feat 测试批量上传图片

fix

.gitignore

@@ -120,6 +120,7 @@ dmypy.json
 
 #runtime produce
 test
+seg_cache
 logs
 seg_result/
 seg_result

app/api/api_design.py

@@ -136,13 +136,16 @@ def design(request_data: DesignModel):
             "process_id": "89"
         }
     """
-    try:
+    logger.info(f"design request item is : @@@@@@:{json.dumps(request_data.dict())}")
-        logger.info(f"design request item is : @@@@@@:{json.dumps(request_data.dict())}")
+    data = generate(request_data=request_data)
-        data = generate(request_data=request_data)
+    logger.info(f"design response @@@@@@:{json.dumps(data)}")
-        logger.info(f"design response @@@@@@:{json.dumps(data)}")
+    # try:
-    except Exception as e:
+    #     logger.info(f"design request item is : @@@@@@:{json.dumps(request_data.dict())}")
-        logger.warning(f"design Run Exception @@@@@@:{e}")
+    #     data = generate(request_data=request_data)
-        raise HTTPException(status_code=404, detail=str(e))
+    #     logger.info(f"design response @@@@@@:{json.dumps(data)}")
+    # except Exception as e:
+    #     logger.warning(f"design Run Exception @@@@@@:{e}")
+    #     raise HTTPException(status_code=404, detail=str(e))
     return ResponseModel(data=data)
 
 

app/core/config.py

@@ -24,11 +24,11 @@ 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"
+    SEG_CACHE_PATH = "../seg_cache/"
 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'
+    SEG_CACHE_PATH = "/seg_cache/"
 
 # RABBITMQ_ENV = ""  # 生产环境
 RABBITMQ_ENV = "-dev"  # 开发环境

app/service/design/items/clothing.py

@@ -37,7 +37,8 @@ class Clothing(object):
                            resize_scale=self.result["resize_scale"],
                            mask=cv2.resize(self.result['mask'], self.result["front_image"].size),
                            gradient_string=self.result['gradient_string'] if 'gradient_string' in self.result.keys() else "",
-                           pattern_image_url=self.result['pattern_image_url']
+                           pattern_image_url=self.result['pattern_image_url'],
+                           pattern_image=self.result['pattern_image']
 
                            )
         layer.insert(front_layer)
@@ -54,7 +55,7 @@ class Clothing(object):
                           resize_scale=self.result["resize_scale"],
                           mask=cv2.resize(self.result['mask'], self.result["front_image"].size),
                           gradient_string=self.result['gradient_string'] if 'gradient_string' in self.result.keys() else "",
-                          pattern_image_url=self.result['pattern_image_url']
+                          pattern_image_url=self.result['pattern_image_url'],
                           )
         layer.insert(back_layer)
 

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

@@ -5,6 +5,7 @@ import numpy as np
 from pymilvus import MilvusClient
 
 from app.core.config import *
+from app.service.utils.decorator import RunTime, ClassCallRunTime
 from ..builder import PIPELINES
 from ...utils.design_ensemble import get_keypoint_result
 
@@ -27,7 +28,7 @@ class KeypointDetection(object):
     # self.client.close()
     # print(f"client close time : {time.time() - start_time}")
 
-    # @ RunTime
+    @ ClassCallRunTime
     def __call__(self, result):
         # logging.info("KeypointDetection run ")
         if result['name'] in ['blouse', 'skirt', 'dress', 'outwear', 'trousers', 'tops', 'bottoms']:  # 查询是否有数据 且类别相同 相同则直接读 不同则推理后更新

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

@@ -1,5 +1,6 @@
 import cv2
 
+from app.service.utils.decorator import RunTime, ClassCallRunTime
 from app.service.utils.oss_client import oss_get_image
 from ..builder import PIPELINES
 
@@ -12,6 +13,7 @@ class LoadImageFromFile(object):
         self.print_dict = print_dict
         # self.minio_client = Minio(f"{MINIO_URL}", access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE)
 
+    @ClassCallRunTime
     def __call__(self, result):
         result['image'], result['pre_mask'] = self.read_image(self.path)
         result['gray'] = cv2.cvtColor(result['image'], cv2.COLOR_BGR2GRAY)

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

@@ -4,6 +4,7 @@ import cv2
 import numpy as np
 from PIL import Image
 
+from app.service.utils.decorator import RunTime, ClassCallRunTime
 from app.service.utils.oss_client import oss_get_image
 from ..builder import PIPELINES
 
