TensorFlow脑洞人脸识别(二)
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本篇博客给大家介绍数据的处理、模型的建立、识别算法
1.数据的处理
比较简单，先上代码：
import osimport threadingimport randomimport timeimport numpy as npimport cv2import tensorflow as tfimport classfierdataList=[]dataLabelList=[]isStarted=FalseisInited=FalsemaxCachedData=200cachedImageList=[]cachedIndexList=[]lock=threading.Lock()imageDir=["face","body","background"]def initSampler(path): global isInited,isStarted if isStarted!=False:return isStarted=True thread=threading.Thread(target=threadFun,args=[path]) thread.setDaemon(True) thread.start()def threadFun(path): global isInited,isStarted if loadImageData(path)!=True: isStarted=False return isInited=True fillCache()def loadImageData(path): global dataList,dataLabelList dataLabelList=[] dataList=[] for dir in imageDir: dirPath=os.path.join(path,dir) if os.path.isdir(dirPath)!=True:continue dataLabelList.append(dir) tempList=[] for image in os.listdir(dirPath): imagePath=os.path.join(dirPath,image) tempList.append(cv2.resize(cv2.imread(imagePath),classfier.IMAGE_SIZE)) dataList.append(tempList) return Trueimport tensorflow as tfinput_image=tf.placeholder(tf.float32,[classfier.IMAGE_SIZE[0],classfier.IMAGE_SIZE[1],3])output_image=tf.image.per_image_standardization(input_image)session=tf.Session()def preProcessImage(image): return session.run([output_image],feed_dict={input_image:image})[0]def fillCache(): global dataList global lock,cachedImageList,cachedIndexList while True: if len(cachedImageList)>=maxCachedData: time.sleep(0.01) continue indexList=[] imageList=[] for i in range(maxCachedData): index=random.randint(0,len(dataList)-1) image=dataList[index][random.randint(0,len(dataList[index])-1)] image=transfromImage(image,0.2,0.2,0.2).astype(np.float32) image=preProcessImage(image) indexList.append(index) imageList.append(image) lock.acquire() cachedIndexList+=indexList cachedImageList+=imageList lock.release()def transfromImage(image,fRandomFactor,hRandomFactor, vRandomFactor): shape=image.shape[0:2] fromPoint=[[0,0],[shape[0]-1,0],[0,shape[1]-1]] moveScale = shape[0] hMoveLength = moveScale * random.uniform(-hRandomFactor, hRandomFactor) vMoveLength = moveScale * random.uniform(-vRandomFactor, vRandomFactor) transformScale=shape[0]/4 toPoint = np.array(fromPoint, np.float32) for i in range(3): toPoint[i][0]+=transformScale*random.uniform(-fRandomFactor,fRandomFactor)+hMoveLength toPoint[i][1] += transformScale * random.uniform(-fRandomFactor, fRandomFactor)+vMoveLength transfromMat=cv2.getAffineTransform(np.array(fromPoint,np.float32),toPoint) return cv2.warpAffine(image,transfromMat,shape,flags=cv2.INTER_LINEAR)def rotateImage(image,randomFactor): shape=image.shape[0:2] angle=random.uniform(-randomFactor,randomFactor) rotateMat=cv2.getRotationMatrix2D((shape[0]/2,shape[1]/2),angle,1) return cv2.warpAffine(image,rotateMat,shape)def check(): if isStarted==True and isInited==False: while True: if isInited==False: time.sleep(5) else: break if isInited!=True: raise Exception("sampler is not inited") if len(dataLabelList)!