| import gradio as gr | |
| from demo import VideoCLSModel | |
| sample_videos = [ | |
| 'data/charades_ego/video/P9SOAEGO.mp4', | |
| 'data/charades_ego/video/6D5DHEGO.mp4', | |
| 'data/charades_ego/video/15AKPEGO.mp4', | |
| 'data/charades_ego/video/X2JTKEGO.mp4', | |
| 'data/charades_ego/video/184EHEGO.mp4', | |
| 'data/charades_ego/video/S8YZIEGO.mp4', | |
| 'data/charades_ego/video/PRODQEGO.mp4', | |
| 'data/charades_ego/video/QLXEXEGO.mp4', | |
| 'data/charades_ego/video/CC0LBEGO.mp4', | |
| 'data/charades_ego/video/FLY2FEGO.mp4' | |
| ] | |
| def main(): | |
| svitt = VideoCLSModel("configs/charades_ego/svitt.yml") | |
| def predict(video_str): | |
| video_file = video_str.split('/')[-1] | |
| for i, item in enumerate(sample_videos): | |
| if video_file in item: | |
| idx = i | |
| break | |
| ft_action, gt_action = svitt.predict(idx) | |
| return gt_action, ft_action | |
| with gr.Blocks() as demo: | |
| gr.Markdown( | |
| """ | |
| # SViTT-Ego for Action Recognition | |
| Choose a sample video and click predict to view the results. | |
| """ | |
| ) | |
| with gr.Row(): | |
| idx = gr.Number(label="Idx", visible=False) | |
| video = gr.Video(label='video', format='mp4', autoplay=True, height=256, width=256) | |
| with gr.Row(): | |
| label = gr.Text(label="Ground Truth") | |
| ours = gr.Text(label="SViTT-Ego prediction") | |
| with gr.Row(): | |
| btn = gr.Button("Predict", variant="primary") | |
| btn.click(predict, inputs=[video], outputs=[label, ours]) | |
| with gr.Column(): | |
| gr.Examples(examples=[[x] for _, x in enumerate(sample_videos)], inputs=[video]) | |
| demo.launch() | |
| if __name__ == "__main__": | |
| main() | |