytes = io.BytesIO(response.read())
-
-        # 转换为PIL图像对象
-        image = Image.open(image_bytes)
-        target_height = 768
-        target_width = 512
-
-        aspect_ratio = image.width / image.height
-        new_width = int(target_height * aspect_ratio)
-
-        resized_image = image.resize((new_width, target_height))
-        left = (target_width - resized_image.width) // 2
-        top = (target_height - resized_image.height) // 2
-        right = target_width - resized_image.width - left
-        bottom = target_height - resized_image.height - top
-        image = ImageOps.expand(resized_image, (left, top, right, bottom), fill="white")
-        image_size = image.size
-        if image.mode in ('RGBA', 'LA') or (image.mode == 'P' and 'transparency' in image.info):
-            # 创建白色背景
-            background = Image.new("RGB", image.size, (255, 255, 255))
-            # 将图片粘贴到白色背景上
-            background.paste(image, mask=image.split()[3])
-            image = np.array(background)
-            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-
-            # 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.cvtColor(image_cv2, cv2.COLOR_BGR2RGB)
-            # image = cv2.resize(image_rbg, (1024, 1024))
-        return image, image_size
-
    def callback(self, result, error):
        if error:
            self.gen_product_data['status'] = "FAILURE"
            self.gen_product_data['message'] = str(error)
-            # self.gen_product_data['data'] = str(error)
            self.redis_client.set(self.tasks_id, json.dumps(self.gen_product_data))
        else:
            # pil图像转成numpy数组
            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}", object_name=f"{self.tasks_id}.png")
-            # logger.info(f"upload image SUCCESS ： {image_url}")
+            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)
@@ -105,13 +65,6 @@ class GenerateProductImage:
        status_data = self.redis_client.get(self.tasks_id)
        return json.loads(status_data), status_data

-    def infer(self, inputs):
-        return self.grpc_client.async_infer(
-            model_name=GPI_MODEL_NAME,
-            inputs=inputs,
-            callback=self.callback
-        )
-
    def get_result(self):
        try:
            prompts = [self.prompt] * self.batch_size
@@ -129,11 +82,10 @@ class GenerateProductImage:
            input_image.set_data_from_numpy(image_obj)
            inputs = [input_text, input_image]

-            ctx = self.infer(inputs)
+            ctx = self.grpc_client.async_infer(model_name=GPI_MODEL_NAME, inputs=inputs, callback=self.callback)
            time_out = 600
            while time_out > 0:
                gen_product_data, _ = self.read_tasks_status()
-                # logger.info(gen_product_data)
                if gen_product_data['status'] in ["REVOKED", "FAILURE"]:
                    ctx.cancel()
                    break
@@ -141,7 +93,6 @@ class GenerateProductImage:
                    break
                time_out -= 1
                time.sleep(0.1)
-                # logger.info(time_out, gen_product_data)
            gen_product_data, _ = self.read_tasks_status()
            return gen_product_data
        except Exception as e:
@@ -153,7 +104,6 @@ class GenerateProductImage:
            dict_gen_product_data, str_gen_product_data = self.read_tasks_status()
            if DEBUG is False:
                self.channel.basic_publish(exchange='', routing_key=GPI_RABBITMQ_QUEUES, body=str_gen_product_data)
-            # self.channel.basic_publish(exchange='', routing_key=GEN_PRODUCT_IMAGE_RABBITMQ_QUEUES, body=str_gen_product_data)
            logger.info(f" [x] Sent to： {GPI_RABBITMQ_QUEUES} data：@@@@  {json.dumps(dict_gen_product_data, indent=4)}")


