111

1.py

@@ -0,0 +1,2 @@
+import face_emo_analysize
+face_emo_analysize.face_emo_analysize()

face_emo_analysize.py

@@ -6,6 +6,21 @@ import time
 import torch
 from PIL import Image
 from torchvision import transforms
+# 示例代码
+import torch
+import torch.nn as nn
+
+# 假设你有一个模型类
+class MyModel(nn.Module):
+    def __init__(self):
+        super(MyModel, self).__init__()
+        self.fc = nn.Linear(10, 10)
+
+    def forward(self, x):
+        return self.fc(x)
+
+
+
 
 
 # 定义预处理函数
@@ -133,151 +148,168 @@ def display_FPS(img, text, margin=1.0, box_scale=1.0):
 def face_emo_analysize():
     # 初始化MediaPipe Face Mesh
     mp_face_mesh = mp.solutions.face_mesh
+    # 创建模型实例并保存
+    model = MyModel()
+    torch.save(model.state_dict(), 'model.pth')
+
+    # 加载模型
+    model = MyModel()
+    model.load_state_dict(torch.load('model.pth', weights_only=True))
+    model.eval()
 
     # 加载PyTorch模型
     name = '0_66_49_wo_gl'
-    pth_model = torch.jit.load('torchscript_model_0_66_37_wo_gl.pth'.format(name)).to(
-        'cuda')
-    pth_model.eval()
-
-    # 定义情感字典
-    DICT_EMO = {0: 'Neutral', 1: 'Happiness', 2: 'Sadness', 3: 'Surprise', 4: 'Fear', 5: 'Disgust', 6: 'Anger'}
-
-    # 打开摄像头
-    cap = cv2.VideoCapture(0)
-    w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-    h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-    fps = np.round(cap.get(cv2.CAP_PROP_FPS))
-
-    # 设置视频写入器
-    path_save_video = 'result2.mp4'
-    vid_writer = cv2.VideoWriter(path_save_video, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
-
-    # 使用MediaPipe Face Mesh进行面部检测
-    emotion_stats = {}
-    with mp_face_mesh.FaceMesh(
-            max_num_faces=1,
-            refine_landmarks=False,
-            min_detection_confidence=0.5,
-            min_tracking_confidence=0.5) as face_mesh:
-        while cap.isOpened():
-            t1 = time.time()
-            success, frame = cap.read()
-            if frame is None: break
-
-            frame_copy = frame.copy()
-            frame_copy.flags.writeable = False
-            frame_copy = cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
-            results = face_mesh.process(frame_copy)
-            frame_copy.flags.writeable = True
-
-            if results.multi_face_landmarks:
-                for fl in results.multi_face_landmarks:
-                    startX, startY, endX, endY = get_box(fl, w, h)
-                    cur_face = frame_copy[startY:endY, startX: endX]
-
-                    # 使用PyTorch模型进行情感预测
-                    cur_face = pth_processing(Image.fromarray(cur_face))
-                    output = torch.nn.functional.softmax(pth_model(cur_face), dim=1).cpu().detach().numpy()[0]
-
-                    # 获取情感类别和概率
-                    cl = np.argmax(output)
-                    label = DICT_EMO[cl]
-                    prob = output[cl]
-
-                    # 记录情感统计信息
-                    if label not in emotion_stats:
-                        emotion_stats[label] = {'start_time': t1, 'duration': 0, 'total_prob': prob, 'count': 1}
-                    else:
-                        emotion_stats[label]['duration'] += (t1 - emotion_stats[label]['start_time'])
-                        emotion_stats[label]['total_prob'] += prob
-                        emotion_stats[label]['count'] += 1
-                        emotion_stats[label]['start_time'] = t1
-
-                    # 显示情感结果和概率
-                    frame = display_EMO_PRED(frame, (startX, startY, endX, endY), label, prob, line_width=3)
-
-            t2 = time.time()
-
-            # 显示FPS
-            frame = display_FPS(frame, 'FPS: {0:.1f}'.format(1 / (t2 - t1)), box_scale=.5)
-
-            # 写入视频
-            vid_writer.write(frame)
-
-            # 显示帧
-            cv2.imshow('Webcam', frame)
-            if cv2.waitKey(1) & 0xFF == ord('\x1b'):
-                break
-
-        # 释放资源
-        vid_writer.release()
-        cap.release()
-        cv2.destroyAllWindows()
-
-        # 打印情感统计信息
-        for emotion, stats in emotion_stats.items():
-            avg_prob = stats['total_prob'] / stats['count']
-            print(f'Emotion: {emotion}, Duration: {stats["duration"]:.2f} seconds, Average Probability: {avg_prob:.2f}')
-
-        # 将视频转换为GIF
-        from moviepy.editor import VideoFileClip
-
-
-        def convert_mp4_to_gif(input_path, output_path, fps=10):
-            clip = VideoFileClip(input_path)
