Class Ximgproc


  • public class Ximgproc
    extends java.lang.Object
    • Constructor Detail

      • Ximgproc

        public Ximgproc()
    • Method Detail

      • niBlackThreshold

        public static void niBlackThreshold​(Mat _src,
                                            Mat _dst,
                                            double maxValue,
                                            int type,
                                            int blockSize,
                                            double k,
                                            int binarizationMethod,
                                            double r)
        Performs thresholding on input images using Niblack's technique or some of the popular variations it inspired. The function transforms a grayscale image to a binary image according to the formulae:
        • THRESH_BINARY \(dst(x,y) = \fork{\texttt{maxValue}}{if \(src(x,y) > T(x,y)\)}{0}{otherwise}\)
        • THRESH_BINARY_INV \(dst(x,y) = \fork{0}{if \(src(x,y) > T(x,y)\)}{\texttt{maxValue}}{otherwise}\) where \(T(x,y)\) is a threshold calculated individually for each pixel.
        The threshold value \(T(x, y)\) is determined based on the binarization method chosen. For classic Niblack, it is the mean minus \( k \) times standard deviation of \(\texttt{blockSize} \times\texttt{blockSize}\) neighborhood of \((x, y)\). The function can't process the image in-place.
        Parameters:
        _src - Source 8-bit single-channel image.
        _dst - Destination image of the same size and the same type as src.
        maxValue - Non-zero value assigned to the pixels for which the condition is satisfied, used with the THRESH_BINARY and THRESH_BINARY_INV thresholding types.
        type - Thresholding type, see cv::ThresholdTypes.
        blockSize - Size of a pixel neighborhood that is used to calculate a threshold value for the pixel: 3, 5, 7, and so on.
        k - The user-adjustable parameter used by Niblack and inspired techniques. For Niblack, this is normally a value between 0 and 1 that is multiplied with the standard deviation and subtracted from the mean.
        binarizationMethod - Binarization method to use. By default, Niblack's technique is used. Other techniques can be specified, see cv::ximgproc::LocalBinarizationMethods.
        r - The user-adjustable parameter used by Sauvola's technique. This is the dynamic range of standard deviation. SEE: threshold, adaptiveThreshold
      • niBlackThreshold

        public static void niBlackThreshold​(Mat _src,
                                            Mat _dst,
                                            double maxValue,
                                            int type,
                                            int blockSize,
                                            double k,
                                            int binarizationMethod)
        Performs thresholding on input images using Niblack's technique or some of the popular variations it inspired. The function transforms a grayscale image to a binary image according to the formulae:
        • THRESH_BINARY \(dst(x,y) = \fork{\texttt{maxValue}}{if \(src(x,y) > T(x,y)\)}{0}{otherwise}\)
        • THRESH_BINARY_INV \(dst(x,y) = \fork{0}{if \(src(x,y) > T(x,y)\)}{\texttt{maxValue}}{otherwise}\) where \(T(x,y)\) is a threshold calculated individually for each pixel.
        The threshold value \(T(x, y)\) is determined based on the binarization method chosen. For classic Niblack, it is the mean minus \( k \) times standard deviation of \(\texttt{blockSize} \times\texttt{blockSize}\) neighborhood of \((x, y)\). The function can't process the image in-place.
        Parameters:
        _src - Source 8-bit single-channel image.
        _dst - Destination image of the same size and the same type as src.
        maxValue - Non-zero value assigned to the pixels for which the condition is satisfied, used with the THRESH_BINARY and THRESH_BINARY_INV thresholding types.
        type - Thresholding type, see cv::ThresholdTypes.
        blockSize - Size of a pixel neighborhood that is used to calculate a threshold value for the pixel: 3, 5, 7, and so on.
        k - The user-adjustable parameter used by Niblack and inspired techniques. For Niblack, this is normally a value between 0 and 1 that is multiplied with the standard deviation and subtracted from the mean.
        binarizationMethod - Binarization method to use. By default, Niblack's technique is used. Other techniques can be specified, see cv::ximgproc::LocalBinarizationMethods. of standard deviation. SEE: threshold, adaptiveThreshold
      • niBlackThreshold

        public static void niBlackThreshold​(Mat _src,
                                            Mat _dst,
                                            double maxValue,
                                            int type,
                                            int blockSize,
                                            double k)
        Performs thresholding on input images using Niblack's technique or some of the popular variations it inspired. The function transforms a grayscale image to a binary image according to the formulae:
        • THRESH_BINARY \(dst(x,y) = \fork{\texttt{maxValue}}{if \(src(x,y) > T(x,y)\)}{0}{otherwise}\)
        • THRESH_BINARY_INV \(dst(x,y) = \fork{0}{if \(src(x,y) > T(x,y)\)}{\texttt{maxValue}}{otherwise}\) where \(T(x,y)\) is a threshold calculated individually for each pixel.
        The threshold value \(T(x, y)\) is determined based on the binarization method chosen. For classic Niblack, it is the mean minus \( k \) times standard deviation of \(\texttt{blockSize} \times\texttt{blockSize}\) neighborhood of \((x, y)\). The function can't process the image in-place.
        Parameters:
        _src - Source 8-bit single-channel image.
        _dst - Destination image of the same size and the same type as src.
        maxValue - Non-zero value assigned to the pixels for which the condition is satisfied, used with the THRESH_BINARY and THRESH_BINARY_INV thresholding types.
        type - Thresholding type, see cv::ThresholdTypes.
        blockSize - Size of a pixel neighborhood that is used to calculate a threshold value for the pixel: 3, 5, 7, and so on.
        k - The user-adjustable parameter used by Niblack and inspired techniques. For Niblack, this is normally a value between 0 and 1 that is multiplied with the standard deviation and subtracted from the mean. Other techniques can be specified, see cv::ximgproc::LocalBinarizationMethods. of standard deviation. SEE: threshold, adaptiveThreshold
      • thinning

        public static void thinning​(Mat src,
                                    Mat dst,
                                    int thinningType)
        Applies a binary blob thinning operation, to achieve a skeletization of the input image. The function transforms a binary blob image into a skeletized form using the technique of Zhang-Suen.
        Parameters:
        src - Source 8-bit single-channel image, containing binary blobs, with blobs having 255 pixel values.
        dst - Destination image of the same size and the same type as src. The function can work in-place.
        thinningType - Value that defines which thinning algorithm should be used. See cv::ximgproc::ThinningTypes
      • thinning

        public static void thinning​(Mat src,
                                    Mat dst)
        Applies a binary blob thinning operation, to achieve a skeletization of the input image. The function transforms a binary blob image into a skeletized form using the technique of Zhang-Suen.
        Parameters:
        src - Source 8-bit single-channel image, containing binary blobs, with blobs having 255 pixel values.
        dst - Destination image of the same size and the same type as src. The function can work in-place.
      • anisotropicDiffusion

