add fine control

add 3d printing feature
speed up with batching
speed up with keeping pipeline in cache
add einstein example
pin Marigold git version
add license, contributing files
add slider feature
add numpy 32bit output

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+files/einstein_depth_fp32.npy filter=lfs diff=lfs merge=lfs -text
+files/einstein_depth_16bit.png filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -1,2 +1,5 @@
 .idea
-.DS_Store
+.DS_Store
+__pycache__
+gradio_cached_examples
+Marigold

CONTRIBUTING.md

@@ -0,0 +1,15 @@
+## Contributing instructions
+
+We appreciate your interest in contributing. Please follow these guidelines:
+
+1. **Discuss Changes:** Start a GitHub issue to talk about your proposed change before proceeding.
+
+2. **Pull Requests:** Avoid unsolicited PRs. Discussion helps align with project goals.
+
+3. **License Agreement:** By submitting a PR, you accept our LICENSE terms.
+
+4. **Legal Compatibility:** Ensure your change complies with our project's objectives and licensing.
+
+5. **Attribution:** Credit third-party code in your PR if used.
+
+Please, feel free to reach out for questions or assistance. Your contributions are valued, and we're excited to work together to enhance this project!

LICENSE.txt

@@ -0,0 +1,177 @@
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
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+      and distribution as defined by Sections 1 through 9 of this document.
+
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+
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+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
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+      the terms of any separate license agreement you may have executed
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+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
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+   7. Disclaimer of Warranty. Unless required by applicable law or
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+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
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+   END OF TERMS AND CONDITIONS

README.md

@@ -4,7 +4,7 @@ emoji: 🏵️
 colorFrom: blue
 colorTo: red
 sdk: gradio
-sdk_version: 3.44.4
+sdk_version: 4.9.1
 app_file: app.py
 pinned: true
 license: cc-by-sa-4.0

app.py

@@ -1,127 +1,564 @@
+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
 
-desc = """
-    <p align="center">
-    <a title="Website" href="https://marigoldmonodepth.github.io/" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
-        <img src="https://www.obukhov.ai/img/badges/badge-website.svg">
-    </a>
-    <a title="arXiv" href="https://arxiv.org/abs/2312.02145" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
-        <img src="https://www.obukhov.ai/img/badges/badge-pdf.svg">
-    </a>
-    <a title="Github" href="https://github.com/prs-eth/marigold" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
-        <img src="https://img.shields.io/github/stars/prs-eth/marigold?label=GitHub%20%E2%98%85&logo=github&color=C8C" alt="badge-github-stars">
-    </a>
-    <a title="Social" href="https://twitter.com/antonobukhov1" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
-        <img src="https://www.obukhov.ai/img/badges/badge-social.svg" alt="social">
-    </a>
-    </p>
-    <p align="justify">
-    Marigold is the new state-of-the-art depth estimator for images in the wild. Upload your image into the pane on the left side, or expore examples listed in the bottom.  
-    </p>
-"""
-
-
-def download_code():
-    os.system('git clone https://github.com/prs-eth/Marigold.git')
-
-
-def find_first_png(directory):
-    for file in os.listdir(directory):
-        if file.lower().endswith(".png"):
-            return os.path.join(directory, file)
-    return None
-
-
-def marigold_process(path_input, path_out_vis=None, path_out_pred=None):
-    if path_out_vis is not None and path_out_pred is not None:
-        return path_out_vis, path_out_pred
-
-    path_input_dir = path_input + ".input"
-    path_output_dir = path_input + ".output"
-    os.makedirs(path_input_dir, exist_ok=True)
+from extrude import extrude_depth_3d
+
+
+def process(
+    pipe,
+    path_input,
+    ensemble_size,
+    denoise_steps,
+    processing_res,
+    path_out_16bit=None,
+    path_out_fp32=None,
+    path_out_vis=None,
+    _input_3d_plane_near=None,
+    _input_3d_plane_far=None,
+    _input_3d_embossing=None,
+    _input_3d_filter_size=None,
+    _input_3d_frame_near=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,
+        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)
-    shutil.copy(path_input, path_input_dir)
 
-    os.system(
-        f"cd Marigold && python3 run.py "
-        f"--input_rgb_dir \"{path_input_dir}\" "
-        f"--output_dir \"{path_output_dir}\" "
+    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],
     )
 
