feat 1.design 取消进度模式测试

2.结果以stream发送到java
3.新增配饰类
fix

app/service/design_fast/design_generate.py

@@ -5,8 +5,8 @@ import time
 from minio import Minio
 
 from app.core.config import *
-from app.service.design_fast.item import BodyItem, TopItem, BottomItem
-from app.service.design_fast.utils.organize import organize_body, organize_clothing
+from app.service.design_fast.item import BodyItem, TopItem, BottomItem, AccessoriesItem
+from app.service.design_fast.utils.organize import organize_body, organize_clothing, organize_accessories
 from app.service.design_fast.utils.progress import final_progress, update_progress
 from app.service.design_fast.utils.synthesis_item import synthesis, synthesis_single, update_base_size_priority
 from app.service.utils.decorator import RunTime
@@ -26,9 +26,14 @@ def process_item(item, basic):
     elif item['type'].lower() in ['blouse', 'outwear', 'dress', 'tops']:
         top_server = TopItem(data=item, basic=basic, minio_client=minio_client)
         item_data = top_server.process()
-    else:
+    elif item['type'].lower() in ['skirt', 'trousers', 'bottoms']:
         bottom_server = BottomItem(data=item, basic=basic, minio_client=minio_client)
         item_data = bottom_server.process()
+    elif item['type'].lower() in ['accessories']:
+        bottom_server = AccessoriesItem(data=item, basic=basic, minio_client=minio_client)
+        item_data = bottom_server.process()
+    else:
+        raise NotImplementedError(f"Item type {item['type']} not implemented")
     return item_data
 
 
@@ -38,6 +43,10 @@ def process_layer(item, layers):
         body_layer = organize_body(item)
         layers.append(body_layer)
         return item['body_image'].size
+    elif item['name'] == 'accessories':
+        front_layer, back_layer = organize_accessories(item)
+        layers.append(front_layer)
+        layers.append(back_layer)
     else:
         front_layer, back_layer = organize_clothing(item)
         layers.append(front_layer)
@@ -57,7 +66,7 @@ def design_generate(request_data):
     def process_object(step, object):
         nonlocal active_threads
         basic = object['basic']
-        items_response = {'layers': []}
+        items_response = {'layers': [], 'objectSign': object['objectSign'] if 'objectSign' in object.keys() else ""}
         if basic['single_overall'] == "overall":
             item_results = []
             for item in object['items']:
@@ -126,6 +135,136 @@ def design_generate(request_data):
     return object_response
 
 
+@RunTime
+def design_generate_v2(request_data):
+    objects_data = request_data.dict()['objects']
+    # process_id = request_data.dict()['process_id']
+    # object_response = {}
+    threads = []
+    active_threads = 0
+    lock = threading.Lock()
+
+    # total = len(objects_data)
+
+    def process_object(step, object):
+        nonlocal active_threads
+        basic = object['basic']
+        items_response = {'layers': [], 'objectSign': object['objectSign'] if 'objectSign' in object.keys() else ""}
+        if basic['single_overall'] == "overall":
+            item_results = []
+            for item in object['items']:
+                item_results.append(process_item(item, basic))
+            layers = []
+            body_size = None
+            for item in item_results:
+                body_size = process_layer(item, layers)
+            layers = sorted(layers, key=lambda s: s.get("priority", float('inf')))
+
+            layers, new_size = update_base_size_priority(layers, body_size)
+
+            for lay in layers:
+                items_response['layers'].append({
+                    'image_category': "body" if lay['name'] == 'mannequin' else lay['name'],
+                    'position': lay['position'],
+                    'priority': lay.get("priority", None),
+                    'resize_scale': lay['resize_scale'] if "resize_scale" in lay.keys() else None,
+                    'image_size': lay['image'] if lay['image'] is None else lay['image'].size,
+                    'gradient_string': lay['gradient_string'] if 'gradient_string' in lay.keys() else "",
+                    'mask_url': lay['mask_url'],
+                    'image_url': lay['image_url'] if 'image_url' in lay.keys() else None,
+                    'pattern_image_url': lay['pattern_image_url'] if 'pattern_image_url' in lay.keys() else None,
+                    # 'back_perspective_url': lay['back_perspective_url'] if 'back_perspective_url' in lay.keys() else None,
+                })
+            items_response['synthesis_url'] = synthesis(layers, new_size, basic)
+        else:
+            item_result = process_item(object['items'][0], basic)
+            items_response['layers'].append({
+                'image_category': f"{item_result['name']}_front",
+                'image_size': item_result['back_image'].size if item_result['back_image'] else None,
+                'position': None,
+                'priority': 0,
+                'image_url': item_result['front_image_url'],
+                'mask_url': item_result['mask_url'],
+                "gradient_string": item_result['gradient_string'] if 'gradient_string' in item_result.keys() else "",
+                'pattern_image_url': item_result['pattern_image_url'] if 'pattern_image_url' in item_result.keys() else None,
+            })
+            items_response['layers'].append({
+                'image_category': f"{item_result['name']}_back",
+                'image_size': item_result['front_image'].size if item_result['front_image'] else None,
+                'position': None,
+                'priority': 0,
+                'image_url': item_result['back_image_url'],
+                'mask_url': item_result['mask_url'],
+                "gradient_string": item_result['gradient_string'] if 'gradient_string' in item_result.keys() else "",
+                '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"
+        headers = {
+            'Accept': "*/*",
+            'Accept-Encoding': "gzip, deflate, br",
+            'User-Agent': "PostmanRuntime-ApipostRuntime/1.1.0",
+            'Connection': "keep-alive",
+            'Content-Type': "application/json"
+        }
+        response = post_request(url, json_data=items_response, headers=headers)
+        if response:
+            # 打印结果
+            logger.info(response.text)
+            logger.info(items_response)
+
+        # update_progress(process_id, total)
+
+        # with lock:
+        #     object_response[step] = items_response
+        #     active_threads -= 1
+
+    for step, object in enumerate(objects_data):
+        t = threading.Thread(target=process_object, args=(step, object))
+        threads.append(t)
+        t.start()
+        # with lock:
+        #     active_threads += 1
+
+    # for t in threads:
+    #     t.join()
+    # final_progress(process_id)
+    # return object_response
+
+
+import requests
+
+
+def post_request(url, data=None, json_data=None, headers=None, auth=None, timeout=5):
+    """
+    发送POST请求的封装函数
+
+    :param url: 接口的URL地址
+    :param data: 要发送的数据（字典形式，用于表单数据等，会自动编码）
+    :param json_data: 要发送的JSON数据（字典形式，会自动转换为JSON字符串）
+    :param headers: 请求头字典
+    :param auth: 认证信息（如 ('username', 'password') 形式用于基本认证）
+    :param timeout: 超时时间，单位为秒
+    :return: 返回接口的响应对象
+    """
+    try:
+        response = requests.post(
+            url,
+            data=data,
+            json=json_data,
+            headers=headers,
+            auth=auth,
+            timeout=timeout
+        )
+        response.raise_for_status()  # 如果请求失败，抛出异常
+        return response
+    except requests.RequestException as e:
+        print(f"POST请求出错: {e}")
+        return None
+
+
 if __name__ == '__main__':
     object_data = {
         "objects": [