=len(dataList): raise Exception("sampler data is wrong")def getBatch(num): global maxCachedData,lock,cachedImageList,cachedIndexList check() if num*2>maxCachedData: maxCachedData=2*num while True: if len(cachedImageList)<=num: print("sleep to get batch") time.sleep(0.01) lock.acquire() imageList=cachedImageList[0:num] cachedImageList=cachedImageList[num:len(cachedImageList)] indexList=cachedIndexList[0:num] cachedIndexList=cachedIndexList[num:len(cachedIndexList)] lock.release() return np.array(imageList),np.array(indexList)def getLabel(index): if index<0 or index>=len(imageDir): raise Exception("invalid index") return imageDir[index]
外部模块调用initSampler(path)方法后，会启动一个线程。这个线程会把所有数据加载到内存里，并预先处理图片放到队列里，以供网络模型使用。处理方法是先用opencv进行一个随机的仿射变换，然后用tensorflow提供的方法去均值并归一化。
外部模块调用getBatch(num)方法可以获取指定数量的minibatch数据。
2.模型的建立
主要参考Alexnet，不过做了很大的简化，上代码
IMAGE_SIZE=(128,128)import threadingimport tensorflow as tfimport numpy as npimport cv2import samplerdef addConvLayer(input,shape,name): with tf.name_scope(name) as scope: weight=tf.Variable(tf.truncated_normal(shape,stddev=0.1),name="weight") bias=tf.Variable(tf.zeros([shape[3]]),name="bias") output=tf.nn.relu(tf.nn.conv2d(input,weight,[1,1,1,1],padding="SAME")+bias,name="output") weightLoss=tf.multiply(tf.nn.l2_loss(weight),0.01) tf.add_to_collection("loss",weightLoss) return outputdef addFullLayer(input,shape,name): with tf.name_scope(name) as scope: weight = tf.Variable(tf.truncated_normal(shape, stddev=0.1), name="weight") bias = tf.Variable(tf.zeros([shape[1]]), name="bias") output=tf.nn.relu(tf.matmul(input,weight)+bias,name="output") weightLoss = tf.multiply(tf.nn.l2_loss(weight), 0.01) tf.add_to_collection("loss",weightLoss) return outputinput_image=tf.placeholder(tf.float32,[IMAGE_SIZE[0],IMAGE_SIZE[1],3])output_image=tf.image.per_image_standardization(input_image)inputImage=tf.placeholder(tf.float32,[None,IMAGE_SIZE[0],IMAGE_SIZE[1],3])inputLabel=tf.placeholder(tf.int32,[None])conv1=addConvLayer(inputImage,[7,7,3,8],"conv1")pool1=tf.nn.max_pool(conv1,[1,4,4,1],[1,4,4,1],padding="SAME")conv2=addConvLayer(pool1,[5,5,8,16],"conv2")pool2=tf.nn.max_pool(conv2,[1,4,4,1],[1,4,4,1],padding="SAME")conv3=addConvLayer(pool2,[5,5,16,32],"conv3")pool3=tf.nn.max_pool(conv3,[1,4,4,1],[1,4,4,1],padding="SAME")featureLength=int(IMAGE_SIZE[0]*IMAGE_SIZE[1]/128)print("feature length="+str(featureLength))reShape=tf.reshape(pool3,[-1,featureLength])fc1=addFullLayer(reShape,[featureLength,32],"fc1")fc2=addFullLayer(fc1,[32,3],"fc2")logit=tf.nn.softmax(fc2)outIndex=tf.argmax(fc2,axis=1)cross_entropy=tf.nn.sparse_softmax_cross_entropy_with_logits( logits=fc2,labels=inputLabel,name="cross_entropy")tf.add_to_collection("loss",tf.reduce_mean(cross_entropy))train_op=tf.train.AdamOptimizer().minimize(tf.add_n(tf.get_collection("loss")))accurate=tf.reduce_sum(tf.cast(tf.equal(tf.cast(inputLabel,tf.int64),tf.argmax(fc2,1)),tf.float32))initer=tf.global_variables_initializer()saver=tf.train.Saver()#######################################################################BATCH=50message=""isStart=Falsepath=Nonedef startTrain(): global isStart,path if isStart==True: return isStart=True