0 前言

🔥这两年开始毕业设计和毕业答辩的要求和难度不断提升，传统的毕设题目缺少创新和亮点，往往达不到毕业答辩的要求，这两年不断有学弟学妹告诉学长自己做的项目系统达不到老师的要求。并且很难找到完整的毕设参考学习资料。

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提供大家参考学习，今天要分享的是

🚩毕业设计 基于深度学习的新闻文本分类算法系统(源码+论文)

🥇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

🧿 项目分享:见文末!

1 项目运行效果

视频效果：

2 技术介绍

2.1 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。

2.2 目前表情识别实现技术

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

importcv2importsysimportjsonimportnumpyasnpfromkeras.modelsimportmodel_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 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 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 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_size=1,verbose=1)angry,fear,happy,sad,surprise,neutral=[probforlstinlist_of_listforprobinlst]return[angry,fear,happy,sad,surprise,neutral]video_capture=cv2.VideoCapture(0)whileTrue:# Capture frame-by-frameret,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)infaces:face_image_gray=img_gray[y:y+h,x:x+w]filename=overlay_memeface(predict_emotion(face_image_gray))printfilename 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.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: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)innose:# 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 nosemustacheWidth=20*nw mustacheHeight=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 clippingifx1<0:x1=0ify1<0:y1=0ifx2>w:x2=wify2>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), (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 framecv2.imshow('Video',frame)ifcv2.waitKey(1)&0xFF==ord('q'):break# When everything is done, release the capturevideo_capture.release()cv2.destroyAllWindows()

篇幅有限，更多详细设计见设计论文

4 最后

项目包含内容

上万字 完整详细设计论文

🧿 项目分享:见文末!