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lfw_eval.py
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lfw_eval.py
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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
import pandas as pd
import matplotlib.pyplot as plt
from matlab_cp2tform import get_similarity_transform_for_cv2
import net_sphere
from mobile_net import MobileNetV2
from resnet import ResNet34, ResNet18, ResNet50
os.environ["CUDA_VISIBLE_DEVICES"] = "5"
def alignment(src_img,src_pts):
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
def KFold(n=6000, 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
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
def find_best_threshold(thresholds, predicts):
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
#
def runNetonLFW(lfwPath, lfwLandmarkPath, lfwPairsPath):
zfile = zipfile.ZipFile(lfwPath)
predicts = []
landmark = {}
with open(lfwLandmarkPath) as f:
landmark_lines = f.readlines()
for line in landmark_lines:
l = line.replace('\n', '').split('\t')
landmark[l[0]] = [int(k) for k in l[1:]]
with open(lfwPairsPath) as f:
pairs_lines = f.readlines()[1:]
for i in range(6000):
p = pairs_lines[i].replace('\n', '').split('\t')
if 3 == len(p):
sameflag = 1
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]))
img1 = alignment(cv2.imdecode(np.frombuffer(zfile.read("lfw/" + name1), np.uint8), 1), landmark[name1])
img2 = alignment(cv2.imdecode(np.frombuffer(zfile.read("lfw/" + name2), np.uint8), 1), landmark[name2])
imglist = [img1, cv2.flip(img1, 1), img2, cv2.flip(img2, 1)]
for i in range(len(imglist)):
imglist[i] = imglist[i].transpose(2, 0, 1).reshape((1, 3, 112, 96))
imglist[i] = (imglist[i] - 127.5) / 128
img = np.vstack(imglist)
img = Variable(torch.from_numpy(img).float(), volatile=True).cuda()
output = net(img)
f = output.data
f1, f2 = f[0], f[2]
cosdistance = f1.dot(f2) / (f1.norm() * f2.norm() + 1e-5)
predicts.append('{}\t{}\t{}\t{}\n'.format(name1, name2, cosdistance, sameflag))
predicts = np.array(list(map(lambda line: line.strip('\n').split('\t'), predicts)))
return predicts
# 绘制相似度分布直方图
def plotSimliarityHist(predicts, savePath):
predDf = pd.DataFrame(predicts)
predDf.columns = ["name1", "name2", "cosdistance", "sameflag"]
pos = predDf[predDf["sameflag"] == '1']
neg = predDf[predDf["sameflag"] == '0']
hist = pd.to_numeric(pos["cosdistance"]).hist()
fig1 = hist.get_figure()
fig1.savefig(os.path.join(savePath, "pos.jpg"))
hist = pd.to_numeric(neg["cosdistance"]).hist()
fig2 = hist.get_figure()
fig2.savefig(os.path.join(savePath, "neg.jpg"))
if __name__ == '__main__':
lfwPath = 'data/LFW/lfw.zip'
modelPath = 'model_file/resnet34_webface_align_m05.pt'
lfwLandmarkPath = 'data/LFW/lfw_landmark.txt'
lfwPairsPath = 'data/LFW/pairs.txt'
histSavePath = "data/"
modelName = modelPath.replace('.', '/').split('/')[1]
print("model:", modelName)
net = ResNet34(feature=True)
net.load_state_dict(torch.load(modelPath))
net.cuda()
net.eval()
net.feature = True
print("load model finished")
predicts = runNetonLFW(lfwPath, lfwLandmarkPath, lfwPairsPath)
print("predict finished!")
plotSimliarityHist(predicts, savePath=histSavePath)
accuracy = []
thd = []
folds = KFold(n=6000, n_folds=10, shuffle=False)
thresholds = np.arange(-1.0, 1.0, 0.005)
for idx, (train, test) in enumerate(folds):
best_thresh = find_best_threshold(thresholds, predicts[train])
accuracy.append(eval_acc(best_thresh, predicts[test]))
thd.append(best_thresh)
print('LFWACC={:.4f} std={:.4f} thd={:.4f}'.format(np.mean(accuracy), np.std(accuracy), np.mean(thd)))