Merge branch 'refs/heads/develop'

.gitignore

@@ -136,4 +136,9 @@ app/logs/*
 /qodana.yaml
 .pth
 .pytorch
-*.png
+*.png
+*.pth
+*.db
+*.npy
+*.pytorch
+*.jpg

app/api/api_attribute_retrieve.py

@@ -35,13 +35,13 @@ def attribute_recognition(request_item: list[AttributeRecognitionModel]):
     """
     try:
         for item in request_item:
-            logger.info(f"attribute_recognition request item is : @@@@@@:{json.dumps(item.dict())}")
+            logger.debug(f"attribute_recognition request item is : @@@@@@:{json.dumps(item.dict())}")
         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)}")
+        logger.debug(f"attribute_recognition response @@@@@@:{json.dumps(data)}")
     except Exception as e:
         logger.warning(f"attribute_recognition Run Exception @@@@@@:{e}")
         raise HTTPException(status_code=404, detail=str(e))

app/api/api_brand_dna.py

@@ -0,0 +1,34 @@
+import json
+import logging
+
+from fastapi import APIRouter, HTTPException
+
+from app.schemas.brand_dna import BrandDnaModel
+from app.schemas.response_template import ResponseModel
+from app.service.brand_dna.service import BrandDna
+
+router = APIRouter()
+logger = logging.getLogger()
+
+
+@router.post("/seg_product")
+def image2sketch(request_item: BrandDnaModel):
+    """
+        创建一个具有以下参数的请求体:
+        - **image_url**: 提取图片url
+        - **is_brand_dna**: 是否提取属性
+
+        示例参数：
+        {
+          "image_url": "test/image2sketch/real_Dress_3200fecdc83d0c556c2bd96aedbd7fbf.jpg_Img.jpg",
+          "is_brand_dna": False
+        }
+    """
+    try:
+        logger.info(f"brand dna request item is : @@@@@@:{json.dumps(request_item.dict())}")
+        service = BrandDna(request_item)
+        result_url = service.get_result()
+    except Exception as e:
+        logger.warning(f"brand dna Run Exception @@@@@@:{e}")
+        raise HTTPException(status_code=404, detail=str(e))
+    return ResponseModel(data=result_url)

app/api/api_design.py

@@ -4,7 +4,7 @@ import os
 
 from fastapi import APIRouter, HTTPException, UploadFile, File, Form, BackgroundTasks
 
-from app.schemas.design import DesignModel, DesignProgressModel, ModelProgressModel, DBGConfigModel
+from app.schemas.design import DesignModel, DesignProgressModel, ModelProgressModel, DBGConfigModel, DesignStreamModel
 from app.schemas.response_template import ResponseModel
 from app.service.design.model_process_service import model_transpose
 from app.service.design_batch.service import start_design_batch_generate
@@ -18,6 +18,14 @@ logger = logging.getLogger()
 @router.post("/design")
 def design(request_data: DesignModel, background_tasks: BackgroundTasks):
     """
+        objects.items.transparent:
+            "transparent":{
+                "mask_url":"test/transparent_test/transparent_mask.png",
+                "scale":0.1
+            },
+            mask_url 为空"" ->  单件衣服透明
+            mask_url 非空"mask_url" ->  区域透明
+
         创建一个具有以下参数的请求体:
         示例参数：
         {
@@ -197,7 +205,7 @@ def design(request_data: DesignModel, background_tasks: BackgroundTasks):
 
 
 @router.post("/design_v2")
-async def design_v2(request_data: DesignModel, background_tasks: BackgroundTasks):
+async def design_v2(request_data: DesignStreamModel, background_tasks: BackgroundTasks):
     """
         创建一个具有以下参数的请求体:
         示例参数：
@@ -445,7 +453,7 @@ async def design(file: UploadFile = File(...),
 
 async def save_request_file(contents, file_name):
     # 创建保存文件的目录（如果不存在）
-    save_dir = os.path.join(os.getcwd(), "design_batch", "request_data")
+    save_dir = os.path.join(os.getcwd(), "service/design_batch", "request_data")
     if not os.path.exists(save_dir):
         os.makedirs(save_dir)
     # 处理文件

app/api/api_generate_image.py

@@ -3,9 +3,10 @@ import logging
 
 from fastapi import APIRouter, BackgroundTasks, HTTPException
 
-from app.schemas.generate_image import GenerateImageModel, GenerateProductImageModel, GenerateSingleLogoImageModel, GenerateRelightImageModel
+from app.schemas.generate_image import GenerateImageModel, GenerateProductImageModel, GenerateSingleLogoImageModel, GenerateRelightImageModel, GenerateMultiViewModel
 from app.schemas.response_template import ResponseModel
 from app.service.generate_image.service_generate_image import GenerateImage, infer_cancel as generate_image_infer_cancel
+from app.service.generate_image.service_generate_multi_view import GenerateMultiView, infer_cancel as generate_multi_view_cancel
 from app.service.generate_image.service_generate_product_image import GenerateProductImage, infer_cancel as generate_product_image_cancel
 from app.service.generate_image.service_generate_relight_image import GenerateRelightImage, infer_cancel as generate_relight_image_cancel
 from app.service.generate_image.service_generate_single_logo import GenerateSingleLogoImage, infer_cancel as generate_single_logo_cancel
@@ -61,6 +62,44 @@ def generate_image(tasks_id: str):
     return ResponseModel(data=data['data'])
 
 
+'''multi view'''
+
+
+@router.post("/generate_multi_view")
+def generate_multi_view(request_item: GenerateMultiViewModel, background_tasks: BackgroundTasks):
+    """
+        创建一个具有以下参数的请求体:
+        - **tasks_id**: 任务id 用于取消生成任务和获取生成结果
+        - **image_url**: 前视角图的输入，minio或S3 url 地址
+
+        示例参数：
+        {
+          "tasks_id": "123-89",
+          "image_url": "aida-collection-element/87/Printboard/842c09cf-7297-42d9-9e6e-9c17d4a13cb5.jpg"
+        }
+    """
+    try:
+        logger.info(f"generate_multi_view request item is : @@@@@@:{json.dumps(request_item.dict())}")
+        service = GenerateMultiView(request_item)
+        background_tasks.add_task(service.get_result)
+    except Exception as e:
+        logger.warning(f"generate_multi_view Run Exception @@@@@@:{e}")
+        raise HTTPException(status_code=404, detail=str(e))
+    return ResponseModel()
+
+
+@router.get("/generate_multi_view_cancel/{tasks_id}")
+def generate_multi_view(tasks_id: str):
+    try:
+        logger.info(f"generate_cancel request item is : @@@@@@:{tasks_id}")
+        data = generate_multi_view_cancel(tasks_id)
+        logger.info(f"generate_cancel response @@@@@@:{data}")
+    except Exception as e:
+        logger.warning(f"generate_cancel Run Exception @@@@@@:{e}")
+        raise HTTPException(status_code=404, detail=str(e))
+    return ResponseModel(data=data['data'])
+
+
 '''single logo'''
 
 

app/api/api_prompt_generation.py

@@ -26,7 +26,7 @@ def prompt_generation(request_data: PromptGenerationImageModel):
     """
     try:
         logger.info(f"prompt_generation request item is : @@@@@@:{request_data}")
-        data = get_translation_from_llama3("[" + request_data.text + "]")
+        data = get_translation_from_llama3(request_data.text)
         logger.info(f"prompt_generation response @@@@@@:{data}")
     except Exception as e:
         logger.warning(f"prompt_generation Run Exception @@@@@@:{e}")

app/api/api_route.py

@@ -1,6 +1,7 @@
 from fastapi import APIRouter
 
 from app.api import api_attribute_retrieve, api_query_image
+from app.api import api_brand_dna
 from app.api import api_brighten
 from app.api import api_chat_robot
 from app.api import api_design
@@ -23,4 +24,5 @@ router.include_router(api_prompt_generation.router, tags=['prompt_generation'],
 router.include_router(api_design_pre_processing.router, tags=['design_pre_processing'], prefix="/api")
 router.include_router(api_image2sketch.router, tags=['api_image2sketch'], prefix="/api")
 router.include_router(api_brighten.router, tags=['api_brighten'], prefix="/api")
-router.include_router(api_query_image.router, tags=['api_query_image'], prefix="/api")
+router.include_router(api_query_image.router, tags=['api_query_image'], prefix="/api")
+router.include_router(api_brand_dna.router, tags=['api_brand_dna'], prefix="/api")

app/api/api_test.py

@@ -4,7 +4,7 @@ import logging
 from fastapi import APIRouter
 from fastapi import HTTPException
 
