import pycolmap from models.SpaTrackV2.models.predictor import Predictor import yaml import easydict import os import numpy as np import cv2 import torch import torchvision.transforms as T from PIL import Image import io import moviepy.editor as mp from models.SpaTrackV2.utils.visualizer import Visualizer import tqdm from models.SpaTrackV2.models.utils import get_points_on_a_grid import glob from rich import print import argparse import decord from models.SpaTrackV2.models.vggt4track.models.vggt_moe import VGGT4Track from models.SpaTrackV2.models.vggt4track.utils.load_fn import preprocess_image from models.SpaTrackV2.models.vggt4track.utils.pose_enc import pose_encoding_to_extri_intri def parse_args(): parser = argparse.ArgumentParser() parser.add_argument("--track_mode", type=str, default="offline") parser.add_argument("--data_type", type=str, default="RGBD") parser.add_argument("--data_dir", type=str, default="assets/example0") parser.add_argument("--video_name", type=str, default="snowboard") parser.add_argument("--grid_size", type=int, default=10) parser.add_argument("--vo_points", type=int, default=756) parser.add_argument("--fps", type=int, default=1) return parser.parse_args() if __name__ == "__main__": args = parse_args() out_dir = args.data_dir + "/results" # fps fps = int(args.fps) mask_dir = args.data_dir + f"/{args.video_name}.png" vggt4track_model = VGGT4Track.from_pretrained("Yuxihenry/SpatialTrackerV2_Front") vggt4track_model.eval() vggt4track_model = vggt4track_model.to("cuda") if args.data_type == "RGBD": npz_dir = args.data_dir + f"/{args.video_name}.npz" data_npz_load = dict(np.load(npz_dir, allow_pickle=True)) #TODO: tapip format video_tensor = data_npz_load["video"] * 255 video_tensor = torch.from_numpy(video_tensor) video_tensor = video_tensor[::fps] depth_tensor = data_npz_load["depths"] depth_tensor = depth_tensor[::fps] intrs = data_npz_load["intrinsics"] intrs = intrs[::fps] extrs = np.linalg.inv(data_npz_load["extrinsics"]) extrs = extrs[::fps] unc_metric = None elif args.data_type == "RGB": vid_dir = os.path.join(args.data_dir, f"{args.video_name}.mp4") video_reader = decord.VideoReader(vid_dir) video_tensor = torch.from_numpy(video_reader.get_batch(range(len(video_reader))).asnumpy()).permute(0, 3, 1, 2) # Convert to tensor and permute to (N, C, H, W) video_tensor = video_tensor[::fps].float() # process the image tensor video_tensor = preprocess_image(video_tensor)[None] with torch.no_grad(): with torch.cuda.amp.autocast(dtype=torch.bfloat16): # Predict attributes including cameras, depth maps, and point maps. predictions = vggt4track_model(video_tensor.cuda()/255) extrinsic, intrinsic = predictions["poses_pred"], predictions["intrs"] depth_map, depth_conf = predictions["points_map"][..., 2], predictions["unc_metric"] depth_tensor = depth_map.squeeze().cpu().numpy() extrs = np.eye(4)[None].repeat(len(depth_tensor), axis=0) extrs = extrinsic.squeeze().cpu().numpy() intrs = intrinsic.squeeze().cpu().numpy() video_tensor = video_tensor.squeeze() #NOTE: 20% of the depth is not reliable # threshold = depth_conf.squeeze()[0].view(-1).quantile(0.6).item() unc_metric = depth_conf.squeeze().cpu().numpy() > 0.5 data_npz_load = {} if os.path.exists(mask_dir): mask_files = mask_dir mask = cv2.imread(mask_files) mask = cv2.resize(mask, (video_tensor.shape[3], video_tensor.shape[2])) mask = mask.sum(axis=-1)>0 else: mask = np.ones_like(video_tensor[0,0].numpy())>0 # get