cars.applications.resampling.bicubic_resampling_app

this module contains the bicubic_resampling application class.

Classes

BicubicResampling

BicubicResampling

Functions

generate_epipolar_images_wrapper(→ Dict[str, ...)

Compute disparity maps from image objects. This function will be run

Module Contents

class cars.applications.resampling.bicubic_resampling_app.BicubicResampling(conf=None)

Bases: cars.applications.resampling.abstract_resampling_app.Resampling

BicubicResampling

used_method

strip_height

step

save_intermediate_data

interpolator_image

interpolator_classif

interpolator_mask

interpolators_edges

orchestrator = None

check_conf(conf)

Check configuration

Parameters:

conf (dict) – configuration to check

Returns:

overloaded configuration

Return type:

dict

pre_run(grid_left, tile_width, tile_height)

Pre run some computations : tiling grid

Parameters:

• grid_left (dict) – left grid

• optimum_tile_size (int) – optimum tile size

Returns:

epipolar_regions_grid, epipolar_regions, opt_epipolar_tile_size, largest_epipolar_region,

run(sensor_image_left, sensor_image_right, grid_left, grid_right, geom_plugin, orchestrator=None, pair_folder=None, pair_key='PAIR_0', margins_fun=None, tile_width=None, tile_height=None, add_classif=True, add_edges=True, epipolar_roi=None, required_bands=None, texture_bands=None)

Run resampling application.

Creates left and right CarsDataset filled with xarray.Dataset, corresponding to sensor images resampled in epipolar geometry.

Parameters:

• sensor_images_left (CarsDataset) – tiled sensor left image Dict Must contain keys : “image”, “geomodel”, “no_data”, “mask”, “classification”. Paths must be absolutes

• sensor_images_right (CarsDataset) – tiled sensor right image Dict Must contain keys : “image”, “geomodel”, “no_data”, “mask”, “classification”. Paths must be absolutes

• grid_left (dict) – left epipolar grid Grid dict contains : - “grid_spacing”, “grid_origin”, “epipolar_size_x”, “epipolar_size_y”, “epipolar_origin_x”, “epipolar_origin_y”, epipolar_spacing_x”, “epipolar_spacing”, “disp_to_alt_ratio”, “path”

• grid_right (dict) – right epipolar grid. Grid dict contains : - “grid_spacing”, “grid_origin”, “epipolar_size_x”, “epipolar_size_y”, “epipolar_origin_x”, “epipolar_origin_y”, epipolar_spacing_x”, “epipolar_spacing”, “disp_to_alt_ratio”, “path”

• orchestrator – orchestrator used

• pair_folder (directory to save files to) – folder used for current pair

• pair_key (str) – pair id

• margins_fun (fun) – margins function to use

• optimum_tile_size (int) – optimum tile size to use

• tile_width (int) – width of tile

• tile_height (int) – height of tile

• add_classif (bool) – add classif to dataset

• epipolar_roi (list(int), [row_min, row_max, col_min, col_max]) – Epipolar roi to use if set. Set None tiles outsize roi

• required_bands (dict) – bands to resample on left and right image

• texture_bands (list) – name of bands used for output texture

Returns:

left epipolar image, right epipolar image. Each CarsDataset contains:

• N x M Delayed tiles. Each tile will be a future xarray Dataset containing:

  • data with keys : “im”, “msk”, “classif”

  • attrs with keys: “margins” with “disp_min” and “disp_max” “transform”, “crs”, “valid_pixels”, “no_data_mask”,

    ”no_data_img”

• attributes containing: “largest_epipolar_region”,”opt_epipolar_tile_size”,

  ”disp_min_tiling”, “disp_max_tiling”

Return type:

Tuple(CarsDataset, CarsDataset)

cars.applications.resampling.bicubic_resampling_app.generate_epipolar_images_wrapper(left_overlaps, right_overlaps, window, epipolar_size_x, epipolar_size_y, left_imgs, right_imgs, grid1, grid2, interpolator_image, interpolator_classif, interpolator_mask, interpolators_edges, step=None, used_disp_min=None, used_disp_max=None, add_classif=True, add_edges=True, mask1=None, mask2=None, edges1=None, edges2=None, left_classifs=None, right_classifs=None, nodata1=0, nodata2=0, saving_info_left=None, saving_info_right=None) → Dict[str, Tuple[xarray.Dataset, xarray.Dataset]]

Compute disparity maps from image objects. This function will be run as a delayed task. If user want to correctly save dataset, the user must provide saving_info_left and right. See cars_dataset.fill_dataset.

Parameters:

• left_overlaps (dict) – Overlaps of left image, with row_min, row_max, col_min and col_max keys.

• right_overlaps (dict) – Overlaps of right image, with row_min, row_max, col_min and col_max keys.

• window (dict) – Window considered in generation, with row_min, row_max, col_min and col_max keys.

Returns:

Left image object, Right image object (if exists)

Returned objects are composed of dataset with :

• cst.EPI_IMAGE

• cst.EPI_MSK (if given)

• cst.EPI_TEXTURE (for left, if given)