-from app.core.config import SR_RABBITMQ_QUEUES, GI_RABBITMQ_QUEUES, GPI_RABBITMQ_QUEUES, GRI_RABBITMQ_QUEUES, OSS
+from app.core.config import SR_RABBITMQ_QUEUES, GI_RABBITMQ_QUEUES, GPI_RABBITMQ_QUEUES, GRI_RABBITMQ_QUEUES, OSS, JAVA_STREAM_API_URL
 from app.schemas.response_template import ResponseModel
 
 logger = logging.getLogger()
@@ -18,6 +18,7 @@ def test(id: int):
         "GI_RABBITMQ_QUEUES": GI_RABBITMQ_QUEUES,
         "GPI_RABBITMQ_QUEUES": GPI_RABBITMQ_QUEUES,
         "GRI_RABBITMQ_QUEUES": GRI_RABBITMQ_QUEUES,
+        "JAVA_STREAM_API_URL": JAVA_STREAM_API_URL,
         "local_oss_server": OSS
     }
     logger.info(json.dumps(data))

app/core/config.py

@@ -34,6 +34,9 @@ else:
 RABBITMQ_ENV = "-dev"  # 开发环境
 # RABBITMQ_ENV = "-local"  # 本地测试环境
 
+JAVA_STREAM_API_URL = os.getenv("JAVA_STREAM_API_URL", "https://api.aida.com.hk/api/third/party/receiveDesignResults")
+
+
 settings = Settings()
 
 # minio 配置
@@ -106,6 +109,12 @@ FAST_GI_MODEL_NAME = 'stable_diffusion_xl'
 GI_MODEL_URL = '10.1.1.240:10061'
 GI_MODEL_NAME = 'flux'
 
+GMV_MODEL_URL = '10.1.1.243:10081'
+GMV_MODEL_NAME = 'multi_view'
+
+GMV_RABBITMQ_QUEUES = os.getenv("GMV_RABBITMQ_QUEUES", f"GenerateMultiView{RABBITMQ_ENV}")
+
+
 GI_MINIO_BUCKET = "aida-users"
 GI_RABBITMQ_QUEUES = os.getenv("GI_RABBITMQ_QUEUES", f"GenerateImage{RABBITMQ_ENV}")
 GI_SYS_IMAGE_URL = "aida-sys-image/generate_image/white_image.jpg"

app/schemas/brand_dna.py

@@ -0,0 +1,6 @@
+from pydantic import BaseModel
+
+
+class BrandDnaModel(BaseModel):
+    image_url: str
+    is_brand_dna: bool

app/schemas/design.py

@@ -6,6 +6,12 @@ class DesignModel(BaseModel):
     process_id: str
 
 
+class DesignStreamModel(BaseModel):
+    objects: list[dict]
+    process_id: str
+    requestId: str
+
+
 class DesignProgressModel(BaseModel):
     process_id: str
 

app/schemas/generate_image.py

@@ -1,6 +1,11 @@
 from pydantic import BaseModel
 
 
+class GenerateMultiViewModel(BaseModel):
+    tasks_id: str
+    image_url: str
+
+
 class GenerateImageModel(BaseModel):
     tasks_id: str
     prompt: str

