app.py

import spaces
import torch

print(f'torch version:{torch.__version__}')
import functools
import gc
import os
import subprocess

import shutil
import sys
import tempfile
import time
from datetime import datetime
from pathlib import Path
import uuid

import cv2
import gradio as gr

from huggingface_hub import hf_hub_download
from PIL import Image

sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

from src.misc.image_io import save_interpolated_video
from src.model.model.anysplat import AnySplat
from src.model.ply_export import export_ply
from src.utils.image import process_image

os.environ["ANYSPLAT_PROCESSED"] = f"{os.getcwd()}/proprocess_results"

from plyfile import PlyData
import numpy as np
import argparse
from io import BytesIO


def process_ply_to_splat(ply_file_path):
    plydata = PlyData.read(ply_file_path)
    vert = plydata["vertex"]
    sorted_indices = np.argsort(
        -np.exp(vert["scale_0"] + vert["scale_1"] + vert["scale_2"])
        / (1 + np.exp(-vert["opacity"]))
    )
    buffer = BytesIO()
    for idx in sorted_indices:
        v = plydata["vertex"][idx]
        position = np.array([v["x"], v["y"], v["z"]], dtype=np.float32)
        scales = np.exp(
            np.array(
                [v["scale_0"], v["scale_1"], v["scale_2"]],
                dtype=np.float32,
            )
        )
        rot = np.array(
            [v["rot_0"], v["rot_1"], v["rot_2"], v["rot_3"]],
            dtype=np.float32,
        )
        SH_C0 = 0.28209479177387814
        color = np.array(
            [
                0.5 + SH_C0 * v["f_dc_0"],
                0.5 + SH_C0 * v["f_dc_1"],
                0.5 + SH_C0 * v["f_dc_2"],
                1 / (1 + np.exp(-v["opacity"])),
            ]
        )
        buffer.write(position.tobytes())
        buffer.write(scales.tobytes())
        buffer.write((color * 255).clip(0, 255).astype(np.uint8).tobytes())
        buffer.write(
            ((rot / np.linalg.norm(rot)) * 128 + 128)
            .clip(0, 255)
            .astype(np.uint8)
            .tobytes()
        )

    return buffer.getvalue()

def save_splat_file(splat_data, output_path):
    with open(output_path, "wb") as f:
        f.write(splat_data)

def get_reconstructed_scene(outdir, model, device):

    image_files = sorted(
        [
            os.path.join(outdir, "images", f)
            for f in os.listdir(os.path.join(outdir, "images"))
        ]
    )
    images = [process_image(img_path) for img_path in image_files]
    images = torch.stack(images, dim=0).unsqueeze(0).to(device)  # [1, K, 3, 448, 448]
    b, v, c, h, w = images.shape

    assert c == 3, "Images must have 3 channels"

    gaussians, pred_context_pose = model.inference((images + 1) * 0.5)

    pred_all_extrinsic = pred_context_pose["extrinsic"]
    pred_all_intrinsic = pred_context_pose["intrinsic"]
    video, depth_colored = save_interpolated_video(
        pred_all_extrinsic,
        pred_all_intrinsic,
        b,
        h,
        w,
        gaussians,
        outdir,
        model.decoder,
    )
    plyfile = os.path.join(outdir, "gaussians.ply")

    export_ply(
        gaussians.means[0],
        gaussians.scales[0],
        gaussians.rotations[0],
        gaussians.harmonics[0],
        gaussians.opacities[0],
        Path(plyfile),
        save_sh_dc_only=True,
    )
    # Clean up
    torch.cuda.empty_cache()
    return plyfile, video, depth_colored


# 2) Handle uploaded video/images --> produce target_dir + images
def extract_frames(input_video, session_id):
    """
    Create a new 'target_dir' + 'images' subfolder, and place user-uploaded
    images or extracted frames from video into it. Return (target_dir, image_paths).
    """
    start_time = time.time()
    gc.collect()
    torch.cuda.empty_cache()

    base_dir = os.path.join(os.environ["ANYSPLAT_PROCESSED"], session_id)
    target_dir = base_dir
    target_dir_images = os.path.join(target_dir, "images")