@@ -165,11 +115,37 @@ def infer_cancel(tasks_id):
    return data


+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 图片内尺寸 并贴到768-512的纯白图像上
+    target_height = 768
+    target_width = 512
+    aspect_ratio = image.width / image.height
+    new_width = int(target_height * aspect_ratio)
+    resized_image = image.resize((new_width, target_height))
+    left = (target_width - resized_image.width) // 2
+    top = (target_height - resized_image.height) // 2
+    right = target_width - resized_image.width - left
+    bottom = target_height - resized_image.height - top
+    image = ImageOps.expand(resized_image, (left, top, right, bottom), fill="white")
+    image_size = image.size
+    if image.mode in ('RGBA', 'LA') or (image.mode == 'P' and 'transparency' in image.info):
+        # 创建白色背景
+        background = Image.new("RGB", image.size, (255, 255, 255))
+        # 将图片粘贴到白色背景上
+        background.paste(image, mask=image.split()[3])
+        image = np.array(background)
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    return image, image_size
+
+
 if __name__ == '__main__':
-    rd = GenerateImageModel(
+    rd = GenerateProductImageModel(
        tasks_id="123-89",
-        prompt="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_067f2f7e-21ba-11ef-8cf5-0242ac170002.png",
+        prompt="",
+        # 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",
    )
    server = GenerateProductImage(rd)
    print(server.get_result())
--- a/app/service/generate_image/service_generate_relight_image.py
+++ b/app/service/generate_image/service_generate_relight_image.py
@@ -22,6 +22,7 @@ from tritonclient.utils import np_to_triton_dtype
 from app.core.config import *
 from app.schemas.generate_image import GenerateRelightImageModel
 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()

@@ -31,71 +32,34 @@ class GenerateRelightImage:
        if DEBUG is False:
            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.minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE)
        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 = "relight_image"
        self.batch_size = 1
        self.prompt = request_data.prompt
-        self.seed = "12345"
-        # TODO aida design 结果图背景改为白色
-        # self.image, self.image_size = self.get_image(request_data.image_url)
-        self.image = request_data.image_url
-        # TODO image 填充并resize成512*768
-
+        self.seed = "1"
+        self.negative_prompt = 'lowres, bad anatomy, bad hands, cropped, worst quality'
+        self.direction = "Right Light"
+        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': 'PENDING', 'message': "pending", 'image_url': ''}
        self.redis_client.set(self.tasks_id, json.dumps(self.gen_product_data))
        self.redis_client.expire(self.tasks_id, 600)

-    def get_image(self, image_url):
-        response = self.minio_client.get_object(image_url.split('/')[0], image_url[image_url.find('/') + 1:])
-        image_bytes = io.BytesIO(response.read())
-
-        # 转换为PIL图像对象
-        image = Image.open(image_bytes)
-        target_height = 768
-        target_width = 512
-
-        aspect_ratio = image.width / image.height
-        new_width = int(target_height * aspect_ratio)
-
-        resized_image = image.resize((new_width, target_height))
-        left = (target_width - resized_image.width) // 2
-        top = (target_height - resized_image.height) // 2
-        right = target_width - resized_image.width - left
-        bottom = target_height - resized_image.height - top
-        image = ImageOps.expand(resized_image, (left, top, right, bottom), fill="white")
-        image_size = image.size
-        if image.mode in ('RGBA', 'LA') or (image.mode == 'P' and 'transparency' in image.info):
-            # 创建白色背景
-            background = Image.new("RGB", image.size, (255, 255, 255))
-            # 将图片粘贴到白色背景上
-            background.paste(image, mask=image.split()[3])
-            image = np.array(background)
-            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-
-            # 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.cvtColor(image_cv2, cv2.COLOR_BGR2RGB)
-            # image = cv2.resize(image_rbg, (1024, 1024))
-        return image, image_size
-
    def callback(self, result, error):
        if error:
            self.gen_product_data['status'] = "FAILURE"
            self.gen_product_data['message'] = str(error)
-            # self.gen_product_data['data'] = str(error)
            self.redis_client.set(self.tasks_id, json.dumps(self.gen_product_data))
        else:
            # pil图像转成numpy数组
            image = result.as_numpy("generated_inpaint_image")
-            image_result = Image.fromarray(np.squeeze(image.astype(np.uint8))).resize(self.image_size)
+            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}", object_name=f"{self.tasks_id}.png")
-            # logger.info(f"upload image SUCCESS ： {image_url}")
+            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)
@@ -105,62 +69,40 @@ class GenerateRelightImage:
        status_data = self.redis_client.get(self.tasks_id)
        return json.loads(status_data), status_data

