Python超详细实时人脸活物识别基于TensorFlow_CNN(卷积神经网络)
活物识别在生活中有广泛应用，设想如果一个人拿着你的照片去刷脸支付，是不是你钱包就要空了。在生活中，有多种方法进行活物（活体）识别，比如动作指令活体检测（让你眨眼睛等）。这里我们是用CNN（卷积神经网络）来对图片进行分析来识别是否为真人。
最终效果:
首先，我们将我们的项目大致分成三步：
1. 产生训练集
2. 训练模型
3. 验证数据
1. 产生训练集
我们知道，视频是由每一帧组成的，换句话说就是由很多张图片组成的。因此我们如果想要产生训练集，首先要将人脸逐帧截图，分别产生真实（Real）和非真实（Fake）的数据集，具体方法如下：
我们先看一下两段视频
一段是手机自拍（Real），另一段是拿另一个手机录屏（Fake），然后我们导入这两个视频然后逐帧截取人脸。此时，我们需要 face_recognition的功能来识别人脸的具体位置。由于本文章是专注于活物识别的，面部识别就直接使用别人的封包了，具体代码如下：
import cv2
import os
import numpy as np
import face_recognition
from PIL import Image
inputpath_real='video/real.mp4'
inputpath_fake='video/fake.mp4'
outputpath_real='train/real/'
outputpath_fake='train/fake/'
'''
inputpathtest_real='video/real_test.mp4'
inputpathtest_fake='video/fake_test.mp4'
outputpathtest_real='train/real_test/'
outputpathtest_fake='train/fake_test/'
'''
def cut_img (inputpath,outputpath,type,width,height):
vs = cv2.VideoCapture(inputpath)
read = 0
while True:
(grabbed, frame) = vs.read()
if not grabbed:
print('This frame is not grabbed')
break
read +=1
rgb_frame = frame[:, :, ::-1]
face_locations = face_recognition.face_locations(rgb_frame)
for face_location in face_locations:
top, right, bottom, left = face_location
cropped= frame [top:bottom, left:right]
img_name=outputpath+'%s_%d.png' %(type,read)
cv2.imwrite(img_name, cropped,[int(cv2.IMWRITE_PNG_COMPRESSION), 9] )
img = Image.open(img_name)
new_image = img.resize((width, height), Image.BILINEAR) #Resize to 50*50
new_image.save(os.path.join(outputpath, os.path.basename(img_name)))
if __name__ == '__main__':
cut_img(inputpath_real,outputpath_real,'real',50,50)
cut_img(inputpath_fake,outputpath_fake,'fake',50,50)
'''
cut_img(inputpathtest_real, outputpathtest_real, 'real_test', 50, 50)
cut_img(inputpathtest_fake, outputpathtest_fake, 'fake_test', 50, 50)
'''
被注释起来的部分是用来产生测试数据的，有兴趣的可以再录两段测试视频（共四段）。代码很简单，我就不解释了，最后会产生如下效果：
这里，所有的图片（约400张）都被resize到50*50，方便导入CNN。这样我们的训练集就产生了。
2. 训练模型
下面就是我们的重头戏，训练模型了。 我们使用TensorFlow的CNN来构件我们的神经网络：
我们先看一下我们的文件路径等设置：
import os
from PIL import Image
import numpy as np
import tensorflow as tf
import face_recognition
import cv2
#文件路径设置（address）
data_dir_real='train/real/'
data_dir_fake='train/fake/'
test='video/test.mp4'
output_test='train/test_output/'
model_path='train/' # CNN model save address
train= Ture # Ture: Training mode ，False: Validation mode
当train=True是为训练模式，False为验证模式。 data_dir_real和data_dir_fake为之前产生图片的地址，test是用来验证模型的视频路径，output_test则是验证视频产生的人脸图片的路径。
首先第一步读取数据：
def read_data(data_dir_real,data_dir_fake):
datas = []
labels = []
fpaths = []
# Read data of real
for fname in os.listdir(data_dir_real):
fpath = os.path.join(data_dir_real, fname) # Record image name and address
fpaths.append(fpath)
image = Image.open(fpath) # Open image
data = np.array(image) / 255.0 # 归一化(Normalization)
datas.append(data)
labels.append(0) # Take real as 0
# Read data of fake
for fname in os.listdir(data_dir_fake):
fpath = os.path.join(data_dir_fake, fname)
fpaths.append(fpath)
image = Image.open(fpath)
data = np.array(image) / 255.0 # 归一化(Normalization)
datas.append(data)
labels.append(1) # Take fake as 1
datas = np.array(datas)
labels = np.array(labels)
return fpaths, datas,labels
