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import functools
import logging
import bisect
import torch.utils.data as data
import cv2
import numpy as np
import glob
from concern.config import Configurable, State
import math
class ImageDataset(data.Dataset, Configurable):
r'''Dataset reading from images.
Args:
Processes: A series of Callable object, which accept as parameter and return the data dict,
typically inherrited the `DataProcess`(data/processes/data_process.py) class.
'''
data_dir = State()
data_list = State()
processes = State(default=[])
def __init__(self, data_dir=None, data_list=None, cmd={}, **kwargs):
self.load_all(**kwargs)
self.data_dir = data_dir or self.data_dir
self.data_list = data_list or self.data_list
if 'train' in self.data_list[0]:
self.is_training = True
else:
self.is_training = False
self.debug = cmd.get('debug', False)
self.image_paths = []
self.gt_paths = []
self.get_all_samples()
def get_all_samples(self):
for i in range(len(self.data_dir)):
with open(self.data_list[i], 'r') as fid:
image_list = fid.readlines()
if self.is_training:
image_path=[self.data_dir[i]+'/train_images/'+timg.strip() for timg in image_list]
#gt_path=[self.data_dir[i]+'/train_gts/gt_'+timg.strip()+'.txt' for timg in image_list]
gt_path=[self.data_dir[i]+'/train_gts/gt_'+timg.strip().split('.')[0]+'.txt' for timg in image_list]
else:
image_path=[self.data_dir[i]+'/test_images/'+timg.strip() for timg in image_list]
#gt_path=[self.data_dir[i]+'/train_gts/gt_'+timg.strip()+'.txt' for timg in image_list]
gt_path=[self.data_dir[i]+'/test_gts/gt_'+timg.strip().split('.')[0]+'.txt' for timg in image_list]
# image_path=[self.data_dir[i]+'/test_images/gt_'+timg.strip() for timg in image_list]
# print(self.data_dir[i])
# if 'TD500' in self.data_list[i] or 'total_text' in self.data_list[i]:
# #gt_path=[self.data_dir[i]+'/test_gts/'+timg.strip()+'.txt' for timg in image_list]
# gt_path=[self.data_dir[i]+'/test_gts/'+timg.strip().split('.')[0]+'.gt' for timg in image_list]
# else:
# gt_path=[self.data_dir[i]+'/test_gts/'+timg.strip().split('.')[0]+'.gt' for timg in image_list]
self.image_paths += image_path
self.gt_paths += gt_path
self.num_samples = len(self.image_paths)
self.targets = self.load_ann()
if self.is_training:
assert len(self.image_paths) == len(self.targets)
def load_ann(self):
res = []
for gt in self.gt_paths:
lines = []
reader = open(gt, 'r').readlines()
for line in reader:
item = {}
parts = line.strip().split(',')
label = parts[-1]
if 'TD' in self.data_dir[0] and label == '1':
label = '###'
line = [i.strip('\ufeff').strip('\xef\xbb\xbf') for i in parts]
if 'icdar' in self.data_dir[0]:
poly = np.array(list(map(float, line[:8]))).reshape((-1, 2)).tolist()
else:
num_points = math.floor((len(line) - 1) / 2) * 2
poly = np.array(list(map(float, line[:num_points]))).reshape((-1, 2)).tolist()
item['poly'] = poly
item['text'] = label
lines.append(item)
res.append(lines)
return res
def __getitem__(self, index, retry=0):
if index >= self.num_samples:
index = index % self.num_samples
data = {}
image_path = self.image_paths[index]
img = cv2.imread(image_path, cv2.IMREAD_COLOR).astype('float32')
if self.is_training:
data['filename'] = image_path
data['data_id'] = image_path
else:
data['filename'] = image_path.split('/')[-1]
data['data_id'] = image_path.split('/')[-1]
data['image'] = img
target = self.targets[index]
data['lines'] = target
if self.processes is not None:
for data_process in self.processes:
data = data_process(data)
return data
def __len__(self):
return len(self.image_paths)
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