cars.applications.point_cloud_fusion.point_cloud_tools
This module is responsible for the transition between triangulation and rasterization steps
Module Contents
Functions
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Combine a list of clouds (and their colors) into a pandas dataframe |
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Add color information for a current cloud_list item |
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Get color type of the tiles and if the same type. |
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Get max number of bands of clouds |
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Filter all points of the cloud DataFrame |
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Add a uint16 mask labeled 'mask_label' to the clouds in clouds_list. |
- cars.applications.point_cloud_fusion.point_cloud_tools.create_combined_cloud(cloud_list: List[xarray.Dataset], dsm_epsg: int, xmin: float = None, xmax: float = None, ymin: int = None, ymax: int = None, epipolar_border_margin: int = 0, margin: float = 0, with_coords: bool = False) Tuple[pandas.DataFrame, int]
Combine a list of clouds (and their colors) into a pandas dataframe structured with the following labels:
- if no colors in input and no mask data present in cloud_list datasets:
labels=[cst.POINTS_CLOUD_VALID_DATA, cst.X, cst.Y, cst.Z] The combined cloud has x, y, z columns along with ‘valid data’ one. The valid data is a mask set to True if the data are not on the epipolar image margin (epipolar_border_margin), otherwise it is set to False.
- if no colors in input and mask data present in cloud_list datasets:
labels=[cst.POINTS_CLOUD_VALID_DATA, cst.X, cst.Y, cst.Z, cst.POINTS_CLOUD_MSK] The mask values are added to the dataframe.
- if colors are set in input and mask data are present in the cloud_list datasets:
labels=[cst.POINTS_CLOUD_VALID_DATA, cst.X, cst.Y, cst.Z, cst.POINTS_CLOUD_MSK, cst.POINTS_CLOUD_CLR_KEY_ROOT+”0”, cst.POINTS_CLOUD_CLR_KEY_ROOT+”1”, cst.POINTS_CLOUD_CLR_KEY_ROOT+”2”] Color channels information are added to the dataframe.
- if colors in input, mask data present in the cloud_list datasets and the with_coords option is activated:
labels=[cst.POINTS_CLOUD_VALID_DATA, cst.X, cst.Y, cst.Z, cst.POINTS_CLOUD_MSK, cst.POINTS_CLOUD_CLR_KEY_ROOT+”0”, cst.POINTS_CLOUD_CLR_KEY_ROOT+”1”, cst.POINTS_CLOUD_CLR_KEY_ROOT+”2” cst.POINTS_CLOUD_COORD_EPI_GEOM_I, cst.POINTS_CLOUD_COORD_EPI_GEOM_J, cst.POINTS_CLOUD_IDX_IM_EPI] The pixel position of the xyz point in the original epipolar image (coord_epi_geom_i, coord_epi_geom_j) are added to the dataframe along with the index of its original cloud in the cloud_list input.
- Parameters
dsm_epsg – epsg code for the CRS of the final output raster
xmin – xmin of the rasterization grid (if None, the whole clouds are combined)
xmax – xmax of the rasterization grid (if None, the whole clouds are combined)
ymin – ymin of the rasterization grid (if None, the whole clouds are combined)
ymax – ymax of the rasterization grid (if None, the whole clouds are combined)
margin – Margin added for each tile, in meter or degree. (default value: 0)
epipolar_border_margin – Margin used to invalidate cells too close to epipolar border. (default value: 0)
with_coords – Option enabling the adding to the combined cloud of information of each point to retrieve their positions in the original epipolar images
- Returns
Tuple formed with the combined clouds and color in a single pandas dataframe and the epsg code
- cars.applications.point_cloud_fusion.point_cloud_tools.add_color_information(cloud_list, worker_logger, nb_data, nb_band_clr, cloud_list_idx, bbox, c_cloud)
Add color information for a current cloud_list item
- Parameters
cloud_list (List(Dataset)) – point cloud dataset
worker_logger (Logger) – logger
nb_data (list[str]) – list of band data
nb_band_clr (int) – number of color band
cloud_list_idx (int) – index of the current point cloud
bbox (list[int]) – bbox of interest
c_cloud (NDArray[float64]) – arranged point cloud
- cars.applications.point_cloud_fusion.point_cloud_tools.get_color_type(clouds)
Get color type of the tiles and if the same type.
- Parameters
cloud_list (xarray Dataset) – list of clouds
- Returns
color type of the tiles list
- Return type
str
- cars.applications.point_cloud_fusion.point_cloud_tools.get_number_bands(cloud_list)
Get max number of bands of clouds
- Parameters
cloud_list (xarray Dataset) – list of clouds
- Returns
max number of band
- Return type
int
- cars.applications.point_cloud_fusion.point_cloud_tools.filter_cloud(cloud: pandas.DataFrame, index_elt_to_remove: List[int], filtered_elt_pos: bool = False) Tuple[pandas.DataFrame, Union[None, pandas.DataFrame]]
Filter all points of the cloud DataFrame which index is in the index_elt_to_remove list.
If filtered_elt_pos is set to True, the information of the removed elements positions in their original epipolar images are returned.
To do so the cloud DataFrame has to be build with the ‘with_coords’ option activated.
- Parameters
cloud – combined cloud as returned by the create_combined_cloud function
index_elt_to_remove – indexes of lines to filter in the cloud DataFrame
filtered_elt_pos – if filtered_elt_pos is set to True, the removed points positions in their original epipolar images are returned, otherwise it is set to None
- Returns
Tuple composed of the filtered cloud DataFrame and the filtered elements epipolar position information (or None for the latter if filtered_elt_pos is set to False or if the cloud Dataframe has not been build with with_coords option)
- cars.applications.point_cloud_fusion.point_cloud_tools.add_cloud_filtering_msk(clouds_list: List[xarray.Dataset], elt_pos_infos: pandas.DataFrame, mask_label: str, mask_value: int = 255)
Add a uint16 mask labeled ‘mask_label’ to the clouds in clouds_list. (in-line function)
TODO only used in tests
- Parameters
clouds_list – Input list of clouds
elt_pos_infos – pandas dataframe composed of cst.POINTS_CLOUD_COORD_EPI_GEOM_I, cst.POINTS_CLOUD_COORD_EPI_GEOM_J, cst.POINTS_CLOUD_IDX_IM_EPI columns as computed in the create_combined_cloud function. Those information are used to retrieve the point position in its original epipolar image.
mask_label – label to give to the mask in the datasets
mask_value – filtered elements value in the mask