| #!/usr/bin/env python | |
| import pathlib | |
| import gradio as gr | |
| import numpy as np | |
| import PIL.Image as Image | |
| from model import Model, random_color, vis_mask | |
| model = Model() | |
| def run(image_path, threshold, max_num_mask): | |
| image = np.asarray(Image.open(image_path).convert('RGB')) | |
| masks = model(image_path, threshold, max_num_mask) | |
| for mask in masks: | |
| image = vis_mask(image, mask, random_color(rgb=True)) | |
| return image | |
| TITLE = 'MaskCut' | |
| DESCRIPTION = 'This is an unofficial demo for https://github.com/facebookresearch/CutLER.' | |
| paths = sorted(pathlib.Path('CutLER/maskcut/imgs').glob('*.jpg')) | |
| demo = gr.Interface( | |
| fn=run, | |
| inputs=[ | |
| gr.Image(label='Input image', type='filepath'), | |
| gr.Slider(label='Threshold used for producing binary graph', | |
| minimum=0, | |
| maximum=1, | |
| value=0.15, | |
| step=0.01), | |
| gr.Slider(label='The maximum number of pseudo-masks per image', | |
| minimum=1, | |
| maximum=20, | |
| value=6, | |
| step=1), | |
| ], | |
| outputs=gr.Image(label='Result', type='numpy'), | |
| examples=[[path.as_posix(), 0.15, 6] for path in paths], | |
| title=TITLE, | |
| description=DESCRIPTION) | |
| demo.queue().launch() | |