基于python神经卷积网络的人脸识别

本文实例为大家分享了基于神经卷积网络的人脸识别，供大家参考，具体内容如下

1.人脸识别整体设计方案

客_服交互流程图：

2.服务端代码展示

sk = socket.socket() 
# s.bind(address) 将套接字绑定到地址。在AF_INET下，以元组（host,port）的形式表示地址。 
sk.bind(("172.29.25.11",8007)) 
# 开始监听传入连接。 
sk.listen(True) 
 
while True: 
 for i in range(100): 
  # 接受连接并返回（conn,address）,conn是新的套接字对象，可以用来接收和发送数据。address是连接客户端的地址。 
  conn,address = sk.accept() 
 
  # 建立图片存储路径 
  path = str(i+1) + '.jpg' 
 
  # 接收图片大小（字节数） 
  size = conn.recv(1024) 
  size_str = str(size,encoding="utf-8") 
  size_str = size_str[2 :] 
  file_size = int(size_str) 
 
  # 响应接收完成 
  conn.sendall(bytes('finish', encoding="utf-8")) 
 
  # 已经接收数据大小 has_size 
  has_size = 0 
  # 创建图片并写入数据 
  f = open(path,"wb") 
  while True: 
   # 获取 
   if file_size == has_size: 
    break 
   date = conn.recv(1024) 
   f.write(date) 
   has_size += len(date) 
  f.close() 
 
  # 图片缩放 
  resize(path) 
  # cut_img(path)：图片裁剪成功返回True；失败返回False 
  if cut_img(path): 
   yuchuli() 
   result = test('test.jpg') 
   conn.sendall(bytes(result,encoding="utf-8")) 
  else: 
   print('falue') 
   conn.sendall(bytes('人眼检测失败，请保持图片眼睛清晰',encoding="utf-8")) 
  conn.close()

3.图片预处理

1）图片缩放

# 根据图片大小等比例缩放图片 
def resize(path): 
 image=cv2.imread(path,0) 
 row,col = image.shape 
 if row >= 2500: 
  x,y = int(row/5),int(col/5) 
 elif row >= 2000: 
  x,y = int(row/4),int(col/4) 
 elif row >= 1500: 
  x,y = int(row/3),int(col/3) 
 elif row >= 1000: 
  x,y = int(row/2),int(col/2) 
 else: 
  x,y = row,col 
 # 缩放函数 
 res=cv2.resize(image,(y,x),interpolation=cv2.INTER_CUBIC) 
 cv2.imwrite(path,res)

2）直方图均衡化和中值滤波

# 直方图均衡化 
eq = cv2.equalizeHist(img) 
# 中值滤波 
lbimg=cv2.medianBlur(eq,3)

3）人眼检测

# -*- coding: utf-8 -*- 
# 检测人眼，返回眼睛数据 
 
import numpy as np 
import cv2 
 
def eye_test(path): 
 # 待检测的人脸路径 
 imagepath = path 
 
 # 获取训练好的人脸参数 
 eyeglasses_cascade = cv2.CascadeClassifier('haarcascade_eye_tree_eyeglasses.xml') 
 
 # 读取图片 
 img = cv2.imread(imagepath) 
 # 转为灰度图像 
 gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) 
 
 # 检测并获取人眼数据 
 eyeglasses = eyeglasses_cascade.detectMultiScale(gray) 
 # 人眼数为2时返回左右眼位置数据 
 if len(eyeglasses) == 2: 
  num = 0 
  for (e_gx,e_gy,e_gw,e_gh) in eyeglasses: 
   cv2.rectangle(img,(e_gx,e_gy),(e_gx+int(e_gw/2),e_gy+int(e_gh/2)),(0,0,255),2) 
   if num == 0: 
    x1,y1 = e_gx+int(e_gw/2),e_gy+int(e_gh/2) 
   else: 
    x2,y2 = e_gx+int(e_gw/2),e_gy+int(e_gh/2) 
   num += 1 
  print('eye_test') 
  return x1,y1,x2,y2 
 else: 
  return False

4）人眼对齐并裁剪

# -*- coding: utf-8 -*- 
# 人眼对齐并裁剪 
 
# 参数含义： 
# CropFace(image, eye_left, eye_right, offset_pct, dest_sz) 
# eye_left is the position of the left eye 
# eye_right is the position of the right eye 
# 比例的含义为：要保留的图像靠近眼镜的百分比， 
# offset_pct is the percent of the image you want to keep next to the eyes (horizontal, vertical direction) 
# 最后保留的图像的大小。 
# dest_sz is the size of the output image 
# 
import sys,math 
from PIL import Image 
from eye_test import eye_test 
 
