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

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_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/core/config.py

@@ -109,6 +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/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/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/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_fast/design_generate.py

@@ -207,7 +207,7 @@ def design_generate_v2(request_data):
             'Connection': "keep-alive",
             'Content-Type': "application/json"
         }
-        logger.info(items_response)
+        # logger.info(items_response)
         response = post_request(url, json_data=items_response, headers=headers)
         if response:
             # 打印结果

app/service/design_fast/pipeline/segmentation.py

@@ -36,11 +36,11 @@ class Segmentation:
             # preview 过模型 不缓存
             if "preview_submit" in result.keys() and result['preview_submit'] == "preview":
                 # 推理获得seg 结果
-                seg_result = get_seg_result(result["image_id"], result['image'])[0]
+                seg_result = get_seg_result(result["image_id"], result['image'])
             # submit 过模型 缓存
             elif "preview_submit" in result.keys() and result['preview_submit'] == "submit":
                 # 推理获得seg 结果
-                seg_result = get_seg_result(result["image_id"], result['image'])[0]
+                seg_result = get_seg_result(result["image_id"], result['image'])
                 self.save_seg_result(seg_result, result['image_id'])
             #  null  正常流程 加载本地缓存 无缓存则过模型
             else:
@@ -49,14 +49,14 @@ class Segmentation:
                 # 判断缓存和实际图片size是否相同
                 if not _ or result["image"].shape[:2] != seg_result.shape:
                     # 推理获得seg 结果
-                    seg_result = get_seg_result(result["image_id"], result['image'])[0]
+                    seg_result = get_seg_result(result["image_id"], result['image'])
                     self.save_seg_result(seg_result, result['image_id'])
             result['seg_result'] = seg_result
 
             # 处理前片后片
-            temp_front = seg_result == 1.0
+            temp_front = seg_result == 1
             result['front_mask'] = (255 * (temp_front + 0).astype(np.uint8))
-            temp_back = seg_result == 2.0
+            temp_back = seg_result == 2
             result['back_mask'] = (255 * (temp_back + 0).astype(np.uint8))
             result['mask'] = result['front_mask'] + result['back_mask']
         return result
@@ -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

@@ -13,7 +13,6 @@ import cv2
 import mmcv
 import numpy as np
 import torch
-import torch.nn.functional as F
 import tritonclient.http as httpclient
 
 from app.core.config import *
@@ -85,7 +84,10 @@ def seg_preprocess(img_path):
     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)
+
+    # 扩充25的白边
+    img = cv2.copyMakeBorder(img, 25, 25, 25, 25, cv2.BORDER_CONSTANT, value=[255, 255, 255])
+    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
 
@@ -114,9 +116,9 @@ def get_seg_result(image_id, image):
 
 # no cache
 def seg_postprocess(image_id, output, ori_shape):
-    seg_logit = F.interpolate(torch.tensor(output).float(), size=ori_shape, scale_factor=None, mode='bilinear', align_corners=False)
+    seg_logit = cv2.resize(output[0][0].astype(np.uint8), (ori_shape[1] + 50, ori_shape[0] + 50))
-    seg_pred = seg_logit.cpu().numpy()
+    seg_logit = seg_logit[25: - 25, 25: - 25]
-    return seg_pred[0]
+    return seg_logit
 
 
 def key_point_show(image_path, key_point_result=None):

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/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:
+        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")
+        # 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"