all data pieces viz = True os.makedirs(out_dir, exist_ok=True) # with open(cfg_dir, "r") as f: # cfg = yaml.load(f, Loader=yaml.FullLoader) # cfg = easydict.EasyDict(cfg) # cfg.out_dir = out_dir # cfg.model.track_num = args.vo_points # print(f"Downloading model from HuggingFace: {cfg.ckpts}") if args.track_mode == "offline": model = Predictor.from_pretrained("Yuxihenry/SpatialTrackerV2-Offline") else: model = Predictor.from_pretrained("Yuxihenry/SpatialTrackerV2-Online") # config the model; the track_num is the number of points in the grid model.spatrack.track_num = args.vo_points model.eval() model.to("cuda") viser = Visualizer(save_dir=out_dir, grayscale=True, fps=10, pad_value=0, tracks_leave_trace=5) grid_size = args.grid_size # get frame H W if video_tensor is None: cap = cv2.VideoCapture(video_path) frame_H = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) frame_W = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) else: frame_H, frame_W = video_tensor.shape[2:] grid_pts = get_points_on_a_grid(grid_size, (frame_H, frame_W), device="cpu") # Sample mask values at grid points and filter out points where mask=0 if os.path.exists(mask_dir): grid_pts_int = grid_pts[0].long() mask_values = mask[grid_pts_int[...,1], grid_pts_int[...,0]] grid_pts = grid_pts[:, mask_values] query_xyt = torch.cat([torch.zeros_like(grid_pts[:, :, :1]), grid_pts], dim=2)[0].numpy() # Run model inference with torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16): ( c2w_traj, intrs, point_map, conf_depth, track3d_pred, track2d_pred, vis_pred, conf_pred, video ) = model.forward(video_tensor, depth=depth_tensor, intrs=intrs, extrs=extrs, queries=query_xyt, fps=1, full_point=False, iters_track=4, query_no_BA=True, fixed_cam=False, stage=1, unc_metric=unc_metric, support_frame=len(video_tensor)-1, replace_ratio=0.2) # resize the results to avoid too large I/O Burden # depth and image, the maximum side is 336 max_size = 336 h, w = video.shape[2:] scale = min(max_size / h, max_size / w) if scale < 1: new_h, new_w = int(h * scale), int(w * scale) video = T.Resize((new_h, new_w))(video) video_tensor = T.Resize((new_h, new_w))(video_tensor) point_map = T.Resize((new_h, new_w))(point_map) conf_depth = T.Resize((new_h, new_w))(conf_depth) track2d_pred[...,:2] = track2d_pred[...,:2] * scale intrs[:,:2,:] = intrs[:,:2,:] * scale if depth_tensor is not None: if isinstance(depth_tensor, torch.Tensor): depth_tensor = T.Resize((new_h, new_w))(depth_tensor) else: depth_tensor = T.Resize((new_h, new_w))(torch.from_numpy(depth_tensor)) if viz: viser.visualize(video=video[None], tracks=track2d_pred[None][...,:2], visibility=vis_pred[None],filename="test") # save as the tapip3d format data_npz_load["coords"] = (torch.einsum("tij,tnj->tni", c2w_traj[:,:3,:3], track3d_pred[:,:,:3].cpu()) + c2w_traj[:,:3,3][:,None,:]).numpy() data_npz_load["extrinsics"] = torch.inverse(c2w_traj).cpu().numpy() data_npz_load["intrinsics"] = intrs.cpu().numpy() depth_save = point_map[:,2,...] depth_save[conf_depth<0.5] = 0 data_npz_load["depths"] = depth_save.cpu().numpy() data_npz_load["video"] = (video_tensor).cpu().numpy()/255 data_npz_load["visibs"] = vis_pred.cpu().numpy() data_npz_load["unc_metric"] = conf_depth.cpu().numpy() np.savez(os.path.join(out_dir, f'result.npz'), **data_npz_load) print(f"Results saved to {out_dir}.\nTo visualize them with tapip3d, run: [bold yellow]python tapip3d_viz.py {out_dir}/result.npz[/bold yellow]")