cars.applications.resampling.bicubic_resampling
this module contains the dense_matching application class.
Module Contents
Classes
BicubicResampling |
Functions
|
Compute disparity maps from image objects. This function will be run |
- class cars.applications.resampling.bicubic_resampling.BicubicResampling(conf=None)
Bases:
cars.applications.resampling.resampling.ResamplingBicubicResampling
- check_conf(conf)
Check configuration
- Parameters
conf (dict) – configuration to check
- Returns
overloaded configuration
- Return type
dict
- pre_run(grid_left, optimum_tile_size)
Pre run some computations : tiling grid
- Parameters
grid_left (CarsDataset) – 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, orchestrator=None, pair_folder=None, pair_key='PAIR_0', margins=None, optimum_tile_size=None, add_color=True)
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”, “color”, “geomodel”, “no_data”, “mask”. Paths must be absolutes
sensor_images_right (CarsDataset) – tiled sensor right image Dict Must contain keys : “image”, “color”, “geomodel”, “no_data”, “mask”. Paths must be absolutes
grid_left –
left epipolar grid Grid CarsDataset contains :
- A single tile stored in [0,0], containing a (N, M, 2) shape
array in xarray Dataset
Attributes containing: “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”, :type grid_left: CarsDataset
grid_right (CarsDataset) –
right epipolar grid. Grid CarsDataset contains :
A single tile stored in [0,0], containing a (N, M, 2) shape array in xarray Dataset
Attributes containing: “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”,
orchestrator – orchestrator used
pair_folder (directory to save files to) – folder used for current pair
pair_key (str) – pair id
margins (xr.Dataset) – margins to use
optimum_tile_size (int) – optimum tile size to use
add_color (bool) – add color image to dataset
- 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”, “color”
- 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”
- Return type
Tuple(CarsDataset, CarsDataset)
- cars.applications.resampling.bicubic_resampling.generate_epipolar_images_wrapper(left_overlaps, right_overlaps, window, initial_margins, epipolar_size_x, epipolar_size_y, img1, img2, grid1, grid2, add_color=True, color1=None, mask1=None, mask2=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.
initial_margins (dict) – Initial margins without crops (used as template)
- 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_COLOR (for left, if given)