cars.applications.resampling.bicubic_resampling
this module contains the dense_matching application class.
Module Contents
Classes
BicubicResampling |
Functions
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Compute disparity maps from image objects. This function will be run |
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Get bounds of sensor images |
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Check if epipolar tiles will be used. |
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Check if tile is in sensor image |
- 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, tile_width, tile_height)
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_fun=None, tile_width=None, tile_height=None, add_color=True, epipolar_roi=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”, “color”, “geomodel”, “no_data”, “mask”, “classification”. Paths must be absolutes
sensor_images_right (CarsDataset) – tiled sensor right image Dict Must contain keys : “image”, “color”, “geomodel”, “no_data”, “mask”, “classification”. 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_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_color (bool) – add color image 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
- 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”, “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.generate_epipolar_images_wrapper(left_overlaps, right_overlaps, window, epipolar_size_x, epipolar_size_y, img1, img2, grid1, grid2, step=None, used_disp_min=None, used_disp_max=None, add_color=True, color1=None, mask1=None, mask2=None, classif1=None, classif2=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_COLOR (for left, if given)
- cars.applications.resampling.bicubic_resampling.get_sensors_bounds(sensor_image_left, sensor_image_right)
Get bounds of sensor images Bounds: BoundingBox(left, bottom, right, top)
- Parameters
sensor_image_left (dict) – left sensor
sensor_image_right (dict) – right sensor
- Returns
left image bounds, right image bounds
- Return type
tuple(list, list)
- cars.applications.resampling.bicubic_resampling.check_tiles_in_sensor(sensor_image_left, sensor_image_right, image_tiling, grid_left, grid_right)
Check if epipolar tiles will be used. A tile is not used if is outside sensor bounds
- Parameters
sensor_image_left (dict) – left sensor
sensor_image_right (dict) – right sensor
image_tiling (np.array) – epipolar tiling grid
grid_left (CarsDataset) – left epipolar grid
grid_right (CarsDataset) – right epipolar grid
- Returns
left in sensor, right in sensor
- Return type
np.array(bool), np.array(bool)
- cars.applications.resampling.bicubic_resampling.check_tile_inclusion(left_sensor_bounds, right_sensor_bounds, sensor_pos_left, sensor_pos_right)
Check if tile is in sensor image
- Parameters
left_sensor_bounds (list) – bounds of left sensor
right_sensor_bounds (list) – bounds of right sensor
sensor_pos_left (np.array) – left sensor position
sensor_pos_right (np.array) – right sensor position
- Returns
left tile in sensor image left, right tile in sensor image right
- Return type
tuple(bool, bool)