Video Analysis

Note

• A general optical flow example can be found at opencv_source_code/samples/gpu/optical_flow.cpp
• A general optical flow example using the Nvidia API can be found at opencv_source_code/samples/gpu/opticalflow_nvidia_api.cpp

gpu::BroxOpticalFlow

class gpu::BroxOpticalFlow

Class computing the optical flow for two images using Brox et al Optical Flow algorithm ([Brox2004]).

class BroxOpticalFlow
{
public:
    BroxOpticalFlow(float alpha_, float gamma_, float scale_factor_, int inner_iterations_, int outer_iterations_, int solver_iterations_);

    //! Compute optical flow
    //! frame0 - source frame (supports only CV_32FC1 type)
    //! frame1 - frame to track (with the same size and type as frame0)
    //! u      - flow horizontal component (along x axis)
    //! v      - flow vertical component (along y axis)
    void operator ()(const GpuMat& frame0, const GpuMat& frame1, GpuMat& u, GpuMat& v, Stream& stream = Stream::Null());

    //! flow smoothness
    float alpha;

    //! gradient constancy importance
    float gamma;

    //! pyramid scale factor
    float scale_factor;

    //! number of lagged non-linearity iterations (inner loop)
    int inner_iterations;

    //! number of warping iterations (number of pyramid levels)
    int outer_iterations;

    //! number of linear system solver iterations
    int solver_iterations;

    GpuMat buf;
};

Note

• An example illustrating the Brox et al optical flow algorithm can be found at opencv_source_code/samples/gpu/brox_optical_flow.cpp

gpu::GoodFeaturesToTrackDetector_GPU

class gpu::GoodFeaturesToTrackDetector_GPU

Class used for strong corners detection on an image.

class GoodFeaturesToTrackDetector_GPU
{
public:
    explicit GoodFeaturesToTrackDetector_GPU(int maxCorners_ = 1000, double qualityLevel_ = 0.01, double minDistance_ = 0.0,
        int blockSize_ = 3, bool useHarrisDetector_ = false, double harrisK_ = 0.04);

    void operator ()(const GpuMat& image, GpuMat& corners, const GpuMat& mask = GpuMat());

    int maxCorners;
    double qualityLevel;
    double minDistance;

    int blockSize;
    bool useHarrisDetector;
    double harrisK;

    void releaseMemory();
};

The class finds the most prominent corners in the image.

See also

goodFeaturesToTrack()

gpu::GoodFeaturesToTrackDetector_GPU::GoodFeaturesToTrackDetector_GPU

Constructor.

C++: gpu::GoodFeaturesToTrackDetector_GPU::GoodFeaturesToTrackDetector_GPU(int maxCorners=1000, double qualityLevel=0.01, double minDistance=0.0, int blockSize=3, bool useHarrisDetector=false, double harrisK=0.04)

Parameters:

• maxCorners – Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned.
• qualityLevel – Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see gpu::cornerMinEigenVal() ) or the Harris function response (see gpu::cornerHarris() ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected.
• minDistance – Minimum possible Euclidean distance between the returned corners.
• blockSize – Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See cornerEigenValsAndVecs() .
• useHarrisDetector – Parameter indicating whether to use a Harris detector (see gpu::cornerHarris()) or gpu::cornerMinEigenVal().
• harrisK – Free parameter of the Harris detector.

gpu::GoodFeaturesToTrackDetector_GPU::operator ()

Finds the most prominent corners in the image.

C++: void gpu::GoodFeaturesToTrackDetector_GPU::operator()(const GpuMat& image, GpuMat& corners, const GpuMat& mask=GpuMat())

Parameters:

• image – Input 8-bit, single-channel image.
• corners – Output vector of detected corners (it will be one row matrix with CV_32FC2 type).
• mask – Optional region of interest. If the image is not empty (it needs to have the type CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.

See also

goodFeaturesToTrack()

gpu::GoodFeaturesToTrackDetector_GPU::releaseMemory

Releases inner buffers memory.