        public static void anisotropicDiffusion​(Mat src,
                                                Mat dst,
                                                float alpha,
                                                float K,
                                                int niters)
        Performs anisotropic diffusion on an image. The function applies Perona-Malik anisotropic diffusion to an image. This is the solution to the partial differential equation: \({\frac {\partial I}{\partial t}}={\mathrm {div}}\left(c(x,y,t)\nabla I\right)=\nabla c\cdot \nabla I+c(x,y,t)\Delta I\) Suggested functions for c(x,y,t) are: \(c\left(\|\nabla I\|\right)=e^{{-\left(\|\nabla I\|/K\right)^{2}}}\) or \( c\left(\|\nabla I\|\right)={\frac {1}{1+\left({\frac {\|\nabla I\|}{K}}\right)^{2}}} \)
        Parameters:
        src - Source image with 3 channels.
        dst - Destination image of the same size and the same number of channels as src .
        alpha - The amount of time to step forward by on each iteration (normally, it's between 0 and 1).
        K - sensitivity to the edges
        niters - The number of iterations
      • createSuperpixelSLIC

        public static SuperpixelSLIC createSuperpixelSLIC​(Mat image,
                                                          int algorithm,
                                                          int region_size,
                                                          float ruler)
        Initialize a SuperpixelSLIC object
        Parameters:
        image - Image to segment
        algorithm - Chooses the algorithm variant to use: SLIC segments image using a desired region_size, and in addition SLICO will optimize using adaptive compactness factor, while MSLIC will optimize using manifold methods resulting in more content-sensitive superpixels.
        region_size - Chooses an average superpixel size measured in pixels
        ruler - Chooses the enforcement of superpixel smoothness factor of superpixel The function initializes a SuperpixelSLIC object for the input image. It sets the parameters of choosed superpixel algorithm, which are: region_size and ruler. It preallocate some buffers for future computing iterations over the given image. For enanched results it is recommended for color images to preprocess image with little gaussian blur using a small 3 x 3 kernel and additional conversion into CieLAB color space. An example of SLIC versus SLICO and MSLIC is ilustrated in the following picture. ![image](pics/superpixels_slic.png)
        Returns:
        automatically generated
      • createSuperpixelSLIC

        public static SuperpixelSLIC createSuperpixelSLIC​(Mat image,
                                                          int algorithm,
                                                          int region_size)
        Initialize a SuperpixelSLIC object
        Parameters:
        image - Image to segment
        algorithm - Chooses the algorithm variant to use: SLIC segments image using a desired region_size, and in addition SLICO will optimize using adaptive compactness factor, while MSLIC will optimize using manifold methods resulting in more content-sensitive superpixels.
        region_size - Chooses an average superpixel size measured in pixels The function initializes a SuperpixelSLIC object for the input image. It sets the parameters of choosed superpixel algorithm, which are: region_size and ruler. It preallocate some buffers for future computing iterations over the given image. For enanched results it is recommended for color images to preprocess image with little gaussian blur using a small 3 x 3 kernel and additional conversion into CieLAB color space. An example of SLIC versus SLICO and MSLIC is ilustrated in the following picture. ![image](pics/superpixels_slic.png)
        Returns:
        automatically generated
      • createSuperpixelSLIC

        public static SuperpixelSLIC createSuperpixelSLIC​(Mat image,
                                                          int algorithm)
        Initialize a SuperpixelSLIC object
        Parameters:
        image - Image to segment
        algorithm - Chooses the algorithm variant to use: SLIC segments image using a desired region_size, and in addition SLICO will optimize using adaptive compactness factor, while MSLIC will optimize using manifold methods resulting in more content-sensitive superpixels. The function initializes a SuperpixelSLIC object for the input image. It sets the parameters of choosed superpixel algorithm, which are: region_size and ruler. It preallocate some buffers for future computing iterations over the given image. For enanched results it is recommended for color images to preprocess image with little gaussian blur using a small 3 x 3 kernel and additional conversion into CieLAB color space. An example of SLIC versus SLICO and MSLIC is ilustrated in the following picture. ![image](pics/superpixels_slic.png)
        Returns:
        automatically generated
      • createSuperpixelSLIC

        public static SuperpixelSLIC createSuperpixelSLIC​(Mat image)
        Initialize a SuperpixelSLIC object
        Parameters:
        image - Image to segment SLIC segments image using a desired region_size, and in addition SLICO will optimize using adaptive compactness factor, while MSLIC will optimize using manifold methods resulting in more content-sensitive superpixels. The function initializes a SuperpixelSLIC object for the input image. It sets the parameters of choosed superpixel algorithm, which are: region_size and ruler. It preallocate some buffers for future computing iterations over the given image. For enanched results it is recommended for color images to preprocess image with little gaussian blur using a small 3 x 3 kernel and additional conversion into CieLAB color space. An example of SLIC versus SLICO and MSLIC is ilustrated in the following picture. ![image](pics/superpixels_slic.png)
        Returns:
        automatically generated
      • findEllipses

        public static void findEllipses​(Mat image,
                                        Mat ellipses,
                                        float scoreThreshold,
                                        float reliabilityThreshold,
                                        float centerDistanceThreshold)
        Finds ellipses fastly in an image using projective invariant pruning. The function detects ellipses in images using projective invariant pruning. For more details about this implementation, please see [JIA2017FAST] Jia, Qi et al, (2017). A Fast Ellipse Detector using Projective Invariant Pruning. IEEE Transactions on Image Processing.
        Parameters:
        image - input image, could be gray or color.
        ellipses - output vector of found ellipses. each vector is encoded as five float $x, y, a, b, radius, score$.
        scoreThreshold - float, the threshold of ellipse score.
        reliabilityThreshold - float, the threshold of reliability.
        centerDistanceThreshold - float, the threshold of center distance.
      • findEllipses

        public static void findEllipses​(Mat image,
                                        Mat ellipses,
                                        float scoreThreshold,
                                        float reliabilityThreshold)
        Finds ellipses fastly in an image using projective invariant pruning. The function detects ellipses in images using projective invariant pruning. For more details about this implementation, please see [JIA2017FAST] Jia, Qi et al, (2017). A Fast Ellipse Detector using Projective Invariant Pruning. IEEE Transactions on Image Processing.
        Parameters:
        image - input image, could be gray or color.
        ellipses - output vector of found ellipses. each vector is encoded as five float $x, y, a, b, radius, score$.
        scoreThreshold - float, the threshold of ellipse score.
        reliabilityThreshold - float, the threshold of reliability.
      • findEllipses

        public static void findEllipses​(Mat image,
                                        Mat ellipses,
                                        float scoreThreshold)
        Finds ellipses fastly in an image using projective invariant pruning. The function detects ellipses in images using projective invariant pruning. For more details about this implementation, please see [JIA2017FAST] Jia, Qi et al, (2017). A Fast Ellipse Detector using Projective Invariant Pruning. IEEE Transactions on Image Processing.
        Parameters:
        image - input image, could be gray or color.
        ellipses - output vector of found ellipses. each vector is encoded as five float $x, y, a, b, radius, score$.
        scoreThreshold - float, the threshold of ellipse score.
      • findEllipses

