TensorFlow练习23: “恶作剧”

前面有一个帖《OpenCV检测场景内是否有移动物体》我用树莓派做了一个简单的Motion Detection，放在卫生间的，它会在我上大号时自动播放音乐。

我一个人租房，几个盆友周末时常会找我玩，他们觉得我做的Motion Detection很垃圾。于是我就想恶搞一下，用TensorFlow做一个“人脸识别”，在我上大号时播放音乐，如果是别人就播放《张震讲鬼故事》（@xingCI说放屁声更搞）。

我的任务的训练一个模型可以区分“我”和“其它人”的脸。注意，上面“人脸识别”我是加引号的，其实并不是真正的人脸识别，充其量就是个图像分类。如果你要使用真正的人脸识别，可以试试现成的库OpenFace+dlib《使用OpenFace进行人脸识别》。

有人已经把TensorFlow移植到了树莓派，项目地址tensorflow-on-raspberry-pi。

准备数据

本帖需要使用到两组数据：一组是包含我脸的图像，另一组包含其它人人脸的图像。

其它人人脸的收集

找一堆图片，只要不包含自己就行，然后使用OpenCV提取图像中的大脸。

我使用的数据集是前面几帖用到的：《妹子图》《大脸》《imagenet》

提取图像中的人脸，我使用OpenCV，据说使用dlib效果更好。

other_peoples_faces.py：

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import cv2 import os import sys   IMAGE_DIR = '图片目录路径'   OUTPUT_DIR = './other_people' if not os.path . exists ( OUTPUT_DIR ) : os . makedirs ( OUTPUT_DIR )   # http://blog.topspeedsnail.com/archives/10511 # wget https://raw.githubusercontent.com/opencv/opencv/master/data/haarcascades/haarcascade_frontalface_default.xml face_haar = cv2 . CascadeClassifier ( "haarcascade_frontalface_default.xml" )   for ( dirpath , dirnames , filenames ) in os . walk ( IMAGE_DIR ) : for filename in filenames : if filename . endswith ( '.jpg' ) : image_path = os.path . join ( dirpath , filename ) print ( 'process: ' , image_path ) img = cv2 . imread ( image_path ) gray_image = cv2 . cvtColor ( img , cv2 . COLOR_BGR2GRAY ) faces = face_haar . detectMultiScale ( gray_image , 1.3 , 5 ) for face_x , face_y , face_w , face_h in faces : face = img [ face_y : face_y + face_h , face_x : face_x + face_w ]   face = cv2 . resize ( face , ( 64 , 64 ) )   cv2 . imshow ( "img" , face ) cv2 . imwrite ( os.path . join ( OUTPUT_DIR , filename ) , face )   key = cv2 . waitKey ( 30 ) & 0xff if key == 27 : sys . exit ( 0 )

