tensorflow74 使用tensorflow dlib opencv做特定人脸识别
来源：互联网 发布：瓣膜成形术 知乎 编辑：程序博客网 时间：2024/06/16 09:07
这个demo效果还是不错的，比单纯的使用opencv判断效率要高。
该blog完整参考 http://tumumu.cn/2017/05/02/deep-learning-face/
01 基本环境
win10 Tensorflow_gpu1.2.1 python3.5.3 dlib opencv
源码：https://github.com/5455945/tensorflow_demo/tree/master/SpecificFaceRecognition
# 该blog完整参考 http://tumumu.cn/2017/05/02/deep-learning-face/# 源码地址:https://github.com/5455945/tensorflow_demo.git# https://github.com/5455945/tensorflow_demo/tree/master/SpecificFaceRecognition# win10 Tensorflow_gpu1.2.1 python3.5.3 dlib opencv# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1# 本实验需要有一个摄像头，笔记本自带的即可# tensorflow_demo\SpecificFaceRecognition\get_my_faces.py 用dlib生成自己脸的jpg图像# tensorflow_demo\SpecificFaceRecognition\get_my_faces_opencv.py 用opencv生成自己脸的jpg图像(效果没有dlib好)# tensorflow_demo\SpecificFaceRecognition\set_other_faces.py 预处理lfw的人脸数据# tensorflow_demo\SpecificFaceRecognition\train_faces.py 人脸识别训练# tensorflow_demo\SpecificFaceRecognition\is_my_face.py 人脸识别测试pip3 install tensorflow==1.2.1pip3 install tensorflow_gpu==1.2.1pip3 install numpy==1.13.1+mklpip3 install opencv-python==3.2.0pip3 install dlib==19.4.0# 一定要注意scikit-learn和scipy的版本pip3 install scikit-learn==0.18.2pip3 install scipy==0.19.102 获取本人图片集
使用get_my_faces.py获取本人的10000张头像照片，保存到./my_faces目录。只需启动get_my_faces.py，坐在电脑前，摆出不同脸部表情和姿势即可。大约1小时左右可采集10000张。 get_my_faces_opencv.py是采用opencv库采集的，速度比dlib的get_my_faces.py快些。dlib效果会好些。
get_my_faces.py
# -*- codeing: utf-8 -*-import cv2import dlibimport osimport sysimport random# 使用摄像头采集某人的人脸数据，保存到./my_faces目录output_dir = './my_faces'size = 64if not os.path.exists(output_dir): os.makedirs(output_dir)# 改变图片的亮度与对比度def relight(img, light=1, bias=0): w = img.shape[1] h = img.shape[0] #image = [] for i in range(0,w): for j in range(0,h): for c in range(3): tmp = int(img[j,i,c]*light + bias) if tmp > 255: tmp = 255 elif tmp < 0: tmp = 0 img[j, i, c] = tmp return img# 使用dlib自带的frontal_face_detector作为我们的特征提取器detector = dlib.get_frontal_face_detector()# 打开摄像头 参数为输入流，可以为摄像头或视频文件camera = cv2.VideoCapture(0)index = 1while True: if (index <= 10000): print('Being processed picture %s' % index) # 从摄像头读取照片 success, img = camera.read() # 转为灰度图片 gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 使用detector进行人脸检测 dets = detector(gray_img, 1) for i, d in enumerate(dets): x1 = d.top() if d.top() > 0 else 0 y1 = d.bottom() if d.bottom() > 0 else 0 x2 = d.left() if d.left() > 0 else 0 y2 = d.right() if d.right() > 0 else 0 face = img[x1:y1, x2:y2] # 调整图片的对比度与亮度， 对比度与亮度值都取随机数，这样能增加样本的多样性 face = relight(face, random.uniform(0.5, 1.5), random.randint(-50, 50)) face = cv2.resize(face, (size,size)) cv2.imshow('image', face) cv2.imwrite(output_dir+'/'+str(index)+'.jpg', face) index += 1 key = cv2.waitKey(30) & 0xff if key == 27: break else: print('Finished!') break03 获取其他人脸图片集
下载http://vis-www.cs.umass.edu/lfw/lfw.tgz人脸数据集。
windows下，可以使用winrar解压，注意要先选[查看文件]，然后再解压，才能解压出所有子目录及文件。
加压后的文件放到./input_img目录下。
然后，使用set_other_people.py处理./input_img目录下的解压文件，把大约13000+张头像预处理到./other_faces目录。
set_other_people.py