        public static void findEllipses​(Mat image,
                                        Mat ellipses)
        Finds ellipses fastly in an image using projective invariant pruning. The function detects ellipses in images using projective invariant pruning. For more details about this implementation, please see [JIA2017FAST] Jia, Qi et al, (2017). A Fast Ellipse Detector using Projective Invariant Pruning. IEEE Transactions on Image Processing.
        Parameters:
        image - input image, could be gray or color.
        ellipses - output vector of found ellipses. each vector is encoded as five float $x, y, a, b, radius, score$.
      • createGraphSegmentation

        public static GraphSegmentation createGraphSegmentation​(double sigma,
                                                                float k,
                                                                int min_size)
        Creates a graph based segmentor
        Parameters:
        sigma - The sigma parameter, used to smooth image
        k - The k parameter of the algorythm
        min_size - The minimum size of segments
        Returns:
        automatically generated
      • createGraphSegmentation

        public static GraphSegmentation createGraphSegmentation​(double sigma,
                                                                float k)
        Creates a graph based segmentor
        Parameters:
        sigma - The sigma parameter, used to smooth image
        k - The k parameter of the algorythm
        Returns:
        automatically generated
      • createGraphSegmentation

        public static GraphSegmentation createGraphSegmentation​(double sigma)
        Creates a graph based segmentor
        Parameters:
        sigma - The sigma parameter, used to smooth image
        Returns:
        automatically generated
      • createGraphSegmentation

        public static GraphSegmentation createGraphSegmentation()
        Creates a graph based segmentor
        Returns:
        automatically generated
      • createSelectiveSearchSegmentationStrategyColor

        public static SelectiveSearchSegmentationStrategyColor createSelectiveSearchSegmentationStrategyColor()
        Create a new color-based strategy
        Returns:
        automatically generated
      • createSelectiveSearchSegmentationStrategySize

        public static SelectiveSearchSegmentationStrategySize createSelectiveSearchSegmentationStrategySize()
        Create a new size-based strategy
        Returns:
        automatically generated
      • createSelectiveSearchSegmentationStrategyTexture

        public static SelectiveSearchSegmentationStrategyTexture createSelectiveSearchSegmentationStrategyTexture()
        Create a new size-based strategy
        Returns:
        automatically generated
      • createSelectiveSearchSegmentationStrategyFill

        public static SelectiveSearchSegmentationStrategyFill createSelectiveSearchSegmentationStrategyFill()
        Create a new fill-based strategy
        Returns:
        automatically generated
      • createSelectiveSearchSegmentationStrategyMultiple

        public static SelectiveSearchSegmentationStrategyMultiple createSelectiveSearchSegmentationStrategyMultiple()
        Create a new multiple strategy
        Returns:
        automatically generated
      • createSelectiveSearchSegmentation

        public static SelectiveSearchSegmentation createSelectiveSearchSegmentation()
        Create a new SelectiveSearchSegmentation class.
        Returns:
        automatically generated
      • createFastLineDetector

        public static FastLineDetector createFastLineDetector​(int length_threshold,
                                                              float distance_threshold,
                                                              double canny_th1,
                                                              double canny_th2,
                                                              int canny_aperture_size,
                                                              boolean do_merge)
        Creates a smart pointer to a FastLineDetector object and initializes it
        Parameters:
        length_threshold - Segment shorter than this will be discarded
        distance_threshold - A point placed from a hypothesis line segment farther than this will be regarded as an outlier
        canny_th1 - First threshold for hysteresis procedure in Canny()
        canny_th2 - Second threshold for hysteresis procedure in Canny()
        canny_aperture_size - Aperturesize for the sobel operator in Canny(). If zero, Canny() is not applied and the input image is taken as an edge image.
        do_merge - If true, incremental merging of segments will be performed
        Returns:
        automatically generated
      • createFastLineDetector

        public static FastLineDetector createFastLineDetector​(int length_threshold,
                                                              float distance_threshold,
                                                              double canny_th1,
                                                              double canny_th2,
                                                              int canny_aperture_size)
        Creates a smart pointer to a FastLineDetector object and initializes it
        Parameters:
        length_threshold - Segment shorter than this will be discarded
        distance_threshold - A point placed from a hypothesis line segment farther than this will be regarded as an outlier
        canny_th1 - First threshold for hysteresis procedure in Canny()
        canny_th2 - Second threshold for hysteresis procedure in Canny()
        canny_aperture_size - Aperturesize for the sobel operator in Canny(). If zero, Canny() is not applied and the input image is taken as an edge image.
        Returns:
        automatically generated
      • createFastLineDetector

        public static FastLineDetector createFastLineDetector​(int length_threshold,
                                                              float distance_threshold,
                                                              double canny_th1,
                                                              double canny_th2)
        Creates a smart pointer to a FastLineDetector object and initializes it
        Parameters:
        length_threshold - Segment shorter than this will be discarded
        distance_threshold - A point placed from a hypothesis line segment farther than this will be regarded as an outlier
        canny_th1 - First threshold for hysteresis procedure in Canny()
        canny_th2 - Second threshold for hysteresis procedure in Canny() If zero, Canny() is not applied and the input image is taken as an edge image.
        Returns:
        automatically generated
      • createFastLineDetector

        public static FastLineDetector createFastLineDetector​(int length_threshold,
                                                              float distance_threshold,
                                                              double canny_th1)
        Creates a smart pointer to a FastLineDetector object and initializes it
        Parameters:
        length_threshold - Segment shorter than this will be discarded
        distance_threshold - A point placed from a hypothesis line segment farther than this will be regarded as an outlier
        canny_th1 - First threshold for hysteresis procedure in Canny() If zero, Canny() is not applied and the input image is taken as an edge image.
        Returns:
        automatically generated
      • createFastLineDetector

        public static FastLineDetector createFastLineDetector​(int length_threshold,
                                                              float distance_threshold)
        Creates a smart pointer to a FastLineDetector object and initializes it
        Parameters:
        length_threshold - Segment shorter than this will be discarded
        distance_threshold - A point placed from a hypothesis line segment farther than this will be regarded as an outlier If zero, Canny() is not applied and the input image is taken as an edge image.
        Returns:
        automatically generated
      • createFastLineDetector

        public static FastLineDetector createFastLineDetector​(int length_threshold)
        Creates a smart pointer to a FastLineDetector object and initializes it
        Parameters:
        length_threshold - Segment shorter than this will be discarded segment farther than this will be regarded as an outlier If zero, Canny() is not applied and the input image is taken as an edge image.
        Returns:
        automatically generated
      • createFastLineDetector

        public static FastLineDetector createFastLineDetector()
        Creates a smart pointer to a FastLineDetector object and initializes it segment farther than this will be regarded as an outlier If zero, Canny() is not applied and the input image is taken as an edge image.
        Returns:
        automatically generated
      • GradientDericheY

