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#!/usr/bin/python3
# -*- coding: utf-8 -*-
# Author: xurongzhong#126.com wechat:pythontesting qq:37391319
# CreateDate: 2018-1-8
# datas.py
import time
import os
import subprocess
from pathlib import Path
import pandas as pd
import numpy as np
def get_live_frr_far(df,colomn1,score,colomn2):
total = len(df)
#print(df.head())
unknow = len(df[df[colomn1] == -1])
df = df[df[colomn1] != -1]
real_number = len(df[df[colomn2] == 0])
photo_number = len(df[df[colomn2] == 1])
num_2d = len(df.loc[df['filename'].str.contains('/2D_photo/')])
num_3d = len(df.loc[df['filename'].str.contains('/3D_photo/')])
# 真人识别为假人
frr_number = len(df.loc[((df[colomn1] > score) & (df[colomn2] == 0))])
# 假人识别为真人
far_number = len(df.loc[((df[colomn1] < score) & (df[colomn2] == 1))])
# 2d假人识别为真人
far_number_2d = far_number_3d = 0
far_number_2d = len(df.loc[((df[colomn1] < score) & (df[colomn2] == 1) &
df['filename'].str.contains('/2D_photo/', regex=False))])
## 3d假人识别为真人
far_number_3d = len(df.loc[((df[colomn1] < score) & (df[colomn2] == 1) &
df['filename'].str.contains('/3D_photo/', regex=False))])
frr = 0 if not real_number else frr_number/float(real_number)
far2d = 0 if not num_2d else far_number_2d/float(num_2d)
far3d = 0 if not num_3d else far_number_3d/float(num_3d)
far = 0 if not photo_number else far_number/float(photo_number)
return (far, frr, total, real_number, frr_number, photo_number, far_number,
num_2d, far_number_2d, far2d, num_3d, far_number_3d, far3d,
unknow, unknow/float(total))
def get_gaze_frr_far(df,colomn1,score):
total = len(df)
unknow = len(df[df[colomn1] == -1])
df = df[df[colomn1] != -1]
real_number = len(df.loc[df['filename'].str.contains('/gaze/')])
no_number = len(df.loc[df['filename'].str.contains('/no_gaze/')])
# 真人识别为假人
frr_number = len(df.loc[(df['score'] < score) & df['filename'].str.contains('/gaze/')])
# 假人识别为真人
far_number = len(df.loc[(df['score'] > score) & df['filename'].str.contains('/no_gaze/')] )
frr = 0 if not real_number else frr_number/float(real_number)
far = 0 if not no_number else far_number/float(no_number)
return (far, frr, total, real_number, frr_number, no_number, far_number, unknow, unknow/float(total))
def load_verify_server_result(names,files,scores,
replace_file="/home/andrew/code/data/tof/base_test_data/vivo-verify-452/./",
replace_name="output/enroll_list/",
):
real_photos = pd.read_csv(files, names=['filename'])
real_photos['filename'] = real_photos['filename'].apply(
lambda x:x.replace(replace_file, ''))
real_photos['person'] = real_photos['filename'].apply(
lambda x:x.split('/')[0])
persons = pd.read_csv(names,names=['person'])
persons['person'] = persons['person'].apply(
lambda x:x.replace(replace_name, ''))
df = pd.read_csv(scores, header=None, engine='c',
na_filter=False, low_memory=False)
df.index = persons['person']
return df, real_photos
def get_verify_errors(df, real_photos, positive=0.7, negative=0.7):
other_errors = []
self_errors = []
self_nums = 0
other_nums = 0
for person in df.index:
print("index: {} {}".format(person,time.ctime()))
row = df.loc[str(person)]
row.index = [real_photos['person'].astype(str), real_photos['filename']]
#print(row)
self = row[str(person)]
self_nums = self_nums + len(self)
self_error = self[(self<positive) & (self>-1)]
for item in self_error.index:
self_errors.append((item, self_error[item]))
#print(self_error)
others = row.drop(person,level=0)
other_nums = other_nums + len(others)
other_error = others[others>=negative]
