app.py

import functools
import os
import shutil
import sys
import git

import gradio as gr
import numpy as np
import torch as torch
from PIL import Image

from gradio_imageslider import ImageSlider


def process(
    pipe,
    path_input,
    ensemble_size,
    denoise_steps,
    processing_res,
    path_out_16bit=None,
    path_out_fp32=None,
    path_out_vis=None,
):
    if path_out_vis is not None:
        return (
            [path_out_16bit, path_out_vis],
            [path_out_16bit, path_out_fp32, path_out_vis],
        )

    input_image = Image.open(path_input)

    pipe_out = pipe(
        input_image,
        ensemble_size=ensemble_size,
        denoising_steps=denoise_steps,
        processing_res=processing_res,
        batch_size=1 if processing_res == 0 else 0,
        show_progress_bar=True,
    )

    depth_pred = pipe_out.depth_np
    depth_colored = pipe_out.depth_colored
    depth_16bit = (depth_pred * 65535.0).astype(np.uint16)

    path_output_dir = os.path.splitext(path_input)[0] + "_output"
    os.makedirs(path_output_dir, exist_ok=True)

    name_base = os.path.splitext(os.path.basename(path_input))[0]
    path_out_fp32 = os.path.join(path_output_dir, f"{name_base}_depth_fp32.npy")
    path_out_16bit = os.path.join(path_output_dir, f"{name_base}_depth_16bit.png")
    path_out_vis = os.path.join(path_output_dir, f"{name_base}_depth_colored.png")

    np.save(path_out_fp32, depth_pred)
    Image.fromarray(depth_16bit).save(path_out_16bit, mode="I;16")
    depth_colored.save(path_out_vis)

    return (
        [path_out_16bit, path_out_vis],
        [path_out_16bit, path_out_fp32, path_out_vis],
    )

def run_demo_server(pipe):
    process_pipe = functools.partial(process, pipe)
    os.environ["GRADIO_ALLOW_FLAGGING"] = "never"

    with gr.Blocks(
        analytics_enabled=False,
        title="Geowizard Depth and Normal Estimation",
        css="""
            #download {
                height: 118px;
            }
            .slider .inner {
                width: 5px;
                background: #FFF;
            }
            .viewport {
                aspect-ratio: 4/3;
            }
        """,
    ) as demo:
        gr.Markdown(
            """
        """
        )

        with gr.Row():
            with gr.Column():
                input_image = gr.Image(
                    label="Input Image",
                    type="filepath",
                )
                with gr.Accordion("Advanced options", open=False):
                    ensemble_size = gr.Slider(
                        label="Ensemble size",
                        minimum=1,
                        maximum=20,
                        step=1,
                        value=10,
                    )
                    denoise_steps = gr.Slider(
                        label="Number of denoising steps",
                        minimum=1,
                        maximum=20,
                        step=1,
                        value=10,
                    )
                    processing_res = gr.Radio(
                        [
                            ("Native", 0),
                            ("Recommended", 768),
                        ],
                        label="Processing resolution",
                        value=768,
                    )
                input_output_16bit = gr.File(
                    label="Predicted depth (16-bit)",
                    visible=False,
                )
                input_output_fp32 = gr.File(
                    label="Predicted depth (32-bit)",
                    visible=False,
                )
                input_output_vis = gr.File(
                    label="Predicted depth (red-near, blue-far)",
                    visible=False,
                )
                with gr.Row():
                    submit_btn = gr.Button(value="Compute Depth", variant="primary")
                    clear_btn = gr.Button(value="Clear")
            with gr.Column():
                output_slider = ImageSlider(
                    label="Predicted depth (red-near, blue-far)",
                    type="filepath",
                    show_download_button=True,
                    show_share_button=True,
                    interactive=False,
                    elem_classes="slider",
                    position=0.25,
                )
                files = gr.Files(
                    label="Depth outputs",
                    elem_id="download",
                    interactive=False,
                )

        demo_3d_header = gr.Markdown(
            """
            <h3 align="center">Depth Maps</h3>
            <p align="justify">
                TBD 
                result (see Pro Tips below).
            </p>
            """,
            render=False,
        )

