mmocr/tools/data/textdet/totaltext_converter.py

import argparse
import glob
import os.path as osp
from functools import partial

import cv2
import mmcv
import numpy as np
import scipy.io as scio
from shapely.geometry import Polygon
from tools.data_converter.common import convert_annotations, is_not_png

from mmocr.utils import drop_orientation


def collect_files(img_dir, gt_dir, split):
    """Collect all images and their corresponding groundtruth files.

    Args:
        img_dir(str): The image directory
        gt_dir(str): The groundtruth directory
        split(str): The split of dataset. Namely: training or test

    Returns:
        files(list): The list of tuples (img_file, groundtruth_file)
    """
    assert isinstance(img_dir, str)
    assert img_dir
    assert isinstance(gt_dir, str)
    assert gt_dir

    # note that we handle png and jpg only. Pls convert others such as gif to
    # jpg or png offline
    suffixes = ['.png', '.PNG', '.jpg', '.JPG', '.jpeg', '.JPEG']
    # suffixes = ['.png']

    imgs_list = []
    for suffix in suffixes:
        imgs_list.extend(glob.glob(osp.join(img_dir, '*' + suffix)))

    imgs_list = [
        drop_orientation(f) if is_not_png(f) else f for f in imgs_list
    ]

    files = []
    if split == 'training':
        for img_file in imgs_list:
            gt_file = gt_dir + '/gt_' + osp.splitext(
                osp.basename(img_file))[0] + '.mat'
            # gt_file = gt_dir + '/' + osp.splitext(
            #    osp.basename(img_file))[0] + '.png'
            files.append((img_file, gt_file))
        assert len(files), f'No images found in {img_dir}'
        print(f'Loaded {len(files)} images from {img_dir}')
    elif split == 'test':
        for img_file in imgs_list:
            gt_file = gt_dir + '/poly_gt_' + osp.splitext(
                osp.basename(img_file))[0] + '.mat'
            files.append((img_file, gt_file))
        assert len(files), f'No images found in {img_dir}'
        print(f'Loaded {len(files)} images from {img_dir}')

    return files


def collect_annotations(files, split, nproc=1):
    """Collect the annotation information.

    Args:
        files(list): The list of tuples (image_file, groundtruth_file)
        split(str): The split of dataset. Namely: training or test
        nproc(int): The number of process to collect annotations

    Returns:
        images(list): The list of image information dicts
    """
    assert isinstance(files, list)
    assert isinstance(split, str)
    assert isinstance(nproc, int)

    load_img_info_with_split = partial(load_img_info, split=split)
    if nproc > 1:
        images = mmcv.track_parallel_progress(
            load_img_info_with_split, files, nproc=nproc)
    else:
        images = mmcv.track_progress(load_img_info_with_split, files)

    return images


def get_contours(gt_path, split):
    """Get the contours and words for each ground_truth file.

    Args:
        gt_path(str): The relative path of the ground_truth mat file
        split(str): The split of dataset: training or test

    Returns:
        contours(list[lists]): A list of lists of contours
        for the text instances
        words(list[list]): A list of lists of words (string)
        for the text instances
    """
    assert isinstance(gt_path, str)
    assert isinstance(split, str)

    contours = []
    words = []
    data = scio.loadmat(gt_path)
    if split == 'training':
        data_polygt = data['gt']
    elif split == 'test':
        data_polygt = data['polygt']

    for i, lines in enumerate(data_polygt):
        X = np.array(lines[1])
        Y = np.array(lines[3])

        point_num = len(X[0])
        word = lines[4]
        if len(word) == 0:
            word = '???'
        else:
            word = word[0]

        if word == '#':
            word = '###'
            continue

        words.append(word)

        arr = np.concatenate([X, Y]).T
        contour = []
        for i in range(point_num):
            contour.append(arr[i][0])
            contour.append(arr[i][1])
        contours.append(np.asarray(contour))

    return contours, words


def load_mat_info(img_info, gt_file, split):
    """Load the information of one ground truth in .mat format.

