以后的程序就不留这个文件啦
# -*- coding: utf-8 -*-
"""
Created on Fri Mar 22 09:59:41 2019
@author: Leon
内容：
人脸验证准确率测试
样本：LFW人脸集，共6000对人脸，中3000对同一身份、3000对不同身份。
"""
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
import sys
import os
import copy
from embeddings1 import load_model
sys.path.append('../align/')
from src.mtcnn import PNet, RNet, ONet
from tools import detect_face, get_model_filenames
import config
import cv2
import h5py
import utils1
def face_verification(img_pairs_list,f):
#++++++++++++++++++++++
margin = 44
image_size = 160
file_paths = get_model_filenames('../align/save_model/all_in_one/')
with tf.Graph().as_default():
with tf.Session() as sess:
# 根据模型文件载入模型
load_model('../model/')
# 得到输入、输出等张量
images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
keep_probability_placeholder= tf.get_default_graph().get_tensor_by_name('keep_probability:0')
saver = tf.train.import_meta_graph(file_paths[0])
saver.restore(sess, file_paths[1])
def pnet_fun(img): return sess.run(
('softmax/Reshape_1:0',
'pnet/conv4-2/BiasAdd:0'),
feed_dict={
'Placeholder:0': img})
def rnet_fun(img): return sess.run(
('softmax_1/softmax:0',
'rnet/conv5-2/rnet/conv5-2:0'),
feed_dict={
'Placeholder_1:0': img})
def onet_fun(img): return sess.run(
('softmax_2/softmax:0',
'onet/conv6-2/onet/conv6-2:0',
'onet/conv6-3/onet/conv6-3:0'),
feed_dict={
'Placeholder_2:0': img})
# 设置可视化进度条相关参数
jd = '\r %2d%%\t [%s%s]'
bar_num_total = 50
total_num = len(img_pairs_list)
result, dist = [],[]
for i in range(len(img_pairs_list)):
# 画进度条
#if i%round(total_num/bar_num_total) == 0 or i == total_num-1:
# bar_num_alright = round(bar_num_total*i/total_num)
# alright = '#'*bar_num_alright
# not_alright = '□'*(bar_num_total-bar_num_alright)
# percent = (bar_num_alright/bar_num_total)*100
# print(jd % (percent,alright,not_alright),end='')
# 读取一对人脸图像
img_pairs = img_pairs_list[i]
img_list = []
img1 = cv2.imread(img_pairs[0])
img2 = cv2.imread(img_pairs[1])
img_size1 = np.asarray(img1.shape)[0:2]
img_size2 = np.asarray(img2.shape)[0:2]
# 检测该对图像中的人脸
bounding_box1,points1=detect_face(img1,50,pnet_fun, rnet_fun, onet_fun,[0.4, 0.5, 0.6],0.7)
bounding_box2,points2=detect_face(img2,50,pnet_fun, rnet_fun, onet_fun,[0.4, 0.5, 0.6],0.7)
# 未检测到人脸，则将结果标为-1，后续计算准确率时排除
if len(bounding_box1)<1 or len(bounding_box2)<1:
result.append(-1)
dist.append(-1)
continue
# 将图片1加入img_list
det = np.squeeze(bounding_box1[0,0:4])
bb = np.zeros(4, dtype=np.int32)
bb[0] = np.maximum(det[0]-margin/2, 0)
bb[1] = np.maximum(det[1]-margin/2, 0)
bb[2] = np.minimum(det[2]+margin/2, img_size1[1])
bb[3] = np.minimum(det[3]+margin/2, img_size1[0])
cropped = img1[bb[1]:bb[3],bb[0]:bb[2],:]
aligned = cv2.resize(cropped, (image_size, image_size))
aligned=utils1.align_face(aligned, points1[:,0])
aligned=cv2.cvtColor(aligned,cv2.COLOR_RGB2GRAY)
aligned=np.float64(np.array(aligned))
aligned=(aligned-127.5)/128.0
img_list.append(aligned)
# 将图片2加入img_list
det = np.squeeze(bounding_box2[0,0:4])
bb = np.zeros(4, dtype=np.int32)
bb[0] = np.maximum(det[0]-margin/2, 0)
bb[1] = np.maximum(det[1]-margin/2, 0)
bb[2] = np.minimum(det[2]+margin/2, img_size2[1])
bb[3] = np.minimum(det[3]+margin/2, img_size2[0])
cropped = img2[bb[1]:bb[3],bb[0]:bb[2],:]
aligned = cv2.resize(cropped, (image_size, image_size))
aligned=utils1.align_face(aligned, points2[:,0])
aligned=cv2.cvtColor(aligned,cv2.COLOR_RGB2GRAY)
aligned=np.float64(np.array(aligned))
aligned=(aligned-127.5)/128.0
