cars_mesh.tools.point_cloud_io
==============================

.. py:module:: cars_mesh.tools.point_cloud_io

.. autoapi-nested-parse::

   Tools to manipulate point clouds



Functions
---------

.. autoapisummary::

   cars_mesh.tools.point_cloud_io.get_offset
   cars_mesh.tools.point_cloud_io.get_scale
   cars_mesh.tools.point_cloud_io.apply_scale_offset
   cars_mesh.tools.point_cloud_io.o3d2df
   cars_mesh.tools.point_cloud_io.las2df
   cars_mesh.tools.point_cloud_io.pkl2df
   cars_mesh.tools.point_cloud_io.ply2df
   cars_mesh.tools.point_cloud_io.csv2df
   cars_mesh.tools.point_cloud_io.df2las
   cars_mesh.tools.point_cloud_io.df2o3d
   cars_mesh.tools.point_cloud_io.df2csv
   cars_mesh.tools.point_cloud_io.deserialize_point_cloud
   cars_mesh.tools.point_cloud_io.serialize_point_cloud
   cars_mesh.tools.point_cloud_io.change_frame
   cars_mesh.tools.point_cloud_io.conversion_utm_to_geo
   cars_mesh.tools.point_cloud_io.convert_color_to_8bits


Module Contents
---------------

.. py:function:: get_offset(arr_max, arr_min)

   Compute offset


.. py:function:: get_scale(arr_max, arr_min, number_values)

   Compute scale


.. py:function:: apply_scale_offset(arr: numpy.ndarray, scale: float, offset: float, is_inverse: bool = False, clip_min: Union[float, None] = None, clip_max: Union[float, None] = None)

   Apply a scale and an offset to the input array

   :param arr: Array to normalize
   :type arr: np.ndarray
   :param scale: Scaling factor
   :type scale: float
   :param offset: Offset factor
   :type offset: float
   :param is_inverse: Whether to denormalize ('inverse') (x = (x' - o) / s) rather than
                      normalize (x' = s * x + o)
   :type is_inverse: bool (default=False)
   :param clip_min: Whether to limit the minimum output value
   :type clip_min: float or None (default=None)
   :param clip_max: Whether to limit the maximum output value
   :type clip_max: float or None (default=None)


.. py:function:: o3d2df(o3d_pcd: open3d.geometry.PointCloud) -> pandas.DataFrame

   Open3D Point Cloud to pandas DataFrame


.. py:function:: las2df(filepath: str) -> pandas.DataFrame

   LAS or LAZ point cloud to pandas DataFrame


.. py:function:: pkl2df(filepath: str) -> pandas.DataFrame

   PKL point cloud to pandas DataFrame


.. py:function:: ply2df(filepath: str) -> pandas.DataFrame

   PLY point cloud to pandas DataFrame


.. py:function:: csv2df(filepath: str) -> pandas.DataFrame

   CSV file to pandas DataFrame


.. py:function:: df2las(filepath: str, df_pcd: pandas.DataFrame, metadata: Union[laspy.LasHeader, None] = None, point_format: int = 8, version: str = '1.4')

   This method serializes a pandas DataFrame in .las


.. py:function:: df2o3d(df_pcd: pandas.DataFrame) -> open3d.geometry.PointCloud

   pandas.DataFrame to Open3D Point Cloud


.. py:function:: df2csv(filepath: str, df_pcd: pandas.DataFrame, **kwargs)

   pandas DataFrame to csv file


.. py:function:: deserialize_point_cloud(filepath: str) -> pandas.DataFrame

   Convert a point cloud to a pandas dataframe


.. py:function:: serialize_point_cloud(filepath: str, df_pcd: pandas.DataFrame, metadata: Union[laspy.LasHeader, None] = None, extension: str = 'las', **kwargs)

   Serialize a point cloud to disk in the format asked by the user


.. py:function:: change_frame(df_pcd, in_epsg, out_epsg) -> pandas.DataFrame

   Change frame in which the points are expressed


.. py:function:: conversion_utm_to_geo(coords: Union[list, tuple, numpy.ndarray], utm_code: int) -> numpy.ndarray

   Conversion points from epsg 32631 to epsg 4326


.. py:function:: convert_color_to_8bits(df_pcd: pandas.DataFrame, q_percent: Union[tuple, list, numpy.ndarray] = (0, 100)) -> pandas.DataFrame

   Convert the colors of the data to 8 bits. It will preserve the relative
   colors between the bands (it is a global normalisation, not a by band
   normalisation).

   :param df_pcd: Point cloud data
   :type df_pcd: pd.DataFrame
   :param q_percent: Whether to clip the colors to discard outliers. First term is the
                     minimum percentage to take into account, the second term is the
                     maximum. By default, no value is clipped.
   :type q_percent: tuple or list or np.ndarray (default=(0, 100))

   :returns: **df_pcd** -- Point cloud data with colors converted to 8bits
   :rtype: pd.DataFrame