cars.applications.resampling.bicubic_resampling_app =================================================== .. py:module:: cars.applications.resampling.bicubic_resampling_app .. autoapi-nested-parse:: this module contains the bicubic_resampling application class. Classes ------- .. autoapisummary:: cars.applications.resampling.bicubic_resampling_app.BicubicResampling Functions --------- .. autoapisummary:: cars.applications.resampling.bicubic_resampling_app.generate_epipolar_images_wrapper Module Contents --------------- .. py:class:: BicubicResampling(conf=None) Bases: :py:obj:`cars.applications.resampling.abstract_resampling_app.Resampling` BicubicResampling .. py:attribute:: used_method .. py:attribute:: strip_height .. py:attribute:: step .. py:attribute:: save_intermediate_data .. py:attribute:: interpolator_image .. py:attribute:: interpolator_classif .. py:attribute:: interpolator_mask .. py:attribute:: interpolators_edges .. py:attribute:: orchestrator :value: None .. py:method:: check_conf(conf) Check configuration :param conf: configuration to check :type conf: dict :return: overloaded configuration :rtype: dict .. py:method:: pre_run(grid_left, tile_width, tile_height) Pre run some computations : tiling grid :param grid_left: left grid :type grid_left: dict :param optimum_tile_size: optimum tile size :type optimum_tile_size: int :return: epipolar_regions_grid, epipolar_regions, opt_epipolar_tile_size, largest_epipolar_region, .. py:method:: 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. :param sensor_images_left: tiled sensor left image Dict Must contain keys : "image", "geomodel", "no_data", "mask", "classification". Paths must be absolutes :type sensor_images_left: CarsDataset :param sensor_images_right: tiled sensor right image Dict Must contain keys : "image", "geomodel", "no_data", "mask", "classification". Paths must be absolutes :type sensor_images_right: CarsDataset :param grid_left: 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" :type grid_left: dict :param grid_right: 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" :type grid_right: dict :param orchestrator: orchestrator used :param pair_folder: folder used for current pair :type pair_folder: directory to save files to :param pair_key: pair id :type pair_key: str :param margins_fun: margins function to use :type margins_fun: fun :param optimum_tile_size: optimum tile size to use :type optimum_tile_size: int :param tile_width: width of tile :type tile_width: int :param tile_height: height of tile :type tile_height: int :param add_classif: add classif to dataset :type add_classif: bool :param epipolar_roi: Epipolar roi to use if set. Set None tiles outsize roi :type epipolar_roi: list(int), [row_min, row_max, col_min, col_max] :param required_bands: bands to resample on left and right image :type required_bands: dict :param texture_bands: name of bands used for output texture :type texture_bands: list :return: 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" :rtype: Tuple(CarsDataset, CarsDataset) .. py:function:: 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. :param left_overlaps: Overlaps of left image, with row_min, row_max, col_min and col_max keys. :type left_overlaps: dict :param right_overlaps: Overlaps of right image, with row_min, row_max, col_min and col_max keys. :type right_overlaps: dict :param window: Window considered in generation, with row_min, row_max, col_min and col_max keys. :type window: dict :return: 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)