app/service/brand_dna/service.py

@@ -0,0 +1,335 @@
+import logging
+
+import cv2
+import mmcv
+import numpy as np
+import pandas as pd
+import torch
+import torch.nn.functional as F
+import tritonclient.http as httpclient
+from minio import Minio
+
+from app.core.config import MINIO_URL, MINIO_ACCESS, MINIO_SECRET, MINIO_SECURE, DESIGN_MODEL_URL
+from app.schemas.brand_dna import BrandDnaModel
+from app.service.attribute.config import local_debug_const
+from app.service.utils.generate_uuid import generate_uuid
+from app.service.utils.new_oss_client import oss_upload_image, oss_get_image
+
+logger = logging.getLogger()
+
+minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE)
+
+
+class BrandDna:
+    def __init__(self, request_item):
+        self.sketch_bucket = "test"
+        self.image_url = request_item.image_url
+        self.is_brand_dna = request_item.is_brand_dna
+        # self.attr_type = pd.read_csv(CATEGORY_PATH)
+        self.attr_type = pd.read_csv(r"E:\workspace\trinity_client_aida\app\service\attribute\config\descriptor\category\category_dis.csv")
+        self.att_client = httpclient.InferenceServerClient(url=DESIGN_MODEL_URL)
+        self.seg_client = httpclient.InferenceServerClient(url='10.1.1.243:30000')
+        # self.const = const
+        self.const = local_debug_const
+
+    # 获取结果
+    def get_result(self):
+        mask, image = self.get_seg_mask()
+        cv2.imshow("", image)
+        cv2.waitKey(0)
+
+        height, width, channels = image.shape
+        result_dict = []
+        white_img = np.ones((height, width, channels), dtype=image.dtype) * 255
+        mask_image = np.zeros((height, width, 3))
+
+        for value in np.unique(mask):
+            if value == 1:
+                outwear_img = white_img.copy()
+                outwear_mask_img = mask_image.copy()
+                outwear_img[mask == value] = image[mask == value]
+                outwear_mask_img[mask == value] = [0, 0, 255]
+
+                cv2.imshow("", outwear_img)
+                cv2.waitKey(0)
+
+                # 预处理之后的input img
+                preprocess_img = self.category_preprocess(outwear_img)
+                # 类别检测
+                category = self.recognition_category(preprocess_img)
+                if category == 'Trousers' or category == 'Skirt':
+                    male_category = 'Bottoms'
+                elif category == 'Blouse' or category == 'Dress':
+                    male_category = 'Tops'
+                else:
+                    male_category = 'Outwear'
+
+                attribute = {}
+                mask_url = ""
+                img_url = ""
+                # 属性检测
+                if self.is_brand_dna:
+                    attribute = self.get_recognition_attribute_result(category, preprocess_img)
+                else:
+                    img_url = self.put_image(outwear_img, f"img/{generate_uuid()}")
+                    mask_url = self.put_image(outwear_mask_img, f"mask/{generate_uuid()}")
+
+                result_dict.append(
+                    {
+                        'category_female': category,
+                        'category_male': male_category,
+                        'mask_url': mask_url,
+                        'img_url': img_url,
+                        'attribute': attribute
+                    }
+                )
+            if value == 2:
+                tops_img = white_img.copy()
+                tops_mask_img = mask_image.copy()
+                tops_img[mask == value] = image[mask == value]
+                tops_mask_img[mask == value] = [0, 0, 255]
+
+                cv2.imshow("", tops_img)
+                cv2.waitKey(0)
+
+                # 预处理之后的input img
+                preprocess_img = self.category_preprocess(tops_img)
+                # 类别检测
+                category = self.recognition_category(preprocess_img)
+                if category == 'Trousers' or category == 'Skirt':
+                    male_category = 'Bottoms'
+                elif category == 'Blouse' or category == 'Dress':
+                    male_category = 'Tops'
+                else:
+                    male_category = 'Outwear'
+
+                # 属性检测
+                attribute = {}
+                img_url = ""
+                mask_url = ""
+                # 属性检测
+                if self.is_brand_dna:
+                    attribute = self.get_recognition_attribute_result(category, preprocess_img)
+                else:
+                    mask_url = self.put_image(tops_mask_img, f"mask/{generate_uuid()}")
+                    img_url = self.put_image(tops_img, f"img/{generate_uuid()}")
+
+                result_dict.append(
+                    {
+                        'category_female': category,
+                        'category_male': male_category,
+                        'mask_url': mask_url,
+                        'img_url': img_url,
+                        'attribute': attribute
+                    }
+                )
+            if value == 3:
+                bottoms_img = white_img.copy()
+                bottoms_mask_img = mask_image.copy()
+                bottoms_img[mask == value] = image[mask == value]
+                bottoms_mask_img[mask == value] = [0, 0, 255]
+
+                cv2.imshow("", bottoms_img)
+                cv2.waitKey(0)
+
+                # 预处理之后的input img
+                preprocess_img = self.category_preprocess(bottoms_img)
+                # 类别检测
+                category = self.recognition_category(preprocess_img)
+                if category == 'Trousers' or category == 'Skirt':
+                    male_category = 'Bottoms'
+                elif category == 'Blouse' or category == 'Dress':
+                    male_category = 'Tops'
+                else:
+                    male_category = 'Outwear'
+
+                attribute = {}
+                img_url = ""
+                mask_url = ""
+                # 属性检测
+                if self.is_brand_dna:
+                    attribute = self.get_recognition_attribute_result(category, preprocess_img)
+                else:
+                    img_url = self.put_image(bottoms_img, f"img/{generate_uuid()}")
+                    mask_url = self.put_image(bottoms_mask_img, f"mask/{generate_uuid()}")
+
+                result_dict.append(
+                    {
+                        'category_female': category,
+                        'category_male': male_category,
+                        'mask_url': mask_url,
+                        'img_url': img_url,
+                        'attribute': attribute
+                    }
+                )
+        return result_dict
+
+    # 获取product mask
+    def get_seg_mask(self):
+        input_image = self.get_image()
+        input_img, ori_shape = self.seg_product_preprocess(input_image)
+        transformed_img = input_img.astype(np.float32)
+
+        inputs = [httpclient.InferInput(f"seg_input__0", transformed_img.shape, datatype="FP32")]
+        inputs[0].set_data_from_numpy(transformed_img, binary_data=True)
+        outputs = [httpclient.InferRequestedOutput(f"seg_output__0", binary_data=True)]
+        results = self.seg_client.infer(model_name=f"seg_product", inputs=inputs, outputs=outputs)
+        inference_output1 = results.as_numpy("seg_output__0")
+        mask = self.product_postprocess(inference_output1, ori_shape)[0]
+        return mask, input_image
+
+    # 获取图片
+    def get_image(self):
+        image = oss_get_image(oss_client=minio_client, bucket=self.image_url.split('/')[0], object_name=self.image_url[self.image_url.find('/') + 1:], data_type="cv2")
+        # 将其转换为彩色图像
+        if len(image.shape) == 3 and image.shape[2] == 4:
+            image = cv2.cvtColor(image, cv2.COLOR_BGRA2BGR)
+        elif len(image.shape) == 2:
+            image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
+        return image
+        # return cv2.imread(self.image_url)
+
+    # 图片上传
+    def put_image(self, image, object_name):
+        try:
+            image_bytes = cv2.imencode('.jpg', image)[1].tobytes()
+            oss_upload_image(oss_client=minio_client, bucket=self.sketch_bucket, object_name=f"{object_name}.jpg", image_bytes=image_bytes)
+            return f"{self.sketch_bucket}/{object_name}.jpg"
+        except Exception as e:
+            logger.warning(e)
+
+    # 服装分割预处理
+    @staticmethod
+    def seg_product_preprocess(image):
+        img = mmcv.imread(image)
+        ori_shape = img.shape[:2]
+        img_scale_w, img_scale_h = ori_shape
+        if ori_shape[0] > 1024:
+            img_scale_w = 1024
+        if ori_shape[1] > 1024:
+            img_scale_h = 1024
+        # 如果图片size任意一边 大于 1024， 则会resize 成1024
+        if ori_shape != (img_scale_w, img_scale_h):
+            # mmcv.imresize(img, img_scale_h, img_scale_w) # 老代码 引以为戒！哈哈哈~ h和w写反了
+            img = cv2.resize(img, (img_scale_h, img_scale_w))
+        img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
+        preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0)
+        return preprocessed_img, ori_shape
+
+    # 类别检测后处理
+    @staticmethod
+    def product_postprocess(output, ori_shape):
+        seg_logit = F.interpolate(torch.tensor(output).float(), size=ori_shape, scale_factor=None, mode='bilinear', align_corners=False)
+        seg_pred = seg_logit.cpu().numpy()
+        return seg_pred[0]
+
+    # 类别检测模型预处理
+    @staticmethod
+    def category_preprocess(img):
+        img = mmcv.imread(img)
+        # ori_shape = img.shape[:2]
+        img_scale = (224, 224)
+        img = cv2.resize(img, img_scale)
+        img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
+        preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0)
+        return preprocessed_img
+
+    # 类别检测
+    def recognition_category(self, image):
+        inputs = [
+            httpclient.InferInput("input__0", image.shape, datatype="FP32")
+        ]
+        inputs[0].set_data_from_numpy(image, binary_data=True)
+        results = self.att_client.infer(model_name="attr_retrieve_category", inputs=inputs)
+        inference_output = torch.from_numpy(results.as_numpy(f'output__0'))
+
+        scores = inference_output.detach().numpy()
+
+        colattr = list(self.attr_type['labelName'])
+        maxsc = np.max(scores[0][:5])
+        indexs = np.argwhere(scores == maxsc)[:, 1]
+
+        return colattr[indexs[0]]
+
+    # 属性检测
+    def recognition_attribute(self, model_name, description, image):
+        attr_type = pd.read_csv(description)
+        inputs = [
+            httpclient.InferInput("input__0", image.shape, datatype="FP32")
+        ]
+        inputs[0].set_data_from_numpy(image, binary_data=True)
+        results = self.att_client.infer(model_name=model_name, inputs=inputs)
+        inference_output = torch.from_numpy(results.as_numpy(f"output__0"))
+        scores = inference_output.detach().numpy()
+        colattr = list(attr_type['labelName'])
+        task = description.split('/')[-1][:-4]
+        maxsc = np.max(scores[0][:5])
+        indexs = np.argwhere(scores == maxsc)[:, 1]
+        attr = {
+            task: []
+        }
+        for i in range(len(indexs)):
+            atr = colattr[indexs[i]]
+            attr[task].append(atr)
+        return attr
+
+    # 获取属性检测结果
+    def get_recognition_attribute_result(self, category, input_img):
+        if category == "Blouse":
+            attr_dict = {}
+            for i in range(len(self.const.top_description_list)):
+                attr_description = self.const.top_description_list[i]
+                attr_model_path = self.const.top_model_list[i]
+                present_dict = self.recognition_attribute(attr_model_path, attr_description, input_img)
+                attr_dict = self.merge(attr_dict, present_dict)
+
+        elif category == 'Trousers' or category == "Skirt":
+            attr_dict = {}
+            for i in range(len(self.const.bottom_description_list)):
+                attr_description = self.const.bottom_description_list[i]
+                attr_model_path = self.const.bottom_model_list[i]
+                present_dict = self.recognition_attribute(attr_model_path, attr_description, input_img)
+                attr_dict = self.merge(attr_dict, present_dict)
+
+        elif category == 'Dress':
+            attr_dict = {}
+            for i in range(len(self.const.dress_description_list)):
+                attr_description = self.const.dress_description_list[i]
+                attr_model_path = self.const.dress_model_list[i]
+                present_dict = self.recognition_attribute(attr_model_path, attr_description, input_img)
+                attr_dict = self.merge(attr_dict, present_dict)
+
+        elif category == 'Outwear':
+            attr_dict = {}
+            for i in range(len(self.const.outwear_description_list)):
+                attr_description = self.const.outwear_description_list[i]
+                attr_model_path = self.const.outwear_model_list[i]
+                present_dict = self.recognition_attribute(attr_model_path, attr_description, input_img)
+                attr_dict = self.merge(attr_dict, present_dict)
+        else:
+            attr_dict = {}
+        return attr_dict
+
+    @staticmethod
+    def merge(dict1, dict2):
+        res = {**dict1, **dict2}
+        return res
+
+
+if __name__ == '__main__':
+    # for path in os.listdir('./test_img'):
+    #     img_path = os.path.join(r'./test_img', path)
+    #     request_item = BrandDnaModel(
+    #         image_url=img_path,
+    #         is_brand_dna=True
+    #     )
+    #     service = BrandDna(request_item)
+    #     result_url = service.get_result()
+    #     print(result_url)
+    request_item = BrandDnaModel(
+        image_url="aida-users/60/product_image/07cb5d5d-5022-44cc-b0d3-cc986cfebad1-2-60.png",
+        is_brand_dna=True
+    )
+    service = BrandDna(request_item)
+    result_url = service.get_result()
+    print(result_url)

app/service/chat_robot/script/prompt.py

@@ -69,3 +69,5 @@ TOOLS_FUNCTIONS_SUFFIX = (
 )
 
