#-*- coding:utf-8 -*-
from __future__ import print_function
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.autograd import Variable
torch.backends.cudnn.bencmark = True
import os,sys,cv2,random,datetime
import argparse
import numpy as np
import zipfile
from dataset import ImageDataset
from matlab_cp2tform import get_similarity_transform_for_cv2
import net_sphere
from matplotlib import pyplot as plt
#图像对齐和裁剪
def alignment(src_img,src_pts):
#使用标准人脸坐标对图像进行仿射
ref_pts = [ [30.2946, 51.6963],[65.5318, 51.5014],
[48.0252, 71.7366],[33.5493, 92.3655],[62.7299, 92.2041] ]
crop_size = (96, 112)
src_pts = np.array(src_pts).reshape(5,2)
s = np.array(src_pts).astype(np.float32)
r = np.array(ref_pts).astype(np.float32)
tfm = get_similarity_transform_for_cv2(s, r)
face_img = cv2.warpAffine(src_img, tfm, crop_size)
return face_img
#k-fold cross validation（k-折叠交叉验证）
#将n份数据分为n_folds份，以次将第i份作为测试集，其余部分作为训练集
def KFold(n=200, n_folds=10, shuffle=False):
folds = []
base = list(range(n))
for i in range(n_folds):
test = base[(i*n//n_folds):((i+1)*n//n_folds)]
train = list(set(base)-set(test))
folds.append([train,test])
return folds
#求解当前阈值时的准确率
def eval_acc(threshold, diff):
y_true = []
y_predict = []
for d in diff:
same = 1 if float(d[2]) > threshold else 0
y_predict.append(same)
y_true.append(int(d[3]))
y_true = np.array(y_true)
y_predict = np.array(y_predict)
accuracy = 1.0*np.count_nonzero(y_true==y_predict)/len(y_true)
return accuracy
#eval_acc和find_best_threshold共同工作，来求试图找到最佳阈值，
#
def find_best_threshold(thresholds, predicts):
#threshould 阈值
best_threshold = best_acc = 0
for threshold in thresholds:
accuracy = eval_acc(threshold, predicts)
if accuracy >= best_acc:
best_acc = accuracy
best_threshold = threshold
return best_threshold
#命令行参数
parser = argparse.ArgumentParser(description='PyTorch sphereface lfw')
parser.add_argument('--net','-n', default='sphere20a', type=str)
parser.add_argument('--lfw', default='../DataSet/lfw.zip', type=str)
parser.add_argument('--model','-m', default='./sphere20a_20171020.pth', type=str)
args = parser.parse_args()
predicts=[]
#加载网络
net = getattr(net_sphere,args.net)()
#加载模型
net.load_state_dict(torch.load(args.model))
#
net.eval()
#
net.feature = True
#加载图片数据
zfile = zipfile.ZipFile(args.lfw)
#加载landmark，每张照片包括五个特征点，共五组坐标
landmark = {}
with open('data/lfw_landmark.txt') as f:
landmark_lines = f.readlines()
#对每一行进行处理
for line in landmark_lines:
l = line.replace('\n','').split('\t')
#将每一组数据转化为字典形式
landmark[l[0]] = [int(k) for k in l[1:]]
#加载pairs
with open('data/pairs.txt') as f:
pairs_lines = f.readlines()[1:]
#range表示测试的图片对数
for i in range(600):
print(str(i)+" start")
p = pairs_lines[i].replace('\n','').split('\t')
# pairs.txt一共有6000行，存在两种形式，
# 分别表示进行对比的两张照片，形式1是同一个人，形式2是不同人：
# name 数字1 数字2
# name 数字1 name数字2
if 3==len(p):
sameflag = 1
#形式例如：Woody_Allen/Woody_Allen_0002.jpg