fpaths, datas,labels = read_data(data_dir_real,data_dir_fake) # Load training set
构建CNN模型：
定义容器：
# Placeholder
datas_placeholder = tf.placeholder(tf.float32,[None, 50,50, 3])
labels_placeholder = tf.placeholder(tf.int32, [None])
dropout_placeholdr = tf.placeholder(tf.float32)
众所周知，图片是由像素组成的，每个像素有3个RGB颜色，图片大小为50*50，因此读取数据的size为[None, 50,50, 3]（None表示图片数目为不确定）
定义卷积层与池化：
# First layer and pooling, 20 kernel, size of kernel is 5
conv0 = tf.layers.conv2d(datas_placeholder, 20, 5, activation=tf.nn.relu)
# pooling filter is 2x2，stride is 2x2
pool0 = tf.layers.max_pooling2d(conv0, [2, 2], [2, 2])
# Second layer
conv1 = tf.layers.conv2d(pool0, 40, 4, activation=tf.nn.relu)
pool1 = tf.layers.max_pooling2d(conv1, [2, 2], [2, 2])
我们使用了2层，并有数个卷积核，卷积核大小为5*5
全连接层：
# Fully Connected Layer
flatten = tf.layers.flatten(pool1)
fc = tf.layers.dense(flatten, 400, activation=tf.nn.relu)
全连接层可以将我们的CNN展开，就好比CNN是眼睛，后面的NN为大脑
加上dropuout防止过拟合，并产生输出层：
# DropOut to prevent overfitting
dropout_fc = tf.layers.dropout(fc, dropout_placeholdr)
logits = tf.layers.dense(dropout_fc, 2,activation=None)
predicted_labels = tf.arg_max(logits, 1)
定义损失函数和优化器并定义模型保存器：
# Mean loss
losses = tf.nn.softmax_cross_entropy_with_logits(
labels=tf.one_hot(labels_placeholder, 2),
logits=logits)
mean_loss = tf.reduce_mean(losses)
# Optimizer
optimizer = tf.train.AdamOptimizer(learning_rate=1e-2).minimize(losses)
saver = tf.train.Saver()
这个时候我们的CNN模型就已经构建好了，我们就可以进入训练模式了：
with tf.Session() as sess:
if train:
print('Training')
sess.run(tf.global_variables_initializer()) # initialization
train_feed = {
datas_placeholder: datas,
labels_placeholder: labels,
dropout_placeholdr: 0.5
} # feed data
for step in range(150): # iterate 150
_, mean_loss_val = sess.run([optimizer, mean_loss],feed_dict=train_feed)
if step % 10 == 0:
print("step = {}\tmean loss = {}".format(step,mean_loss_val)) # print mean loss every 10 step
saver.save(sess, model_path) # save model
print('Training finished，save')
这里有一点要注意的地方，我这里选择的迭代150次，每10次计算一次平均损失。你也可以自己改变我之前的模型结构，然后手动选择迭代次数，但需要注意的是，当你训练完毕的时候，要注意他的平均损失足够的低，0.01以下，不然会非常不准。
好了这个时候，我们的模型已经训练完毕了，我们就要开始验证我们的模型了。
3. 验证数据
else:
print('Validation')
vs = cv2.VideoCapture(test) # Load video
saver.restore(sess, model_path) # load model
read = 0
while True:
(grabbed, frame) = vs.read()
if not grabbed:
print('This frame is not grabbed')
break
read += 1
rgb_frame = frame[:, :, ::-1]
face_locations = face_recognition.face_locations(rgb_frame) # Find faces
for face_location in face_locations:
datas = []
top, right, bottom, left = face_location # Obtain face location
cropped = frame[top:bottom, left:right]
img_name = output_test + 'test_%d.png' % (read)
cv2.imwrite(img_name, cropped, [int(cv2.IMWRITE_PNG_COMPRESSION), 9]) # Cut face images
img = Image.open(img_name) # Open Img
new_image = img.resize((50, 50), Image.BILINEAR) # Resize to 50*50
new_image.save(os.path.join(output_test, os.path.basename(img_name))) # save resize img
data = np.array(new_image) / 255.0
datas.append(data)
datas = np.array(datas)
label_name_dict = {
0: "Real",
1: "Fake"
} # Create dir
test_feed_dict = {
datas_placeholder: datas,
dropout_placeholdr: 0
}
predicted_labels_val = sess.run(predicted_labels, feed_dict=test_feed_dict) # Feed data
