cars.applications.holes_detection.holes_detection_tools
This module contains function for holes detection.
Module Contents
Functions
Finds all roi existing in binary msk and stores their coverage as |
|
|
Calculates bbox of masked region(s) if mask exists for |
|
Convert disparity dataset to mask correspoding to all classes |
- cars.applications.holes_detection.holes_detection_tools.get_roi_coverage_as_poly_with_margins(msk_values: numpy.ndarray, row_offset=0, col_offset=0, margin=0) List[shapely.geometry.Polygon]
Finds all roi existing in binary msk and stores their coverage as list of Polygon
- Parameters
msk_values (np.ndarray) – msk layer of left/right epipolar image dataset
row_offset (int) – offset on row to apply
col_offset (int) – offset on col to apply
margin (int) – margin added to bbox in case masked region is localized at tile border (to ensure later disparity values at mask border extraction)
- Returns
list of polygon
- cars.applications.holes_detection.holes_detection_tools.localize_masked_areas(dataset: xarray.Dataset, classification: List[str], row_offset: int = 0, col_offset: int = 0, margin: int = 0) numpy.ndarray
Calculates bbox of masked region(s) if mask exists for input image file (see configuration “mask” and “mask_classes” in input .json configuration file)
- Parameters
dataset (CarsDataset) – epipolar image dataset
classification (List of str) – label of masked region to use
row_offset (int) – offset on row to apply
col_offset (int) – offset on col to apply
margin (int) – margin added to bbox in case masked region is localized at tile border (to ensure later disparity values at mask border extraction)
- Returns
bounding box of masked area(s)
- cars.applications.holes_detection.holes_detection_tools.classif_to_stacked_array(disp_map, class_index)
Convert disparity dataset to mask correspoding to all classes
- Parameters
disp_map (xarray Dataset) – disparity dataset
class_index (list of str) – classification tags