cars.applications.dense_matching.dense_matching_tools
This module is responsible for the dense matching algorithms: - thus it creates a disparity map from a pair of images
Module Contents
Functions
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Get margins for the dense matching steps |
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Get masks dictionary from the disparity map in output of pandora. |
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Create the disparity dataset. |
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This function will compute disparity. |
Compute optimal tile size according to estimated memory usage |
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Estimate color image of reference from the disparity map and the secondary |
- cars.applications.dense_matching.dense_matching_tools.get_margins(disp_min, disp_max, corr_cfg)
Get margins for the dense matching steps
- Parameters
disp_min (int) – Minimum disparity
disp_max (int) – Maximum disparity
corr_cfg (dict) – Correlator configuration
- Returns
margins of the matching algorithm used
- cars.applications.dense_matching.dense_matching_tools.get_masks_from_pandora(disp: xarray.Dataset, compute_disparity_masks: bool) Dict[str, numpy.ndarray]
Get masks dictionary from the disparity map in output of pandora.
- Parameters
disp – disparity map (pandora output)
compute_disparity_masks – compute_disparity_masks activation status
- Returns
masks dictionary
- cars.applications.dense_matching.dense_matching_tools.add_color(output_dataset: xarray.Dataset, color: numpy.ndarray = None, color_mask: numpy.ndarray = None, band_im: list = None)
” Add color and color mask to dataset
- Parameters
output_dataset – output dataset
color – color array
color_mask – color mask array
band_im – list of band names
- cars.applications.dense_matching.dense_matching_tools.add_classification(output_dataset: xarray.Dataset, classif: numpy.ndarray = None, band_classif: list = None)
” Add classification to dataset
- Parameters
output_dataset – output dataset
classif – classif array
band_im – list of band names
- cars.applications.dense_matching.dense_matching_tools.create_disp_dataset(disp: xarray.Dataset, ref_dataset: xarray.Dataset, compute_disparity_masks: bool = False, generate_performance_map=False, perf_ambiguity_threshold=0.6, disp_to_alt_ratio=None) xarray.Dataset
Create the disparity dataset.
- Parameters
disp – disparity map (result of pandora)
ref_dataset – reference dataset for the considered disparity map
compute_disparity_masks – compute_disparity_masks activation status
generate_performance_map (bool) – True if generate performance map
perf_ambiguity_threshold (float) – ambiguity threshold used for performance map
disp_to_alt_ratio (float) – disp to alti ratio used for performance map
- Returns
disparity dataset as used in cars
- cars.applications.dense_matching.dense_matching_tools.add_confidence(output_dataset: xarray.Dataset, disp: xarray.Dataset, ref_roi: List[int])
” Add confidences to dataset
- Parameters
output_dataset – output dataset
disp – disp xarray
- cars.applications.dense_matching.dense_matching_tools.add_performance_map(output_dataset: xarray.Dataset, disp: xarray.Dataset, ref_roi: List[int], perf_ambiguity_threshold: float, disp_to_alt_ratio: float)
” Add performance map to dataset
- Parameters
output_dataset – output dataset
disp – disp xarray
perf_ambiguity_threshold (float) – ambiguity threshold used for performance map
disp_to_alt_ratio (float) – disp to alti ratio used for performance map
- cars.applications.dense_matching.dense_matching_tools.compute_disparity(left_dataset, right_dataset, corr_cfg, disp_min=None, disp_max=None, use_sec_disp=True, compute_disparity_masks=False, generate_performance_map=False, perf_ambiguity_threshold=0.6, disp_to_alt_ratio=None) Dict[str, xarray.Dataset]
This function will compute disparity.
- Parameters
left_dataset (xarray.Dataset) – Dataset containing left image and mask
right_dataset (xarray.Dataset) – Dataset containing right image and mask
corr_cfg (dict) – Correlator configuration
disp_min (int) – Minimum disparity (if None, value is taken from left dataset)
disp_max (int) – Maximum disparity (if None, value is taken from left dataset)
use_sec_disp (bool) – Boolean activating the use of the secondary disparity map
compute_disparity_masks (Boolean) – Activation of compute_disparity_masks mode
generate_performance_map (bool) – True if generate performance map
perf_ambiguity_threshold (float) – ambiguity threshold used for performance map
disp_to_alt_ratio (float) – disp to alti ratio used for performance map
- Returns
Dictionary of disparity dataset. Keys are (if it is computed by Pandora):
’ref’ for the left to right disparity map
’sec’ for the right to left disparity map
- cars.applications.dense_matching.dense_matching_tools.optimal_tile_size_pandora_plugin_libsgm(disp_min: int, disp_max: int, min_tile_size: int, max_tile_size: int, max_ram_per_worker: int, tile_size_rounding: int = 50, margin: int = 0) int
Compute optimal tile size according to estimated memory usage (pandora_plugin_libsgm) Returned optimal tile size will be at least equal to tile_size_rounding.
- Parameters
disp_min – Minimum disparity to explore
disp_max – Maximum disparity to explore
min_tile_size – Minimal tile size
max_tile_size – Maximal tile size
max_ram_per_worker – amount of RAM allocated per worker
tile_size_rounding – Optimal tile size will be aligned to multiples of tile_size_rounding
margin – margin to remove to the computed tile size (as a percent of the computed tile size)
- Returns
Optimal tile size according to benchmarked memory usage
- cars.applications.dense_matching.dense_matching_tools.estimate_color_from_disparity(disp_ref_to_sec: xarray.Dataset, sec_ds: xarray.Dataset) xarray.Dataset
Estimate color image of reference from the disparity map and the secondary color image.
- Parameters
disp_ref_to_sec – disparity map
sec_ds – secondary image dataset
- Returns
interpolated reference color image dataset