app.py

import os
import time
import torch
import numpy as np
import gradio as gr
import urllib.parse
import tempfile
import subprocess
from dust3r.losses import L21
from spann3r.model import Spann3R
from spann3r.datasets import Demo
from torch.utils.data import DataLoader
import trimesh
from scipy.spatial.transform import Rotation
from transformers import AutoModelForImageSegmentation
from torchvision import transforms
from PIL import Image
import open3d as o3d
from backend_utils import improved_multiway_registration, pts2normal, point2mesh, combine_and_clean_point_clouds
from gs_utils import point2gs
from gradio.helpers import Examples as GradioExamples
from gradio.utils import get_cache_folder
from pathlib import Path

# Default values
DEFAULT_CKPT_PATH = './checkpoints/spann3r.pth'
DEFAULT_DUST3R_PATH = 'https://huggingface.co/camenduru/dust3r/resolve/main/DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth'
DEFAULT_DEVICE = 'cuda:0' if torch.cuda.is_available() else 'cpu'

OPENGL = np.array([[1, 0, 0, 0],
                   [0, -1, 0, 0],
                   [0, 0, -1, 0],
                   [0, 0, 0, 1]])

class Examples(GradioExamples):
    def __init__(self, *args, directory_name=None, **kwargs):
        super().__init__(*args, **kwargs, _initiated_directly=False)
        if directory_name is not None:
            self.cached_folder = get_cache_folder() / directory_name
            self.cached_file = Path(self.cached_folder) / "log.csv"
            self.create()

def export_geometry(geometry):
    output_path = tempfile.mktemp(suffix='.obj')

    # Apply rotation
    rot = np.eye(4)
    rot[:3, :3] = Rotation.from_euler('y', np.deg2rad(180)).as_matrix()
    transform = np.linalg.inv(OPENGL @ rot)
    geometry.transform(transform)

    o3d.io.write_triangle_mesh(output_path, geometry, write_ascii=False, compressed=True)
    
    return output_path


def extract_frames(video_path: str, duration: float = 20.0, fps: float = 3.0) -> str:
    temp_dir = tempfile.mkdtemp()
    output_path = os.path.join(temp_dir, "%03d.jpg")
    
    filter_complex = f"select='if(lt(t,{duration}),1,0)',fps={fps}"

    command = [
        "ffmpeg",
        "-i", video_path,
        "-vf", filter_complex,
        "-vsync", "0",
        output_path
    ]
    
    subprocess.run(command, check=True)
    return temp_dir

def cat_meshes(meshes):
    vertices, faces, colors = zip(*[(m['vertices'], m['faces'], m['face_colors']) for m in meshes])
    n_vertices = np.cumsum([0]+[len(v) for v in vertices])
    for i in range(len(faces)):
        faces[i][:] += n_vertices[i]

    vertices = np.concatenate(vertices)
    colors = np.concatenate(colors)
    faces = np.concatenate(faces)
    return dict(vertices=vertices, face_colors=colors, faces=faces)

def load_ckpt(model_path_or_url, verbose=True):
    if verbose:
        print('... loading model from', model_path_or_url)
    is_url = urllib.parse.urlparse(model_path_or_url).scheme in ('http', 'https')
    
    if is_url:
        ckpt = torch.hub.load_state_dict_from_url(model_path_or_url, map_location='cpu', progress=verbose)
    else:
        ckpt = torch.load(model_path_or_url, map_location='cpu')
    return ckpt

def load_model(ckpt_path, device):
    model = Spann3R(dus3r_name=DEFAULT_DUST3R_PATH, 
                    use_feat=False).to(device)
    
    model.load_state_dict(load_ckpt(ckpt_path)['model'])
    model.eval()
    return model

def pts3d_to_trimesh(img, pts3d, valid=None):
    H, W, THREE = img.shape
    assert THREE == 3
    assert img.shape == pts3d.shape

    vertices = pts3d.reshape(-1, 3)

    # make squares: each pixel == 2 triangles
    idx = np.arange(len(vertices)).reshape(H, W)
    idx1 = idx[:-1, :-1].ravel()  # top-left corner
    idx2 = idx[:-1, +1:].ravel()  # right-left corner
    idx3 = idx[+1:, :-1].ravel()  # bottom-left corner
    idx4 = idx[+1:, +1:].ravel()  # bottom-right corner
    faces = np.concatenate((
        np.c_[idx1, idx2, idx3],
        np.c_[idx3, idx2, idx1],  # same triangle, but backward (cheap solution to cancel face culling)
        np.c_[idx2, idx3, idx4],
        np.c_[idx4, idx3, idx2],  # same triangle, but backward (cheap solution to cancel face culling)
    ), axis=0)

    # prepare triangle colors
    face_colors = np.concatenate((
        img[:-1, :-1].reshape(-1, 3),
        img[:-1, :-1].reshape(-1, 3),
        img[+1:, +1:].reshape(-1, 3),
        img[+1:, +1:].reshape(-1, 3)
    ), axis=0)

