本週被教師留了CNN作業,沒有任何資料，便自學，留此筆記
PS：
主要程式碼來自於https://www.cnblogs.com/mu---mu/p/FaceRecognition-tensorflow.html

執行環境：

pip install tensorflow
pip install numpy
pip install dlib
pip install opencv-python

本人照片集

通過攝像頭拍攝照片採集本人照片集，原始碼使用10000張照片作為樣本，由於個人電腦限制，只採取了800張作為樣本集
檔案：get_my_faces.py

dlib版

import cv2
import dlib
import os
import sys
import random

output_dir = './my_faces'
size = 64

if not os.path.exists(output_dir):
    os.makedirs(output_dir)

# 改變圖片的亮度與對比度
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自帶的frontal_face_detector作為我們的特徵提取器
detector = dlib.get_frontal_face_detector()
# 開啟攝像頭 引數為輸入流，可以為攝像頭或視訊檔案
camera = cv2.VideoCapture(0)

index = 1
while True:
    if (index <= 10000):
        print('Being processed picture %s' % index)
        # 從攝像頭讀取照片
        success, img = camera.read()
        # 轉為灰度圖片
        gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        # 使用detector進行人臉檢測
        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]
            # 調整圖片的對比度與亮度， 對比度與亮度值都取隨機數，這樣能增加樣本的多樣性
            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
 

opencv版

import cv2
import os
import sys
import random

out_dir = './my_faces'
if not os.path.exists(out_dir):
    os.makedirs(out_dir)


# 改變亮度與對比度
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


# 獲取分類器
haar = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

# 開啟攝像頭 引數為輸入流，可以為攝像頭或視訊檔案
camera = cv2.VideoCapture(0)

n = 1
while 1:
    if (n <= 10000):
        print('It`s processing %s image.' % n)
        # 讀幀
        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, (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
 

他人圖片集

收集他人面容圖片，放入專案目錄/input_img目錄下
檔案：set_other_people.py

# -*- codeing: utf-8 -*-
import sys
import os
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自帶的frontal_face_detector作為我們的特徵提取器
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
            # 從檔案讀取圖片
            img = cv2.imread(img_path)
            # 轉為灰度圖片
            gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
            # 使用detector進行人臉檢測 dets為返回的結果
            dets = detector(gray_img, 1)

            #使用enumerate 函式遍歷序列中的元素以及它們的下標
            #下標i即為人臉序號
            #left：人臉左邊距離圖片左邊界的距離 ；right：人臉右邊距離圖片左邊界的距離 
            #top：人臉上邊距離圖片上邊界的距離 ；bottom：人臉下邊距離圖片上邊界的距離
            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]
                # 調整圖片的尺寸
                face = cv2.resize(face, (size,size))
                cv2.imshow('image',face)
                # 儲存圖片
                cv2.imwrite(output_dir+'/'+str(index)+'.jpg', face)
                index += 1

            key = cv2.waitKey(30) & 0xff
            if key == 27:
                sys.exit(0)
 

訓練模型

檔案：train_faces.py

import tensorflow as tf
import cv2
import numpy as np
import os
import random
import sys
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
        # //表示整除符號
        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)
            # 將圖片放大， 擴充圖片邊緣部分
            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)
# 將圖片資料與標籤轉換成陣列
imgs = np.array(imgs)
labs = np.array([[0,1] if lab == my_faces_path else [1,0] for lab in labs])
# 隨機劃分測試集與訓練集
train_x,test_x,train_y,test_y = train_test_split(imgs, labs, test_size=0.05, random_state=random.randint(0,100))
# 引數：圖片資料的總數，圖片的高、寬、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)
# 將資料轉換成小於1的數
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)))
# 圖片塊，每次取100張圖片
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():
    # 第一層
    W1 = weightVariable([3,3,3,32]) # 卷積核大小(3,3)， 輸入通道(3)， 輸出通道(32)
    b1 = biasVariable([32])
    # 卷積
    conv1 = tf.nn.relu(conv2d(x, W1) + b1)
    # 池化
    pool1 = maxPool(conv1)
    # 減少過擬合，隨機讓某些權重不更新
    drop1 = dropout(pool1, keep_prob_5)

    # 第二層
    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)

    # 第三層
    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)

    # 全連線層
    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)

    # 輸出層
    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)
    # 比較標籤是否相等，再求的所有數的平均值，tf.cast(強制轉換型別)
    accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(out, 1), tf.argmax(y_, 1)), tf.float32))
    # 將loss與accuracy儲存以供tensorboard使用
    tf.summary.scalar('loss', cross_entropy)
    tf.summary.scalar('accuracy', accuracy)
    merged_summary_op = tf.summary.merge_all()
    # 資料儲存器的初始化
    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)張圖片
            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]
                # 開始訓練資料，同時訓練三個變數，返回三個資料
                _,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)
                # 列印損失
                print(n*num_batch+i, loss)

                if (n*num_batch+i) % 100 == 0:
                    # 獲取測試資料的準確率
                    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)
                    # 準確率大於0.98時儲存並退出
                    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()

進行識別

檔案：is_my_face.py

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自帶的frontal_face_detector作為我們的特徵提取器
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]
        # 調整圖片的尺寸
        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()