def create_fisheye_spec(**kw_args):
fisheye_sensor_spec = habitat_sim.FisheyeSensorDoubleSphereSpec()
fisheye_sensor_spec.uuid = "fisheye_sensor"
fisheye_sensor_spec.sensor_type = habitat_sim.SensorType.COLOR
fisheye_sensor_spec.sensor_model_type = (
habitat_sim.FisheyeSensorModelType.DOUBLE_SPHERE)
# The default value (alpha, xi) is set to match the lens "BF2M2020S23" found in Table 3 of this paper:
# Vladyslav Usenko, Nikolaus Demmel and Daniel Cremers: The Double Sphere
# Camera Model, The International Conference on 3D Vision (3DV), 2018
# You can find the intrinsic parameters for the other lenses in the same table as well.
# fisheye_sensor_spec.alpha = 0.59 # default
# fisheye_sensor_spec.xi = -0.18 # default
fisheye_sensor_spec.resolution = [
settings["height"], settings["width"]
]
fisheye_sensor_spec.focal_length = [
min(settings["height"], settings["width"]) * 0.5
] * 2
# The default principal_point_offset is the middle of the image
fisheye_sensor_spec.principal_point_offset = mn.Vector2(
settings["height"] // 2,
settings["width"] // 2,
)
fisheye_sensor_spec.position = [0, settings["sensor_height"], 0]
for k in kw_args:
setattr(fisheye_sensor_spec, k, kw_args[k])
return fisheye_sensor_spec
[[255, 255, 255], [128, 128, 128], [0, 0, 0]], dtype=np.uint8
)
top_down_map = recolor_map[top_down_map]
grid_dimensions = (top_down_map.shape[0], top_down_map.shape[1])
# convert world trajectory points to maps module grid points
trajectory = [
maps.to_grid(
path_point[2],
path_point[0],
grid_dimensions,
pathfinder=sim.pathfinder,
)
for path_point in path_points
]
grid_tangent = mn.Vector2(
trajectory[1][1] - trajectory[0][1], trajectory[1][0] - trajectory[0][0]
)
path_initial_tangent = grid_tangent / grid_tangent.length()
initial_angle = math.atan2(path_initial_tangent[0], path_initial_tangent[1])
# draw the agent and trajectory on the map
maps.draw_path(top_down_map, trajectory)
maps.draw_agent(
top_down_map, trajectory[0], initial_angle, agent_radius_px=8
)
print("\nDisplay the map with agent and path overlay:")
display_map(top_down_map)
# @markdown 4. (optional) Place agent and render images at trajectory points (if found).
display_path_agent_renders = True # @param{type:"boolean"}
if display_path_agent_renders:
print("Rendering observations at path points:")
