app.py

# build upon InstantSplat https://huggingface.co/spaces/kairunwen/InstantSplat/blob/main/app.py
import os, subprocess, shlex, sys, gc
import numpy as np
import shutil
import argparse
import gradio as gr
import uuid
import glob
import re

import spaces

subprocess.run(shlex.split("pip install wheel/diff_gaussian_rasterization-0.0.0-cp310-cp310-linux_x86_64.whl"))
subprocess.run(shlex.split("pip install wheel/simple_knn-0.0.0-cp310-cp310-linux_x86_64.whl"))
# subprocess.run(shlex.split("pip install wheel/curope-0.0.0-cp310-cp310-linux_x86_64.whl"))

GRADIO_CACHE_FOLDER = './gradio_cache_folder'


def get_dust3r_args_parser():
    parser = argparse.ArgumentParser()
    parser.add_argument("--image_size", type=int, default=512, choices=[512, 224], help="image size")
    parser.add_argument("--model_path", type=str, default="submodules/dust3r/checkpoints/DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth", help="path to the model weights")
    parser.add_argument("--device", type=str, default='cuda', help="pytorch device")
    parser.add_argument("--batch_size", type=int, default=1)
    parser.add_argument("--schedule", type=str, default='linear')
    parser.add_argument("--lr", type=float, default=0.01)
    parser.add_argument("--niter", type=int, default=300)
    parser.add_argument("--focal_avg", type=bool, default=True)
    parser.add_argument("--n_views", type=int, default=3)
    parser.add_argument("--base_path", type=str, default=GRADIO_CACHE_FOLDER) 
    return parser


def natural_sort(l): 
    convert = lambda text: int(text) if text.isdigit() else text.lower()
    alphanum_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key.split('/')[-1])]
    return sorted(l, key=alphanum_key)

def cmd(command):
    print(command)
    os.system(command)

@spaces.GPU(duration=150)
def process(inputfiles, input_path='demo'):
    if inputfiles:
        frames = natural_sort(inputfiles)
    else:
        frames = natural_sort(glob.glob('./assets/example/' + input_path + '/*'))
    if len(frames) > 20:
        stride = int(np.ceil(len(frames) / 20))
        frames = frames[::stride]
    
    # Create a temporary directory to store the selected frames
    temp_dir = os.path.join(GRADIO_CACHE_FOLDER, str(uuid.uuid4()))
    os.makedirs(temp_dir, exist_ok=True)
    
    # Copy the selected frames to the temporary directory
    for i, frame in enumerate(frames):
        shutil.copy(frame, f"{temp_dir}/{i:04d}.{frame.split('.')[-1]}")

    imgs_path = temp_dir
    output_path = f'./results/{input_path}/output'
    cmd(f"python dynamic_predictor/launch.py --mode=eval_pose_custom \
        --pretrained=Kai422kx/das3r \
        --dir_path={imgs_path} \
        --output_dir={output_path} \
        --use_pred_mask ")
    
    cmd(f"python utils/rearrange.py --output_dir={output_path}")
    output_path = f'{output_path}_rearranged'

    print(output_path)
    cmd(f"python train_gui.py -s {output_path} -m {output_path} --iter 2000")
    cmd(f"python render.py -s {output_path} -m {output_path} --iter 2000 --get_video")

    output_video_path = f"{output_path}/rendered.mp4"
    output_ply_path = f"{output_path}/point_cloud/iteration_2000/point_cloud.ply"
    return  output_video_path, output_ply_path, output_ply_path



_TITLE = '''DAS3R'''
_DESCRIPTION = '''
<div style="display: flex; justify-content: center; align-items: center;">
    <div style="width: 100%; text-align: center; font-size: 30px;">
        <strong>DAS3R: Dynamics-Aware Gaussian Splatting for Static Scene Reconstruction</strong>
    </div>
</div> 
<p></p>


<div align="center">
    <a style="display:inline-block" href="https://arxiv.org/abs/2412.19584"><img src="https://img.shields.io/badge/ArXiv-2412.19584-b31b1b.svg?logo=arXiv" alt='arxiv'></a>
    <a style="display:inline-block" href="https://kai422.github.io/DAS3R/"><img src='https://img.shields.io/badge/Project-Website-blue.svg'></a>&nbsp;
    <a style="display:inline-block" href="https://github.com/kai422/DAS3R"><img src='https://img.shields.io/badge/GitHub-%23121011.svg?logo=github&logoColor=white'></a>&nbsp;
</div>
<p></p>


* Official demo of [DAS3R: Dynamics-Aware Gaussian Splatting for Static Scene Reconstruction](https://kai422.github.io/DAS3R/).
* You can explore the sample results by clicking the sequence names at the bottom of the page.
* Due to GPU memory and time constraints, the total processing frame number is constrained at 20 and the iterations for GS training is constrained at 2000. We apply uniform sampling when the total number of input frames exceeds 20.  
* This Gradio demo is built upon InstantSplat, which can be found at [https://huggingface.co/spaces/kairunwen/InstantSplat](https://huggingface.co/spaces/kairunwen/InstantSplat).

'''

block = gr.Blocks().queue()
with block:
    with gr.Row():
        with gr.Column(scale=1):
            # gr.Markdown('# ' + _TITLE)
            gr.Markdown(_DESCRIPTION)
    
    with gr.Row(variant='panel'):
        with gr.Tab("Input"):
            inputfiles = gr.File(file_count="multiple", label="images")
            input_path = gr.Textbox(visible=False, label="example_path")
            button_gen = gr.Button("RUN")

    with gr.Row(variant='panel'):
        with gr.Tab("Output"):
            with gr.Column(scale=2):
                with gr.Group():
                    output_model = gr.Model3D(
                        label="3D Dense Model under Gaussian Splats Formats, need more time to visualize",
                        interactive=False,
                        camera_position=[0.5, 0.5, 1],  # 稍微偏移一点，以便更好地查看模型
                    )
                    gr.Markdown(
                        """
                        <div class="model-description">
                           &nbsp;&nbsp;Use the left mouse button to rotate, the scroll wheel to zoom, and the right mouse button to move.
                        </div>
                        """
                    )    
                output_file = gr.File(label="ply")
            with gr.Column(scale=1):
                output_video = gr.Video(label="video")
                
    button_gen.click(process, inputs=[inputfiles], outputs=[output_video, output_file, output_model])
    
    gr.Examples(
        examples=[
            "davis-dog",
            # "sintel-market_2",
        ],
        inputs=[input_path],
        outputs=[output_video, output_file, output_model],
        fn=lambda x: process(inputfiles=None, input_path=x),
        cache_examples=True,
        label='Sparse-view Examples'
    )
block.launch(server_name="0.0.0.0", share=False)