C++: void gpu::GoodFeaturesToTrackDetector_GPU::releaseMemory()

gpu::FarnebackOpticalFlow

class gpu::FarnebackOpticalFlow

Class computing a dense optical flow using the Gunnar Farneback’s algorithm.

class CV_EXPORTS FarnebackOpticalFlow
{
public:
    FarnebackOpticalFlow()
    {
        numLevels = 5;
        pyrScale = 0.5;
        fastPyramids = false;
        winSize = 13;
        numIters = 10;
        polyN = 5;
        polySigma = 1.1;
        flags = 0;
    }

    int numLevels;
    double pyrScale;
    bool fastPyramids;
    int winSize;
    int numIters;
    int polyN;
    double polySigma;
    int flags;

    void operator ()(const GpuMat &frame0, const GpuMat &frame1, GpuMat &flowx, GpuMat &flowy, Stream &s = Stream::Null());

    void releaseMemory();

private:
    /* hidden */
};

gpu::FarnebackOpticalFlow::operator ()

Computes a dense optical flow using the Gunnar Farneback’s algorithm.

C++: void gpu::FarnebackOpticalFlow::operator()(const GpuMat& frame0, const GpuMat& frame1, GpuMat& flowx, GpuMat& flowy, Stream& s=Stream::Null())

Parameters:

• frame0 – First 8-bit gray-scale input image
• frame1 – Second 8-bit gray-scale input image
• flowx – Flow horizontal component
• flowy – Flow vertical component
• s – Stream

See also

calcOpticalFlowFarneback()

gpu::FarnebackOpticalFlow::releaseMemory

Releases unused auxiliary memory buffers.

C++: void gpu::FarnebackOpticalFlow::releaseMemory()

gpu::PyrLKOpticalFlow

class gpu::PyrLKOpticalFlow

Class used for calculating an optical flow.

class PyrLKOpticalFlow
{
public:
    PyrLKOpticalFlow();

    void sparse(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts,
        GpuMat& status, GpuMat* err = 0);

    void dense(const GpuMat& prevImg, const GpuMat& nextImg, GpuMat& u, GpuMat& v, GpuMat* err = 0);

    Size winSize;
    int maxLevel;
    int iters;
    bool useInitialFlow;

    void releaseMemory();
};

The class can calculate an optical flow for a sparse feature set or dense optical flow using the iterative Lucas-Kanade method with pyramids.

See also

calcOpticalFlowPyrLK()

Note

• An example of the Lucas Kanade optical flow algorithm can be found at opencv_source_code/samples/gpu/pyrlk_optical_flow.cpp

gpu::PyrLKOpticalFlow::sparse

Calculate an optical flow for a sparse feature set.

C++: void gpu::PyrLKOpticalFlow::sparse(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts, GpuMat& status, GpuMat* err=0)

Parameters:

• prevImg – First 8-bit input image (supports both grayscale and color images).
• nextImg – Second input image of the same size and the same type as prevImg .
• prevPts – Vector of 2D points for which the flow needs to be found. It must be one row matrix with CV_32FC2 type.
• nextPts – Output vector of 2D points (with single-precision floating-point coordinates) containing the calculated new positions of input features in the second image. When useInitialFlow is true, the vector must have the same size as in the input.
• status – Output status vector (CV_8UC1 type). Each element of the vector is set to 1 if the flow for the corresponding features has been found. Otherwise, it is set to 0.
• err – Output vector (CV_32FC1 type) that contains the difference between patches around the original and moved points or min eigen value if getMinEigenVals is checked. It can be NULL, if not needed.

See also

calcOpticalFlowPyrLK()

gpu::PyrLKOpticalFlow::dense

Calculate dense optical flow.

C++: void gpu::PyrLKOpticalFlow::dense(const GpuMat& prevImg, const GpuMat& nextImg, GpuMat& u, GpuMat& v, GpuMat* err=0)

Parameters:

• prevImg – First 8-bit grayscale input image.
• nextImg – Second input image of the same size and the same type as prevImg .
• u – Horizontal component of the optical flow of the same size as input images, 32-bit floating-point, single-channel
• v – Vertical component of the optical flow of the same size as input images, 32-bit floating-point, single-channel
• err – Output vector (CV_32FC1 type) that contains the difference between patches around the original and moved points or min eigen value if getMinEigenVals is checked. It can be NULL, if not needed.

gpu::PyrLKOpticalFlow::releaseMemory

Releases inner buffers memory.

C++: void gpu::PyrLKOpticalFlow::releaseMemory()

gpu::interpolateFrames

Interpolates frames (images) using provided optical flow (displacement field).

C++: void gpu::interpolateFrames(const GpuMat& frame0, const GpuMat& frame1, const GpuMat& fu, const GpuMat& fv, const GpuMat& bu, const GpuMat& bv, float pos, GpuMat& newFrame, GpuMat& buf, Stream& stream=Stream::Null())

Parameters:

• frame0 – First frame (32-bit floating point images, single channel).
• frame1 – Second frame. Must have the same type and size as frame0 .
• fu – Forward horizontal displacement.
• fv – Forward vertical displacement.
• bu – Backward horizontal displacement.
• bv – Backward vertical displacement.
• pos – New frame position.
• newFrame – Output image.
• buf – Temporary buffer, will have width x 6*height size, CV_32FC1 type and contain 6 GpuMat: occlusion masks for first frame, occlusion masks for second, interpolated forward horizontal flow, interpolated forward vertical flow, interpolated backward horizontal flow, interpolated backward vertical flow.
• stream – Stream for the asynchronous version.

gpu::FGDStatModel

class gpu::FGDStatModel

Class used for background/foreground segmentation.

class FGDStatModel
{
public:
    struct Params
    {
        ...
    };

    explicit FGDStatModel(int out_cn = 3);
    explicit FGDStatModel(const cv::gpu::GpuMat& firstFrame, const Params& params = Params(), int out_cn = 3);

    ~FGDStatModel();

    void create(const cv::gpu::GpuMat& firstFrame, const Params& params = Params());
    void release();

    int update(const cv::gpu::GpuMat& curFrame);

    //8UC3 or 8UC4 reference background image
    cv::gpu::GpuMat background;

    //8UC1 foreground image
    cv::gpu::GpuMat foreground;

    std::vector< std::vector<cv::Point> > foreground_regions;
};

The class discriminates between foreground and background pixels by building and maintaining a model of the background. Any pixel which does not fit this model is then deemed to be foreground. The class implements algorithm described in [FGD2003].

The results are available through the class fields:

cv::gpu::GpuMat background

The output background image.

cv::gpu::GpuMat foreground

The output foreground mask as an 8-bit binary image.

cv::gpu::GpuMat foreground_regions

The output foreground regions calculated by findContours().

gpu::FGDStatModel::FGDStatModel

Constructors.

C++: gpu::FGDStatModel::FGDStatModel(int out_cn=3)

C++: gpu::FGDStatModel::FGDStatModel(const cv::gpu::GpuMat& firstFrame, const Params& params=Params(), int out_cn=3)

Parameters:

• firstFrame – First frame from video stream. Supports 3- and 4-channels input ( CV_8UC3 and CV_8UC4 ).
• params – Algorithm’s parameters. See [FGD2003] for explanation.
• out_cn – Channels count in output result and inner buffers. Can be 3 or 4. 4-channels version requires more memory, but works a bit faster.

See also

gpu::FGDStatModel::create()

gpu::FGDStatModel::create

Initializes background model.

C++: void gpu::FGDStatModel::create(const cv::gpu::GpuMat& firstFrame, const Params& params=Params())

Parameters:

• firstFrame – First frame from video stream. Supports 3- and 4-channels input ( CV_8UC3 and CV_8UC4 ).
• params – Algorithm’s parameters. See [FGD2003] for explanation.

gpu::FGDStatModel::release

Releases all inner buffer’s memory.

C++: void gpu::FGDStatModel::release()

gpu::FGDStatModel::update

Updates the background model and returns foreground regions count.

C++: int gpu::FGDStatModel::update(const cv::gpu::GpuMat& curFrame)

Parameters:

• curFrame – Next video frame.

gpu::MOG_GPU

class gpu::MOG_GPU

Gaussian Mixture-based Backbround/Foreground Segmentation Algorithm.

class MOG_GPU
{
public:
    MOG_GPU(int nmixtures = -1);

    void initialize(Size frameSize, int frameType);

    void operator()(const GpuMat& frame, GpuMat& fgmask, float learningRate = 0.0f, Stream& stream = Stream::Null());

    void getBackgroundImage(GpuMat& backgroundImage, Stream& stream = Stream::Null()) const;

    void release();

    int history;
    float varThreshold;
    float backgroundRatio;
    float noiseSigma;
};

The class discriminates between foreground and background pixels by building and maintaining a model of the background. Any pixel which does not fit this model is then deemed to be foreground. The class implements algorithm described in [MOG2001].

See also

BackgroundSubtractorMOG

Note

• An example on gaussian mixture based background/foreground segmantation can be found at opencv_source_code/samples/gpu/bgfg_segm.cpp

gpu::MOG_GPU::MOG_GPU

The constructor.

C++: gpu::MOG_GPU::MOG_GPU(int nmixtures=-1)

Parameters:

• nmixtures – Number of Gaussian mixtures.