# -*- codeing: utf-8 -*-import sysimport osimport cv2import dlib# 下载 lfw.tgz 并解压所有文件到./input_img# wget http://vis-www.cs.umass.edu/lfw/lfw.tgzinput_dir = './input_img'output_dir = './other_faces'size = 64if not os.path.exists(output_dir): os.makedirs(output_dir)# 使用dlib自带的frontal_face_detector作为我们的特征提取器detector = dlib.get_frontal_face_detector()index = 1for (path, dirnames, filenames) in os.walk(input_dir): for filename in filenames: if filename.endswith('.jpg'): print('Being processed picture %s' % index) img_path = path + '/' + filename # 从文件读取图片 img = cv2.imread(img_path) # 转为灰度图片 gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 使用detector进行人脸检测 dets为返回的结果 dets = detector(gray_img, 1) # 使用enumerate 函数遍历序列中的元素以及它们的下标 # 下标i即为人脸序号 # left：人脸左边距离图片左边界的距离 ；right：人脸右边距离图片左边界的距离 # top：人脸上边距离图片上边界的距离 ；bottom：人脸下边距离图片上边界的距离 for i, d in enumerate(dets): x1 = d.top() if d.top() > 0 else 0 y1 = d.bottom() if d.bottom() > 0 else 0 x2 = d.left() if d.left() > 0 else 0 y2 = d.right() if d.right() > 0 else 0 # img[y:y+h, x:x+w] face = img[x1:y1, x2:y2] # 调整图片的尺寸 face = cv2.resize(face, (size, size)) cv2.imshow('image', face) # 保存图片 cv2.imwrite(output_dir + '/' + str(index) + '.jpg', face) index += 1 key = cv2.waitKey(30) & 0xff if key == 27: sys.exit(0)04 训练模型
使用train_faces.py来训练模型，模型保持到./model目录下
train_faces.py
# -*- codeing: utf-8 -*-import tensorflow as tfimport cv2import numpy as npimport osimport randomimport sysfrom sklearn.model_selection import train_test_split# 使用./my_faces和./other_faces中的人脸数据训练，保持模型到./model中my_faces_path = './my_faces'other_faces_path = './other_faces'model_path = './model'if not os.path.exists(model_path): os.makedirs(model_path)size = 64imgs = []labs = []def getPaddingSize(img): h, w, _ = img.shape top, bottom, left, right = (0, 0, 0, 0) longest = max(h, w) if w < longest: tmp = longest - w # //表示整除符号 left = tmp // 2 right = tmp - left elif h < longest: tmp = longest - h top = tmp // 2 bottom = tmp - top else: pass return top, bottom, left, rightdef readData(path , h = size, w = size): for filename in os.listdir(path): if filename.endswith('.jpg'): filename = path + '/' + filename img = cv2.imread(filename) top, bottom, left, right = getPaddingSize(img) # 将图片放大， 扩充图片边缘部分 img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value = [0, 0, 0]) img = cv2.resize(img, (h, w)) imgs.append(img) labs.append(path)readData(my_faces_path)readData(other_faces_path)# 将图片数据与标签转换成数组imgs = np.array(imgs)labs = np.array([[0, 1] if lab == my_faces_path else [1, 0] for lab in labs])# 随机划分测试集与训练集train_x, test_x, train_y, test_y = train_test_split(imgs, labs, test_size = 0.05, random_state = random.randint(0, 100))# 参数：图片数据的总数，图片的高、宽、通道train_x = train_x.reshape(train_x.shape[0], size, size, 3)test_x = test_x.reshape(test_x.shape[0], size, size, 3)# 将数据转换成小于1的数train_x = train_x.astype('float32') / 255.0test_x = test_x.astype('float32') / 255.0print('train size: %s, test size: %s' % (len(train_x), len(test_x)))# 图片块，每次取100张图片batch_size = 100num_batch = len(train_x) // batch_sizex = tf.placeholder(tf.float32, [None, size, size, 3])y_ = tf.placeholder(tf.float32, [None, 2])keep_prob_5 = tf.placeholder(tf.float32)keep_prob_75 = tf.placeholder(tf.float32)def weightVariable(shape): init = tf.random_normal(shape, stddev = 0.01) return tf.Variable(init)def biasVariable(shape): init = tf.random_normal(shape) return tf.Variable(init)def conv2d(x, W): return tf.nn.conv2d(x, W, strides = [1, 1, 1, 1], padding = 'SAME')def maxPool(x): return tf.nn.max_pool(x, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'SAME')def dropout(x, keep): return tf.nn.dropout(x, keep)def cnnLayer(): # 第一层 W1 = weightVariable([3, 3, 3, 32]) # 卷积核大小(3,3)， 输入通道(3)， 输出通道(32) b1 = biasVariable([32]) # 卷积 conv1 = tf.nn.relu(conv2d(x, W1) + b1) # 池化 pool1 = maxPool(conv1) # 减少过拟合，随机让某些权重不更新 drop1 = dropout(pool1, keep_prob_5) # 第二层 W2 = weightVariable([3, 3, 32, 64]) b2 = biasVariable([64]) conv2 = tf.nn.relu(conv2d(drop1, W2) + b2) pool2 = maxPool(conv2) drop2 = dropout(pool2, keep_prob_5) # 第三层 W3 = weightVariable([3, 3, 64, 64]) b3 = biasVariable([64]) conv3 = tf.nn.relu(conv2d(drop2, W3) + b3) pool3 = maxPool(conv3) drop3 = dropout(pool3, keep_prob_5) # 全连接层 Wf = weightVariable([8 * 8 * 64, 512]) bf = biasVariable([512]) drop3_flat = tf.reshape(drop3, [-1, 8 * 8 * 64]) dense = tf.nn.relu(tf.matmul(drop3_flat, Wf) + bf) dropf = dropout(dense, keep_prob_75) # 输出层 Wout = weightVariable([512, 2]) bout = weightVariable([2]) #out = tf.matmul(dropf, Wout) + bout out = tf.add(tf.matmul(dropf, Wout), bout) return outdef cnnTrain(): out = cnnLayer() cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = out, labels = y_)) train_step = tf.train.AdamOptimizer(0.01).minimize(cross_entropy) # 比较标签是否相等，再求的所有数的平均值，tf.cast(强制转换类型) accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(out, 1), tf.argmax(y_, 1)), tf.float32)) # 将loss与accuracy保存以供tensorboard使用 tf.summary.scalar('loss', cross_entropy) tf.summary.scalar('accuracy', accuracy) merged_summary_op = tf.summary.merge_all() # 数据保存器的初始化 saver = tf.train.Saver() with tf.Session() as sess: sess.run(tf.global_variables_initializer()) summary_writer = tf.summary.FileWriter('./tmp', graph = tf.get_default_graph()) for n in range(10): # 每次取128(batch_size)张图片 for i in range(num_batch): batch_x = train_x[i*batch_size : (i + 1) * batch_size] batch_y = train_y[i*batch_size : (i + 1) * batch_size] # 开始训练数据，同时训练三个变量，返回三个数据 _, loss, summary = sess.run([train_step, cross_entropy, merged_summary_op], feed_dict = {x:batch_x,y_:batch_y, keep_prob_5:0.5, keep_prob_75:0.75}) summary_writer.add_summary(summary, n * num_batch + i) # 打印损失 # print("loss ", n*num_batch + i, loss) if (n * num_batch + i) % 100 == 0: # 获取测试数据的准确率 acc = accuracy.eval({x:test_x, y_:test_y, keep_prob_5:1.0, keep_prob_75:1.0}) print(n * num_batch + i, "acc:", acc, " loss:", loss) # 准确率大于0.98时保存并退出 if acc > 0.98 and n > 2: saver.save(sess, model_path + '/train_faces.model', global_step = n * num_batch + i) sys.exit(0) print('accuracy less 0.98, exited!')cnnTrain()'''train size: 22782, test size: 12000 acc: 0.560833 loss: 0.760013100 acc: 0.923333 loss: 0.280099200 acc: 0.945833 loss: 0.255821300 acc: 0.953333 loss: 0.246161400 acc: 0.958333 loss: 0.113214500 acc: 0.9625 loss: 0.183178600 acc: 0.964167 loss: 0.119886700 acc: 0.971667 loss: 0.134483800 acc: 0.943333 loss: 0.142579900 acc: 0.953333 loss: 0.1438541000 acc: 0.958333 loss: 0.1671311100 acc: 0.965 loss: 0.104531200 acc: 0.975833 loss: 0.1325731300 acc: 0.976667 loss: 0.1919871400 acc: 0.9825 loss: 0.0590191'''05 使用模型进行识别
使用is_my_face.py来验证模型，检测到是自己的脸时，返回true。
