Class cv::cuda::StereoBeliefPropagation#

Class computing stereo correspondence using the belief propagation algorithm. : View details

#include <opencv2/cudastereo.hpp>

Collaboration diagram for cv::cuda::StereoBeliefPropagation:

Public Member Functions#

Public Member Functions inherited from cv::Algorithm

Return	Name	Description
	`Algorithm()`
	`~Algorithm()`
`void`	`clear()`	Clears the algorithm state.
`bool`	`empty()`	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
`String`	`getDefaultName()`
`void`	`read(const FileNode & fn)`	Reads algorithm parameters from a file storage.
`void`	`save(const String & filename)`
`void`	`write(` `const Ptr< FileStorage > & fs,` `const String & name = String() )`
`void`	`write(FileStorage & fs)`	Stores algorithm parameters in a file storage.
`void`	`write(` `FileStorage & fs,` `const String & name )`

Static Public Member Functions#

Static Public Member Functions inherited from cv::Algorithm

Return	Name	Description
`static Ptr< _Tp >`	`load(` `const String & filename,` `const String & objname = String() )`	Loads algorithm from the file.
`static Ptr< _Tp >`	`loadFromString(` `const String & strModel,` `const String & objname = String() )`	Loads algorithm from a String.
`static Ptr< _Tp >`	`read(const FileNode & fn)`	Reads algorithm from the file node.

Additional Inherited Members#

Detailed Description#

Class computing stereo correspondence using the belief propagation algorithm. :

The class implements algorithm described in Felzenszwalb2006 . 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 Felzenszwalb2006 .

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:

\[10 \cdot 2^{levels-1} \cdot max \_ data \_ term < SHRT \_ MAX\]

Member Function Documentation#

compute()#

void cv::cuda::StereoBeliefPropagation::compute(
InputArray data,
OutputArray disparity,
Stream & stream = Stream::Null() )

Enables the stereo correspondence operator that finds the disparity for the specified data cost.

Parameters

data — User-specified data cost, a matrix of msg_type type and Size(*ndisp, ) 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.

compute()#

void cv::cuda::StereoBeliefPropagation::compute(
InputArray left,
InputArray right,
OutputArray disparity,
Stream & stream )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

getDataWeight()#

double cv::cuda::StereoBeliefPropagation::getDataWeight()

data weight

getDiscSingleJump()#

double cv::cuda::StereoBeliefPropagation::getDiscSingleJump()

discontinuity single jump

getMaxDataTerm()#

double cv::cuda::StereoBeliefPropagation::getMaxDataTerm()

truncation of data cost

getMaxDiscTerm()#

double cv::cuda::StereoBeliefPropagation::getMaxDiscTerm()

truncation of discontinuity cost

getMsgType()#

int cv::cuda::StereoBeliefPropagation::getMsgType()

type for messages (CV_16SC1 or CV_32FC1)

getNumIters()#

int cv::cuda::StereoBeliefPropagation::getNumIters()

number of BP iterations on each level

getNumLevels()#

int cv::cuda::StereoBeliefPropagation::getNumLevels()

number of levels

setDataWeight()#

void cv::cuda::StereoBeliefPropagation::setDataWeight(double data_weight)

setDiscSingleJump()#

void cv::cuda::StereoBeliefPropagation::setDiscSingleJump(double disc_single_jump)

setMaxDataTerm()#

void cv::cuda::StereoBeliefPropagation::setMaxDataTerm(double max_data_term)

setMaxDiscTerm()#

void cv::cuda::StereoBeliefPropagation::setMaxDiscTerm(double max_disc_term)

setMsgType()#

void cv::cuda::StereoBeliefPropagation::setMsgType(int msg_type)

setNumIters()#

void cv::cuda::StereoBeliefPropagation::setNumIters(int iters)

setNumLevels()#

void cv::cuda::StereoBeliefPropagation::setNumLevels(int levels)

estimateRecommendedParams()#

static void cv::cuda::StereoBeliefPropagation::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 ).

Source file#

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

opencv2/cudastereo.hpp