import tensorflow as tf
import cv2
import dlib
import sys
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

size = 64

x = tf.placeholder(tf.float32, [None, size, size, 3])#tf.placeholder(tf.float32, shape=[None, img_height, img_width, channels])x代表的是输入图片的浮点数张量
y_ = tf.placeholder(tf.float32, [None, 2])
#防止欠拟合
keep_prob_5 = tf.placeholder(tf.float32)
keep_prob_75 = tf.placeholder(tf.float32)
#初始化权重
def weightVariable(shape):
    init = tf.random_normal(shape, stddev=0.01)
    return tf.Variable(init)
#初始化偏置项
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):#使用dropout进行防止过拟合
    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，提供一定的空间方差
    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)#激活函数是ReLU
    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


output = cnnLayer()
predict = tf.argmax(output, 1)

init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
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):
       
        cv2.imshow('img', img)
        key = cv2.waitKey(30) & 0xff
        if key == 27:
            sys.exit(0)

    for i, d in enumerate(dets):#枚举函数
        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(' 王金玮 %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()
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint('.'))