cars.applications.dense_matching.census_mccnn_sgm
this module contains the dense_matching application class.
Module Contents
Classes
Census SGM & MCCNN SGM matching class |
Functions
|
Compute disparity maps from image objects. |
- class cars.applications.dense_matching.census_mccnn_sgm.CensusMccnnSgm(conf=None)
Bases:
cars.applications.dense_matching.dense_matching.DenseMatching
Census SGM & MCCNN SGM matching class
- check_conf(conf)
Check configuration
- Parameters
conf (dict) – configuration to check
- Returns
overloaded configuration
- Return type
dict
- get_margins(grid_left, disp_min=None, disp_max=None)
Get Margins needed by matching method, to use during resampling
- Parameters
grid_left – left epipolar grid
disp_min – minimum disparity
disp_max – maximum disparity
- Returns
margins, updated disp_min, updated disp_max
- get_optimal_tile_size(disp_min, disp_max)
Get the optimal tile size to use during dense matching.
- Parameters
disp_min – minimum disparity
disp_max – maximum disparity
- Returns
optimal tile size
- run(epipolar_images_left, epipolar_images_right, orchestrator=None, pair_folder=None, pair_key='PAIR_0', mask1_ignored_by_corr: List[int] = None, mask2_ignored_by_corr: List[int] = None, mask1_set_to_ref_alt: List[int] = None, mask2_set_to_ref_alt: List[int] = None, disp_min=None, disp_max=None)
Run Matching application.
Create left and right CarsDataset filled with xarray.Dataset , corresponding to epipolar disparities, on the same geometry that epipolar_images_left and epipolar_images_right.
- Parameters
epipolar_images_left (CarsDataset) –
tiled left epipolar 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”, “epipolar_regions_grid”
epipolar_images_right (CarsDataset) –
tiled right epipolar 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”, “epipolar_regions_grid”
orchestrator – orchestrator used
pair_folder (str) – folder used for current pair
pair_key (str) – pair id
mask1_ignored_by_corr (list) – values used in left mask to ignore in correlation
mask2_ignored_by_corr (list) – values used in right mask to ignore in correlation
mask1_set_to_ref_alt (list) – values used in left mask to altitude to ref
mask2_set_to_ref_alt (list) – values used in right mask to altitude to ref
disp_min (int) – minimum disparity
disp_max (int) – maximum disparity
- Returns
left disparity map, right disparity map: Each CarsDataset contains:
- N x M Delayed tiles. Each tile will be a future xarray Dataset containing:
data with keys : “disp”, “disp_msk”
attrs with keys: profile, window, overlaps
- attributes containing:
- ”largest_epipolar_region”,”opt_epipolar_tile_size”,
”epipolar_regions_grid”
- Return type
Tuple(CarsDataset, CarsDataset)
- cars.applications.dense_matching.census_mccnn_sgm.compute_disparity(left_image_object: xarray.Dataset, right_image_object: xarray.Dataset, corr_cfg: dict, mask1_ignored_by_corr: List[int] = None, mask2_ignored_by_corr: List[int] = None, mask1_set_to_ref_alt: List[int] = None, mask2_set_to_ref_alt: List[int] = None, disp_min=None, disp_max=None, use_sec_disp=False, saving_info_left=None, saving_info_right=None, compute_disparity_masks=False) Dict[str, Tuple[xarray.Dataset, xarray.Dataset]]
Compute disparity maps from image objects. This function will be run as a delayed task.
User must provide saving infos to save properly created datasets
- Parameters
left_image_object –
tiled Left image - dataset with :
cst.EPI_IMAGE
cst.EPI_MSK (if given)
cst.EPI_COLOR (for left, if given)
right_image_object (xr.Dataset) – tiled Right image
corr_cfg (dict) – Correlator configuration
disp_min (int) – minimum disparity
disp_max (int) – maximum disparity
use_sec_disp (bool) – Boolean activating the use of the secondary disparity map
compute_disparity_masks (bool) – Compute all the disparity pandora masks(disable by default)
- Returns
Left disparity object, Right disparity object (if exists)
- Returned objects are composed of :
dataset (None for right object if use_sec_disp not activated) with :
cst_disp.MAP
cst_disp.VALID
cst.EPI_COLOR