1: ("白天-室内-正常光-非走动-720的7人间的中间位置", "白天-室内-正常光"),

2: ("白天-室内-正常光-非走动-路演区第4排(低)中间位置", "白天-室内-正常光"),

3: ("白天-室内-正常光-非走动-海翔通往荔园路的楼下通道门口", "白天-室内-正常光"),

4: ("白天-室内-正常光-非走动-701健身区域", "白天-室内-正常光"),

5: ("白天-室内-正常光-非走动-电梯间内", "白天-室内-正常光"),

6: ("白天-室内-正常光-走动-七楼楼道", "白天-室内-正常光"),

7: ("白天-室内-正常光-走动-717办公室", "白天-室内-正常光"),

8: ("白天-室内-正常光-动作-平躺", "白天-室内-正常光"),

9: ("白天-室内-正常光-动作-侧躺", "白天-室内-正常光"),

10: ("白天-室内-暗光-非走动-720办公室4人间关灯后", "白天-室内-暗光"),

11: ("白天-室内-逆光-非走动-路演区窗口逆光", "白天-室内-逆光"),

12: ("白天-室内-逆光-非走动-717办公室过道顶灯光", "白天-室内-逆光"),

13: ("白天-室内-逆光-非走动-厕所头顶灯光", "白天-室内-逆光"),

14: ("白天-室内-逆光-非走动-701健身区域", "白天-室内-逆光"),

15: ("白天-室内-逆光-走动-路演区窗口逆光", "白天-室内-逆光"),

16: ("白天-室内-逆光-走动-717办公室过道顶灯光", "白天-室内-逆光"),

17: ("白天-室内-逆光-走动-厕所头顶灯光", "白天-室内-逆光"),

18: ("白天-室外-正常光-非走动-海翔楼下空旷处（品骏快递）（背阴处正常光）",

"白天-室外-正常光"),

19: ("白天-室外-正常光-非走动-荔园路树荫下", "白天-室外-正常光"),

20: ("白天-室外-正常光-走动-海翔楼下空旷处（品骏快递）（背阴处正常光）",

"白天-室外-正常光"),

21: ("白天-室外-正常光-走动-荔园路树荫下", "白天-室外-正常光"),

22: ("白天-室外-逆光-非走动-海翔楼下空旷处 (品骏快递)(建议中午左右)（背对太阳）",

"白天-室外-逆光"),

23: ("白天-室外-逆光-非走动-荔园路树荫下（建议中午左右）（背对太阳）",

"白天-室外-逆光"),

24: ("白天-室外-逆光-非走动-（阴天）海翔通往荔园路的楼下通道门口（人物背对门口）",

"白天-室外-逆光"),

25: ("白天-室外-逆光-非走动-（阴天）手机朝上，背朝天空", "白天-室外-逆光"),

26: ("白天-室外-逆光-走动-海翔楼下空旷处（品骏快递）（建议中午左右)（背对太阳）",

"白天-室外-逆光"),

27: ("白天-室外-逆光-走动-荔园路树荫下（建议中午左右）（背对太阳）",

"白天-室外-逆光"),

28: ("白天-室外-逆光-走动-（阴天）手机朝上，背朝天空", "白天-室外-逆光"),

29: ("白天-室外-强光-非走动-海翔楼下空旷处（建议中午左右）（面对太阳）",

"白天-室外-强光"),

30: ("白天-室外-强光-非走动-荔园路树荫下（面对太阳）", "白天-室外-强光"),

31: ("白天-室外-强光-走动-海翔楼下空旷处（建议中午左右）（面对太阳）",

"白天-室外-强光"),

32: ("白天-室外-强光-走动-荔园路树荫下（面对太阳）", "白天-室外-强光"),

33: ("白天-室内-正常光-表情", "白天-室内-正常光-表情"),

34: ("白天-室内-正常光-不戴眼镜注册，戴近视眼镜认证", "白天-室内-正常光-脸"),

35: ("白天-室内-正常光-不戴眼镜注册，戴墨镜认证", "白天-室内-正常光-脸"),

36: ("白天-室内-正常光-戴近视眼镜注册，不戴眼镜认证", "白天-室内-正常光-脸"),

37: ("白天-室内-正常光-戴近视眼镜注册，戴墨镜认证", "白天-室内-正常光-脸"),

38: ("晚上-室内-正常光-夜晚路演区沙发位置", "晚上室内正常光"),

39: ("晚上-室内-正常光-七楼过道", "晚上室内正常光"),

40: ("晚上-室内-正常光-701健身区域", "晚上室内正常光"),

41: ("晚上-室内-正常光-平躺（路演区域沙发位置）", "晚上室内正常光"),

42: ("晚上-室内-正常光-侧躺（路演区域沙发位置）", "晚上室内正常光"),

43: ("晚上-室内-暗光-夜晚路演区沙发位置（19：00左右关灯）", "晚上室内暗光"),

44: ("晚上-室内-暗光-7楼710的客梯间（关灯后）", "晚上室内暗光"),

45: ("晚上-室内-暗光-720的4人间关灯后", "晚上室内暗光"),

46: ("晚上-室内-暗光-平躺（720的4人间关灯后）", "晚上室内暗光"),

47: ("晚上-室内-暗光-侧躺（720的4人间关灯后）", "晚上室内暗光"),

48: ("晚上-室外-暗光-海翔楼下空旷处（品骏快递）（人物背景无灯光）",

"晚上室外暗光"),

49: ("晚上-室外-暗光-海翔楼下上坡处路灯下（广州银行）", "晚上室外暗光"),

50: ("晚上-室外-强光-海翔楼下上坡处路灯下（广州银行）（面朝路灯）",

"晚上室外强光"),

'''

total_number = compare_number = live_number = success_number = \

test_number = 0

print(datas)

for row in datas:

print(row)

num, case_name, total, compare, live, success, test, r1, r2, r3 = row

if num in values:

total_number += total

compare_number += compare

live_number += live

success_number += success

test_number += test

return ["====", maps[values[0]][0], total_number, compare_number,

live_number, success_number, test_number,