-    path_out_vis = find_first_png(path_output_dir + "/depth_colored")
-    assert path_out_vis is not None, "Processing failed"
-    path_out_pred = find_first_png(path_output_dir + "/depth_bw")
-    assert path_out_pred is not None, "Processing failed"
-
-    return path_out_vis, path_out_pred
-
-
-iface = gr.Interface(
-    title="Marigold Depth Estimation",
-    description=desc,
-    thumbnail="marigold_logo_square.jpg",
-    fn=marigold_process,
-    inputs=[
-        gr.Image(
-            label="Input Image",
-            type="filepath",
-        ),
-        gr.Image(
-            label="Predicted depth (red-near, blue-far)",
-            type="filepath",
-            visible=False,
-        ),
-        gr.Image(
-            label="Predicted depth",
-            type="filepath",
-            visible=False,
-        ),
-    ],
-    outputs=[
-        gr.Image(
-            label="Predicted depth (red-near, blue-far)",
-            type="pil",
-        ),
-        gr.Image(
-            label="Predicted depth",
-            type="pil",
-            elem_classes="imgdownload",
-        ),
-    ],
-    allow_flagging="never",
-    examples=[
-        [
-            os.path.join(os.path.dirname(__file__), "files/bee.jpg"),
-            os.path.join(os.path.dirname(__file__), "files/bee_vis.jpg"),
-            os.path.join(os.path.dirname(__file__), "files/bee_pred.jpg"),
-        ],
-        [
-            os.path.join(os.path.dirname(__file__), "files/cat.jpg"),
-            os.path.join(os.path.dirname(__file__), "files/cat_vis.jpg"),
-            os.path.join(os.path.dirname(__file__), "files/cat_pred.jpg"),
-        ],
-        [
-            os.path.join(os.path.dirname(__file__), "files/swings.jpg"),
-            os.path.join(os.path.dirname(__file__), "files/swings_vis.jpg"),
-            os.path.join(os.path.dirname(__file__), "files/swings_pred.jpg"),
-        ],
-    ],
-    css="""
-    .viewport {
-        aspect-ratio: 4/3;
-    }
-    .imgdownload {
-        height: 64px;
-    }
-    """,
-    cache_examples=True,
-)
+
+def process_3d(
+    input_image,
+    files,
+    size_longest_px,
+    size_longest_cm,
+    filter_size,
+    plane_near,
+    plane_far,
+    embossing,
+    frame_thickness,
+    frame_near,
+    frame_far,
+):
+    if input_image is None or len(files) < 1:
+        raise gr.Error("Please upload an image (or use examples) and compute depth first")
+
+    if plane_near >= plane_far:
+        raise gr.Error("NEAR plane must have a value smaller than the FAR plane")
+
+    def _process_3d(size_longest_px, filter_size, vertex_colors, scene_lights, output_model_scale=None):
+        image_rgb = input_image
+        image_depth = files[0]
+
+        image_rgb_basename, image_rgb_ext = os.path.splitext(image_rgb)
+        image_depth_basename, image_depth_ext = os.path.splitext(image_depth)
+
+        image_rgb_content = Image.open(image_rgb)
+        image_rgb_w, image_rgb_h = image_rgb_content.width, image_rgb_content.height
+        image_rgb_d = max(image_rgb_w, image_rgb_h)
+        image_new_w = size_longest_px * image_rgb_w // image_rgb_d
+        image_new_h = size_longest_px * image_rgb_h // image_rgb_d
+
+        image_rgb_new = image_rgb_basename + f"_{size_longest_px}" + image_rgb_ext
+        image_depth_new = image_depth_basename + f"_{size_longest_px}" + image_depth_ext
+        image_rgb_content.resize((image_new_w, image_new_h), Image.LANCZOS).save(
+            image_rgb_new
+        )
+        Image.open(image_depth).resize((image_new_w, image_new_h), Image.LANCZOS).save(
+            image_depth_new
+        )
+
+        path_glb, path_stl = extrude_depth_3d(
+            image_rgb_new,
+            image_depth_new,
+            output_model_scale=size_longest_cm * 10 if output_model_scale is None else output_model_scale,
+            filter_size=filter_size,
+            coef_near=plane_near,
+            coef_far=plane_far,
+            emboss=embossing / 100,
+            f_thic=frame_thickness / 100,
+            f_near=frame_near / 100,
+            f_back=frame_far / 100,
+            vertex_colors=vertex_colors,
+            scene_lights=scene_lights,
+        )
+
+        return path_glb, path_stl
+
+    path_viewer_glb, _ = _process_3d(256, filter_size, vertex_colors=False, scene_lights=True, output_model_scale=1)
+    path_files_glb, path_files_stl = _process_3d(size_longest_px, filter_size, vertex_colors=True, scene_lights=False)
+
+    # sanitize 3d viewer glb path to keep babylon.js happy
+    path_viewer_glb_sanitized = os.path.join(os.path.dirname(path_viewer_glb), "preview.glb")
+    if path_viewer_glb_sanitized != path_viewer_glb:
+        os.rename(path_viewer_glb, path_viewer_glb_sanitized)
+        path_viewer_glb = path_viewer_glb_sanitized
+
+    return path_viewer_glb, [path_files_glb, path_files_stl]
+
+
+def run_demo_server(pipe):
+    process_pipe = functools.partial(process, pipe)
+    os.environ["GRADIO_ALLOW_FLAGGING"] = "never"
+
+    with gr.Blocks(
+        analytics_enabled=False,
+        title="Marigold Depth Estimation",
+        css="""
+            #download {
+                height: 118px;
+            }
+            .slider .inner {
+                width: 5px;
+                background: #FFF;
+            }
+            .viewport {
+                aspect-ratio: 4/3;
+            }
+        """,
+    ) as demo:
+        gr.Markdown(
+            """
+            <h1 align="center">Marigold Depth Estimation</h1>