app/service/design_fast/item.py

@@ -1,4 +1,4 @@
-from app.service.design_fast.pipeline import LoadImage, KeyPoint, Segmentation, Color, PrintPainting, Scaling, Split, LoadBodyImage, ContourDetection, BackPerspective
+from app.service.design_fast.pipeline import LoadImage, KeyPoint, Segmentation, Color, PrintPainting, Scaling, Split, LoadBodyImage, ContourDetection
 
 
 class BaseItem:
@@ -9,6 +9,27 @@ class BaseItem:
         self.result.update(basic)
 
 
+class AccessoriesItem(BaseItem):
+    def __init__(self, data, basic, minio_client):
+        super().__init__(data, basic)
+        self.Accessories_pipeline = [
+            LoadImage(minio_client),
+            # KeyPoint(),
+            ContourDetection(),
+            # Segmentation(minio_client),
+            # BackPerspective(minio_client),
+            Color(minio_client),
+            PrintPainting(minio_client),
+            Scaling(),
+            Split(minio_client)
+        ]
+
+    def process(self):
+        for item in self.Accessories_pipeline:
+            self.result = item(self.result)
+        return self.result
+
+
 class TopItem(BaseItem):
     def __init__(self, data, basic, minio_client):
         super().__init__(data, basic)

app/service/design_fast/pipeline/loading.py

@@ -74,6 +74,8 @@ class LoadImage:
             keypoint = 'head_point'
         elif name == 'earring':
             keypoint = 'ear_point'
+        elif name == 'accessories':
+            keypoint = "accessories"
         else:
             raise KeyError(f"{name} does not belong to item category list: blouse, outwear, dress, trousers, skirt, "
                            f"bag, shoes, hairstyle, earring.")

app/service/design_fast/pipeline/scale.py

@@ -18,7 +18,7 @@ class Scaling:
                  -
                  int(result['body_point_test'][result['keypoint'] + '_right'][0])) ** 2 + 1
             )
-            
+
             if distance_clo == 0:
                 result['scale'] = 1
             else:
@@ -46,4 +46,6 @@ class Scaling:
             result['scale'] = result['scale_bag']
         elif result['keypoint'] == 'ear_point':
             result['scale'] = result['scale_earrings']
+        else:
+            result['scale'] = 1
         return result

app/service/design_fast/pipeline/split.py

@@ -21,7 +21,7 @@ class Split(object):
     def __call__(self, result):
         try:
 