 TUTORIAL_TOOL_RETURN = "Commencing the systematic tutorial guide now."
+
+GET_LANGUAGE_PREFIX = "Please identify the language. Only output the language name"

app/service/chat_robot/script/service/CallQWen.py

@@ -1,4 +1,5 @@
 import json
+import logging
 
 from dashscope import Generation
 from retry import retry
@@ -7,7 +8,8 @@ from urllib3.exceptions import NewConnectionError
 from app.core.config import *
 from app.service.chat_robot.script.callbacks.qwen_callback_handler import QWenCallbackHandler
 from app.service.chat_robot.script.database import CustomDatabase
-from app.service.chat_robot.script.prompt import FASHION_CHAT_BOT_PREFIX, TOOLS_FUNCTIONS_SUFFIX, TUTORIAL_TOOL_RETURN
+from app.service.chat_robot.script.prompt import FASHION_CHAT_BOT_PREFIX, TOOLS_FUNCTIONS_SUFFIX, TUTORIAL_TOOL_RETURN, \
+    GET_LANGUAGE_PREFIX
 from app.service.search_image_with_text.service import query
 
 get_database_table_description = "Input is an empty string, output is a comma separated list of tables in the database."
@@ -159,10 +161,11 @@ def search_from_internet(message):
         model='qwen-turbo',
         api_key=QWEN_API_KEY,
         messages=message,
-        tools=tools,
+        prompt='The output must be in English.Keep the final result under 200 words.'
+        # tools=tools,
         # seed=random.randint(1, 10000),  # 设置随机数种子seed，如果没有设置，则随机数种子默认为1234
-        result_format='message',  # 将输出设置为message形式
-        enable_search='True'
+        # result_format='message',  # 将输出设置为message形式
+        # enable_search='True'
     )
     return response
 
@@ -198,14 +201,9 @@ def get_response(messages):
 
 
 def call_with_messages(message, gender):
+    global tool_info
     user_input = message
     print('\n')
-    # messages = [
-    #     {
-    #         "content": input('请输入：'),  # 提问示例："现在几点了？" "一个小时后几点" "北京天气如何？"
-    #         "role": "user"
-    #     }
-    # ]
 
     messages = [
         {
@@ -223,14 +221,10 @@ def call_with_messages(message, gender):
         }
     ]
 
-    # 模型的第一轮调用
-    # first_response = get_response(messages)
-    # assistant_output = first_response.output.choices[0].message
-    # print(f"\n大模型第一轮输出信息：{first_response}\n")
-    # messages.append(assistant_output)
     flag = True
     count = 1
-    result_content = "我是一个时尚AI助手，请问有什么可以帮您"
+    # result_content = "我是一个时尚AI助手，请问有什么可以帮您"
+    result_content = "I am a fashion AI assistant, how can I help you?"
     response_type = "chat"
 
     while flag and count <= 3:
@@ -244,44 +238,53 @@ def call_with_messages(message, gender):
             print(f"最终答案：{assistant_output.content}")  # 此处直接返回模型的回复，您可以根据您的业务，选择当无需调用工具时最终回复的内容
             result_content = assistant_output.content
             break
-        # 如果模型选择的工具是search_from_internet
-        # elif assistant_output.tool_calls[0]['function']['name'] == 'search_from_internet':
-        #     tool_info = {"name": "search_from_internet", "role": "tool"}
-        #     user_input = json.loads(assistant_output.tool_calls[0]['function']['arguments'])['user_input']
-        #     tool_info['content'] = search_from_internet(user_input)
-        # 如果模型选择的工具是get_database_table
-        elif assistant_output.tool_calls[0]['function']['name'] == 'get_database_table':
-            tool_info = {"name": "get_database_table", "role": "tool", 'content': get_database_table()}
-        # 如果模型选择的工具是get_table_info
-        elif assistant_output.tool_calls[0]['function']['name'] == 'get_table_info':
-            tool_info = {"name": "get_table_info", "role": "tool"}
-            table_names = json.loads(assistant_output.tool_calls[0]['function']['arguments'])['table_names']
-            tool_info['content'] = get_table_info(table_names)
-        # 如果模型选择的工具是query_database
-        elif assistant_output.tool_calls[0]['function']['name'] == 'query_database':
-            tool_info = {"name": "query_database", "role": "tool"}
-            sql_string = json.loads(assistant_output.tool_calls[0]['function']['arguments'])['sql_string']
-            tool_info['content'] = query_database(sql_string)
+        # 如果模型选择的工具是internet_search
+        elif assistant_output.tool_calls[0]['function']['name'] == 'internet_search':
+            tool_info = {"name": "search_from_internet", "role": "tool"}
+            content = json.loads(assistant_output.tool_calls[0]['function']['arguments'])
+            message = [
+                {'role': 'assistant', 'content': content['query']}
+            ]
+            tool_info['content'] = search_from_internet(message)
             flag = False
-            result_content = tool_info['content']
-            response_type = "image"
+            result_content = tool_info['content'].output.text
+        # 如果模型选择的工具是get_database_table
+        # elif assistant_output.tool_calls[0]['function']['name'] == 'get_database_table':
+        #     tool_info = {"name": "get_database_table", "role": "tool", 'content': get_database_table()}
+        # # 如果模型选择的工具是get_table_info
+        # elif assistant_output.tool_calls[0]['function']['name'] == 'get_table_info':
+        #     tool_info = {"name": "get_table_info", "role": "tool"}
+        #     table_names = json.loads(assistant_output.tool_calls[0]['function']['arguments'])['table_names']
+        #     tool_info['content'] = get_table_info(table_names)
+        # # 如果模型选择的工具是query_database
+        # elif assistant_output.tool_calls[0]['function']['name'] == 'query_database':
+        #     tool_info = {"name": "query_database", "role": "tool"}
+        #     sql_string = json.loads(assistant_output.tool_calls[0]['function']['arguments'])['sql_string']
+        #     tool_info['content'] = query_database(sql_string)
+        #     flag = False
+        #     result_content = tool_info['content']
+        #     response_type = "image"
         elif assistant_output.tool_calls[0]['function']['name'] == 'tutorial_tool':
             tool_info = {"name": "tutorial_tool", "role": "tool", 'content': tutorial_tool()}
             flag = False
             result_content = tool_info['content']
         elif assistant_output.tool_calls[0]['function']['name'] == 'get_image_from_vector_db':
+            content = json.loads(assistant_output.tool_calls[0]['function']['arguments'])
             tool_info = {"name": "get_image_from_vector_db", "role": "tool",
-                         'content': get_image_from_vector_db(gender, user_input)}
+                         'content': get_image_from_vector_db(gender, content['parameters']['content'])}
             flag = False
             result_content = tool_info['content']
             response_type = "image"
+        else:
+            tool_info = {"name": assistant_output.tool_calls[0]['function']['name'], 'content': 'null'}
+            logging.info(assistant_output.tool_calls[0]['function']['name'] + "(unknown tools)")
+            flag = False
 
         print(f"工具输出信息：{tool_info['content']}\n")
         messages.append(tool_info)
         count += 1
 
