opencv+tensorflow+cnn实现人脸识别

1.获取我的人脸数据：

注意以下有一个引用库import cv2
为顺利找到依赖库函数，需要先安装库：

pip install opencv-python

• 使用opencv打开摄像头，获取人脸
• 对图像做一些预处理，如处理成64*64大小的图片
• 获取期间，做一些明暗处理，以增加图像的噪声干扰，使得训练出来的模型具备一定的泛化能力
• 共获取200张照片

#!/usr/bin/python #coding=utf-8 ''' face detect


''' # pylint: disable=invalid-name import os import random import numpy as np import cv2 def createdir(*args): ''' create dir''' for item in args: if not os.path.exists(item):
            os.makedirs(item)

IMGSIZE = 64 def getpaddingSize(shape): ''' get size to make image to be a square rect ''' h, w = shape
    longest = max(h, w)
    result = (np.array([longest]*4, int) - np.array([h, h, w, w], int)) // 2 return result.tolist() def dealwithimage(img, h=64, w=64): ''' dealwithimage ''' #img = cv2.imread(imgpath) top, bottom, left, right = getpaddingSize(img.shape[0:2])
    img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=[0, 0, 0])
    img = cv2.resize(img, (h, w)) return img def relight(imgsrc, alpha=1, bias=0): '''relight''' imgsrc = imgsrc.astype(float)
    imgsrc = imgsrc * alpha + bias
    imgsrc[imgsrc < 0] = 0 imgsrc[imgsrc > 255] = 255 imgsrc = imgsrc.astype(np.uint8) return imgsrc def getfacefromcamera(outdir): createdir(outdir)
    camera = cv2.VideoCapture(0)
    haar = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
    n = 1 while 1: if (n <= 200):
            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, (IMGSIZE, IMGSIZE)) #could deal with face to train face = relight(face, random.uniform(0.5, 1.5), random.randint(-50, 50))
                cv2.imwrite(os.path.join(outdir, str(n)+'.jpg'), face)

                cv2.putText(img, 'haha', (f_x, f_y - 20), cv2.FONT_HERSHEY_SIMPLEX, 1, 255, 2) #显示名字 img = cv2.rectangle(img, (f_x, f_y), (f_x + f_w, f_y + f_h), (255, 0, 0), 2)
                n+=1 cv2.imshow('img', img)
            key = cv2.waitKey(30) & 0xff if key == 27: break else: break camera.release()
    cv2.destroyAllWindows() if __name__ == '__main__':
    name = input('please input yourename: ')
    getfacefromcamera(os.path.join('./image/trainfaces', name)) 

执行完以后效果是这样的（原谅我作了处理（-——-））

Inked捕获_LI.jpg

2.创建CNN网络：

#!/usr/bin/python #coding=utf-8 ''' face detect convolution''' # pylint: disable=invalid-name import os import logging as log import matplotlib.pyplot as plt import common import numpy as np from tensorflow.examples.tutorials.mnist import input_data import tensorflow as tf import cv2

SIZE = 64 x_data = tf.placeholder(tf.float32, [None, SIZE, SIZE, 3])
y_data = tf.placeholder(tf.float32, [None, None])

keep_prob_5 = tf.placeholder(tf.float32)
keep_prob_75 = tf.placeholder(tf.float32) def weightVariable(shape): ''' build weight variable''' init = tf.random_normal(shape, stddev=0.01) #init = tf.truncated_normal(shape, stddev=0.01) return tf.Variable(init) def biasVariable(shape): ''' build bias variable''' init = tf.random_normal(shape) #init = tf.truncated_normal(shape, stddev=0.01) return tf.Variable(init) def conv2d(x, W): ''' conv2d by 1, 1, 1, 1''' return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') def maxPool(x): ''' max pooling''' return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') def dropout(x, keep): ''' drop out''' return tf.nn.dropout(x, keep) def cnnLayer(classnum): ''' create cnn layer''' # 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])
    conv1 = tf.nn.relu(conv2d(x_data, W1) + b1)
    pool1 = maxPool(conv1) # 减少过拟合，随机让某些权重不更新 drop1 = dropout(pool1, keep_prob_5) # 32 * 32 * 32 多个输入channel 被filter内积掉了 # 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) # 64 * 16 * 16 # 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) # 64 * 8 * 8 # 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, classnum])
    bout = weightVariable([classnum]) #out = tf.matmul(dropf, Wout) + bout out = tf.add(tf.matmul(dropf, Wout), bout) return out def train(train_x, train_y, tfsavepath): ''' train''' log.debug('train')
    out = cnnLayer(train_y.shape[1])
    cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=out, labels=y_data))
    train_step = tf.train.AdamOptimizer(0.01).minimize(cross_entropy)
    accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(out, 1), tf.argmax(y_data, 1)), tf.float32))

