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Dependents: RZ_A2M_Mbed_samples
StereoSGBM Class Reference
[Camera Calibration and 3D Reconstruction]
The class implements the modified H. More...
#include <calib3d.hpp>
Inherits cv::StereoMatcher.
Public Member Functions | |
| virtual CV_WRAP void | compute (InputArray left, InputArray right, OutputArray disparity)=0 |
| Computes disparity map for the specified stereo pair. | |
| virtual CV_WRAP void | clear () |
| Clears the algorithm state. | |
| virtual void | write (FileStorage &fs) const |
| Stores algorithm parameters in a file storage. | |
| virtual void | read (const FileNode &fn) |
| Reads algorithm parameters from a file storage. | |
| virtual bool | empty () const |
| Returns true if the Algorithm is empty (e.g. | |
| virtual CV_WRAP void | save (const String &filename) const |
| Saves the algorithm to a file. | |
| virtual CV_WRAP String | getDefaultName () const |
| Returns the algorithm string identifier. | |
Static Public Member Functions | |
| static CV_WRAP Ptr< StereoSGBM > | create (int minDisparity, int numDisparities, int blockSize, 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. | |
| template<typename _Tp > | |
| static Ptr< _Tp > | read (const FileNode &fn) |
| Reads algorithm from the file node. | |
| template<typename _Tp > | |
| static Ptr< _Tp > | load (const String &filename, const String &objname=String()) |
| Loads algorithm from the file. | |
| template<typename _Tp > | |
| static Ptr< _Tp > | loadFromString (const String &strModel, const String &objname=String()) |
| Loads algorithm from a String. | |
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=StereoSGBMMODE_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 BT98 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
Definition at line 1805 of file calib3d.hpp.
Member Function Documentation
| virtual CV_WRAP void clear | ( | ) | [virtual, inherited] |
Clears the algorithm state.
Reimplemented in DescriptorMatcher, and FlannBasedMatcher.
| virtual CV_WRAP void compute | ( | InputArray | left, |
| InputArray | right, | ||
| OutputArray | disparity | ||
| ) | [pure virtual, inherited] |
Computes disparity map for the specified stereo pair.
- Parameters:
-
left Left 8-bit single-channel image. right Right image of the same size and the same type as the left one. disparity Output disparity map. It has the same size as the input images. Some algorithms, like StereoBM or StereoSGBM compute 16-bit fixed-point disparity map (where each disparity value has 4 fractional bits), whereas other algorithms output 32-bit floating-point disparity map.
| static CV_WRAP Ptr<StereoSGBM> create | ( | int | minDisparity, |
| int | numDisparities, | ||
| int | blockSize, | ||
| 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] |
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 bool empty | ( | ) | const [virtual, inherited] |
| virtual CV_WRAP String getDefaultName | ( | ) | const [virtual, inherited] |
Returns the algorithm string identifier.
This string is used as top level xml/yml node tag when the object is saved to a file or string.
| static Ptr<_Tp> load | ( | const String & | filename, |
| const String & | objname = String() |
||
| ) | [static, inherited] |
Loads algorithm from the file.
- Parameters:
-
filename Name of the file to read. objname The optional name of the node to read (if empty, the first top-level node will be used)
This is static template method of Algorithm. It's usage is following (in the case of SVM):
Ptr<SVM> svm = Algorithm::load<SVM>("my_svm_model.xml");
In order to make this method work, the derived class must overwrite Algorithm::read(const FileNode& fn).
| static Ptr<_Tp> loadFromString | ( | const String & | strModel, |
| const String & | objname = String() |
||
| ) | [static, inherited] |
Loads algorithm from a String.
- Parameters:
-
strModel The string variable containing the model you want to load. objname The optional name of the node to read (if empty, the first top-level node will be used)
This is static template method of Algorithm. It's usage is following (in the case of SVM):
Ptr<SVM> svm = Algorithm::loadFromString<SVM>(myStringModel);
| virtual void read | ( | const FileNode & | fn ) | [virtual, inherited] |
Reads algorithm parameters from a file storage.
Reimplemented in Feature2D, DescriptorMatcher, and FlannBasedMatcher.
Reads algorithm from the file node.
This is static template method of Algorithm. It's usage is following (in the case of SVM):
cv::FileStorage fsRead("example.xml", FileStorage::READ); Ptr<SVM> svm = Algorithm::read<SVM>(fsRead.root());
In order to make this method work, the derived class must overwrite Algorithm::read(const FileNode& fn) and also have static create() method without parameters (or with all the optional parameters)
Reimplemented in Feature2D, DescriptorMatcher, and FlannBasedMatcher.
| virtual CV_WRAP void save | ( | const String & | filename ) | const [virtual, inherited] |
Saves the algorithm to a file.
In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).
| virtual void write | ( | FileStorage & | fs ) | const [virtual, inherited] |
Stores algorithm parameters in a file storage.
Reimplemented in Feature2D, DescriptorMatcher, and FlannBasedMatcher.
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