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import os
import numpy as np
import cv2
import h5py
import argparse
import matplotlib.pyplot as plt
DT = 0.02
# JOINT_NAMES = ["waist", "shoulder", "elbow", "forearm_roll", "wrist_angle", "wrist_rotate"]
JOINT_NAMES = ["joint0", "joint1", "joint2", "joint3", "joint4", "joint5"]
STATE_NAMES = JOINT_NAMES + ["gripper"]
BASE_STATE_NAMES = ["linear_vel", "angular_vel"]
def load_hdf5(dataset_dir, dataset_name):
dataset_path = os.path.join(dataset_dir, dataset_name + '.hdf5')
if not os.path.isfile(dataset_path):
print(f'Dataset does not exist at \n{dataset_path}\n')
exit()
with h5py.File(dataset_path, 'r') as root:
is_sim = root.attrs['sim']
compressed = root.attrs.get('compress', False)
qpos = root['/observations/qpos'][()]
qvel = root['/observations/qvel'][()]
if 'effort' in root.keys():
effort = root['/observations/effort'][()]
else:
effort = None
action = root['/action'][()]
base_action = root['/base_action'][()]
image_dict = dict()
for cam_name in root[f'/observations/images/'].keys():
image_dict[cam_name] = root[f'/observations/images/{cam_name}'][()]
if compressed:
compress_len = root['/compress_len'][()]
if compressed:
for cam_id, cam_name in enumerate(image_dict.keys()):
# un-pad and uncompress
padded_compressed_image_list = image_dict[cam_name]
image_list = []
for frame_id, padded_compressed_image in enumerate(padded_compressed_image_list): # [:1000] to save memory
image_len = int(compress_len[cam_id, frame_id])
compressed_image = padded_compressed_image
image = cv2.imdecode(compressed_image, 1)
image_list.append(image)
image_dict[cam_name] = image_list
return qpos, qvel, effort, action, base_action, image_dict
def main(args):
dataset_dir = args['dataset_dir']
episode_idx = args['episode_idx']
task_name = args['task_name']
dataset_name = f'episode_{episode_idx}'
qpos, qvel, effort, action, base_action, image_dict = load_hdf5(os.path.join(dataset_dir, task_name), dataset_name)
print('hdf5 loaded!!')
save_videos(image_dict, action, DT, video_path=os.path.join(dataset_dir, dataset_name + '_video.mp4'))
visualize_joints(qpos, action, plot_path=os.path.join(dataset_dir, dataset_name + '_qpos.png'))
visualize_base(base_action, plot_path=os.path.join(dataset_dir, dataset_name + '_base_action.png'))
def save_videos(video, actions, dt, video_path=None):
cam_names = list(video.keys())
all_cam_videos = []
for cam_name in cam_names:
all_cam_videos.append(video[cam_name])
all_cam_videos = np.concatenate(all_cam_videos, axis=2) # width dimension
n_frames, h, w, _ = all_cam_videos.shape
fps = int(1 / dt)
out = cv2.VideoWriter(video_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
for t in range(n_frames):
image = all_cam_videos[t]
image = image[:, :, [2, 1, 0]] # swap B and R channel
cv2.imshow("images",image)
cv2.waitKey(30)
print("episode_id: ", t, "left: ", np.round(actions[t][:7], 3), "right: ", np.round(actions[t][7:], 3), "\n")
out.write(image)
out.release()
print(f'Saved video to: {video_path}')
def visualize_joints(qpos_list, command_list, plot_path=None, ylim=None, label_overwrite=None):
if label_overwrite:
label1, label2 = label_overwrite
else:
label1, label2 = 'State', 'Command'
qpos = np.array(qpos_list) # ts, dim
command = np.array(command_list)
num_ts, num_dim = qpos.shape
h, w = 2, num_dim
num_figs = num_dim
fig, axs = plt.subplots(num_figs, 1, figsize=(8, 2 * num_dim))
# plot joint state
all_names = [name + '_left' for name in STATE_NAMES] + [name + '_right' for name in STATE_NAMES]
for dim_idx in range(num_dim):
ax = axs[dim_idx]
ax.plot(qpos[:, dim_idx], label=label1, color='orangered')
ax.set_title(f'Joint {dim_idx}: {all_names[dim_idx]}')
ax.legend()
# plot arm command
# for dim_idx in range(num_dim):
# ax = axs[dim_idx]
# ax.plot(command[:, dim_idx], label=label2)
# ax.legend()
if ylim:
for dim_idx in range(num_dim):
ax = axs[dim_idx]
ax.set_ylim(ylim)
plt.tight_layout()
plt.savefig(plot_path)
print(f'Saved qpos plot to: {plot_path}')
plt.close()
def visualize_base(readings, plot_path=None):
readings = np.array(readings) # ts, dim
num_ts, num_dim = readings.shape
num_figs = num_dim
fig, axs = plt.subplots(num_figs, 1, figsize=(8, 2 * num_dim))
# plot joint state
all_names = BASE_STATE_NAMES
for dim_idx in range(num_dim):
ax = axs[dim_idx]
ax.plot(readings[:, dim_idx], label='raw')
ax.plot(np.convolve(readings[:, dim_idx], np.ones(20)/20, mode='same'), label='smoothed_20')
ax.plot(np.convolve(readings[:, dim_idx], np.ones(10)/10, mode='same'), label='smoothed_10')
ax.plot(np.convolve(readings[:, dim_idx], np.ones(5)/5, mode='same'), label='smoothed_5')
ax.set_title(f'Joint {dim_idx}: {all_names[dim_idx]}')
ax.legend()
plt.tight_layout()
plt.savefig(plot_path)
print(f'Saved effort plot to: {plot_path}')
plt.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--dataset_dir', action='store', type=str, help='Dataset dir.', required=True)
parser.add_argument('--task_name', action='store', type=str, help='Task name.',
default="aloha_mobile_dummy", required=False)
parser.add_argument('--episode_idx', action='store', type=int, help='Episode index.',default=0, required=False)
main(vars(parser.parse_args()))
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