import os from pathlib import Path import shutil import numpy as np import tqdm from PIL import Image def convert_pas21(input, output): img = np.asarray(Image.open(input)) assert img.dtype == np.uint8 # do nothing Image.fromarray(img).save(output) def convert_pas20(input, output): img = np.array(Image.open(input)) img[img == 0] = 255 img = img - 1 img[img == 254] = 255 assert img.dtype == np.uint8 # do nothing Image.fromarray(img).save(output) if __name__ == "__main__": dataset_dir = Path(os.getenv("DETECTRON2_DATASETS", "datasets")) / "pascal_voc_d2" voc_dir = Path(os.getenv("DETECTRON2_DATASETS", "datasets")) / "VOCdevkit/VOC2012" for split in ["training", "validation"]: if split == "training": img_name_path = voc_dir / "ImageSets/Segmentation/train.txt" else: img_name_path = voc_dir / "ImageSets/Segmentation/val.txt" img_dir = voc_dir / "JPEGImages" ann_dir = voc_dir / "SegmentationClass" output_img_dir = dataset_dir / "images" / split output_ann_dir_21 = dataset_dir / "annotations_pascal21" / split output_ann_dir_20 = dataset_dir / "annotations_pascal20" / split output_img_dir.mkdir(parents=True, exist_ok=True) output_ann_dir_21.mkdir(parents=True, exist_ok=True) output_ann_dir_20.mkdir(parents=True, exist_ok=True) with open(img_name_path) as f: for line in tqdm.tqdm(f.readlines()): img_name = line.strip() img_path = img_dir / f"{img_name}.jpg" ann_path = ann_dir / f"{img_name}.png" # print(f'copy2 {output_img_dir}') shutil.copy2(img_path, output_img_dir) # print(f"convert {ann_dir} to {output_ann_dir / f'{img_name}.png'}") convert_pas21(ann_path, output_ann_dir_21 / f"{img_name}.png") convert_pas20(ann_path, output_ann_dir_20 / f"{img_name}.png")