竞赛 深度学习人脸表情识别算法 - opencv python 机器视觉

0 前言

🔥 优质竞赛项目系列，今天要分享的是

🚩 深度学习人脸表情识别系统

该项目较为新颖，适合作为竞赛课题方向，学长非常推荐！

🥇学长这里给一个题目综合评分(每项满分5分)

• 难度系数：3分
• 工作量：3分
• 创新点：4分

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https://gitee.com/dancheng-senior/postgraduate

1 技术介绍

1.1 技术概括

面部表情识别技术源于1971年心理学家Ekman和Friesen的一项研究，他们提出人类主要有六种基本情感，每种情感以唯一的表情来反映当时的心理活动，这六种情感分别是愤怒（anger）、高兴(happiness)、悲伤
(sadness)、惊讶(surprise)、厌恶(disgust)和恐惧(fear)。

尽管人类的情感维度和表情复杂度远不是数字6可以量化的，但总体而言，这6种也差不多够描述了。

1.2 目前表情识别实现技术

2 实现效果

废话不多说，先上实现效果

3 深度学习表情识别实现过程

3.1 网络架构

面部表情识别CNN架构（改编自 埃因霍芬理工大学PARsE结构图）

其中，通过卷积操作来创建特征映射，将卷积核挨个与图像进行卷积，从而创建一组要素图，并在其后通过池化（pooling）操作来降维。

3.2 数据

主要来源于kaggle比赛，下载地址。
有七种表情类别： (0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral).
数据是48x48 灰度图，格式比较奇葩。
第一列是情绪分类，第二列是图像的numpy，第三列是train or test。

3.3 实现流程

3.4 部分实现代码

​

    import cv2
    import sys
    import json
    import numpy as np
    from keras.models import model_from_json

    emotions = ['angry', 'fear', 'happy', 'sad', 'surprise', 'neutral']
    cascPath = sys.argv[1]
    
    faceCascade = cv2.CascadeClassifier(cascPath)
    noseCascade = cv2.CascadeClassifier(cascPath)

    # load json and create model arch
    json_file = open('model.json','r')
    loaded_model_json = json_file.read()
    json_file.close()
    model = model_from_json(loaded_model_json)
    
    # load weights into new model
    model.load_weights('model.h5')
    
    # overlay meme face
    def overlay_memeface(probs):
        if max(probs) > 0.8:
            emotion = emotions[np.argmax(probs)]
            return 'meme_faces/{}-{}.png'.format(emotion, emotion)
        else:
            index1, index2 = np.argsort(probs)[::-1][:2]
            emotion1 = emotions[index1]
            emotion2 = emotions[index2]
            return 'meme_faces/{}-{}.png'.format(emotion1, emotion2)
    
    def predict_emotion(face_image_gray): # a single cropped face
        resized_img = cv2.resize(face_image_gray, (48,48), interpolation = cv2.INTER_AREA)
        # cv2.imwrite(str(index)+'.png', resized_img)
        image = resized_img.reshape(1, 1, 48, 48)
        list_of_list = model.predict(image, batch_size=1, verbose=1)
        angry, fear, happy, sad, surprise, neutral = [prob for lst in list_of_list for prob in lst]
        return [angry, fear, happy, sad, surprise, neutral]
    
    video_capture = cv2.VideoCapture(0)
    while True:
        # Capture frame-by-frame
        ret, frame = video_capture.read()
    
        img_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY,1)

        faces = faceCascade.detectMultiScale(
            img_gray,
            scaleFactor=1.1,
            minNeighbors=5,
            minSize=(30, 30),
            flags=cv2.cv.CV_HAAR_SCALE_IMAGE
        )
    