@@ -13,7 +14,7 @@ class Painting(object):
     def __init__(self, painting_flag=True):
         self.painting_flag = painting_flag
 
-    # @ RunTime
+    @ClassCallRunTime
     def __call__(self, result):
         if result['name'] not in ['hairstyle', 'earring'] and self.painting_flag and result['color'] != 'none':
             dim_image_h, dim_image_w = result['image'].shape[0:2]
@@ -86,7 +87,7 @@ class PrintPainting(object):
     def __init__(self, print_flag=True):
         self.print_flag = print_flag
 
-    # @ RunTime
+    @ClassCallRunTime
     def __call__(self, result):
         single_print = result['print']['single']
         overall_print = result['print']['overall']
@@ -236,7 +237,6 @@ class PrintPainting(object):
 
                     print_background = cv2.cvtColor(np.array(source_image_pil), cv2.COLOR_RGBA2BGR)
                     mask_background = cv2.cvtColor(np.array(source_image_pil_mask), cv2.COLOR_RGBA2BGR)
-                    print(1)
                 else:
                     mask = self.get_mask_inv(image)
                     mask = np.expand_dims(mask, axis=2)

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

@@ -2,6 +2,7 @@ import math
 
 import cv2
 
+from app.service.utils.decorator import ClassCallRunTime
 from ..builder import PIPELINES
 
 
@@ -10,7 +11,7 @@ class Scaling(object):
     def __init__(self):
         pass
 
-    # @ RunTime
+    @ClassCallRunTime
     def __call__(self, result):
         if result['keypoint'] in ['waistband', 'shoulder', 'head_point']:
             # milvus_db_keypoint_cache

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

@@ -1,3 +1,9 @@
+import os
+
+import numpy as np
+
+from app.core.config import SEG_CACHE_PATH
+from app.service.utils.decorator import ClassCallRunTime
 from ..builder import PIPELINES
 from ...utils.design_ensemble import get_seg_result
 
@@ -9,6 +15,32 @@ class Segmentation(object):
         self.device = device
         self.debug = debug
 
+    @ClassCallRunTime
     def __call__(self, result):
-        result['seg_result'] = get_seg_result(result["image_id"], result['image'])
+        _, seg_result = self.load_seg_result(result["image_id"])
+        if not _:
+            result['seg_result'] = get_seg_result(result["image_id"], result['image'])
+            self.save_seg_result(result['seg_result'][0], result['image_id'])
         return result
+
+    @staticmethod
+    def save_seg_result(seg_result, image_id):
+        file_path = f"{SEG_CACHE_PATH}{image_id}.npy"
+        try:
+            np.save(file_path, seg_result)
+            print("保存成功", os.path.abspath(file_path))
+        except Exception as e:
+            print(f"保存失败: {e}")
+
+    @staticmethod
+    def load_seg_result(image_id):
+        file_path = f"{SEG_CACHE_PATH}{image_id}.npy"
+        try:
+            seg_result = np.load(file_path)
+            return True, seg_result
+        except FileNotFoundError:
+            print("文件不存在")
+            return False, None
+        except Exception as e:
+            print(f"加载失败: {e}")
+            return False, None

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

@@ -5,6 +5,7 @@ import numpy as np
 from PIL import Image
 from cv2 import cvtColor, COLOR_BGR2RGBA
 
+from app.service.utils.decorator import ClassCallRunTime
 from app.service.utils.generate_uuid import generate_uuid
 from ..builder import PIPELINES
 from ...utils.conversion_image import rgb_to_rgba
@@ -17,6 +18,7 @@ class Split(object):
     Split image into front and back layer according to the segmentation result
     """
 
+    @ClassCallRunTime
     # KNet
     def __call__(self, result):
         try:
@@ -66,7 +68,7 @@ class Split(object):
                 # 创建中间图层
             result_pattern_image_rgba = rgb_to_rgba(result['pattern_image'], result['mask'])
             result_pattern_image_pil = Image.fromarray(cvtColor(result_pattern_image_rgba, COLOR_BGR2RGBA))
-            _, result['pattern_image_url'], _ = upload_png_mask(result_pattern_image_pil, f'{generate_uuid()}')
+            result['pattern'], result['pattern_image_url'], _ = upload_png_mask(result_pattern_image_pil, f'{generate_uuid()}')
             return result
         except Exception as e:
             logging.warning(f"split runtime exception : {e} image_id : {result['image_id']}")

app/service/design/service.py

@@ -1,4 +1,8 @@
 import concurrent.futures
+import io
+
+import cv2
+from PIL import Image
 