Default constructor sets all parameters to default values.

gpu::MOG_GPU::operator()

Updates the background model and returns the foreground mask.

C++: void gpu::MOG_GPU::operator()(const GpuMat& frame, GpuMat& fgmask, float learningRate=0.0f, Stream& stream=Stream::Null())

Parameters:

• frame – Next video frame.
• fgmask – The output foreground mask as an 8-bit binary image.
• stream – Stream for the asynchronous version.

gpu::MOG_GPU::getBackgroundImage

Computes a background image.

C++: void gpu::MOG_GPU::getBackgroundImage(GpuMat& backgroundImage, Stream& stream=Stream::Null()) const

Parameters:

• backgroundImage – The output background image.
• stream – Stream for the asynchronous version.

gpu::MOG_GPU::release

Releases all inner buffer’s memory.

C++: void gpu::MOG_GPU::release()

gpu::MOG2_GPU

class gpu::MOG2_GPU

Gaussian Mixture-based Background/Foreground Segmentation Algorithm.

class MOG2_GPU
{
public:
    MOG2_GPU(int nmixtures = -1);

    void initialize(Size frameSize, int frameType);

    void operator()(const GpuMat& frame, GpuMat& fgmask, float learningRate = 0.0f, Stream& stream = Stream::Null());

    void getBackgroundImage(GpuMat& backgroundImage, Stream& stream = Stream::Null()) const;

    void release();

    // parameters
    ...
};

The class discriminates between foreground and background pixels by building and maintaining a model of the background. Any pixel which does not fit this model is then deemed to be foreground. The class implements algorithm described in [MOG2004].

Here are important members of the class that control the algorithm, which you can set after constructing the class instance:

float backgroundRatio

Threshold defining whether the component is significant enough to be included into the background model ( corresponds to TB=1-cf from the paper??which paper??). cf=0.1 => TB=0.9 is default. For alpha=0.001, it means that the mode should exist for approximately 105 frames before it is considered foreground.

float varThreshold

Threshold for the squared Mahalanobis distance that helps decide when a sample is close to the existing components (corresponds to Tg). If it is not close to any component, a new component is generated. 3 sigma => Tg=3*3=9 is default. A smaller Tg value generates more components. A higher Tg value may result in a small number of components but they can grow too large.

float fVarInit

Initial variance for the newly generated components. It affects the speed of adaptation. The parameter value is based on your estimate of the typical standard deviation from the images. OpenCV uses 15 as a reasonable value.

float fVarMin

Parameter used to further control the variance.

float fVarMax

Parameter used to further control the variance.

float fCT

Complexity reduction parameter. This parameter defines the number of samples needed to accept to prove the component exists. CT=0.05 is a default value for all the samples. By setting CT=0 you get an algorithm very similar to the standard Stauffer&Grimson algorithm.

uchar nShadowDetection

The value for marking shadow pixels in the output foreground mask. Default value is 127.

float fTau

Shadow threshold. The shadow is detected if the pixel is a darker version of the background. Tau is a threshold defining how much darker the shadow can be. Tau= 0.5 means that if a pixel is more than twice darker then it is not shadow. See [ShadowDetect2003].

bool bShadowDetection

Parameter defining whether shadow detection should be enabled.

See also

BackgroundSubtractorMOG2

gpu::MOG2_GPU::MOG2_GPU

The constructor.

C++: gpu::MOG2_GPU::MOG2_GPU(int nmixtures=-1)

Parameters:

• nmixtures – Number of Gaussian mixtures.

Default constructor sets all parameters to default values.

gpu::MOG2_GPU::operator()

Updates the background model and returns the foreground mask.

C++: void gpu::MOG2_GPU::operator()(const GpuMat& frame, GpuMat& fgmask, float learningRate=-1.0f, Stream& stream=Stream::Null() )

Parameters:

• frame – Next video frame.
• fgmask – The output foreground mask as an 8-bit binary image.
• stream – Stream for the asynchronous version.

gpu::MOG2_GPU::getBackgroundImage

Computes a background image.

C++: void gpu::MOG2_GPU::getBackgroundImage(GpuMat& backgroundImage, Stream& stream=Stream::Null()) const

Parameters:

• backgroundImage – The output background image.
• stream – Stream for the asynchronous version.

gpu::MOG2_GPU::release

Releases all inner buffer’s memory.

