feat: 增加 hanoi 跳过过滤条件（存在问题）

utils.py

@@ -106,6 +106,53 @@ class label_filter:
             if len(results) > 0:
                 return True, np.array(results)
         return False, None
+    # '''
+    # description: 对模型推理推理结果使用 threshold 和其他条件过滤 默认阈值为 0.5
+    # param {*} self
+    # param {*} data get_resp 返回的数据
+    # return {bool,array}
+    # '''    
+    # def filter_box_custom(self, data, ymax_range):  
+    #     if len(data) > 0:  
+    #         expect_boxes = (data[:, 1] > self.threshold) & (data[:, 0] > -1)  
+    #         np_boxes = data[expect_boxes, :]  
+    #         results = [  
+    #             [  
+    #                 item[0],  # 'label':  
+    #                 item[1],  # 'score':  
+    #                 item[2],  # 'xmin':  
+    #                 item[3],  # 'ymin':  
+    #                 item[4],  # 'xmax':  
+    #                 item[5],  # 'ymax':  
+    #                 not (ymax_range[0] < item[3] < ymax_range[1]), # 如果 ymin 处在范围内则返回 False，认为该目标不符合要求
+    #                 not (ymax_range[0] < item[5] < ymax_range[1]) # 如果 ymax 处在范围内则返回 False，认为该目标不符合要求
+    #             ]  
+    #             for item in np_boxes  
+    #         ]  
+    #         if len(results) > 0:  
+    #             return True, np.array(results)  
+    #     return False, None
+    
+    #原来的函数
+    def filter_box_custom(self,data):
+        if len(data) > 0:
+            expect_boxes = (data[:, 1] > self.threshold) & (data[:, 0] > -1)
+            np_boxes = data[expect_boxes, :]
+            results = [
+                [
+                    item[0], # 'label': 
+                    item[1], # 'score': 
+                    item[2], # 'xmin':
+                    item[3], # 'ymin':
+                    item[4], # 'xmax':
+                    item[5]  # 'ymax':
+                ]
+                for item in np_boxes
+                if item[5] < 180
+            ]
+            if len(results) > 0:
+                return True, np.array(results)
+        return False, None
     '''
     description: 根据传入的标签过滤，返回该标签的个数、box
     param {*} self
@@ -198,7 +245,7 @@ class label_filter:
     def get_two(self, target_label, label):
         response = self.get_resp()
         if response['code'] == 0:
-            ret, results = self.filter_box(response['data'])
+            ret, results = self.filter_box_custom(response['data'])
             if ret:
                 expect_boxes = (results[:, 0] == target_label.value)
                 boxes = results[expect_boxes, :]
@@ -216,6 +263,35 @@ class label_filter:
                     label_bool = False
                 return (target_bool, label_bool, target_box)
         return (False, False, None)
+    
+    # '''
+    # description: 查询两个目标 只有 target_label 返回 box
+    # param {*} self
+    # param {*} tlabel
+    # return {[bool]}
+    # ''' 
+    # def get_two_hanoi(self, target_label, label, ymax_range):
+    #     response = self.get_resp()
+    #     if response['code'] == 0:
+    #         ret, results = self.filter_box_custom(response['data'], ymax_range)
+    #         if ret:
+    #             expect_boxes = (results[:, 0] == target_label.value)
+    #             boxes = results[expect_boxes, :]
+    #             if len(boxes) != 0:
+    #                 target_bool = True
+    #                 target_box = boxes[:, 2:]
+    #             else:
+    #                 target_bool = False
+    #                 target_box = None
+    #             expect_boxes = (results[:, 0] == label.value)
+    #             boxes = results[expect_boxes, :]
+    #             if len(boxes) != 0:
+    #                 label_bool = True
+    #             else:
+    #                 label_bool = False
+    #             return (target_bool, label_bool, target_box)
+    #     return (False, False, None)
+
     '''
     description: 判断传入的多目标标签是否存在，存在返回 True
     param {*} self