-    final_output = {"output": result_content}
-    final_output["response_type"] = response_type
+    final_output = {"output": result_content, "response_type": response_type}
     QWenCallbackHandler.on_chain_end(qwen, final_output)
 
     # 模型的第二轮调用，对工具的输出进行总结
@@ -298,5 +301,23 @@ def tutorial_tool():
     return TUTORIAL_TOOL_RETURN
 
 
+def get_language(message: str) -> str:
+    messages = [
+        {
+            "content": message,  # 用户message
+            "role": "user"
+        },
+        {
+            "content": GET_LANGUAGE_PREFIX,  # ai message
+            "role": "assistant"
+        }
+    ]
+
+    first_response = get_response(messages)
+    assistant_output = first_response.output.choices[0].message.content
+    logging.info(f"大模型输出信息：{first_response}\n判断用户输入的语言为：{assistant_output}")
+    return assistant_output
+
+
 if __name__ == '__main__':
-    call_with_messages()
+    get_language("")

app/service/design/items/pipelines/segmentation.py

@@ -53,7 +53,7 @@ class Segmentation(object):
         file_path = f"{SEG_CACHE_PATH}{image_id}.npy"
         try:
             np.save(file_path, seg_result)
-            logger.info(f"保存成功 ：{os.path.abspath(file_path)}")
+            logger.debug(f"保存成功 ：{os.path.abspath(file_path)}")
         except Exception as e:
             logger.error(f"保存失败: {e}")
 
@@ -64,7 +64,7 @@ class Segmentation(object):
             seg_result = np.load(file_path)
             return True, seg_result
         except FileNotFoundError:
-            logger.warning("文件不存在")
+            # logger.warning("文件不存在")
             return False, None
         except Exception as e:
             logger.error(f"加载失败: {e}")

app/service/design_batch/design_batch_celery.py

@@ -12,7 +12,7 @@ from app.service.design_batch.utils.save_json import oss_upload_json
 from app.service.design_batch.utils.synthesis_item import update_base_size_priority, synthesis, synthesis_single
 
 id_lock = threading.Lock()
-celery_app = Celery('tasks', broker='amqp://guest:guest@10.1.2.213:5672//', backend='rpc://')
+celery_app = Celery('tasks', broker=f'amqp://rabbit:123456@18.167.251.121:5672//', backend='rpc://', BROKER_CONNECTION_RETRY_ON_STARTUP=True)
 celery_app.conf.worker_log_format = '%(asctime)s %(filename)s [line:%(lineno)d] %(levelname)s %(message)s'
 celery_app.conf.worker_hijack_root_logger = False
 logging.getLogger('pika').setLevel(logging.WARNING)
@@ -120,7 +120,7 @@ def batch_design(objects_data, tasks_id, json_name):
 
     for t in threads:
         t.join()
-
+    logger.debug(object_response)
     oss_upload_json(minio_client, object_response, json_name)
     publish_status(tasks_id, "ok", json_name)
     return object_response

app/service/design_batch/pipeline/segmentation.py

@@ -51,19 +51,19 @@ class Segmentation:
         file_path = f"seg_cache/{image_id}.npy"
         try:
             np.save(file_path, seg_result)
-            logger.info(f"保存成功 ：{os.path.abspath(file_path)}")
+            logger.debug(f"保存成功 ：{os.path.abspath(file_path)}")
         except Exception as e:
             logger.error(f"保存失败: {e}")
 
     @staticmethod
     def load_seg_result(image_id):
         file_path = f"seg_cache/{image_id}.npy"
-        logger.info(f"load seg file name is :{SEG_CACHE_PATH}{image_id}.npy")
+        # logger.info(f"load seg file name is :{SEG_CACHE_PATH}{image_id}.npy")
         try:
             seg_result = np.load(file_path)
             return True, seg_result
         except FileNotFoundError:
-            logger.warning("文件不存在")
+            # logger.warning("文件不存在")
             return False, None
         except Exception as e:
             logger.error(f"加载失败: {e}")

app/service/design_batch/service.py

@@ -5,7 +5,7 @@ from app.service.design_batch.utils.MQ import publish_status
 
 
 async def start_design_batch_generate(data, file):
-    generate_clothes_task = batch_design.delay(json.loads(file.decode())['objects'], data.total, data.tasks_id)
+    generate_clothes_task = batch_design.delay(json.loads(file.decode())['objects'], data.tasks_id, data.file_name)
     print(generate_clothes_task)
     publish_status(data.tasks_id, "0/100", "")
     return {"task_id": data.tasks_id}

app/service/design_batch/test.py

@@ -157,6 +157,6 @@ if __name__ == '__main__':
         ],
         "process_id": "83"
     }
-    task_id = 1
+    task_id = 10086
     json_name = "test.json"
     batch_design.delay(data['objects'], task_id, json_name)

app/service/design_batch/utils/MQ.py

@@ -2,9 +2,11 @@ import json
 
 import pika
 
+from app.core.config import RABBITMQ_PARAMS
+
 
 def publish_status(task_id, progress, result):
-    connection = pika.BlockingConnection(pika.ConnectionParameters('10.1.2.213'))
+    connection = pika.BlockingConnection(pika.ConnectionParameters(**RABBITMQ_PARAMS))
     channel = connection.channel()
     channel.queue_declare(queue='DesignBatch', durable=True)
     message = {'task_id': task_id, 'progress': progress, "result": result}

app/service/design_batch/utils/save_json.py

@@ -1,13 +1,21 @@
+import io
 import json
 import logging
+import os
 
 logger = logging.getLogger()
 
 
 def oss_upload_json(oss_client, json_data, object_name):
     try:
-        with open(f"app/service/design_batch/response_json/{object_name}", 'w') as file:
+        save_dir = os.path.join(os.getcwd(), "service/design_batch", "response_data")
+        if not os.path.exists(save_dir):
+            os.makedirs(save_dir)
+        # 处理文件
+        file_path = os.path.join(save_dir, object_name)
+        with open(file_path, 'w') as file:
             json.dump(json_data, file, indent=4)
-        oss_client.fput_object("test", object_name, f"app/service/design_batch/response_json/{object_name}")
+        json_bytes = json.dumps(json_data).encode('utf-8')
+        oss_client.put_object("test", object_name, io.BytesIO(json_bytes), length=len(json_bytes), content_type="application/json")
     except Exception as e:
         logger.warning(str(e))

app/service/design_fast/design_generate.py

@@ -139,6 +139,7 @@ def design_generate(request_data):
 @RunTime
 def design_generate_v2(request_data):
     objects_data = request_data.dict()['objects']
+    request_id = request_data.requestId
     threads = []
 
     def process_object(step, object):
@@ -146,7 +147,7 @@ def design_generate_v2(request_data):
         items_response = {
             'layers': [],
             'objectSign': object['objectSign'] if 'objectSign' in object.keys() else "",
-            'requestId': object['requestId'] if 'requestId' in object.keys() else ""
+            'requestId': request_id
         }
         if basic['single_overall'] == "overall":
             item_results = []
@@ -197,9 +198,8 @@ def design_generate_v2(request_data):
                 'pattern_image_url': item_result['pattern_image_url'] if 'pattern_image_url' in item_result.keys() else None,
             })
             items_response['synthesis_url'] = synthesis_single(item_result['front_image'], item_result['back_image'])
-
         #  发送结果给java端
-        url = "https://3998-117-143-125-51.ngrok-free.app/api/third/party/receiveDesignResults"
+        url = JAVA_STREAM_API_URL
         headers = {
             'Accept': "*/*",
             'Accept-Encoding': "gzip, deflate, br",
@@ -207,11 +207,11 @@ def design_generate_v2(request_data):
             'Connection': "keep-alive",
             'Content-Type': "application/json"
         }
+        # logger.info(items_response)
         response = post_request(url, json_data=items_response, headers=headers)
         if response:
             # 打印结果
             logger.info(response.text)
-            logger.info(items_response)
 
     for step, object in enumerate(objects_data):
         t = threading.Thread(target=process_object, args=(step, object))

app/service/design_fast/pipeline/segmentation.py

@@ -66,19 +66,19 @@ class Segmentation:
         file_path = f"{SEG_CACHE_PATH}{image_id}.npy"
         try:
             np.save(file_path, seg_result)
-            logger.info(f"保存成功 ：{os.path.abspath(file_path)}")
+            logger.debug(f"保存成功 ：{os.path.abspath(file_path)}")
         except Exception as e:
             logger.error(f"保存失败: {e}")
 