predicted_label_name = label_name_dict[int(predicted_labels_val)] # Label transformation
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 3) # Create rectangle to label face
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, predicted_label_name, (left + 10, bottom), font, 2, (255, 255, 255)) # put text of face result
cv2.imshow('Video', frame)
if cv2.waitKey(1) & 0xFF == ord('q'): # Type q to quit
break
按Q键退出，大致效果如下：
这里呢，训练集为我本人，测试集为我室友，来充分验证模型的泛化性。但经本人亲测，由于训练集可能不足，导致整个预测的准确率跟光线强弱十分相关，因此建议大家在学习时候，尽量在相同的光照环境下进行测试，并手机在录制时候也尽量保持设置一致，否则可能误差会较大。这里，由于我室友隐私问题，我不放出视频文件，仅提供我个人照片文件供大家学习。建议大家自己录制视频进行学习，有问题也请积极留言。
最终代码如下：
产生训练数据：
import cv2
import os
import numpy as np
import face_recognition
from PIL import Image
inputpath_real='video/real.mp4'
inputpath_fake='video/fake.mp4'
outputpath_real='train/real/'
outputpath_fake='train/fake/'
'''
inputpathtest_real='video/real_test.mp4'
inputpathtest_fake='video/fake_test.mp4'
outputpathtest_real='train/real_test/'
outputpathtest_fake='train/fake_test/'
'''
def cut_img (inputpath,outputpath,type,width,height):
vs = cv2.VideoCapture(inputpath)
read = 0
while True:
(grabbed, frame) = vs.read()
if not grabbed:
print('This frame is not grabbed')
break
read +=1
rgb_frame = frame[:, :, ::-1]
face_locations = face_recognition.face_locations(rgb_frame)
for face_location in face_locations:
top, right, bottom, left = face_location
cropped= frame [top:bottom, left:right]
img_name=outputpath+'%s_%d.png' %(type,read)
cv2.imwrite(img_name, cropped,[int(cv2.IMWRITE_PNG_COMPRESSION), 9] )
img = Image.open(img_name)
new_image = img.resize((width, height), Image.BILINEAR)
new_image.save(os.path.join(outputpath, os.path.basename(img_name)))
if __name__ == '__main__':
cut_img(inputpath_real,outputpath_real,'real',50,50)
cut_img(inputpath_fake,outputpath_fake,'fake',50,50)
'''
cut_img(inputpathtest_real, outputpathtest_real, 'real_test', 50, 50)
cut_img(inputpathtest_fake, outputpathtest_fake, 'fake_test', 50, 50)
'''
训练及验证代码：
import os
from PIL import Image
import numpy as np
import tensorflow as tf
import face_recognition
import cv2
#文件路径设置（address）
data_dir_real='train/real/'
data_dir_fake='train/fake/'
test='video/test.mp4'
output_test='train/test_output/'
model_path='train/'
train= False # Ture: Training mode ，False: Validation mode
# 读取训练集（Load training set)
def read_data(data_dir_real,data_dir_fake):
datas = []
labels = []
fpaths = []
# Read data of real
for fname in os.listdir(data_dir_real):
fpath = os.path.join(data_dir_real, fname) # Record image name and address
fpaths.append(fpath)
image = Image.open(fpath) # Open image
data = np.array(image) / 255.0 # 归一化(Normalization)
datas.append(data)
labels.append(0) # Take real as 0
# Read data of fake
for fname in os.listdir(data_dir_fake):
fpath = os.path.join(data_dir_fake, fname)
fpaths.append(fpath)
image = Image.open(fpath)
data = np.array(image) / 255.0 # 归一化(Normalization)
datas.append(data)
labels.append(1) # Take fake as 1
datas = np.array(datas)
labels = np.array(labels)
return fpaths, datas,labels
fpaths, datas,labels = read_data(data_dir_real,data_dir_fake) # Load training set