percentage(compare_number, total_number),

percentage(live_number, total_number),

result = ''

for line in open(name):

result += line + "<br>"

if type_ ==0:

tag = old_tag = None

values = []

title = ["用例编号", "测试用例", "重试总次数", "比对成功次数", "活体成功次数",

"成功次数", "测试次数", "比对通过率", "活体通过率", "用户通过率"]

else:

title = ["用例编号", "重试总次数", "比对成功次数", "活体成功次数",

"睁闭眼通过次数","成功次数", "测试次数", "比对通过率", "活体通过率",

"用户通过率", "睁闭眼通过率"]

datas = [title, ]

for i in range(1, number + 1):

total_number = compare_number = live_number = eye_number = \

success_number = test_number = 0

if type_ ==0:

old_tag = tag

tag = maps[i][1]

# 用例标签与上一用例不一致时，需要对前面用例进行汇总

if (old_tag is not None) and values and old_tag != tag:

datas.append(count(datas, values))

values = []

values.append(i)

# 没有数据的生成空表,有数据则统计

if i not in results:

if type_ ==0:

datas.append([i, maps[i][1], 0, 0, 0, 0, 0, 0, 0, 0, ])

else:

datas.append([i, 0, 0, 0, 0, 0, 0, 0, 0, 0,0])

else:

for row in results[i]:

if type_ == 0:

print(row)

total, compare, live, success, test = row

else:

total, compare, live, eye, success, test = row

total_number += total

compare_number += compare

live_number += live

success_number += success

test_number += test

if type_ != 0:

eye_number += eye

if type_ == 0:

result = [i, maps[i][0], total_number, compare_number,

live_number,success_number, test_number]

else:

result = [i, total_number, compare_number, live_number,

eye_number, success_number, test_number,]

result.append(percentage(compare_number, total_number))

result.append(percentage(live_number, total_number))

result.append(percentage(success_number, test_number))

if type_ != 0:

result.append(percentage(eye_number, total_number))

datas.append(result)

# 最后的用例需要进行汇总

if type_ ==0:

if i == len(maps):

datas.append(count(datas, values))

values = []

try:

writer = pandas.ExcelWriter(output)

df = pandas.DataFrame(datas)

df.to_excel(writer, sheet_name='output', index=False)

writer.save()

except IOError:

if Path(name).exists():

print("{0} Exists，Now Delete it!".format(name))

try:

shutil.rmtree(name)

time.sleep(0.5)

except Exception as info:

print('Error: shutil.rmtree {}'.format(name))

print(info)

traceback.print_exc()

print('Please close file and directories and continue...')

print("mkdir {0} .".format(name))

labels = []

for file_ in files:

if real in file_:

labels.append(1)

else:

labels.append(0)

label_name='output/label.txt'):

types = filetype.split(",")

# print(src)

if len(types)>1:

filetype = types[0]

# print(src, filetype)

files = find_files_by_type(src,filetype)

# print(files)

files.sort()

if len(types)==2:

files2 = find_files_by_type(src,types[1])

files2.sort()

files = concat_list(files, files2, sep=' ')

labels = get_labels(files, real)

output_file(file_name, files)

p = Path(src)