+            <p align="center">
+            <a title="Website" href="https://marigoldmonodepth.github.io/" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+                <img src="https://www.obukhov.ai/img/badges/badge-website.svg">
+            </a>
+            <a title="arXiv" href="https://arxiv.org/abs/2312.02145" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+                <img src="https://www.obukhov.ai/img/badges/badge-pdf.svg">
+            </a>
+            <a title="Github" href="https://github.com/prs-eth/marigold" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+                <img src="https://img.shields.io/github/stars/prs-eth/marigold?label=GitHub%20%E2%98%85&logo=github&color=C8C" alt="badge-github-stars">
+            </a>
+            <a title="Social" href="https://twitter.com/antonobukhov1" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+                <img src="https://www.obukhov.ai/img/badges/badge-social.svg" alt="social">
+            </a>
+            </p>
+            <p align="justify">
+                Marigold is the new state-of-the-art depth estimator for images in the wild. 
+                Upload your image into the <b>left</b> side, or click any of the <b>examples</b> below.
+                The result will be computed and appear on the <b>right</b> in the output comparison window.
+                <b style="color: red;">NEW</b>: Scroll down to the new 3D printing part of the demo! 
+            </p>
+        """
+        )
+
+        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">3D Printing Depth Maps</h3>
+            <p align="justify">
+                This part of the demo uses Marigold depth maps estimated in the previous step to create a 
+                3D-printable model. The models are watertight, with correct normals, and exported in the STL format.
+                We recommended creating the first model with the default parameters and iterating on it until the best 
+                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,
+        )
+
+        gr.Examples(
+            fn=submit_depth_fn,
+            examples=[
+                [
+                    "files/bee.jpg",
+                    10,  # ensemble_size
+                    10,  # denoise_steps
+                    768,  # processing_res
+                    "files/bee_depth_16bit.png",
+                    "files/bee_depth_fp32.npy",
+                    "files/bee_depth_colored.png",
+                    0.0,  # plane_near
+                    0.5,  # plane_far
+                    20,  # embossing
+                    3,  # filter_size
+                    0,  # frame_near
+                ],
+                [
+                    "files/cat.jpg",
+                    10,  # ensemble_size
+                    10,  # denoise_steps
+                    768,  # processing_res
+                    "files/cat_depth_16bit.png",
+                    "files/cat_depth_fp32.npy",
+                    "files/cat_depth_colored.png",
+                    0.0,  # plane_near
+                    0.3,  # plane_far
+                    20,  # embossing
+                    3,  # filter_size
+                    0,  # frame_near
+                ],
+                [
+                    "files/swings.jpg",
+                    10,  # ensemble_size
+                    10,  # denoise_steps
+                    768,  # processing_res
+                    "files/swings_depth_16bit.png",
+                    "files/swings_depth_fp32.npy",
+                    "files/swings_depth_colored.png",
+                    0.05,  # plane_near
+                    0.25,  # plane_far
+                    10,  # embossing
+                    1,  # filter_size
+                    0,  # frame_near
+                ],
+                [
+                    "files/einstein.jpg",
+                    10,  # ensemble_size
+                    10,  # denoise_steps
+                    768,  # processing_res
+                    "files/einstein_depth_16bit.png",
+                    "files/einstein_depth_fp32.npy",
+                    "files/einstein_depth_colored.png",
+                    0.0,  # plane_near
+                    0.5,  # plane_far
+                    50,  # embossing
+                    3,  # filter_size
+                    -15,  # frame_near
+                ],
+            ],
+            inputs=inputs,
+            outputs=outputs,
+            cache_examples=True,
+        )
+
+        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().launch(server_name="0.0.0.0", server_port=7860)
+
+
+def prefetch_hf_cache(pipe):
+    process(pipe, "files/bee.jpg", 1, 1, 64)
+    shutil.rmtree("files/bee_output")
+
+
+def main():
+    REPO_URL = "https://github.com/prs-eth/Marigold.git"
+    REPO_HASH = "22437a9d"
+    REPO_DIR = "Marigold"
+    CHECKPOINT = "Bingxin/Marigold"
+
+    if os.path.isdir(REPO_DIR):
+        shutil.rmtree(REPO_DIR)
+    repo = git.Repo.clone_from(REPO_URL, REPO_DIR)
+    repo.git.checkout(REPO_HASH)
+
+    sys.path.append(os.path.join(os.getcwd(), REPO_DIR))
+
+    from marigold import MarigoldPipeline
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    pipe = MarigoldPipeline.from_pretrained(CHECKPOINT)
+    try:
+        import xformers
+        pipe.enable_xformers_memory_efficient_attention()
+    except:
+        pass  # run without xformers
+
+    pipe = pipe.to(device)
+    prefetch_hf_cache(pipe)
+    run_demo_server(pipe)
 