 # 计算两个坐标的距离 
def Distance(p1,p2): 
 dx = p2[0]- p1[0] 
 dy = p2[1]- p1[1] 
 return math.sqrt(dx*dx+dy*dy) 
 
 # 根据参数，求仿射变换矩阵和变换后的图像。 
def ScaleRotateTranslate(image, angle, center =None, new_center =None, scale =None, resample=Image.BICUBIC): 
 if (scale is None)and (center is None): 
  return image.rotate(angle=angle, resample=resample) 
 nx,ny = x,y = center 
 sx=sy=1.0 
 if new_center: 
  (nx,ny) = new_center 
 if scale: 
  (sx,sy) = (scale, scale) 
 cosine = math.cos(angle) 
 sine = math.sin(angle) 
 a = cosine/sx 
 b = sine/sx 
 c = x-nx*a-ny*b 
 d =-sine/sy 
 e = cosine/sy 
 f = y-nx*d-ny*e 
 return image.transform(image.size, Image.AFFINE, (a,b,c,d,e,f), resample=resample) 
 
 # 根据所给的人脸图像，眼睛坐标位置，偏移比例，输出的大小，来进行裁剪。 
def CropFace(image, eye_left=(0,0), eye_right=(0,0), offset_pct=(0.2,0.2), dest_sz = (70,70)): 
 # calculate offsets in original image 计算在原始图像上的偏移。 
 offset_h = math.floor(float(offset_pct[0])*dest_sz[0]) 
 offset_v = math.floor(float(offset_pct[1])*dest_sz[1]) 
 # get the direction 计算眼睛的方向。 
 eye_direction = (eye_right[0]- eye_left[0], eye_right[1]- eye_left[1]) 
 # calc rotation angle in radians 计算旋转的方向弧度。 
 rotation =-math.atan2(float(eye_direction[1]),float(eye_direction[0])) 
 # distance between them # 计算两眼之间的距离。 
 dist = Distance(eye_left, eye_right) 
 # calculate the reference eye-width 计算最后输出的图像两只眼睛之间的距离。 
 reference = dest_sz[0]-2.0*offset_h 
 # scale factor # 计算尺度因子。 
 scale =float(dist)/float(reference) 
 # rotate original around the left eye # 原图像绕着左眼的坐标旋转。 
 image = ScaleRotateTranslate(image, center=eye_left, angle=rotation) 
 # crop the rotated image # 剪切 
 crop_xy = (eye_left[0]- scale*offset_h, eye_left[1]- scale*offset_v) # 起点 
 crop_size = (dest_sz[0]*scale, dest_sz[1]*scale) # 大小 
 image = image.crop((int(crop_xy[0]),int(crop_xy[1]),int(crop_xy[0]+crop_size[0]),int(crop_xy[1]+crop_size[1]))) 
 # resize it 重置大小 
 image = image.resize(dest_sz, Image.ANTIALIAS) 
 return image 
 
def cut_img(path): 
 image = Image.open(path) 
 
 # 人眼识别成功返回True；否则，返回False 
 if eye_test(path): 
  print('cut_img') 
  # 获取人眼数据 
  leftx,lefty,rightx,righty = eye_test(path) 
 
  # 确定左眼和右眼位置 
  if leftx > rightx: 
   temp_x,temp_y = leftx,lefty 
   leftx,lefty = rightx,righty 
   rightx,righty = temp_x,temp_y 
 
  # 进行人眼对齐并保存截图 
  CropFace(image, eye_left=(leftx,lefty), eye_right=(rightx,righty), offset_pct=(0.30,0.30), dest_sz=(92,112)).save('test.jpg') 
  return True 
 else: 
  print('falue') 
  return False

4.用神经卷积网络训练数据

# -*- coding: utf-8 -*- 
 
from numpy import * 
import cv2 
import tensorflow as tf 
 
# 图片大小 
TYPE = 112*92 
# 训练人数 
PEOPLENUM = 42 
# 每人训练图片数 
TRAINNUM = 15 #( train_face_num ) 
# 单人训练人数加测试人数 
EACH = 21 #( test_face_num + train_face_num ) 
 
# 2维=>1维 
def img2vector1(filename): 
 img = cv2.imread(filename,0) 
 row,col = img.shape 
 vector1 = zeros((1,row*col)) 
 vector1 = reshape(img,(1,row*col)) 
 return vector1 
 
# 获取人脸数据 
def ReadData(k): 
 path = 'face_flip/' 
 train_face = zeros((PEOPLENUM*k,TYPE),float32) 
 train_face_num = zeros((PEOPLENUM*k,PEOPLENUM)) 
 test_face = zeros((PEOPLENUM*(EACH-k),TYPE),float32) 
 test_face_num = zeros((PEOPLENUM*(EACH-k),PEOPLENUM)) 
 
