| from mmseg.apis import inference_segmentor, init_segmentor |
| import mmcv |
| import glob |
| import os |
| from tqdm import tqdm |
| import argparse |
| from PIL import Image |
| import numpy as np |
| from skimage.io import imsave |
| import pdb |
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| parser = argparse.ArgumentParser(description="") |
| parser.add_argument("--config_file", default='./work_dirs/upernet_swin_base_patch4_window12_512x512_160k_egohos_handobj2_pretrain_480x360_22K/upernet_swin_base_patch4_window12_512x512_160k_egohos_handobj2_pretrain_480x360_22K.py', type=str) |
| parser.add_argument("--checkpoint_file", default='./work_dirs/upernet_swin_base_patch4_window12_512x512_160k_egohos_handobj2_pretrain_480x360_22K/best_mIoU_iter_42000.pth', type=str) |
| parser.add_argument("--img_dir", default='../data/train/image', type=str) |
| parser.add_argument("--pred_seg_dir", default='./work_dirs/upernet_swin_base_patch4_window12_512x512_160k_egohos_handobj2_pretrain_480x360_22K/outputs/train_seg', type=str) |
| args = parser.parse_args() |
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| os.makedirs(args.pred_seg_dir, exist_ok = True) |
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| model = init_segmentor(args.config_file, args.checkpoint_file, device='cuda:0') |
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| alpha = 0.5 |
| for file in tqdm(glob.glob(args.img_dir + '/*')): |
| fname = os.path.basename(file).split('.')[0] |
| img = np.array(Image.open(os.path.join(args.img_dir, fname + '.jpg'))) |
| seg_result = inference_segmentor(model, file)[0] |
| imsave(os.path.join(args.pred_seg_dir, fname + '.png'), seg_result.astype(np.uint8)) |
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