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, max_ram_per_worker)
Get the optimal tile size to use during dense matching.
- Parameters
disp_min – minimum disparity
disp_max – maximum disparity
max_ram_per_worker – maximum ram per worker
- Returns
optimal tile size
- run(epipolar_images_left, epipolar_images_right, orchestrator=None, pair_folder=None, pair_key='PAIR_0', disp_min=None, disp_max=None, compute_disparity_masks=False, disp_to_alt_ratio=None)
Run Matching application.
Create CarsDataset filled with xarray.Dataset, corresponding to epipolar disparities, on the same geometry than epipolar_images_left.
- 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_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”
orchestrator – orchestrator used
pair_folder (str) – folder used for current pair
pair_key (str) – pair id
disp_min (int) – minimum disparity
disp_max (int) – maximum disparity
disp_to_alt_ratio (float) – disp to alti ratio used for performance map
- Returns
disparity map: The 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”
- Return type
- cars.applications.dense_matching.census_mccnn_sgm.compute_disparity(left_image_object: xarray.Dataset, right_image_object: xarray.Dataset, corr_cfg: dict, disp_min=None, disp_max=None, saving_info=None, compute_disparity_masks=False, generate_performance_map=False, perf_ambiguity_threshold=0.6, disp_to_alt_ratio=None) 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
compute_disparity_masks (bool) – Compute all the disparity pandora masks(disable by default)
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
Left to right disparity dataset Returned dataset is composed of :
cst_disp.MAP
cst_disp.VALID
cst.EPI_COLOR