"""split major.txt into major_tain.txt and major_test.txt"""

f = open('/mnt/data/scenetext/online/txts/version2/file_path_all_2.txt', 'r')

image_list = [f_name.strip() for f_name in f.readlines()]

image_list = np.array(image_list)

split(image_list, )

num_image = len(image_list)

num_train = int(num_image * ratio)

rng = range(0, num_image)

random.shuffle(rng)

train_list = image_list[rng[:num_train]]

test_list = image_list[rng[num_train:]]

write_txt(train_list, test_list)

f_train = open('/mnt/data/scenetext/online/txts/version2/train.txt', 'w')

f_test = open('/mnt/data/scenetext/online/txts/version2/test.txt', 'w')

for train in train_list:

f_train.write('%s\n' %train)

for test in test_list:

f_test.write('%s\n' %test)

f_train.close()

"""remove last field in ICDAR15 gt data"""

icdar15_data_set = '/mnt/data/scenetext/dataset/icdar13-15/icdar2015/4.1.text_localization/ch4_training_localization_transcription_gt'

os.chdir(icdar15_data_set)

gt_txt_list = os.listdir('.')

for gt_txt in gt_txt_list:

f_save = open(os.path.join('../gt_non_difficult', gt_txt), 'w')

with codecs.open(gt_txt, "r", encoding="utf-8-sig") as f_gt:

frs = f_gt.readlines()

for line in frs:

line = line.strip()

if line.split(',')[-1] == '###': continue # icdar15 test only for non_difficulty

line = ''.join(s + ',' for s in line.split(',', 8)[:8])

line = line[:-1]

f_save.write(line+'\n')

"""check if all the annotation is ordered"""

os.chdir(cfg.ICDAR15_DATA_ROOT)

image_list = os.listdir(cfg.ICDAR15_SUB_IMAGE)

major_list = map(lambda f_name: f_name.split('.')[0], image_list)

# os.chdir(cfg.AUGMENTATION_DATA_ROOT)

# image_list = os.listdir(cfg.AUGMENTATION_SUB_IMAGE)

for (ind, major) in enumerate(major_list):

image_path = os.path.join(cfg.ICDAR15_DATA_ROOT, cfg.ICDAR15_SUB_IMAGE, '{}.jpg'.format(major))

gt_path = os.path.join(cfg.ICDAR15_DATA_ROOT, cfg.ICDAR15_SUB_TXT, 'gt_{}.txt'.format(major))

# image_path = os.path.join(cfg.AUGMENTATION_DATA_ROOT, cfg.AUGMENTATION_SUB_IMAGE, '{}.jpg'.format(major))

# gt_path = os.path.join(cfg.AUGMENTATION_DATA_ROOT, cfg.AUGMENTATION_SUB_TXT, '{}.txt'.format(major))

gt_quadrilaterals = parse_Quadrilateral(gt_path)

non_ordered_quads = filter_clockwise_quads(gt_quadrilaterals)

if len(non_ordered_quads) > 0:

im = cv2.imread(image_path)

"""

result_dir = '/mnt/data/scenetext/dataset/icdar13-15/icdar2015/4.1.text_localization/result/model.0.0.1/submit'

confs = np.arange(0.4, 1, 0.1)

major_list = os.listdir(result_dir)

import zipfile

import shutil

for conf in confs:

zip_file_name = os.path.join(result_dir.rsplit('/', 1)[0], 'submit-{}'.format(conf))

zip_file = zipfile.ZipFile(zip_file_name, 'w' )

save_dir = os.path.join(result_dir.rsplit('/', 1)[0], 'submit{}'.format(conf))

for major in major_list:

gt_path = os.path.join(result_dir, major)

filtered_lines = [line.strip() for line in open(gt_path).readlines() if

float(line.strip().split(',')[-1]) > conf]

if not os.path.exists(save_dir):

os.makedirs(save_dir)

f_txt = open(os.path.join(save_dir, major), 'w')

for line in filtered_lines:

f_txt.write(line.rsplit(',', 1)[0]+'\n')

f_txt.close()

zip_file.write(os.path.join(save_dir, major), os.path.basename(os.path.join(save_dir, major)))

zip_file.close()

with open('/home/netease/scenetext/crnn-git/data/char4/1_2.png', 'r') as f:

imageBin = f.read()

imageBuf = np.fromstring(imageBin, dtype=np.uint8)

img = cv2.imdecode(imageBuf, cv2.IMREAD_GRAYSCALE)