        public static void GradientDericheY​(Mat op,
                                            Mat dst,
                                            double alpha,
                                            double omega)
        Applies Y Deriche filter to an image. For more details about this implementation, please see http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.476.5736&rep=rep1&type=pdf
        Parameters:
        op - automatically generated
        dst - automatically generated
        alpha - automatically generated
        omega - automatically generated
      • GradientDericheX

        public static void GradientDericheX​(Mat op,
                                            Mat dst,
                                            double alpha,
                                            double omega)
        Applies X Deriche filter to an image. For more details about this implementation, please see http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.476.5736&rep=rep1&type=pdf
        Parameters:
        op - automatically generated
        dst - automatically generated
        alpha - automatically generated
        omega - automatically generated
      • createDisparityWLSFilter

        public static DisparityWLSFilter createDisparityWLSFilter​(StereoMatcher matcher_left)
        Convenience factory method that creates an instance of DisparityWLSFilter and sets up all the relevant filter parameters automatically based on the matcher instance. Currently supports only StereoBM and StereoSGBM.
        Parameters:
        matcher_left - stereo matcher instance that will be used with the filter
        Returns:
        automatically generated
      • createRightMatcher

        public static StereoMatcher createRightMatcher​(StereoMatcher matcher_left)
        Convenience method to set up the matcher for computing the right-view disparity map that is required in case of filtering with confidence.
        Parameters:
        matcher_left - main stereo matcher instance that will be used with the filter
        Returns:
        automatically generated
      • createDisparityWLSFilterGeneric

        public static DisparityWLSFilter createDisparityWLSFilterGeneric​(boolean use_confidence)
        More generic factory method, create instance of DisparityWLSFilter and execute basic initialization routines. When using this method you will need to set-up the ROI, matchers and other parameters by yourself.
        Parameters:
        use_confidence - filtering with confidence requires two disparity maps (for the left and right views) and is approximately two times slower. However, quality is typically significantly better.
        Returns:
        automatically generated
      • readGT

        public static int readGT​(java.lang.String src_path,
                                 Mat dst)
        Function for reading ground truth disparity maps. Supports basic Middlebury and MPI-Sintel formats. Note that the resulting disparity map is scaled by 16.
        Parameters:
        src_path - path to the image, containing ground-truth disparity map
        dst - output disparity map, CV_16S depth
        Returns:
        returns zero if successfully read the ground truth
      • computeMSE

        public static double computeMSE​(Mat GT,
                                        Mat src,
                                        Rect ROI)
        Function for computing mean square error for disparity maps
        Parameters:
        GT - ground truth disparity map
        src - disparity map to evaluate
        ROI - region of interest
        Returns:
        returns mean square error between GT and src
      • computeBadPixelPercent

        public static double computeBadPixelPercent​(Mat GT,
                                                    Mat src,
                                                    Rect ROI,
                                                    int thresh)
        Function for computing the percent of "bad" pixels in the disparity map (pixels where error is higher than a specified threshold)
        Parameters:
        GT - ground truth disparity map
        src - disparity map to evaluate
        ROI - region of interest
        thresh - threshold used to determine "bad" pixels
        Returns:
        returns mean square error between GT and src
      • computeBadPixelPercent

        public static double computeBadPixelPercent​(Mat GT,
                                                    Mat src,
                                                    Rect ROI)
        Function for computing the percent of "bad" pixels in the disparity map (pixels where error is higher than a specified threshold)
        Parameters:
        GT - ground truth disparity map
        src - disparity map to evaluate
        ROI - region of interest
        Returns:
        returns mean square error between GT and src
      • getDisparityVis

        public static void getDisparityVis​(Mat src,
                                           Mat dst,
                                           double scale)
        Function for creating a disparity map visualization (clamped CV_8U image)
        Parameters:
        src - input disparity map (CV_16S depth)
        dst - output visualization
        scale - disparity map will be multiplied by this value for visualization
      • getDisparityVis

        public static void getDisparityVis​(Mat src,
                                           Mat dst)
        Function for creating a disparity map visualization (clamped CV_8U image)
        Parameters:
        src - input disparity map (CV_16S depth)
        dst - output visualization
      • createSuperpixelLSC

        public static SuperpixelLSC createSuperpixelLSC​(Mat image,
                                                        int region_size,
                                                        float ratio)
        Class implementing the LSC (Linear Spectral Clustering) superpixels
        Parameters:
        image - Image to segment
        region_size - Chooses an average superpixel size measured in pixels
        ratio - Chooses the enforcement of superpixel compactness factor of superpixel The function initializes a SuperpixelLSC object for the input image. It sets the parameters of superpixel algorithm, which are: region_size and ruler. It preallocate some buffers for future computing iterations over the given image. An example of LSC is ilustrated in the following picture. For enanched results it is recommended for color images to preprocess image with little gaussian blur with a small 3 x 3 kernel and additional conversion into CieLAB color space. ![image](pics/superpixels_lsc.png)
        Returns:
        automatically generated
      • createSuperpixelLSC

        public static SuperpixelLSC createSuperpixelLSC​(Mat image,
                                                        int region_size)
        Class implementing the LSC (Linear Spectral Clustering) superpixels
        Parameters:
        image - Image to segment
        region_size - Chooses an average superpixel size measured in pixels The function initializes a SuperpixelLSC object for the input image. It sets the parameters of superpixel algorithm, which are: region_size and ruler. It preallocate some buffers for future computing iterations over the given image. An example of LSC is ilustrated in the following picture. For enanched results it is recommended for color images to preprocess image with little gaussian blur with a small 3 x 3 kernel and additional conversion into CieLAB color space. ![image](pics/superpixels_lsc.png)
        Returns:
        automatically generated
      • createSuperpixelLSC

        public static SuperpixelLSC createSuperpixelLSC​(Mat image)
        Class implementing the LSC (Linear Spectral Clustering) superpixels
        Parameters:
        image - Image to segment The function initializes a SuperpixelLSC object for the input image. It sets the parameters of superpixel algorithm, which are: region_size and ruler. It preallocate some buffers for future computing iterations over the given image. An example of LSC is ilustrated in the following picture. For enanched results it is recommended for color images to preprocess image with little gaussian blur with a small 3 x 3 kernel and additional conversion into CieLAB color space. ![image](pics/superpixels_lsc.png)
        Returns:
        automatically generated
      • RadonTransform

        public static void RadonTransform​(Mat src,
                                          Mat dst,
                                          double theta,
                                          double start_angle,
                                          double end_angle,
                                          boolean crop,
                                          boolean norm)
        Calculate Radon Transform of an image. This function calculates the Radon Transform of a given image in any range. See https://engineering.purdue.edu/~malcolm/pct/CTI_Ch03.pdf for detail. If the input type is CV_8U, the output will be CV_32S. If the input type is CV_32F or CV_64F, the output will be CV_64F The output size will be num_of_integral x src_diagonal_length. If crop is selected, the input image will be crop into square then circle, and output size will be num_of_integral x min_edge.
        Parameters:
        src - automatically generated
        dst - automatically generated
        theta - automatically generated
        start_angle - automatically generated
        end_angle - automatically generated
        crop - automatically generated
        norm - automatically generated
      • RadonTransform

