import os
import shutil
from tqdm import tqdm
import cv2
import numpy as np

# 对mask进行膨胀

root_folder = '../data/video_dataset/YoutubeVOS/train'
mask_folder = os.path.join(root_folder,'mask')
mask_dilate_folder = os.path.join(root_folder,'mask_dilate')

os.makedirs(mask_dilate_folder,exist_ok=True)
# m_1 = len(os.listdir(mask_folder))
# m_2 = len(os.listdir(mask_dilate_folder))
# print(m_1,m_2)
# exit(0)
vid_list = os.listdir(mask_folder)
pbar = tqdm(enumerate(vid_list),total=len(vid_list))
for i, vid_name in pbar:
    # folder_path = '/mnt/workspace/workgroup/sihui.jsh/layer_diff_dataset/YoutubeVOS/mask/0043f083b5'
    folder_path = os.path.join(mask_folder,vid_name)
    # folder_path = '/mnt/workspace/workgroup/sihui.jsh/layer_diff_dataset/YoutubeVOS/mask/0043f083b5'
    folder_path_ = os.path.join(mask_dilate_folder,vid_name)
    os.makedirs(folder_path_,exist_ok=True)
    file_list = os.listdir(folder_path)
    file_list = [i for i in file_list if i.endswith('.png')]
    file_list.sort()

    # pbar = tqdm(enumerate(file_list),total=len(file_list))
    for i, image_name in enumerate(file_list):
        gt = cv2.imread(os.path.join(folder_path,image_name),cv2.IMREAD_GRAYSCALE)
        # gt = cv2.resize(gt,(512,512))
        kernel = np.ones((40, 40), np.uint8)
        gt = cv2.dilate(gt,kernel,20)
        gt = gt.clip(0, 255).round().astype('uint8')
        cv2.imwrite(os.path.join(folder_path_,image_name),gt)
    # exit()