    # remove invalid faces
    if valid is not None:
        assert valid.shape == (H, W)
        valid_idxs = valid.ravel()
        valid_faces = valid_idxs[faces].all(axis=-1)
        faces = faces[valid_faces]
        face_colors = face_colors[valid_faces]

    assert len(faces) == len(face_colors)
    return dict(vertices=vertices, face_colors=face_colors, faces=faces)

model = load_model(DEFAULT_CKPT_PATH, DEFAULT_DEVICE)
birefnet = AutoModelForImageSegmentation.from_pretrained('zhengpeng7/BiRefNet', trust_remote_code=True)
birefnet.to(DEFAULT_DEVICE)
birefnet.eval()

def extract_object(birefnet, image):
    # Data settings
    image_size = (1024, 1024)
    transform_image = transforms.Compose([
        transforms.Resize(image_size),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])

    input_images = transform_image(image).unsqueeze(0).to(DEFAULT_DEVICE)

    # Prediction
    with torch.no_grad():
        preds = birefnet(input_images)[-1].sigmoid().cpu()
    pred = preds[0].squeeze()
    pred_pil = transforms.ToPILImage()(pred)
    mask = pred_pil.resize(image.size)
    return mask

def generate_mask(image: np.ndarray):
    # Convert numpy array to PIL Image
    pil_image = Image.fromarray((image * 255).astype(np.uint8))
    
    # Extract object and get mask
    mask = extract_object(birefnet, pil_image)
    
    # Convert mask to numpy array
    mask_np = np.array(mask) / 255.0
    return mask_np

def center_pcd(pcd: o3d.geometry.PointCloud, normalize=False) -> o3d.geometry.PointCloud:
    # Convert to numpy array
    points = np.asarray(pcd.points)
    
    # Compute centroid
    centroid = np.mean(points, axis=0)
    
    # Center the point cloud
    centered_points = points - centroid
    
    if normalize:
         # Compute the maximum distance from the center
        max_distance = np.max(np.linalg.norm(centered_points, axis=1))
        
        # Normalize the point cloud
        normalized_points = centered_points / max_distance
        
        # Create a new point cloud with the normalized points
        normalized_pcd = o3d.geometry.PointCloud()
        normalized_pcd.points = o3d.utility.Vector3dVector(normalized_points)
        
        # If the original point cloud has colors, normalize them too
        if pcd.has_colors():
            normalized_pcd.colors = pcd.colors
        
        # If the original point cloud has normals, copy them
        if pcd.has_normals():
            normalized_pcd.normals = pcd.normals
        
        return normalized_pcd
    else:
        pcd.points = o3d.utility.Vector3dVector(centered_points)
        return pcd

@torch.no_grad()
def reconstruct(video_path, conf_thresh, kf_every, 
                remove_background=False):
    # Extract frames from video
    demo_path = extract_frames(video_path)
    
    # Load dataset
    dataset = Demo(ROOT=demo_path, resolution=224, full_video=True, kf_every=kf_every)
    dataloader = DataLoader(dataset, batch_size=1, shuffle=False, num_workers=0)
    batch = next(iter(dataloader))
    
    for view in batch:
        view['img'] = view['img'].to(DEFAULT_DEVICE, non_blocking=True)
    
    demo_name = os.path.basename(video_path)
    print(f'Started reconstruction for {demo_name}')
    
    start = time.time()
    preds, preds_all = model.forward(batch)
    end = time.time()
    fps = len(batch) / (end - start)
    print(f'Finished reconstruction for {demo_name}, FPS: {fps:.2f}')
    
    # Process results
    pcds = []
    for j, view in enumerate(batch):
        image = view['img'].permute(0, 2, 3, 1).cpu().numpy()[0]
        image = (image + 1) / 2
        pts = preds[j]['pts3d' if j==0 else 'pts3d_in_other_view'].detach().cpu().numpy()[0]
        pts_normal = pts2normal(preds[j]['pts3d' if j==0 else 'pts3d_in_other_view'][0]).cpu().numpy()
        conf = preds[j]['conf'][0].cpu().data.numpy()
        conf_sig = (conf - 1) / conf
        if remove_background:
            mask = generate_mask(image)
        else:
            mask = np.ones_like(conf)
            
        combined_mask = (conf_sig > conf_thresh) & (mask > 0.5)
        
        pcd = o3d.geometry.PointCloud()
        pcd.points = o3d.utility.Vector3dVector(pts[combined_mask])
        pcd.colors = o3d.utility.Vector3dVector(image[combined_mask])
        pcd.normals = o3d.utility.Vector3dVector(pts_normal[combined_mask])
        pcds.append(pcd)
    
    pcd_combined = combine_and_clean_point_clouds(pcds, voxel_size=0.001)
    pcd_combined = center_pcd(pcd_combined, normalize=True)
    o3d_geometry = point2mesh(pcd_combined)
        