 
 if __name__ == "__main__":
-    download_code()
-    iface.queue().launch(server_name="0.0.0.0", server_port=7860)
+    main()

extrude.py

@@ -0,0 +1,322 @@
+import math
+import os
+
+import numpy as np
+import pygltflib
+import trimesh
+from PIL import Image, ImageFilter
+
+
+def quaternion_multiply(q1, q2):
+    x1, y1, z1, w1 = q1
+    x2, y2, z2, w2 = q2
+    return [
+        w1 * x2 + x1 * w2 + y1 * z2 - z1 * y2,
+        w1 * y2 - x1 * z2 + y1 * w2 + z1 * x2,
+        w1 * z2 + x1 * y2 - y1 * x2 + z1 * w2,
+        w1 * w2 - x1 * x2 - y1 * y2 - z1 * z2,
+    ]
+
+
+def glb_add_lights(path_input, path_output):
+    """
+    Adds directional lights in the horizontal plane to the glb file.
+    :param path_input: path to input glb
+    :param path_output: path to output glb
+    :return: None
+    """
+    glb = pygltflib.GLTF2().load(path_input)
+
+    N = 3  # default max num lights in Babylon.js is 4
+    angle_step = 2 * math.pi / N
+    elevation_angle = math.radians(75)
+
+    light_colors = [
+        [1.0, 0.0, 0.0],
+        [0.0, 1.0, 0.0],
+        [0.0, 0.0, 1.0],
+    ]
+
+    lights_extension = {
+        "lights": [
+            {"type": "directional", "color": light_colors[i], "intensity": 2.0}
+            for i in range(N)
+        ]
+    }
+
+    if "KHR_lights_punctual" not in glb.extensionsUsed:
+        glb.extensionsUsed.append("KHR_lights_punctual")
+    glb.extensions["KHR_lights_punctual"] = lights_extension
+
+    light_nodes = []
+    for i in range(N):
+        angle = i * angle_step
+
+        pos_rot = [0.0, 0.0, math.sin(angle / 2), math.cos(angle / 2)]
+        elev_rot = [math.sin(elevation_angle / 2), 0.0, 0.0, math.cos(elevation_angle / 2)]
+        rotation = quaternion_multiply(pos_rot, elev_rot)
+
+        node = {
+            "rotation": rotation,
+            "extensions": {"KHR_lights_punctual": {"light": i}},
+        }
+        light_nodes.append(node)
+
+    light_node_indices = list(range(len(glb.nodes), len(glb.nodes) + N))
+    glb.nodes.extend(light_nodes)
+
+    root_node_index = glb.scenes[glb.scene].nodes[0]
+    root_node = glb.nodes[root_node_index]
+    if hasattr(root_node, "children"):
+        root_node.children.extend(light_node_indices)
+    else:
+        root_node.children = light_node_indices
+
+    glb.save(path_output)
+
+
+def extrude_depth_3d(
+    path_rgb,
+    path_depth,
+    output_model_scale=100,
+    filter_size=3,
+    coef_near=0.0,
+    coef_far=1.0,
+    emboss=0.3,
+    f_thic=0.05,
+    f_near=-0.15,
+    f_back=0.01,
+    vertex_colors=True,
+    scene_lights=True,
+):
+    f_far_inner = -emboss
+    f_far_outer = f_far_inner - f_back
+
+    f_near = max(f_near, f_far_inner)
+
+    depth_image = Image.open(path_depth)
+    assert depth_image.mode == "I", depth_image.mode
+    depth_image = depth_image.filter(ImageFilter.MedianFilter(size=filter_size))
+
+    w, h = depth_image.size
+    d_max = max(w, h)
+    depth_image = np.array(depth_image).astype(np.double)
+    z_min, z_max = np.min(depth_image), np.max(depth_image)
+    depth_image = (depth_image.astype(np.double) - z_min) / (z_max - z_min)
+    depth_image[depth_image < coef_near] = coef_near
+    depth_image[depth_image > coef_far] = coef_far
+    depth_image = emboss * (depth_image - coef_near) / (coef_far - coef_near)
+    rgb_image = np.array(
+        Image.open(path_rgb).convert("RGB").resize((w, h), Image.Resampling.LANCZOS)
+    )
+
+    w_norm = w / float(d_max - 1)
+    h_norm = h / float(d_max - 1)
+    w_half = w_norm / 2
+    h_half = h_norm / 2
+
+    x, y = np.meshgrid(np.arange(w), np.arange(h))
+    x = x / float(d_max - 1) - w_half  # [-w_half, w_half]
+    y = -y / float(d_max - 1) + h_half  # [-h_half, h_half]
+    z = -depth_image  # -depth_emboss (far) - 0 (near)
+    vertices_2d = np.stack((x, y, z), axis=-1)
+    vertices = vertices_2d.reshape(-1, 3)
+    colors = rgb_image[:, :, :3].reshape(-1, 3) / 255.0
+
+    faces = []
+    for y in range(h - 1):
+        for x in range(w - 1):
+            idx = y * w + x
+            faces.append([idx, idx + w, idx + 1])
+            faces.append([idx + 1, idx + w, idx + 1 + w])
+
+    # OUTER frame
+
+    nv = len(vertices)
+    vertices = np.append(
+        vertices,
+        [
+            [-w_half - f_thic, -h_half - f_thic, f_near],  # 00
+            [-w_half - f_thic, -h_half - f_thic, f_far_outer],  # 01
+            [w_half + f_thic, -h_half - f_thic, f_near],  # 02
+            [w_half + f_thic, -h_half - f_thic, f_far_outer],  # 03
+            [w_half + f_thic, h_half + f_thic, f_near],  # 04
+            [w_half + f_thic, h_half + f_thic, f_far_outer],  # 05
+            [-w_half - f_thic, h_half + f_thic, f_near],  # 06
+            [-w_half - f_thic, h_half + f_thic, f_far_outer],  # 07
+        ],
+        axis=0,
+    )
+    faces.extend(
+        [
+            [nv + 0, nv + 1, nv + 2],
+            [nv + 2, nv + 1, nv + 3],
+            [nv + 2, nv + 3, nv + 4],
+            [nv + 4, nv + 3, nv + 5],
+            [nv + 4, nv + 5, nv + 6],
+            [nv + 6, nv + 5, nv + 7],
+            [nv + 6, nv + 7, nv + 0],
+            [nv + 0, nv + 7, nv + 1],
+        ]
+    )
+    colors = np.append(colors, [[0.5, 0.5, 0.5]] * 8, axis=0)
+
+    # INNER frame
+
+    nv = len(vertices)
+    vertices_left_data = vertices_2d[:, 0]  # H x 3
+    vertices_left_frame = vertices_2d[:, 0].copy()  # H x 3
+    vertices_left_frame[:, 2] = f_near
+    vertices = np.append(vertices, vertices_left_data, axis=0)
+    vertices = np.append(vertices, vertices_left_frame, axis=0)
+    colors = np.append(colors, [[0.5, 0.5, 0.5]] * (2 * h), axis=0)
+    for i in range(h - 1):
+        nvi_d = nv + i
+        nvi_f = nvi_d + h
+        faces.append([nvi_d, nvi_f, nvi_d + 1])
+        faces.append([nvi_d + 1, nvi_f, nvi_f + 1])
+
+    nv = len(vertices)
+    vertices_right_data = vertices_2d[:, -1]  # H x 3
+    vertices_right_frame = vertices_2d[:, -1].copy()  # H x 3
+    vertices_right_frame[:, 2] = f_near
+    vertices = np.append(vertices, vertices_right_data, axis=0)
+    vertices = np.append(vertices, vertices_right_frame, axis=0)
+    colors = np.append(colors, [[0.5, 0.5, 0.5]] * (2 * h), axis=0)
+    for i in range(h - 1):
+        nvi_d = nv + i
+        nvi_f = nvi_d + h
+        faces.append([nvi_d, nvi_d + 1, nvi_f])
+        faces.append([nvi_d + 1, nvi_f + 1, nvi_f])
+
+    nv = len(vertices)
+    vertices_top_data = vertices_2d[0, :]  # H x 3
+    vertices_top_frame = vertices_2d[0, :].copy()  # H x 3
+    vertices_top_frame[:, 2] = f_near
+    vertices = np.append(vertices, vertices_top_data, axis=0)
+    vertices = np.append(vertices, vertices_top_frame, axis=0)
+    colors = np.append(colors, [[0.5, 0.5, 0.5]] * (2 * w), axis=0)
+    for i in range(w - 1):
+        nvi_d = nv + i
+        nvi_f = nvi_d + w
+        faces.append([nvi_d, nvi_d + 1, nvi_f])
+        faces.append([nvi_d + 1, nvi_f + 1, nvi_f])
+
+    nv = len(vertices)
+    vertices_bottom_data = vertices_2d[-1, :]  # H x 3
+    vertices_bottom_frame = vertices_2d[-1, :].copy()  # H x 3
+    vertices_bottom_frame[:, 2] = f_near
+    vertices = np.append(vertices, vertices_bottom_data, axis=0)
+    vertices = np.append(vertices, vertices_bottom_frame, axis=0)
