PyElas

This is the Python version of libelas (LIBrary for Efficient LArge-scale Stereo matching) by Andreas Geiger.

Apart from fixing warnings reported by GCC on Linux, almost nothing has been changed on the original code.

This library depends on numpy, as it returns numpy, because the disparity maps are returned as numpy array.

The original library was released under the GNU General Public License, so this is as well.

Usage

import cv2
from elas import Elas

# Images can be loaded with any library, as long as they can be accessed with buffer protocol
left = cv2.imread('left.jpg')
right = cv2.imread('right.jpg')
# Images must be grayscale
# In particular, they must be 2D buffers, stored in row-major/C order
left = cv2.cvtColor(left, cv2.COLOR_BGR2GRAY)
right = cv2.cvtColor(right, cv2.COLOR_BGR2GRAY)

# Construct the Elas object. You can pass your customized parameters...
e = Elas()
# ...or, optionally, you can set parameters as object members.
# Then compute the disparities. The results will be returned as numpy arrays
disp_left, disp_right = e.process(left, right)

Parameters

Parameter	Type	Description	Default value (robotics)	Defult value (Middlebury)
disp_min	int	Min disparity	0	0
disp_max	int	Max disparity	255	255
support_threshold	float	Max uniqueness ratio (best vs. second best support match)	0.85	0.95
support_texture	int	Min texture for support points	10	10
candidate_stepsize	int	Step size of regular grid on which support points are matched	5	5
incon_window_size	int	Window size of inconsistent support point check	5	5
incon_threshold	int	Disparity similarity threshold for support point to be considered consistent	5	5
incon_min_support	int	Minimum number of consistent support points	5	5
add_corners	bool	Add support points at image corners with nearest neighbor disparities	False	True
grid_size	int	Size of neighborhood for additional support point extrapolation	20	20
beta	float	Image likelihood parameter	0.02	0.02
gamma	float	Prior constant	3	5
sigma	float	Prior sigma	1	1
sradius	float	Prior sigma radius	2	3
match_texture	int	Min texture for dense matching	1	0
lr_threshold	int	Disparity threshold for left/right consistency check	2	2
speckle_sim_threshold	float	Similarity threshold for speckle segmentation	1	1
speckle_size	int	Maximal size of a speckle (small speckles get removed)	200	200
ipol_gap_width	int	Interpolate small gaps (left<->right, top<->bottom)	3	5000
filter_median	bool	Optional median filter (approximated)	False	True
filter_adaptive_mean	bool	Optional adaptive mean filter (approximated)	True	False
postprocess_only_left	bool	Save time by not postprocessing the right image	True	False
subsampling	bool	Save time by only computing disparities for each 2nd pixel	False	False