C++: void gpu::MOG2_GPU::release()

gpu::GMG_GPU

class gpu::GMG_GPU

Class used for background/foreground segmentation.

class GMG_GPU
{
public:
    GMG_GPU();

    void initialize(Size frameSize, float min = 0.0f, float max = 255.0f);

    void operator ()(const GpuMat& frame, GpuMat& fgmask, float learningRate = -1.0f, Stream& stream = Stream::Null());

    void release();

    int    maxFeatures;
    float  learningRate;
    int    numInitializationFrames;
    int    quantizationLevels;
    float  backgroundPrior;
    float  decisionThreshold;
    int    smoothingRadius;

    ...
};

The class discriminates between foreground and background pixels by building and maintaining a model of the background. Any pixel which does not fit this model is then deemed to be foreground. The class implements algorithm described in [GMG2012].

Here are important members of the class that control the algorithm, which you can set after constructing the class instance:

int maxFeatures

Total number of distinct colors to maintain in histogram.

float learningRate

Set between 0.0 and 1.0, determines how quickly features are “forgotten” from histograms.

int numInitializationFrames

Number of frames of video to use to initialize histograms.

int quantizationLevels

Number of discrete levels in each channel to be used in histograms.

float backgroundPrior

Prior probability that any given pixel is a background pixel. A sensitivity parameter.

float decisionThreshold

Value above which pixel is determined to be FG.

float smoothingRadius

Smoothing radius, in pixels, for cleaning up FG image.

gpu::GMG_GPU::GMG_GPU

The default constructor.

C++: gpu::GMG_GPU::GMG_GPU()

Default constructor sets all parameters to default values.

gpu::GMG_GPU::initialize

Initialize background model and allocates all inner buffers.

C++: void gpu::GMG_GPU::initialize(Size frameSize, float min=0.0f, float max=255.0f)

Parameters:

• frameSize – Input frame size.
• min – Minimum value taken on by pixels in image sequence. Usually 0.
• max – Maximum value taken on by pixels in image sequence, e.g. 1.0 or 255.

gpu::GMG_GPU::operator()

Updates the background model and returns the foreground mask

C++: void gpu::GMG_GPU::operator()(const GpuMat& frame, GpuMat& fgmask, float learningRate=-1.0f, Stream& stream=Stream::Null() )

Parameters:

• frame – Next video frame.
• fgmask – The output foreground mask as an 8-bit binary image.
• stream – Stream for the asynchronous version.

gpu::GMG_GPU::release

Releases all inner buffer’s memory.

C++: void gpu::GMG_GPU::release()

gpu::VideoWriter_GPU

Video writer class.

class gpu::VideoWriter_GPU

The class uses H264 video codec.

Note

Currently only Windows platform is supported.

Note

• An example on how to use the videoWriter class can be found at opencv_source_code/samples/gpu/video_writer.cpp

gpu::VideoWriter_GPU::VideoWriter_GPU

Constructors.

C++: gpu::VideoWriter_GPU::VideoWriter_GPU()

C++: gpu::VideoWriter_GPU::VideoWriter_GPU(const std::string& fileName, cv::Size frameSize, double fps, SurfaceFormat format=SF_BGR)

C++: gpu::VideoWriter_GPU::VideoWriter_GPU(const std::string& fileName, cv::Size frameSize, double fps, const EncoderParams& params, SurfaceFormat format=SF_BGR)

C++: gpu::VideoWriter_GPU::VideoWriter_GPU(const cv::Ptr<EncoderCallBack>& encoderCallback, cv::Size frameSize, double fps, SurfaceFormat format=SF_BGR)

C++: gpu::VideoWriter_GPU::VideoWriter_GPU(const cv::Ptr<EncoderCallBack>& encoderCallback, cv::Size frameSize, double fps, const EncoderParams& params, SurfaceFormat format=SF_BGR)

Parameters:

• fileName – Name of the output video file. Only AVI file format is supported.
• frameSize – Size of the input video frames.
• fps – Framerate of the created video stream.
• params – Encoder parameters. See gpu::VideoWriter_GPU::EncoderParams .
• format – Surface format of input frames ( SF_UYVY , SF_YUY2 , SF_YV12 , SF_NV12 , SF_IYUV , SF_BGR or SF_GRAY). BGR or gray frames will be converted to YV12 format before encoding, frames with other formats will be used as is.
• encoderCallback – Callbacks for video encoder. See gpu::VideoWriter_GPU::EncoderCallBack . Use it if you want to work with raw video stream.