    # Create coarse result
    coarse_output_path = export_geometry(o3d_geometry)
    
    yield coarse_output_path, None
    
    transformed_pcds, _, _ = improved_multiway_registration(pcds, voxel_size=0.01)
    transformed_pcds = center_pcd(transformed_pcds)
    
    # Create coarse result
    refined_output_path = tempfile.mktemp(suffix='.ply')
    point2gs(refined_output_path, transformed_pcds)
    yield coarse_output_path, refined_output_path

    # Clean up temporary directory
    os.system(f"rm -rf {demo_path}")

example_videos = [os.path.join('./examples', f) for f in os.listdir('./examples') if f.endswith(('.mp4', '.webm'))]

# Update the Gradio interface with improved layout
with gr.Blocks(
        title="StableRecon: 3D Reconstruction from Video",
        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 iface:
    gr.Markdown(
        """
        # StableRecon: Making Video to 3D easy
        <p align="center">
            <a title="Github" href="https://github.com/Stable-X/StableRecon" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
                <img src="https://img.shields.io/github/stars/Stable-X/StableRecon?label=GitHub%20%E2%98%85&logo=github&color=C8C" alt="badge-github-stars">
            </a>
            <a title="Social" href="https://x.com/ychngji6" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
                <img src="https://www.obukhov.ai/img/badges/badge-social.svg" alt="social">
            </a>
        </p>
        
        <div style="background-color: #f0f0f0; padding: 10px; border-radius: 5px; margin-bottom: 20px;">
            <strong>📢 About StableRecon:</strong> This is an experimental open-source project building on <a href="https://github.com/naver/dust3r" target="_blank">dust3r</a> and <a href="https://github.com/HengyiWang/spann3r" target="_blank">spann3r</a>. We're exploring video-to-3D conversion, using spann3r for tracking and implementing our own backend and meshing. While it's a work in progress with plenty of room for improvement, we're excited to share it with the community. We welcome your feedback, especially on failure cases, as we continue to develop and refine this tool.
        </div>
        """
    )
    with gr.Row():
        with gr.Column(scale=1):
            video_input = gr.Video(label="Input Video", sources=["upload"])
            with gr.Row():
                conf_thresh = gr.Slider(0, 1, value=1e-3, label="Confidence Threshold")
                kf_every = gr.Slider(1, 30, step=1, value=1, label="Keyframe Interval")
            with gr.Row():
                remove_background = gr.Checkbox(label="Remove Background", value=False)
            reconstruct_btn = gr.Button("Start Reconstruction")
        
        with gr.Column(scale=2):
            with gr.Tab("3D Models"):
                with gr.Group():
                    initial_model = gr.Model3D(label="Initial 3D Model", display_mode="solid", 
                                               clear_color=[0.0, 0.0, 0.0, 0.0])
                    gr.Markdown(
                        """
                        <div class="model-description">
                        This is the initial 3D model generated from the video. Finish within 10 seconds.
                        </div>
                        """
                    )
                
                with gr.Group():
                    optimized_model = gr.Model3D(label="Optimized 3D Model", display_mode="solid", 
                                                 clear_color=[0.0, 0.0, 0.0, 0.0])
                    gr.Markdown(
                        """
                        <div class="model-description">
                        This is the optimized 3D model with improved accuracy and detail using Gaussian Splatting. Finish within 60 seconds.
                        </div>
                        """
                    )
            
            with gr.Tab("Help"):
                gr.Markdown(
                    """
                    ## How to use this tool:
                    1. Upload a video of the object you want to reconstruct.
                    2. Adjust the Confidence Threshold and Keyframe Interval if needed.
                    3. Choose whether to remove the background.
                    4. Click "Start Reconstruction" to begin the process.
                    5. The Initial 3D Model will appear first, giving you a quick preview.
                    6. Once processing is complete, the Optimized 3D Model will show the final result.
                    
                    ### Tips:
                    - For best results, ensure your video captures the object from multiple angles.
                    - If the model appears noisy, try increasing the Confidence Threshold.
                    - Experiment with different Keyframe Intervals to balance speed and accuracy.
                    """
                )
    
    Examples(
        fn=reconstruct,
        examples=sorted([
            os.path.join("examples", name) 
            for name in os.listdir(os.path.join("examples")) if name.endswith('.webm')
        ]),
        inputs=[video_input],
        outputs=[initial_model, optimized_model],
        directory_name="examples_video",
        cache_examples=False,
    )
    
    reconstruct_btn.click(
        fn=reconstruct,
        inputs=[video_input, conf_thresh, kf_every, remove_background],
        outputs=[initial_model, optimized_model]
    )

if __name__ == "__main__":
    iface.launch(server_name="0.0.0.0")