OpenCV
3.4.20-dev
Open Source Computer Vision
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Class computing stereo correspondence using the belief propagation algorithm. : More...
#include <opencv2/cudastereo.hpp>
Public Member Functions | |
virtual void | compute (InputArray left, InputArray right, OutputArray disparity, Stream &stream)=0 |
virtual void | compute (InputArray data, OutputArray disparity, Stream &stream=Stream::Null())=0 |
Enables the stereo correspondence operator that finds the disparity for the specified data cost. More... | |
virtual double | getDataWeight () const =0 |
data weight More... | |
virtual double | getDiscSingleJump () const =0 |
discontinuity single jump More... | |
virtual double | getMaxDataTerm () const =0 |
truncation of data cost More... | |
virtual double | getMaxDiscTerm () const =0 |
truncation of discontinuity cost More... | |
virtual int | getMsgType () const =0 |
type for messages (CV_16SC1 or CV_32FC1) More... | |
virtual int | getNumIters () const =0 |
number of BP iterations on each level More... | |
virtual int | getNumLevels () const =0 |
number of levels More... | |
virtual void | setDataWeight (double data_weight)=0 |
virtual void | setDiscSingleJump (double disc_single_jump)=0 |
virtual void | setMaxDataTerm (double max_data_term)=0 |
virtual void | setMaxDiscTerm (double max_disc_term)=0 |
virtual void | setMsgType (int msg_type)=0 |
virtual void | setNumIters (int iters)=0 |
virtual void | setNumLevels (int levels)=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 (FileStorage &fs, const String &name) const |
void | write (const Ptr< FileStorage > &fs, const String &name=String()) const |
Static Public Member Functions | |
static void | estimateRecommendedParams (int width, int height, int &ndisp, int &iters, int &levels) |
Uses a heuristic method to compute the recommended parameters ( ndisp, iters and levels ) for the specified image size ( width and height ). 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 | |
Public Types inherited from cv::StereoMatcher | |
enum | { DISP_SHIFT = 4, DISP_SCALE = (1 << DISP_SHIFT) } |
Protected Member Functions inherited from cv::Algorithm | |
void | writeFormat (FileStorage &fs) const |
Class computing stereo correspondence using the belief propagation algorithm. :
The class implements algorithm described in [72] . It can compute own data cost (using a truncated linear model) or use a user-provided data cost.
\[width \_ step \cdot height \cdot ndisp \cdot 4 \cdot (1 + 0.25)\]
and for data cost storage:
\[width\_step \cdot height \cdot ndisp \cdot (1 + 0.25 + 0.0625 + \dotsm + \frac{1}{4^{levels}})\]
width_step is the number of bytes in a line including padding.
StereoBeliefPropagation uses a truncated linear model for the data cost and discontinuity terms:
\[DataCost = data \_ weight \cdot \min ( \lvert Img_Left(x,y)-Img_Right(x-d,y) \rvert , max \_ data \_ term)\]
\[DiscTerm = \min (disc \_ single \_ jump \cdot \lvert f_1-f_2 \rvert , max \_ disc \_ term)\]
For more details, see [72] .
By default, StereoBeliefPropagation uses floating-point arithmetics and the CV_32FC1 type for messages. But it can also use fixed-point arithmetics and the CV_16SC1 message type for better performance. To avoid an overflow in this case, the parameters must satisfy the following requirement:
\[10 \cdot 2^{levels-1} \cdot max \_ data \_ term < SHRT \_ MAX\]
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This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
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Enables the stereo correspondence operator that finds the disparity for the specified data cost.
data | User-specified data cost, a matrix of msg_type type and Size(<image columns>*ndisp, <image rows>) size. |
disparity | Output disparity map. If disparity is empty, the output type is CV_16SC1 . Otherwise, the type is retained. In 16-bit signed format, the disparity values do not have fractional bits. |
stream | Stream for the asynchronous version. |
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Uses a heuristic method to compute the recommended parameters ( ndisp, iters and levels ) for the specified image size ( width and height ).
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data weight
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discontinuity single jump
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truncation of data cost
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truncation of discontinuity cost
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type for messages (CV_16SC1 or CV_32FC1)
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number of BP iterations on each level
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number of levels
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