The constructors initialize video writer. FFMPEG is used to write videos. User can implement own multiplexing with gpu::VideoWriter_GPU::EncoderCallBack .

gpu::VideoWriter_GPU::open

Initializes or reinitializes video writer.

C++: void gpu::VideoWriter_GPU::open(const std::string& fileName, cv::Size frameSize, double fps, SurfaceFormat format=SF_BGR)

C++: void gpu::VideoWriter_GPU::open(const std::string& fileName, cv::Size frameSize, double fps, const EncoderParams& params, SurfaceFormat format=SF_BGR)

C++: void gpu::VideoWriter_GPU::open(const cv::Ptr<EncoderCallBack>& encoderCallback, cv::Size frameSize, double fps, SurfaceFormat format=SF_BGR)

C++: void gpu::VideoWriter_GPU::open(const cv::Ptr<EncoderCallBack>& encoderCallback, cv::Size frameSize, double fps, const EncoderParams& params, SurfaceFormat format=SF_BGR)

The method opens video writer. Parameters are the same as in the constructor gpu::VideoWriter_GPU::VideoWriter_GPU() . The method throws Exception if error occurs.

gpu::VideoWriter_GPU::isOpened

Returns true if video writer has been successfully initialized.

C++: bool gpu::VideoWriter_GPU::isOpened() const

gpu::VideoWriter_GPU::close

Releases the video writer.

C++: void gpu::VideoWriter_GPU::close()

gpu::VideoWriter_GPU::write

Writes the next video frame.

C++: void gpu::VideoWriter_GPU::write(const cv::gpu::GpuMat& image, bool lastFrame=false)

Parameters:

• image – The written frame.
• lastFrame – Indicates that it is end of stream. The parameter can be ignored.

The method write the specified image to video file. The image must have the same size and the same surface format as has been specified when opening the video writer.

gpu::VideoWriter_GPU::EncoderParams

struct gpu::VideoWriter_GPU::EncoderParams

Different parameters for CUDA video encoder.

struct EncoderParams
{
    int       P_Interval;      //    NVVE_P_INTERVAL,
    int       IDR_Period;      //    NVVE_IDR_PERIOD,
    int       DynamicGOP;      //    NVVE_DYNAMIC_GOP,
    int       RCType;          //    NVVE_RC_TYPE,
    int       AvgBitrate;      //    NVVE_AVG_BITRATE,
    int       PeakBitrate;     //    NVVE_PEAK_BITRATE,
    int       QP_Level_Intra;  //    NVVE_QP_LEVEL_INTRA,
    int       QP_Level_InterP; //    NVVE_QP_LEVEL_INTER_P,
    int       QP_Level_InterB; //    NVVE_QP_LEVEL_INTER_B,
    int       DeblockMode;     //    NVVE_DEBLOCK_MODE,
    int       ProfileLevel;    //    NVVE_PROFILE_LEVEL,
    int       ForceIntra;      //    NVVE_FORCE_INTRA,
    int       ForceIDR;        //    NVVE_FORCE_IDR,
    int       ClearStat;       //    NVVE_CLEAR_STAT,
    int       DIMode;          //    NVVE_SET_DEINTERLACE,
    int       Presets;         //    NVVE_PRESETS,
    int       DisableCabac;    //    NVVE_DISABLE_CABAC,
    int       NaluFramingType; //    NVVE_CONFIGURE_NALU_FRAMING_TYPE
    int       DisableSPSPPS;   //    NVVE_DISABLE_SPS_PPS

    EncoderParams();
    explicit EncoderParams(const std::string& configFile);

    void load(const std::string& configFile);
    void save(const std::string& configFile) const;
};

gpu::VideoWriter_GPU::EncoderParams::EncoderParams

Constructors.

C++: gpu::VideoWriter_GPU::EncoderParams::EncoderParams()

C++: gpu::VideoWriter_GPU::EncoderParams::EncoderParams(const std::string& configFile)

Parameters:

• configFile – Config file name.

Creates default parameters or reads parameters from config file.

gpu::VideoWriter_GPU::EncoderParams::load

Reads parameters from config file.

C++: void gpu::VideoWriter_GPU::EncoderParams::load(const std::string& configFile)

Parameters:

• configFile – Config file name.

gpu::VideoWriter_GPU::EncoderParams::save

Saves parameters to config file.

