image_demo.py

# Copyright (c) OpenMMLab. All rights reserved.
import os
import os.path as osp
from argparse import ArgumentParser

import cv2
import mmcv
import mmcv_custom  # noqa: F401,F403
import mmseg_custom  # noqa: F401,F403
from mmcv.runner import load_checkpoint
from mmseg.apis import inference_segmentor, init_segmentor, show_result_pyplot
from mmseg.core import get_classes
from mmseg.core.evaluation import get_palette


def test_single_image(model, img_name, out_dir, color_palette, opacity):
    # check img_name is an image file or not
    assumed_imgformat = ('.png', '.jpg', '.jpeg', '.tiff', '.bmp', '.gif')
    if (not img_name.lower().endswith(assumed_imgformat)):
        print(f'Skip {img_name} because it is not an image file.')
        return

    result = inference_segmentor(model, img_name)

    # show the results
    if hasattr(model, 'module'):
        model = model.module
    img = model.show_result(img_name, result,
                            palette=color_palette,
                            show=False, opacity=opacity)

    # save the results
    mmcv.mkdir_or_exist(out_dir)
    out_path = osp.join(out_dir, osp.basename(img_name))
    cv2.imwrite(out_path, img)
    print(f'Result is save at {out_path}')


def main():
    parser = ArgumentParser()
    parser.add_argument(
        'img', help='Image file or a directory contains images')
    parser.add_argument('config', help='Config file')
    parser.add_argument('checkpoint', help='Checkpoint file')
    parser.add_argument('--out', type=str, default='demo', help='out dir')
    parser.add_argument(
        '--device', default='cuda:0', help='Device used for inference')
    parser.add_argument(
        '--palette',
        default='ade20k',
        choices=['ade20k', 'cityscapes', 'cocostuff'],
        help='Color palette used for segmentation map')
    parser.add_argument(
        '--opacity',
        type=float,
        default=0.5,
        help='Opacity of painted segmentation map. In (0, 1] range.')
    args = parser.parse_args()

    # build the model from a config file and a checkpoint file
    model = init_segmentor(args.config, checkpoint=None, device=args.device)
    checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
    if 'CLASSES' in checkpoint.get('meta', {}):
        model.CLASSES = checkpoint['meta']['CLASSES']
    else:
        model.CLASSES = get_classes(args.palette)

    # check arg.img is directory of a single image.
    if osp.isdir(args.img):
        for img in sorted(os.listdir(args.img)):
            test_single_image(model, osp.join(args.img, img),
                              args.out, get_palette(args.palette), args.opacity)
    else:
        test_single_image(model, args.img, args.out,
                          get_palette(args.palette), args.opacity)


if __name__ == '__main__':
    main()