    # Clean up if somehow that folder already exists
    if os.path.exists(target_dir):
        shutil.rmtree(target_dir)
    os.makedirs(target_dir)
    os.makedirs(target_dir_images)

    image_paths = []

    if input_video is not None:
        if isinstance(input_video, dict) and "name" in input_video:
            video_path = input_video["name"]
        else:
            video_path = input_video

        vs = cv2.VideoCapture(video_path)
        fps = vs.get(cv2.CAP_PROP_FPS)
        frame_interval = int(fps * 1)  # 1 frame/sec

        count = 0
        video_frame_num = 0
        while True:
            gotit, frame = vs.read()
            if not gotit:
                break
            count += 1
            if count % frame_interval == 0:
                image_path = os.path.join(
                    target_dir_images, f"{video_frame_num:06}.png"
                )
                cv2.imwrite(image_path, frame)
                image_paths.append(image_path)
                video_frame_num += 1

    # Sort final images for gallery
    image_paths = sorted(image_paths)

    end_time = time.time()
    print(
        f"Files copied to {target_dir_images}; took {end_time - start_time:.3f} seconds"
    )
    return target_dir, image_paths


def update_gallery_on_upload(input_video, session_id):
    """
    Whenever user uploads or changes files, immediately handle them
    and show in the gallery. Return (target_dir, image_paths).
    If nothing is uploaded, returns "None" and empty list.
    """
    if not input_video and not input_images:
        return None, None, None
        
    target_dir, image_paths = extract_frames(input_video, session_id)
    return None, target_dir, image_paths

@spaces.GPU()
def generate_splats_from_video(video_path, session_id=None):
    
    if session_id is None:
        session_id = uuid.uuid4().hex

    images_folder, image_paths = extract_frames(video_path, session_id)
    plyfile, rgb_vid, depth_vid = generate_splats_from_images(images_folder, session_id)

    return plyfile, rgb_vid, depth_vid, image_paths
    
@spaces.GPU()
def generate_splats_from_images(images_folder, session_id=None):

    if session_id is None:
        session_id = uuid.uuid4().hex
    
    start_time = time.time()
    gc.collect()
    torch.cuda.empty_cache()

    base_dir = os.path.join(os.environ["ANYSPLAT_PROCESSED"], session_id)

    all_files = (
        sorted(os.listdir(images_folder))
        if os.path.isdir(images_folder)
        else []
    )
    all_files = [f"{i}: {filename}" for i, filename in enumerate(all_files)]

    print("Running run_model...")
    with torch.no_grad():
        plyfile, video, depth_colored = get_reconstructed_scene(base_dir, model, device)

    end_time = time.time()
    print(f"Total time: {end_time - start_time:.2f} seconds (including IO)")

    return plyfile, video, depth_colored

def cleanup(request: gr.Request):
    """
    Clean up session-specific directories and temporary files when the user session ends.
    
    This function is triggered when the Gradio demo is unloaded (e.g., when the user
    closes the browser tab or navigates away). It removes all temporary files and
    directories created during the user's session to free up storage space.
    
    Args:
        request (gr.Request): Gradio request object containing session information
    """
    sid = request.session_hash
    if sid:
        d1 = os.path.join(os.environ["ANYSPLAT_PROCESSED"], sid)
        shutil.rmtree(d1, ignore_errors=True)
        
def start_session(request: gr.Request):
    """
    Initialize a new user session and return the session identifier.
    
    This function is triggered when the Gradio demo loads and creates a unique
    session hash that will be used to organize outputs and temporary files
    for this specific user session.
    
    Args:
        request (gr.Request): Gradio request object containing session information
        
    Returns:
        str: Unique session hash identifier
    """
    return request.session_hash


if __name__ == "__main__":
    share = True
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    
    # Load model
    model = AnySplat.from_pretrained(
        "lhjiang/anysplat"
    )
    model = model.to(device)
    model.eval()
    for param in model.parameters():
        param.requires_grad = False

    theme = gr.themes.Ocean()
    theme.set(
        checkbox_label_background_fill_selected="*button_primary_background_fill",
        checkbox_label_text_color_selected="*button_primary_text_color",
    )
    css = """
        #col-container {
            margin: 0 auto;
            max-width: 1024px;
        }
        """
    with gr.Blocks(css=css, title="AnySplat Demo", theme=theme) as demo:
        session_state = gr.State()
        demo.load(start_session, outputs=[session_state])
                