     @staticmethod
     def load_seg_result(image_id):
         file_path = f"{SEG_CACHE_PATH}{image_id}.npy"
-        logger.info(f"load seg file name is :{SEG_CACHE_PATH}{image_id}.npy")
+        # logger.info(f"load seg file name is :{SEG_CACHE_PATH}{image_id}.npy")
         try:
             seg_result = np.load(file_path)
             return True, seg_result
         except FileNotFoundError:
-            logger.warning("文件不存在")
+            # logger.warning("文件不存在")
             return False, None
         except Exception as e:
             logger.error(f"加载失败: {e}")

app/service/design_fast/utils/design_ensemble.py

@@ -25,6 +25,7 @@ from app.core.config import *
 
 def keypoint_preprocess(img_path):
     img = mmcv.imread(img_path)
+    img = cv2.copyMakeBorder(img, 25, 25, 25, 25, cv2.BORDER_CONSTANT, value=[255, 255, 255])
     img_scale = (256, 256)
     h, w = img.shape[:2]
     img = cv2.resize(img, img_scale)
@@ -62,7 +63,11 @@ def keypoint_postprocess(output, scale_factor):
     scale_matrix = np.diag(scale_factor)
     nan = np.isinf(scale_matrix)
     scale_matrix[nan] = 0
-    return np.ceil(np.dot(segment_result, scale_matrix) * 4)
+    # 应用缩放因子
+    scaled_result = np.ceil(np.dot(segment_result, scale_matrix) * 4)
+    # 补偿边框偏移
+    compensated_result = scaled_result - 25
+    return compensated_result
 
 
 """

app/service/design_pre_processing/service.py

@@ -266,7 +266,7 @@ class DesignPreprocessing:
             seg_result = np.load(file_path)
             return True, seg_result
         except FileNotFoundError:
-            logging.info("文件不存在")
+            # logging.info("文件不存在")
             return False, None
         except Exception as e:
             logging.warning(f"加载失败: {e}")
@@ -277,7 +277,7 @@ class DesignPreprocessing:
         file_path = f"{SEG_CACHE_PATH}{image_id}.npy"
         try:
             np.save(file_path, seg_result)
-            logging.info(f"保存成功,{os.path.abspath(file_path)}")
+            logging.debug(f"保存成功,{os.path.abspath(file_path)}")
         except Exception as e:
             logging.warning(f"保存失败: {e}")
 

app/service/generate_image/service_generate_multi_view.py

@@ -0,0 +1,126 @@
+#!/usr/bin/env python
+# -*- coding: UTF-8 -*-
+"""
+@Project ：trinity_client
+@File    ：service_att_recognition.py
+@Author  ：周成融
+@Date    ：2023/7/26 12:01:05
+@detail  ：
+"""
+import json
+import logging
+import time
+
+import numpy as np
+import redis
+import tritonclient.grpc as grpcclient
+
+from app.core.config import *
+from app.schemas.generate_image import GenerateMultiViewModel
+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()
+
+
+class GenerateMultiView:
+    def __init__(self, request_data):
+        if DEBUG is False:
+            self.connection = pika.BlockingConnection(pika.ConnectionParameters(**RABBITMQ_PARAMS))
+            self.channel = self.connection.channel()
+        # self.connection = pika.BlockingConnection(pika.ConnectionParameters(**RABBITMQ_PARAMS))
+        # self.channel = self.connection.channel()
+        # self.minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE)
+        self.grpc_client = grpcclient.InferenceServerClient(url=GMV_MODEL_URL)
+
+        self.redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True)
+        self.image = self.get_image(request_data.image_url)
+        self.tasks_id = request_data.tasks_id
+        self.user_id = self.tasks_id[self.tasks_id.rfind('-') + 1:]
+        self.generate_data = {'tasks_id': self.tasks_id, 'status': 'PENDING', 'message': "pending", 'image_url': ''}
+        self.redis_client.set(self.tasks_id, json.dumps(self.generate_data))
+        self.redis_client.expire(self.tasks_id, 600)
+
+    def get_image(self, image_url):
+        try:
+            image = oss_get_image(bucket=image_url.split('/')[0], object_name=image_url[image_url.find('/') + 1:], data_type="PIL")
+            return image
+        except Exception as e:
+            logger.error(e)
+
+    def callback(self, result, error):
+        if error:
+            self.generate_data['status'] = "FAILURE"
+            self.generate_data['message'] = str(error)
+            # self.generate_data['data'] = str(error)
+            self.redis_client.set(self.tasks_id, json.dumps(self.generate_data))
+        else:
+            # pil图像转成numpy数组
+            images = result.as_numpy("generated_image")
+            # for id, img in enumerate(images):
+            #     cv2.imwrite(f"{id}.png", img)
+            # image_url = ""
+            image_url = upload_png_sd(images[6], user_id=self.user_id, category="multi_view", file_name=f"{self.tasks_id}.png")
+            # logger.info(f"upload image SUCCESS ： {image_url}")
+            self.generate_data['status'] = "SUCCESS"
+            self.generate_data['message'] = "success"
+            self.generate_data['image_url'] = str(image_url)
+            self.redis_client.set(self.tasks_id, json.dumps(self.generate_data))
+
+    def read_tasks_status(self):
+        status_data = self.redis_client.get(self.tasks_id)
+        return json.loads(status_data), status_data
+
+    def get_result(self):
+        try:
+            images = [np.array(self.image).astype(np.uint8)] * 1
+
+            image_obj = np.array(images, dtype=np.uint8)
+
+            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=GMV_MODEL_NAME, inputs=inputs, callback=self.callback)
+
+            time_out = 600
+            generate_data = None
+            while time_out > 0:
+                generate_data, _ = self.read_tasks_status()
+                if generate_data['status'] in ["REVOKED", "FAILURE"]:
+                    ctx.cancel()
+                    break
+                elif generate_data['status'] == "SUCCESS":
+                    break
+                time_out -= 1
+                time.sleep(0.1)
+            return generate_data
+        except Exception as e:
+            self.generate_data['status'] = "FAILURE"
+            self.generate_data['message'] = str(e)
+            self.redis_client.set(self.tasks_id, json.dumps(self.generate_data))
+            raise Exception(str(e))
+        finally:
+            dict_generate_data, str_generate_data = self.read_tasks_status()
+            if DEBUG is False:
+                self.channel.basic_publish(exchange='', routing_key=GMV_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)}")
+
+
+def infer_cancel(tasks_id):
+    redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True)
+    data = {'tasks_id': tasks_id, 'status': 'REVOKED', 'message': "revoked", 'data': 'revoked'}
+    generate_data = json.dumps(data)
+    redis_client.set(tasks_id, generate_data)
+    return data
+
+
+if __name__ == '__main__':
+    rd = GenerateMultiViewModel(
+        tasks_id="123-89",
+        image_url="aida-sys-image/images/female/outwear/0628000123.jpg",
+    )
+    server = GenerateMultiView(rd)
+    print(server.get_result())

