| 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 | |
| # 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): | |
| """ | |
| 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() | |
| # Create a unique folder name | |
| timestamp = datetime.now().strftime("%Y%m%d_%H%M%S_%f") | |
| target_dir = f"input_images_{timestamp}" | |
| 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): | |
| """ | |
| 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) | |
| return None, target_dir, image_paths | |
| @spaces.GPU() | |
| # 4) Reconstruction: uses the target_dir plus any viz parameters | |
| def gradio_demo( | |
| target_dir, | |
| ): | |
| """ | |
| Perform reconstruction using the already-created target_dir/images. | |
| """ | |
| if not os.path.isdir(target_dir) or target_dir == "None": | |
| return None, None, None | |
| start_time = time.time() | |
| gc.collect() | |
| torch.cuda.empty_cache() | |
| # Prepare frame_filter dropdown | |
| target_dir_images = os.path.join(target_dir, "images") | |
| all_files = ( | |
| sorted(os.listdir(target_dir_images)) | |
| if os.path.isdir(target_dir_images) | |
| 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( | |
| target_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 clear_fields(): | |
| """ | |
| Clears the 3D viewer, the stored target_dir, and empties the gallery. | |
| """ | |
| return None, None, None | |
| if __name__ == "__main__": | |
| server_name = "127.0.0.1" | |
| server_port = None | |
| 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 = """ | |
| .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: | |
| gr.Markdown( | |
| """ | |
| <h1 style='text-align: center;'>AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Views</h1> | |
| """ | |
| ) | |
| with gr.Row(): | |
| gr.Markdown( | |
| """ | |
| <p align="center"> | |
| <a title="Website" href="https://city-super.github.io/anysplat/" 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/pdf/2505.23716" 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/OpenRobotLab/AnySplat" target="_blank" rel="noopener noreferrer" style="display: inline-block;"> | |
| <img src="https://img.shields.io/badge/Github-Page-black" alt="badge-github-stars"> | |
| </a> | |
| </p> | |
| """ | |
| ) | |
| with gr.Row(): | |
| gr.Markdown( | |
| """ | |
| ### Getting Started: | |
| 1. Upload Your Data: Use the "Upload Video" or "Upload Images" buttons on the left to provide your input. Videos will be automatically split into individual frames (one frame per second). | |
| 2. Preview: Your uploaded images will appear in the gallery on the left. | |
| 3. Reconstruct: Click the "Reconstruct" button to start the 3D reconstruction process. | |
| 4. Visualize: The reconstructed 3D Gaussian Splat will appear in the viewer on the right, along with the rendered RGB and depth videos. The trajectory of the rendered video is obtained by interpolating the estimated input image poses. | |
| <strong style="color: #0ea5e9;">Please note:</strong> <span style="color: #0ea5e9; font-weight: bold;">The generated splats are large in size, so they may not load successfully in the Hugging Face demo. You can download the .ply file and render it using other viewers, such as [SuperSplat](https://playcanvas.com/supersplat/editor).</span> | |
| """ | |
| ) | |
| 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.Row(): | |
| with gr.Column(scale=2): | |
| with gr.Tabs(): | |
| with gr.Tab("Input Data"): | |
| input_video = gr.Video(label="Upload Video", interactive=True) | |
| input_images = gr.File( | |
| file_count="multiple", | |
| label="Upload Images", | |
| interactive=True, | |
| ) | |
| image_gallery = gr.Gallery( | |
| label="Preview", | |
| columns=4, | |
| height="300px", | |
| show_download_button=True, | |
| object_fit="contain", | |
| preview=True, | |
| ) | |
| with gr.Column(scale=4): | |
| with gr.Tabs(): | |
| with gr.Tab("AnySplat Output"): | |
| with gr.Column(): | |
| reconstruction_output = gr.Model3D( | |
| label="3D Reconstructed Gaussian Splat", | |
| height=540, | |
| 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, | |
| ) | |
| with gr.Row(): | |
| submit_btn = gr.Button( | |
| "Reconstruct", scale=1, variant="primary" | |
| ) | |
| clear_btn = gr.ClearButton( | |
| [ | |
| input_video, | |
| input_images, | |
| reconstruction_output, | |
| target_dir_output, | |
| image_gallery, | |
| rgb_video, | |
| depth_video, | |
| ], | |
| scale=1, | |
| ) | |
| # ---------------------- 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",], | |
| ] | |
| def example_pipeline( | |
| input_images, | |
| input_video, | |
| dataset_name, | |
| scene_name, | |
| num_images_str, | |
| image_type, | |
| is_example, | |
| ): | |
| """ | |
| 1) Copy example images to new target_dir | |
| 2) Reconstruct | |
| 3) Return model3D + logs + new_dir + updated dropdown + gallery | |
| We do NOT return is_example. It's just an input. | |
| """ | |
| target_dir, image_paths = handle_uploads(input_video, input_images) | |
| plyfile, video, depth_colored = gradio_demo(target_dir) | |
| return plyfile, video, depth_colored, target_dir, image_paths | |
| gr.Markdown("Click any row to load an example.", elem_classes=["example-log"]) | |
| 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, | |
| ) | |
| gr.Markdown("<p style='text-align: center; font-style: italic; color: #666;'>We thank VGGT for their excellent gradio implementation!</p>") | |
| submit_btn.click( | |
| fn=clear_fields, | |
| inputs=[], | |
| outputs=[reconstruction_output, rgb_video, depth_video], | |
| ).then( | |
| fn=gradio_demo, | |
| inputs=[ | |
| target_dir_output, | |
| ], | |
| outputs=[reconstruction_output, rgb_video, depth_video], | |
| ).then( | |
| fn=lambda: "False", inputs=[], outputs=[is_example] | |
| ) | |
| input_video.change( | |
| fn=update_gallery_on_upload, | |
| inputs=[input_video, input_images], | |
| outputs=[reconstruction_output, target_dir_output, image_gallery], | |
| ) | |
| input_images.change( | |
| fn=update_gallery_on_upload, | |
| inputs=[input_video, input_images], | |
| outputs=[reconstruction_output, target_dir_output, image_gallery], | |
| ) | |
| # demo.launch(share=share, server_name=server_name, server_port=server_port) | |
| demo.queue().launch(show_error=True, share=True) | |
| # We thank VGGT for their excellent gradio implementation | |