layer_diff_dataset / layer_diff_dataset /change_mask copy 2.py
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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()