import os import cv2 import torch import numpy as np import gradio as gr import trimesh import sys import os sys.path.append('vggsfm_code/') import shutil from datetime import datetime from vggsfm_code.hf_demo import demo_fn from omegaconf import DictConfig, OmegaConf from viz_utils.viz_fn import add_camera, apply_density_filter_np import glob # from scipy.spatial.transform import Rotation import PIL import gc import open3d as o3d # import spaces # @spaces.GPU def vggsfm_demo( input_video, input_image, query_frame_num, max_query_pts=4096, ): import time start_time = time.time() gc.collect() torch.cuda.empty_cache() debug = False timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") max_input_image = 25 target_dir = f"input_images_{timestamp}" if os.path.exists(target_dir): shutil.rmtree(target_dir) os.makedirs(target_dir) target_dir_images = target_dir + "/images" os.makedirs(target_dir_images) if debug: predictions = torch.load("predictions_scene2.pth") else: if input_video is not None: if not isinstance(input_video, str): input_video = input_video["video"]["path"] cfg_file = "vggsfm_code/cfgs/demo.yaml" cfg = OmegaConf.load(cfg_file) if input_image is not None: if len(input_image)<3: return None, "Please input at least three frames" input_image = sorted(input_image) input_image = input_image[:max_input_image] # Copy files to the new directory for file_name in input_image: shutil.copy(file_name, target_dir_images) elif input_video is not None: vs = cv2.VideoCapture(input_video) fps = vs.get(cv2.CAP_PROP_FPS) frame_rate = 1 frame_interval = int(fps * frame_rate) video_frame_num = 0 count = 0 while video_frame_num<=max_input_image: (gotit, frame) = vs.read() count +=1 if not gotit: break if count % frame_interval == 0: cv2.imwrite(target_dir_images+"/"+f"{video_frame_num:06}.png", frame) video_frame_num+=1 if video_frame_num<3: return None, "Please input at least three frames" else: return None, "Input format incorrect" cfg.query_frame_num = query_frame_num cfg.max_query_pts = max_query_pts print(f"Files have been copied to {target_dir_images}") cfg.SCENE_DIR = target_dir # try: predictions = demo_fn(cfg) # except: # return None, "Something seems to be incorrect. Please verify that your inputs are formatted correctly. If the issue persists, kindly create a GitHub issue for further assistance." glbscene = vggsfm_predictions_to_glb(predictions) glbfile = target_dir + "/glbscene.glb" glbscene.export(file_obj=glbfile) # glbscene.export(file_obj=glbfile, line_settings= {'point_size': 20}) del predictions gc.collect() torch.cuda.empty_cache() print(input_image) print(input_video) end_time = time.time() execution_time = end_time - start_time print(f"Execution time: {execution_time} seconds") return glbfile, "Success" def vggsfm_predictions_to_glb(predictions, sphere=False): # del predictions['reconstruction'] # torch.save(predictions, "predictions_scene2.pth") # learned from https://github.com/naver/dust3r/blob/main/dust3r/viz.py points3D = predictions["points3D"].cpu().numpy() points3D_rgb = predictions["points3D_rgb"].cpu().numpy() points3D_rgb = (points3D_rgb*255).astype(np.uint8) extrinsics_opencv = predictions["extrinsics_opencv"].cpu().numpy() intrinsics_opencv = predictions["intrinsics_opencv"].cpu().numpy() raw_image_paths = predictions["raw_image_paths"] images = predictions["images"].permute(0,2,3,1).cpu().numpy() images = (images*255).astype(np.uint8) glbscene = trimesh.Scene() if True: pcd = o3d.geometry.PointCloud() pcd.points = o3d.utility.Vector3dVector(points3D) pcd.colors = o3d.utility.Vector3dVector(points3D_rgb) cl, ind = pcd.remove_statistical_outlier(nb_neighbors=20, std_ratio=1.0) filtered_pcd = pcd.select_by_index(ind) print(f"Filter out {len(points3D) - len(filtered_pcd.points)} 3D points") points3D = np.asarray(filtered_pcd.points) points3D_rgb = np.asarray(filtered_pcd.colors) if sphere: # TOO SLOW print("testing sphere") # point_size = 0.02 else: point_cloud = trimesh.PointCloud(points3D, colors=points3D_rgb) glbscene.add_geometry(point_cloud) camera_edge_colors = [(255, 0, 0), (0, 0, 255), (0, 255, 0), (255, 0, 255), (255, 204, 0), (0, 204, 204), (128, 255, 255), (255, 128, 255), (255, 255, 128), (0, 0, 0), (128, 128, 128)] frame_num = len(extrinsics_opencv) extrinsics_opencv_4x4 = np.zeros((frame_num, 4, 4)) extrinsics_opencv_4x4[:, :3, :4] = extrinsics_opencv extrinsics_opencv_4x4[:, 3, 3] = 1 for idx in range(frame_num): cam_from_world = extrinsics_opencv_4x4[idx] cam_to_world = np.linalg.inv(cam_from_world) cur_cam_color = camera_edge_colors[idx % len(camera_edge_colors)] cur_focal = intrinsics_opencv[idx, 0, 0] add_camera(glbscene, cam_to_world, cur_cam_color, image=None, imsize=(1024,1024), focal=None,screen_width=0.35) opengl_mat = np.array([[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]]) rot = np.eye(4) rot[:3, :3] = Rotation.from_euler('y', np.deg2rad(180)).as_matrix() glbscene.apply_transform(np.linalg.inv(np.linalg.inv(extrinsics_opencv_4x4[0]) @ opengl_mat @ rot)) # Calculate the bounding box center and apply the translation # bounding_box = glbscene.bounds # center = (bounding_box[0] + bounding_box[1]) / 2 # translation = np.eye(4) # translation[:3, 3] = -center # glbscene.apply_transform(translation) # glbfile = "glbscene.glb" # glbscene.export(file_obj=glbfile) return glbscene apple_video = "vggsfm_code/examples/videos/apple_video.mp4" british_museum_video = "vggsfm_code/examples/videos/british_museum_video.mp4" cake_video = "vggsfm_code/examples/videos/cake_video.mp4" bonsai_video = "vggsfm_code/examples/videos/bonsai_video.mp4" face_video = "vggsfm_code/examples/videos/in2n_face_video.mp4" apple_images = glob.glob(f'vggsfm_code/examples/apple/images/*') bonsai_images = glob.glob(f'vggsfm_code/examples/bonsai/images/*') cake_images = glob.glob(f'vggsfm_code/examples/cake/images/*') british_museum_images = glob.glob(f'vggsfm_code/examples/british_museum/images/*') face_images = glob.glob(f'vggsfm_code/examples/in2n_face/images/*') with gr.Blocks() as demo: gr.Markdown("# 🏛️ VGGSfM: Visual Geometry Grounded Deep Structure From Motion") gr.Markdown("""