 from app.core.config import PRIORITY_DICT
 from app.service.design.core.layer import Layer
@@ -6,6 +10,7 @@ from app.service.design.items import build_item
 from app.service.design.utils.redis_utils import Redis
 from app.service.design.utils.synthesis_item import synthesis, synthesis_single
 from app.service.utils.decorator import RunTime
+from app.service.utils.oss_client import oss_upload_image
 
 
 def process_item(item, layers):
@@ -43,6 +48,7 @@ def final_progress(process_id):
 @RunTime
 def generate(request_data):
     return_response = {}
+    return_png_mask = []
     request_data = request_data.dict()
     assert "process_id" in request_data.keys(), "Need process_id parameters"
 
@@ -55,14 +61,15 @@ def generate(request_data):
         # 获取处理结果
         for future in concurrent.futures.as_completed(futures):
             obj = futures[future]
-
+            return_response[obj] = future.result()[0]
-            result = future.result()
+            return_png_mask.extend(future.result()[1])
-            return_response[obj] = result
     final_progress(process_id)
+    upload_results = process_images(return_png_mask)
     return return_response
 
 
 def process_object(cfg, process_id, total):
+    uploaded_images = []
     basic_info = cfg.get('basic')
     items_response = {
         'layers': []
@@ -83,6 +90,15 @@ def process_object(cfg, process_id, total):
             layers = sorted(layers.layer, key=lambda s: s.get("priority", float('inf')))
         else:
             layers = sorted(layers.layer, key=lambda x: PRIORITY_DICT.get(x['name'], float('inf')))
+        # 上传所有图片
+        for layer in layers:
+            if 'image' in layer.keys() and layer['image'] is not None:
+                uploaded_images.append({'image_obj': layer['image'], 'image_url': layer['image_url']})
+            if 'pattern_image' in layer.keys() and layer['pattern_image'] is not None:
+                uploaded_images.append({'image_obj': layer['pattern_image'], 'image_url': layer['pattern_image_url']})
+            if 'mask' in layer.keys() and layer['mask'] is not None and layer['mask_url'] is not None:
+                uploaded_images.append({'image_obj': layer['mask'], 'image_url': layer['mask_url']})
+
         # 合成
         items_response['synthesis_url'] = synthesis(layers, body_size)
 
@@ -131,4 +147,34 @@ def process_object(cfg, process_id, total):
                 items_response['synthesis_url'] = synthesis_single(item.result['front_image'], item.result['back_image'])
                 break
     update_progress(process_id, total)
-    return items_response
+    return items_response, uploaded_images
+
+
+@RunTime
+def process_images(images):
+    with concurrent.futures.ThreadPoolExecutor() as executor:
+        results = list(executor.map(upload_images, images))
+    # results = []
+    # for image in images:
+    #     results.append(upload_images(image))
+    return results
+
+
+@RunTime
+def upload_images(image_obj):
+    bucket_name = image_obj['image_url'].split("/", 1)[0]
+    object_name = image_obj['image_url'].split("/", 1)[1]
+    if isinstance(image_obj['image_obj'], Image.Image):
+        image_data = io.BytesIO()
+        image_obj['image_obj'].save(image_data, format='PNG')
+        image_data.seek(0)
+        image_bytes = image_data.read()
+        req = oss_upload_image(bucket=bucket_name, object_name=object_name, image_bytes=image_bytes)
+        return image_obj['image_url']
+    else:
+        mask_inverted = cv2.bitwise_not(image_obj['image_obj'])
+        # 将掩模的3通道转换为4通道，白色部分不透明，黑色部分透明
+        rgba_image = cv2.cvtColor(mask_inverted, cv2.COLOR_BGR2BGRA)
+        rgba_image[rgba_image[:, :, 0] == 0] = [0, 0, 0, 0]
+        req = oss_upload_image(bucket=bucket_name, object_name=object_name, image_bytes=cv2.imencode('.png', rgba_image)[1])
+        return image_obj['image_url']

app/service/design/utils/upload_image.py

@@ -13,33 +13,43 @@ import logging
 import cv2
 
 from app.core.config import *
+from app.service.utils.decorator import RunTime
 from app.service.utils.oss_client import oss_upload_image
 