name1 = p[0]+'/'+p[0]+'_'+'{:04}.jpg'.format(int(p[1]))
name2 = p[0]+'/'+p[0]+'_'+'{:04}.jpg'.format(int(p[2]))
if 4==len(p):
sameflag = 0
name1 = p[0]+'/'+p[0]+'_'+'{:04}.jpg'.format(int(p[1]))
name2 = p[2]+'/'+p[2]+'_'+'{:04}.jpg'.format(int(p[3]))
#分别加载两张照片，并对其进行图像对齐
org_img1=cv2.imdecode(np.frombuffer(zfile.read("lfw/lfw/"+name1),np.uint8),1)
org_img2=cv2.imdecode(np.frombuffer(zfile.read("lfw/lfw/"+name2),np.uint8),1)
img1 = alignment(org_img1,landmark[name1])
img2 = alignment(org_img2,landmark[name2])
#1.对输出图像使用cv2进行展示
# cv2.imshow("org_img1", org_img1)
# cv2.imshow("org_img2", org_img2)
# cv2.imshow("img1",img1)
# cv2.imshow("img2", img2)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
#2.对输出图像使用matplotlib进行展示
fig_new=plt.figure()
img_list=[[org_img1,221],[org_img2,222],[img1,223],[img2,224]]
for p,q in img_list:
ax=fig_new.add_subplot(q)
p = p[:, :, (2, 1, 0)]
ax.imshow(p)
plt.show()
#cv.flip图像翻转,第二个参数：1：水平翻转，0：垂直翻转，-1：水平垂直翻转
imglist = [img1,cv2.flip(img1,1),img2,cv2.flip(img2,1)]
#分别对图片进行
for m in range(len(imglist)):
imglist[m] = imglist[m].transpose(2, 0, 1).reshape((1,3,112,96))
imglist[m] = (imglist[m]-127.5)/128.0
# p.vstack: 垂直（按照行顺序）的把数组给堆叠起来
#******举例******
# import numpy as np
# a = [1, 2, 3]
# b = [4, 5, 6]
# print(np.vstack((a, b)))
#
# 输出：
# [[1 2 3]
# [4 5 6]]
img = np.vstack(imglist)
#将numpy形式转化为variable形式
img = Variable(torch.from_numpy(img).float(),volatile=True)
output = net(img)
#得到计算结果,f1和f2均为512维向量形式
f = output.data
f1,f2 = f[0],f[2]
#计算二者的余弦相似度，后面加上常量是为了防止分母为0
#关于余弦相似度请自行百度或google
#这里给出一个简单说明的链接：http://blog.csdn.net/huangfei711/article/details/78469614
#a*b/|a||b|
cosdistance = f1.dot(f2)/(f1.norm()*f2.norm()+1e-5)
predicts.append('{}\t{}\t{}\t{}\n'.format(name1,name2,cosdistance,sameflag))
print(str(i) + " end")
#准确率
accuracy = []
#（最佳）阈值
thd = []
#k-fold cross validation（k-折叠交叉验证）
#folds的形式为[[train,test],[train,test].....]
folds = KFold(n=600, n_folds=10, shuffle=False)
#取数组为-1到1，步长为0.005
thresholds = np.arange(-1.0, 1.0, 0.005)
# 此处为原作者code，疑似有误，已做修改
# predicts = np.array(map(lambda line:frd.append(line.strip('\n').split()), predicts))
predicts = np.array([k.strip('\n').split() for k in predicts])
for idx, (train, test) in enumerate(folds):
# predicts[train/test]形式为：
# [['Doris_Roberts/Doris_Roberts_0001.jpg'
# 'Doris_Roberts/Doris_Roberts_0003.jpg' '0.6532696413605743' '1'],.....]
#寻找最佳阈值
best_thresh = find_best_threshold(thresholds, predicts[train])
#通过上面的得到的最佳阈值来对test数据集进行测试得到准确率
accuracy.append(eval_acc(best_thresh, predicts[test]))
#thd阈值
thd.append(best_thresh)
#np.mean：计算均值，np.std：计算标准差
#输出结果分别为：准确率均值，准确率标准差，阈值均值
print('LFWACC={:.4f} std={:.4f} thd={:.4f}'.format(np.mean(accuracy), np.std(accuracy), np.mean(thd)))
#例如结果为 LFWACC=0.9800 std=0.0600 thd=0.3490
#则说明准确率为98%，准确率标准差为0.06，阈值的均值为0.3490
#因此我们可以认为余弦相似度大于0.3490的两张图片里是同一个人