mmsegmentation/tools/dataset_converters/vaihingen.py

# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import glob
import math
import os
import os.path as osp
import tempfile
import zipfile

import mmcv
import numpy as np
from mmengine.utils import ProgressBar, mkdir_or_exist


def parse_args():
    parser = argparse.ArgumentParser(
        description='Convert vaihingen dataset to mmsegmentation format')
    parser.add_argument('dataset_path', help='vaihingen folder path')
    parser.add_argument('--tmp_dir', help='path of the temporary directory')
    parser.add_argument('-o', '--out_dir', help='output path')
    parser.add_argument(
        '--clip_size',
        type=int,
        help='clipped size of image after preparation',
        default=512)
    parser.add_argument(
        '--stride_size',
        type=int,
        help='stride of clipping original images',
        default=256)
    args = parser.parse_args()
    return args


def clip_big_image(image_path, clip_save_dir, to_label=False):
    # Original image of Vaihingen dataset is very large, thus pre-processing
    # of them is adopted. Given fixed clip size and stride size to generate
    # clipped image, the intersection　of width and height is determined.
    # For example, given one 5120 x 5120 original image, the clip size is
    # 512 and stride size is 256, thus it would generate 20x20 = 400 images
    # whose size are all 512x512.
    image = mmcv.imread(image_path)

    h, w, c = image.shape
    cs = args.clip_size
    ss = args.stride_size

    num_rows = math.ceil((h - cs) / ss) if math.ceil(
        (h - cs) / ss) * ss + cs >= h else math.ceil((h - cs) / ss) + 1
    num_cols = math.ceil((w - cs) / ss) if math.ceil(
        (w - cs) / ss) * ss + cs >= w else math.ceil((w - cs) / ss) + 1

    x, y = np.meshgrid(np.arange(num_cols + 1), np.arange(num_rows + 1))
    xmin = x * cs
    ymin = y * cs

    xmin = xmin.ravel()
    ymin = ymin.ravel()
    xmin_offset = np.where(xmin + cs > w, w - xmin - cs, np.zeros_like(xmin))
    ymin_offset = np.where(ymin + cs > h, h - ymin - cs, np.zeros_like(ymin))
    boxes = np.stack([
        xmin + xmin_offset, ymin + ymin_offset,
        np.minimum(xmin + cs, w),
        np.minimum(ymin + cs, h)
    ],
                     axis=1)

    if to_label:
        color_map = np.array([[0, 0, 0], [255, 255, 255], [255, 0, 0],
                              [255, 255, 0], [0, 255, 0], [0, 255, 255],
                              [0, 0, 255]])
        flatten_v = np.matmul(
            image.reshape(-1, c),
            np.array([2, 3, 4]).reshape(3, 1))
        out = np.zeros_like(flatten_v)
        for idx, class_color in enumerate(color_map):
            value_idx = np.matmul(class_color,
                                  np.array([2, 3, 4]).reshape(3, 1))
            out[flatten_v == value_idx] = idx
        image = out.reshape(h, w)

    for box in boxes:
        start_x, start_y, end_x, end_y = box
        clipped_image = image[start_y:end_y,
                              start_x:end_x] if to_label else image[
                                  start_y:end_y, start_x:end_x, :]
        area_idx = osp.basename(image_path).split('_')[3].strip('.tif')
        mmcv.imwrite(
            clipped_image.astype(np.uint8),
            osp.join(clip_save_dir,
                     f'{area_idx}_{start_x}_{start_y}_{end_x}_{end_y}.png'))


def main():
    splits = {
        'train': [
            'area1', 'area11', 'area13', 'area15', 'area17', 'area21',
            'area23', 'area26', 'area28', 'area3', 'area30', 'area32',
            'area34', 'area37', 'area5', 'area7'
        ],
        'val': [
            'area6', 'area24', 'area35', 'area16', 'area14', 'area22',
            'area10', 'area4', 'area2', 'area20', 'area8', 'area31', 'area33',
            'area27', 'area38', 'area12', 'area29'
        ],
    }

    dataset_path = args.dataset_path
    if args.out_dir is None:
        out_dir = osp.join('data', 'vaihingen')
    else:
        out_dir = args.out_dir

    print('Making directories...')
    mkdir_or_exist(osp.join(out_dir, 'img_dir', 'train'))
    mkdir_or_exist(osp.join(out_dir, 'img_dir', 'val'))
    mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'train'))
    mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'val'))

    zipp_list = glob.glob(os.path.join(dataset_path, '*.zip'))
    print('Find the data', zipp_list)

    with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:
        for zipp in zipp_list:
            zip_file = zipfile.ZipFile(zipp)
            zip_file.extractall(tmp_dir)
            src_path_list = glob.glob(os.path.join(tmp_dir, '*.tif'))
            if 'ISPRS_semantic_labeling_Vaihingen' in zipp:
                src_path_list = glob.glob(
                    os.path.join(os.path.join(tmp_dir, 'top'), '*.tif'))
            if 'ISPRS_semantic_labeling_Vaihingen_ground_truth_eroded_COMPLETE' in zipp:  # noqa
                src_path_list = glob.glob(os.path.join(tmp_dir, '*.tif'))
                # delete unused area9 ground truth
                for area_ann in src_path_list:
                    if 'area9' in area_ann:
                        src_path_list.remove(area_ann)
            prog_bar = ProgressBar(len(src_path_list))
            for i, src_path in enumerate(src_path_list):
                area_idx = osp.basename(src_path).split('_')[3].strip('.tif')
                data_type = 'train' if area_idx in splits['train'] else 'val'
                if 'noBoundary' in src_path:
                    dst_dir = osp.join(out_dir, 'ann_dir', data_type)
                    clip_big_image(src_path, dst_dir, to_label=True)
                else:
                    dst_dir = osp.join(out_dir, 'img_dir', data_type)
                    clip_big_image(src_path, dst_dir, to_label=False)
                prog_bar.update()

        print('Removing the temporary files...')

    print('Done!')


if __name__ == '__main__':
    args = parse_args()
    main()