Welcome to VGGSfM demo! This space demonstrates 3D reconstruction from input image frames.

To get started quickly, you can click on our examples (the bottom of the page) . If you want to reconstruct your own data, simply:

If both images and videos are uploaded, the demo will only reconstruct the uploaded images. By default, we extract 1 image frame per second from the input video . To prevent crashes on the Hugging Face space, we currently limit reconstruction to the first 25 image frames.

SfM methods are designed for rigid/static reconstruction . When dealing with dynamic/moving inputs, these methods may still work by focusing on the rigid parts of the scene. However, to ensure high-quality results, it is better to minimize the presence of moving objects in the input data.

The reconstruction should typically take up to 90 seconds . If it takes longer, the input data is likely not well-conditioned or the query images/points are set too high.

If you meet any problem, feel free to create an issue in our GitHub Repo ⭐

(Please note that running reconstruction on Hugging Face space is slower than on a local machine.)

""") with gr.Row(): with gr.Column(scale=1): input_video = gr.Video(label="Input video", interactive=True) input_images = gr.File(file_count="multiple", label="Input Images", interactive=True) num_query_images = gr.Slider(minimum=1, maximum=10, step=1, value=4, label="Number of query images (key frames)", info="More query images usually lead to better reconstruction at lower speeds. If the viewpoint differences between your images are minimal, you can set this value to 1. ") num_query_points = gr.Slider(minimum=600, maximum=6000, step=1, value=2048, label="Number of query points", info="More query points usually lead to denser reconstruction at lower speeds.") with gr.Column(scale=3): reconstruction_output = gr.Model3D(label="Reconstruction", height=520, zoom_speed=1, pan_speed=1) log_output = gr.Textbox(label="Log") with gr.Row(): submit_btn = gr.Button("Reconstruct", scale=1) # submit_btn = gr.Button("Reconstruct", scale=1, elem_attributes={"style": "background-color: blue; color: white;"}) clear_btn = gr.ClearButton([input_video, input_images, num_query_images, num_query_points, reconstruction_output, log_output], scale=1) examples = [ [face_video, face_images, 4, 2048], [british_museum_video, british_museum_images, 1, 4096], [apple_video, apple_images, 6, 2048], [bonsai_video, bonsai_images, 3, 2048], # [cake_video, cake_images, 3, 2048], ] gr.Examples(examples=examples, inputs=[input_video, input_images, num_query_images, num_query_points], outputs=[reconstruction_output, log_output], # Provide outputs fn=vggsfm_demo, # Provide the function cache_examples=True, ) submit_btn.click( vggsfm_demo, [input_video, input_images, num_query_images, num_query_points], [reconstruction_output, log_output], concurrency_limit=1 ) # demo.launch(debug=True, share=True) demo.queue(max_size=20).launch(show_error=True, share=True) # demo.queue(max_size=20, concurrency_count=1).launch(debug=True, share=True) ######################################################################################################################## # else: # import glob # files = glob.glob(f'vggsfm_code/examples/cake/images/*', recursive=True) # vggsfm_demo(files, None, None) # demo.queue(max_size=20, concurrency_count=1).launch(debug=True, share=True)