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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()
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