for item in other_error.index:
other_errors.append([person,item[1], other_error.loc[item]])
#print(other_error)
df_person_errors = pd.DataFrame(self_errors,columns=['filename','score'])
df_other_errors = pd.DataFrame(other_errors,columns=['person','filename','score'])
return df_person_errors, df_other_errors, self_nums, other_nums
def get_verify_frr_far(selfs_num, others_num, df_person_errors, df_other_errors, colomn, score):
frr_num = len(df_person_errors[df_person_errors[colomn] < score])
far_num = len(df_other_errors[df_other_errors[colomn] > score])
frr = 0 if not frr_num else frr_num/float(selfs_num)
far = 0 if not far_num else far_num/float(others_num)
return (far, frr, selfs_num + others_num, selfs_num, frr_num, others_num, far_num)
def get_verify_server_result(
names,files,scores, score=0.7, output_dir="./",
replace_file="/home/andrew/code/data/tof/base_test_data/vivo-verify-452/./",
replace_name="output/enroll_list/", error_name="verify_error.xlsx"):
df, real_photos = load_verify_server_result(
names, files, scores, replace_file=replace_file,
replace_name=replace_name)
df_person_errors, df_other_errors, selfs_num, others_num = \
get_verify_errors(df, real_photos, positive=0.9, negative=0.7)
writer = pd.ExcelWriter(error_name)
df_person_errors.to_excel(writer, sheet_name='本人识别分值低于0.9', index=False)
df_other_errors.to_excel(writer, sheet_name='他人识别高于0.7', index=False)
values = [0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80,
0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.90]
results = []
for value in values:
result = get_verify_frr_far(
selfs_num, others_num, df_person_errors, df_other_errors, 'score',
value)
results.append([value, *result])
df4 = pd.DataFrame(
results,
columns=["Threshold","FAR", "FRR", "number","real_number", "frr_number",
"no_number", "far_number"])
df4.to_excel(writer, sheet_name='FAR_FRR', index=False)
writer.save()
def check_process(name):
cmd = "ps afx | grep -i '{}' | grep -v grep |wc -l".format(name)
result = subprocess.check_output(cmd, shell=True)
return True if int(result.strip()) else False
def wait_until_stop(name,sep=5):
print("Waiting " + name)
time.sleep(sep)
while check_process(name):
time.sleep(sep)
def get_liveness_server_result(scores, files, labels, score=0.99,
replace='/home/andrew/code/data/tof/base_test_data/vivo-liveness/',
error_name="live_error.xlsx",type_=''):
cases = {
"01": "注册",
"02": "全脸-稳定拍摄",
"03": "全脸-晃动拍摄",
"04": "半脸-鼻子以下超出画面",
"05": "半脸-眉毛以上超出画面",
"06": "遮挡大部分五官",
"07": "遮挡部分五官",
"08": "手机平放桌面",
"09": "一睁一闭",
"10": "闭眼(戴墨镜、裸眼、普通眼镜) ",
"11": "闭眼(戴墨镜、普通眼镜下滑挡住眼睛) ",
"12": "闭眼(手机晃动)",
"13": "注视",
"14": "非注视",
"15": "侧躺、平躺"}
def rename(name):
type_ = os.path.dirname(name.replace(replace,"").split()[-1])
last = type_.split('/')[-1]
if last in cases and replace:
type_ = type_.replace(last,cases[last])
return type_
df_score = pd.read_csv(scores, header=None, names=['score'], engine='c',
na_filter=False, low_memory=False)
df_file = pd.read_csv(files, header=None, names=['filename'])
df_label = pd.read_csv(labels, header=None, names=['label'], engine='c',
na_filter=False, low_memory=False)
df = pd.concat([df_label, df_score, df_file], axis=1)
df['type'] = df['filename'].apply(rename)
# print(df.head())
results =[]
for name, group in df.groupby('type'):
result = get_live_frr_far(group, 'score', score, 'label')
results.append([name, *result[:-8], *result[-2:]])
for name, group in df.groupby('label'):
result = get_live_frr_far(group, 'score', score, 'label')
results.append([name, *result[:-8], *result[-2:]])