+    colors = np.append(colors, [[0.5, 0.5, 0.5]] * (2 * w), axis=0)
+    for i in range(w - 1):
+        nvi_d = nv + i
+        nvi_f = nvi_d + w
+        faces.append([nvi_d, nvi_f, nvi_d + 1])
+        faces.append([nvi_d + 1, nvi_f, nvi_f + 1])
+
+    # FRONT frame
+
+    nv = len(vertices)
+    vertices = np.append(
+        vertices,
+        [
+            [-w_half - f_thic, -h_half - f_thic, f_near],
+            [-w_half - f_thic, h_half + f_thic, f_near],
+        ],
+        axis=0,
+    )
+    vertices = np.append(vertices, vertices_left_frame, axis=0)
+    colors = np.append(colors, [[0.5, 0.5, 0.5]] * (2 + h), axis=0)
+    for i in range(h - 1):
+        faces.append([nv, nv + 2 + i + 1, nv + 2 + i])
+    faces.append([nv, nv + 2, nv + 1])
+
+    nv = len(vertices)
+    vertices = np.append(
+        vertices,
+        [
+            [w_half + f_thic, h_half + f_thic, f_near],
+            [w_half + f_thic, -h_half - f_thic, f_near],
+        ],
+        axis=0,
+    )
+    vertices = np.append(vertices, vertices_right_frame, axis=0)
+    colors = np.append(colors, [[0.5, 0.5, 0.5]] * (2 + h), axis=0)
+    for i in range(h - 1):
+        faces.append([nv, nv + 2 + i, nv + 2 + i + 1])
+    faces.append([nv, nv + h + 1, nv + 1])
+
+    nv = len(vertices)
+    vertices = np.append(
+        vertices,
+        [
+            [w_half + f_thic, h_half + f_thic, f_near],
+            [-w_half - f_thic, h_half + f_thic, f_near],
+        ],
+        axis=0,
+    )
+    vertices = np.append(vertices, vertices_top_frame, axis=0)
+    colors = np.append(colors, [[0.5, 0.5, 0.5]] * (2 + w), axis=0)
+    for i in range(w - 1):
+        faces.append([nv, nv + 2 + i, nv + 2 + i + 1])
+    faces.append([nv, nv + 1, nv + 2])
+
+    nv = len(vertices)
+    vertices = np.append(
+        vertices,
+        [
+            [-w_half - f_thic, -h_half - f_thic, f_near],
+            [w_half + f_thic, -h_half - f_thic, f_near],
+        ],
+        axis=0,
+    )
+    vertices = np.append(vertices, vertices_bottom_frame, axis=0)
+    colors = np.append(colors, [[0.5, 0.5, 0.5]] * (2 + w), axis=0)
+    for i in range(w - 1):
+        faces.append([nv, nv + 2 + i + 1, nv + 2 + i])
+    faces.append([nv, nv + 1, nv + w + 1])
+
+    # BACK frame
+
+    nv = len(vertices)
+    vertices = np.append(
+        vertices,
+        [
+            [-w_half - f_thic, -h_half - f_thic, f_far_outer],  # 00
+            [w_half + f_thic, -h_half - f_thic, f_far_outer],  # 01
+            [w_half + f_thic, h_half + f_thic, f_far_outer],  # 02
+            [-w_half - f_thic, h_half + f_thic, f_far_outer],  # 03
+        ],
+        axis=0,
+    )
+    faces.extend(
+        [
+            [nv + 0, nv + 2, nv + 1],
+            [nv + 2, nv + 0, nv + 3],
+        ]
+    )
+    colors = np.append(colors, [[0.5, 0.5, 0.5]] * 4, axis=0)
+
+    trimesh_kwargs = {}
+    if vertex_colors:
+        trimesh_kwargs["vertex_colors"] = colors
+    mesh = trimesh.Trimesh(vertices=vertices, faces=faces, **trimesh_kwargs)
+
+    mesh.merge_vertices()
+
+    current_max_dimension = max(mesh.extents)
+    scaling_factor = output_model_scale / current_max_dimension
+    mesh.apply_scale(scaling_factor)
+
+    path_out_base = os.path.splitext(path_depth)[0].replace("_16bit", "")
+    path_out_glb = path_out_base + ".glb"
+    path_out_stl = path_out_base + ".stl"
+
+    mesh.export(path_out_glb, file_type="glb")
+    if scene_lights:
+        glb_add_lights(path_out_glb, path_out_glb)
+
+    mesh.export(path_out_stl, file_type="stl")
+
+    return path_out_glb, path_out_stl

