| 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]) |
