mirror of https://github.com/open-mmlab/mmocr.git
312 lines
9.1 KiB
Python
312 lines
9.1 KiB
Python
import argparse
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import glob
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import os.path as osp
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from functools import partial
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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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from shapely.geometry import Polygon
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from mmocr.utils import convert_annotations, drop_orientation, is_not_png
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def collect_files(img_dir, gt_dir, split):
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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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split(str): The split of dataset. Namely: training or test
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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 = [
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drop_orientation(f) if is_not_png(f) else f for f in imgs_list
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]
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files = []
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if split == 'training':
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for img_file in imgs_list:
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gt_file = osp.join(
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gt_dir,
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'poly_gt_' + osp.splitext(osp.basename(img_file))[0] + '.mat')
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files.append((img_file, gt_file))
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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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elif split == 'test':
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for img_file in imgs_list:
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gt_file = osp.join(
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gt_dir,
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'poly_gt_' + osp.splitext(osp.basename(img_file))[0] + '.mat')
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files.append((img_file, gt_file))
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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, split, 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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split(str): The split of dataset. Namely: training or test
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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(split, str)
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assert isinstance(nproc, int)
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load_img_info_with_split = partial(load_img_info, split=split)
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if nproc > 1:
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images = mmcv.track_parallel_progress(
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load_img_info_with_split, files, nproc=nproc)
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else:
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images = mmcv.track_progress(load_img_info_with_split, files)
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return images
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def get_contours(gt_path, split):
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"""Get the contours and words for each ground_truth 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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split(str): The split of dataset: training or test
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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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assert isinstance(split, str)
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contours = []
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words = []
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data = scio.loadmat(gt_path)
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if split == 'training':
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data_polygt = data['polygt']
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elif split == 'test':
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data_polygt = data['polygt']
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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:
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word = '???'
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else:
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word = word[0]
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if word == '#':
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word = '###'
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continue
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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, split):
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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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split(str): The split of dataset: training or test
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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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assert isinstance(split, str)
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contours, words = get_contours(gt_file, split)
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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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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=[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, split):
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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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split(str): The split of dataset: training or test
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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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assert isinstance(split, str)
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img_file, gt_file = files
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# read imgs with ignoring orientations
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img = mmcv.imread(img_file, 'unchanged')
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# read imgs with orientations as dataloader does when training and testing
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img_color = mmcv.imread(img_file, 'color')
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# make sure imgs have no orientation info, or annotation gt is wrong.
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assert img.shape[0:2] == img_color.shape[0:2]
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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 split == 'training':
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img_info = load_mat_info(img_info, gt_file, split)
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elif split == 'test':
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img_info = load_mat_info(img_info, gt_file, split)
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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('-o', '--out-dir', help='output path')
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parser.add_argument(
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'--split-list',
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nargs='+',
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help='a list of splits. e.g., "--split_list training test"')
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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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out_dir = args.out_dir if args.out_dir else root_path
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mmcv.mkdir_or_exist(out_dir)
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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 args.split_list:
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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), split)
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image_infos = collect_annotations(files, split, nproc=args.nproc)
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convert_annotations(image_infos, osp.join(out_dir, json_name))
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if __name__ == '__main__':
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main()
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