app/service/generate_image/service_generate_product_image.py

@@ -1,4 +1,196 @@
-#!/usr/bin/env python
+# #!/usr/bin/env python
+# # -*- coding: UTF-8 -*-
+# """
+# @Project ：trinity_client
+# @File    ：service_att_recognition.py
+# @Author  ：周成融
+# @Date    ：2023/7/26 12:01:05
+# @detail  ：
+# """
+# import json
+# import logging
+# import time
+#
+# import cv2
+# import numpy as np
+# import redis
+# import tritonclient.grpc as grpcclient
+# from PIL import Image
+# from tritonclient.utils import np_to_triton_dtype
+#
+# from app.core.config import *
+# 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()
+#
+#
+# class GenerateProductImage:
+#     def __init__(self, request_data):
+#         if DEBUG is False:
+#             self.connection = pika.BlockingConnection(pika.ConnectionParameters(**RABBITMQ_PARAMS))
+#             self.channel = self.connection.channel()
+#         # self.connection = pika.BlockingConnection(pika.ConnectionParameters(**RABBITMQ_PARAMS))
+#         # self.channel = self.connection.channel()
+#         # self.minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE)
+#         self.grpc_client = grpcclient.InferenceServerClient(url=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.image_strength = request_data.image_strength
+#         self.batch_size = 1
+#         self.product_type = request_data.product_type
+#         self.prompt = request_data.prompt
+#         self.image, self.image_size, self.left, self.top = pre_processing_image(request_data.image_url)
+#         self.tasks_id = request_data.tasks_id
+#         self.user_id = self.tasks_id[self.tasks_id.rfind('-') + 1:]
+#         self.gen_product_data = {'tasks_id': self.tasks_id, 'status': 'PENDING', 'message': "pending", 'image_url': ''}
+#         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:
+#             # 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)
+#             cropped_image = post_processing_image(image_result, self.left, self.top)
+#             image_url = upload_SDXL_image(cropped_image, user_id=self.user_id, category=f"{self.category}", file_name=f"{self.tasks_id}.png")
+#             self.gen_product_data['status'] = "SUCCESS"
+#             self.gen_product_data['message'] = "success"
+#             self.gen_product_data['image_url'] = str(image_url)
+#             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:
+#             prompts = [self.prompt] * self.batch_size
+#             self.image = cv2.cvtColor(self.image, cv2.COLOR_BGR2RGB)
+#             self.image = cv2.resize(self.image, (1024, 1024))
+#             images = [self.image.astype(np.uint8)] * self.batch_size
+#
+#             if self.product_type == "single":
+#                 text_obj = np.array(prompts, dtype="object").reshape(-1, 1)
+#                 image_obj = np.array(images, dtype=np.uint8).reshape((-1, 1024, 1024, 3))
+#                 image_strength_obj = np.array(self.image_strength, dtype=np.float32).reshape(-1, 1)
+#             else:
+#                 text_obj = np.array(prompts, dtype="object").reshape((1))
+#                 image_obj = np.array(images, dtype=np.uint8).reshape((1024, 1024, 3))
+#                 image_strength_obj = np.array(self.image_strength, dtype=np.float32).reshape((1))
+#
+#             # 假设 prompts、images 和 self.image_strength 已经定义
+#
+#             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_image_strength = grpcclient.InferInput("image_strength", image_strength_obj.shape, np_to_triton_dtype(image_strength_obj.dtype))
+#
+#             input_text.set_data_from_numpy(text_obj)
+#             input_image.set_data_from_numpy(image_obj)
+#             input_image_strength.set_data_from_numpy(image_strength_obj)
+#
+#             inputs = [input_text, input_image, input_image_strength]
+#
+#             if self.product_type == "single":
+#                 ctx = self.grpc_client.async_infer(model_name="stable_diffusion_xl_cnet_inpaint", inputs=inputs, callback=self.callback)
+#             else:
+#                 ctx = self.grpc_client.async_infer(model_name=GPI_MODEL_NAME_OVERALL, inputs=inputs, callback=self.callback)
+#
+#             time_out = 600
+#             while time_out > 0:
+#                 gen_product_data, _ = self.read_tasks_status()
+#                 if gen_product_data['status'] in ["REVOKED", "FAILURE"]:
+#                     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=GPI_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)}")
+#
+#
+# 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
+#
+#
+# def pre_processing_image(image_url):
+#     image = oss_get_image(bucket=image_url.split('/')[0], object_name=image_url[image_url.find('/') + 1:], data_type="PIL")
+#     # resize 原图至1024*1024
+#     image = image.resize((int(1024 / image.height * image.width), 1024))
+#
+#     # 原始图片的尺寸
+#     width, height = image.size
+#
+#     new_height, new_width = 1024, 1024
+#     # 创建一个新的画布，大小为添加 padding 后的尺寸，并设置为白色背景
+#     pad_image = Image.new('RGBA', (new_width, new_height), (0, 0, 0, 0))
+#
+#     # 将原始图片粘贴到新的画布中心
+#     left = (new_width - width) // 2
+#     top = (new_height - height) // 2
+#     pad_image.paste(image, (left, top))
+#
+#     # 将画布 resize 成宽度 1024，长度 1024
+#     resized_image = pad_image.resize((1024, 1024))
+#     image_size = (1024, 1024)
+#
+#     if resized_image.mode in ('RGBA', 'LA') or (resized_image.mode == 'P' and 'transparency' in resized_image.info):
+#         # 创建白色背景
+#         background = Image.new("RGB", image_size, (255, 255, 255))
+#         # 将图片粘贴到白色背景上
+#         background.paste(resized_image, mask=resized_image.split()[3])
+#         image = np.array(background)
+#         image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+#     return image, image_size, left, top
+#
+#
+# def post_processing_image(image, left, top):
+#     resized_image = image.resize((int(image.width * (768 / image.height)), 768))
+#     # 计算裁剪的坐标
+#     left = (resized_image.width - 512) // 2
+#     upper = 0
+#     right = left + 512
+#     lower = 768
+#
+#     # 进行裁剪
+#     cropped_image = resized_image.crop((left, upper, right, lower))
+#     return cropped_image
+#
+#
+# if __name__ == '__main__':
+#     rd = GenerateProductImageModel(
+#         tasks_id="123-89",
+#         # prompt="",
+#         image_strength=0.7,
+#         prompt="The best quality, masterpiece,outwear, 8K realistic, HUD",
+#         image_url="aida-results/result_53381ada-ac64-11ef-ae9d-0242ac150002.png",
+#         product_type="overall"
+#     )
+#     server = GenerateProductImage(rd)
+#     print(server.get_result())
+
+# 旧版product
+# !/usr/bin/env python
 # -*- coding: UTF-8 -*-
 """
 @Project ：trinity_client
@@ -34,14 +226,14 @@ class GenerateProductImage:
         # 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=GPI_MODEL_URL)
+        self.grpc_client = grpcclient.InferenceServerClient(url="10.1.1.243:18001")
         self.redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True)
         self.category = "product_image"
         self.image_strength = request_data.image_strength
         self.batch_size = 1
         self.product_type = request_data.product_type
         self.prompt = request_data.prompt
-        self.image, self.image_size, self.left, self.top = pre_processing_image(request_data.image_url)
+        self.image = pre_processing_image(request_data.image_url)
         self.tasks_id = request_data.tasks_id
         self.user_id = self.tasks_id[self.tasks_id.rfind('-') + 1:]
         self.gen_product_data = {'tasks_id': self.tasks_id, 'status': 'PENDING', 'message': "pending", 'image_url': ''}
@@ -55,10 +247,13 @@ class GenerateProductImage:
             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)
-            cropped_image = post_processing_image(image_result, self.left, self.top)
-            image_url = upload_SDXL_image(cropped_image, user_id=self.user_id, category=f"{self.category}", file_name=f"{self.tasks_id}.png")
+            if self.product_type == "single":
+                image = result.as_numpy("generated_cnet_image")
+            else:
+                image = result.as_numpy("generated_inpaint_image")
+            image_result = Image.fromarray(np.squeeze(image.astype(np.uint8)))
+            # cropped_image = post_processing_image(image_result, self.left, self.top)
+            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)
@@ -71,17 +266,18 @@ class GenerateProductImage:
     def get_result(self):
         try:
             prompts = [self.prompt] * self.batch_size
-            self.image = cv2.cvtColor(self.image, cv2.COLOR_BGR2RGB)
-            self.image = cv2.resize(self.image, (1024, 1024))
+
+            self.image = cv2.cvtColor(self.image, cv2.COLOR_RGBA2RGB)
+            # self.image = cv2.resize(self.image, (1024, 1024))
             images = [self.image.astype(np.uint8)] * self.batch_size
 
             if self.product_type == "single":
-                text_obj = np.array(prompts, dtype="object").reshape(-1, 1)
-                image_obj = np.array(images, dtype=np.uint8).reshape((-1, 1024, 1024, 3))
-                image_strength_obj = np.array(self.image_strength, dtype=np.float32).reshape(-1, 1)
+                text_obj = np.array(prompts, dtype="object").reshape((-1, 1))
+                image_obj = np.array(images, dtype=np.uint8).reshape((-1, 768, 512, 3))
+                image_strength_obj = np.array(self.image_strength, dtype=np.float32).reshape((-1, 1))
             else:
                 text_obj = np.array(prompts, dtype="object").reshape((1))
-                image_obj = np.array(images, dtype=np.uint8).reshape((1024, 1024, 3))
+                image_obj = np.array(images, dtype=np.uint8).reshape((768, 512, 3))
                 image_strength_obj = np.array(self.image_strength, dtype=np.float32).reshape((1))
 
