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/')])

num_3d_high = len(df.loc[df['filename'].str.contains('/3D_Highcost/')])

num_3d_low = len(df.loc[df['filename'].str.contains('/3D_Lowcost/')])

# 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))])

far_number_3d_high = len(df.loc[((df[colomn1] < score) & (df[colomn2] == 1) &

df['filename'].str.contains('/3D_Highcost/', regex=False))])

far_number_3d_low = len(df.loc[((df[colomn1] < score) & (df[colomn2] == 1) &

df['filename'].str.contains('/3D_Lowcost/', 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)

far3d_high = 0 if not num_3d_high else far_number_3d_high/float(num_3d_high)

far3d_low = 0 if not num_3d_low else far_number_3d_low/float(num_3d_low)

far = 0 if not photo_number else far_number/float(photo_number)

return (far, frr, total, real_number, frr_number, photo_number, far_number,

unknow, unknow/float(total),

num_2d, far_number_2d, far2d, num_3d, far_number_3d, far3d,

num_3d_high, far_number_3d_high, far3d_high,

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)

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, ''))

score = np.fromfile(scores, dtype=np.float32)

score = score.reshape(len(persons), len(real_photos))

df = pd.DataFrame(score, columns=real_photos['filename'])

df.index = persons['person']

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)]

#print(row)

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'])

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)

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)

#print(df.head())

#print(real_photos.head())

df.to_csv("count.csv")

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)

#print(df_person_errors.head())

#print(df_other_errors.head())

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)

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[:9]])

for name, group in df.groupby('label'):

result = get_live_frr_far(group, 'score', score, 'label')

results.append([name, *result[:9]])

# 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[:9]])

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)

#print(results)

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])

columns=["Threshold","FAR","FRR","total",

"real_num","frr_num", "photo_num", "far_num", "unknow","unknow_rate",

'num_2d', 'far_number_2d', 'far2d',

'num_3d', 'far_number_3d', 'far3d',

'num_3d_high', 'far_number_3d_high', 'far3d_high',

'num_3d_low', 'far_number_3d_low', 'far3d_low']

df4 = pd.DataFrame(results, columns=columns)

df4.to_excel(writer, sheet_name='FAR_FRR', index=False)

writer.save()

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)

error_name="eye_error.xlsx", type_=""):

df = pd.read_csv(values, sep=' |,', engine='python',

names=['left_score','left_valid','right_score','right_valid','name'])

df_unknow = df[df['left_score'] == -1]

df_error = df[df['left_score'] == -2]

df2 = df[df['left_score'] > -1]

close_error = df2[df2['name'].str.contains('/close/') & ((df2['left_score'] > 9.5) | (df2['right_score'] > 9.5))]

open_error = df2[df2['name'].str.contains('/open/') & (df2['left_score'] < 9.5) & (df2['right_score'] < 9.5)]

invalid_error = df2[df2['name'].str.contains('/invalid/') & ((df2['left_valid'] > 9.5) | (df2['right_valid'] > 9.5))]

valid_error = df2[df2['name'].str.contains('/valid/') & (df2['left_valid'] < 9.5) & (df2['right_valid'] < 9.5)]

writer = pd.ExcelWriter(error_name)

df_unknow.to_excel(writer, sheet_name='未认识人脸', index=False)

df_error.to_excel(writer, sheet_name='图片格式错误', index=False)

close_error.to_excel(writer, sheet_name='闭眼识别为睁眼', index=False)

open_error.to_excel(writer, sheet_name='睁眼识别为闭眼', index=False)

invalid_error.to_excel(writer, sheet_name='无效识别为有效', index=False)

valid_error.to_excel(writer, sheet_name='有效识别为无效', index=False)

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:

#for i in range(len(people)):

#flabel.write(i)

#flabel.write('\n')

label_enroll.tofile(flabel, sep=' ')

print('', file = flabel)