CNN tensorflow 人脸识别

数据材料

这是一个小型的人脸数据库，一共有40个人，每个人有10张照片作为样本数据。这些图片都是黑白照片，意味着这些图片都只有灰度0-255，没有rgb三通道。于是我们需要对这张大图片切分成一个个的小脸。整张图片大小是1190 × 942，一共有20 × 20张照片。那么每张照片的大小就是（1190 / 20）× （942 / 20）= 57 × 47 （大约，以为每张图片之间存在间距）。

问题解决：

10类样本，利用CNN训练可以分类10类数据的神经网络，与手写字符识别类似olivettifaces.gif

 

#coding=utf-8
#http://www.jianshu.com/p/3e5ddc44aa56
#tensorflow 1.3.1
#python 3.6
import os
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import matplotlib.patches as patches
import numpy
from PIL import Image

#获取dataset
def load_data(dataset_path):
img = Image.open(dataset_path)
# 定义一个20 × 20的训练样本，一共有40个人，每个人都10张样本照片
img_ndarray = np.asarray(img, dtype='float64') / 256
#img_ndarray = np.asarray(img, dtype='float32') / 32

# 记录脸数据矩阵，57 * 47为每张脸的像素矩阵
faces = np.empty((400, 57 * 47))

for row in range(20):
for column in range(20):
faces[20 * row + column] = np.ndarray.flatten(
img_ndarray[row * 57: (row + 1) * 57, column * 47 : (column + 1) * 47]
)

label = np.zeros((400, 40))
for i in range(40):
label[i * 10: (i + 1) * 10, i] = 1

# 将数据分成训练集，验证集，测试集
train_data = np.empty((320, 57 * 47))
train_label = np.zeros((320, 40))
vaild_data = np.empty((40, 57 * 47))
vaild_label = np.zeros((40, 40))
test_data = np.empty((40, 57 * 47))
test_label = np.zeros((40, 40))

for i in range(40):
train_data[i * 8: i * 8 + 8] = faces[i * 10: i * 10 + 8]
train_label[i * 8: i * 8 + 8] = label[i * 10: i * 10 + 8]

vaild_data[i] = faces[i * 10 + 8]
vaild_label[i] = label[i * 10 + 8]

test_data[i] = faces[i * 10 + 9]
test_label[i] = label[i * 10 + 9]

train_data = train_data.astype('float32')
vaild_data = vaild_data.astype('float32')
test_data = test_data.astype('float32')

return [
(train_data, train_label),
(vaild_data, vaild_label),
(test_data, test_label)
]

def convolutional_layer(data, kernel_size, bias_size, pooling_size):
kernel = tf.get_variable("conv", kernel_size, initializer=tf.random_normal_initializer())
bias = tf.get_variable('bias', bias_size, initializer=tf.random_normal_initializer())

conv = tf.nn.conv2d(data, kernel, strides=[1, 1, 1, 1], padding='SAME')
linear_output = tf.nn.relu(tf.add(conv, bias))
pooling = tf.nn.max_pool(linear_output, ksize=pooling_size, strides=pooling_size, padding="SAME")
return pooling

def linear_layer(data, weights_size, biases_size):
weights = tf.get_variable("weigths", weights_size, initializer=tf.random_normal_initializer())
biases = tf.get_variable("biases", biases_size, initializer=tf.random_normal_initializer())
return tf.add(tf.matmul(data, weights), biases)

def convolutional_neural_network(data):
# 根据类别个数定义最后输出层的神经元
n_ouput_layer = 40

kernel_shape1=[5, 5, 1, 32]
kernel_shape2=[5, 5, 32, 64]
full_conn_w_shape = [15 * 12 * 64, 1024]
out_w_shape = [1024, n_ouput_layer]

bias_shape1=[32]
bias_shape2=[64]
full_conn_b_shape = [1024]
out_b_shape = [n_ouput_layer]

data = tf.reshape(data, [-1, 57, 47, 1])

# 经过第一层卷积神经网络后，得到的张量shape为：[batch, 29, 24, 32]
with tf.variable_scope("conv_layer1") as layer1:
layer1_output = convolutional_layer(
data=data,
kernel_size=kernel_shape1,
bias_size=bias_shape1,
pooling_size=[1, 2, 2, 1]
)
# 经过第二层卷积神经网络后，得到的张量shape为：[batch, 15, 12, 64]
with tf.variable_scope("conv_layer2") as layer2:
layer2_output = convolutional_layer(
data=layer1_output,
kernel_size=kernel_shape2,
bias_size=bias_shape2,
pooling_size=[1, 2, 2, 1]
)
with tf.variable_scope("full_connection") as full_layer3:
# 讲卷积层张量数据拉成2-D张量只有有一列的列向量
layer2_output_flatten = tf.contrib.layers.flatten(layer2_output)
layer3_output = tf.nn.relu(
linear_layer(
data=layer2_output_flatten,
weights_size=full_conn_w_shape,
biases_size=full_conn_b_shape
)
)
# layer3_output = tf.nn.dropout(layer3_output, 0.8)
with tf.variable_scope("output") as output_layer4:
output = linear_layer(
data=layer3_output,
weights_size=out_w_shape,
biases_size=out_b_shape
)

