Mask annotation issue

[UdonDa]

Hi, @weders

Thank you for sharing your nice work! I have checked your proposed dataset. I find the mask does not look enough to cover the object region. I follow the run_real.sh to create inpainted images. Is this the correct way to generate a training set? Or, your limitation?

https://github.com/nianticlabs/nerf-object-removal/blob/main/run_real.sh

import os
from torchvision.utils import save_image
import numpy as np
import cv2
import torch

def vis_object_removal_016_140():
    ROOT = '/dataset/nerf-object-removal/016'
    imgid = 140
    
    img_path = os.path.join(ROOT, 'lama_images_input_real', f'{imgid}.png')
    mask_path = os.path.join(ROOT, 'lama_images_input_real', f'{imgid}_mask001.png')
    inpainted_img_path = os.path.join(ROOT, 'lama_images_output_real', f'{imgid}_mask001.png')
    
    img = cv2.imread(img_path)
    inpainted_img = cv2.imread(inpainted_img_path)
    inpainted_img = cv2.resize(inpainted_img, (img.shape[1], img.shape[0]), interpolation=cv2.INTER_NEAREST)
    
    mask = cv2.imread(mask_path)
    mask = cv2.resize(mask, (img.shape[1], img.shape[0]), interpolation=cv2.INTER_NEAREST)
    
    print('img:', img.shape, img.min(), img.max())
    print('inpainted_img:', inpainted_img.shape, inpainted_img.min(), inpainted_img.max())
    print('mask:', mask.shape, mask.min(), mask.max())

    masked_img = img * (1 - mask / 255.)
    
    vis = np.concatenate([inpainted_img, img, mask, masked_img], axis=1)
    cv2.imwrite(f'tmp/vis_{imgid}.jpg', vis)
    
    
    
if __name__ == '__main__':
    vis_object_removal_016_140()