img_list.append(aligned)
images = np.stack(img_list)
images=images.reshape(images.shape[0],image_size, image_size,1)
# 将两个人脸转化为512维的向量
feed_dict = {
images_placeholder: images, phase_train_placeholder:False,keep_probability_placeholder:1.0}
emb = sess.run(embeddings, feed_dict=feed_dict)
# 计算两个人脸向量的距离
ed = np.sqrt(np.sum(np.square(emb[0]-emb[1])))
#ed = np.mean(np.square(np.subtract(emb[0], emb[1])))
#x1_norm = np.sqrt(np.sum(np.square(emb[0])))
#x2_norm = np.sqrt(np.sum(np.square(emb[1])))
# 内积
#x1_x2 = np.sum(np.multiply(emb[0], emb[1]))
#ed = x1_x2 / (x1_norm * x2_norm)
dist.append(ed)
# 根据得出的人脸间的距离，判断是否属于同一个人
f.write(str(ed)+'\n') # 自带文件关闭功能，不需要再写f.close()
if ed<=1.1:
result.append(1)
else:
result.append(0)
return result,dist
#+++++++++++++++++++++++
def get_img_pairs_list(pairs_txt_path,img_path):
""" 指定图片组合及其所在文件，返回各图片对的绝对路径
Args:
pairs_txt_path：图片pairs文件，里面是6000对图片名字的组合
img_path：图片所在文件夹
return:
img_pairs_list：深度为2的list，每一个二级list存放的是一对图片的绝对路径
"""
file = open(pairs_txt_path)
img_pairs_list,labels = [],[]
while 1:
img_pairs = []
line = file.readline().replace('\n','')
if line == '':
break
line_list = line.split('\t')
if len(line_list) == 3:
# 图片路径示例：
# 'C:\Users\thinkpad1\Desktop\image_set\lfw_funneled\Tina_Fey\Tina_Fey_0001.jpg'
img_pairs.append(img_path+'\\'+line_list[0]+'\\'+line_list[0]+'_'+('000'+line_list[1])[-4:]+'.jpg')
img_pairs.append(img_path+'\\'+line_list[0]+'\\'+line_list[0]+'_'+('000'+line_list[2])[-4:]+'.jpg')
labels.append(1)
elif len(line_list) == 4:
img_pairs.append(img_path+'\\'+line_list[0]+'\\'+line_list[0]+'_'+('000'+line_list[1])[-4:]+'.jpg')
img_pairs.append(img_path+'\\'+line_list[2]+'\\'+line_list[2]+'_'+('000'+line_list[3])[-4:]+'.jpg')
labels.append(0)
else:
continue
img_pairs_list.append(img_pairs)
return img_pairs_list,labels
def roc(dist,labels):
TP_list,TN_list,FP_list,FN_list,TPR,FPR = [],[],[],[],[],[]
for t in range(180):
threh = 0.1+t*0.01
TP,TN,FP,FN = 0,0,0,0
for i in range(len(dist)):
if labels[i]==1 and dist[i]!=-1:
if dist[i]<threh:
TP += 1
else:
FN += 1
elif labels[i]==0 and dist[i]!=-1:
if dist[i]>=threh:
TN += 1
else:
FP += 1
TP_list.append(TP)
TN_list.append(TN)
FP_list.append(FP)
FN_list.append(FN)
TPR.append(TP/(TP+FN))
FPR.append(FP/(FP+TN))
return TP_list,TN_list,FP_list,FN_list,TPR,FPR
if __name__ == '__main__':
pairs_txt_path = 'D:/lfw_funneled/pairs.txt'
img_path = 'D:/lfw_funneled'
img_pairs_list,labels = get_img_pairs_list(pairs_txt_path,img_path)
f=open("test.txt", "w")
result,dist = face_verification(img_pairs_list,f)
num_right, num_total = 0, 0
num_total = len([r for r in result if r != -1])
num_right = len([result[i] for i in range(len(result)) if result[i] == labels[i]])
for i in range(len(labels)):
if(dist[i]<0):
continue
if(labels[i]==1):
plt.scatter(dist[i],i,color='red',marker='.')
elif(labels[i]==0):
plt.scatter(dist[i],i,color='blue',marker='.')
plt.show()
print("人脸验证测试完毕")
print("阈值为1.1，共%d对人脸，准确率%2.4f%%"%(num_total, round(100*num_right/num_total,4)))
f.close()
TP_list,TN_list,FP_list,FN_list,TPR,FPR = roc(dist,labels)
plt.plot(FPR,TPR,label='Roc')
plt.plot([0, 1], [0, 1], '--', color=(0.6, 0.6, 0.6), label='Luck')
plt.xlabel('FPR')
plt.ylabel('TPR')
plt.legend()
plt.show()
plt.plot(np.linspace(0.1,1.89,180),TP_list,label='TP')
plt.plot(np.linspace(0.1,1.89,180),TN_list,label='TN')
plt.plot(np.linspace(0.1,1.89,180),FP_list,label='FP')
plt.plot(np.linspace(0.1,1.89,180),FN_list,label='FN')
plt.legend()
plt.show()