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 import tqdm from models.moge.train.losses import ( affine_invariant_global_loss, ) 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="dyn_check") parser.add_argument("--video_name", type=str, default="snowboard") parser.add_argument("--grid_size", type=int, default=2) parser.add_argument("--vo_points", type=int, default=1024) parser.add_argument("--fps", type=int, default=1) return parser.parse_args() if __name__ == "__main__": args = parse_args() out_dir = args.data_dir + "/saved" # fps fps = int(args.fps) mask_dir = args.data_dir + f"/{args.video_name}.png" # dyn check root dir dyn_check_root = "/mnt/bn/xyxdata/data/4d_data/dyn_check" dyn_check_list = os.listdir(dyn_check_root) dyn_check_list = [os.path.join(dyn_check_root, i) for i in dyn_check_list if os.path.isdir(os.path.join(dyn_check_root, i))] # get the video name vggt4track_model = VGGT4Track.from_pretrained("Yuxihenry/SpatialTrackerV2_Front") vggt4track_model.eval() vggt4track_model = vggt4track_model.to("cuda") if args.track_mode == "offline": model = Predictor.from_pretrained("Yuxihenry/SpatialTrackerV2-Offline") else: model = Predictor.from_pretrained("Yuxihenry/SpatialTrackerV2-Online") # set the fps MAX_LEN = 2000 wind_size = 60 overlap = 16 non_overlap = wind_size - overlap model.S_wind = 500 for dyn_check_dir in tqdm.tqdm(dyn_check_list): img_files = sorted(glob.glob(os.path.join(dyn_check_dir, "dense", "images", "*.png"))) # fps = max(1, len(img_files) // MAX_LEN) fps = 2 video_tensor = torch.stack([torch.from_numpy(cv2.imread(i)).permute(2, 0, 1) for i in img_files])[::fps].float() raw_len = len(video_tensor) wind_num = max((raw_len - wind_size) // non_overlap + 1, 1) if (wind_num-1)*non_overlap + wind_size < raw_len: wind_num += 1 # record the intermediate results video_tensor = preprocess_image(video_tensor) T_vid, _, H, W = video_tensor.shape points_map_list = np.zeros((T_vid, H, W, 3)) extrs_list = np.zeros((T_vid, 4, 4)) intrs_list = np.zeros((T_vid, 3, 3)) unc_metric_list = np.zeros((T_vid, H, W)) for i in tqdm.tqdm(range(wind_num)): start_idx = i * non_overlap end_idx = start_idx + wind_size video_tensor_i = video_tensor[start_idx:end_idx] # run the model video_tensor_i = video_tensor_i.float().clone() # process the image tensor video_tensor_i = video_tensor_i[None] with torch.no_grad(): with torch.cuda.amp.autocast(dtype=torch.bfloat16): # Predict attributes including cameras, depth maps, and point maps. if i > 0: overlap_len_i = min(overlap, depth_tensor.shape[0]) prev_intrs = intrs_list[start_idx:start_idx+overlap_len_i] fx_prev, fy_prev = prev_intrs[:,0,0], prev_intrs[:,1,1] else: fx_prev, fy_prev = None, None predictions = vggt4track_model(video_tensor_i.cuda()/255, fx_prev=fx_prev, fy_prev=fy_prev) 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 # update the list if i == 0: points_map_list[start_idx:end_idx] = predictions["points_map"].squeeze().cpu().numpy() extrs_list[start_idx:end_idx] = extrs intrs_list[start_idx:end_idx] = intrs unc_metric_list[start_idx:end_idx] = unc_metric else: # merge the list next_clip_points = predictions["points_map"][:overlap_len_i].cuda().float().clone() prev_clip_points = torch.from_numpy(points_map_list[start_idx:start_idx+overlap_len_i]).cuda().float() mask_i = torch.from_numpy(unc_metric_list[start_idx:start_idx+overlap_len_i]).cuda().bool() loss_i, _, scale_x = affine_invariant_global_loss(next_clip_points, prev_clip_points, mask_i, align_resolution=32) # update the list scale_mean = scale_x.mean() current_points = predictions["points_map"].clone() current_points = current_points * scale_mean #NOTE: chain the results points_map_list[start_idx:end_idx] = current_points.squeeze().cpu().numpy() intrs_list[start_idx:end_idx] = predictions["intrs"].squeeze().cpu().numpy() unc_metric_list[start_idx:end_idx] = unc_metric prev_extr = extrs_list[start_idx:start_idx+overlap_len_i][:1] extrs_list[start_idx:end_idx] = prev_extr@extrs # get the final results intrs = intrs_list.astype(np.float32) extrs = extrs_list.astype(np.float32) unc_metric = np.bool_(unc_metric_list) depth_tensor = points_map_list[..., 2] 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) # 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=6, 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() data_npz_load["fps"] = fps np.savez(os.path.join(out_dir, f'{dyn_check_dir.split("/")[-1]}.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]")