    saver = tf.train.Saver() with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        batch_size = 10 num_batch = len(train_x) // 10 for n in range(10):
            r = np.random.permutation(len(train_x))
            train_x = train_x[r, :]
            train_y = train_y[r, :] 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]
                _, loss = sess.run([train_step, cross_entropy],\
                                   feed_dict={x_data:batch_x, y_data:batch_y,
                                              keep_prob_5:0.75, keep_prob_75:0.75})

                print(n*num_batch+i, loss) # 获取测试数据的准确率 acc = accuracy.eval({x_data:train_x, y_data:train_y, keep_prob_5:1.0, keep_prob_75:1.0})
        print('after 10 times run: accuracy is ', acc)
        saver.save(sess, tfsavepath) def validate(test_x, tfsavepath): ''' validate ''' output = cnnLayer(2) #predict = tf.equal(tf.argmax(output, 1), tf.argmax(y_data, 1)) predict = output

    saver = tf.train.Saver() with tf.Session() as sess: #sess.run(tf.global_variables_initializer()) saver.restore(sess, tfsavepath)
        res = sess.run([predict, tf.argmax(output, 1)],
                       feed_dict={x_data: test_x,
                                  keep_prob_5:1.0, keep_prob_75: 1.0}) return res if __name__ == '__main__': pass 

使用tf创建3层cnn，3 * 3的filter，输入为rgb所以：

• 第一层的channel是3，图像宽高为64，输出32个filter，maxpooling是缩放一倍
• 第二层的输入为32个channel，宽高是32，输出为64个filter，maxpooling是缩放一倍
• 第三层的输入为64个channel，宽高是16，输出为64个filter，maxpooling是缩放一倍
  所以最后输入的图像是8 * 8 * 64，卷积层和全连接层都设置了dropout参数

将输入的8 * 8 * 64的多维度，进行flatten，映射到512个数据上，然后进行softmax，输出到onehot类别上，类别的输入根据采集的人员的个数来确定。

3.识别人脸分类

def getfileandlabel(filedir): ''' get path and host paire and class index to name''' dictdir = dict([[name, os.path.join(filedir, name)] \ for name in os.listdir(filedir) if os.path.isdir(os.path.join(filedir, name))]) #for (path, dirnames, _) in os.walk(filedir) for dirname in dirnames]) dirnamelist, dirpathlist = dictdir.keys(), dictdir.values()
    indexlist = list(range(len(dirnamelist))) return list(zip(dirpathlist, onehot(indexlist))), dict(zip(indexlist, dirnamelist))
    
pathlabelpair, indextoname = getfileandlabel('./image/trainfaces')
train_x, train_y = readimage(pathlabelpair)
train_x = train_x.astype(np.float32) / 255.0 myconv.train(train_x, train_y, savepath) 

• 将人脸从子目录内读出来，根据不同的人名，分配不同的onehot值，这里是按照遍历的顺序分配序号，然后训练，完成之后会保存checkpoint
• 图像识别之前将像素值转换为0到1的范围
• 需要多次训练的话，把checkpoint下面的上次训练结果删除，代码有个判断，有上一次的训练结果，就不会再训练了

4.识别图像

def testfromcamera(chkpoint): camera = cv2.VideoCapture(0)
    haar = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
    pathlabelpair, indextoname = getfileandlabel('./image/trainfaces')
    output = myconv.cnnLayer(len(pathlabelpair)) #predict = tf.equal(tf.argmax(output, 1), tf.argmax(y_data, 1)) predict = output

    saver = tf.train.Saver() with tf.Session() as sess: #sess.run(tf.global_variables_initializer()) saver.restore(sess, chkpoint)
        
        n = 1 while 1: if (n <= 20000):
                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, (IMGSIZE, IMGSIZE)) #could deal with face to train test_x = np.array([face])
                    test_x = test_x.astype(np.float32) / 255.0 res = sess.run([predict, tf.argmax(output, 1)],\
                                   feed_dict={myconv.x_data: test_x,\
                                   myconv.keep_prob_5:1.0, myconv.keep_prob_75: 1.0})
                    print(res)

                    cv2.putText(img, indextoname[res[1][0]], (f_x, f_y - 20), cv2.FONT_HERSHEY_SIMPLEX, 1, 255, 2) #显示名字 img = cv2.rectangle(img, (f_x, f_y), (f_x + f_w, f_y + f_h), (255, 0, 0), 2)
                    n+=1 cv2.imshow('img', img)
                key = cv2.waitKey(30) & 0xff if key == 27: break else: break camera.release()
    cv2.destroyAllWindows() 

• 从训练的结果中恢复训练识别的参数，然后用于新的识别判断
• 打开摄像头，采集到图片之后，进行人脸检测，检测出来之后，进行人脸识别，根据结果对应到人员名字，显示在图片中人脸的上面

5.测试效果如下

测试结果.PNG

作者：AugustRush_a2ec
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來源：简书
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