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https://github.com/PaddlePaddle/PaddleOCR.git
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68099c2d5b
* Add custom detection and recognition model usage instructions in re * update * Add custom detection and recognition model usage instructions in re * add db net for benchmark * rename benckmark to PaddleOCR_benchmark * add addict to req * rename
88 lines
2.9 KiB
Python
88 lines
2.9 KiB
Python
# -*- coding: utf-8 -*-
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# @Time : 2019/12/4 13:12
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# @Author : zhoujun
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import copy
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from paddle.io import Dataset
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from data_loader.modules import *
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class BaseDataSet(Dataset):
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def __init__(self,
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data_path: str,
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img_mode,
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pre_processes,
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filter_keys,
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ignore_tags,
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transform=None,
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target_transform=None):
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assert img_mode in ['RGB', 'BRG', 'GRAY']
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self.ignore_tags = ignore_tags
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self.data_list = self.load_data(data_path)
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item_keys = [
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'img_path', 'img_name', 'text_polys', 'texts', 'ignore_tags'
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]
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for item in item_keys:
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assert item in self.data_list[
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0], 'data_list from load_data must contains {}'.format(
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item_keys)
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self.img_mode = img_mode
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self.filter_keys = filter_keys
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self.transform = transform
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self.target_transform = target_transform
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self._init_pre_processes(pre_processes)
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def _init_pre_processes(self, pre_processes):
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self.aug = []
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if pre_processes is not None:
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for aug in pre_processes:
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if 'args' not in aug:
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args = {}
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else:
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args = aug['args']
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if isinstance(args, dict):
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cls = eval(aug['type'])(**args)
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else:
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cls = eval(aug['type'])(args)
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self.aug.append(cls)
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def load_data(self, data_path: str) -> list:
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"""
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把数据加载为一个list：
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:params data_path: 存储数据的文件夹或者文件
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return a dict ,包含了，'img_path','img_name','text_polys','texts','ignore_tags'
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"""
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raise NotImplementedError
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def apply_pre_processes(self, data):
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for aug in self.aug:
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data = aug(data)
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return data
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def __getitem__(self, index):
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try:
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data = copy.deepcopy(self.data_list[index])
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im = cv2.imread(data['img_path'], 1
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if self.img_mode != 'GRAY' else 0)
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if self.img_mode == 'RGB':
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im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
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data['img'] = im
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data['shape'] = [im.shape[0], im.shape[1]]
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data = self.apply_pre_processes(data)
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if self.transform:
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data['img'] = self.transform(data['img'])
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data['text_polys'] = data['text_polys'].tolist()
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if len(self.filter_keys):
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data_dict = {}
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for k, v in data.items():
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if k not in self.filter_keys:
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data_dict[k] = v
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return data_dict
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else:
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return data
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except:
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return self.__getitem__(np.random.randint(self.__len__()))
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def __len__(self):
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return len(self.data_list)
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