Delete face_emo_analysize.py

face_emo_analysize.py

@@ -1,283 +0,0 @@
-import cv2
-import mediapipe as mp
-import math
-import numpy as np
-import time
-import torch
-from PIL import Image
-from torchvision import transforms
-
-
-# 定义预处理函数
-def pth_processing(fp):
-    class PreprocessInput(torch.nn.Module):
-        def __init__(self):
-            super(PreprocessInput, self).__init__()
-
-        def forward(self, x):
-            x = x.to(torch.float32)
-            x = torch.flip(x, dims=(0,))
-            x[0, :, :] -= 91.4953
-            x[1, :, :] -= 103.8827
-            x[2, :, :] -= 131.0912
-            return x
-
-    def get_img_torch(img):
-        ttransform = transforms.Compose([
-            transforms.PILToTensor(),
-            PreprocessInput()
-        ])
-        img = img.resize((224, 224), Image.Resampling.NEAREST)
-        img = ttransform(img)
-        img = torch.unsqueeze(img, 0).to('cuda')
-        return img
-
-    return get_img_torch(fp)
-
-
-# 定义坐标归一化函数
-def norm_coordinates(normalized_x, normalized_y, image_width, image_height):
-    x_px = min(math.floor(normalized_x * image_width), image_width - 1)
-    y_px = min(math.floor(normalized_y * image_height), image_height - 1)
-    return x_px, y_px
-
-
-# 定义获取面部边界框的函数
-def get_box(fl, w, h):
-    idx_to_coors = {}
-    for idx, landmark in enumerate(fl.landmark):
-        landmark_px = norm_coordinates(landmark.x, landmark.y, w, h)
-        if landmark_px:
-            idx_to_coors[idx] = landmark_px
-
-    x_min = np.min(np.asarray(list(idx_to_coors.values()))[:, 0])
-    y_min = np.min(np.asarray(list(idx_to_coors.values()))[:, 1])
-    endX = np.max(np.asarray(list(idx_to_coors.values()))[:, 0])
-    endY = np.max(np.asarray(list(idx_to_coors.values()))[:, 1])
-
-    (startX, startY) = (max(0, x_min), max(0, y_min))
-    (endX, endY) = (min(w - 1, endX), min(h - 1, endY))
-    return startX, startY, endX, endY
-
-
-# 定义显示情感预测结果的函数
-def display_EMO_PRED(img, box, label='', prob=0.0, color=(128, 128, 128), txt_color=(255, 255, 255), line_width=2):
-    lw = line_width or max(round(sum(img.shape) / 2 * 0.003), 2)
-    text2_color = (255, 0, 255)
-    p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
-    cv2.rectangle(img, p1, p2, text2_color, thickness=lw, lineType=cv2.LINE_AA)
-    font = cv2.FONT_HERSHEY_SIMPLEX
-
-    tf = max(lw - 1, 1)
-    text_fond = (0, 0, 0)
-
-    # 获取情感标签的文本尺寸
-    label_width, label_height = cv2.getTextSize(label, font, lw / 3, tf)[0]
-
-    # 显示情感标签
-    cv2.putText(img, label,
-                (p1[0], p1[1] - round(((p2[0] - p1[0]) * 20) / 360)), font,
-                lw / 3, text_fond, thickness=tf, lineType=cv2.LINE_AA)
-    cv2.putText(img, label,
-                (p1[0], p1[1] - round(((p2[0] - p1[0]) * 20) / 360)), font,
-                lw / 3, text2_color, thickness=tf, lineType=cv2.LINE_AA)
-
-    # 显示情感概率
-    prob_text = f"{prob:.2f}"
-    prob_width, prob_height = cv2.getTextSize(prob_text, font, lw / 3, tf)[0]
-    cv2.putText(img, prob_text,
-                (p1[0] + label_width + 5, p1[1] - round(((p2[0] - p1[0]) * 20) / 360)), font,
-                lw / 3, text_fond, thickness=tf, lineType=cv2.LINE_AA)
-    cv2.putText(img, prob_text,
-                (p1[0] + label_width + 5, p1[1] - round(((p2[0] - p1[0]) * 20) / 360)), font,
-                lw / 3, text2_color, thickness=tf, lineType=cv2.LINE_AA)
-
-    return img
-
-
-# 定义显示FPS的函数
-def display_FPS(img, text, margin=1.0, box_scale=1.0):
-    img_h, img_w, _ = img.shape
-    line_width = int(min(img_h, img_w) * 0.001)  # line width
-    thickness = max(int(line_width / 3), 1)  # font thickness
-
-    font_face = cv2.FONT_HERSHEY_SIMPLEX
-    font_color = (0, 0, 0)
-    font_scale = thickness / 1.5
-
-    t_w, t_h = cv2.getTextSize(text, font_face, font_scale, None)[0]
-
-    margin_n = int(t_h * margin)
-    sub_img = img[0 + margin_n: 0 + margin_n + t_h + int(2 * t_h * box_scale),
-              img_w - t_w - margin_n - int(2 * t_h * box_scale): img_w - margin_n]
-
-    white_rect = np.ones(sub_img.shape, dtype=np.uint8) * 255
-
-    img[0 + margin_n: 0 + margin_n + t_h + int(2 * t_h * box_scale),
-    img_w - t_w - margin_n - int(2 * t_h * box_scale):img_w - margin_n] = cv2.addWeighted(sub_img, 0.5, white_rect, .5,
-                                                                                          1.0)
-
-    cv2.putText(img=img,
-                text=text,
-                org=(img_w - t_w - margin_n - int(2 * t_h * box_scale) // 2,
-                     0 + margin_n + t_h + int(2 * t_h * box_scale) // 2),
-                fontFace=font_face,
-                fontScale=font_scale,
-                color=font_color,
-                thickness=thickness,
-                lineType=cv2.LINE_AA,
-                bottomLeftOrigin=False)
-
-    return img
-
-def face_emo_analysize():
-    # 初始化MediaPipe Face Mesh
-    mp_face_mesh = mp.solutions.face_mesh
-
-    # 加载PyTorch模型
-    name = '0_66_49_wo_gl'
-    pth_model = torch.jit.load('torchscript_model_0_66_49_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)