#print(str(p))

files = []

for file_name in p.glob('**/*.{0}'.format(filetype)):

files.append(str(file_name))

directories=None, one_directory=True):

'''

check_directory(dst)

p = Path(src)

for file_ext in types.split(','):

for file_name in p.glob('**/*.{0}'.format(file_ext)):

# print(file_name)

if directories is not None:

flag = False

for directory in directories.split(','):

if os.sep + directory + os.sep in str(file_name):

flag = True

if not flag:

continue

if not one_directory:

dirname = str(file_name.parent).replace(src, dst)

dst_filename = "{}{}{}".format(

dirname, os.sep, file_name.name)

if not Path(dirname).exists():

print("mkdir {}".format(dirname))

Path(dirname).mkdir(parents=True, exist_ok=True)

else:

dst_filename = "{}{}{}".format(

dst, os.sep, Path(file_name).name)

print("Copying {} to {}".format(file_name, dst_filename))

'''

datas = {}

for file_ext in file_types.split(','):

datas[file_ext] = count_number_by_filetype(directory, file_ext, output)

'''

'''

datas = {}

dirs = glob.glob("{0}/*/".format(directory))

if output:

print('\n', file_type, ':\n')

for dir_ in (dirs):

files = glob.glob("{0}/*.{1}".format(dir_, file_type))

datas[int(dir_.split(os.sep)[-2].lstrip('0'))] = len(files)

for seq in range(1,len(maps) + 1):

if seq not in datas:

datas[seq] = 0

if output:

print(datas[seq])

all_data_frames = []

for file_name in files:

df = pandas.read_excel(file_name, index_col=index_col, usecols=usecols)

all_data_frames.append(df)

df = pandas.concat(all_data_frames, ignore_index=True)

if strips:

for item in strips:

df[item] = df[item].str.replace(strips[item], "")

result = {}

for line in open(filename):

if line.strip():

if change:

value, key = line.split(sep)

else:

key, value = line.split(sep)

key = key.strip()

value = value.strip()

if basename:

key = os.path.basename(key)

if multi:

if key not in result:

result[key] = []

result[key].append(value)

else:

result[key] = value

'''

result = collections.OrderedDict()

for line in open(filename):

item = line.strip()

if not item:

continue

key = item.split('/')[value]

if basename:

item = os.path.basename(item)

result[key] = item

if is_file:

return hashlib.md5(open(content,'rb').read()).hexdigest()

else:

"""

df1.fillna(method='ffill',inplace=True)

df2.fillna(method='ffill',inplace=True)

columns1 = set(df1.columns)

columns2 = set(df2.columns)

for item in columns2 - columns1:

df1[item] = 0

for item in columns1 - columns2:

df2[item] = 0

key1 = list(df1[key])

key2 = list(df2[key])

df = df1.iloc[0:0]

for item in set(key1)|set(key2):

if item in set(key1)&set(key2):

value = df1.iloc[key1.index(item)] + df2.iloc[key2.index(item)]

value[key] = df1.iloc[key1.index(item)][key]

for name in fixes:

value[name] = df1.iloc[key1.index(item)][name]

elif item in set(df1[key]):

value = df1.iloc[key1.index(item)]

else:

value = df2.iloc[key2.index(item)]

df = pandas.concat([df, value.to_frame().T])

if sorts:

df = df.sort_values(by=sorts)

if sorts:

df = df.loc[:,columns]

'''输出列表为文本文件'''

f = open(name,'w')

for item in items:

f.write("{}\n".format(item))

'''将列名中的文件字符串只保留文件名'''

if full:

return [ os.path.splitext(x.strip())[0] for x in items]

else:

results = {}

base_human = r'{}{}human_test'.format(diretory, os.sep)

base_paper = r'{}{}paper'.format(diretory, os.sep)

base_noface = r'{}{}noface'.format(diretory, os.sep)

human_photos = get_filename(glob.glob('{}{}*.*'.format(base_human,os.sep)))

human_photos = get_filename(human_photos)

paper_photos = get_filename(glob.glob('{}{}*.*'.format(base_paper,os.sep)))

paper_photos = get_filename(paper_photos)

noface_photos = get_filename(glob.glob('{}{}*.*'.format(base_noface,os.sep)))

noface_photos = get_filename(noface_photos)

results['human_test'] = human_photos

results['paper'] = paper_photos

results['noface'] = noface_photos

names=['name','left','top','length','height','v','score']

df = pandas.read_csv(filename, names=names, sep='\s', engine='python')

rename = lambda x: os.path.basename(x)

