The class implements the modified H. Hirschmuller algorithm [126] that differs from the original one as follows:
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#include <opencv2/calib3d.hpp>
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| static Ptr< 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) |
| | Creates StereoSGBM object.
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| static Ptr< _Tp > | load (const String &filename, const String &objname=String()) |
| | Loads algorithm from the file.
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| static Ptr< _Tp > | loadFromString (const String &strModel, const String &objname=String()) |
| | Loads algorithm from a String.
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| template<typename _Tp > |
| static Ptr< _Tp > | read (const FileNode &fn) |
| | Reads algorithm from the file node.
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The class implements the modified H. Hirschmuller algorithm [126] 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 from [28] 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
◆ anonymous enum
| Enumerator |
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| MODE_SGBM | |
| MODE_HH | |
| MODE_SGBM_3WAY | |
| MODE_HH4 | |
◆ create()
| static Ptr< StereoSGBM > cv::StereoSGBM::create |
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int | minDisparity = 0, |
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int | numDisparities = 16, |
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int | blockSize = 3, |
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int | P1 = 0, |
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int | P2 = 0, |
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int | disp12MaxDiff = 0, |
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int | preFilterCap = 0, |
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int | uniquenessRatio = 0, |
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int | speckleWindowSize = 0, |
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int | speckleRange = 0, |
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int | mode = StereoSGBM::MODE_SGBM ) |
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static |
| Python: |
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| cv.StereoSGBM.create( | [, minDisparity[, numDisparities[, blockSize[, P1[, P2[, disp12MaxDiff[, preFilterCap[, uniquenessRatio[, speckleWindowSize[, speckleRange[, mode]]]]]]]]]]] | ) -> | retval |
| cv.StereoSGBM_create( | [, minDisparity[, numDisparities[, blockSize[, P1[, P2[, disp12MaxDiff[, preFilterCap[, uniquenessRatio[, speckleWindowSize[, speckleRange[, mode]]]]]]]]]]] | ) -> | retval |
Creates StereoSGBM object.
- Parameters
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| 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*blockSize*blockSize and 32*number_of_image_channels*blockSize*blockSize , 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.
◆ getMode()
| virtual int cv::StereoSGBM::getMode |
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const |
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pure virtual |
| Python: |
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| cv.StereoSGBM.getMode( | | ) -> | retval |
◆ getP1()
| virtual int cv::StereoSGBM::getP1 |
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const |
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pure virtual |
| Python: |
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| cv.StereoSGBM.getP1( | | ) -> | retval |
◆ getP2()
| virtual int cv::StereoSGBM::getP2 |
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const |
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pure virtual |
| Python: |
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| cv.StereoSGBM.getP2( | | ) -> | retval |
◆ getPreFilterCap()
| virtual int cv::StereoSGBM::getPreFilterCap |
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const |
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pure virtual |
| Python: |
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| cv.StereoSGBM.getPreFilterCap( | | ) -> | retval |
◆ getUniquenessRatio()
| virtual int cv::StereoSGBM::getUniquenessRatio |
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const |
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pure virtual |
| Python: |
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| cv.StereoSGBM.getUniquenessRatio( | | ) -> | retval |
◆ setMode()
| virtual void cv::StereoSGBM::setMode |
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int | mode | ) |
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pure virtual |
| Python: |
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| cv.StereoSGBM.setMode( | mode | ) -> | None |
◆ setP1()
| virtual void cv::StereoSGBM::setP1 |
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int | P1 | ) |
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pure virtual |
| Python: |
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| cv.StereoSGBM.setP1( | P1 | ) -> | None |
◆ setP2()
| virtual void cv::StereoSGBM::setP2 |
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int | P2 | ) |
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pure virtual |
| Python: |
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| cv.StereoSGBM.setP2( | P2 | ) -> | None |
◆ setPreFilterCap()
| virtual void cv::StereoSGBM::setPreFilterCap |
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int | preFilterCap | ) |
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pure virtual |
| Python: |
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| cv.StereoSGBM.setPreFilterCap( | preFilterCap | ) -> | None |
◆ setUniquenessRatio()
| virtual void cv::StereoSGBM::setUniquenessRatio |
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int | uniquenessRatio | ) |
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pure virtual |
| Python: |
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| cv.StereoSGBM.setUniquenessRatio( | uniquenessRatio | ) -> | None |
The documentation for this class was generated from the following file:
Public Types inherited from cv::StereoMatcher
1.12.0