# Placeholder
datas_placeholder = tf.placeholder(tf.float32,[None, 50,50, 3])
labels_placeholder = tf.placeholder(tf.int32, [None])
dropout_placeholdr = tf.placeholder(tf.float32)
# First layer and pooling, 20 kernel, size of kernel is 5
conv0 = tf.layers.conv2d(datas_placeholder, 20, 5, activation=tf.nn.relu)
# pooling filter is 2x2，stride is 2x2
pool0 = tf.layers.max_pooling2d(conv0, [2, 2], [2, 2])
# Second layer
conv1 = tf.layers.conv2d(pool0, 40, 4, activation=tf.nn.relu)
pool1 = tf.layers.max_pooling2d(conv1, [2, 2], [2, 2])
# Fully Connected Layer
flatten = tf.layers.flatten(pool1)
fc = tf.layers.dense(flatten, 400, activation=tf.nn.relu)
# DropOut to prevent overfitting
dropout_fc = tf.layers.dropout(fc, dropout_placeholdr)
logits = tf.layers.dense(dropout_fc, 2,activation=None)
predicted_labels = tf.arg_max(logits, 1)
# Mean loss
losses = tf.nn.softmax_cross_entropy_with_logits(
labels=tf.one_hot(labels_placeholder, 2),
logits=logits)
mean_loss = tf.reduce_mean(losses)
# Optimizer
optimizer = tf.train.AdamOptimizer(learning_rate=1e-2).minimize(losses)
saver = tf.train.Saver()
with tf.Session() as sess:
if train:
print('Training')
sess.run(tf.global_variables_initializer()) # initialization
train_feed = {
datas_placeholder: datas,
labels_placeholder: labels,
dropout_placeholdr: 0.5
} # feed data
for step in range(150): # iterate 150
_, mean_loss_val = sess.run([optimizer, mean_loss],feed_dict=train_feed)
if step % 10 == 0:
print("step = {}\tmean loss = {}".format(step,mean_loss_val)) # print mean loss every 10 step
saver.save(sess, model_path) # save model
print('Training finished，save')
else:
print('Validation')
vs = cv2.VideoCapture(test) # Load video
saver.restore(sess, model_path) # load model
read = 0
while True:
(grabbed, frame) = vs.read()
if not grabbed:
print('This frame is not grabbed')
break
read += 1
rgb_frame = frame[:, :, ::-1]
face_locations = face_recognition.face_locations(rgb_frame) # Find faces
for face_location in face_locations:
datas = []
top, right, bottom, left = face_location # Obtain face location
cropped = frame[top:bottom, left:right]
img_name = output_test + 'test_%d.png' % (read)
cv2.imwrite(img_name, cropped, [int(cv2.IMWRITE_PNG_COMPRESSION), 9]) # Cut face images
img = Image.open(img_name) # Open Img
new_image = img.resize((50, 50), Image.BILINEAR) # Resize to 50*50
new_image.save(os.path.join(output_test, os.path.basename(img_name))) # save resize img
data = np.array(new_image) / 255.0
datas.append(data)
datas = np.array(datas)
label_name_dict = {
0: "Real",
1: "Fake"
} # Create dir
test_feed_dict = {
datas_placeholder: datas,
dropout_placeholdr: 0
}
predicted_labels_val = sess.run(predicted_labels, feed_dict=test_feed_dict) # Feed data
predicted_label_name = label_name_dict[int(predicted_labels_val)] # Label transformation
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 3) # Create rectangle to label face
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, predicted_label_name, (left + 10, bottom), font, 2, (255, 255, 255)) # put text of face result
cv2.imshow('Video', frame)
if cv2.waitKey(1) & 0xFF == ord('q'): # Type q to quit
break
完整代码和文件可以查看我的Github代码链接
https://github.com/Connor666/Tutorial