C++: void gpu::VideoWriter_GPU::EncoderParams::save(const std::string& configFile) const

Parameters:

• configFile – Config file name.

gpu::VideoWriter_GPU::EncoderCallBack

class gpu::VideoWriter_GPU::EncoderCallBack

Callbacks for CUDA video encoder.

class EncoderCallBack
{
public:
    enum PicType
    {
        IFRAME = 1,
        PFRAME = 2,
        BFRAME = 3
    };

    virtual ~EncoderCallBack() {}

    virtual unsigned char* acquireBitStream(int* bufferSize) = 0;
    virtual void releaseBitStream(unsigned char* data, int size) = 0;
    virtual void onBeginFrame(int frameNumber, PicType picType) = 0;
    virtual void onEndFrame(int frameNumber, PicType picType) = 0;
};

gpu::VideoWriter_GPU::EncoderCallBack::acquireBitStream

Callback function to signal the start of bitstream that is to be encoded.

C++: virtual uchar* gpu::VideoWriter_GPU::EncoderCallBack::acquireBitStream(int* bufferSize) = 0

Callback must allocate buffer for CUDA encoder and return pointer to it and it’s size.

gpu::VideoWriter_GPU::EncoderCallBack::releaseBitStream

Callback function to signal that the encoded bitstream is ready to be written to file.

C++: virtual void gpu::VideoWriter_GPU::EncoderCallBack::releaseBitStream(unsigned char* data, int size) = 0

gpu::VideoWriter_GPU::EncoderCallBack::onBeginFrame

Callback function to signal that the encoding operation on the frame has started.

C++: virtual void gpu::VideoWriter_GPU::EncoderCallBack::onBeginFrame(int frameNumber, PicType picType) = 0

Parameters:

• picType – Specify frame type (I-Frame, P-Frame or B-Frame).

gpu::VideoWriter_GPU::EncoderCallBack::onEndFrame

Callback function signals that the encoding operation on the frame has finished.

C++: virtual void gpu::VideoWriter_GPU::EncoderCallBack::onEndFrame(int frameNumber, PicType picType) = 0

Parameters:

• picType – Specify frame type (I-Frame, P-Frame or B-Frame).

gpu::VideoReader_GPU

Class for reading video from files.

class gpu::VideoReader_GPU

Note

Currently only Windows and Linux platforms are supported.

Note

• An example on how to use the videoReader class can be found at opencv_source_code/samples/gpu/video_reader.cpp

gpu::VideoReader_GPU::Codec

Video codecs supported by gpu::VideoReader_GPU .

C++: enum gpu::VideoReader_GPU::Codec

MPEG1 = 0

MPEG2

MPEG4

VC1

H264

JPEG

H264_SVC

H264_MVC

Uncompressed_YUV420 = (('I'<<24)|('Y'<<16)|('U'<<8)|('V'))

Y,U,V (4:2:0)

Uncompressed_YV12 = (('Y'<<24)|('V'<<16)|('1'<<8)|('2'))

Y,V,U (4:2:0)

Uncompressed_NV12 = (('N'<<24)|('V'<<16)|('1'<<8)|('2'))

Y,UV (4:2:0)

Uncompressed_YUYV = (('Y'<<24)|('U'<<16)|('Y'<<8)|('V'))

YUYV/YUY2 (4:2:2)

Uncompressed_UYVY = (('U'<<24)|('Y'<<16)|('V'<<8)|('Y'))

UYVY (4:2:2)

gpu::VideoReader_GPU::ChromaFormat

Chroma formats supported by gpu::VideoReader_GPU .

C++: enum gpu::VideoReader_GPU::ChromaFormat

Monochrome = 0

YUV420

YUV422

YUV444

gpu::VideoReader_GPU::FormatInfo

struct gpu::VideoReader_GPU::FormatInfo

Struct providing information about video file format.

struct FormatInfo
{
    Codec codec;
    ChromaFormat chromaFormat;
    int width;
    int height;
};

gpu::VideoReader_GPU::VideoReader_GPU

Constructors.

C++: gpu::VideoReader_GPU::VideoReader_GPU()

C++: gpu::VideoReader_GPU::VideoReader_GPU(const std::string& filename)

C++: gpu::VideoReader_GPU::VideoReader_GPU(const cv::Ptr<VideoSource>& source)

Parameters:

• filename – Name of the input video file.
• source – Video file parser implemented by user.

The constructors initialize video reader. FFMPEG is used to read videos. User can implement own demultiplexing with gpu::VideoReader_GPU::VideoSource .

gpu::VideoReader_GPU::open

Initializes or reinitializes video reader.

