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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import os.path as osp | |
| from typing import Dict, List, Tuple | |
| import mmcv | |
| from mmengine import mkdir_or_exist | |
| from mmocr.registry import DATA_PACKERS | |
| from mmocr.utils import bbox2poly, crop_img, poly2bbox, warp_img | |
| from .base import BasePacker | |
| class TextRecogPacker(BasePacker): | |
| """Text recogntion packer. It is used to pack the parsed annotation info | |
| to: | |
| .. code-block:: python | |
| { | |
| "metainfo": | |
| { | |
| "dataset_type": "TextRecogDataset", | |
| "task_name": "textrecog", | |
| }, | |
| "data_list": | |
| [ | |
| { | |
| "img_path": "textrecog_imgs/train/test_img.jpg", | |
| "instances": | |
| [ | |
| { | |
| "text": "GRAND" | |
| } | |
| ] | |
| } | |
| ] | |
| } | |
| """ | |
| def pack_instance(self, sample: Tuple) -> Dict: | |
| """Pack the text info to a recognition instance. | |
| Args: | |
| samples (Tuple): A tuple of (img_name, text). | |
| split (str): The split of the instance. | |
| Returns: | |
| Dict: The packed instance. | |
| """ | |
| img_name, text = sample | |
| img_name = osp.relpath(img_name, self.data_root) | |
| packed_instance = dict(instances=[dict(text=text)], img_path=img_name) | |
| return packed_instance | |
| def add_meta(self, sample: List) -> Dict: | |
| """Add meta information to the sample. | |
| Args: | |
| sample (List): A list of samples of the dataset. | |
| Returns: | |
| Dict: A dict contains the meta information and samples. | |
| """ | |
| meta = { | |
| 'metainfo': { | |
| 'dataset_type': 'TextRecogDataset', | |
| 'task_name': 'textrecog' | |
| }, | |
| 'data_list': sample | |
| } | |
| return meta | |
| class TextRecogCropPacker(TextRecogPacker): | |
| """Text recognition packer with image cropper. It is used to pack the | |
| parsed annotation info and crop out the word images from the full-size | |
| ones. | |
| Args: | |
| crop_with_warp (bool): Whether to crop the text from the original | |
| image using opencv warpPerspective. | |
| jitter (bool): (Applicable when crop_with_warp=True) | |
| Whether to jitter the box. | |
| jitter_ratio_x (float): (Applicable when crop_with_warp=True) | |
| Horizontal jitter ratio relative to the height. | |
| jitter_ratio_y (float): (Applicable when crop_with_warp=True) | |
| Vertical jitter ratio relative to the height. | |
| long_edge_pad_ratio (float): (Applicable when crop_with_warp=False) | |
| The ratio of padding the long edge of the cropped image. | |
| Defaults to 0.1. | |
| short_edge_pad_ratio (float): (Applicable when crop_with_warp=False) | |
| The ratio of padding the short edge of the cropped image. | |
| Defaults to 0.05. | |
| """ | |
| def __init__(self, | |
| crop_with_warp: bool = False, | |
| jitter: bool = False, | |
| jitter_ratio_x: float = 0.0, | |
| jitter_ratio_y: float = 0.0, | |
| long_edge_pad_ratio: float = 0.0, | |
| short_edge_pad_ratio: float = 0.0, | |
| **kwargs): | |
| super().__init__(**kwargs) | |
| self.crop_with_warp = crop_with_warp | |
| self.jitter = jitter | |
| self.jrx = jitter_ratio_x | |
| self.jry = jitter_ratio_y | |
| self.lepr = long_edge_pad_ratio | |
| self.sepr = short_edge_pad_ratio | |
| # Crop converter crops the images of textdet to patches | |
| self.cropped_img_dir = 'textrecog_imgs' | |
| self.crop_save_path = osp.join(self.data_root, self.cropped_img_dir) | |
| mkdir_or_exist(self.crop_save_path) | |
| mkdir_or_exist(osp.join(self.crop_save_path, self.split)) | |
| def pack_instance(self, sample: Tuple) -> List: | |
| """Crop patches from image. | |
| Args: | |
| samples (Tuple): A tuple of (img_name, text). | |
| Return: | |
| List: The list of cropped patches. | |
| """ | |
| def get_box(instance: Dict) -> List: | |
| if 'box' in instance: | |
| return bbox2poly(instance['box']).tolist() | |
| if 'poly' in instance: | |
| return bbox2poly(poly2bbox(instance['poly'])).tolist() | |
| def get_poly(instance: Dict) -> List: | |
| if 'poly' in instance: | |
| return instance['poly'] | |
| if 'box' in instance: | |
| return bbox2poly(instance['box']).tolist() | |
| data_list = [] | |
| img_path, instances = sample | |
| img = mmcv.imread(img_path) | |
| for i, instance in enumerate(instances): | |
| if instance['ignore']: | |
| continue | |
| if self.crop_with_warp: | |
| poly = get_poly(instance) | |
| patch = warp_img(img, poly, self.jitter, self.jrx, self.jry) | |
| else: | |
| box = get_box(instance) | |
| patch = crop_img(img, box, self.lepr, self.sepr) | |
| if patch.shape[0] == 0 or patch.shape[1] == 0: | |
| continue | |
| text = instance['text'] | |
| patch_name = osp.splitext( | |
| osp.basename(img_path))[0] + f'_{i}' + osp.splitext( | |
| osp.basename(img_path))[1] | |
| dst_path = osp.join(self.crop_save_path, self.split, patch_name) | |
| mmcv.imwrite(patch, dst_path) | |
| rec_instance = dict( | |
| instances=[dict(text=text)], | |
| img_path=osp.join(self.cropped_img_dir, self.split, | |
| patch_name)) | |
| data_list.append(rec_instance) | |
| return data_list | |
| def add_meta(self, sample: List) -> Dict: | |
| # Since the TextRecogCropConverter packs all of the patches in a single | |
| # image into a list, we need to flatten the list. | |
| sample = [item for sublist in sample for item in sublist] | |
| return super().add_meta(sample) | |