-            clip.write_gif(output_path, fps=fps)
-    #此时我们获得了各表情的持续时间与平均概率,我们可以计算大小,如果负向情绪大于正向情绪那么情感就是负的，再计算平均值即可.
-        positive_emotions = ['Happiness', 'Surprise']
-        negative_emotions = ['Anger', 'Fear', 'Sadness', 'Disgust']
-
-        # 初始化正向和负向情感的统计信息
-        positive_stats = {'duration': 0, 'total_prob': 0, 'count': 0}
-        negative_stats = {'duration': 0, 'total_prob': 0, 'count': 0}
-
-        # 统计正向和负向情感��持续时间和概率
-        for emotion, stats in emotion_stats.items():
-            if emotion in positive_emotions:
-                positive_stats['duration'] += stats['duration']
-                positive_stats['total_prob'] += stats['total_prob']
-                positive_stats['count'] += stats['count']
-            elif emotion in negative_emotions:
-                negative_stats['duration'] += stats['duration']
-                negative_stats['total_prob'] += stats['total_prob']
-                negative_stats['count'] += stats['count']
-
-        # 计算正向和负向情感的平均概率
-        if positive_stats['count'] > 0:
-            positive_avg_prob = positive_stats['total_prob'] / positive_stats['count']
-        else:
-            positive_avg_prob = 0
-
-        if negative_stats['count'] > 0:
-            negative_avg_prob = negative_stats['total_prob'] / negative_stats['count']
-        else:
-            negative_avg_prob = 0
-
-        # 比较正向和负向情感的持续时间
-        if negative_stats['duration'] > positive_stats['duration']:
-            print(f'负向情感持续时间更长: {negative_stats["duration"]:.2f} seconds')
-            print(f'负向情感的平均概率: {negative_avg_prob:.2f}')
-            outcome = "负向,概率:"+str(negative_avg_prob)
-            return outcome
-        else:
-            print(f'正向情感持续时间更长: {positive_stats["duration"]:.2f} seconds')
-            print(f'正向情感的平均概率: {positive_avg_prob:.2f}')
-            outcome = "正向,概率:"+str(positive_avg_prob)
-            return outcome
-        # 将视频转换为GIF
-        from moviepy.editor import VideoFileClip
-
-
-        def convert_mp4_to_gif(input_path, output_path, fps=10):
-            clip = VideoFileClip(input_path)
-            clip.write_gif(output_path, fps=fps)
-
-        # 示例使用
-        input_video_path = "result.mp4"
-        output_gif_path = "result.gif"
-
-        convert_mp4_to_gif(input_video_path, output_gif_path)
+    #pth_model = torch.jit.load('torchscript_model_0_66_37_wo_gl.pth'.format(name)).to(
+     #   'cuda')
+    model_path = 'torchscript_model_0_66_37_wo_gl.pth'
+    try:
+        model = torch.jit.load(model_path)
+        model.to('cuda')
+        model.eval()
+    except Exception as e:
+        #print(f"Failed to load model: {e}"
+
+
+       # pth_model.eval()
+
+        # 定义情感字典
+        DICT_EMO = {0: 'Neutral', 1: 'Happiness', 2: 'Sadness', 3: 'Surprise', 4: 'Fear', 5: 'Disgust', 6: 'Anger'}
+
+        # 打开摄像头
+        cap = cv2.VideoCapture(0)
+        w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        fps = np.round(cap.get(cv2.CAP_PROP_FPS))
+
+        # 设置视频写入器
+        path_save_video = 'result2.mp4'
+        vid_writer = cv2.VideoWriter(path_save_video, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
+
+        # 使用MediaPipe Face Mesh进行面部检测
+        emotion_stats = {}
+        with mp_face_mesh.FaceMesh(
+                max_num_faces=1,
+                refine_landmarks=False,
+                min_detection_confidence=0.5,
+                min_tracking_confidence=0.5) as face_mesh:
+            while cap.isOpened():
+                t1 = time.time()
+                success, frame = cap.read()
+                if frame is None: break
+
+                frame_copy = frame.copy()
+                frame_copy.flags.writeable = False
+                frame_copy = cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
+                results = face_mesh.process(frame_copy)
+                frame_copy.flags.writeable = True
+
+                if results.multi_face_landmarks:
+                    for fl in results.multi_face_landmarks:
+                        startX, startY, endX, endY = get_box(fl, w, h)
+                        cur_face = frame_copy[startY:endY, startX: endX]
+
+                        # 使用PyTorch模型进行情感预测
+                        cur_face = pth_processing(Image.fromarray(cur_face))
+                        output = torch.nn.functional.softmax(pth_model(cur_face), dim=1).cpu().detach().numpy()[0]
+
+                        # 获取情感类别和概率
