deprecate marigold-lcm demo

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.gitignore

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-.idea
-.DS_Store
-__pycache__

README.md

@@ -1,28 +1,53 @@
 ---
-title: Marigold-LCM Depth Estimation
-emoji: 🏵️
+title: Marigold-LCM Depth Estimation (Deprecated)
+emoji: 🔴
 colorFrom: blue
 colorTo: red
-sdk: gradio
-sdk_version: 4.32.2
-app_file: app.py
-pinned: true
+sdk: static
+pinned: false
 license: cc-by-sa-4.0
 models:
 - prs-eth/marigold-depth-lcm-v1-0
-hf_oauth: true
-hf_oauth_expiration_minutes: 43200
 ---
 
-This is a demo of Marigold-LCM, the state-of-the-art depth estimator for images in the wild.
-It combines the power of the original Marigold 10-step estimator and the Latent Consistency Models, delivering high-quality results in as little as one step.
-Find out more in our CVPR 2024 Oral paper titled ["Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation"](https://arxiv.org/abs/2312.02145)
-
-```
-@InProceedings{ke2023repurposing,
-  title={Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation},
-  author={Bingxin Ke and Anton Obukhov and Shengyu Huang and Nando Metzger and Rodrigo Caye Daudt and Konrad Schindler},
-  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
-  year={2024}
-}
-```
+<h2 align="center">Marigold-LCM Depth Estimation</h2>
+<p align="center"><span style="color: red;"><b>This demo is deprecated.</b></span></p>
+<p align="center"><b>Fast and reliable</b> single-step estimation is now available in the original Marigold
+    <a title="Image Depth" href="https://huggingface.co/spaces/prs-eth/marigold" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+        <img src="https://img.shields.io/badge/%F0%9F%A4%97%20Image%20Depth%20-Demo-yellow" style="vertical-align: middle;" alt="imagedepth">
+    </a>
+</p>
+<p align="center"><b>3D-printable bas-relief</b> models creation is now available in a separate
+    <a title="Depth-to-3D" href="https://huggingface.co/spaces/prs-eth/depth-to-3d-print" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+        <img src="https://img.shields.io/badge/%F0%9F%A4%97%20Depth--to--3D%20-Demo-yellow" style="vertical-align: middle;" alt="depthto3d">
+    </a>
+</p>
+<p align="center"><b>Video depth</b> processing function is improved and availble in a separate
+    <a title="Video Depth" href="https://huggingface.co/spaces/prs-eth/rollingdepth" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+        <img src="https://img.shields.io/badge/%F0%9F%A4%97%20Video%20Depth%20-Demo-yellow" style="vertical-align: middle;" alt="videodepth">
+    </a>
+</p>
+<p align="center"><span style="color: blue;"><b>Check out other Marigold resources:</b></span></p>
+<p align="center">
+<a title="Website" href="https://marigoldmonodepth.github.io/" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+    <img src="https://img.shields.io/badge/%E2%99%A5%20Project%20-Website-blue">
+</a>
+<a title="arXiv" href="https://arxiv.org/abs/2312.02145" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+    <img src="https://img.shields.io/badge/%F0%9F%93%84%20Read%20-Paper-AF3436">
+</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="Image Normals" href="https://huggingface.co/spaces/prs-eth/marigold-normals" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+    <img src="https://img.shields.io/badge/%F0%9F%A4%97%20Image%20Normals%20-Demo-yellow" alt="imagedepth">
+</a>
+<a title="Image Intrinsics" href="https://huggingface.co/spaces/prs-eth/marigold-iid" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+    <img src="https://img.shields.io/badge/%F0%9F%A4%97%20Image%20Intrinsics%20-Demo-yellow" alt="imagedepth">
+</a>
+<a title="LiDAR Depth" href="https://huggingface.co/spaces/prs-eth/marigold-dc" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+    <img src="https://img.shields.io/badge/%F0%9F%A4%97%20LiDAR%20Depth%20-Demo-yellow" alt="imagedepth">
+</a>
+<a title="Social" href="https://twitter.com/antonobukhov1" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+    <img src="https://shields.io/twitter/follow/:?label=Subscribe%20for%20updates!" alt="social">
+</a>
+</p>

app.py

@@ -1,1109 +0,0 @@
-# Copyright 2024 Anton Obukhov, ETH Zurich. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# --------------------------------------------------------------------------
-# If you find this code useful, we kindly ask you to cite our paper in your work.
-# Please find bibtex at: https://github.com/prs-eth/Marigold#-citation
-# More information about the method can be found at https://marigoldmonodepth.github.io
-# --------------------------------------------------------------------------
-from __future__ import annotations
-
-import functools
-import os
-import tempfile
-import warnings
-import zipfile
-from io import BytesIO
-
-import diffusers
-import gradio as gr
-import imageio as imageio
-import numpy as np
-import spaces
-import torch as torch
-from PIL import Image
-from diffusers import MarigoldDepthPipeline
-from gradio_imageslider import ImageSlider
-from huggingface_hub import login
-from tqdm import tqdm
-
-from extrude import extrude_depth_3d
-from gradio_patches.examples import Examples
-from gradio_patches.flagging import FlagMethod, HuggingFaceDatasetSaver
-
-warnings.filterwarnings(
-    "ignore", message=".*LoginButton created outside of a Blocks context.*"
-)
-
-default_seed = 2024
-default_batch_size = 4
-
-default_image_num_inference_steps = 4
-default_image_ensemble_size = 1
-default_image_processing_resolution = 768
-default_image_reproducuble = True
-
-default_video_depth_latent_init_strength = 0.1
-default_video_num_inference_steps = 1
-default_video_ensemble_size = 1
-default_video_processing_resolution = 768
-default_video_out_max_frames = 450
-
-default_bas_plane_near = 0.0
-default_bas_plane_far = 1.0
-default_bas_embossing = 20
-default_bas_num_inference_steps = 4
-default_bas_ensemble_size = 1
-default_bas_processing_resolution = 768
-default_bas_size_longest_px = 512
-default_bas_size_longest_cm = 10
-default_bas_filter_size = 3
-default_bas_frame_thickness = 5
-default_bas_frame_near = 1
-default_bas_frame_far = 1
-
-default_share_always_show_hf_logout_btn = True
-default_share_always_show_accordion = False
-
-
-def process_image_check(path_input):
-    if path_input is None:
-        raise gr.Error(
-            "Missing image in the first pane: upload a file or use one from the gallery below."
-        )
-
-
-def process_image(
-    pipe,
-    path_input,
-    num_inference_steps=default_image_num_inference_steps,
-    ensemble_size=default_image_ensemble_size,
-    processing_resolution=default_image_processing_resolution,
-):
-    name_base, name_ext = os.path.splitext(os.path.basename(path_input))
-    print(f"Processing image {name_base}{name_ext}")
-
-    path_output_dir = tempfile.mkdtemp()
-    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")
-
-    input_image = Image.open(path_input)
-
-    generator = torch.Generator(device=pipe.device).manual_seed(default_seed)
-
-    pipe_out = pipe(
-        input_image,
-        num_inference_steps=num_inference_steps,
-        ensemble_size=ensemble_size,
-        processing_resolution=processing_resolution,
-        batch_size=1 if processing_resolution == 0 else default_batch_size,
-        generator=generator,
-    )
-
-    depth_pred = pipe_out.prediction[0, :, :, 0]
-    depth_colored = pipe.image_processor.visualize_depth(pipe_out.prediction)[0]
-    depth_16bit = pipe.image_processor.export_depth_to_16bit_png(pipe_out.prediction)[0]
-
-    np.save(path_out_fp32, depth_pred)
-    depth_16bit.save(path_out_16bit)
-    depth_colored.save(path_out_vis)
-
-    return (
-        [path_out_16bit, path_out_vis],
-        [path_out_16bit, path_out_fp32, path_out_vis],
-    )
-
-
-def process_video(
-    pipe,
-    path_input,
-    depth_latent_init_strength=default_video_depth_latent_init_strength,
-    num_inference_steps=default_video_num_inference_steps,
-    ensemble_size=default_video_ensemble_size,
-    processing_resolution=default_video_processing_resolution,
-    out_max_frames=default_video_out_max_frames,
-    progress=gr.Progress(),
-):
-    if path_input is None:
-        raise gr.Error(
-            "Missing video in the first pane: upload a file or use one from the gallery below."
-        )
-
-    name_base, name_ext = os.path.splitext(os.path.basename(path_input))
-    print(f"Processing video {name_base}{name_ext}")
-
-    path_output_dir = tempfile.mkdtemp()
-    path_out_vis = os.path.join(path_output_dir, f"{name_base}_depth_colored.mp4")
-    path_out_16bit = os.path.join(path_output_dir, f"{name_base}_depth_16bit.zip")
-
-    generator = torch.Generator(device=pipe.device).manual_seed(default_seed)
-
-    reader, writer, zipf = None, None, None
-    try:
-        pipe.vae, pipe.vae_tiny = pipe.vae_tiny, pipe.vae
-
-        reader = imageio.get_reader(path_input)
-
-        meta_data = reader.get_meta_data()
-        fps = meta_data["fps"]
-        size = meta_data["size"]
-        max_orig = max(size)
-        duration_sec = meta_data["duration"]
-        total_frames = int(fps * duration_sec)
-
-        out_duration_sec = out_max_frames / fps
-        if duration_sec > out_duration_sec:
-            gr.Warning(
-                f"Only the first ~{int(out_duration_sec)} seconds will be processed; "
-                f"use alternative setups such as ComfyUI Marigold node for full processing"
-            )
-
-        writer = imageio.get_writer(path_out_vis, fps=fps)
-
-        zipf = zipfile.ZipFile(path_out_16bit, "w", zipfile.ZIP_DEFLATED)
-
-        last_frame_latent = None
-        latent_common = torch.randn(
-            (
-                1,
-                4,
-                (768 * size[1] + 7 * max_orig) // (8 * max_orig),
-                (768 * size[0] + 7 * max_orig) // (8 * max_orig),
-            ),
-            generator=generator,
-            device=pipe.device,
-            dtype=torch.float16,
-        )
-
-        out_frame_id = 0
-        pbar = tqdm(desc="Processing Video", total=min(out_max_frames, total_frames))
-
-        for frame_id, frame in enumerate(reader):
-            out_frame_id += 1
-            pbar.update(1)
-            if out_frame_id > out_max_frames:
-                break
-
-            frame_pil = Image.fromarray(frame)
-
-            latents = latent_common
-            if last_frame_latent is not None:
-                assert (
-                    last_frame_latent.shape == latent_common.shape
-                ), f"{last_frame_latent.shape}, {latent_common.shape}"
-                latents = (
-                    1 - depth_latent_init_strength
-                ) * latents + depth_latent_init_strength * last_frame_latent
-
-            pipe_out = pipe(
-                frame_pil,
-                num_inference_steps=num_inference_steps,
-                ensemble_size=ensemble_size,
-                processing_resolution=processing_resolution,
-                match_input_resolution=False,
-                batch_size=1,
-                latents=latents,
-                output_latent=True,
-            )
-
-            last_frame_latent = pipe_out.latent
-
-            processed_frame = pipe.image_processor.visualize_depth(  # noqa
-                pipe_out.prediction
-            )[0]
-            processed_frame = imageio.core.util.Array(np.array(processed_frame))
-            writer.append_data(processed_frame)
-
-            archive_path = os.path.join(
-                f"{name_base}_depth_16bit", f"{out_frame_id:05d}.png"
-            )
-            img_byte_arr = BytesIO()
-            processed_frame = pipe.image_processor.export_depth_to_16bit_png(
-                pipe_out.prediction
-            )[0]
-            processed_frame.save(img_byte_arr, format="png")
-            img_byte_arr.seek(0)
-            zipf.writestr(archive_path, img_byte_arr.read())
-    finally:
-        if zipf is not None:
-            zipf.close()
-
-        if writer is not None:
-            writer.close()
-
-        if reader is not None:
-            reader.close()
-
-        pipe.vae, pipe.vae_tiny = pipe.vae_tiny, pipe.vae
-
-    return (
-        path_out_vis,
-        [path_out_vis, path_out_16bit],
-    )
-
-
-def process_bas(
-    pipe,
-    path_input,
-    plane_near=default_bas_plane_near,
-    plane_far=default_bas_plane_far,
-    embossing=default_bas_embossing,
-    num_inference_steps=default_bas_num_inference_steps,
-    ensemble_size=default_bas_ensemble_size,
-    processing_resolution=default_bas_processing_resolution,
-    size_longest_px=default_bas_size_longest_px,
-    size_longest_cm=default_bas_size_longest_cm,
-    filter_size=default_bas_filter_size,
-    frame_thickness=default_bas_frame_thickness,
-    frame_near=default_bas_frame_near,
-    frame_far=default_bas_frame_far,
-):
-    if path_input is None:
-        raise gr.Error(
-            "Missing image in the first pane: upload a file or use one from the gallery below."
-        )
-
-    if plane_near >= plane_far:
-        raise gr.Error("NEAR plane must have a value smaller than the FAR plane")
-
-    name_base, name_ext = os.path.splitext(os.path.basename(path_input))
-    print(f"Processing bas-relief {name_base}{name_ext}")
-
-    path_output_dir = tempfile.mkdtemp()
-
-    input_image = Image.open(path_input)
-
-    generator = torch.Generator(device=pipe.device).manual_seed(default_seed)
-
-    pipe_out = pipe(
-        input_image,
-        num_inference_steps=num_inference_steps,
-        ensemble_size=ensemble_size,
-        processing_resolution=processing_resolution,
-        generator=generator,
-    )
-
-    depth_pred = pipe_out.prediction[0, :, :, 0] * 65535
-
-    def _process_3d(
-        size_longest_px,
-        filter_size,
-        vertex_colors,
-        scene_lights,
-        output_model_scale=None,
-        prepare_for_3d_printing=False,
-        zip_outputs=False,
-    ):
-        image_rgb_w, image_rgb_h = input_image.width, input_image.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 = os.path.join(
-            path_output_dir, f"{name_base}_rgb_{size_longest_px}{name_ext}"
-        )
-        image_depth_new = os.path.join(
-            path_output_dir, f"{name_base}_depth_{size_longest_px}.png"
-        )
-        input_image.resize((image_new_w, image_new_h), Image.LANCZOS).save(
-            image_rgb_new
-        )
-        Image.fromarray(depth_pred).convert(mode="F").resize(
-            (image_new_w, image_new_h), Image.BILINEAR
-        ).convert("I").save(image_depth_new)
-
-        path_glb, path_stl, path_obj = 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,
-            prepare_for_3d_printing=prepare_for_3d_printing,
-            zip_outputs=zip_outputs,
-        )
-
-        return path_glb, path_stl, path_obj
-
-    path_viewer_glb, _, _ = _process_3d(
-        256, filter_size, vertex_colors=False, scene_lights=True, output_model_scale=1
-    )
-    path_files_glb, path_files_stl, path_files_obj = _process_3d(
-        size_longest_px,
-        filter_size,
-        vertex_colors=True,
-        scene_lights=False,
-        prepare_for_3d_printing=True,
-        zip_outputs=True,
-    )
-
-    return path_viewer_glb, [path_files_glb, path_files_stl, path_files_obj]
-
-
-def run_demo_server(pipe, hf_writer=None):
-    process_pipe_image = spaces.GPU(functools.partial(process_image, pipe))
-    process_pipe_video = spaces.GPU(
-        functools.partial(process_video, pipe), duration=120
-    )
-    process_pipe_bas = spaces.GPU(functools.partial(process_bas, pipe))
-
-    gradio_theme = gr.themes.Default()
-
-    with gr.Blocks(
-        theme=gradio_theme,
-        title="Marigold-LCM Depth Estimation",
-        css="""
-            #download {
-                height: 118px;
-            }
-            .slider .inner {
-                width: 5px;
-                background: #FFF;
-            }
-            .viewport {
-                aspect-ratio: 4/3;
-            }
-            .tabs button.selected {
-                font-size: 20px !important;
-                color: crimson !important;
-            }
-            h1 {
-                text-align: center;
-                display: block;
-            }
-            h2 {
-                text-align: center;
-                display: block;
-            }
-            h3 {
-                text-align: center;
-                display: block;
-            }
-            .md_feedback li {
-                margin-bottom: 0px !important;
-            }
-        """,
-        head="""
-            <script async src="https://www.googletagmanager.com/gtag/js?id=G-1FWSVCGZTG"></script>
-            <script>
-                window.dataLayer = window.dataLayer || [];
-                function gtag() {dataLayer.push(arguments);}
-                gtag('js', new Date());
-                gtag('config', 'G-1FWSVCGZTG');
-            </script>
-        """,
-    ) as demo:
-        if hf_writer is not None:
-            print("Creating login button")
-            share_login_btn = gr.LoginButton(size="sm", scale=1, render=False)
-            print("Created login button")
-            share_login_btn.activate()
-            print("Activated login button")
-
-        gr.Markdown(
-            """
-            # Marigold-LCM Depth Estimation
-            <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-LCM is the fast version of Marigold, the state-of-the-art depth estimator for images in the 
-                wild. It combines the power of the original Marigold 10-step estimator and the Latent Consistency 
-                Models, delivering high-quality results in as little as <b>one step</b>. We provide three functions 
-                in this demo: Image, Video, and Bas-relief 3D processing — <b>see the tabs below</b>. Upload your 
-                content into the <b>first</b> pane, or click any of the <b>examples</b> below. Wait a second (for 
-                images and 3D) or a minute (for videos), and interact with the result in the <b>second</b> pane. To 
-                avoid queuing, fork the demo into your profile.
-                <a href="https://huggingface.co/spaces/prs-eth/marigold">
-                    The original Marigold demo is also available
-                </a>.
-            </p>
-        """
-        )
-
-        def get_share_instructions(is_full):
-            out = (
-                "### Help us improve Marigold! If the output is not what you expected, "
-                "you can help us by sharing it with us privately.\n"
-            )
-            if is_full:
-                out += (
-                    "1. Sign into your Hugging Face account using the button below.\n"
-                    "1. Signing in may reset the demo and results; in that case, process the image again.\n"
-                )
-            out += "1. Review and agree to the terms of usage and enter an optional message to us.\n"
-            out += "1. Click the 'Share' button to submit the image to us privately.\n"
-            return out
-
-        def get_share_conditioned_on_login(profile: gr.OAuthProfile | None):
-            state_logged_out = profile is None
-            return get_share_instructions(is_full=state_logged_out), gr.Button(
-                visible=(state_logged_out or default_share_always_show_hf_logout_btn)
-            )
-
-        with gr.Tabs(elem_classes=["tabs"]):
-            with gr.Tab("Image"):
-                with gr.Row():
-                    with gr.Column():
-                        image_input = gr.Image(
-                            label="Input Image",
-                            type="filepath",
-                        )
-                        with gr.Row():
-                            image_submit_btn = gr.Button(
-                                value="Compute Depth", variant="primary"
-                            )
-                            image_reset_btn = gr.Button(value="Reset")
-                        with gr.Accordion("Advanced options", open=False):
-                            image_num_inference_steps = gr.Slider(
-                                label="Number of denoising steps",
-                                minimum=1,
-                                maximum=4,
-                                step=1,
-                                value=default_image_num_inference_steps,
-                            )
-                            image_ensemble_size = gr.Slider(
-                                label="Ensemble size",
-                                minimum=1,
-                                maximum=10,
-                                step=1,
-                                value=default_image_ensemble_size,
-                            )
-                            image_processing_resolution = gr.Radio(
-                                [
-                                    ("Native", 0),
-                                    ("Recommended", 768),
-                                ],
-                                label="Processing resolution",
-                                value=default_image_processing_resolution,
-                            )
-                    with gr.Column():
-                        image_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,
-                        )
-                        image_output_files = gr.Files(
-                            label="Depth outputs",
-                            elem_id="download",
-                            interactive=False,
-                        )
-
-                        if hf_writer is not None:
-                            with gr.Accordion(
-                                "Feedback",
-                                open=False,
-                                visible=default_share_always_show_accordion,
-                            ) as share_box:
-                                share_instructions = gr.Markdown(
-                                    get_share_instructions(is_full=True),
-                                    elem_classes="md_feedback",
-                                )
-                                share_transfer_of_rights = gr.Checkbox(
-                                    label="(Optional) I own or hold necessary rights to the submitted image. By "
-                                    "checking this box, I grant an irrevocable, non-exclusive, transferable, "
-                                    "royalty-free, worldwide license to use the uploaded image, including for "
-                                    "publishing, reproducing, and model training. [transfer_of_rights]",
-                                    scale=1,
-                                )
-                                share_content_is_legal = gr.Checkbox(
-                                    label="By checking this box, I acknowledge that my uploaded content is legal and "
-                                    "safe, and that I am solely responsible for ensuring it complies with all "
-                                    "applicable laws and regulations. Additionally, I am aware that my Hugging Face "
-                                    "username is collected. [content_is_legal]",
-                                    scale=1,
-                                )
-                                share_reason = gr.Textbox(
-                                    label="(Optional) Reason for feedback",
-                                    max_lines=1,
-                                    interactive=True,
-                                )
-                                with gr.Row():
-                                    share_login_btn.render()
-                                    share_share_btn = gr.Button(
-                                        "Share", variant="stop", scale=1
-                                    )
-
-                Examples(
-                    fn=process_pipe_image,
-                    examples=[
-                        os.path.join("files", "image", name)
-                        for name in [
-                            "arc.jpeg",
-                            "berries.jpeg",
-                            "butterfly.jpeg",
-                            "cat.jpg",
-                            "concert.jpeg",
-                            "dog.jpeg",
-                            "doughnuts.jpeg",
-                            "einstein.jpg",
-                            "food.jpeg",
-                            "glasses.jpeg",
-                            "house.jpg",
-                            "lake.jpeg",
-                            "marigold.jpeg",
-                            "portrait_1.jpeg",
-                            "portrait_2.jpeg",
-                            "pumpkins.jpg",
-                            "puzzle.jpeg",
-                            "road.jpg",
-                            "scientists.jpg",
-                            "surfboards.jpeg",
-                            "surfer.jpeg",
-                            "swings.jpg",
-                            "switzerland.jpeg",
-                            "teamwork.jpeg",
-                            "wave.jpeg",
-                        ]
-                    ],
-                    inputs=[image_input],
-                    outputs=[image_output_slider, image_output_files],
-                    cache_examples=True,
-                    directory_name="examples_image",
-                )
-
-            with gr.Tab("Video"):
-                with gr.Row():
-                    with gr.Column():
-                        video_input = gr.Video(
-                            label="Input Video",
-                            sources=["upload"],
-                        )
-                        with gr.Row():
-                            video_submit_btn = gr.Button(
-                                value="Compute Depth", variant="primary"
-                            )
-                            video_reset_btn = gr.Button(value="Reset")
-                    with gr.Column():
-                        video_output_video = gr.Video(
-                            label="Output video depth (red-near, blue-far)",
-                            interactive=False,
-                        )
-                        video_output_files = gr.Files(
-                            label="Depth outputs",
-                            elem_id="download",
-                            interactive=False,
-                        )
-                Examples(
-                    fn=process_pipe_video,
-                    examples=[
-                        os.path.join("files", "video", name)
-                        for name in [
-                            "cab.mp4",
-                            "elephant.mp4",
-                            "obama.mp4",
-                        ]
-                    ],
-                    inputs=[video_input],
-                    outputs=[video_output_video, video_output_files],
-                    cache_examples=True,
-                    directory_name="examples_video",
-                )
-
-            with gr.Tab("Bas-relief (3D)"):
-                gr.Markdown(
-                    """
-                    <p align="justify">
-                        This part of the demo uses Marigold-LCM to create a bas-relief model. 
-                        The models are watertight, with correct normals, and exported in the STL format, which makes 
-                        them <b>3D-printable</b>.
-                    </p>
-                    """,
-                )
-                with gr.Row():
-                    with gr.Column():
-                        bas_input = gr.Image(
-                            label="Input Image",
-                            type="filepath",
-                        )
-                        with gr.Row():
-                            bas_submit_btn = gr.Button(
-                                value="Create 3D", variant="primary"
-                            )
-                            bas_reset_btn = gr.Button(value="Reset")
-                        with gr.Accordion("3D printing demo: Main options", open=True):
-                            bas_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=default_bas_plane_near,
-                            )
-                            bas_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=default_bas_plane_far,
-                            )
-                            bas_embossing = gr.Slider(
-                                label="Embossing level",
-                                minimum=0,
-                                maximum=100,
-                                step=1,
-                                value=default_bas_embossing,
-                            )
-                        with gr.Accordion(
-                            "3D printing demo: Advanced options", open=False
-                        ):
-                            bas_num_inference_steps = gr.Slider(
-                                label="Number of denoising steps",
-                                minimum=1,
-                                maximum=4,
-                                step=1,
-                                value=default_bas_num_inference_steps,
-                            )
-                            bas_ensemble_size = gr.Slider(
-                                label="Ensemble size",
-                                minimum=1,
-                                maximum=10,
-                                step=1,
-                                value=default_bas_ensemble_size,
-                            )
-                            bas_processing_resolution = gr.Radio(
-                                [
-                                    ("Native", 0),
-                                    ("Recommended", 768),
-                                ],
-                                label="Processing resolution",
-                                value=default_bas_processing_resolution,
-                            )
-                            bas_size_longest_px = gr.Slider(
-                                label="Size (px) of the longest side",
-                                minimum=256,
-                                maximum=1024,
-                                step=256,
-                                value=default_bas_size_longest_px,
-                            )
-                            bas_size_longest_cm = gr.Slider(
-                                label="Size (cm) of the longest side",
-                                minimum=1,
-                                maximum=100,
-                                step=1,
-                                value=default_bas_size_longest_cm,
-                            )
-                            bas_filter_size = gr.Slider(
-                                label="Size (px) of the smoothing filter",
-                                minimum=1,
-                                maximum=5,
-                                step=2,
-                                value=default_bas_filter_size,
-                            )
-                            bas_frame_thickness = gr.Slider(
-                                label="Frame thickness",
-                                minimum=0,
-                                maximum=100,
-                                step=1,
-                                value=default_bas_frame_thickness,
-                            )
-                            bas_frame_near = gr.Slider(
-                                label="Frame's near plane offset",
-                                minimum=-100,
-                                maximum=100,
-                                step=1,
-                                value=default_bas_frame_near,
-                            )
-                            bas_frame_far = gr.Slider(
-                                label="Frame's far plane offset",
-                                minimum=1,
-                                maximum=10,
-                                step=1,
-                                value=default_bas_frame_far,
-                            )
-                    with gr.Column():
-                        bas_output_viewer = gr.Model3D(
-                            camera_position=(75.0, 90.0, 1.25),
-                            elem_classes="viewport",
-                            label="3D preview (low-res, relief highlight)",
-                            interactive=False,
-                        )
-                        bas_output_files = gr.Files(
-                            label="3D model outputs (high-res)",
-                            elem_id="download",
-                            interactive=False,
-                        )
-                Examples(
-                    fn=process_pipe_bas,
-                    examples=[
-                        [
-                            "files/basrelief/coin.jpg",  # input
-                            0.0,  # plane_near
-                            0.66,  # plane_far
-                            15,  # embossing
-                            4,  # num_inference_steps
-                            4,  # ensemble_size
-                            768,  # processing_resolution
-                            512,  # size_longest_px
-                            10,  # size_longest_cm
-                            3,  # filter_size
-                            5,  # frame_thickness
-                            0,  # frame_near
-                            1,  # frame_far
-                        ],
-                        [
-                            "files/basrelief/einstein.jpg",  # input
-                            0.0,  # plane_near
-                            0.5,  # plane_far
-                            50,  # embossing
-                            2,  # num_inference_steps
-                            1,  # ensemble_size
-                            768,  # processing_resolution
-                            512,  # size_longest_px
-                            10,  # size_longest_cm
-                            3,  # filter_size
-                            5,  # frame_thickness
-                            -25,  # frame_near
-                            1,  # frame_far
-                        ],
-                        [
