增加设备传图数判断,优化识别标签

Controller/AiController.py

@@ -125,15 +125,8 @@ class AiView(View):
             file_post_three = request_dict.get('fileThree', None)
             file_post_four = request_dict.get('fileFour', None)
 
-            file_post_one = file_post_one.replace(' ', '+')
-            file_post_two = file_post_two.replace(' ', '+')
-            file_post_three = file_post_three.replace(' ', '+')
-            file_post_four = file_post_four.replace(' ', '+')
-
-            file_post_one = base64.b64decode(file_post_one)
-            file_post_two = base64.b64decode(file_post_two)
-            file_post_three = base64.b64decode(file_post_three)
-            file_post_four = base64.b64decode(file_post_four)
+            if not all([file_post_one,file_post_two,file_post_three,file_post_four]):
+                return response.json(444)
 
             file_list = [file_post_one, file_post_two, file_post_three, file_post_four]
             del file_post_one, file_post_two, file_post_three, file_post_four
@@ -143,7 +136,8 @@ class AiView(View):
                 os.makedirs(dir_path)
             file_path_list = []
             for i, val in enumerate(file_list):
-                # file_path = dir_path + '/' + str(i) + '.jpg'
+                val = val.replace(' ', '+')
+                val = base64.b64decode(val)
                 file_path = "{dir_path}/{n_time}_{i}.jpg".format(dir_path=dir_path, n_time=n_time, i=i)
                 file_path_list.append(file_path)
                 with open(file_path, 'wb') as f:
@@ -183,7 +177,7 @@ class AiView(View):
             if len(labels['label_list']) == 0:
                 logger.info('没有识别到任何标签-----------------')
                 return response.json(10055)
-            event_type_list = labels['label_type']
+            event_type_list = labels['user_detect_group']
             label_list = ','.join(labels['label_list'])
             logger.info(event_type_list)
             logger.info(label_list)
@@ -259,7 +253,7 @@ class AiView(View):
                 kwargs = {
                     'uid': uid,
                     'channel': channel,
-                    'event_type': labels['label_type'],
+                    'event_type': event_type_list,
                     'n_time': n_time,
                     'appBundleId': appBundleId,
                     'token_val': token_val,
@@ -316,7 +310,7 @@ class AiView(View):
         label_name = []
         logger.info('--------识别到的标签-------')
         logger.info(labels)
-        labels_type = {
+        all_labels_type = {
             '1': ['Person', 'Human'],  # 人
             '2': ['Dog', 'Pet', 'Canine', 'Animal', 'Puppy'],  # 动物
             '3': ['Car', 'Vehicle', 'Transportation', 'Automobile']  # 车
@@ -326,29 +320,27 @@ class AiView(View):
             label_name.append(label['Name'])
             for Parents in label['Parents']:
                 label_name.append(Parents['Name'])
-
         #删除用户没有选择的ai识别类型, 并且得出最终识别结果
         user_detect_list = user_detect_group.split(',')
         user_detect_list = [i.strip() for i in user_detect_list]
-        label_result_list = []
-        new_labels_type = labels_type
-        for key, label_type in labels_type.items():
+        conform_label_list = []
+        conform_user_d_group = set()
+        for key, label_type_val in all_labels_type.items():
             if key in user_detect_list:
-                new_labels_type[key] = label_type
-                for label in label_type:
+                for label in label_type_val:
                     if label in label_name:
-                        label_result_list.append(label)
-
+                        conform_user_d_group.add(key)
+                        conform_label_list.append(label)
         #找出标签边框线位置信息
         boundingBoxList = []
         for label in labels:
-            if label['Name'] in label_result_list:
+            if label['Name'] in conform_label_list:
                 for boundingBox in label['Instances']:
                     boundingBoxList.append(boundingBox['BoundingBox'])
 
         #找出边框位置信息对应的单图位置并重新计算位置比
         merge_image_height = image_size['height']
-        merge_image_width = image_size['width']
+        # merge_image_width = image_size['width']
         single_height = merge_image_height//image_size['num']
         new_bounding_box_dict = {}
         new_bounding_box_dict[n_time+'_0'] = []
@@ -368,14 +360,11 @@ class AiView(View):
                     boxDict['Height'] = merge_image_height*val['Height']/single_height
                     boxDict['Top'] = ((merge_image_height*val['Top'])-(i*single_height))/single_height #减去前i张图片的高度
                     boxDict['Left'] = val['Left']
-                    boxDict['picName'] = "{n_time}_{i}".format(n_time=n_time,i=i)
-                    # new_bounding_box_list.append(boxDict)
-                    # new_bounding_box_list.append(boxDict)
                     new_bounding_box_dict["{n_time}_{i}".format(n_time=n_time,i=i)].append(boxDict)
         # exit(new_bounding_box_list)
-        user_labels_list = list(new_labels_type.keys())
-        user_labels_list.sort()
-        return {'label_type': user_labels_list, 'label_list': label_result_list,
+        conform_user_d_group = list(conform_user_d_group)
+        conform_user_d_group.sort()
+        return {'user_detect_group': conform_user_d_group, 'label_list': conform_label_list,
                 'new_bounding_box_dict':new_bounding_box_dict}