 
 # @RunTime
-def upload_png_mask(front_image, object_name, mask=None):
+# def upload_png_mask(front_image, object_name, mask=None):
-    try:
+#     try:
-        mask_url = None
+#         mask_url = None
-        if mask is not None:
+#         if mask is not None:
-            mask_inverted = cv2.bitwise_not(mask)
+#             mask_inverted = cv2.bitwise_not(mask)
-            # 将掩模的3通道转换为4通道，白色部分不透明，黑色部分透明
+#             # 将掩模的3通道转换为4通道，白色部分不透明，黑色部分透明
-            rgba_image = cv2.cvtColor(mask_inverted, cv2.COLOR_BGR2BGRA)
+#             rgba_image = cv2.cvtColor(mask_inverted, cv2.COLOR_BGR2BGRA)
-            rgba_image[rgba_image[:, :, 0] == 0] = [0, 0, 0, 0]
+#             rgba_image[rgba_image[:, :, 0] == 0] = [0, 0, 0, 0]
-            # image_bytes = io.BytesIO()
+#             # image_bytes = io.BytesIO()
-            # image_bytes.write(cv2.imencode('.png', rgba_image)[1].tobytes())
+#             # image_bytes.write(cv2.imencode('.png', rgba_image)[1].tobytes())
-            # image_bytes.seek(0)
+#             # image_bytes.seek(0)
-            # mask_url = f"{AIDA_CLOTHING}/{minio_client.put_object('aida-clothing', f'mask/mask_{object_name}.png', image_bytes, len(image_bytes.getvalue()), content_type='image/png').object_name}"
+#             # mask_url = f"{AIDA_CLOTHING}/{minio_client.put_object('aida-clothing', f'mask/mask_{object_name}.png', image_bytes, len(image_bytes.getvalue()), content_type='image/png').object_name}"
-            # oss upload ####################
+#             # oss upload ####################
-            req = oss_upload_image(bucket=AIDA_CLOTHING, object_name=f"mask/mask_{object_name}.png", image_bytes=cv2.imencode('.png', rgba_image)[1])
+#             req = oss_upload_image(bucket=AIDA_CLOTHING, object_name=f"mask/mask_{object_name}.png", image_bytes=cv2.imencode('.png', rgba_image)[1])
-            mask_url = f"{AIDA_CLOTHING}/mask/mask_{object_name}.png"
+#             mask_url = f"{AIDA_CLOTHING}/mask/mask_{object_name}.png"
+#
+#         image_data = io.BytesIO()
+#         front_image.save(image_data, format='PNG')
+#         image_data.seek(0)
+#         image_bytes = image_data.read()
+#         # image_url = f"{AIDA_CLOTHING}/{minio_client.put_object('aida-clothing', f'image/image_{object_name}.png', io.BytesIO(image_bytes), len(image_bytes), content_type='image/png').object_name}"
+#         req = oss_upload_image(bucket=AIDA_CLOTHING, object_name=f"image/image_{object_name}.png", image_bytes=image_bytes)
+#         image_url = f"{AIDA_CLOTHING}/image/image_{object_name}.png"
+#         return front_image, image_url, mask_url
+#     except Exception as e:
+#         logging.warning(f"upload_png_mask runtime exception : {e}")
 
-        image_data = io.BytesIO()
+
-        front_image.save(image_data, format='PNG')
+@RunTime
-        image_data.seek(0)
+def upload_png_mask(front_image, object_name, mask=None):
-        image_bytes = image_data.read()
+    mask_url = None
-        # image_url = f"{AIDA_CLOTHING}/{minio_client.put_object('aida-clothing', f'image/image_{object_name}.png', io.BytesIO(image_bytes), len(image_bytes), content_type='image/png').object_name}"
+    if mask is not None:
-        req = oss_upload_image(bucket=AIDA_CLOTHING, object_name=f"image/image_{object_name}.png", image_bytes=image_bytes)
+        mask_url = f"{AIDA_CLOTHING}/mask/mask_{object_name}.png"
-        image_url = f"{AIDA_CLOTHING}/image/image_{object_name}.png"
+    image_url = f"{AIDA_CLOTHING}/image/image_{object_name}.png"
-        return front_image, image_url, mask_url
+    return front_image, image_url, mask_url
-    except Exception as e:
-        logging.warning(f"upload_png_mask runtime exception : {e}")

app/service/utils/decorator.py

@@ -1,5 +1,5 @@
-import time
 import logging
+import time
 
 
 def RunTime(func):
@@ -12,3 +12,16 @@ def RunTime(func):
         return res
 
     return wrapper
+
+
+def ClassCallRunTime(func):
+    def wrapper(*args, **kwargs):
+        start_time = time.time()
+        result = func(*args, **kwargs)
+        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")
+        return result
+
+    return wrapper