Python quickstart for loading egocentric training data

pip install firsthand-ego

# 1. list what you have access to
fh ls
# EP_0117_KITCHEN_A   78.4 s   kitchen/knife_dice        shipped
# EP_0118_KITCHEN_A   41.2 s   kitchen/pour_decant       shipped

# 2. pull one episode in the format your loader wants
fh get EP_0117_KITCHEN_A --format rlds --out ./data

# 3. or stream a whole skill line without staging it locally
fh stream --skill kitchen/knife_dice --format webdataset | your_trainer

from firsthand_ego import Episode
import torch

ep = Episode.open("data/EP_0117_KITCHEN_A")

# frames and hand pose arrive already aligned on the head-camera clock
for step in ep.steps(rate_hz=30):
    rgb    = torch.from_numpy(step.head_rgb)          # [2160, 3840, 3]
    depth  = torch.from_numpy(step.depth_mm)          # [480, 848] uint16
    hands  = torch.from_numpy(step.hand_joints)       # [2, 21, 3] metres
    valid  = torch.from_numpy(step.hand_visible)      # [2, 21] bool
    action = step.action                               # ("dice", "onion")
    assert step.sync_error_ms < 2.0