def _construct_dataset(num_episodes):
episodes = []
for ii in range(num_episodes):
episode = Episode(
episode_id=str(ii),
scene_id="scene_id_" + str(ii % 10),
start_position=[0, 0, 0],
start_rotation=[0, 0, 0, 1],
)
episodes.append(episode)
dataset = Dataset()
dataset.episodes = episodes
return dataset
scene_id = house + os.sep + house + ".glb"
elif DATASET == "gibson":
scene_id = house + ".glb"
else:
raise NotImplementedError("Not implemented for this dataset")
dummy_episode = NavigationEpisode(
episode_id=str(ii),
goals=[NavigationGoal([0, 0, 0])],
scene_id=scene_id,
start_position=[0, 0, 0],
start_rotation=[0, 0, 0, 1],
)
episodes.append(dummy_episode)
dataset = Dataset()
dataset.episodes = episodes
json = dataset.to_json().encode("utf-8")
with gzip.GzipFile(
os.path.join(OUTPUT_PATH, "dataset_one_ep_per_scene.json.gz"),
"w") as fout:
fout.write(json)
env = habitat.Env(config=config, dataset=dataset)
episodes = []
episode_id = 0
for ii in tqdm.tqdm(range(len(scenes))):
try:
env.reset()
for jj in range(NUM_EPS_PER_LOCATION):
episode = generator.generate_pointnav_episode(env, episode_id)
def transform_rgb_bgr(image):
return image[:, :, [2, 1, 0]]
apartment = "office_3"
ep = NavigationEpisode(
episode_id="0",
scene_id=f"/raid/workspace/alexandrug/Replica-Dataset/dataset"
f"/{apartment}/habitat/mesh_semantic.ply",
start_position=[-0.6644544, -1.020689, -0.4631982],
start_rotation=[0, 0.163276, 0, 0.98658],
goals=[NavigationGoal(position=[-2.452475, -1.02069, 1.156327])],
info={"geodesic_distance": 0.001},
)
dataset = Dataset()
dataset.episodes = [ep]
def draw_top_down_map(info, heading, output_size):
top_down_map = maps.colorize_topdown_map(
info["top_down_map"]["map"], info["top_down_map"]["fog_of_war_mask"])
original_map_size = top_down_map.shape[:2]
map_scale = np.array(
(1, original_map_size[1] * 1.0 / original_map_size[0]))
new_map_size = np.round(output_size * map_scale).astype(np.int32)
# OpenCV expects w, h but map size is in h, w
top_down_map = cv2.resize(top_down_map, (new_map_size[1], new_map_size[0]))
map_agent_pos = info["top_down_map"]["agent_map_coord"]
map_agent_pos = np.round(map_agent_pos * new_map_size /
original_map_size).astype(np.int32)