df['name'] = df['name'].apply(rename)

if return_dict:

results = {}

for num in range(len(df)):

row = df.iloc[num]

results[row['name']] = (

row['left'],row['top'],row['left'] + row['length'],

row['top'] + row['height'])

return results

else:

names = name.split('/')

if 'noface' in name:

filename = "double/{}/{}".format(names[-2],names[-1])

else:

filename = "{}{}/{}".format(

"20180228_双通人脸检测_zhourong/Image/33941/双通活体检测全集数据/",

names[-2],names[-1])

filenames,shuangtong_photos, output_file=False,

out_filename="/home/andrew/code/detection_results.txt"):

print(filenames)

cols = [0,3,6]

df_paper = pandas.read_excel(filenames['paper'],usecols=cols)

df_human = pandas.read_excel(filenames['human_test'],usecols=cols)

#df_noface = pandas.read_excel(filenames['noface'],usecols=cols)

df = pandas.concat([df_paper, df_human])

df['图片的路径'] = df['图片的路径'].apply(rename_shuangtong)

print(df["人脸检测时间"].mean())

if output_file:

# 生成北京需要的测试结果

detection_results = ""

for num in range(len(df)):

row = df.iloc[num]

name = row['图片的路径']

result = row['测试结果']

# left, top, right, bottom

#if os.path.basename(name) in shuangtong_photos['noface']:

#if result != '未检测到人脸':

#detection_result = "{0}\n".format(name)

##detection_results = detection_results + detection_result

#else:

#print("Error: Find face in {}".format(os.path.basename(name)))

#else:

if result != '未检测到人脸':

sore = float(result.split(':')[-1].strip())

temps = result.split('[')

left, top = temps[1].strip(']').split(',')

right, bottom = temps[2].split(']')[0].split(',')

detection_result = "{0} {1} {2} {3} {4} 1 {5} \n".format(

name,left, top, int(right) - int(left), int(bottom) - int(top), sore)

detection_results = detection_results + detection_result

else:

detection_result = "{0}\n".format(name)

#detection_results = detection_results + detection_result

f = open(out_filename, 'wb')

f.write(detection_results.encode(encoding='utf_8', errors='strict'))

f.close()

else:

'''获取深圳的XLS结果文件列表'''

p = Path(directoy)

files = p.glob('**/*.{0}'.format("xls"))

xls_files = {}

for filename in files:

version = re.search('v\d+\.\d+\.\d+',str(filename)).group()

if version not in xls_files:

xls_files[version] = {}

if "双通" in str(filename):

if "paper" in str(filename):

xls_files[version]['paper'] = str(filename)

elif "human" in str(filename):

xls_files[version]['human_test'] = str(filename)

else:

xls_files[version]['noface'] = str(filename)

else:

xls_files[version]['tongyong'] = str(filename)

result = []

for line in open(filename):

item = line.strip()

if item:

if basename:

item = os.path.basename(item)

result.append(item)

result = []

for i in range(len(list1)):

try:

result.append("{}{}{}".format(list1[i], sep, list2[i]))

except Exception as info:

print('Error: concat_list')

print(i)

print(len(list1), len(list2))

#print(list1)

#print(list2)

if len(list1) > i:

print(list1[i])

else:

print(list2[i])

print(info)

traceback.print_exc()

continue

list1 = file2list(file1)

list2 = file2list(file2)

result = concat_list(list1, list2, sep)

print("directory: {}".format(src))

files1 = find_files_by_type(src, type1)

print("{}: {}".format(type1, len(files1)))

files1_name = get_filename_without_ext(files1, full=True)

files2 = find_files_by_type(src, type2)

print("{}: {}".format(type2, len(files2)))

files2_name = get_filename_without_ext(files2, full=True)

print(set(files1_name)^set(files2_name))

for name in set(files1_name)^set(files2_name):

#print(name)

if name in files1_name:

location = files1[files1_name.index(name)]

else:

location = files2[files2_name.index(name)]

print(location)

if flag:

print("--remove {}".format(location))

results = set()

for root, dirs, files in os.walk(input_directory):

if files:

results.add(root)

'''

if type(files) is list:

df = concat_excel(files)

else:

df = pandas.read_excel(files)

df.index = df[u'识别图片的路径'].apply(

lambda x:os.path.basename(x.split()[0]))

df_server = pandas.read_csv(server_file, sep='\s', names=server_columns)

df_server.index = df_server['filename'].apply(

lambda x:os.path.basename(x.split()[0]))

print(df.index)

print(df_server.index)

df['server_score'] = df_server['server_score']

df['diff_score'] = df['server_score'] - df[key]