青岛网站制作百家号,怎么注册免费个人网站,网站后台文字编辑器,济宁做网站文章目录 0 前言1 技术介绍1.1 技术概括1.2 目前表情识别实现技术 2 实现效果3 深度学习表情识别实现过程3.1 网络架构3.2 数据3.3 实现流程3.4 部分实现代码 4 最后 0 前言
#x1f525; 优质竞赛项目系列#xff0c;今天要分享的是
#x1f6a9; 深度学习人脸表情识别系… 文章目录 0 前言1 技术介绍1.1 技术概括1.2 目前表情识别实现技术 2 实现效果3 深度学习表情识别实现过程3.1 网络架构3.2 数据3.3 实现流程3.4 部分实现代码 4 最后 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 更多资料, 项目分享
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 (0Angry, 1Disgust, 2Fear, 3Happy, 4Sad, 5Surprise, 6Neutral). 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 cv2import sysimport jsonimport numpy as npfrom keras.models import model_from_jsonemotions [angry, fear, happy, sad, surprise, neutral]cascPath sys.argv[1]faceCascade cv2.CascadeClassifier(cascPath)noseCascade cv2.CascadeClassifier(cascPath)# load json and create model archjson_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 modelmodel.load_weights(model.h5)# overlay meme facedef 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 faceresized_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_size1, verbose1)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-frameret, frame video_capture.read()img_gray cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY,1)faces faceCascade.detectMultiScale(img_gray,scaleFactor1.1,minNeighbors5,minSize(30, 30),flagscv2.cv.CV_HAAR_SCALE_IMAGE)# Draw a rectangle around the facesfor (x, y, w, h) in faces:face_image_gray img_gray[y:yh, x:xw]filename overlay_memeface(predict_emotion(face_image_gray))print filenamememe 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.shapeorig_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:yh, x:xw]roi_color frame[y:yh, x:xw]# 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),(nxnw,nynh),(255,0,0),2)# The mustache should be three times the width of the nosemustacheWidth 20 * nwmustacheHeight mustacheWidth * origMustacheHeight / origMustacheWidth# Center the mustache on the bottom of the nosex1 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 wif y2 h:y2 h# Re-calculate the width and height of the mustache imagemustacheWidth (x2 - x1)mustacheHeight (y2 - y1)# Re-size the original image and the masks to the mustache sizes# calcualted abovemustache 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 imageroi 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 isroi_fg cv2.bitwise_and(mustache,mustache,mask mask)# join the roi_bg and roi_fgdst cv2.add(roi_bg,roi_fg)# place the joined image, saved to dst back over the original imageroi_color[y1:y2, x1:x2] dstbreak# cv2.rectangle(frame, (x, y), (xw, yh), (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 framecv2.imshow(Video, frame)if cv2.waitKey(1) 0xFF ord(q):break# When everything is done, release the capturevideo_capture.release()cv2.destroyAllWindows()
需要完整代码以及学长训练好的模型联系学长获取
4 最后 更多资料, 项目分享
https://gitee.com/dancheng-senior/postgraduate
