mirror of https://github.com/open-mmlab/mmocr.git
410 lines
12 KiB
Python
410 lines
12 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import glob
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import os
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import os.path as osp
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import re
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import cv2
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import mmcv
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import numpy as np
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import scipy.io as scio
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import yaml
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from shapely.geometry import Polygon
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from mmocr.utils import dump_ocr_data
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def collect_files(img_dir, gt_dir):
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"""Collect all images and their corresponding groundtruth files.
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Args:
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img_dir (str): The image directory
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gt_dir (str): The groundtruth directory
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Returns:
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files (list): The list of tuples (img_file, groundtruth_file)
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"""
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assert isinstance(img_dir, str)
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assert img_dir
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assert isinstance(gt_dir, str)
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assert gt_dir
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# note that we handle png and jpg only. Pls convert others such as gif to
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# jpg or png offline
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suffixes = ['.png', '.PNG', '.jpg', '.JPG', '.jpeg', '.JPEG']
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# suffixes = ['.png']
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imgs_list = []
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for suffix in suffixes:
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imgs_list.extend(glob.glob(osp.join(img_dir, '*' + suffix)))
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imgs_list = sorted(imgs_list)
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ann_list = sorted(
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osp.join(gt_dir, gt_file) for gt_file in os.listdir(gt_dir))
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files = list(zip(imgs_list, ann_list))
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assert len(files), f'No images found in {img_dir}'
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print(f'Loaded {len(files)} images from {img_dir}')
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return files
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def collect_annotations(files, nproc=1):
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"""Collect the annotation information.
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Args:
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files (list): The list of tuples (image_file, groundtruth_file)
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nproc (int): The number of process to collect annotations
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Returns:
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images (list): The list of image information dicts
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"""
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assert isinstance(files, list)
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assert isinstance(nproc, int)
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if nproc > 1:
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images = mmcv.track_parallel_progress(
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load_img_info, files, nproc=nproc)
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else:
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images = mmcv.track_progress(load_img_info, files)
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return images
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def get_contours_mat(gt_path):
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"""Get the contours and words for each ground_truth mat file.
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Args:
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gt_path (str): The relative path of the ground_truth mat file
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Returns:
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contours (list[lists]): A list of lists of contours
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for the text instances
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words (list[list]): A list of lists of words (string)
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for the text instances
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"""
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assert isinstance(gt_path, str)
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contours = []
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words = []
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data = scio.loadmat(gt_path)
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# 'gt' for the latest version; 'polygt' for the legacy version
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keys = data.keys()
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if 'gt' in keys:
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data_polygt = data.get('gt')
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elif 'polygt' in keys:
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data_polygt = data.get('polygt')
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else:
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raise NotImplementedError
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for i, lines in enumerate(data_polygt):
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X = np.array(lines[1])
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Y = np.array(lines[3])
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point_num = len(X[0])
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word = lines[4]
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if len(word) == 0 or word == '#':
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word = '###'
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else:
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word = word[0]
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words.append(word)
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arr = np.concatenate([X, Y]).T
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contour = []
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for i in range(point_num):
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contour.append(arr[i][0])
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contour.append(arr[i][1])
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contours.append(np.asarray(contour))
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return contours, words
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def load_mat_info(img_info, gt_file):
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"""Load the information of one ground truth in .mat format.
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Args:
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img_info (dict): The dict of only the image information
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gt_file (str): The relative path of the ground_truth mat
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file for one image
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Returns:
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img_info(dict): The dict of the img and annotation information
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"""
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assert isinstance(img_info, dict)
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assert isinstance(gt_file, str)
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contours, texts = get_contours_mat(gt_file)
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anno_info = []
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for contour, text in zip(contours, texts):
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if contour.shape[0] == 2:
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continue
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category_id = 1
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coordinates = np.array(contour).reshape(-1, 2)
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polygon = Polygon(coordinates)
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iscrowd = 1 if text == '###' else 0
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area = polygon.area
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# convert to COCO style XYWH format
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min_x, min_y, max_x, max_y = polygon.bounds
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bbox = [min_x, min_y, max_x - min_x, max_y - min_y]
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anno = dict(
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iscrowd=iscrowd,
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category_id=category_id,
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bbox=bbox,
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area=area,
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text=text,
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segmentation=[contour])
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anno_info.append(anno)
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img_info.update(anno_info=anno_info)
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return img_info
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def process_line(line, contours, words):
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"""Get the contours and words by processing each line in the gt file.
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Args:
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line(str): The line in gt file containing annotation info
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contours(list[lists]): A list of lists of contours
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for the text instances
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words(list[list]): A list of lists of words (string)
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for the text instances
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Returns:
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contours (list[lists]): A list of lists of contours
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for the text instances
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words (list[list]): A list of lists of words (string)
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for the text instances
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"""
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line = '{' + line.replace('[[', '[').replace(']]', ']') + '}'
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ann_dict = re.sub('([0-9]) +([0-9])', r'\1,\2', line)
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ann_dict = re.sub('([0-9]) +([ 0-9])', r'\1,\2', ann_dict)
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ann_dict = re.sub('([0-9]) -([0-9])', r'\1,-\2', ann_dict)
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ann_dict = ann_dict.replace("[u',']", "[u'#']")
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ann_dict = yaml.safe_load(ann_dict)
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X = np.array([ann_dict['x']])
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Y = np.array([ann_dict['y']])
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if len(ann_dict['transcriptions']) == 0:
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word = '###'
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else:
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word = ann_dict['transcriptions'][0]
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if len(ann_dict['transcriptions']) > 1:
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for ann_word in ann_dict['transcriptions'][1:]:
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word += ',' + ann_word
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word = str(eval(word))
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words.append(word)
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point_num = len(X[0])
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arr = np.concatenate([X, Y]).T
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contour = []
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for i in range(point_num):
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contour.append(arr[i][0])
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contour.append(arr[i][1])
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contours.append(np.asarray(contour))
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return contours, words
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def get_contours_txt(gt_path):
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"""Get the contours and words for each ground_truth txt file.