    Args:
        img_info(dict): The dict of only the image information
        gt_file(str): The relative path of the ground_truth mat
        file for one image
        split(str): The split of dataset: training or test

    Returns:
        img_info(dict): The dict of the img and annotation information
    """
    assert isinstance(img_info, dict)
    assert isinstance(gt_file, str)
    assert isinstance(split, str)

    contours, words = get_contours(gt_file, split)
    anno_info = []
    for contour in contours:
        if contour.shape[0] == 2:
            continue
        category_id = 1
        coordinates = np.array(contour).reshape(-1, 2)
        polygon = Polygon(coordinates)
        iscrowd = 0

        area = polygon.area
        # convert to COCO style XYWH format
        min_x, min_y, max_x, max_y = polygon.bounds
        bbox = [min_x, min_y, max_x - min_x, max_y - min_y]

        anno = dict(
            iscrowd=iscrowd,
            category_id=category_id,
            bbox=bbox,
            area=area,
            segmentation=[contour])
        anno_info.append(anno)

    img_info.update(anno_info=anno_info)

    return img_info


def load_png_info(gt_file, img_info):
    """Load the information of one ground truth in .png format.

    Args:
        gt_file(str): The relative path of the ground_truth file for one image
        img_info(dict): The dict of only the image information

    Returns:
        img_info(dict): The dict of the img and annotation information
    """
    assert isinstance(gt_file, str)
    assert isinstance(img_info, dict)
    gt_img = cv2.imread(gt_file, 0)
    contours, _ = cv2.findContours(gt_img, cv2.RETR_EXTERNAL,
                                   cv2.CHAIN_APPROX_SIMPLE)

    anno_info = []
    for contour in contours:
        if contour.shape[0] == 2:
            continue
        category_id = 1
        xy = np.array(contour).flatten().tolist()

        coordinates = np.array(contour).reshape(-1, 2)
        polygon = Polygon(coordinates)
        iscrowd = 0

        area = polygon.area
        # convert to COCO style XYWH format
        min_x, min_y, max_x, max_y = polygon.bounds
        bbox = [min_x, min_y, max_x - min_x, max_y - min_y]

        anno = dict(
            iscrowd=iscrowd,
            category_id=category_id,
            bbox=bbox,
            area=area,
            segmentation=[xy])
        anno_info.append(anno)

    img_info.update(anno_info=anno_info)

    return img_info


def load_img_info(files, split):
    """Load the information of one image.

    Args:
        files(tuple): The tuple of (img_file, groundtruth_file)
        split(str): The split of dataset: training or test

    Returns:
        img_info(dict): The dict of the img and annotation information
    """
    assert isinstance(files, tuple)
    assert isinstance(split, str)

    img_file, gt_file = files
    # read imgs with ignoring orientations
    img = mmcv.imread(img_file, 'unchanged')
    # read imgs with orientations as dataloader does when training and testing
    img_color = mmcv.imread(img_file, 'color')
    # make sure imgs have no orientation info, or annotation gt is wrong.
    assert img.shape[0:2] == img_color.shape[0:2]

    split_name = osp.basename(osp.dirname(img_file))
    img_info = dict(
        # remove img_prefix for filename
        file_name=osp.join(split_name, osp.basename(img_file)),
        height=img.shape[0],
        width=img.shape[1],
        # anno_info=anno_info,
        segm_file=osp.join(split_name, osp.basename(gt_file)))

    if split == 'training':
        img_info = load_mat_info(img_info, gt_file, split)
    elif split == 'test':
        img_info = load_mat_info(img_info, gt_file, split)
    else:
        raise NotImplementedError

    return img_info


def parse_args():
    parser = argparse.ArgumentParser(
        description='Convert totaltext annotations to COCO format')
    parser.add_argument('root_path', help='totaltext root path')
    parser.add_argument('-o', '--out-dir', help='output path')
    parser.add_argument(
        '--split-list',
        nargs='+',
        help='a list of splits. e.g., "--split_list training test"')

    parser.add_argument(
        '--nproc', default=1, type=int, help='number of process')
    args = parser.parse_args()
    return args


def main():
    args = parse_args()
    root_path = args.root_path
    out_dir = args.out_dir if args.out_dir else root_path
    mmcv.mkdir_or_exist(out_dir)

    img_dir = osp.join(root_path, 'imgs')
    gt_dir = osp.join(root_path, 'annotations')

    set_name = {}
    for split in args.split_list:
        set_name.update({split: 'instances_' + split + '.json'})
        assert osp.exists(osp.join(img_dir, split))

    for split, json_name in set_name.items():
        print(f'Converting {split} into {json_name}')
        with mmcv.Timer(
                print_tmpl='It takes {}s to convert totaltext annotation'):
            files = collect_files(
                osp.join(img_dir, split), osp.join(gt_dir, split), split)
            image_infos = collect_annotations(files, split, nproc=args.nproc)
            convert_annotations(image_infos, osp.join(out_dir, json_name))


if __name__ == '__main__':
    main()