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| import argparse import glob import math import os import os.path as osp import tempfile import zipfile from tqdm import tqdm
from PIL import Image import numpy as np
def parse_args(arg_list): parser = argparse.ArgumentParser( description='Convert potsdam dataset to mmsegmentation format') parser.add_argument('dataset_path', help='potsdam 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(arg_list) return args
def clip_big_image(image_path, clip_save_dir, args, to_label=False): image = Image.open(image_path) image = np.array(image)
h, w, c = image.shape clip_size = args.clip_size stride_size = args.stride_size
num_rows = math.ceil((h - clip_size) / stride_size) if math.ceil( (h - clip_size) / stride_size) * stride_size + clip_size >= h else math.ceil( (h - clip_size) / stride_size) + 1 num_cols = math.ceil((w - clip_size) / stride_size) if math.ceil( (w - clip_size) / stride_size) * stride_size + clip_size >= w else math.ceil( (w - clip_size) / stride_size) + 1
x, y = np.meshgrid(np.arange(num_cols + 1), np.arange(num_rows + 1)) xmin = x * clip_size ymin = y * clip_size
xmin = xmin.ravel() ymin = ymin.ravel() xmin_offset = np.where(xmin + clip_size > w, w - xmin - clip_size, np.zeros_like(xmin)) ymin_offset = np.where(ymin + clip_size > h, h - ymin - clip_size, np.zeros_like(ymin)) boxes = np.stack([ xmin + xmin_offset, ymin + ymin_offset, np.minimum(xmin + clip_size, w), np.minimum(ymin + clip_size, 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, :] idx_i, idx_j = osp.basename(image_path).split('_')[2:4]
clipped_image = Image.fromarray(clipped_image.astype(np.uint8)) clipped_image.save( fp=osp.join(clip_save_dir, f'{idx_i}_{idx_j}_{start_x}_{start_y}_{end_x}_{end_y}.png'), format='PNG', compress_level=1 )
def main(): args = parse_args(["D:/Dataset/Potsdam"]) splits = { 'train': [ '2_10', '2_11', '2_12', '3_10', '3_11', '3_12', '4_10', '4_11', '4_12', '5_10', '5_11', '5_12', '6_10', '6_11', '6_12', '6_7', '6_8', '6_9', '7_10', '7_11', '7_12', '7_7', '7_8', '7_9' ], 'val': [ '5_15', '6_15', '6_13', '3_13', '4_14', '6_14', '5_14', '2_13', '4_15', '2_14', '5_13', '4_13', '3_14', '7_13' ] }
dataset_path = args.dataset_path if args.out_dir is None: out_dir = osp.join('data', 'potsdam') else: out_dir = args.out_dir
print('Making directories...') if not osp.exists(osp.join(out_dir, 'img_dir', 'train')): os.makedirs(osp.join(out_dir, 'img_dir', 'train')) if not osp.exists(osp.join(out_dir, 'img_dir', 'val')): os.makedirs(osp.join(out_dir, 'img_dir', 'val'))
if not osp.exists(osp.join(out_dir, 'ann_dir', 'train')): os.makedirs(osp.join(out_dir, 'ann_dir', 'train')) if not osp.exists(osp.join(out_dir, 'ann_dir', 'val')): os.makedirs(osp.join(out_dir, 'ann_dir', 'val'))
zipp_list = glob.glob(os.path.join(dataset_path, '*.zip')) print('Find the data', zipp_list)
for zipp in zipp_list: with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir: zip_file = zipfile.ZipFile(zipp) zip_file.extractall(tmp_dir) src_path_list = glob.glob(os.path.join(tmp_dir, '*.tif')) if not len(src_path_list): sub_tmp_dir = os.path.join(tmp_dir, os.listdir(tmp_dir)[0]) src_path_list = glob.glob(os.path.join(sub_tmp_dir, '*.tif'))
prog_bar = tqdm(src_path_list[:2]) for src_path in prog_bar: idx_i, idx_j = osp.basename(src_path).split('_')[2:4] data_type = 'train' if f'{idx_i}_{idx_j}' in splits[ 'train'] else 'val' if 'label' in src_path: dst_dir = osp.join(out_dir, 'ann_dir', data_type) clip_big_image(src_path, dst_dir, args, to_label=True) else: dst_dir = osp.join(out_dir, 'img_dir', data_type) clip_big_image(src_path, dst_dir, args, to_label=False)
print('Removing the temporary files...')
print('Done!')
if __name__ == '__main__': main()
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