init

.gitignore

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+/.settings

.project

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+<?xml version="1.0" encoding="UTF-8"?>
+<projectDescription>
+	<name>faceisme</name>
+	<comment></comment>
+	<projects>
+	</projects>
+	<buildSpec>
+		<buildCommand>
+			<name>org.python.pydev.PyDevBuilder</name>
+			<arguments>
+			</arguments>
+		</buildCommand>
+	</buildSpec>
+	<natures>
+		<nature>org.python.pydev.pythonNature</nature>
+	</natures>
+</projectDescription>

.pydevproject

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+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<?eclipse-pydev version="1.0"?><pydev_project>
+<pydev_property name="org.python.pydev.PYTHON_PROJECT_INTERPRETER">Default</pydev_property>
+<pydev_property name="org.python.pydev.PYTHON_PROJECT_VERSION">python 2.7</pydev_property>
+</pydev_project>

README.md

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+#Usage:
+get_my_faces，通过电脑摄像头获取很多不同的人脸
+
+set_other_people，下载好的其他人脸处理。
+
+train_faces训练人脸
+
+is_my_faces，判断是否是本人

src/python/get_my_faces_by_dlib.py

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+#coding=utf-8
+'''
+Created on 2017年9月12日
+@vsersion:python 3.6
+@author: liuyuqi
+'''
+
+import os
+import random
+import sys
+
+import cv2
+import dlib
+
+output_dir = './my_faces'
+size = 64
+
+if not os.path.exists(output_dir):
+    os.makedirs(output_dir)
+
+# 改变图片的亮度与对比度
+def relight(img, light=1, bias=0):
+    w = img.shape[1]
+    h = img.shape[0]
+    #image = []
+    for i in range(0,w):
+        for j in range(0,h):
+            for c in range(3):
+                tmp = int(img[j,i,c]*light + bias)
+                if tmp > 255:
+                    tmp = 255
+                elif tmp < 0:
+                    tmp = 0
+                img[j,i,c] = tmp
+    return img
+
+#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()
+# 打开摄像头 参数为输入流，可以为摄像头或视频文件
+camera = cv2.VideoCapture(0)
+
+index = 1
+while True:
+    if (index <= 10000):
+        print('Being processed picture %s' % index)
+        # 从摄像头读取照片
+        success, img = camera.read()
+        # 转为灰度图片
+        gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        # 使用detector进行人脸检测
+        dets = detector(gray_img, 1)
+
+        for i, d in enumerate(dets):
+            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]
+            # 调整图片的对比度与亮度， 对比度与亮度值都取随机数，这样能增加样本的多样性
+            face = relight(face, random.uniform(0.5, 1.5), random.randint(-50, 50))
+
+            face = cv2.resize(face, (size,size))
+
+            cv2.imshow('image', face)
+
+            cv2.imwrite(output_dir+'/'+str(index)+'.jpg', face)
+
+            index += 1
+        key = cv2.waitKey(30) & 0xff
+        if key == 27:
+            break
+    else:
+        print('Finished!')
+        break

src/python/get_my_faces_by_opencv.py

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+# coding=utf-8
+'''
+Created on 2017年9月12日
+@vsersion:python 3.6
+@author: liuyuqi
+'''
+import os
+import random
+import sys
+from time import sleep
+
+import cv2
+
+out_dir = './my_faces'
+if not os.path.exists(out_dir):
+    os.makedirs(out_dir)
+
+
+# 改变亮度与对比度
+def relight(img, alpha=1, bias=0):
+    w = img.shape[1]
+    h = img.shape[0]
+    #image = []
+    for i in range(0, w):
+        for j in range(0, h):
+            for c in range(3):
+                tmp = int(img[j, i, c] * alpha + bias)
+                if tmp > 255:
+                    tmp = 255
+                elif tmp < 0:
+                    tmp = 0
+                img[j, i, c] = tmp
+    return img
+
+
+# 获取分类器
+# D:\Program-Files\opencv\build\share\OpenCV\haarcascades\haarcascade_frontalface_default.xml
+haar = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
+
+# 打开摄像头 参数为输入流，可以为摄像头或视频文件
+camera = cv2.VideoCapture(0)
+
+n = 1
+while 1:
+    if (n <= 100):
+        print('It`s processing %s image.' % n)
+        # 读帧
+        success, img = camera.read()
+
+# 转换为灰度图
+        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, (64, 64))
+            '''
+            if n % 3 == 1:
+                face = relight(face, 1, 50)
+            elif n % 3 == 2:
+                face = relight(face, 0.5, 0)
+            '''
+            face = relight(face, random.uniform(
+                0.5, 1.5), random.randint(-50, 50))
+            cv2.imshow('img', face)
+            cv2.imwrite(out_dir + '/' + str(n) + '.jpg', face)
+            n += 1
+        key = cv2.waitKey(30) & 0xff
+        if key == 27:
+            break
+    else:
+        break
+    sleep(10)

src/python/is_my_face.py

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+#coding=utf-8
+'''
+Created on 2017年9月12日
+@vsersion:python 3.6
+@author: liuyuqi
+'''
+output = cnnLayer()  
+predict = tf.argmax(output, 1)  
+
+saver = tf.train.Saver()  
+sess = tf.Session()  
+saver.restore(sess, tf.train.latest_checkpoint('.'))  
+
+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):
+        #print('Can`t get face.')
+        cv2.imshow('img', img)
+        key = cv2.waitKey(30) & 0xff  
+        if key == 27:
+            sys.exit(0)
+
+    for i, d in enumerate(dets):
+        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('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) & 0xff
+        if key == 27:
+            sys.exit(0)
+
+sess.close()

src/python/set_other_people.py

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+#coding=utf-8
+'''
+Created on 2017年9月12日
+@vsersion:python 3.6
+@author: liuyuqi
+'''
+import os
+import sys
+
+import cv2
+import dlib
+
+input_dir = './input_img'
+output_dir = './other_faces'
+size = 64
+
+if not os.path.exists(output_dir):
+    os.makedirs(output_dir)
+
+#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()
+
+index = 1
+for (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 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
+                # 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) & 0xff
+            if key == 27:
+                sys.exit(0)

src/python/train_faces.py

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+#coding=utf-8
+'''
+Created on 2017年9月12日
+@vsersion:python 3.6
+@author: liuyuqi
+'''
+import os
+import random
+import sys
+
+import numpy as np
+
+import cv2
+import tensorflow as tf
+from sklearn.model_selection import train_test_split
+
+my_faces_path = './my_faces'
+other_faces_path = './other_faces'
+size = 64
+
+imgs = []
+labs = []
+
+def getPaddingSize(img):
+    h, w, _ = img.shape
+    top, 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 // 2
+        right = tmp - left
+    elif 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 = 100
+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) % 100 == 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)
+                    # 准确率大于0.98时保存并退出
+                    if acc > 0.98 and n > 2:
+                        saver.save(sess, './train_faces.model', global_step=n*num_batch+i)
+                        sys.exit(0)
+        print('accuracy less 0.98, exited!')
+
+cnnTrain()