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