 # 建立42个人的训练人脸集和测试人脸集 
 for i in range(PEOPLENUM): 
  # 单前获取人 
  people_num = i + 1 
  for j in range(k): 
   #获取图片路径 
   filename = path + 's' + str(people_num) + '/' + str(j+1) + '.jpg' 
   #2维=>1维 
   img = img2vector1(filename) 
 
   #train_face:每一行为一幅图的数据；train_face_num:储存每幅图片属于哪个人 
   train_face[i*k+j,:] = img/255 
   train_face_num[i*k+j,people_num-1] = 1 
 
  for j in range(k,EACH): 
   #获取图片路径 
   filename = path + 's' + str(people_num) + '/' + str(j+1) + '.jpg' 
 
   #2维=>1维 
   img = img2vector1(filename) 
 
   # test_face:每一行为一幅图的数据；test_face_num:储存每幅图片属于哪个人 
   test_face[i*(EACH-k)+(j-k),:] = img/255 
   test_face_num[i*(EACH-k)+(j-k),people_num-1] = 1 
 
 return train_face,train_face_num,test_face,test_face_num 
 
# 获取训练和测试人脸集与对应lable 
train_face,train_face_num,test_face,test_face_num = ReadData(TRAINNUM) 
 
# 计算测试集成功率 
def compute_accuracy(v_xs, v_ys): 
 global prediction 
 y_pre = sess.run(prediction, feed_dict={xs: v_xs, keep_prob: 1}) 
 correct_prediction = tf.equal(tf.argmax(y_pre,1), tf.argmax(v_ys,1)) 
 accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) 
 result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys, keep_prob: 1}) 
 return result 
 
# 神经元权重 
def weight_variable(shape): 
 initial = tf.truncated_normal(shape, stddev=0.1) 
 return tf.Variable(initial) 
 
# 神经元偏置 
def bias_variable(shape): 
 initial = tf.constant(0.1, shape=shape) 
 return tf.Variable(initial) 
 
# 卷积 
def conv2d(x, W): 
 # stride [1, x_movement, y_movement, 1] 
 # Must have strides[0] = strides[3] = 1 
 return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') 
 
# 最大池化，x,y步进值均为2 
def max_pool_2x2(x): 
 # stride [1, x_movement, y_movement, 1] 
 return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME') 
 
 
# define placeholder for inputs to network 
xs = tf.placeholder(tf.float32, [None, 10304])/255. # 112*92 
ys = tf.placeholder(tf.float32, [None, PEOPLENUM]) # 42个输出 
keep_prob = tf.placeholder(tf.float32) 
x_image = tf.reshape(xs, [-1, 112, 92, 1]) 
# print(x_image.shape) # [n_samples, 112,92,1] 
 
# 第一层卷积层 
W_conv1 = weight_variable([5,5, 1,32]) # patch 5x5, in size 1, out size 32 
b_conv1 = bias_variable([32]) 
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) # output size 112x92x32 
h_pool1 = max_pool_2x2(h_conv1)       # output size 56x46x64 
 
 
# 第二层卷积层 
W_conv2 = weight_variable([5,5, 32, 64]) # patch 5x5, in size 32, out size 64 
b_conv2 = bias_variable([64]) 
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) # output size 56x46x64 
h_pool2 = max_pool_2x2(h_conv2)       # output size 28x23x64 
 
 
# 第一层神经网络全连接层 
W_fc1 = weight_variable([28*23*64, 1024]) 
b_fc1 = bias_variable([1024]) 
# [n_samples, 28, 23, 64] ->> [n_samples, 28*23*64] 
h_pool2_flat = tf.reshape(h_pool2, [-1, 28*23*64]) 
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) 
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) 
 
# 第二层神经网络全连接层 
W_fc2 = weight_variable([1024, PEOPLENUM]) 
b_fc2 = bias_variable([PEOPLENUM]) 
prediction = tf.nn.softmax((tf.matmul(h_fc1_drop, W_fc2) + b_fc2)) 
 
 
# 交叉熵损失函数 
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = tf.matmul(h_fc1_drop, W_fc2)+b_fc2, labels=ys)) 
regularizers = tf.nn.l2_loss(W_fc1) + tf.nn.l2_loss(b_fc1) +tf.nn.l2_loss(W_fc2) + tf.nn.l2_loss(b_fc2) 
# 将正则项加入损失函数 
cost += 5e-4 * regularizers 
# 优化器优化误差值 
train_step = tf.train.AdamOptimizer(1e-4).minimize(cost) 
 
sess = tf.Session() 
init = tf.global_variables_initializer() 
saver = tf.train.Saver() 
sess.run(init) 
 
