File size: 2,831 Bytes
5f3481c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | 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 # FIXME, if it is 31, it is overlapping with RIGHT_OBJ2
# all_classes = [LEFT_HAND, RIGHT_HAND, LEFT_OBJ1, LEFT_OBJ2, RIGHT_OBJ1, RIGHT_OBJ2, TWO_OBJ1, TWO_OBJ2]
# all_classes = [LEFT_HAND, RIGHT_HAND, LEFT_OBJ1, RIGHT_OBJ1, TWO_OBJ1]
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 all objects
for i, cls in enumerate(all_classes):
new_ann[ann == cls] = i + 1 # (cls // 10) + 1
# new_ann[new_ann == 255] = len(all_classes) + 1 # for all objects
new_ann[new_ann == 255] = 0 # for all objects
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'))
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