pandora.matching_cost.cpp.matching_cost_cpp

Functions

compute_matching_costs(img_left, imgs_right, cv, ...)	Given a left image and right images (multiple when doing subpixellic), compute the Census matching costs for all disparities, with the given window.
reverse_cost_volume(left_cv, disp_min)	Create the right_cv from the left_one by reindexing (i,j,d) -> (i, j + d, -d)
reverse_disp_range(left_min, left_max)	Create the right disp ranges from the left disp ranges

Module Contents

pandora.matching_cost.cpp.matching_cost_cpp.compute_matching_costs(img_left, imgs_right, cv, disps, census_width, census_height)

Given a left image and right images (multiple when doing subpixellic), compute the Census matching costs for all disparities, with the given window.

Parameters:

• img_left (2D np.array (row, col) dtype = np.float32) – the left image

• imgs_right (List of 2D np.array (row, col) dtype = np.float32) – the right images

• cv (3D np.array (row, col, disps) dtype = np.float32) – cost volume to fill

• disps (np.array (disps) dtype = np.float32) – the disparities to sample, sorted

• census_width (int) – the width of the census window

• census_height (int) – the height of the census window

Returns:

the filled cost volume

Return type:

3D np.array (row, col, disps) dtype = np.float32

pandora.matching_cost.cpp.matching_cost_cpp.reverse_cost_volume(left_cv, disp_min)

Create the right_cv from the left_one by reindexing (i,j,d) -> (i, j + d, -d) :param left_cv: the 3D cost_colume data array, with dimensions row, col, disp :type left_cv: np.ndarray(dtype=float32) :param disp_min: the minimum of the right disparities :type min_disp: int64 :return: The right cost volume data :rtype: 3D np.ndarray of type float32

pandora.matching_cost.cpp.matching_cost_cpp.reverse_disp_range(left_min, left_max)

Create the right disp ranges from the left disp ranges :param left_min: the 2D left disp min array, with dimensions row, col :type left_min: np.ndarray(dtype=float32) :param left_max: the 2D left disp max array, with dimensions row, col :type left_max: np.ndarray(dtype=float32) :return: The min and max disp ranges for the right image :rtype: Tuple[np.ndarray(dtype=float32), np.ndarray(dtype=float32)]