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Running
on
A10G
Running
on
A10G
| import gradio as gr | |
| from utils import colorize | |
| from PIL import Image | |
| import tempfile | |
| def predict_depth(model, image): | |
| depth = model.infer_pil(image) | |
| return depth | |
| def create_demo(model): | |
| gr.Markdown("### Depth Prediction demo") | |
| with gr.Row(): | |
| input_image = gr.Image(label="Input Image", type='pil', elem_id='img-display-input').style(height="auto") | |
| depth_image = gr.Image(label="Depth Map", elem_id='img-display-output') | |
| raw_file = gr.File(label="16-bit raw depth, multiplier:256") | |
| submit = gr.Button("Submit") | |
| def on_submit(image): | |
| depth = predict_depth(model, image) | |
| colored_depth = colorize(depth, cmap='gray_r') | |
| tmp = tempfile.NamedTemporaryFile(suffix='.png', delete=False) | |
| raw_depth = Image.fromarray((depth*256).astype('uint16')) | |
| raw_depth.save(tmp.name) | |
| return [colored_depth, tmp.name] | |
| submit.click(on_submit, inputs=[input_image], outputs=[depth_image, raw_file]) | |
| examples = gr.Examples(examples=["examples/person_1.jpeg", "examples/person_2.jpeg", "examples/person-leaves.png", "examples/living-room.jpeg"], | |
| inputs=[input_image]) |