             # 假设 prompts、images 和 self.image_strength 已经定义
@@ -97,9 +293,9 @@ class GenerateProductImage:
             inputs = [input_text, input_image, input_image_strength]
 
             if self.product_type == "single":
-                ctx = self.grpc_client.async_infer(model_name="stable_diffusion_xl_cnet_inpaint", inputs=inputs, callback=self.callback)
+                ctx = self.grpc_client.async_infer(model_name="stable_diffusion_1_5_cnet", inputs=inputs, callback=self.callback)
             else:
-                ctx = self.grpc_client.async_infer(model_name=GPI_MODEL_NAME_OVERALL, inputs=inputs, callback=self.callback)
+                ctx = self.grpc_client.async_infer(model_name="diffusion_ensemble_all", inputs=inputs, callback=self.callback)
 
             time_out = 600
             while time_out > 0:
@@ -135,33 +331,26 @@ def infer_cancel(tasks_id):
 
 def pre_processing_image(image_url):
     image = oss_get_image(bucket=image_url.split('/')[0], object_name=image_url[image_url.find('/') + 1:], data_type="PIL")
-    # resize 原图至1024*1024
-    image = image.resize((int(1024 / image.height * image.width), 1024))
-
-    # 原始图片的尺寸
+    # 调整图片高度为768像素，保持宽高比
     width, height = image.size
+    new_height = 768
+    new_width = int(width * (new_height / height))
+    resized_image = image.resize((new_width, new_height))
 
-    new_height, new_width = 1024, 1024
-    # 创建一个新的画布，大小为添加 padding 后的尺寸，并设置为白色背景
-    pad_image = Image.new('RGBA', (new_width, new_height), (0, 0, 0, 0))
+    # 创建一个512x768的透明图片
+    result_image = Image.new("RGBA", (512, 768), (255, 255, 255, 255))
 
-    # 将原始图片粘贴到新的画布中心
-    left = (new_width - width) // 2
-    top = (new_height - height) // 2
-    pad_image.paste(image, (left, top))
+    # 计算需要粘贴的位置，使图片居中
+    x_offset = (512 - new_width) // 2
+    y_offset = 0
 
-    # 将画布 resize 成宽度 1024，长度 1024
-    resized_image = pad_image.resize((1024, 1024))
-    image_size = (1024, 1024)
+    # 将调整大小后的图片粘贴到透明图片上
+    result_image.paste(resized_image, (x_offset, y_offset), mask=resized_image.split()[3])
 
-    if resized_image.mode in ('RGBA', 'LA') or (resized_image.mode == 'P' and 'transparency' in resized_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(resized_image, mask=resized_image.split()[3])
-        image = np.array(background)
-        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    return image, image_size, left, top
+    image = np.array(result_image)
+
+    # image = cv2.cvtColor(image, cv2.COLOR_BGRA2RGBA)
+    return image
 
 
 def post_processing_image(image, left, top):
@@ -182,9 +371,9 @@ if __name__ == '__main__':
         tasks_id="123-89",
         # prompt="",
         image_strength=0.7,
-        prompt="The best quality, masterpiece,outwear, 8K realistic, HUD",
-        image_url="aida-results/result_53381ada-ac64-11ef-ae9d-0242ac150002.png",
-        product_type="overall"
+        prompt="The best quality, masterpiece, real image.,high quality clothing details,8K realistic,HDR",
+        image_url="aida-results/result_40c7924e-e220-11ef-8ea2-0242ac150003.png",
+        product_type="single"
     )
     server = GenerateProductImage(rd)
     print(server.get_result())

app/service/prompt_generation/chatgpt_for_translation.py

@@ -8,6 +8,7 @@ from requests import RequestException
 from retry import retry
 
 from app.core.config import QWEN_API_KEY
+from app.service.chat_robot.script.service.CallQWen import get_language
 
 logger = logging.getLogger(__name__)
 
@@ -93,7 +94,13 @@ def get_translation_from_llama3(text):
 
     # prompt = f"System: {prefix_for_llama}\nUser:[{text}]"
 
-    # 创建请求的负载
+    # 先获取用户输入文本的语言
+    language = get_language(text)
+
+    if 'English' in language:
+        return text
+
+    # 创建请求的负载 translator是自定义的翻译模型
     payload = {
         "model": "translator",
         "prompt": f"[{text}]",
@@ -117,6 +124,26 @@ def get_translation_from_llama3(text):
         print(response.text)
 
 
+# 在llama3中创建一个翻译模型
+# def create_model_with_llama(text):
+#     url = "http://localhost:11434/api/create"
+#     # url = "http://10.1.1.240:1143/api/generate"
+#
+#     # prompt = f"System: {prefix_for_llama}\nUser:[{text}]"
+#
+#     # 创建翻译器的配置文件
+#     payload = {
+#         "model": "translator",
+#         "modelfile": "FROM llama3\nSYSTEM Translate everything within the brackets [] into English."
+#                      "Never translate or modify any English input."
+#                      "The input must be fully translated into coherent English sentences."
+#     }
+#
+#     # 将负载转换为 JSON 格式
+#     headers = {'Content-Type': 'application/json'}
+#     response = requests.post(url, data=json.dumps(payload), headers=headers)
+
+
 def main():
     """Main function"""
     text = get_translation_from_llama3("[火焰]")

app/service/utils/decorator.py

@@ -7,9 +7,9 @@ def RunTime(func):
         t1 = time.time()
         res = func(*args, **kwargs)
         t2 = time.time()
-        # if t2 - t1 > 0.05:
-        #     logging.info(f"function：【{func.__name__}】,runtime：【{str(t2 - t1)}】s")
-        logging.info(f"function：【{func.__name__}】,runtime：【{str(t2 - t1)}】s")
+        if t2 - t1 > 0.05:
+            logging.info(f"function：【{func.__name__}】,runtime：【{str(t2 - t1)}】s")
+        # logging.info(f"function：【{func.__name__}】,runtime：【{str(t2 - t1)}】s")
         return res
 
     return wrapper
@@ -22,7 +22,8 @@ def ClassCallRunTime(func):
         end_time = time.time()
         execution_time = end_time - start_time
         class_name = args[0].__class__.__name__  # 获取类名
-        print(f"class name: {class_name} , run time is : {execution_time} s")
+        if execution_time > 0.05:
+            logging.info(f"class name: {class_name} , run time is : {execution_time} s")
         return result
 
     return wrapper

app/service/utils/new_oss_client.py

@@ -82,7 +82,7 @@ if __name__ == '__main__':
     # url = "aida-users/89/sketchboard/female/Dress/e6724ab7-8d3f-4677-abe0-c3e42ab7af85.jpeg"
     # url = "aida-users/87/print/956614a2-7e75-4fbe-9ed0-c1831e37a2c9-4-87.png"
     # url = "aida-users/89/single_logo/123-89.png"
-    url = "aida-users/89/test/123-89.png"
+    url = "aida-users/89/123-89.png"
 
     # url = "aida-collection-element/12148/Sketchboard/95ea577b-305b-4a62-b30a-39c0dd3ddb3f.png"
     read_type = "2"