return output;

def train_facedata(dataset, model_dir,model_path):
# train_set_x = data[0][0]
# train_set_y = data[0][1]
# valid_set_x = data[1][0]
# valid_set_y = data[1][1]
# test_set_x = data[2][0]
# test_set_y = data[2][1]
# X = tf.placeholder(tf.float32, shape=(None, None), name="x-input")  # 输入数据
# Y = tf.placeholder(tf.float32, shape=(None, None), name='y-input')  # 输入标签

batch_size = 40

# train_set_x, train_set_y = dataset[0]
# valid_set_x, valid_set_y = dataset[1]
# test_set_x, test_set_y = dataset[2]
train_set_x = dataset[0][0]
train_set_y = dataset[0][1]
valid_set_x = dataset[1][0]
valid_set_y = dataset[1][1]
test_set_x = dataset[2][0]
test_set_y = dataset[2][1]

X = tf.placeholder(tf.float32, [batch_size, 57 * 47])
Y = tf.placeholder(tf.float32, [batch_size, 40])

predict = convolutional_neural_network(X)
cost_func = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=predict, labels=Y))
optimizer = tf.train.AdamOptimizer(1e-2).minimize(cost_func)

# 用于保存训练的最佳模型
saver = tf.train.Saver()
#model_dir = './model'
#model_path = model_dir + '/best.ckpt'
with tf.Session() as session:
# 若不存在模型数据，需要训练模型参数
if not os.path.exists(model_path + ".index"):
session.run(tf.global_variables_initializer())
best_loss = float('Inf')
for epoch in range(20):
epoch_loss = 0
for i in range((int)(np.shape(train_set_x)[0] / batch_size)):
x = train_set_x[i * batch_size: (i + 1) * batch_size]
y = train_set_y[i * batch_size: (i + 1) * batch_size]
_, cost = session.run([optimizer, cost_func], feed_dict={X: x, Y: y})
epoch_loss += cost

print(epoch, ' : ', epoch_loss)
if best_loss > epoch_loss:
best_loss = epoch_loss
if not os.path.exists(model_dir):
os.mkdir(model_dir)
print("create the directory: %s" % model_dir)
save_path = saver.save(session, model_path)
print("Model saved in file: %s" % save_path)

# 恢复数据并校验和测试
saver.restore(session, model_path)
correct = tf.equal(tf.argmax(predict,1), tf.argmax(Y,1))
valid_accuracy = tf.reduce_mean(tf.cast(correct,'float'))
print('valid set accuracy: ', valid_accuracy.eval({X: valid_set_x, Y: valid_set_y}))

test_pred = tf.argmax(predict, 1).eval({X: test_set_x})
test_true = np.argmax(test_set_y, 1)
test_correct = correct.eval({X: test_set_x, Y: test_set_y})
incorrect_index = [i for i in range(np.shape(test_correct)[0]) if not test_correct[i]]
for i in incorrect_index:
print('picture person is %i, but mis-predicted as person %i'
%(test_true[i], test_pred[i]))
plot_errordata(incorrect_index, "olivettifaces.gif")

#画出在测试集中错误的数据
def plot_errordata(error_index, dataset_path):
img = mpimg.imread(dataset_path)
plt.imshow(img)
currentAxis = plt.gca()
for index in error_index:
row = index // 2
column = index % 2
currentAxis.add_patch(
patches.Rectangle(
xy=(
47 * 9 if column == 0 else 47 * 19,
row * 57
),
width=47,
height=57,
linewidth=1,
edgecolor='r',
facecolor='none'
)
)
plt.savefig("result.png")
plt.show()

def main():
dataset_path = "olivettifaces.gif"
data = load_data(dataset_path)
model_dir = './model'
model_path = model_dir + '/best.ckpt'
train_facedata(data, model_dir, model_path)

if __name__ == "__main__" :
main()

 C:\python36\python.exe X:/DeepLearning/code/face/TensorFlow_CNN_face/facerecognition_main.py
valid set accuracy:  0.825
picture person is 0, but mis-predicted as person 23
picture person is 6, but mis-predicted as person 38
picture person is 8, but mis-predicted as person 34
picture person is 15, but mis-predicted as person 11
picture person is 24, but mis-predicted as person 7
picture person is 29, but mis-predicted as person 7
picture person is 33, but mis-predicted as person 39