        public static void RadonTransform​(Mat src,
                                          Mat dst,
                                          double theta,
                                          double start_angle,
                                          double end_angle,
                                          boolean crop)
        Calculate Radon Transform of an image. This function calculates the Radon Transform of a given image in any range. See https://engineering.purdue.edu/~malcolm/pct/CTI_Ch03.pdf for detail. If the input type is CV_8U, the output will be CV_32S. If the input type is CV_32F or CV_64F, the output will be CV_64F The output size will be num_of_integral x src_diagonal_length. If crop is selected, the input image will be crop into square then circle, and output size will be num_of_integral x min_edge.
        Parameters:
        src - automatically generated
        dst - automatically generated
        theta - automatically generated
        start_angle - automatically generated
        end_angle - automatically generated
        crop - automatically generated
      • RadonTransform

        public static void RadonTransform​(Mat src,
                                          Mat dst,
                                          double theta,
                                          double start_angle,
                                          double end_angle)
        Calculate Radon Transform of an image. This function calculates the Radon Transform of a given image in any range. See https://engineering.purdue.edu/~malcolm/pct/CTI_Ch03.pdf for detail. If the input type is CV_8U, the output will be CV_32S. If the input type is CV_32F or CV_64F, the output will be CV_64F The output size will be num_of_integral x src_diagonal_length. If crop is selected, the input image will be crop into square then circle, and output size will be num_of_integral x min_edge.
        Parameters:
        src - automatically generated
        dst - automatically generated
        theta - automatically generated
        start_angle - automatically generated
        end_angle - automatically generated
      • RadonTransform

        public static void RadonTransform​(Mat src,
                                          Mat dst,
                                          double theta,
                                          double start_angle)
        Calculate Radon Transform of an image. This function calculates the Radon Transform of a given image in any range. See https://engineering.purdue.edu/~malcolm/pct/CTI_Ch03.pdf for detail. If the input type is CV_8U, the output will be CV_32S. If the input type is CV_32F or CV_64F, the output will be CV_64F The output size will be num_of_integral x src_diagonal_length. If crop is selected, the input image will be crop into square then circle, and output size will be num_of_integral x min_edge.
        Parameters:
        src - automatically generated
        dst - automatically generated
        theta - automatically generated
        start_angle - automatically generated
      • RadonTransform

        public static void RadonTransform​(Mat src,
                                          Mat dst,
                                          double theta)
        Calculate Radon Transform of an image. This function calculates the Radon Transform of a given image in any range. See https://engineering.purdue.edu/~malcolm/pct/CTI_Ch03.pdf for detail. If the input type is CV_8U, the output will be CV_32S. If the input type is CV_32F or CV_64F, the output will be CV_64F The output size will be num_of_integral x src_diagonal_length. If crop is selected, the input image will be crop into square then circle, and output size will be num_of_integral x min_edge.
        Parameters:
        src - automatically generated
        dst - automatically generated
        theta - automatically generated
      • RadonTransform

        public static void RadonTransform​(Mat src,
                                          Mat dst)
        Calculate Radon Transform of an image. This function calculates the Radon Transform of a given image in any range. See https://engineering.purdue.edu/~malcolm/pct/CTI_Ch03.pdf for detail. If the input type is CV_8U, the output will be CV_32S. If the input type is CV_32F or CV_64F, the output will be CV_64F The output size will be num_of_integral x src_diagonal_length. If crop is selected, the input image will be crop into square then circle, and output size will be num_of_integral x min_edge.
        Parameters:
        src - automatically generated
        dst - automatically generated
      • createQuaternionImage

        public static void createQuaternionImage​(Mat img,
                                                 Mat qimg)
        creates a quaternion image.
        Parameters:
        img - automatically generated
        qimg - automatically generated
      • qconj

        public static void qconj​(Mat qimg,
                                 Mat qcimg)
        calculates conjugate of a quaternion image.
        Parameters:
        qimg - automatically generated
        qcimg - automatically generated
      • qunitary

        public static void qunitary​(Mat qimg,
                                    Mat qnimg)
        divides each element by its modulus.
        Parameters:
        qimg - automatically generated
        qnimg - automatically generated
      • qmultiply

        public static void qmultiply​(Mat src1,
                                     Mat src2,
                                     Mat dst)
        Calculates the per-element quaternion product of two arrays
        Parameters:
        src1 - automatically generated
        src2 - automatically generated
        dst - automatically generated
      • qdft

        public static void qdft​(Mat img,
                                Mat qimg,
                                int flags,
                                boolean sideLeft)
        Performs a forward or inverse Discrete quaternion Fourier transform of a 2D quaternion array.
        Parameters:
        img - automatically generated
        qimg - automatically generated
        flags - automatically generated
        sideLeft - automatically generated
      • colorMatchTemplate

        public static void colorMatchTemplate​(Mat img,
                                              Mat templ,
                                              Mat result)
        Compares a color template against overlapped color image regions.
        Parameters:
        img - automatically generated
        templ - automatically generated
        result - automatically generated
      • createSuperpixelSEEDS

        public static SuperpixelSEEDS createSuperpixelSEEDS​(int image_width,
                                                            int image_height,
                                                            int image_channels,
                                                            int num_superpixels,
                                                            int num_levels,
                                                            int prior,
                                                            int histogram_bins,
                                                            boolean double_step)
        Initializes a SuperpixelSEEDS object.
        Parameters:
        image_width - Image width.
        image_height - Image height.
        image_channels - Number of channels of the image.
        num_superpixels - Desired number of superpixels. Note that the actual number may be smaller due to restrictions (depending on the image size and num_levels). Use getNumberOfSuperpixels() to get the actual number.
        num_levels - Number of block levels. The more levels, the more accurate is the segmentation, but needs more memory and CPU time.
        prior - enable 3x3 shape smoothing term if >0. A larger value leads to smoother shapes. prior must be in the range [0, 5].
        histogram_bins - Number of histogram bins.
        double_step - If true, iterate each block level twice for higher accuracy. The function initializes a SuperpixelSEEDS object for the input image. It stores the parameters of the image: image_width, image_height and image_channels. It also sets the parameters of the SEEDS superpixel algorithm, which are: num_superpixels, num_levels, use_prior, histogram_bins and double_step. The number of levels in num_levels defines the amount of block levels that the algorithm use in the optimization. The initialization is a grid, in which the superpixels are equally distributed through the width and the height of the image. The larger blocks correspond to the superpixel size, and the levels with smaller blocks are formed by dividing the larger blocks into 2 x 2 blocks of pixels, recursively until the smaller block level. An example of initialization of 4 block levels is illustrated in the following figure. ![image](pics/superpixels_blocks.png)
        Returns:
        automatically generated
      • createSuperpixelSEEDS