4万多图片，我只提取了1万张脸，应该够使了。

图像大小 64×64

上面是OpenCV做的人脸检测，有了这个数据集又可以反过来训练TensorFlow版本的人脸检测。

斗大熊的脸

给自己拍照1万张，这是我一次拍照最多的一回。

在镜头前摇头晃脑、摆pose，戴眼镜、耳机，仰天45，写代码，呲牙咧嘴，玩手机。。。一定要多样化，直到拍1万张大脸。

训练模型

训练数据有了，下面开始训练。

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import tensorflow as tf import cv2 import numpy as np import os from sklearn . model_selection import train_test_split import random import sys   my_image_path = 'my_faces' others_image_path = 'other_people'   image_data = [ ] label_data = [ ]   def get_padding_size ( image ) : h , w , _ = image . shape longest_edge = max ( h , w ) top , bottom , left , right = ( 0 , 0 , 0 , 0 ) if h < longest_edge : dh = longest_edge - h top = dh / / 2 bottom = dh - top elif w < longest_edge : dw = longest_edge - w left = dw / / 2 right = dw - left else : pass return top , bottom , left , right   def read_data ( img_path , image_h = 64 , image_w = 64 ) : for filename in os . listdir ( img_path ) : if filename . endswith ( '.jpg' ) : filepath = os.path . join ( img_path , filename ) image = cv2 . imread ( filepath )   top , bottom , left , right = get_padding_size ( image ) image_pad = cv2 . copyMakeBorder ( image , top , bottom , left , right , cv2 . BORDER_CONSTANT , value = [ 0 , 0 , 0 ] ) image = cv2 . resize ( image_pad , ( image_h , image_w ) )   image_data . append ( image ) label_data . append ( img_path )   read_data ( others_image_path ) read_data ( my_image_path )   image_data = np . array ( image_data ) label_data = np . array ( [ [ 0 , 1 ] if label == 'my_faces' else [ 1 , 0 ] for label in label_data ] )   train_x , test_x , train_y , test_y = train_test_split ( image_data , label_data , test_size = 0.05 , random_state = random . randint ( 0 , 100 ) )   # image (height=64 width=64 channel=3) train_x = train_x . reshape ( train_x . shape [ 0 ] , 64 , 64 , 3 ) test_x = test_x . reshape ( test_x . shape [ 0 ] , 64 , 64 , 3 )   # nomalize train_x = train_x . astype ( 'float32' ) / 255.0 test_x = test_x . astype ( 'float32' ) / 255.0   print ( len ( train_x ) , len ( train_y ) ) print ( len ( test_x ) , len ( test_y ) )   ############################################################# batch_size = 128 num_batch = len ( train_x ) / / batch _size   X = tf . placeholder ( tf . float32 , [ None , 64 , 64 , 3 ] )    # 图片大小64x64 channel=3 Y = tf . placeholder ( tf . float32 , [ None , 2 ] )   keep_prob_5 = tf . placeholder ( tf . float32 ) keep_prob_75 = tf . placeholder ( tf . float32 )   def panda_joke_cnn ( ) :   W_c1 = tf . Variable ( tf . random_normal ( [ 3 , 3 , 3 , 32 ] , stddev = 0.01 ) ) b_c1 = tf . Variable ( tf . random_normal ( [ 32 ] ) ) conv1 = tf . nn . relu ( tf . nn . bias_add ( tf . nn . conv2d ( X , W_c1 , strides = [ 1 , 1 , 1 , 1 ] , padding = 'SAME' ) , b_c1 ) ) conv1 = tf . nn . max_pool ( conv1 , ksize = [ 1 , 2 , 2 , 1 ] , strides = [ 1 , 2 , 2 , 1 ] , padding = 'SAME' ) conv1 = tf . nn . dropout ( conv1 , keep_prob_5 )   W_c2 = tf . Variable ( tf . random_normal ( [ 3 , 3 , 32 , 64 ] , stddev = 0.01 ) ) b_c2 = tf . Variable ( tf . random_normal ( [ 64 ] ) ) conv2 = tf . nn . relu ( tf . nn . bias_add ( tf . nn . conv2d ( conv1 , W_c2 , strides = [ 1 , 1 , 1 , 1 ] , padding = 'SAME' ) , b_c2 ) ) conv2 = tf . nn . max_pool ( conv2 , ksize = [ 1 , 2 , 2 , 1 ] , strides = [ 1 , 2 , 2 , 1 ] , padding = 'SAME' ) conv2 = tf . nn . dropout ( conv2 , keep_prob_5 )   W_c3 = tf . Variable ( tf . random_normal ( [ 3 , 3 , 64 , 64 ] , stddev = 0.01 ) ) b_c3 = tf . Variable ( tf . random_normal ( [ 64 ] ) ) conv3 = tf . nn . relu ( tf . nn . bias_add ( tf . nn . conv2d ( conv2 , W_c3 , strides = [ 1 , 1 , 1 , 1 ] , padding = 'SAME' ) , b_c3 ) ) conv3 = tf . nn . max_pool ( conv3 , ksize = [ 1 , 2 , 2 , 1 ] , strides = [ 1 , 2 , 2 , 1 ] , padding = 'SAME' ) conv3 = tf . nn . dropout ( conv3 , keep_prob_5 )   # Fully connected layer W_d = tf . Variable ( tf . random_normal ( [ 8 * 16 * 32 , 512 ] , stddev = 0.01 ) ) b_d = tf . Variable ( tf . random_normal ( [ 512 ] ) ) dense = tf . reshape ( conv3 , [ - 1 , W_d . get_shape ( ) . as_list ( ) [ 0 ] ] ) dense = tf . nn . relu ( tf . add ( tf . matmul ( dense , W_d ) , b_d ) ) dense = tf . nn . dropout ( dense , keep_prob_75 )   W_out = tf . Variable ( tf . random_normal ( [ 512 , 2 ] , stddev = 0.01 ) ) b_out = tf . Variable ( tf . random_normal ( [ 2 ] ) ) out = tf . add ( tf . matmul ( dense , W_out ) , b_out ) return out   def train_cnn ( ) : output = panda_joke_cnn ( )   loss = tf . reduce_mean ( tf . nn . softmax_cross_entropy_with_logits ( output , Y ) ) optimizer = tf . train . AdamOptimizer ( learning_rate = 0.001 ) . minimize ( loss )   accuracy = tf . reduce_mean ( tf . cast ( tf . equal ( tf . argmax ( output , 1 ) , tf . argmax ( Y , 1 ) ) , tf . float32 ) )   tf . summary . scalar ( "loss" , loss ) tf . summary . scalar ( "accuracy" , accuracy ) merged_summary_op = tf . summary . merge_all ( )   saver = tf . train . Saver ( ) with tf . Session ( ) as sess : sess . run ( tf . global_variables_initializer ( ) )   summary_writer = tf . summary . FileWriter ( './log' , graph = tf . get_default_graph ( ) )   for e in range ( 50 ) : 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_ , summary = sess . run ( [ optimizer , loss , 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 , e * num_batch + i ) print ( e * num_batch + i , loss_ )   if ( e * num_batch + i ) % 100 == 0 : acc = accuracy . eval ( { X : test_x , Y : test_y , keep_prob_5 : 1.0 , keep_prob_75 : 1.0 } ) print ( e * num_batch + i , acc ) # save model if acc > 0.98 : saver . save ( sess , "i_am_a_joke.model" , global_step = e * num_batch + i ) sys . exit ( 0 )   train_cnn ( )

准确率曲线：

下面要做的就是在树莓派上使用模型，代码示例：