OpenCV  3.4.12
Open Source Computer Vision
Public Types | Public Member Functions | Static Public Member Functions | List of all members
cv::StereoSGBM Class Referenceabstract

The class implements the modified H. Hirschmuller algorithm [97] that differs from the original one as follows: More...

#include <opencv2/calib3d.hpp>

Inheritance diagram for cv::StereoSGBM:
cv::StereoMatcher cv::Algorithm

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. More...
 
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. More...
 
virtual bool empty () const
 Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read. More...
 
virtual String getDefaultName () const
 
virtual void read (const FileNode &fn)
 Reads algorithm parameters from a file storage. More...
 
virtual void save (const String &filename) const
 
virtual void write (FileStorage &fs) const
 Stores algorithm parameters in a file storage. More...
 
void write (const Ptr< FileStorage > &fs, const String &name=String()) const
 simplified API for language bindings This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. More...
 

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. More...
 
- Static Public Member Functions inherited from cv::Algorithm
template<typename _Tp >
static Ptr< _Tp > load (const String &filename, const String &objname=String())
 Loads algorithm from the file. More...
 
template<typename _Tp >
static Ptr< _Tp > loadFromString (const String &strModel, const String &objname=String())
 Loads algorithm from a String. More...
 
template<typename _Tp >
static Ptr< _Tp > read (const FileNode &fn)
 Reads algorithm from the file node. More...
 

Additional Inherited Members

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

Detailed Description

The class implements the modified H. Hirschmuller algorithm [97] that differs from the original one as follows:

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

anonymous enum
Enumerator
MODE_SGBM 
MODE_HH 
MODE_SGBM_3WAY 
MODE_HH4 

Member Function Documentation

◆ create()

static Ptr<StereoSGBM> 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
minDisparityMinimum possible disparity value. Normally, it is zero but sometimes rectification algorithms can shift images, so this parameter needs to be adjusted accordingly.
numDisparitiesMaximum disparity minus minimum disparity. The value is always greater than zero. In the current implementation, this parameter must be divisible by 16.
blockSizeMatched block size. It must be an odd number >=1 . Normally, it should be somewhere in the 3..11 range.
P1The first parameter controlling the disparity smoothness. See below.
P2The 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*blockSize*blockSize and 32*number_of_image_channels*blockSize*blockSize , respectively).
disp12MaxDiffMaximum allowed difference (in integer pixel units) in the left-right disparity check. Set it to a non-positive value to disable the check.
preFilterCapTruncation 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.
uniquenessRatioMargin 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.
speckleWindowSizeMaximum 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.
speckleRangeMaximum 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.
modeSet 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.

◆ getMode()

virtual int cv::StereoSGBM::getMode ( ) const
pure virtual
Python:
retval=cv.StereoSGBM.getMode()

◆ getP1()

virtual int cv::StereoSGBM::getP1 ( ) const
pure virtual
Python:
retval=cv.StereoSGBM.getP1()

◆ getP2()

virtual int cv::StereoSGBM::getP2 ( ) const
pure virtual
Python:
retval=cv.StereoSGBM.getP2()

◆ getPreFilterCap()

virtual int cv::StereoSGBM::getPreFilterCap ( ) const
pure virtual
Python:
retval=cv.StereoSGBM.getPreFilterCap()

◆ getUniquenessRatio()

virtual int cv::StereoSGBM::getUniquenessRatio ( ) const
pure virtual
Python:
retval=cv.StereoSGBM.getUniquenessRatio()

◆ setMode()

virtual void cv::StereoSGBM::setMode ( int  mode)
pure virtual
Python:
None=cv.StereoSGBM.setMode(mode)

◆ setP1()

virtual void cv::StereoSGBM::setP1 ( int  P1)
pure virtual
Python:
None=cv.StereoSGBM.setP1(P1)

◆ setP2()

virtual void cv::StereoSGBM::setP2 ( int  P2)
pure virtual
Python:
None=cv.StereoSGBM.setP2(P2)

◆ setPreFilterCap()

virtual void cv::StereoSGBM::setPreFilterCap ( int  preFilterCap)
pure virtual
Python:
None=cv.StereoSGBM.setPreFilterCap(preFilterCap)

◆ setUniquenessRatio()

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: