| 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().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]+'/test_gts/gt_'+timg.strip().split('.')[0]+'.txt' 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) |
|
|