files/bee_depth_fp32.npy

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+oid sha256:82bfba1b08942eea990bcda9f120bc75274f432de56d221fadbf428f85c6f22c
+size 1398912

files/cat_depth_fp32.npy

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+oid sha256:b44fba3ec47bc4591d47ee2ddf8594934a89728fb8fc8d50bc9596dfe2cee831
+size 1550292

files/einstein_depth_fp32.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6306511f81c545ef9997176e067ed44d0ee6c4277788f19ffc109586be88a2f7
+size 4194432

files/swings_depth_fp32.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b5728d846cd554d4a5e1d0e5f71d622135bca36164026b8e49668acdfa20e070
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requirements.txt

@@ -1,24 +1,13 @@
-gradio==3.44.4
-gradio_client==0.5.1
-trimesh==3.23.5
+gradio==4.9.1
+gradio-imageslider==0.0.16
+GitPython==3.1.40
+pygltflib==1.16.1
+trimesh==4.0.5
 
-accelerate
-diffusers==0.20.1
-h5py
-matplotlib
-numpy==1.26.1
-omegaconf
-opencv-python
-pandas
-scipy==1.11.3
-tabulate
-tensorboard
+accelerate>=0.22.0
+diffusers>=0.20.1
+matplotlib==3.8.2
+scipy==1.11.4
 torch==2.0.1
-torchaudio
-torchvision
-torchshow
-tqdm
-transformers
-triton
-wandb==0.14.0
-xformers
+transformers>=4.32.1
+xformers==0.0.21