        target_dir_output = gr.Textbox(label="Target Dir", visible=False, value="None")
        is_example = gr.Textbox(label="is_example", visible=False, value="None")
        num_images = gr.Textbox(label="num_images", visible=False, value="None")
        dataset_name = gr.Textbox(label="dataset_name", visible=False, value="None")
        scene_name = gr.Textbox(label="scene_name", visible=False, value="None")
        image_type = gr.Textbox(label="image_type", visible=False, value="None")

        with gr.Column(elem_id="col-container"):

            gr.HTML(
                """
                <div style="text-align: center;">
                    <p style="font-size:16px; display: inline; margin: 0;">
                        <strong>AnySplat</strong> – Feed-forward 3D Gaussian Splatting from Unconstrained Views
                    </p>
                    <a href="https://github.com/OpenRobotLab/AnySplat" style="display: inline-block; vertical-align: middle; margin-left: 0.5em;">
                        <img src="https://img.shields.io/badge/GitHub-Repo-blue" alt="GitHub Repo">
                    </a>
                </div>
                """
            )
        
            with gr.Row():
                with gr.Column():
                    input_video = gr.Video(label="Upload Video", interactive=True, height=512)

                    submit_btn = gr.Button(
                        "Reconstruct", scale=1, variant="primary"
                    )
        
                    image_gallery = gr.Gallery(
                        label="Preview",
                        columns=4,
                        height="300px",
                        show_download_button=True,
                        object_fit="contain",
                        preview=True,
                    )
    
                with gr.Column():
                    with gr.Column():
                        reconstruction_output = gr.Model3D(
                            label="3D Reconstructed Gaussian Splat",
                            height=512,
                            zoom_speed=0.5,
                            pan_speed=0.5,
                            camera_position=[20, 20, 20],
                        )
                
                    with gr.Row():
                        rgb_video = gr.Video(
                            label="RGB Video", interactive=False, autoplay=True
                        )
                        depth_video = gr.Video(
                            label="Depth Video",
                            interactive=False,
                            autoplay=True,
                        )
                    with gr.Row():
                        examples = [
                            ["examples/video/re10k_1eca36ec55b88fe4.mp4"],
                            ["examples/video/bungeenerf_colosseum.mp4"],
                            ["examples/video/fox.mp4"],
                            ["examples/video/matrixcity_street.mp4"],
                            ["examples/video/vrnerf_apartment.mp4"],
                            # [None, "examples/video/vrnerf_kitchen.mp4", "vrnerf", "kitchen", "17", "Real", "True",],
                            # [None, "examples/video/vrnerf_riverview.mp4", "vrnerf", "riverview", "12", "Real", "True",],
                            # [None, "examples/video/vrnerf_workshop.mp4", "vrnerf", "workshop", "32", "Real", "True",],
                            # [None, "examples/video/fillerbuster_ramen.mp4", "fillerbuster", "ramen", "32", "Real", "True",],
                            # [None, "examples/video/meganerf_rubble.mp4", "meganerf", "rubble", "10", "Real", "True",],
                            # [None, "examples/video/llff_horns.mp4", "llff", "horns", "12", "Real", "True",],
                            # [None, "examples/video/llff_fortress.mp4", "llff", "fortress", "7", "Real", "True",],
                            # [None, "examples/video/dtu_scan_106.mp4", "dtu", "scan_106", "20", "Real", "True",],
                            # [None, "examples/video/horizongs_hillside_summer.mp4", "horizongs", "hillside_summer", "55", "Synthetic", "True",],
                            # [None, "examples/video/kitti360.mp4", "kitti360", "kitti360", "64", "Real", "True",],
                        ]
                
                        gr.Examples(
                            examples=examples,
                            inputs=[
                                input_video
                            ],
                            outputs=[
                                reconstruction_output,
                                rgb_video,
                                depth_video,
                                image_gallery
                            ],
                            fn=generate_splats_from_video,
                            cache_examples=True,
                        )
                            
               
        submit_btn.click(
            fn=generate_splats_from_images,
            inputs=[target_dir_output, session_state],
            outputs=[reconstruction_output, rgb_video, depth_video])

        input_video.upload(
            fn=update_gallery_on_upload,
            inputs=[input_video, session_state],
            outputs=[reconstruction_output, target_dir_output, image_gallery],
        )

        demo.unload(cleanup)
        demo.queue()
        demo.launch(show_error=True, share=True)