OpenCV  4.1.0 Open Source Computer Vision
cv::StereoSGBM Class Referenceabstract

The class implements the modified H. Hirschmuller algorithm. More...

#include <opencv2/calib3d.hpp>

Inheritance diagram for cv::StereoSGBM:

Public Types

enum  {
MODE_SGBM = 0,
MODE_HH = 1,
MODE_SGBM_3WAY = 2,
MODE_HH4 = 3
}

Public Types inherited from cv::StereoMatcher
enum  {
DISP_SHIFT = 4,
DISP_SCALE = (1 << DISP_SHIFT)
}

Public Member Functions

virtual int getMode () const =0

virtual int getP1 () const =0

virtual int getP2 () const =0

virtual int getPreFilterCap () const =0

virtual int getUniquenessRatio () const =0

virtual void setMode (int mode)=0

virtual void setP1 (int P1)=0

virtual void setP2 (int P2)=0

virtual void setPreFilterCap (int preFilterCap)=0

virtual void setUniquenessRatio (int uniquenessRatio)=0

Public Member Functions inherited from cv::StereoMatcher
virtual void compute (InputArray left, InputArray right, OutputArray disparity)=0
Computes disparity map for the specified stereo pair.

virtual int getBlockSize () const =0

virtual int getDisp12MaxDiff () const =0

virtual int getMinDisparity () const =0

virtual int getNumDisparities () const =0

virtual int getSpeckleRange () const =0

virtual int getSpeckleWindowSize () const =0

virtual void setBlockSize (int blockSize)=0

virtual void setDisp12MaxDiff (int disp12MaxDiff)=0

virtual void setMinDisparity (int minDisparity)=0

virtual void setNumDisparities (int numDisparities)=0

virtual void setSpeckleRange (int speckleRange)=0

virtual void setSpeckleWindowSize (int speckleWindowSize)=0

Public Member Functions inherited from cv::Algorithm
Algorithm ()

virtual ~Algorithm ()

virtual void clear ()
Clears the algorithm state.

virtual bool empty () const
Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.

virtual String getDefaultName () const

virtual void read (const FileNode &fn)
Reads algorithm parameters from a file storage.

virtual void save (const String &filename) const

virtual void write (FileStorage &fs) const
Stores algorithm parameters in a file storage.

void write (const Ptr< FileStorage > &fs, const String &name=String()) const
simplified API for language bindingsThis is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Static Public Member Functions

static Ptr< StereoSGBMcreate (int minDisparity=0, int numDisparities=16, int blockSize=3, int P1=0, int P2=0, int disp12MaxDiff=0, int preFilterCap=0, int uniquenessRatio=0, int speckleWindowSize=0, int speckleRange=0, int mode=StereoSGBM::MODE_SGBM)
Creates StereoSGBM object.

Protected Member Functions inherited from cv::Algorithm
void writeFormat (FileStorage &fs) const

Detailed Description

The class implements the modified H. Hirschmuller algorithm.

HH08 that differs from the original one as follows:

• By default, the algorithm is single-pass, which means that you consider only 5 directions instead of 8. Set mode=StereoSGBM::MODE_HH in createStereoSGBM to run the full variant of the algorithm but beware that it may consume a lot of memory.
• The algorithm matches blocks, not individual pixels. Though, setting blockSize=1 reduces the blocks to single pixels.
• Mutual information cost function is not implemented. Instead, a simpler Birchfield-Tomasi sub-pixel metric fromBT98 is used. Though, the color images are supported as well.
• Some pre- and post- processing steps from K. Konolige algorithm StereoBM are included, for example: pre-filtering (StereoBM::PREFILTER_XSOBEL type) and post-filtering (uniqueness check, quadratic interpolation and speckle filtering).
Note
• (Python) An example illustrating the use of the StereoSGBM matching algorithm can be found at opencv_source_code/samples/python/stereo_match.py

Member Enumeration Documentation

 anonymous enum
Enumerator
MODE_SGBM
MODE_HH
MODE_SGBM_3WAY
MODE_HH4

Member Function Documentation

 static Ptr cv::StereoSGBM::create ( int minDisparity = 0, int numDisparities = 16, int blockSize = 3, int P1 = 0, int P2 = 0, int disp12MaxDiff = 0, int preFilterCap = 0, int uniquenessRatio = 0, int speckleWindowSize = 0, int speckleRange = 0, int mode = StereoSGBM::MODE_SGBM )
static
Python:
retval=cv.StereoSGBM_create([, minDisparity[, numDisparities[, blockSize[, P1[, P2[, disp12MaxDiff[, preFilterCap[, uniquenessRatio[, speckleWindowSize[, speckleRange[, mode]]]]]]]]]]])

Creates StereoSGBM object.