-                            "files/basrelief/food.jpeg",  # input
-                            0.0,  # plane_near
-                            1.0,  # plane_far
-                            20,  # embossing
-                            2,  # num_inference_steps
-                            4,  # ensemble_size
-                            768,  # processing_resolution
-                            512,  # size_longest_px
-                            10,  # size_longest_cm
-                            3,  # filter_size
-                            5,  # frame_thickness
-                            -5,  # frame_near
-                            1,  # frame_far
-                        ],
-                    ],
-                    inputs=[
-                        bas_input,
-                        bas_plane_near,
-                        bas_plane_far,
-                        bas_embossing,
-                        bas_num_inference_steps,
-                        bas_ensemble_size,
-                        bas_processing_resolution,
-                        bas_size_longest_px,
-                        bas_size_longest_cm,
-                        bas_filter_size,
-                        bas_frame_thickness,
-                        bas_frame_near,
-                        bas_frame_far,
-                    ],
-                    outputs=[bas_output_viewer, bas_output_files],
-                    cache_examples=True,
-                    directory_name="examples_bas",
-                )
-
-        ### Image tab
-
-        if hf_writer is not None:
-            image_submit_btn.click(
-                fn=process_image_check,
-                inputs=image_input,
-                outputs=None,
-                preprocess=False,
-                queue=False,
-            ).success(
-                get_share_conditioned_on_login,
-                None,
-                [share_instructions, share_login_btn],
-                queue=False,
-            ).then(
-                lambda: (
-                    gr.Button(value="Share", interactive=True),
-                    gr.Accordion(visible=True),
-                    False,
-                    False,
-                    "",
-                ),
-                None,
-                [
-                    share_share_btn,
-                    share_box,
-                    share_transfer_of_rights,
-                    share_content_is_legal,
-                    share_reason,
-                ],
-                queue=False,
-            ).then(
-                fn=process_pipe_image,
-                inputs=[
-                    image_input,
-                    image_num_inference_steps,
-                    image_ensemble_size,
-                    image_processing_resolution,
-                ],
-                outputs=[image_output_slider, image_output_files],
-                concurrency_limit=1,
-            )
-        else:
-            image_submit_btn.click(
-                fn=process_image_check,
-                inputs=image_input,
-                outputs=None,
-                preprocess=False,
-                queue=False,
-            ).success(
-                fn=process_pipe_image,
-                inputs=[
-                    image_input,
-                    image_num_inference_steps,
-                    image_ensemble_size,
-                    image_processing_resolution,
-                ],
-                outputs=[image_output_slider, image_output_files],
-                concurrency_limit=1,
-            )
-
-        image_reset_btn.click(
-            fn=lambda: (
-                None,
-                None,
-                None,
-                default_image_ensemble_size,
-                default_image_num_inference_steps,
-                default_image_processing_resolution,
-            ),
-            inputs=[],
-            outputs=[
-                image_input,
-                image_output_slider,
-                image_output_files,
-                image_ensemble_size,
-                image_num_inference_steps,
-                image_processing_resolution,
-            ],
-            queue=False,
-        )
-
-        if hf_writer is not None:
-            image_reset_btn.click(
-                fn=lambda: (
-                    gr.Button(value="Share", interactive=True),
-                    gr.Accordion(visible=default_share_always_show_accordion),
-                ),
-                inputs=[],
-                outputs=[
-                    share_share_btn,
-                    share_box,
-                ],
-                queue=False,
-            )
-
-        ### Share functionality
-
-        if hf_writer is not None:
-            share_components = [
-                image_input,
-                image_num_inference_steps,
-                image_ensemble_size,
-                image_processing_resolution,
-                image_output_slider,
-                share_content_is_legal,
-                share_transfer_of_rights,
-                share_reason,
-            ]
-
-            hf_writer.setup(share_components, "shared_data")
-            share_callback = FlagMethod(hf_writer, "Share", "", visual_feedback=True)
-
-            def share_precheck(
-                hf_content_is_legal,
-                image_output_slider,
-                profile: gr.OAuthProfile | None,
-            ):
-                if profile is None:
-                    raise gr.Error(
-                        "Log into the Space with your Hugging Face account first."
-                    )
-                if image_output_slider is None or image_output_slider[0] is None:
-                    raise gr.Error("No output detected; process the image first.")
-                if not hf_content_is_legal:
-                    raise gr.Error(
-                        "You must consent that the uploaded content is legal."
-                    )
-                return gr.Button(value="Sharing in progress", interactive=False)
-
-            share_share_btn.click(
-                share_precheck,
-                [share_content_is_legal, image_output_slider],
-                share_share_btn,
-                preprocess=False,
-                queue=False,
-            ).success(
-                share_callback,
-                inputs=share_components,
-                outputs=share_share_btn,
-                preprocess=False,
-                queue=False,
-            )
-
-        ### Video tab
-
-        video_submit_btn.click(
-            fn=process_pipe_video,
-            inputs=[video_input],
-            outputs=[video_output_video, video_output_files],
-            concurrency_limit=1,
-        )
-
-        video_reset_btn.click(
-            fn=lambda: (None, None, None),
-            inputs=[],
-            outputs=[video_input, video_output_video, video_output_files],
-            concurrency_limit=1,
-        )
-
-        ### Bas-relief tab