thread=threading.Thread(target=threadFun,args=[tf.get_default_graph()]) thread.setDaemon(True) thread.start()def setSavePath(savePath): global path path=savePathdef threadFun(graph): global message,isStart,path sess=tf.Session(graph=graph) sess.run([initer]) iterCount = np.zeros([200], np.float32) accurateCount = np.zeros([200], np.float32) curIter=-1 while True: curIter+=1 trainData=sampler.getBatch(BATCH) retAcc=sess.run([accurate,train_op],feed_dict={inputImage:trainData[0],inputLabel:trainData[1]})[0] iterCount[curIter % 200] = 50 accurateCount[curIter % 200] = retAcc message="iter:"+str(curIter)+" cur:"+str(int(retAcc))\ +" accurate:"+str(np.sum(accurateCount)/np.sum(iterCount)) if path!=None: saver.save(sess,path) sess.close() isStart=False path = None message="" return#######################################################################classfySess=Nonedef loadModule(path): global classfySess if classfySess!=None: classfySess.close() classfySess=tf.Session() saver.restore(sess=classfySess,save_path=path)def recognizeImage(image): if classfySess==None: raise Exception("classfy session is not inited") image=cv2.resize(image,IMAGE_SIZE).astype(np.float32) image=classfySess.run([output_image],feed_dict={input_image:image})[0] probability=classfySess.run([logit],feed_dict={inputImage:np.array([image])})[0][0] returnDic={} returnDic[sampler.getLabel(0)]=probability[0] returnDic[sampler.getLabel(1)]=probability[1] returnDic[sampler.getLabel(2)]=probability[2] return returnDic
构建模型的代码在最前面，比较简单，不介绍。模型用了3个卷积层2个全连接层，激活函数采用relu。所有权值均采用了L2正则化。
外部模块调用startTrain()可以开始模型的训练，不过在此之前需要先初始化数据处理模块。调用setSavePath(savePath)可以保存模型到指定路径并结束训练。
外部模块调用loadModule(path)可以载入指定的模型，之后就可以recognizeImage(image)对图像进行识别，这个方法返回一个字典，里面储存图片对应类型的概率。
3.识别算法
方法第一篇已经介绍过了，直接上代码吧！
import classfierMIN_SREACH_SIZE=40subRegionList=[(0,0.6,0,1),(0.4,1,0,1),(0,1,0,0.6),(0,1,0.4,1),(0.2,0.8,0.2,0.8)]def getFaceRegion(image): retDic,retRect=recognizeFace(image,(0,image.shape[0],0,image.shape[1])) if retDic["background"] >= 1.0 / 3: return None return retRect[0]/image.shape[0],retRect[1]/image.shape[0],retRect[2]/image.shape[1],retRect[3]/image.shape[1]def recognizeFace(image,rect): retDic=classfier.recognizeImage(image[rect[0]:rect[1],rect[2]:rect[3]]) if retDic["background"]>=1.0/3: return retDic,rect width=rect[1]-rect[0] height=rect[3]-rect[2] if width<=MIN_SREACH_SIZE or height<=MIN_SREACH_SIZE: return retDic,rect goodDic=retDic goodRect=rect for subRegion in subRegionList: subRect=[] subRect.append(rect[0]+int(subRegion[0]*width)) subRect.append(rect[0] + int(subRegion[1] * width)) subRect.append(rect[2] + int(subRegion[2] * height)) subRect.append(rect[2] + int(subRegion[3] * height)) subRetDic,subRetRect=recognizeFace(image,subRect) if subRetDic["face"]>goodDic["face"]: goodDic=subRetDic goodRect=subRetRect return goodDic,goodRect
外部模块调用getFaceRegion(image)可以获取图片对应的人脸区域，再次之前需要初始化人脸识别模型模块。
recognizeFace(image,rect)方法先判断此图片的类型，如果是“face”或“body”,且图片尺寸大于MIN_SREACH_SIZE，则会按照subRegionList对图片进行分割，递归调用自身，尝试获取“face”概率最高的分割区域
4．总结
没什么好总结的，下一篇介绍运行结果
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