# 训练1000次，每50次输出测试集测试结果 
for i in range(1000): 
 sess.run(train_step, feed_dict={xs: train_face, ys: train_face_num, keep_prob: 0.5}) 
 if i % 50 == 0: 
  print(sess.run(prediction[0],feed_dict= {xs: test_face,ys: test_face_num,keep_prob: 1})) 
  print(compute_accuracy(test_face,test_face_num)) 
# 保存训练数据 
save_path = saver.save(sess,'my_data/save_net.ckpt')

5.用神经卷积网络测试数据

# -*- coding: utf-8 -*- 
# 两层神经卷积网络加两层全连接神经网络 
 
from numpy import * 
import cv2 
import tensorflow as tf 
 
# 神经网络最终输出个数 
PEOPLENUM = 42 
 
# 2维=>1维 
def img2vector1(img): 
 row,col = img.shape 
 vector1 = zeros((1,row*col),float32) 
 vector1 = reshape(img,(1,row*col)) 
 return vector1 
 
# 神经元权重 
def weight_variable(shape): 
 initial = tf.truncated_normal(shape, stddev=0.1) 
 return tf.Variable(initial) 
 
# 神经元偏置 
def bias_variable(shape): 
 initial = tf.constant(0.1, shape=shape) 
 return tf.Variable(initial) 
 
# 卷积 
def conv2d(x, W): 
 # stride [1, x_movement, y_movement, 1] 
 # Must have strides[0] = strides[3] = 1 
 return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') 
 
# 最大池化，x,y步进值均为2 
def max_pool_2x2(x): 
 # stride [1, x_movement, y_movement, 1] 
 return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME') 
 
# define placeholder for inputs to network 
xs = tf.placeholder(tf.float32, [None, 10304])/255. # 112*92 
ys = tf.placeholder(tf.float32, [None, PEOPLENUM]) # 42个输出 
keep_prob = tf.placeholder(tf.float32) 
x_image = tf.reshape(xs, [-1, 112, 92, 1]) 
# print(x_image.shape) # [n_samples, 112,92,1] 
 
# 第一层卷积层 
W_conv1 = weight_variable([5,5, 1,32]) # patch 5x5, in size 1, out size 32 
b_conv1 = bias_variable([32]) 
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) # output size 112x92x32 
h_pool1 = max_pool_2x2(h_conv1)       # output size 56x46x64 
 
 
# 第二层卷积层 
W_conv2 = weight_variable([5,5, 32, 64]) # patch 5x5, in size 32, out size 64 
b_conv2 = bias_variable([64]) 
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) # output size 56x46x64 
h_pool2 = max_pool_2x2(h_conv2)       # output size 28x23x64 
 
 
# 第一层神经网络全连接层 
W_fc1 = weight_variable([28*23*64, 1024]) 
b_fc1 = bias_variable([1024]) 
# [n_samples, 28, 23, 64] ->> [n_samples, 28*23*64] 
h_pool2_flat = tf.reshape(h_pool2, [-1, 28*23*64]) 
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) 
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) 
 
# 第二层神经网络全连接层 
W_fc2 = weight_variable([1024, PEOPLENUM]) 
b_fc2 = bias_variable([PEOPLENUM]) 
prediction = tf.nn.softmax((tf.matmul(h_fc1_drop, W_fc2) + b_fc2)) 
 
sess = tf.Session() 
init = tf.global_variables_initializer() 
 
# 下载训练数据 
saver = tf.train.Saver() 
saver.restore(sess,'my_data/save_net.ckpt') 
 
# 返回签到人名 
def find_people(people_num): 
 if people_num == 41: 
  return '任童霖' 
 elif people_num == 42: 
  return 'LZT' 
 else: 
  return 'another people' 
 
def test(path): 
 # 获取处理后人脸 
 img = cv2.imread(path,0)/255 
 test_face = img2vector1(img) 
 print('true_test') 
 
 # 计算输出比重最大的人及其所占比重 
 prediction1 = sess.run(prediction,feed_dict={xs:test_face,keep_prob:1}) 
 prediction1 = prediction1[0].tolist() 
 people_num = prediction1.index(max(prediction1))+1 
 result = max(prediction1)/sum(prediction1) 
 print(result,find_people(people_num)) 
 
 # 神经网络输出最大比重大于0.5则匹配成功 
 if result > 0.50: 
  # 保存签到数据 
  qiandaobiao = load('save.npy') 
  qiandaobiao[people_num-1] = 1 
  save('save.npy',qiandaobiao) 
 
  # 返回 人名+签到成功 
  print(find_people(people_num) + '已签到') 
  result = find_people(people_num) + ' 签到成功' 
 else: 
  result = '签到失败' 
 return result

神经卷积网络入门简介

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持三水点靠木。