-            if result['name'] in ('outwear', 'dress', 'blouse', 'skirt', 'trousers', 'tops', 'bottoms'):
+            if result['name'] in ('outwear', 'dress', 'blouse', 'skirt', 'trousers', 'tops', 'bottoms','accessories'):
                 front_mask = result['front_mask']
                 back_mask = result['back_mask']
                 rgba_image = rgb_to_rgba(result['final_image'], front_mask + back_mask)

app/service/design_fast/utils/organize.py

@@ -55,6 +55,45 @@ def organize_clothing(layer):
     return front_layer, back_layer
 
 
+def organize_accessories(layer):
+    # 起始坐标
+    start_point = (0, 0)
+    # 前片数据
+    front_layer = dict(priority=layer['priority'] if layer.get("layer_order", False) else PRIORITY_DICT.get(f'{layer["name"].lower()}_front', None),
+                       name=f'{layer["name"].lower()}_front',
+                       image=layer["front_image"],
+                       # mask_image=layer['front_mask_image'],
+                       image_url=layer['front_image_url'],
+                       mask_url=layer['mask_url'],
+                       sacle=layer['scale'],
+                       clothes_keypoint=(0, 0),
+                       position=start_point,
+                       resize_scale=layer["resize_scale"],
+                       mask=cv2.resize(layer['mask'], layer["front_image"].size),
+                       gradient_string=layer['gradient_string'] if 'gradient_string' in layer.keys() else "",
+                       pattern_image_url=layer['pattern_image_url'],
+                       pattern_image=layer['pattern_image'],
+                       # back_perspective_url=layer['back_perspective_url'] if 'back_perspective_url' in layer.keys() else ""
+                       )
+    # 后片数据
+    back_layer = dict(priority=-layer.get("priority", 0) if layer.get("layer_order", False) else PRIORITY_DICT.get(f'{layer["name"].lower()}_back', None),
+                      name=f'{layer["name"].lower()}_back',
+                      image=layer["back_image"],
+                      # mask_image=layer['back_mask_image'],
+                      image_url=layer['back_image_url'],
+                      mask_url=layer['mask_url'],
+                      sacle=layer['scale'],
+                      clothes_keypoint=(0, 0),
+                      position=start_point,
+                      resize_scale=layer["resize_scale"],
+                      mask=cv2.resize(layer['mask'], layer["front_image"].size),
+                      gradient_string=layer['gradient_string'] if 'gradient_string' in layer.keys() else "",
+                      pattern_image_url=layer['pattern_image_url'],
+                      # back_perspective_url=layer['back_perspective_url'] if 'back_perspective_url' in layer.keys() else ""
+                      )
+    return front_layer, back_layer
+
+
 def calculate_start_point(keypoint_type, scale, clothes_point, body_point, offset, resize_scale):
     """
     Align left

app/service/design_fast/utils/synthesis_item.py

@@ -79,9 +79,11 @@ def synthesis(data, size, basic_info):
         _, binary_body_mask = cv2.threshold(body_mask, 127, 255, cv2.THRESH_BINARY)
         top_outer_mask = np.array(binary_body_mask)
         bottom_outer_mask = np.array(binary_body_mask)
+        accessories_outer_mask = np.array(binary_body_mask)
 
         top = True
         bottom = True
+        accessories = True
         i = len(data)
         while i:
             i -= 1
@@ -109,10 +111,23 @@ def synthesis(data, size, basic_info):
                 background = np.zeros_like(top_outer_mask)
                 background[all_y_start:all_y_end, all_x_start:all_x_end] = sketch_mask[mask_y_start:mask_y_end, mask_x_start:mask_x_end]
                 bottom_outer_mask = background + bottom_outer_mask
+            elif accessories and data[i]['name'] in ['accessories_front']:
+                mask_shape = data[i]['mask'].shape
+                y_offset, x_offset = data[i]['adaptive_position']
+                # 初始化叠加区域的起始和结束位置
+                all_y_start, all_y_end, mask_y_start, mask_y_end = positioning(all_mask_shape=all_mask_shape[0], mask_shape=mask_shape[0], offset=y_offset)
+                all_x_start, all_x_end, mask_x_start, mask_x_end = positioning(all_mask_shape=all_mask_shape[1], mask_shape=mask_shape[1], offset=x_offset)
+                # 将叠加区域赋值为相应的像素值
+                _, sketch_mask = cv2.threshold(data[i]['mask'], 127, 255, cv2.THRESH_BINARY)
+                background = np.zeros_like(top_outer_mask)
+                background[all_y_start:all_y_end, all_x_start:all_x_end] = sketch_mask[mask_y_start:mask_y_end, mask_x_start:mask_x_end]
+                accessories_outer_mask = background + accessories_outer_mask
+                pass
             elif bottom is False and top is False:
                 break
 
         all_mask = cv2.bitwise_or(top_outer_mask, bottom_outer_mask)
+        all_mask = cv2.bitwise_or(all_mask, accessories_outer_mask)
 
         for layer in data:
             if layer['image'] is not None: