Image Segmentation
Transformers
PyTorch
upernet
Inference Endpoints
test2 / demo /image_demo.py
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from argparse import ArgumentParser
from mmseg.apis import inference_segmentor, init_segmentor, show_result_pyplot
from mmseg.core.evaluation import get_palette
def main():
parser = ArgumentParser()
parser.add_argument('img', help='Image file')
parser.add_argument('config', help='Config file')
parser.add_argument('checkpoint', help='Checkpoint file')
parser.add_argument(
'--device', default='cuda:0', help='Device used for inference')
parser.add_argument(
'--palette',
default='cityscapes',
help='Color palette used for segmentation map')
args = parser.parse_args()
# build the model from a config file and a checkpoint file
model = init_segmentor(args.config, args.checkpoint, device=args.device)
# test a single image
result = inference_segmentor(model, args.img)
# show the results
show_result_pyplot(model, args.img, result, get_palette(args.palette))
if __name__ == '__main__':
main()