| from pathlib import Path |
| from tkinter import RIGHT |
| from PIL import Image |
| import numpy as np |
| import matplotlib.pyplot as plt |
| import shutil |
| import cv2 |
| import tqdm |
|
|
| LEFT_HAND = 61 |
| RIGHT_HAND = 71 |
| LEFT_OBJ1 = 1 |
| LEFT_OBJ2 = 11 |
| RIGHT_OBJ1 = 21 |
| RIGHT_OBJ2 = 31 |
| TWO_OBJ1 = 41 |
| TWO_OBJ2 = 51 |
| |
| |
| all_classes = [LEFT_HAND, RIGHT_HAND, LEFT_OBJ1, RIGHT_OBJ1, TWO_OBJ1] |
|
|
| def load_ann(ann_path): |
| ann = Image.open(ann_path).convert('P') |
| return np.array(ann) |
|
|
| def load_img(img_path): |
| img = Image.open(img_path) |
| return np.array(img) |
|
|
| def split_data(all_img_lst, train_frac, test_frac): |
| """Given the list of all images, randomly sample a portion of index as training, |
| validation and test set. The weight is specified as train_frac, val_frac, test_frac |
| """ |
| leng = len(all_img_lst) |
| permuted_img = np.array(all_img_lst)[np.random.permutation(leng)] |
| train_idx = int(leng * train_frac) |
| test_idx = int(leng * test_frac) |
| return permuted_img[:train_idx], permuted_img[train_idx + test_idx:], permuted_img[train_idx:train_idx + test_idx] |
|
|
| def convert_ann(ann): |
| new_ann = np.zeros_like(ann) |
| new_ann[ann > 0] = 255 |
| for i, cls in enumerate(all_classes): |
| new_ann[ann == cls] = i + 1 |
| |
| new_ann[new_ann == 255] = 0 |
| return new_ann |
|
|
| def cv2_imsave(path, img): |
| if img.shape[-1] == 3: |
| img = img[...,::-1] |
| cv2.imwrite(str(path), img) |
|
|
| def convert_and_save(vis_paths, save_path): |
| save_img_path = save_path.joinpath('img') |
| save_img_path.mkdir(exist_ok=True, parents=True) |
| save_anno_path = save_path.joinpath('anno') |
| save_anno_path.mkdir(exist_ok=True) |
| for vis_path in tqdm.tqdm(vis_paths): |
| vis_path = str(vis_path) |
| img_path = vis_path.replace('_draw', '') |
| ann_path = img_path.replace('jpg', 'png') |
| img = load_img(img_path) |
| ann = load_ann(ann_path) |
| new_ann = convert_ann(ann) |
| cv2_imsave(str(save_anno_path.joinpath(Path(ann_path).name)), new_ann) |
| cv2_imsave(str(save_img_path.joinpath(Path(img_path).name)), img) |
|
|
|
|
|
|
| if __name__ == "__main__": |
|
|
| vis_paths = list(Path('all_raw').glob('*_draw.jpg')) |
| train_paths, val_paths, test_paths = split_data(vis_paths, 0.8, 0.1) |
| save_path = Path('/mnt/volume2/Data/zlz/EgoHandObject/data/epic/obj1_hand_2k') |
| convert_and_save(train_paths, save_path.joinpath('train')) |
| convert_and_save(val_paths, save_path.joinpath('val')) |
| convert_and_save(test_paths, save_path.joinpath('test')) |
|
|
|
|