-    def infer(self, inputs):
-        return self.grpc_client.async_infer(
-            model_name=GRI_MODEL_NAME,
-            inputs=inputs,
-            callback=self.callback
-        )
-
    def get_result(self):
        try:
-            direction = "Right Light"
-            negative_prompt = 'lowres, bad anatomy, bad hands, cropped, worst quality'
-            self.prompt = 'beautiful woman, detailed face, sunshine, outdoor, warm atmosphere'
            prompts = [self.prompt] * self.batch_size
-            text_obj = np.array(prompts, dtype="object").reshape((-1, 1))
-            input_text = grpcclient.InferInput(
-                "prompt", text_obj.shape, np_to_triton_dtype(text_obj.dtype)
-            )
+            image = cv2.cvtColor(self.image, cv2.COLOR_BGR2RGB)
+            image = cv2.resize(image, (512, 768))
+            images = [image.astype(np.uint8)] * self.batch_size
+            seeds = [self.seed] * self.batch_size
+            nagetive_prompts = [self.negative_prompt] * self.batch_size
+            directions = [self.direction] * self.batch_size
+
+            text_obj = np.array(prompts, dtype="object").reshape((1))
+            image_obj = np.array(images, dtype=np.uint8).reshape((768, 512, 3))
+            na_text_obj = np.array(nagetive_prompts, dtype="object").reshape((1))
+            seed_obj = np.array(seeds, dtype="object").reshape((1))
+            direction_obj = np.array(directions, dtype="object").reshape((1))
+
+            input_text = grpcclient.InferInput("prompt", text_obj.shape, np_to_triton_dtype(text_obj.dtype))
+            input_image = grpcclient.InferInput("input_image", image_obj.shape, "UINT8")
+            input_natext = grpcclient.InferInput("negative_prompt", na_text_obj.shape, np_to_triton_dtype(na_text_obj.dtype))
+            input_seed = grpcclient.InferInput("seed", seed_obj.shape, np_to_triton_dtype(seed_obj.dtype))
+            input_direction = grpcclient.InferInput("direction", direction_obj.shape, np_to_triton_dtype(direction_obj.dtype))
+
            input_text.set_data_from_numpy(text_obj)
+            input_image.set_data_from_numpy(image_obj)
+            input_natext.set_data_from_numpy(na_text_obj)
+            input_seed.set_data_from_numpy(seed_obj)
+            input_direction.set_data_from_numpy(direction_obj)

-            negative_prompts = [negative_prompt] * self.batch_size
-            text_obj_neg = np.array(negative_prompts, dtype="object").reshape((-1, 1))
-            input_text_neg = grpcclient.InferInput(
-                "negative_prompt", text_obj_neg.shape, np_to_triton_dtype(text_obj_neg.dtype)
-            )
-            input_text_neg.set_data_from_numpy(text_obj_neg)
+            inputs = [input_text, input_natext, input_image, input_seed, input_direction]