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Args:
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gt_path (str): The relative path of the ground_truth mat file
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Returns:
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contours (list[lists]): A list of lists of contours
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for the text instances
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words (list[list]): A list of lists of words (string)
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for the text instances
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"""
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assert isinstance(gt_path, str)
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contours = []
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words = []
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with open(gt_path) as f:
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tmp_line = ''
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for idx, line in enumerate(f):
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line = line.strip()
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if idx == 0:
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tmp_line = line
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continue
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if not line.startswith('x:'):
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tmp_line += ' ' + line
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continue
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else:
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complete_line = tmp_line
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tmp_line = line
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contours, words = process_line(complete_line, contours, words)
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if tmp_line != '':
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contours, words = process_line(tmp_line, contours, words)
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words = ['###' if word == '#' else word for word in words]
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return contours, words
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def load_txt_info(gt_file, img_info):
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"""Load the information of one ground truth in .txt format.
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Args:
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img_info (dict): The dict of only the image information
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gt_file (str): The relative path of the ground_truth mat
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file for one image
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Returns:
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img_info(dict): The dict of the img and annotation information
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"""
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contours, texts = get_contours_txt(gt_file)
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anno_info = []
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for contour, text in zip(contours, texts):
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if contour.shape[0] == 2:
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continue
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category_id = 1
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coordinates = np.array(contour).reshape(-1, 2)
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polygon = Polygon(coordinates)
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iscrowd = 1 if text == '###' else 0
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area = polygon.area
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# convert to COCO style XYWH format
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min_x, min_y, max_x, max_y = polygon.bounds
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bbox = [min_x, min_y, max_x - min_x, max_y - min_y]
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anno = dict(
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iscrowd=iscrowd,
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category_id=category_id,
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bbox=bbox,
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area=area,
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text=text,
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segmentation=[contour])
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anno_info.append(anno)
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img_info.update(anno_info=anno_info)
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return img_info
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def load_png_info(gt_file, img_info):
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"""Load the information of one ground truth in .png format.
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Args:
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gt_file (str): The relative path of the ground_truth file for one image
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img_info (dict): The dict of only the image information
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Returns:
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img_info (dict): The dict of the img and annotation information
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"""
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assert isinstance(gt_file, str)
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assert isinstance(img_info, dict)
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gt_img = cv2.imread(gt_file, 0)
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contours, _ = cv2.findContours(gt_img, cv2.RETR_EXTERNAL,
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cv2.CHAIN_APPROX_SIMPLE)
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anno_info = []
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for contour in contours:
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if contour.shape[0] == 2:
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continue
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category_id = 1
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xy = np.array(contour).flatten().tolist()
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coordinates = np.array(contour).reshape(-1, 2)
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polygon = Polygon(coordinates)
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iscrowd = 0
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area = polygon.area
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# convert to COCO style XYWH format
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min_x, min_y, max_x, max_y = polygon.bounds
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bbox = [min_x, min_y, max_x - min_x, max_y - min_y]
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anno = dict(
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iscrowd=iscrowd,
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category_id=category_id,
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bbox=bbox,
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area=area,
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segmentation=[xy])
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anno_info.append(anno)
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img_info.update(anno_info=anno_info)
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return img_info
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def load_img_info(files):
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"""Load the information of one image.
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Args:
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files (tuple): The tuple of (img_file, groundtruth_file)
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Returns:
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img_info (dict): The dict of the img and annotation information
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"""
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assert isinstance(files, tuple)
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img_file, gt_file = files
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# read imgs while ignoring orientations
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img = mmcv.imread(img_file, 'unchanged')
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split_name = osp.basename(osp.dirname(img_file))
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img_info = dict(
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# remove img_prefix for filename
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file_name=osp.join(split_name, osp.basename(img_file)),
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height=img.shape[0],
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width=img.shape[1],
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# anno_info=anno_info,
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segm_file=osp.join(split_name, osp.basename(gt_file)))
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if osp.splitext(gt_file)[1] == '.mat':
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img_info = load_mat_info(img_info, gt_file)
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elif osp.splitext(gt_file)[1] == '.txt':
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img_info = load_txt_info(gt_file, img_info)
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else:
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raise NotImplementedError
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return img_info
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def parse_args():
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parser = argparse.ArgumentParser(
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description='Convert totaltext annotations to COCO format')
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parser.add_argument('root_path', help='Totaltext root path')
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parser.add_argument(
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'--nproc', default=1, type=int, help='Number of process')
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args = parser.parse_args()
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return args
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def main():
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args = parse_args()
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root_path = args.root_path
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img_dir = osp.join(root_path, 'imgs')
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gt_dir = osp.join(root_path, 'annotations')
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set_name = {}
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for split in ['training', 'test']:
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set_name.update({split: 'instances_' + split + '.json'})
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assert osp.exists(osp.join(img_dir, split))
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for split, json_name in set_name.items():
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print(f'Converting {split} into {json_name}')
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with mmcv.Timer(
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print_tmpl='It takes {}s to convert totaltext annotation'):
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files = collect_files(
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osp.join(img_dir, split), osp.join(gt_dir, split))
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image_infos = collect_annotations(files, nproc=args.nproc)
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dump_ocr_data(image_infos, osp.join(root_path, json_name),
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'textdet')
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if __name__ == '__main__':
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main()
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