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 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"


# 1) Core model inference
def get_reconstructed_scene(outdir, model, device):
    # Load Images
    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"

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

    # Save the results
    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 handle_uploads(input_video, input_images, 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 = []

    # --- Handle images ---
    if input_images is not None:
        for file_data in input_images:
            if isinstance(file_data, dict) and "name" in file_data:
                file_path = file_data["name"]
            else:
                file_path = file_data
            dst_path = os.path.join(target_dir_images, os.path.basename(file_path))
            shutil.copy(file_path, dst_path)
            image_paths.append(dst_path)

    # --- Handle video ---
    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


# 3) Update gallery on upload
def update_gallery_on_upload(input_video, input_images, 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 = handle_uploads(input_video, input_images, session_id)
    return None, target_dir, image_paths


@spaces.GPU()
def generate_splat(images_folder, session_id=None):

    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 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;
        }

        .custom-log * {
            font-style: italic;
            font-size: 22px !important;
            background-image: linear-gradient(120deg, #0ea5e9 0%, #6ee7b7 60%, #34d399 100%);
            -webkit-background-clip: text;
            background-clip: text;
            font-weight: bold !important;
            color: transparent !important;
            text-align: center !important;
        }
        
        .example-log * {
            font-style: italic;
            font-size: 16px !important;
            background-image: linear-gradient(120deg, #0ea5e9 0%, #6ee7b7 60%, #34d399 100%);
            -webkit-background-clip: text;
            background-clip: text;
            color: transparent !important;
        }
        
        #my_radio .wrap {
            display: flex;
            flex-wrap: nowrap;
            justify-content: center;
            align-items: center;
        }

        #my_radio .wrap label {
            display: flex;
            width: 50%;
            justify-content: center;
            align-items: center;
            margin: 0;
            padding: 10px 0;
            box-sizing: border-box;
        }
        """
    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.Markdown(
                """ # AnySplat – Feed-forward 3D Gaussian Splatting from Unconstrained Views
        
                • Source: [Github](https://github.com/OpenRobotLab/AnySplat)  """
            )
        
            with gr.Row():
                with gr.Column():
                    input_video = gr.Video(label="Upload Video", interactive=True, height=512)
                    input_images = gr.File(
                        file_count="multiple",
                        label="Upload Images",
                        interactive=True,
                        visible=False
                    )
                    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():
                        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,
                            )
        

            # ---------------------- Examples section ----------------------
    
            # examples = [
            #     [None, "examples/video/re10k_1eca36ec55b88fe4.mp4", "re10k", "1eca36ec55b88fe4", "2", "Real", "True",],
            #     [None, "examples/video/bungeenerf_colosseum.mp4", "bungeenerf", "colosseum", "8", "Synthetic", "True",],
            #     [None, "examples/video/fox.mp4", "InstantNGP", "fox", "14", "Real", "True",],
            #     [None, "examples/video/matrixcity_street.mp4", "matrixcity", "street", "32", "Synthetic", "True",],
            #     [None, "examples/video/vrnerf_apartment.mp4", "vrnerf", "apartment", "32", "Real", "True",],
            #     [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_images,
            #         input_video,
            #         dataset_name,
            #         scene_name,
            #         num_images,
            #         image_type,
            #         is_example,
            #     ],
            #     outputs=[
            #         reconstruction_output,
            #         rgb_video,
            #         depth_video,
            #         target_dir_output,
            #         image_gallery,
            #     ],
            #     fn=example_pipeline,
            #     cache_examples=False,
            #     examples_per_page=50,
            # )
            
        submit_btn.click(
            fn=generate_splat,
            inputs=[target_dir_output,],
            outputs=[reconstruction_output, rgb_video, depth_video])

        input_video.change(
            fn=update_gallery_on_upload,
            inputs=[input_video, input_images, session_state],
            outputs=[reconstruction_output, target_dir_output, image_gallery],
        )
        input_images.change(
            fn=update_gallery_on_upload,
            inputs=[input_video, input_images, session_state],
            outputs=[reconstruction_output, target_dir_output, image_gallery],
        )
        demo.queue().launch(show_error=True, share=True)