-            seed = np.array(self.seed, dtype="object").reshape((-1, 1))
-            input_seed = grpcclient.InferInput(
-                "seed", seed.shape, np_to_triton_dtype(seed.dtype)
-            )
-            input_seed.set_data_from_numpy(seed)
-
-            input_images = [self.image] * self.batch_size
-            text_obj_images = np.array(input_images, dtype="object").reshape((-1, 1))
-            input_input_images = grpcclient.InferInput(
-                "input_image", text_obj_images.shape, np_to_triton_dtype(text_obj_images.dtype)
-            )
-            input_input_images.set_data_from_numpy(text_obj_images)
-
-            directions = [direction] * self.batch_size
-            text_obj_directions = np.array(directions, dtype="object").reshape((-1, 1))
-            input_directions = grpcclient.InferInput(
-                "direction", text_obj_directions.shape, np_to_triton_dtype(text_obj_directions.dtype)
-            )
-            input_directions.set_data_from_numpy(text_obj_directions)
-
-            output_img = grpcclient.InferRequestedOutput("generated_image")
-            request_start = time.time()
-
-            inputs = [input_text, input_text_neg, input_input_images, input_seed, input_directions]
-
-            ctx = self.infer(inputs)
+            ctx = self.grpc_client.async_infer(model_name=GRI_MODEL_NAME, inputs=inputs, callback=self.callback)
            time_out = 600
            while time_out > 0:
                gen_product_data, _ = self.read_tasks_status()
-                # logger.info(gen_product_data)
                if gen_product_data['status'] in ["REVOKED", "FAILURE"]:
                    ctx.cancel()
                    break
@@ -168,7 +110,6 @@ class GenerateRelightImage:
                    break
                time_out -= 1
                time.sleep(0.1)
-                # logger.info(time_out, gen_product_data)
            gen_product_data, _ = self.read_tasks_status()
            return gen_product_data
        except Exception as e:
@@ -179,9 +120,8 @@ class GenerateRelightImage:
        finally:
            dict_gen_product_data, str_gen_product_data = self.read_tasks_status()
            if DEBUG is False:
-                self.channel.basic_publish(exchange='', routing_key=GPI_RABBITMQ_QUEUES, body=str_gen_product_data)
-            # self.channel.basic_publish(exchange='', routing_key=GEN_PRODUCT_IMAGE_RABBITMQ_QUEUES, body=str_gen_product_data)
-            logger.info(f" [x] Sent to： {GPI_RABBITMQ_QUEUES} data：@@@@  {json.dumps(dict_gen_product_data, indent=4)}")
+                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):
@@ -195,8 +135,9 @@ def infer_cancel(tasks_id):
 if __name__ == '__main__':
    rd = GenerateRelightImageModel(
        tasks_id="123-89",
-        prompt="beautiful woman, detailed face, sunshine, outdoor, warm atmosphere",
-        image_url="/workspace/i3.png",
+        # prompt="beautiful woman, detailed face, sunshine, outdoor, warm atmosphere",
+        prompt="Colorful black",
+        image_url='aida-users/89/product_image/123-89.png'
    )
    server = GenerateRelightImage(rd)
    print(server.get_result())
--- a/app/service/generate_image/service_generate_single_logo.py
+++ b/app/service/generate_image/service_generate_single_logo.py
@@ -31,8 +31,6 @@ class GenerateSingleLogoImage:
        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=GSL_MODEL_URL)
        self.redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True)
@@ -51,23 +49,15 @@ class GenerateSingleLogoImage:
        status_data = self.redis_client.get(self.tasks_id)
        return json.loads(status_data), status_data

-    def infer(self, inputs):
-        return self.grpc_client.async_infer(
-            model_name=GSL_MODEL_NAME,
-            inputs=inputs,
-            callback=self.callback
-        )
-
    def callback(self, result, error):
        if error:
            self.gen_single_logo_data['status'] = "FAILURE"
            self.gen_single_logo_data['message'] = str(error)
-            # self.generate_data['data'] = str(error)
            self.redis_client.set(self.tasks_id, json.dumps(self.gen_single_logo_data))
        else:
            image = result.as_numpy("generated_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}", object_name=f"{self.tasks_id}.png")
+            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_single_logo_data['status'] = "SUCCESS"
            self.gen_single_logo_data['message'] = "success"
            self.gen_single_logo_data['image_url'] = str(image_url)
@@ -81,25 +71,19 @@ class GenerateSingleLogoImage:
            input_text = grpcclient.InferInput("prompt", text_obj.shape, np_to_triton_dtype(text_obj.dtype))
            input_text.set_data_from_numpy(text_obj)

-            # negative_prompts
            text_obj_neg = np.array(self.negative_prompts, dtype="object").reshape((-1, 1))
-            # print('text obj neg: ', text_obj_neg)
            input_text_neg = grpcclient.InferInput("negative_prompt", text_obj_neg.shape, np_to_triton_dtype(text_obj_neg.dtype))
            input_text_neg.set_data_from_numpy(text_obj_neg)