        public static SuperpixelSEEDS createSuperpixelSEEDS​(int image_width,
                                                            int image_height,
                                                            int image_channels,
                                                            int num_superpixels,
                                                            int num_levels,
                                                            int prior,
                                                            int histogram_bins)
        Initializes a SuperpixelSEEDS object.
        Parameters:
        image_width - Image width.
        image_height - Image height.
        image_channels - Number of channels of the image.
        num_superpixels - Desired number of superpixels. Note that the actual number may be smaller due to restrictions (depending on the image size and num_levels). Use getNumberOfSuperpixels() to get the actual number.
        num_levels - Number of block levels. The more levels, the more accurate is the segmentation, but needs more memory and CPU time.
        prior - enable 3x3 shape smoothing term if >0. A larger value leads to smoother shapes. prior must be in the range [0, 5].
        histogram_bins - Number of histogram bins. The function initializes a SuperpixelSEEDS object for the input image. It stores the parameters of the image: image_width, image_height and image_channels. It also sets the parameters of the SEEDS superpixel algorithm, which are: num_superpixels, num_levels, use_prior, histogram_bins and double_step. The number of levels in num_levels defines the amount of block levels that the algorithm use in the optimization. The initialization is a grid, in which the superpixels are equally distributed through the width and the height of the image. The larger blocks correspond to the superpixel size, and the levels with smaller blocks are formed by dividing the larger blocks into 2 x 2 blocks of pixels, recursively until the smaller block level. An example of initialization of 4 block levels is illustrated in the following figure. ![image](pics/superpixels_blocks.png)
        Returns:
        automatically generated
      • createSuperpixelSEEDS

        public static SuperpixelSEEDS createSuperpixelSEEDS​(int image_width,
                                                            int image_height,
                                                            int image_channels,
                                                            int num_superpixels,
                                                            int num_levels,
                                                            int prior)
        Initializes a SuperpixelSEEDS object.
        Parameters:
        image_width - Image width.
        image_height - Image height.
        image_channels - Number of channels of the image.
        num_superpixels - Desired number of superpixels. Note that the actual number may be smaller due to restrictions (depending on the image size and num_levels). Use getNumberOfSuperpixels() to get the actual number.
        num_levels - Number of block levels. The more levels, the more accurate is the segmentation, but needs more memory and CPU time.
        prior - enable 3x3 shape smoothing term if >0. A larger value leads to smoother shapes. prior must be in the range [0, 5]. The function initializes a SuperpixelSEEDS object for the input image. It stores the parameters of the image: image_width, image_height and image_channels. It also sets the parameters of the SEEDS superpixel algorithm, which are: num_superpixels, num_levels, use_prior, histogram_bins and double_step. The number of levels in num_levels defines the amount of block levels that the algorithm use in the optimization. The initialization is a grid, in which the superpixels are equally distributed through the width and the height of the image. The larger blocks correspond to the superpixel size, and the levels with smaller blocks are formed by dividing the larger blocks into 2 x 2 blocks of pixels, recursively until the smaller block level. An example of initialization of 4 block levels is illustrated in the following figure. ![image](pics/superpixels_blocks.png)
        Returns:
        automatically generated
      • createSuperpixelSEEDS

        public static SuperpixelSEEDS createSuperpixelSEEDS​(int image_width,
                                                            int image_height,
                                                            int image_channels,
                                                            int num_superpixels,
                                                            int num_levels)
        Initializes a SuperpixelSEEDS object.
        Parameters:
        image_width - Image width.
        image_height - Image height.
        image_channels - Number of channels of the image.
        num_superpixels - Desired number of superpixels. Note that the actual number may be smaller due to restrictions (depending on the image size and num_levels). Use getNumberOfSuperpixels() to get the actual number.
        num_levels - Number of block levels. The more levels, the more accurate is the segmentation, but needs more memory and CPU time. must be in the range [0, 5]. The function initializes a SuperpixelSEEDS object for the input image. It stores the parameters of the image: image_width, image_height and image_channels. It also sets the parameters of the SEEDS superpixel algorithm, which are: num_superpixels, num_levels, use_prior, histogram_bins and double_step. The number of levels in num_levels defines the amount of block levels that the algorithm use in the optimization. The initialization is a grid, in which the superpixels are equally distributed through the width and the height of the image. The larger blocks correspond to the superpixel size, and the levels with smaller blocks are formed by dividing the larger blocks into 2 x 2 blocks of pixels, recursively until the smaller block level. An example of initialization of 4 block levels is illustrated in the following figure. ![image](pics/superpixels_blocks.png)
        Returns:
        automatically generated
      • covarianceEstimation

        public static void covarianceEstimation​(Mat src,
                                                Mat dst,
                                                int windowRows,
                                                int windowCols)
        Computes the estimated covariance matrix of an image using the sliding window forumlation.
        Parameters:
        src - The source image. Input image must be of a complex type.
        dst - The destination estimated covariance matrix. Output matrix will be size (windowRows*windowCols, windowRows*windowCols).
        windowRows - The number of rows in the window.
        windowCols - The number of cols in the window. The window size parameters control the accuracy of the estimation. The sliding window moves over the entire image from the top-left corner to the bottom right corner. Each location of the window represents a sample. If the window is the size of the image, then this gives the exact covariance matrix. For all other cases, the sizes of the window will impact the number of samples and the number of elements in the estimated covariance matrix.
      • createEdgeAwareInterpolator

        public static EdgeAwareInterpolator createEdgeAwareInterpolator()
        Factory method that creates an instance of the EdgeAwareInterpolator.
        Returns:
        automatically generated
      • createRICInterpolator

        public static RICInterpolator createRICInterpolator()
        Factory method that creates an instance of the RICInterpolator.
        Returns:
        automatically generated
      • fourierDescriptor

        public static void fourierDescriptor​(Mat src,
                                             Mat dst,
                                             int nbElt,
                                             int nbFD)
        Fourier descriptors for planed closed curves For more details about this implementation, please see CITE: PersoonFu1977
        Parameters:
        src - automatically generated
        dst - automatically generated
        nbElt - automatically generated
        nbFD - automatically generated
      • fourierDescriptor

        public static void fourierDescriptor​(Mat src,
                                             Mat dst,
                                             int nbElt)
        Fourier descriptors for planed closed curves For more details about this implementation, please see CITE: PersoonFu1977
        Parameters:
        src - automatically generated
        dst - automatically generated
        nbElt - automatically generated
      • fourierDescriptor

        public static void fourierDescriptor​(Mat src,
                                             Mat dst)
        Fourier descriptors for planed closed curves For more details about this implementation, please see CITE: PersoonFu1977
        Parameters:
        src - automatically generated
        dst - automatically generated
      • transformFD

        public static void transformFD​(Mat src,
                                       Mat t,
                                       Mat dst,
                                       boolean fdContour)
        transform a contour
        Parameters:
        src - automatically generated
        t - automatically generated
        dst - automatically generated
        fdContour - automatically generated
      • transformFD

        public static void transformFD​(Mat src,
                                       Mat t,
                                       Mat dst)
        transform a contour
        Parameters:
        src - automatically generated
        t - automatically generated
        dst - automatically generated
      • contourSampling

