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服务器之家。

原文链接：https://blog.csdn.net/Lxingmo/article/details/76146646