案例▍自己动手构建一个人脸识别模型----看她是否认识您

import sysimport osimport cv2import dlib

input_dir = './data/face_recog/my_faces'output_dir = './data/my_faces'size = 64

if not os.path.exists(output_dir):os.makedirs(output_dir)

%matplotlib inlineindex = 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 0y1 = d.bottom() if d.bottom() > 0 else 0x2 = d.left() if d.left() > 0 else 0y2 = 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) & 0xffif key == 27:sys.exit(0)

input_dir = './data/face_recog/other_faces'output_dir = './data/other_faces'size = 64

if not os.path.exists(output_dir):os.makedirs(output_dir)

import tensorflow as tfimport cv2import numpy as npimport osimport randomimport sysfrom sklearn.model_selection import train_test_split

my_faces_path = './data/my_faces'other_faces_path = './data/other_faces'size = 64

imgs = []labs = []
#重新创建图形变量tf.reset_default_graph()
def getPaddingSize(img):h, w, _ = img.shapetop, 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 // 2right = tmp - leftelif h < longest: tmp = longest - h top = tmp // 2 bottom = tmp - top else: pass return top, bottom, left, right def 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')/255.0 test_x = test_x.astype('float32')/255.0 print('train size:%s, test size:%s' % (len(train_x), len(test_x))) # 图片块，每次取100张图片 batch_size = 20 num_batch = len(train_x) // batch_size x = 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.matmul(dropf, Wout) + bout out = tf.add(tf.matmul(dropf, Wout), bout) return out def 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(n*num_batch+i, loss) if (n*num_batch+i) % 40 == 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) # 由于数据不多，这里设为准确率大于0.80时保存并退出 if acc > 0.8 and n > 2:#saver.save(sess, './train_face_model/train_faces.model',global_step=n*num_batch+i)saver.save(sess, './train_face_model/train_faces.model')#sys.exit(0)#print('accuracy less 0.80, exited!')
cnnTrain()

%matplotlib inline
input_dir='./data/face_recog/test_faces'index=1
output = cnnLayer()predict = tf.argmax(output, 1)
#先加载 meta graph并恢复权重变量saver = tf.train.import_meta_graph('./train_face_model/train_faces.model.meta')sess = tf.Session()
saver.restore(sess, tf.train.latest_checkpoint('./train_face_model/'))#saver.restore(sess,tf.train.latest_checkpoint('./my_test_model/'))
def is_my_face(image):sess.run(tf.global_variables_initializer())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 Trueelse: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()for (path, dirnames, filenames) in os.walk(input_dir):for filename in filenames:if filename.endswith('.jpg'):print('Being processed picture %s' % index)index+=1img_path = path+'/'+filename# 从文件读取图片img = cv2.imread(img_path)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) & 0xffif key == 27:sys.exit(0)for i, d in enumerate(dets):x1 = d.top() if d.top() > 0 else 0y1 = d.bottom() if d.bottom() > 0 else 0x2 = d.left() if d.left() > 0 else 0y2 = d.right() if d.right() > 0 else 0face = 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) & 0xffif key == 27:sys.exit(0)
sess.close()