-            # seed
            seed = np.array(self.seed, dtype="object").reshape((-1, 1))
            input_seed = grpcclient.InferInput("seed", seed.shape, np_to_triton_dtype(seed.dtype))
            input_seed.set_data_from_numpy(seed)
-
            inputs = [input_text, input_text_neg, input_seed]
-
-            ctx = self.infer(inputs)
+            ctx = self.grpc_client.async_infer(model_name=GSL_MODEL_NAME, inputs=inputs, callback=self.callback)
            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
@@ -107,7 +91,6 @@ class GenerateSingleLogoImage:
                    break
                time_out -= 1
                time.sleep(0.1)
-                # logger.info(time_out, generate_data)
            return generate_data
        except Exception as e:
            raise Exception(str(e))
@@ -115,7 +98,6 @@ class GenerateSingleLogoImage:
            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)
-            # 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)}")


--- a/app/service/generate_image/utils/upload_sd_image.py
+++ b/app/service/generate_image/utils/upload_sd_image.py
@@ -16,8 +16,11 @@ from PIL import Image
 from minio import Minio

 from app.core.config import *
+from app.service.utils.oss_client import oss_upload_image

 minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=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)


@@ -34,36 +37,34 @@ minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET
 #     except Exception as e:
 #         print(f'上传到 S3 失败: {e}')

-def upload_SDXL_image(image, user_id, category, object_name):
+def upload_SDXL_image(image, user_id, category, file_name):
    try:
        image_data = io.BytesIO()
        image.save(image_data, format='PNG')
        image_data.seek(0)
        image_bytes = image_data.read()
-        minio_req = minio_client.put_object(
-            GI_MINIO_BUCKET,
-            f'{user_id}/{category}/{object_name}',
-            io.BytesIO(image_bytes),
-            len(image_bytes),
-            content_type='image/jpeg'
-        )
-        image_url = f"aida-users/{minio_req.object_name}"
+
+        # minio_req = minio_client.put_object(
+        #     GI_MINIO_BUCKET,
+        #     f'{user_id}/{category}/{file_name}',
+        #     io.BytesIO(image_bytes),
+        #     len(image_bytes),
+        #     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)
+        image_url = f"aida-users/{object_name}"
        return image_url
    except Exception as e:
        logging.warning(f"upload_png_mask runtime exception : {e}")


-def upload_png_sd(image, user_id, category, object_name):
+def upload_png_sd(image, user_id, category, file_name):
    try:
        _, img_byte_array = cv2.imencode('.jpg', image)
-        minio_req = minio_client.put_object(
-            GI_MINIO_BUCKET,
-            f'{user_id}/{category}/{object_name}',
-            io.BytesIO(img_byte_array),
-            len(img_byte_array),
-            content_type='image/jpeg'
-        )
-        image_url = f"aida-users/{minio_req.object_name}"
+        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)
+        image_url = f"aida-users/{object_name}"
        return image_url
    except Exception as e:
        logging.warning(f"upload_png_mask runtime exception : {e}")
--- a/app/service/super_resolution/service.py
+++ b/app/service/super_resolution/service.py
@@ -1,17 +1,15 @@
-import io
+import json
 import logging
 import time
-import minio.error
-import redis
-import json
 import cv2
+import minio.error
 import numpy as np
+import redis
 import torch
 import tritonclient.grpc as grpcclient
-from minio import Minio
 from app.core.config import *
 from app.schemas.super_resolution import SuperResolutionModel
-from app.service.utils.decorator import RunTime
+from app.service.utils.oss_client import oss_get_image, oss_upload_image

 logger = logging.getLogger()