C++: void gpu::VideoReader_GPU::open(const std::string& filename)

C++: void gpu::VideoReader_GPU::open(const cv::Ptr<VideoSource>& source)

The method opens video reader. Parameters are the same as in the constructor gpu::VideoReader_GPU::VideoReader_GPU() . The method throws Exception if error occurs.

gpu::VideoReader_GPU::isOpened

Returns true if video reader has been successfully initialized.

C++: bool gpu::VideoReader_GPU::isOpened() const

gpu::VideoReader_GPU::close

Releases the video reader.

C++: void gpu::VideoReader_GPU::close()

gpu::VideoReader_GPU::read

Grabs, decodes and returns the next video frame.

C++: bool gpu::VideoReader_GPU::read(GpuMat& image)

If no frames has been grabbed (there are no more frames in video file), the methods return false . The method throws Exception if error occurs.

gpu::VideoReader_GPU::format

Returns information about video file format.

C++: FormatInfo gpu::VideoReader_GPU::format() const

The method throws Exception if video reader wasn’t initialized.

gpu::VideoReader_GPU::dumpFormat

Dump information about video file format to specified stream.

C++: void gpu::VideoReader_GPU::dumpFormat(std::ostream& st)

Parameters:

• st – Output stream.

The method throws Exception if video reader wasn’t initialized.

gpu::VideoReader_GPU::VideoSource

class gpu::VideoReader_GPU::VideoSource

Interface for video demultiplexing.

class VideoSource
{
public:
    VideoSource();
    virtual ~VideoSource() {}

    virtual FormatInfo format() const = 0;
    virtual void start() = 0;
    virtual void stop() = 0;
    virtual bool isStarted() const = 0;
    virtual bool hasError() const = 0;

protected:
    bool parseVideoData(const unsigned char* data, size_t size, bool endOfStream = false);
};

User can implement own demultiplexing by implementing this interface.

gpu::VideoReader_GPU::VideoSource::format

Returns information about video file format.

C++: virtual FormatInfo gpu::VideoReader_GPU::VideoSource::format() const = 0

gpu::VideoReader_GPU::VideoSource::start

Starts processing.

C++: virtual void gpu::VideoReader_GPU::VideoSource::start() = 0

Implementation must create own thread with video processing and call periodic gpu::VideoReader_GPU::VideoSource::parseVideoData() .

gpu::VideoReader_GPU::VideoSource::stop

Stops processing.

C++: virtual void gpu::VideoReader_GPU::VideoSource::stop() = 0

gpu::VideoReader_GPU::VideoSource::isStarted

Returns true if processing was successfully started.

C++: virtual bool gpu::VideoReader_GPU::VideoSource::isStarted() const = 0

gpu::VideoReader_GPU::VideoSource::hasError

Returns true if error occured during processing.

C++: virtual bool gpu::VideoReader_GPU::VideoSource::hasError() const = 0

gpu::VideoReader_GPU::VideoSource::parseVideoData

Parse next video frame. Implementation must call this method after new frame was grabbed.

C++: bool gpu::VideoReader_GPU::VideoSource::parseVideoData(const uchar* data, size_t size, bool endOfStream=false)

Parameters:

• data – Pointer to frame data. Can be NULL if endOfStream if true .
• size – Size in bytes of current frame.
• endOfStream – Indicates that it is end of stream.

[Brox2004]

Brox, A. Bruhn, N. Papenberg, J. Weickert. High accuracy optical flow estimation based on a theory for warping. ECCV 2004.

[FGD2003]

(1, 2, 3) Liyuan Li, Weimin Huang, Irene Y.H. Gu, and Qi Tian. Foreground Object Detection from Videos Containing Complex Background. ACM MM2003 9p, 2003.

[MOG2001]

KadewTraKuPong and R. Bowden. An improved adaptive background mixture model for real-time tracking with shadow detection. Proc. 2nd European Workshop on Advanced Video-Based Surveillance Systems, 2001

[MOG2004]

Zivkovic. Improved adaptive Gausian mixture model for background subtraction. International Conference Pattern Recognition, UK, August, 2004

[ShadowDetect2003]

Prati, Mikic, Trivedi and Cucchiarra. Detecting Moving Shadows.... IEEE PAMI, 2003

[GMG2012]

Godbehere, A. Matsukawa and K. Goldberg. Visual Tracking of Human Visitors under Variable-Lighting Conditions for a Responsive Audio Art Installation. American Control Conference, Montreal, June 2012