def __init__(self, config, mode, logger, seed=None):

super(SimpleDataSet, self).__init__()

self.logger = logger

self.mode = mode.lower()

global_config = config['Global']

dataset_config = config[mode]['dataset']

loader_config = config[mode]['loader']

self.delimiter = dataset_config.get('delimiter', '\t')

label_file_list = dataset_config.pop('label_file_list')

data_source_num = len(label_file_list)

ratio_list = dataset_config.get("ratio_list", 1.0)

if isinstance(ratio_list, (float, int)):

ratio_list = [float(ratio_list)] * int(data_source_num)

assert len(

ratio_list

) == data_source_num, "The length of ratio_list should be the same as the file_list."

self.data_dir = dataset_config['data_dir']

self.do_shuffle = loader_config['shuffle']

self.seed = seed

logger.info("Initialize indexs of datasets:%s" % label_file_list)

self.data_lines = self.get_image_info_list(label_file_list, ratio_list)

self.data_idx_order_list = list(range(len(self.data_lines)))

if self.mode == "train" and self.do_shuffle:

self.shuffle_data_random()

self.set_epoch_as_seed(self.seed, dataset_config)

self.ops = create_operators(dataset_config['transforms'], global_config)

self.ext_op_transform_idx = dataset_config.get("ext_op_transform_idx",

2)

self.need_reset = True in [x < 1 for x in ratio_list]

def set_epoch_as_seed(self, seed, dataset_config):

if self.mode == 'train':

try:

border_map_id = [index

for index, dictionary in enumerate(dataset_config['transforms'])

if 'MakeBorderMap' in dictionary][0]

shrink_map_id = [index

for index, dictionary in enumerate(dataset_config['transforms'])

if 'MakeShrinkMap' in dictionary][0]

dataset_config['transforms'][border_map_id]['MakeBorderMap'][

'epoch'] = seed if seed is not None else 0

dataset_config['transforms'][shrink_map_id]['MakeShrinkMap'][

'epoch'] = seed if seed is not None else 0

except Exception as E:

print(E)

return

def get_image_info_list(self, file_list, ratio_list):

if isinstance(file_list, str):

file_list = [file_list]

data_lines = []

for idx, file in enumerate(file_list):

with open(file, "rb") as f:

lines = f.readlines()

if self.mode == "train" or ratio_list[idx] < 1.0:

random.seed(self.seed)

lines = random.sample(lines,

round(len(lines) * ratio_list[idx]))

data_lines.extend(lines)

return data_lines

def shuffle_data_random(self):

random.seed(self.seed)

random.shuffle(self.data_lines)

return

def _try_parse_filename_list(self, file_name):

# multiple images -> one gt label

if len(file_name) > 0 and file_name[0] == "[":

try:

info = json.loads(file_name)

file_name = random.choice(info)

except:

pass

return file_name

def get_ext_data(self):

ext_data_num = 0

for op in self.ops:

if hasattr(op, 'ext_data_num'):

ext_data_num = getattr(op, 'ext_data_num')

break

load_data_ops = self.ops[:self.ext_op_transform_idx]

ext_data = []

while len(ext_data) < ext_data_num:

file_idx = self.data_idx_order_list[np.random.randint(self.__len__(

))]

data_line = self.data_lines[file_idx]

data_line = data_line.decode('utf-8')

substr = data_line.strip("\n").split(self.delimiter)

file_name = substr[0]

file_name = self._try_parse_filename_list(file_name)

label = substr[1]

img_path = os.path.join(self.data_dir, file_name)

data = {'img_path': img_path, 'label': label}

if not os.path.exists(img_path):

continue

with open(data['img_path'], 'rb') as f:

img = f.read()

data['image'] = img

data = transform(data, load_data_ops)

if data is None:

continue

if 'polys' in data.keys():

if data['polys'].shape[1] != 4:

continue

ext_data.append(data)

return ext_data

def __getitem__(self, idx):

file_idx = self.data_idx_order_list[idx]

data_line = self.data_lines[file_idx]

try:

data_line = data_line.decode('utf-8')

substr = data_line.strip("\n").split(self.delimiter)

file_name = substr[0]

file_name = self._try_parse_filename_list(file_name)

label = substr[1]

img_path = os.path.join(self.data_dir, file_name)

data = {'img_path': img_path, 'label': label}

if not os.path.exists(img_path):

raise Exception("{} does not exist!".format(img_path))

with open(data['img_path'], 'rb') as f:

img = f.read()

data['image'] = img

data['ext_data'] = self.get_ext_data()

outs = transform(data, self.ops)

except:

self.logger.error(

"When parsing line {}, error happened with msg: {}".format(

data_line, traceback.format_exc()))

outs = None

if outs is None:

# during evaluation, we should fix the idx to get same results for many times of evaluation.

rnd_idx = np.random.randint(self.__len__(

)) if self.mode == "train" else (idx + 1) % self.__len__()

return self.__getitem__(rnd_idx)

return outs

def __len__(self):

def __init__(self, config, mode, logger, seed=None):

super(MultiScaleDataSet, self).__init__(config, mode, logger, seed)

self.ds_width = config[mode]['dataset'].get('ds_width', False)

if self.ds_width:

self.wh_aware()

def wh_aware(self):

data_line_new = []

wh_ratio = []

for lins in self.data_lines:

data_line_new.append(lins)

lins = lins.decode('utf-8')

name, label, w, h = lins.strip("\n").split(self.delimiter)

wh_ratio.append(float(w) / float(h))

self.data_lines = data_line_new

self.wh_ratio = np.array(wh_ratio)

self.wh_ratio_sort = np.argsort(self.wh_ratio)

self.data_idx_order_list = list(range(len(self.data_lines)))

def resize_norm_img(self, data, imgW, imgH, padding=True):

img = data['image']

h = img.shape[0]

w = img.shape[1]

if not padding:

resized_image = cv2.resize(

img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)

resized_w = imgW

else:

ratio = w / float(h)

if math.ceil(imgH * ratio) > imgW:

resized_w = imgW

else:

resized_w = int(math.ceil(imgH * ratio))

resized_image = cv2.resize(img, (resized_w, imgH))

resized_image = resized_image.astype('float32')

resized_image = resized_image.transpose((2, 0, 1)) / 255

resized_image -= 0.5

resized_image /= 0.5

padding_im = np.zeros((3, imgH, imgW), dtype=np.float32)

padding_im[:, :, :resized_w] = resized_image

valid_ratio = min(1.0, float(resized_w / imgW))

data['image'] = padding_im

data['valid_ratio'] = valid_ratio

return data

def __getitem__(self, properties):

# properites is a tuple, contains (width, height, index)

img_height = properties[1]

idx = properties[2]

if self.ds_width and properties[3] is not None:

wh_ratio = properties[3]

img_width = img_height * (1 if int(round(wh_ratio)) == 0 else

int(round(wh_ratio)))

file_idx = self.wh_ratio_sort[idx]

else:

file_idx = self.data_idx_order_list[idx]

img_width = properties[0]

wh_ratio = None

data_line = self.data_lines[file_idx]

try:

data_line = data_line.decode('utf-8')

substr = data_line.strip("\n").split(self.delimiter)

file_name = substr[0]

file_name = self._try_parse_filename_list(file_name)

label = substr[1]

img_path = os.path.join(self.data_dir, file_name)

data = {'img_path': img_path, 'label': label}

if not os.path.exists(img_path):

raise Exception("{} does not exist!".format(img_path))

with open(data['img_path'], 'rb') as f:

img = f.read()

data['image'] = img

data['ext_data'] = self.get_ext_data()

outs = transform(data, self.ops[:-1])

if outs is not None:

outs = self.resize_norm_img(outs, img_width, img_height)

outs = transform(outs, self.ops[-1:])

except:

self.logger.error(

"When parsing line {}, error happened with msg: {}".format(

data_line, traceback.format_exc()))

outs = None

if outs is None:

# during evaluation, we should fix the idx to get same results for many times of evaluation.

rnd_idx = (idx + 1) % self.__len__()

return self.__getitem__([img_width, img_height, rnd_idx, wh_ratio])