        demo_3d = gr.Row(render=False)
        with demo_3d:
            with gr.Column():
                with gr.Accordion("3D printing demo: Main options", open=True):
                    plane_near = gr.Slider(
                        label="Relative position of the near plane (between 0 and 1)",
                        minimum=0.0,
                        maximum=1.0,
                        step=0.001,
                        value=0.0,
                    )
                    plane_far = gr.Slider(
                        label="Relative position of the far plane (between near and 1)",
                        minimum=0.0,
                        maximum=1.0,
                        step=0.001,
                        value=1.0,
                    )
                    embossing = gr.Slider(
                        label="Embossing level",
                        minimum=0,
                        maximum=100,
                        step=1,
                        value=20,
                    )
                with gr.Accordion("3D printing demo: Advanced options", open=False):
                    size_longest_px = gr.Slider(
                        label="Size (px) of the longest side",
                        minimum=256,
                        maximum=1024,
                        step=256,
                        value=512,
                    )
                    size_longest_cm = gr.Slider(
                        label="Size (cm) of the longest side",
                        minimum=1,
                        maximum=100,
                        step=1,
                        value=10,
                    )
                    filter_size = gr.Slider(
                        label="Size (px) of the smoothing filter",
                        minimum=1,
                        maximum=5,
                        step=2,
                        value=3,
                    )
                    frame_thickness = gr.Slider(
                        label="Frame thickness",
                        minimum=0,
                        maximum=100,
                        step=1,
                        value=5,
                    )
                    frame_near = gr.Slider(
                        label="Frame's near plane offset",
                        minimum=-100,
                        maximum=100,
                        step=1,
                        value=1,
                    )
                    frame_far = gr.Slider(
                        label="Frame's far plane offset",
                        minimum=1,
                        maximum=10,
                        step=1,
                        value=1,
                    )
                with gr.Row():
                    submit_3d = gr.Button(value="Create 3D", variant="primary")
                    clear_3d = gr.Button(value="Clear 3D")
                gr.Markdown(
                    """
                    <h5 align="center">Pro Tips</h5>
                    <ol>
                      <li><b>Re-render with new parameters</b>: Click "Clear 3D" and then "Create 3D".</li>
                      <li><b>Adjust 3D scale and cut-off focus</b>: Set the frame's near plane offset to the 
                          minimum and use 3D preview to evaluate depth scaling. Repeat until the scale is correct and 
                          everything important is in the focus. Set the optimal value for frame's near 
                          plane offset as a last step.</li>
                      <li><b>Increase details</b>: Decrease size of the smoothing filter (also increases noise).</li>
                    </ol>
                    """
                )

            with gr.Column():
                viewer_3d = gr.Model3D(
                    camera_position=(75.0, 90.0, 1.25),
                    elem_classes="viewport",
                    label="3D preview (low-res, relief highlight)",
                    interactive=False,
                )
                files_3d = gr.Files(
                    label="3D model outputs (high-res)",
                    elem_id="download",
                    interactive=False,
                )

        blocks_settings_depth = [ensemble_size, denoise_steps, processing_res]
        blocks_settings_3d = [plane_near, plane_far, embossing, size_longest_px, size_longest_cm, filter_size,
                              frame_thickness, frame_near, frame_far]
        blocks_settings = blocks_settings_depth + blocks_settings_3d
        map_id_to_default = {b._id: b.value for b in blocks_settings}

        inputs = [
            input_image,
            ensemble_size,
            denoise_steps,
            processing_res,
            input_output_16bit,
            input_output_fp32,
            input_output_vis,
            plane_near,
            plane_far,
            embossing,
            filter_size,
            frame_near,
        ]
        outputs = [
            submit_btn,
            input_image,
            output_slider,
            files,
        ]

        def submit_depth_fn(*args):
            out = list(process_pipe(*args))
            out = [gr.Button(interactive=False), gr.Image(interactive=False)] + out
            return out

        submit_btn.click(
            fn=submit_depth_fn,
            inputs=inputs,
            outputs=outputs,
            concurrency_limit=1,
        )

        demo_3d_header.render()
        demo_3d.render()

        def clear_fn():
            out = []
            for b in blocks_settings:
                out.append(map_id_to_default[b._id])
            out += [
                gr.Button(interactive=True),
                gr.Button(interactive=True),
                gr.Image(value=None, interactive=True),
                None, None, None, None, None, None, None,
            ]
            return out

        clear_btn.click(
            fn=clear_fn,
            inputs=[],
            outputs=blocks_settings + [
                submit_btn,
                submit_3d,
                input_image,
                input_output_16bit,
                input_output_fp32,
                input_output_vis,
                output_slider,
                files,
                viewer_3d,
                files_3d,
            ],
        )

        def submit_3d_fn(*args):
            out = list(process_3d(*args))
            out = [gr.Button(interactive=False)] + out
            return out

        submit_3d.click(
            fn=submit_3d_fn,
            inputs=[
                input_image,
                files,
                size_longest_px,
                size_longest_cm,
                filter_size,
                plane_near,
                plane_far,
                embossing,
                frame_thickness,
                frame_near,
                frame_far,
            ],
            outputs=[submit_3d, viewer_3d, files_3d],
            concurrency_limit=1,
        )

        def clear_3d_fn():
            return [gr.Button(interactive=True), None, None]

        clear_3d.click(
            fn=clear_3d_fn,
            inputs=[],
            outputs=[submit_3d, viewer_3d, files_3d],
        )

        demo.queue(
            api_open=False,
        ).launch(
            server_name="0.0.0.0",
            server_port=7860,
        )




def main():

    REPO_URL = "https://github.com/lemonaddie/geowizard.git"
    CHECKPOINT = "lemonaddie/Geowizard"
    REPO_DIR = "geowizard"
    
    if os.path.isdir(REPO_DIR):
        shutil.rmtree(REPO_DIR)
    
    repo = git.Repo.clone_from(REPO_URL, REPO_DIR)
    sys.path.append(os.path.join(os.getcwd(), REPO_DIR))
    
    from pipeline.depth_normal_pipeline_clip_cfg import DepthNormalEstimationPipeline

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")    
    pipe = DepthNormalEstimationPipeline.from_pretrained(CHECKPOINT)
    
    try:
        import xformers
        pipe.enable_xformers_memory_efficient_attention()
    except:
        pass  # run without xformers

    pipe = pipe.to(device)
    run_demo_server(pipe)


if __name__ == "__main__":
    main()
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