添加人脸接口

ai_demo.py

@@ -2,6 +2,7 @@ from PIL import Image
 from util import *
 import json
 
+
 detector = FaceDetector('./weights/detection.onnx')
 fer = HSEmotionRecognizer('./weights/emotion.onnx')
 
@@ -26,7 +27,7 @@ def porcess_video(input_video_path):
         if ret:
             frame_counter += 1
             ## 每30帧进行处理一次
-            if frame_counter % 30 == 0:
+            if frame_counter % 60 == 0:
                 image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
                 boxes = detect_face(image)
                 if boxes is not None:
@@ -38,9 +39,10 @@ def porcess_video(input_video_path):
                         emotion, scores,pred = fer.predict_emotions(np.array(pil_image), logits=False)
                         ###### 做应用逻辑
                         print("frame_id:",frame_counter)
-                        print("emotion",emotion)
                         print("scores",scores[pred])
-                        emotion_reg_list.append(emotion)
+                        emotion_utf_8 = emotion.encode('utf-8').decode('utf-8')
+                        print("emotion_utf_8",emotion_utf_8)
+                        emotion_reg_list.append(emotion_utf_8)
                         emotion_score_list.append(float(scores[pred]))
                         print("idx_class",idx_class)
                         ######
@@ -57,9 +59,10 @@ def porcess_video(input_video_path):
         "emotion_reg_list":emotion_reg_list,
         "emotion_score_list":emotion_score_list
     }
+    print("data:",data)
     json_str = json.dumps(data)
     print("json_str:",json_str)
-    return json_str
+    return json_str,emotion_reg_list,emotion_score_list
 
 if __name__ == '__main__':
     #自己录制一段视频

all_server_open.py

@@ -7,6 +7,7 @@ from datetime import datetime, date
 # 导入蓝图
 from example_info_add import example_info_add_bp
 from user_add import user_add_bp
+from example_ai_info import example_info_ai_bp
 
 
 # 极简JSON编码器（处理特殊数据类型）
@@ -27,6 +28,7 @@ app.config['JSON_SORT_KEYS'] = False
 
 # 注册所有蓝图
 app.register_blueprint(example_info_add_bp)
+app.register_blueprint(example_info_ai_bp)
 app.register_blueprint(user_add_bp)
 
 if __name__ == '__main__':

example_ai_info.py

@@ -0,0 +1,70 @@
+from flask import Blueprint, request, jsonify
+
+from ai_demo import porcess_video
+
+example_info_ai_bp = Blueprint('example_ai_info', __name__)
+
+"""
+API接口：添加example_info数据
+接口路径：POST /api/example_info/ai
+
+请求体示例：
+{
+  "video_path": "/videos/test1.mp4"
+}
+
+字段说明：
+- video_path (可选)：字符串，视频文件路径
+"""
+
+@example_info_ai_bp.route('/api/example_info/ai', methods=['POST'])
+def add_example_ai_info():
+    try:
+        data = request.get_json()
+        print("data:",data)
+
+        # 验证必需字段
+        if not data:
+            return jsonify({
+                "success": False,
+                "message": "请求数据不能为空"
+            }), 400
+
+        # 获取请求数据
+        video_path = data.get('video_path')
+
+        json_info,emotion_reg_list,emotion_score_list=porcess_video(video_path)
+        # 构建SQL语句
+        emotion_state = "happy"
+        confidence = 0.1
+        flag = False
+        if len(emotion_reg_list) > 0:
+            emotion_state = emotion_reg_list[0]
+            flag = True
+        
+        if len(emotion_score_list) > 0:
+            confidence = emotion_score_list[0]
+        if flag:
+        # if result['success']:
+            return jsonify({
+                "success": True,
+                "message": "人脸分析成功",
+                "data": {
+                    "emotion_state": emotion_state,
+                    "video_path": video_path,
+                    "confidence": confidence
+                }
+            }), 201
+        else:
+            return jsonify({
+                "success": False,
+                "message": "添加失败",
+                "error": result.get('error', '未知错误')
+            }), 500
+            
+    except Exception as e:
+        return jsonify({
+            "success": False,
+            "message": "服务器内部错误",
+            "error": str(e)
+        }), 500

util.py

@@ -204,14 +204,22 @@ class HSEmotionRecognizer:
         self.mean = [0.485, 0.456, 0.406]
         self.std = [0.229, 0.224, 0.225]
 
-        self.idx_to_class = {0: 'ANGER',
-                             1: 'DISGUST',
-                             2: 'FEAR',
-                             3: 'HAPPINESS',
-                             4: 'NEUTRAL',
-                             5: 'SADNESS',
-                             6: 'SURPRISE'}
-
+        # self.idx_to_class = {0: 'ANGER',
+        #                      1: 'DISGUST',
+        #                      2: 'FEAR',
+        #                      3: 'HAPPINESS',
+        #                      4: 'NEUTRAL',
+        #                      5: 'SADNESS',
+        #                      6: 'SURPRISE'}
+        self.idx_to_class = {
+                        0: '愤怒',
+                        1: '厌恶',
+                        2: '恐惧',
+                        3: '快乐',
+                        4: '中性',
+                        5: '悲伤',
+                        6: '惊讶'
+                    }
         self.ort_session = ort.InferenceSession(path, providers=['CUDAExecutionProvider'])
 
     def preprocess(self, img):