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 import glob # from scipy.spatial.transform import Rotation import PIL # import spaces # @spaces.GPU def vggsfm_demo( input_video, input_image, query_frame_num, max_query_pts=4096, ): 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) timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") max_input_image = 20 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 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) print(input_image) print(input_video) return glbfile, "Success" def vggsfm_predictions_to_glb(predictions): # 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() 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)) # glbfile = "glbscene.glb" # glbscene.export(file_obj=glbfile) return glbscene # apple_video = "vggsfm_code/examples/videos/apple_video.mp4" # os.path.join(os.path.dirname(__file__), "apple_video.mp4") british_museum_video = "vggsfm_code/examples/videos/british_museum_video.mp4" # os.path.join(os.path.dirname(__file__), "british_museum_video.mp4") cake_video = "vggsfm_code/examples/videos/cake_video.mp4" # os.path.join(os.path.dirname(__file__), "cake_video.mp4") # apple_images = glob.glob(f'vggsfm_code/examples/apple/images/*') cake_images = glob.glob(f'vggsfm_code/examples/cake/images/*') british_museum_images = glob.glob(f'vggsfm_code/examples/british_museum/images/*') if True: demo = gr.Interface( title="🎨 VGGSfM: Visual Geometry Grounded Deep Structure From Motion", fn=vggsfm_demo, inputs=[ gr.Video(label="Input video", interactive=True), gr.File(file_count="multiple", label="Input Images", interactive=True), gr.Slider(minimum=1, maximum=10, step=1, value=5, label="Number of query images"), gr.Slider(minimum=512, maximum=4096, step=1, value=1024, label="Number of query points"), ], outputs=[gr.Model3D(label="Reconstruction"), gr.Textbox(label="Log")], examples=[ # [apple_video, apple_images, 5, 2048], [cake_video, cake_images, 3, 4096], [british_museum_video, british_museum_images, 2, 4096],], cache_examples=True, # allow_flagging=False, allow_flagging='never', # Updated from False to 'never' concurrency_limit=1, # Added concurrency_limit to Interface description = """

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

To get started quickly, you can click on our examples. 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 one image frame per second from the input video. To prevent crashes on the Hugging Face space, we currently limit reconstruction to the first 20 image frames.

For more details, check our GitHub Repo ⭐

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

""", ) demo.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)