+                        cl = np.argmax(output)
+                        label = DICT_EMO[cl]
+                        prob = output[cl]
+
+                        # 记录情感统计信息
+                        if label not in emotion_stats:
+                            emotion_stats[label] = {'start_time': t1, 'duration': 0, 'total_prob': prob, 'count': 1}
+                        else:
+                            emotion_stats[label]['duration'] += (t1 - emotion_stats[label]['start_time'])
+                            emotion_stats[label]['total_prob'] += prob
+                            emotion_stats[label]['count'] += 1
+                            emotion_stats[label]['start_time'] = t1
+
+                        # 显示情感结果和概率
+                        frame = display_EMO_PRED(frame, (startX, startY, endX, endY), label, prob, line_width=3)
+
+                t2 = time.time()
+
+                # 显示FPS
+                frame = display_FPS(frame, 'FPS: {0:.1f}'.format(1 / (t2 - t1)), box_scale=.5)
+
+                # 写入视频
+                vid_writer.write(frame)
+
+                # 显示帧
+                cv2.imshow('Webcam', frame)
+                if cv2.waitKey(1) & 0xFF == ord('\x1b'):
+                    break
+
+            # 释放资源
+            vid_writer.release()
+            cap.release()
+            cv2.destroyAllWindows()
+
+            # 打印情感统计信息
+            for emotion, stats in emotion_stats.items():
+                avg_prob = stats['total_prob'] / stats['count']
+                print(f'Emotion: {emotion}, Duration: {stats["duration"]:.2f} seconds, Average Probability: {avg_prob:.2f}')
+
+            # 将视频转换为GIF
+            from moviepy.editor import VideoFileClip
+
+
+            def convert_mp4_to_gif(input_path, output_path, fps=10):
+                clip = VideoFileClip(input_path)
+                clip.write_gif(output_path, fps=fps)
+        #此时我们获得了各表情的持续时间与平均概率,我们可以计算大小,如果负向情绪大于正向情绪那么情感就是负的，再计算平均值即可.
+            positive_emotions = ['Happiness', 'Surprise']
+            negative_emotions = ['Anger', 'Fear', 'Sadness', 'Disgust']
+
+            # 初始化正向和负向情感的统计信息
+            positive_stats = {'duration': 0, 'total_prob': 0, 'count': 0}
+            negative_stats = {'duration': 0, 'total_prob': 0, 'count': 0}
+
+            # 统计正向和负向情感的持续时间和概率
+            for emotion, stats in emotion_stats.items():
+                if emotion in positive_emotions:
+                    positive_stats['duration'] += stats['duration']
+                    positive_stats['total_prob'] += stats['total_prob']
+                    positive_stats['count'] += stats['count']
+                elif emotion in negative_emotions:
+                    negative_stats['duration'] += stats['duration']
+                    negative_stats['total_prob'] += stats['total_prob']
+                    negative_stats['count'] += stats['count']
+
+            # 计算正向和负向情感的平均概率
+            if positive_stats['count'] > 0:
+                positive_avg_prob = positive_stats['total_prob'] / positive_stats['count']
+            else:
+                positive_avg_prob = 0
+
+            if negative_stats['count'] > 0:
+                negative_avg_prob = negative_stats['total_prob'] / negative_stats['count']
+            else:
+                negative_avg_prob = 0
+
+            # 比较正向和负向情感的持续时间
+            if negative_stats['duration'] > positive_stats['duration']:
+                print(f'负向情感持续时间更长: {negative_stats["duration"]:.2f} seconds')
+                print(f'负向情感的平均概率: {negative_avg_prob:.2f}')
+                outcome = "负向,概率:"+str(negative_avg_prob)
+                return outcome
+            else:
+                print(f'正向情感持续时间更长: {positive_stats["duration"]:.2f} seconds')
+                print(f'正向情感的平均概率: {positive_avg_prob:.2f}')
+                outcome = "正向,概率:"+str(positive_avg_prob)
+                return outcome
+            # 将视频转换为GIF
+            from moviepy.editor import VideoFileClip
+
+
+            def convert_mp4_to_gif(input_path, output_path, fps=10):
+                clip = VideoFileClip(input_path)
+                clip.write_gif(output_path, fps=fps)
+
+            # 示例使用
+            input_video_path = "result.mp4"
+            output_gif_path = "result.gif"
+
+            convert_mp4_to_gif(input_video_path, output_gif_path)

model.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:694736e9a1f66d1925fd74f90b165c8de09f690c4ccdf1ec73722b37db3f5373
+size 1868