        # Draw a rectangle around the faces
        for (x, y, w, h) in faces:
    
            face_image_gray = img_gray[y:y+h, x:x+w]
            filename = overlay_memeface(predict_emotion(face_image_gray))
    
            print filename
            meme = cv2.imread(filename,-1)
            # meme = (meme/256).astype('uint8')
            try:
                meme.shape[2]
            except:
                meme = meme.reshape(meme.shape[0], meme.shape[1], 1)
            # print meme.dtype
            # print meme.shape
            orig_mask = meme[:,:,3]
            # print orig_mask.shape
            # memegray = cv2.cvtColor(orig_mask, cv2.COLOR_BGR2GRAY)
            ret1, orig_mask = cv2.threshold(orig_mask, 10, 255, cv2.THRESH_BINARY)
            orig_mask_inv = cv2.bitwise_not(orig_mask)
            meme = meme[:,:,0:3]
            origMustacheHeight, origMustacheWidth = meme.shape[:2]
    
            roi_gray = img_gray[y:y+h, x:x+w]
            roi_color = frame[y:y+h, x:x+w]
    
            # Detect a nose within the region bounded by each face (the ROI)
            nose = noseCascade.detectMultiScale(roi_gray)
    
            for (nx,ny,nw,nh) in nose:
                # Un-comment the next line for debug (draw box around the nose)
                #cv2.rectangle(roi_color,(nx,ny),(nx+nw,ny+nh),(255,0,0),2)
    
                # The mustache should be three times the width of the nose
                mustacheWidth =  20 * nw
                mustacheHeight = mustacheWidth * origMustacheHeight / origMustacheWidth
    
                # Center the mustache on the bottom of the nose
                x1 = nx - (mustacheWidth/4)
                x2 = nx + nw + (mustacheWidth/4)
                y1 = ny + nh - (mustacheHeight/2)
                y2 = ny + nh + (mustacheHeight/2)
    
                # Check for clipping
                if x1 < 0:
                    x1 = 0
                if y1 < 0:
                    y1 = 0
                if x2 > w:
                    x2 = w
                if y2 > h:
                    y2 = h

                # Re-calculate the width and height of the mustache image
                mustacheWidth = (x2 - x1)
                mustacheHeight = (y2 - y1)
    
                # Re-size the original image and the masks to the mustache sizes
                # calcualted above
                mustache = cv2.resize(meme, (mustacheWidth,mustacheHeight), interpolation = cv2.INTER_AREA)
                mask = cv2.resize(orig_mask, (mustacheWidth,mustacheHeight), interpolation = cv2.INTER_AREA)
                mask_inv = cv2.resize(orig_mask_inv, (mustacheWidth,mustacheHeight), interpolation = cv2.INTER_AREA)
    
                # take ROI for mustache from background equal to size of mustache image
                roi = roi_color[y1:y2, x1:x2]
    
                # roi_bg contains the original image only where the mustache is not
                # in the region that is the size of the mustache.
                roi_bg = cv2.bitwise_and(roi,roi,mask = mask_inv)
    
                # roi_fg contains the image of the mustache only where the mustache is
                roi_fg = cv2.bitwise_and(mustache,mustache,mask = mask)
    
                # join the roi_bg and roi_fg
                dst = cv2.add(roi_bg,roi_fg)
    
                # place the joined image, saved to dst back over the original image
                roi_color[y1:y2, x1:x2] = dst
    
                break
    
        #     cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
        #     angry, fear, happy, sad, surprise, neutral = predict_emotion(face_image_gray)
        #     text1 = 'Angry: {}     Fear: {}   Happy: {}'.format(angry, fear, happy)
        #     text2 = '  Sad: {} Surprise: {} Neutral: {}'.format(sad, surprise, neutral)
        #
        # cv2.putText(frame, text1, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 2, (255, 0, 0), 3)
        # cv2.putText(frame, text2, (50, 150), cv2.FONT_HERSHEY_SIMPLEX, 2, (255, 0, 0), 3)
    
        # Display the resulting frame
        cv2.imshow('Video', frame)
    
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
    
    # When everything is done, release the capture
    video_capture.release()
    cv2.destroyAllWindows()

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4 最后

🧿 更多资料, 项目分享：

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