""" ARCTIC Hand Data Reader Only reads hand (MANO) parameters from raw_seqs. No body model forward pass, no object data, no splits needed. Usage: python scripts_data/hand_reader.py python scripts_data/hand_reader.py --mano_p ./unpack/arctic_data/data/raw_seqs/s01/box_grab_01.mano.npy python scripts_data/hand_reader.py --mano_p ./unpack/arctic_data/data/raw_seqs/s01/box_grab_01.mano.npy --frame 10 """ import argparse import json import os.path as op import sys from glob import glob import numpy as np import torch sys.path = ["."] + sys.path DATA_ROOT = "./data" # view 0 = ego cam (head-mounted, moves every frame) # view 1-8 = 8 fixed allocentric cameras VIEW_NAMES = ["ego", "allo_1", "allo_2", "allo_3", "allo_4", "allo_5", "allo_6", "allo_7", "allo_8"] def load_hand_seq(mano_p): """ Load one sequence. Mirrors construct_loader() in src/arctic/preprocess_dataset.py but keeps only hand fields. Returns a dict with: right / left -> MANO params (tensors) ego_cam -> per-frame ego camera (world2ego, K, dist8) static_cams -> 8 fixed allo cameras from misc.json (world2cam, K) meta -> sid, seq_name, num_frames, gender """ # -- MANO (same loading as preprocess_dataset.py construct_loader) -- data = np.load(mano_p, allow_pickle=True).item() num_frames = len(data["right"]["rot"]) def _load_hand(side): return { "rot": torch.FloatTensor(data[side]["rot"]), # (F, 3) "pose": torch.FloatTensor(data[side]["pose"]), # (F, 45) "trans": torch.FloatTensor(data[side]["trans"]), # (F, 3) "shape": torch.FloatTensor(data[side]["shape"]).repeat(num_frames, 1), # (F, 10) "fitting_err": data[side]["fitting_err"], # (F,) } right = _load_hand("right") left = _load_hand("left") # sanity check from preprocess_dataset.py assert len(right["fitting_err"]) > 50, f"Too few frames: {mano_p}" # -- Ego camera (same as preprocess_dataset.py construct_loader) -- ego_p = mano_p.replace("mano.npy", "egocam.dist.npy") egocam = np.load(ego_p, allow_pickle=True).item() R_ego = torch.FloatTensor(egocam["R_k_cam_np"]) # (F, 3, 3) T_ego = torch.FloatTensor(egocam["T_k_cam_np"]) # (F, 3, 1) K_ego = torch.FloatTensor(egocam["intrinsics"]) # (3, 3) dist8 = torch.FloatTensor(egocam["dist8"]) # (8,) # build homogeneous transform, same as preprocess_dataset.py world2ego = torch.zeros((num_frames, 4, 4)) world2ego[:, :3, :3] = R_ego world2ego[:, :3, 3] = T_ego[:, :, 0] world2ego[:, 3, 3] = 1.0 # -- Static allo cameras (same as process_seqs.py statcams) -- sid = mano_p.split("/")[-2] misc_p = op.join(DATA_ROOT, "misc.json") with open(misc_p) as f: misc = json.load(f) sub = misc[sid] world2cam = torch.FloatTensor(np.array(sub["world2cam"])) # (8, 4, 4) allo_K = torch.FloatTensor(np.array(sub["intris_mat"])) # (8, 3, 3) image_size = np.array(sub["image_size"]) # (9, 2) [w, h] seq_name = mano_p.split("/")[-1].replace(".mano.npy", "") return { "right": right, "left": left, "ego_cam": { "world2ego": world2ego, # (F, 4, 4) "K": K_ego, # (3, 3) "dist8": dist8, # (8,) }, "static_cams": { "world2cam": world2cam, # (8, 4, 4) world -> each allo cam "K": allo_K, # (8, 3, 3) "image_size": image_size, # (9, 2) }, "meta": { "sid": sid, "seq_name": seq_name, "num_frames": num_frames, "gender": sub["gender"], }, } def get_frame(seq_data, idx): """Return data for a single frame (0-indexed).""" n = seq_data["meta"]["num_frames"] assert 0 <= idx < n, f"Frame {idx} out of range (0-{n-1})" def _frame_hand(h): return {k: (v[idx] if isinstance(v, torch.Tensor) else v[idx]) for k, v in h.items()} frame = { "right": _frame_hand(seq_data["right"]), "left": _frame_hand(seq_data["left"]), "world2ego": seq_data["ego_cam"]["world2ego"][idx], # (4, 4) "K_ego": seq_data["ego_cam"]["K"], # (3, 3) "dist8": seq_data["ego_cam"]["dist8"], # (8,) "static_cams": seq_data["static_cams"], } # attach image paths if cropped_images are available img_dir = op.join(DATA_ROOT, "cropped_images", seq_data["meta"]["sid"], seq_data["meta"]["seq_name"]) if op.exists(img_dir): fname = f"{idx + 1:05d}.jpg" frame["images"] = { name: p for name, view_id in zip(VIEW_NAMES, range(9)) if op.exists(p := op.join(img_dir, str(view_id), fname)) } return frame # -------------------------------------------------------------------------- # CLI helpers # -------------------------------------------------------------------------- def print_summary(seq_data): m = seq_data["meta"] print(f"\n=== Sequence: {m['sid']}/{m['seq_name']} ===") print(f"Frames : {m['num_frames']}") print(f"Subject: {m['sid']} gender={m['gender']}") print(f"\nRight hand shape (10,): {seq_data['right']['shape'][0].numpy()}") print(f"Left hand shape (10,): {seq_data['left']['shape'][0].numpy()}") print(f"\nEgo cam K (3x3):\n{seq_data['ego_cam']['K'].numpy()}") print(f"Ego cam dist8: {seq_data['ego_cam']['dist8'].numpy()}") print(f"\nAllo cams: {seq_data['static_cams']['world2cam'].shape[0]}") print(f"Image sizes (w x h):\n{seq_data['static_cams']['image_size']}") def print_frame(frame, idx): print(f"\n--- Frame {idx} ---") for side in ["right", "left"]: h = frame[side] print(f"\n[{side} hand]") print(f" rot (3,) : {h['rot'].numpy()}") print(f" trans (3,) : {h['trans'].numpy()}") print(f" pose (45,) first 6: {h['pose'].numpy()[:6]}") print(f" shape (10,) : {h['shape'].numpy()}") print(f" fitting_err : {h['fitting_err']:.4f}") print(f"\n[Ego world2ego row 0-1]:\n{frame['world2ego'].numpy()[:2]}") if "images" in frame: print(f"\n[Available images]") for view, path in frame["images"].items(): print(f" {view:8s}: {path}") def construct_args(): parser = argparse.ArgumentParser() parser.add_argument("--mano_p", type=str, default=None) parser.add_argument("--frame", type=int, default=0) return parser.parse_args() def main(): args = construct_args() if args.mano_p is not None: mano_p = args.mano_p else: candidates = glob(op.join(DATA_ROOT, "raw_seqs", "*", "*.mano.npy")) assert candidates, "No .mano.npy files found. Unzip raw_seqs.zip first." mano_p = sorted(candidates)[0] print(f"Loading: {mano_p}") seq_data = load_hand_seq(mano_p) print_summary(seq_data) frame = get_frame(seq_data, args.frame) print_frame(frame, args.frame) if __name__ == "__main__": main()