        public static void contourSampling​(Mat src,
                                           Mat out,
                                           int nbElt)
        Contour sampling .
        Parameters:
        src - automatically generated
        out - automatically generated
        nbElt - automatically generated
      • createContourFitting

        public static ContourFitting createContourFitting​(int ctr,
                                                          int fd)
        create ContourFitting algorithm object
        Parameters:
        ctr - number of Fourier descriptors equal to number of contour points after resampling.
        fd - Contour defining second shape (Target).
        Returns:
        automatically generated
      • createContourFitting

        public static ContourFitting createContourFitting​(int ctr)
        create ContourFitting algorithm object
        Parameters:
        ctr - number of Fourier descriptors equal to number of contour points after resampling.
        Returns:
        automatically generated
      • createContourFitting

        public static ContourFitting createContourFitting()
        create ContourFitting algorithm object
        Returns:
        automatically generated
      • createScanSegment

        public static ScanSegment createScanSegment​(int image_width,
                                                    int image_height,
                                                    int num_superpixels,
                                                    int slices,
                                                    boolean merge_small)
        Initializes a ScanSegment object. The function initializes a ScanSegment object for the input image. It stores the parameters of the image: image_width and image_height. It also sets the parameters of the F-DBSCAN superpixel algorithm, which are: num_superpixels, threads, and merge_small.
        Parameters:
        image_width - Image width.
        image_height - Image height.
        num_superpixels - Desired number of superpixels. Note that the actual number may be smaller due to restrictions (depending on the image size). Use getNumberOfSuperpixels() to get the actual number.
        slices - Number of processing threads for parallelisation. Setting -1 uses the maximum number of threads. In practice, four threads is enough for smaller images and eight threads for larger ones.
        merge_small - merge small segments to give the desired number of superpixels. Processing is much faster without merging, but many small segments will be left in the image.
        Returns:
        automatically generated
      • createScanSegment

        public static ScanSegment createScanSegment​(int image_width,
                                                    int image_height,
                                                    int num_superpixels,
                                                    int slices)
        Initializes a ScanSegment object. The function initializes a ScanSegment object for the input image. It stores the parameters of the image: image_width and image_height. It also sets the parameters of the F-DBSCAN superpixel algorithm, which are: num_superpixels, threads, and merge_small.
        Parameters:
        image_width - Image width.
        image_height - Image height.
        num_superpixels - Desired number of superpixels. Note that the actual number may be smaller due to restrictions (depending on the image size). Use getNumberOfSuperpixels() to get the actual number.
        slices - Number of processing threads for parallelisation. Setting -1 uses the maximum number of threads. In practice, four threads is enough for smaller images and eight threads for larger ones. much faster without merging, but many small segments will be left in the image.
        Returns:
        automatically generated
      • createScanSegment

        public static ScanSegment createScanSegment​(int image_width,
                                                    int image_height,
                                                    int num_superpixels)
        Initializes a ScanSegment object. The function initializes a ScanSegment object for the input image. It stores the parameters of the image: image_width and image_height. It also sets the parameters of the F-DBSCAN superpixel algorithm, which are: num_superpixels, threads, and merge_small.
        Parameters:
        image_width - Image width.
        image_height - Image height.
        num_superpixels - Desired number of superpixels. Note that the actual number may be smaller due to restrictions (depending on the image size). Use getNumberOfSuperpixels() to get the actual number. of threads. In practice, four threads is enough for smaller images and eight threads for larger ones. much faster without merging, but many small segments will be left in the image.
        Returns:
        automatically generated
      • FastHoughTransform

        public static void FastHoughTransform​(Mat src,
                                              Mat dst,
                                              int dstMatDepth,
                                              int angleRange,
                                              int op,
                                              int makeSkew)
        Calculates 2D Fast Hough transform of an image. The function calculates the fast Hough transform for full, half or quarter range of angles.
        Parameters:
        src - automatically generated
        dst - automatically generated
        dstMatDepth - automatically generated
        angleRange - automatically generated
        op - automatically generated
        makeSkew - automatically generated
      • FastHoughTransform

        public static void FastHoughTransform​(Mat src,
                                              Mat dst,
                                              int dstMatDepth,
                                              int angleRange,
                                              int op)
        Calculates 2D Fast Hough transform of an image. The function calculates the fast Hough transform for full, half or quarter range of angles.
        Parameters:
        src - automatically generated
        dst - automatically generated
        dstMatDepth - automatically generated
        angleRange - automatically generated
        op - automatically generated
      • FastHoughTransform

        public static void FastHoughTransform​(Mat src,
                                              Mat dst,
                                              int dstMatDepth,
                                              int angleRange)
        Calculates 2D Fast Hough transform of an image. The function calculates the fast Hough transform for full, half or quarter range of angles.
        Parameters:
        src - automatically generated
        dst - automatically generated
        dstMatDepth - automatically generated
        angleRange - automatically generated
      • FastHoughTransform

        public static void FastHoughTransform​(Mat src,
                                              Mat dst,
                                              int dstMatDepth)
        Calculates 2D Fast Hough transform of an image. The function calculates the fast Hough transform for full, half or quarter range of angles.
        Parameters:
        src - automatically generated
        dst - automatically generated
        dstMatDepth - automatically generated
      • createEdgeDrawing

        public static EdgeDrawing createEdgeDrawing()
        Creates a smart pointer to a EdgeDrawing object and initializes it
        Returns:
        automatically generated
      • createEdgeBoxes

        public static EdgeBoxes createEdgeBoxes​(float alpha,
                                                float beta,
                                                float eta,
                                                float minScore,
                                                int maxBoxes,
                                                float edgeMinMag,
                                                float edgeMergeThr,
                                                float clusterMinMag,
                                                float maxAspectRatio,
                                                float minBoxArea,
                                                float gamma,
                                                float kappa)
        Creates a Edgeboxes
        Parameters:
        alpha - step size of sliding window search.
        beta - nms threshold for object proposals.
        eta - adaptation rate for nms threshold.
        minScore - min score of boxes to detect.
        maxBoxes - max number of boxes to detect.
        edgeMinMag - edge min magnitude. Increase to trade off accuracy for speed.
        edgeMergeThr - edge merge threshold. Increase to trade off accuracy for speed.
        clusterMinMag - cluster min magnitude. Increase to trade off accuracy for speed.
        maxAspectRatio - max aspect ratio of boxes.
        minBoxArea - minimum area of boxes.
        gamma - affinity sensitivity.
        kappa - scale sensitivity.
        Returns:
        automatically generated
      • createEdgeBoxes

