【毕业设计】深度学习人脸表情识别系统 - python

文章目录

0 前言

🔥 Hi，大家好，这里是丹成学长的毕设系列文章！

🔥 对毕设有任何疑问都可以问学长哦!

这两年开始，各个学校对毕设的要求越来越高，难度也越来越大… 毕业设计耗费时间，耗费精力，甚至有些题目即使是专业的老师或者硕士生也需要很长时间，所以一旦发现问题，一定要提前准备，避免到后面措手不及，草草了事。

为了大家能够顺利以及最少的精力通过毕设，学长分享优质毕业设计项目，今天要分享的新项目是

🚩 基于深度学习的人脸表情识别

🥇学长这里给一个题目综合评分(每项满分5分)

• 难度系数：4分
• 工作量：4分
• 创新点：3分

🧿 选题指导, 项目分享：

https://gitee.com/yaa-dc/BJH/blob/master/gg/cc/README.md

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 facedefoverlay_memeface(probs):ifmax(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)defpredict_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)whileTrue:# 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 facesfor(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 clippingif x1 <0:
                x1 =0if y1 <0:
                y1 =0if 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()

4 最后