Parameters
 minDisparity Minimum possible disparity value. Normally, it is zero but sometimes rectification algorithms can shift images, so this parameter needs to be adjusted accordingly. numDisparities Maximum disparity minus minimum disparity. The value is always greater than zero. In the current implementation, this parameter must be divisible by 16. blockSize Matched block size. It must be an odd number >=1 . Normally, it should be somewhere in the 3..11 range. P1 The first parameter controlling the disparity smoothness. See below. P2 The second parameter controlling the disparity smoothness. The larger the values are, the smoother the disparity is. P1 is the penalty on the disparity change by plus or minus 1 between neighbor pixels. P2 is the penalty on the disparity change by more than 1 between neighbor pixels. The algorithm requires P2 > P1 . See stereo_match.cpp sample where some reasonably good P1 and P2 values are shown (like 8*number_of_image_channels*SADWindowSize*SADWindowSize and 32*number_of_image_channels*SADWindowSize*SADWindowSize , respectively). disp12MaxDiff Maximum allowed difference (in integer pixel units) in the left-right disparity check. Set it to a non-positive value to disable the check. preFilterCap Truncation value for the prefiltered image pixels. The algorithm first computes x-derivative at each pixel and clips its value by [-preFilterCap, preFilterCap] interval. The result values are passed to the Birchfield-Tomasi pixel cost function. uniquenessRatio Margin in percentage by which the best (minimum) computed cost function value should "win" the second best value to consider the found match correct. Normally, a value within the 5-15 range is good enough. speckleWindowSize Maximum size of smooth disparity regions to consider their noise speckles and invalidate. Set it to 0 to disable speckle filtering. Otherwise, set it somewhere in the 50-200 range. speckleRange Maximum disparity variation within each connected component. If you do speckle filtering, set the parameter to a positive value, it will be implicitly multiplied by 16. Normally, 1 or 2 is good enough. mode Set it to StereoSGBM::MODE_HH to run the full-scale two-pass dynamic programming algorithm. It will consume O(W*H*numDisparities) bytes, which is large for 640x480 stereo and huge for HD-size pictures. By default, it is set to false .

The first constructor initializes StereoSGBM with all the default parameters. So, you only have to set StereoSGBM::numDisparities at minimum. The second constructor enables you to set each parameter to a custom value.

 virtual int cv::StereoSGBM::getMode ( ) const
pure virtual
Python:
retval=cv.StereoSGBM.getMode()
 virtual int cv::StereoSGBM::getP1 ( ) const
pure virtual
Python:
retval=cv.StereoSGBM.getP1()
 virtual int cv::StereoSGBM::getP2 ( ) const
pure virtual
Python:
retval=cv.StereoSGBM.getP2()
 virtual int cv::StereoSGBM::getPreFilterCap ( ) const
pure virtual
Python:
retval=cv.StereoSGBM.getPreFilterCap()
 virtual int cv::StereoSGBM::getUniquenessRatio ( ) const
pure virtual
Python:
retval=cv.StereoSGBM.getUniquenessRatio()
 virtual void cv::StereoSGBM::setMode ( int mode )
pure virtual
Python:
None=cv.StereoSGBM.setMode(mode)
 virtual void cv::StereoSGBM::setP1 ( int P1 )
pure virtual
Python:
None=cv.StereoSGBM.setP1(P1)
 virtual void cv::StereoSGBM::setP2 ( int P2 )
pure virtual
Python:
None=cv.StereoSGBM.setP2(P2)
 virtual void cv::StereoSGBM::setPreFilterCap ( int preFilterCap )
pure virtual
Python:
None=cv.StereoSGBM.setPreFilterCap(preFilterCap)
 virtual void cv::StereoSGBM::setUniquenessRatio ( int uniquenessRatio )
pure virtual
Python:
None=cv.StereoSGBM.setUniquenessRatio(uniquenessRatio)

The documentation for this class was generated from the following file: