feat

fix  design pipeline 剔除边缘检测任务，直接用分割

app/service/design/items/bottom.py

@@ -10,6 +10,7 @@ class Bottom(Clothing):
                 dict(type='LoadImageFromFile', path=kwargs['path'], color=kwargs['color'], print_dict=kwargs['print']),
                 dict(type='KeypointDetection'),
                 dict(type='ContourDetection'),
+                # dict(type='Segmentation'),
                 dict(type='Painting', painting_flag=True),
                 dict(type='PrintPainting', print_flag=True),
                 dict(type='Scaling'),

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

@@ -9,18 +9,22 @@ from ...utils.design_ensemble import get_seg_result
 
 @PIPELINES.register_module()
 class Segmentation(object):
-    def __init__(self, device='cpu', show=False, debug=None):
-        self.show = show
-        self.device = device
-        self.debug = debug
 
     # @ClassCallRunTime
     def __call__(self, result):
+        # 本地查询seg 缓存是否存在
         _, seg_result = self.load_seg_result(result["image_id"])
         result['seg_result'] = seg_result
         if not _:
-            result['seg_result'] = get_seg_result(result["image_id"], result['image'])
+            # 推理获得seg 结果
-            self.save_seg_result(result['seg_result'][0], result['image_id'])
+            seg_result = get_seg_result(result["image_id"], result['image'])[0]
+            self.save_seg_result(seg_result, result['image_id'])
+        # 处理前片后片
+        temp_front = seg_result == 1.0
+        result['front_mask'] = (255 * (temp_front + 0).astype(np.uint8))
+        temp_back = seg_result == 2.0
+        result['back_mask'] = (255 * (temp_back + 0).astype(np.uint8))
+        result['mask'] = result['front_mask'] + result['back_mask']
         return result
 
     @staticmethod

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

@@ -22,29 +22,10 @@ class Split(object):
     # KNet
     def __call__(self, result):
         try:
-            if 'mask' not in result.keys():
-                raise KeyError(f'Cannot find mask in result dict, please check ContourDetection is included in process pipelines.')
-            if 'seg_result' not in result.keys():  # 没过seg模型
-                result['front_mask'] = result['mask'].copy()
-                result['back_mask'] = np.zeros_like(result['mask'])
-            else:
-                temp_front = result['seg_result'] == 1.0
-                result['front_mask'] = (result['mask'] * (temp_front + 0).astype(np.uint8))
-                temp_back = result['seg_result'] == 2.0
-                result['back_mask'] = (result['mask'] * (temp_back + 0).astype(np.uint8))
 
             if result['name'] in ('outwear', 'dress', 'blouse', 'skirt', 'trousers', 'tops', 'bottoms'):
-                if len(result['front_mask'].shape) > 2:
+                front_mask = result['front_mask']
-                    front_mask = result['front_mask'][0]
+                back_mask = result['back_mask']
-                else:
-                    front_mask = result['front_mask']
-
-                if len(result['back_mask'].shape) > 2:
-                    back_mask = result['back_mask'][0]
-                else:
-                    back_mask = result['back_mask']
-
-                # rgba_image = rgb_to_rgba((result['final_image'].shape[0], result['final_image'].shape[1]), result['final_image'], front_mask + back_mask)
                 rgba_image = rgb_to_rgba(result['final_image'], front_mask + back_mask)
                 new_size = (int(rgba_image.shape[1] * result["scale"] * result["resize_scale"][0]), int(rgba_image.shape[0] * result["scale"] * result["resize_scale"][1]))
                 rgba_image = cv2.resize(rgba_image, new_size)

app/service/design/items/top.py

@@ -9,8 +9,8 @@ class Top(Clothing):
             pipeline = [
                 dict(type='LoadImageFromFile', path=kwargs['path'], color=kwargs['color'], print_dict=kwargs['print']),
                 dict(type='KeypointDetection'),
-                dict(type='ContourDetection'),
+                # dict(type='ContourDetection'),
-                dict(type='Segmentation', device='cpu', show=False, debug=kwargs['debug']),
+                dict(type='Segmentation'),
                 dict(type='Painting', painting_flag=True),
                 dict(type='PrintPainting', print_flag=True),
                 # dict(type='ImageShow', key=['image', 'mask', 'seg_visualize', 'pattern_image']),