habitat_extensions/sensors.py [313:367]:
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    r"""Estimates the top-down occupancy based on current depth-map.

    Args:
        sim: reference to the simulator for calculating task observations.
        config: contains the MAP_SCALE, MAP_SIZE, HEIGHT_THRESH fields to
                decide grid-size, extents of the projection, and the thresholds
                for determining obstacles and explored space.
    """

    def __init__(self, sim: Simulator, config: Config, *args: Any, **kwargs: Any):
        self._sim = sim

        super().__init__(config=config)

        # Map statistics
        self.map_size = self.config.MAP_SIZE
        self.map_scale = self.config.MAP_SCALE
        if self.config.MAX_SENSOR_RANGE > 0:
            self.max_forward_range = self.config.MAX_SENSOR_RANGE
        else:
            self.max_forward_range = self.map_size * self.map_scale

        # Agent height for pointcloud tranforms
        self.camera_height = self._sim.habitat_config.DEPTH_SENSOR.POSITION[1]

        # Compute intrinsic matrix
        depth_H = self._sim.habitat_config.DEPTH_SENSOR.HEIGHT
        depth_W = self._sim.habitat_config.DEPTH_SENSOR.WIDTH
        hfov = float(self._sim.habitat_config.DEPTH_SENSOR.HFOV) * np.pi / 180
        vfov = 2 * np.arctan((depth_H / depth_W) * np.tan(hfov / 2.0))
        self.intrinsic_matrix = np.array(
            [
                [1 / np.tan(hfov / 2.0), 0.0, 0.0, 0.0],
                [0.0, 1 / np.tan(vfov / 2.0), 0.0, 0.0],
                [0.0, 0.0, 1, 0],
                [0.0, 0.0, 0, 1],
            ]
        )
        self.inverse_intrinsic_matrix = np.linalg.inv(self.intrinsic_matrix)

        # Height thresholds for obstacles
        self.height_thresh = self.config.HEIGHT_THRESH

        # Depth processing
        self.min_depth = float(self._sim.habitat_config.DEPTH_SENSOR.MIN_DEPTH)
        self.max_depth = float(self._sim.habitat_config.DEPTH_SENSOR.MAX_DEPTH)

        # Pre-compute a grid of locations for depth projection
        W = self._sim.habitat_config.DEPTH_SENSOR.WIDTH
        H = self._sim.habitat_config.DEPTH_SENSOR.HEIGHT
        self.proj_xs, self.proj_ys = np.meshgrid(
            np.linspace(-1, 1, W), np.linspace(1, -1, H)
        )

    def _get_uuid(self, *args: Any, **kwargs: Any):
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habitat_extensions/sensors.py [496:550]:
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    r"""Estimates the top-down wall occupancy based on current depth-map.

    Args:
        sim: reference to the simulator for calculating task observations.
        config: contains the MAP_SCALE, MAP_SIZE, HEIGHT_THRESH fields to
                decide grid-size, extents of the projection, and the thresholds
                for determining obstacles and explored space.
    """

    def __init__(self, sim: Simulator, config: Config, *args: Any, **kwargs: Any):
        self._sim = sim

        super().__init__(config=config)

        # Map statistics
        self.map_size = self.config.MAP_SIZE
        self.map_scale = self.config.MAP_SCALE
        if self.config.MAX_SENSOR_RANGE > 0:
            self.max_forward_range = self.config.MAX_SENSOR_RANGE
        else:
            self.max_forward_range = self.map_size * self.map_scale

        # Agent height for pointcloud tranforms
        self.camera_height = self._sim.habitat_config.DEPTH_SENSOR.POSITION[1]

        # Compute intrinsic matrix
        depth_H = self._sim.habitat_config.DEPTH_SENSOR.HEIGHT
        depth_W = self._sim.habitat_config.DEPTH_SENSOR.WIDTH
        hfov = float(self._sim.habitat_config.DEPTH_SENSOR.HFOV) * np.pi / 180
        vfov = 2 * np.arctan((depth_H / depth_W) * np.tan(hfov / 2.0))
        self.intrinsic_matrix = np.array(
            [
                [1 / np.tan(hfov / 2.0), 0.0, 0.0, 0.0],
                [0.0, 1 / np.tan(vfov / 2.0), 0.0, 0.0],
                [0.0, 0.0, 1, 0],
                [0.0, 0.0, 0, 1],
            ]
        )
        self.inverse_intrinsic_matrix = np.linalg.inv(self.intrinsic_matrix)

        # Height thresholds for obstacles
        self.height_thresh = self.config.HEIGHT_THRESH

        # Depth processing
        self.min_depth = float(self._sim.habitat_config.DEPTH_SENSOR.MIN_DEPTH)
        self.max_depth = float(self._sim.habitat_config.DEPTH_SENSOR.MAX_DEPTH)

        # Pre-compute a grid of locations for depth projection
        W = self._sim.habitat_config.DEPTH_SENSOR.WIDTH
        H = self._sim.habitat_config.DEPTH_SENSOR.HEIGHT
        self.proj_xs, self.proj_ys = np.meshgrid(
            np.linspace(-1, 1, W), np.linspace(1, -1, H)
        )

    def _get_uuid(self, *args: Any, **kwargs: Any):
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