is_my_face.py
# -*- codeing: utf-8 -*-import tensorflow as tfimport cv2import dlibimport numpy as npimport osimport randomimport sysfrom sklearn.model_selection import train_test_split# 使用摄像头采集人脸，使用./model中的模型检测是否为特定的人脸my_faces_path = './my_faces'other_faces_path = './other_faces'model_path = './model'size = 64imgs = []labs = []def getPaddingSize(img): h, w, _ = img.shape top, bottom, left, right = (0, 0, 0, 0) longest = max(h, w) if w < longest: tmp = longest - w # //表示整除符号 left = tmp // 2 right = tmp - left elif h < longest: tmp = longest - h top = tmp // 2 bottom = tmp - top else: pass return top, bottom, left, rightdef readData(path , h = size, w = size): for filename in os.listdir(path): if filename.endswith('.jpg'): filename = path + '/' + filename img = cv2.imread(filename) top,bottom,left,right = getPaddingSize(img) # 将图片放大， 扩充图片边缘部分 img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value = [0, 0, 0]) img = cv2.resize(img, (h, w)) imgs.append(img) labs.append(path)readData(my_faces_path)readData(other_faces_path)# 将图片数据与标签转换成数组imgs = np.array(imgs)labs = np.array([[0, 1] if lab == my_faces_path else [1, 0] for lab in labs])# 随机划分测试集与训练集train_x, test_x, train_y, test_y = train_test_split(imgs, labs, test_size = 0.05, random_state = random.randint(0, 100))# 参数：图片数据的总数，图片的高、宽、通道train_x = train_x.reshape(train_x.shape[0], size, size, 3)test_x = test_x.reshape(test_x.shape[0], size, size, 3)# 将数据转换成小于1的数train_x = train_x.astype('float32') / 255.0test_x = test_x.astype('float32') / 255.0print('train size:%s, test size:%s' % (len(train_x), len(test_x)))# 图片块，每次取128张图片batch_size = 128num_batch = len(train_x) // 128x = tf.placeholder(tf.float32, [None, size, size, 3])y_ = tf.placeholder(tf.float32, [None, 2])keep_prob_5 = tf.placeholder(tf.float32)keep_prob_75 = tf.placeholder(tf.float32)def weightVariable(shape): init = tf.random_normal(shape, stddev = 0.01) return tf.Variable(init)def biasVariable(shape): init = tf.random_normal(shape) return tf.Variable(init)def conv2d(x, W): return tf.nn.conv2d(x, W, strides = [1, 1, 1, 1], padding = 'SAME')def maxPool(x): return tf.nn.max_pool(x, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'SAME')def dropout(x, keep): return tf.nn.dropout(x, keep)def cnnLayer(): # 第一层 W1 = weightVariable([3, 3, 3, 32]) # 卷积核大小(3,3)， 输入通道(3)， 输出通道(32) b1 = biasVariable([32]) # 卷积 conv1 = tf.nn.relu(conv2d(x, W1) + b1) # 池化 pool1 = maxPool(conv1) # 减少过拟合，随机让某些权重不更新 drop1 = dropout(pool1, keep_prob_5) # 第二层 W2 = weightVariable([3, 3, 32, 64]) b2 = biasVariable([64]) conv2 = tf.nn.relu(conv2d(drop1, W2) + b2) pool2 = maxPool(conv2) drop2 = dropout(pool2, keep_prob_5) # 第三层 W3 = weightVariable([3, 3, 64, 64]) b3 = biasVariable([64]) conv3 = tf.nn.relu(conv2d(drop2, W3) + b3) pool3 = maxPool(conv3) drop3 = dropout(pool3, keep_prob_5) # 全连接层 Wf = weightVariable([8*16*32, 512]) bf = biasVariable([512]) drop3_flat = tf.reshape(drop3, [-1, 8*16*32]) dense = tf.nn.relu(tf.matmul(drop3_flat, Wf) + bf) dropf = dropout(dense, keep_prob_75) # 输出层 Wout = weightVariable([512, 2]) bout = weightVariable([2]) out = tf.add(tf.matmul(dropf, Wout), bout) return outoutput = cnnLayer()predict = tf.argmax(output, 1)saver = tf.train.Saver()sess = tf.Session()saver.restore(sess, tf.train.latest_checkpoint(model_path))def is_my_face(image): res = sess.run(predict, feed_dict={x: [image/255.0], keep_prob_5:1.0, keep_prob_75: 1.0}) if res[0] == 1: return True else: return False# 使用dlib自带的frontal_face_detector作为我们的特征提取器detector = dlib.get_frontal_face_detector()cam = cv2.VideoCapture(0)while True: _, img = cam.read() gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) dets = detector(gray_image, 1) if not len(dets): # print('Can`t get face.') cv2.imshow('img', img) key = cv2.waitKey(30) & 0xff if key == 27: sys.exit(0) for i, d in enumerate(dets): x1 = d.top() if d.top() > 0 else 0 y1 = d.bottom() if d.bottom() > 0 else 0 x2 = d.left() if d.left() > 0 else 0 y2 = d.right() if d.right() > 0 else 0 face = img[x1:y1, x2:y2] # 调整图片的尺寸 face = cv2.resize(face, (size, size)) print('Is this my face? %s' % is_my_face(face)) cv2.rectangle(img, (x2, x1), (y2, y1), (255, 0, 0), 3) cv2.imshow('image', img) key = cv2.waitKey(30) & 0xff if key == 27: sys.exit(0)sess.close()'''train size:22782, test size:1200Is this my face? TrueIs this my face? TrueIs this my face? True...'''06 关于opencv获取特定人脸数据
这个使用opencv的代码还需要完善，需要多个分类器组合使用，这里仅仅给出了一个分类器haarcascade_frontalface_default.xml，效果不是很好。opencv自带的分类器在opencv源码的data目录下面。
get_my_faces_opencv.py
import cv2import osimport sysimport random# 这个使用opencv的代码还需要完善# 需要更多的分类器，并且判断准确的人脸后才保存# 这里贴出来仅供参考out_dir = './my_faces1'if not os.path.exists(out_dir): os.makedirs(out_dir)# 改变亮度与对比度def relight(img, alpha=1, bias=0): w = img.shape[1] h = img.shape[0] #image = [] for i in range(0,w): for j in range(0,h): for c in range(3): tmp = int(img[j,i,c]*alpha + bias) if tmp > 255: tmp = 255 elif tmp < 0: tmp = 0 img[j,i,c] = tmp return img# 获取分类器haar = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')# 打开摄像头 参数为输入流，可以为摄像头或视频文件camera = cv2.VideoCapture(0)n = 1while 1: if (n <= 10000): print('It`s processing %s image.' % n) # 读帧 success, img = camera.read() gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) faces = haar.detectMultiScale(gray_img, 1.3, 5) for f_x, f_y, f_w, f_h in faces: face = img[f_y:f_y+f_h, f_x:f_x+f_w] face = cv2.resize(face, (64,64)) ''' if n % 3 == 1: face = relight(face, 1, 50) elif n % 3 == 2: face = relight(face, 0.5, 0) ''' face = relight(face, random.uniform(0.5, 1.5), random.randint(-50, 50)) cv2.imshow('img', face) cv2.imwrite(out_dir+'/'+str(n)+'.jpg', face) n+=1 key = cv2.waitKey(30) & 0xff if key == 27: break else: break阅读全文
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