-
-        bas_submit_btn.click(
-            fn=process_pipe_bas,
-            inputs=[
-                bas_input,
-                bas_plane_near,
-                bas_plane_far,
-                bas_embossing,
-                bas_num_inference_steps,
-                bas_ensemble_size,
-                bas_processing_resolution,
-                bas_size_longest_px,
-                bas_size_longest_cm,
-                bas_filter_size,
-                bas_frame_thickness,
-                bas_frame_near,
-                bas_frame_far,
-            ],
-            outputs=[bas_output_viewer, bas_output_files],
-            concurrency_limit=1,
-        )
-
-        bas_reset_btn.click(
-            fn=lambda: (
-                gr.Button(interactive=True),
-                None,
-                None,
-                None,
-                default_bas_plane_near,
-                default_bas_plane_far,
-                default_bas_embossing,
-                default_bas_num_inference_steps,
-                default_bas_ensemble_size,
-                default_bas_processing_resolution,
-                default_bas_size_longest_px,
-                default_bas_size_longest_cm,
-                default_bas_filter_size,
-                default_bas_frame_thickness,
-                default_bas_frame_near,
-                default_bas_frame_far,
-            ),
-            inputs=[],
-            outputs=[
-                bas_submit_btn,
-                bas_input,
-                bas_output_viewer,
-                bas_output_files,
-                bas_plane_near,
-                bas_plane_far,
-                bas_embossing,
-                bas_num_inference_steps,
-                bas_ensemble_size,
-                bas_processing_resolution,
-                bas_size_longest_px,
-                bas_size_longest_cm,
-                bas_filter_size,
-                bas_frame_thickness,
-                bas_frame_near,
-                bas_frame_far,
-            ],
-            concurrency_limit=1,
-        )
-
-        ### Server launch
-
-        demo.queue(
-            api_open=False,
-        ).launch(
-            server_name="0.0.0.0",
-            server_port=7860,
-        )
-
-
-def main():
-    CHECKPOINT = "prs-eth/marigold-depth-lcm-v1-0"
-    CROWD_DATA = "crowddata-marigold-depth-lcm-v1-0-space-v1-0"
-
-    os.system("pip freeze")
-
-    if "HF_TOKEN_LOGIN" in os.environ:
-        login(token=os.environ["HF_TOKEN_LOGIN"])
-
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-    pipe = MarigoldDepthPipeline.from_pretrained(
-        CHECKPOINT, variant="fp16", torch_dtype=torch.float16
-    ).to(device)
-    pipe.vae_tiny = diffusers.AutoencoderTiny.from_pretrained(
-        "madebyollin/taesd", torch_dtype=torch.float16
-    ).to(device)
-    pipe.set_progress_bar_config(disable=True)
-
-    try:
-        import xformers
-
-        pipe.enable_xformers_memory_efficient_attention()
-    except:
-        pass  # run without xformers
-
-    hf_writer = None
-    if "HF_TOKEN_LOGIN_WRITE_CROWD" in os.environ:
-        hf_writer = HuggingFaceDatasetSaver(
-            os.getenv("HF_TOKEN_LOGIN_WRITE_CROWD"),
-            CROWD_DATA,
-            private=True,
-            info_filename="dataset_info.json",
-            separate_dirs=True,
-        )
-
-    run_demo_server(pipe, hf_writer)
-
-
-if __name__ == "__main__":
-    main()

extrude.py

@@ -1,400 +0,0 @@
-# Copyright 2024 Anton Obukhov, ETH Zurich. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# --------------------------------------------------------------------------
-# If you find this code useful, we kindly ask you to cite our paper in your work.
-# Please find bibtex at: https://github.com/prs-eth/Marigold#-citation
-# More information about the method can be found at https://marigoldmonodepth.github.io
-# --------------------------------------------------------------------------
-
-
-import math
-import os
-import zipfile
-
-import numpy as np
-import pygltflib
-import trimesh
-from PIL import Image
-from scipy.ndimage import median_filter
-
-
-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,
-    path_out_base=None,
-    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,
-    prepare_for_3d_printing=False,
-    zip_outputs=False,
-):
-    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)
-
-    w, h = depth_image.size
-    d_max = max(w, h)
-    depth_image = np.array(depth_image).astype(np.double)
-    depth_image = median_filter(depth_image, size=filter_size)
-    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)
-
-    if prepare_for_3d_printing:
-        rotation_mat = trimesh.transformations.rotation_matrix(
-            np.radians(90), [-1, 0, 0]
-        )
-        mesh.apply_transform(rotation_mat)
-
-    if path_out_base is None:
-        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"
-    path_out_obj = path_out_base + ".obj"
-
-    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")
-    mesh.export(path_out_obj, file_type="obj")
-
-    if zip_outputs:
-        with zipfile.ZipFile(path_out_glb + ".zip", "w", zipfile.ZIP_DEFLATED) as zipf:
-            arcname = os.path.basename(os.path.splitext(path_out_glb)[0]) + ".glb"
-            zipf.write(path_out_glb, arcname=arcname)
-            path_out_glb = path_out_glb + ".zip"
-        with zipfile.ZipFile(path_out_stl + ".zip", "w", zipfile.ZIP_DEFLATED) as zipf:
-            arcname = os.path.basename(os.path.splitext(path_out_stl)[0]) + ".stl"
-            zipf.write(path_out_stl, arcname=arcname)
-            path_out_stl = path_out_stl + ".zip"
-        with zipfile.ZipFile(path_out_obj + ".zip", "w", zipfile.ZIP_DEFLATED) as zipf:
-            arcname = os.path.basename(os.path.splitext(path_out_obj)[0]) + ".obj"
-            zipf.write(path_out_obj, arcname=arcname)
-            path_out_obj = path_out_obj + ".zip"
-
-    return path_out_glb, path_out_stl, path_out_obj
-
-
-if __name__ == "__main__":
-    img_rgb = "files/basrelief/einstein.jpg"
-    img_depth = "gradio_cached_examples/examples_image/Depth outputs/54d74157894322bdc77c/einstein_depth_16bit.png"
-    Image.open(img_rgb).resize((512, 512), Image.LANCZOS).save(
-        "einstein_3d_tex_512.jpg"
-    )
-    Image.open(img_depth).convert(mode="F").resize((512, 512), Image.BILINEAR).convert(
-        "I"
-    ).save("einstein_3d_depth_512.png")
-    extrude_depth_3d(
-        "einstein_3d_tex_512.jpg",
-        "einstein_3d_depth_512.png",
-        path_out_base="einstein_3d_out",
-        output_model_scale=100,
-        filter_size=3,
-        coef_near=0.0,
-        coef_far=0.5,
-        emboss=0.5,
-        f_thic=0.05,
-        f_near=-0.25,
-        f_back=0.01,
-        vertex_colors=True,
-        scene_lights=True,
-        prepare_for_3d_printing=True,
-    )

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