app1.py

import spaces
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

@spaces.GPU
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,
 batch_size=1 if processing_res == 0 else 0,
 show_progress_bar=True,
 )

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

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

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

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

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



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

 with gr.Blocks(
 analytics_enabled=False,
 title="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=1,
 )
 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
 ],
 ],
 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,
 ],
 )

 demo.queue(
 api_open=False,
 ).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/lemonaddie/geowizard.git"
 CHECKPOINT = "lemonaddie/Geowizard"
 REPO_DIR = "geowizard"
 
 if os.path.isdir(REPO_DIR):
 shutil.rmtree(REPO_DIR)
 
 repo = git.Repo.clone_from(REPO_URL, REPO_DIR)
 sys.path.append(os.path.join(os.getcwd(), REPO_DIR))

 from pipeline.depth_normal_pipeline_clip_cfg import DepthNormalEstimationPipeline

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

 try:
 import xformers
 pipe.enable_xformers_memory_efficient_attention()
 except:
 pass # run without xformers

 pipe = pipe.to('cuda')
 prefetch_hf_cache(pipe)
 run_demo_server(pipe)


if __name__ == "__main__":
 main()