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refactor(cameras): cameras API extension (#2808)
* feat(cameras): add new read_latest() method * fix(cameras): fix threading bug + clear state * refactor(cameras): multiple improvements * feat(camera): add context manager to camera base class * chore(camera): slight modifications to opencv * test(cameras): update opencv tests according to the changes * refactor(cameras): reflect desing changes to realsense + deal with depth * test(cameras): fix realsense tests accordingly to new changes * refactor(cameras): update reachymini and zmq accordingly * chore: wrap resource sensitive examples into a try/finally * test(cameras): add test for new read_latest * test(cameras): fix problem with image artifact in opencv tests * test(cameras): fix test_read_latest_high_frequency expectations * Apply suggestions from code review 1 Co-authored-by: Caroline Pascal <caroline8.pascal@gmail.com> Signed-off-by: Steven Palma <imstevenpmwork@ieee.org> * chore(cameras): address feedback * feat(cameras): add max_age_ms check in read_latest * test(cameras): fix read_latest tests * chore(redundancies): removing redundancies in Reachy 2 camera class * fix(warmup): replacing the arbitrary time.sleep in by an actual warmup in the RealSense camera class * chore(format): formatting latest changes * chore(warning): adding a "to be implemented" warning for read_latest() in Camera base class * chore(warning): making read_latest() warning message shorter and clearer --------- Signed-off-by: Steven Palma <imstevenpmwork@ieee.org> Co-authored-by: Caroline Pascal <caroline8.pascal@gmail.com>
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@@ -142,38 +142,24 @@ def main():
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listener, events = init_keyboard_listener()
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init_rerun(session_name="phone_so100_evaluate")
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if not robot.is_connected:
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raise ValueError("Robot is not connected!")
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try:
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if not robot.is_connected:
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raise ValueError("Robot is not connected!")
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print("Starting evaluate loop...")
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episode_idx = 0
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for episode_idx in range(NUM_EPISODES):
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log_say(f"Running inference, recording eval episode {episode_idx + 1} of {NUM_EPISODES}")
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print("Starting evaluate loop...")
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episode_idx = 0
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for episode_idx in range(NUM_EPISODES):
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log_say(f"Running inference, recording eval episode {episode_idx + 1} of {NUM_EPISODES}")
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# Main record loop
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record_loop(
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robot=robot,
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events=events,
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fps=FPS,
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policy=policy,
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preprocessor=preprocessor, # Pass the pre and post policy processors
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postprocessor=postprocessor,
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dataset=dataset,
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control_time_s=EPISODE_TIME_SEC,
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single_task=TASK_DESCRIPTION,
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display_data=True,
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teleop_action_processor=make_default_teleop_action_processor(),
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robot_action_processor=robot_ee_to_joints_processor,
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robot_observation_processor=robot_joints_to_ee_pose_processor,
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)
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# Reset the environment if not stopping or re-recording
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if not events["stop_recording"] and ((episode_idx < NUM_EPISODES - 1) or events["rerecord_episode"]):
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log_say("Reset the environment")
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# Main record loop
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record_loop(
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robot=robot,
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events=events,
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fps=FPS,
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policy=policy,
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preprocessor=preprocessor, # Pass the pre and post policy processors
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postprocessor=postprocessor,
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dataset=dataset,
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control_time_s=EPISODE_TIME_SEC,
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single_task=TASK_DESCRIPTION,
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display_data=True,
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@@ -182,24 +168,41 @@ def main():
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robot_observation_processor=robot_joints_to_ee_pose_processor,
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)
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if events["rerecord_episode"]:
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log_say("Re-record episode")
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events["rerecord_episode"] = False
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events["exit_early"] = False
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dataset.clear_episode_buffer()
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continue
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# Reset the environment if not stopping or re-recording
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if not events["stop_recording"] and (
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(episode_idx < NUM_EPISODES - 1) or events["rerecord_episode"]
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):
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log_say("Reset the environment")
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record_loop(
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robot=robot,
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events=events,
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fps=FPS,
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control_time_s=EPISODE_TIME_SEC,
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single_task=TASK_DESCRIPTION,
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display_data=True,
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teleop_action_processor=make_default_teleop_action_processor(),
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robot_action_processor=robot_ee_to_joints_processor,
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robot_observation_processor=robot_joints_to_ee_pose_processor,
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)
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# Save episode
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dataset.save_episode()
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episode_idx += 1
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if events["rerecord_episode"]:
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log_say("Re-record episode")
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events["rerecord_episode"] = False
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events["exit_early"] = False
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dataset.clear_episode_buffer()
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continue
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# Clean up
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log_say("Stop recording")
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robot.disconnect()
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listener.stop()
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# Save episode
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dataset.save_episode()
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episode_idx += 1
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finally:
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# Clean up
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log_say("Stop recording")
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robot.disconnect()
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listener.stop()
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dataset.finalize()
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dataset.push_to_hub()
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dataset.finalize()
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dataset.push_to_hub()
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if __name__ == "__main__":
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