@@ -24,7 +22,7 @@ class SuperResolution:
        self.user_id = self.tasks_id[self.tasks_id.rfind('-') + 1:]
        self.sr_image_url = data.sr_image_url
        self.sr_xn = data.sr_xn
-        self.minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE)
+        # self.minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE)
        self.redis_client.set(self.tasks_id, json.dumps({'status': 'PENDING', 'message': "pending", 'data': ''}))
        self.redis_client.expire(self.tasks_id, 600)
        self.connection = pika.BlockingConnection(pika.ConnectionParameters(**RABBITMQ_PARAMS))
@@ -33,16 +31,25 @@ class SuperResolution:
    # @RunTime
    def read_image(self):
        try:
-            image_data = self.minio_client.get_object(self.sr_image_url.split("/", 1)[0], self.sr_image_url.split("/", 1)[1])
+            img = oss_get_image(bucket=self.sr_image_url.split("/", 1)[0], object_name=self.sr_image_url.split("/", 1)[1], data_type="cv2")
        except minio.error.S3Error as e:
            sr_data = json.dumps({'tasks_id': self.tasks_id, 'status': 'ERROR', 'message': f'{e}'})
            self.channel.basic_publish(exchange='', routing_key=SR_RABBITMQ_QUEUES, body=sr_data)
            logger.info(f" [x] Sent {sr_data}")
            raise FileNotFoundError(f"Image '{self.sr_image_url.split('/', 1)[1]}' not found in bucket '{self.sr_image_url.split('/', 1)[0]}'")
-        img = np.frombuffer(image_data.data, np.uint8)  # 转成8位无符号整型
-        img = cv2.imdecode(img, cv2.IMREAD_COLOR).astype(np.float32) / 255.  # 解码
        return img

+        # try:
+        #     image_data = self.minio_client.get_object(self.sr_image_url.split("/", 1)[0], self.sr_image_url.split("/", 1)[1])
+        # except minio.error.S3Error as e:
+        #     sr_data = json.dumps({'tasks_id': self.tasks_id, 'status': 'ERROR', 'message': f'{e}'})
+        #     self.channel.basic_publish(exchange='', routing_key=SR_RABBITMQ_QUEUES, body=sr_data)
+        #     logger.info(f" [x] Sent {sr_data}")
+        #     raise FileNotFoundError(f"Image '{self.sr_image_url.split('/', 1)[1]}' not found in bucket '{self.sr_image_url.split('/', 1)[0]}'")
+        # img = np.frombuffer(image_data.data, np.uint8)  # 转成8位无符号整型
+        # img = cv2.imdecode(img, cv2.IMREAD_COLOR).astype(np.float32) / 255.  # 解码
+        # return img
+
    def read_tasks_status(self):
        status_data = json.loads(self.redis_client.get(self.tasks_id))
        logging.info(f"{self.tasks_id} ===> {status_data}")
@@ -101,8 +108,10 @@ class SuperResolution:
    def upload_img_sr(self, image):
        try:
            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')
-            image_url = f"aida-users/{res.object_name}"
+            # 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}"
            return image_url
        except Exception as e:
            logger.warning(f"upload_png_mask runtime exception : {e}")
--- a/app/service/utils/oss_client.py
+++ b/app/service/utils/oss_client.py
@@ -0,0 +1,70 @@
+import io
+import logging
+from io import BytesIO
+
+import boto3
+import cv2
+import numpy as np
+from PIL import Image
+from minio import Minio
+
+from app.core.config import *
+
+logger = logging.getLogger()
+
+
+#  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:
+        if OSS == "minio":
+            oss_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE)
+            image_data = oss_client.get_object(bucket_name=bucket, object_name=object_name)
+        else:
+            oss_client = boto3.client('s3', aws_access_key_id=S3_ACCESS_KEY, aws_secret_access_key=S3_AWS_SECRET_ACCESS_KEY, region_name=S3_REGION_NAME)
+            image_data = oss_client.get_object(Bucket=bucket, Key=object_name)['Body']
+        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)
+        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:
+        if OSS == "minio":
+            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')
+        else:
+            oss_client = boto3.client('s3', aws_access_key_id=S3_ACCESS_KEY, aws_secret_access_key=S3_AWS_SECRET_ACCESS_KEY, region_name=S3_REGION_NAME)
+            req = oss_client.put_object(Bucket=AIDA_CLOTHING, Key=object_name, Body=image_bytes, ContentType='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 = "aida-users/89/single_logo/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/relight_image/123-89.png'
+    # url = "aida-users/89/sketchboard/female/Dress/e6724ab7-8d3f-4677-abe0-c3e42ab7af85.jpeg"
+    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()