# 真人识别为假人
df1 = df.loc[((df['score'] > score) & (df['label'] == 0))]
# 假人识别为真人
df2 = df.loc[((df['score'] < score) & (df['label'] == 1))]
result = get_live_frr_far(df, 'score', score, 'label')
results.append(["All", *result[:-8], *result[-2:]])
writer = pd.ExcelWriter(error_name)
df1.to_excel(writer, sheet_name='真人识别为假人', index=False)
df2.to_excel(writer, sheet_name='假人识别为真人', index=False)
df3 = pd.DataFrame(results, columns=[
"类别","far", "frr", "总数","真人总数","真人识别为假人", "假人总数",
"假人识别为真人","未识别数","未识别率"])
df3.to_excel(writer, sheet_name='分类统计', index=False)
results = []
values = [0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 0.999]
for value in values:
result = get_live_frr_far(df, 'score', value, 'label')
results.append([value, *result])
df4 = pd.DataFrame(results, columns=["Threshold","FAR","FRR","total",
"real_num","frr_num", "photo_num", "far_num",'num_2d', 'far_number_2d',
'far2d', 'num_3d', 'far_number_3d', 'far3d',"unknow","unknow_rate"])
df4.to_excel(writer, sheet_name='FAR_FRR', index=False)
writer.save()
def get_gaze_server_result(scores, files, labels, score=0.3,
error_name="gaze_error.xlsx", type_=""):
values = []
for i in range(18):
values.append(i*0.05+0.1)
df_score = pd.read_csv(scores, header=None, names=['score'])
df_file = pd.read_csv(files, header=None, names=['filename'])
df_label = pd.read_csv(labels, header=None, names=['label'])
df = pd.concat([df_label, df_score, df_file], axis=1)
df1 = df.loc[(df['score'] < score) & df['filename'].str.contains('/gaze/')]
df2 = df.loc[(df['score'] > score) & df['filename'].str.contains('/no_gaze/')]
writer = pd.ExcelWriter(error_name)
df1.to_excel(writer, sheet_name='注视识别为非注视', index=False)
df2.to_excel(writer, sheet_name='非注视识别为注视', index=False)
results = []
for value in values:
result = get_gaze_frr_far(df, 'score', value)
results.append([value, *result])
df4 = pd.DataFrame(
results,
columns=["Threshold","FAR", "FRR", "number","real_number", "frr_number",
"no_number", "far_number","unknow","unknow_rate"])
df4.to_excel(writer, sheet_name='FAR_FRR', index=False)
writer.save()
def build_verify_input(directory, output,filetype='ir'):
peoples = {}
files = []
p = Path(directory)
root = "{}{}".format(directory.rstrip(os.sep), os.sep)
print('root', root,"filetype", filetype)
for file_name in p.glob('**/*.{0}'.format(filetype)):
file_str = str(file_name)
#print(file_str)
people = file_str.split(os.sep)[-3]
if not people in peoples:
peoples[people] = []
peoples[people].append(file_str.replace(root,""))
enroll_list = "{}{}enroll_list".format(output, os.sep)
if not os.path.exists(enroll_list):
os.makedirs(enroll_list)
label_enroll = np.array([], dtype=int)
label_real = np.array([], dtype=int)
with open("{}{}i_enroll.txt".format(output, os.sep), 'w') as i_enroll:
with open("{}{}i_real.txt".format(output, os.sep), 'w') as i_real:
for i, key in enumerate(peoples.keys()):
print("{}/enroll_list/{}".format(
output.rstrip(os.sep),key), file=i_enroll)
label_enroll = np.append(label_enroll, i)
with open("{}{}{}".format(enroll_list, os.sep, key), 'w') as en:
for imgname in peoples[key]:
if '/enroll/' in imgname.lower():
#print(imgname)
print("{}{}".format(root,imgname), file=en)
else:
print("{}{}".format(root,imgname), file=i_real)
label_real = np.append(label_real, i)
with open("{}{}labels.txt".format(output, os.sep), 'w') as flabel:
label_enroll.tofile(flabel, sep=' ')
print('', file = flabel)
label_real.tofile(flabel, sep=' ')
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