        public static EdgeBoxes createEdgeBoxes​(float alpha,
                                                float beta,
                                                float eta,
                                                float minScore,
                                                int maxBoxes,
                                                float edgeMinMag,
                                                float edgeMergeThr,
                                                float clusterMinMag,
                                                float maxAspectRatio,
                                                float minBoxArea,
                                                float gamma)
        Creates a Edgeboxes
        Parameters:
        alpha - step size of sliding window search.
        beta - nms threshold for object proposals.
        eta - adaptation rate for nms threshold.
        minScore - min score of boxes to detect.
        maxBoxes - max number of boxes to detect.
        edgeMinMag - edge min magnitude. Increase to trade off accuracy for speed.
        edgeMergeThr - edge merge threshold. Increase to trade off accuracy for speed.
        clusterMinMag - cluster min magnitude. Increase to trade off accuracy for speed.
        maxAspectRatio - max aspect ratio of boxes.
        minBoxArea - minimum area of boxes.
        gamma - affinity sensitivity.
        Returns:
        automatically generated
      • createEdgeBoxes

        public static EdgeBoxes createEdgeBoxes​(float alpha,
                                                float beta,
                                                float eta,
                                                float minScore,
                                                int maxBoxes,
                                                float edgeMinMag,
                                                float edgeMergeThr,
                                                float clusterMinMag,
                                                float maxAspectRatio,
                                                float minBoxArea)
        Creates a Edgeboxes
        Parameters:
        alpha - step size of sliding window search.
        beta - nms threshold for object proposals.
        eta - adaptation rate for nms threshold.
        minScore - min score of boxes to detect.
        maxBoxes - max number of boxes to detect.
        edgeMinMag - edge min magnitude. Increase to trade off accuracy for speed.
        edgeMergeThr - edge merge threshold. Increase to trade off accuracy for speed.
        clusterMinMag - cluster min magnitude. Increase to trade off accuracy for speed.
        maxAspectRatio - max aspect ratio of boxes.
        minBoxArea - minimum area of boxes.
        Returns:
        automatically generated
      • createEdgeBoxes

        public static EdgeBoxes createEdgeBoxes​(float alpha,
                                                float beta,
                                                float eta,
                                                float minScore,
                                                int maxBoxes,
                                                float edgeMinMag,
                                                float edgeMergeThr,
                                                float clusterMinMag,
                                                float maxAspectRatio)
        Creates a Edgeboxes
        Parameters:
        alpha - step size of sliding window search.
        beta - nms threshold for object proposals.
        eta - adaptation rate for nms threshold.
        minScore - min score of boxes to detect.
        maxBoxes - max number of boxes to detect.
        edgeMinMag - edge min magnitude. Increase to trade off accuracy for speed.
        edgeMergeThr - edge merge threshold. Increase to trade off accuracy for speed.
        clusterMinMag - cluster min magnitude. Increase to trade off accuracy for speed.
        maxAspectRatio - max aspect ratio of boxes.
        Returns:
        automatically generated
      • createEdgeBoxes

        public static EdgeBoxes createEdgeBoxes​(float alpha,
                                                float beta,
                                                float eta,
                                                float minScore,
                                                int maxBoxes,
                                                float edgeMinMag,
                                                float edgeMergeThr,
                                                float clusterMinMag)
        Creates a Edgeboxes
        Parameters:
        alpha - step size of sliding window search.
        beta - nms threshold for object proposals.
        eta - adaptation rate for nms threshold.
        minScore - min score of boxes to detect.
        maxBoxes - max number of boxes to detect.
        edgeMinMag - edge min magnitude. Increase to trade off accuracy for speed.
        edgeMergeThr - edge merge threshold. Increase to trade off accuracy for speed.
        clusterMinMag - cluster min magnitude. Increase to trade off accuracy for speed.
        Returns:
        automatically generated
      • createEdgeBoxes

        public static EdgeBoxes createEdgeBoxes​(float alpha,
                                                float beta,
                                                float eta,
                                                float minScore,
                                                int maxBoxes,
                                                float edgeMinMag,
                                                float edgeMergeThr)
        Creates a Edgeboxes
        Parameters:
        alpha - step size of sliding window search.
        beta - nms threshold for object proposals.
        eta - adaptation rate for nms threshold.
        minScore - min score of boxes to detect.
        maxBoxes - max number of boxes to detect.
        edgeMinMag - edge min magnitude. Increase to trade off accuracy for speed.
        edgeMergeThr - edge merge threshold. Increase to trade off accuracy for speed.
        Returns:
        automatically generated
      • createEdgeBoxes

        public static EdgeBoxes createEdgeBoxes​(float alpha,
                                                float beta,
                                                float eta,
                                                float minScore,
                                                int maxBoxes,
                                                float edgeMinMag)
        Creates a Edgeboxes
        Parameters:
        alpha - step size of sliding window search.
        beta - nms threshold for object proposals.
        eta - adaptation rate for nms threshold.
        minScore - min score of boxes to detect.
        maxBoxes - max number of boxes to detect.
        edgeMinMag - edge min magnitude. Increase to trade off accuracy for speed.
        Returns:
        automatically generated
      • createEdgeBoxes

        public static EdgeBoxes createEdgeBoxes​(float alpha,
                                                float beta,
                                                float eta,
                                                float minScore,
                                                int maxBoxes)
        Creates a Edgeboxes
        Parameters:
        alpha - step size of sliding window search.
        beta - nms threshold for object proposals.
        eta - adaptation rate for nms threshold.
        minScore - min score of boxes to detect.
        maxBoxes - max number of boxes to detect.
        Returns:
        automatically generated
      • createEdgeBoxes

        public static EdgeBoxes createEdgeBoxes​(float alpha,
                                                float beta,
                                                float eta,
                                                float minScore)
        Creates a Edgeboxes
        Parameters:
        alpha - step size of sliding window search.
        beta - nms threshold for object proposals.
        eta - adaptation rate for nms threshold.
        minScore - min score of boxes to detect.
        Returns:
        automatically generated
      • createEdgeBoxes

        public static EdgeBoxes createEdgeBoxes​(float alpha,
                                                float beta,
                                                float eta)
        Creates a Edgeboxes
        Parameters:
        alpha - step size of sliding window search.
        beta - nms threshold for object proposals.
        eta - adaptation rate for nms threshold.
        Returns:
        automatically generated
      • createEdgeBoxes

        public static EdgeBoxes createEdgeBoxes​(float alpha,
                                                float beta)
        Creates a Edgeboxes
        Parameters:
        alpha - step size of sliding window search.
        beta - nms threshold for object proposals.
        Returns:
        automatically generated
      • createEdgeBoxes

        public static EdgeBoxes createEdgeBoxes​(float alpha)
        Creates a Edgeboxes
        Parameters:
        alpha - step size of sliding window search.
        Returns:
        automatically generated
      • createEdgeBoxes

        public static EdgeBoxes createEdgeBoxes()
        Creates a Edgeboxes
        Returns:
        automatically generated
      • createDTFilter

        public static DTFilter createDTFilter​(Mat guide,
                                              double sigmaSpatial,
                                              double sigmaColor,
                                              int mode,
                                              int numIters)
        Factory method, create instance of DTFilter and produce initialization routines.
        Parameters:
        guide - guided image (used to build transformed distance, which describes edge structure of guided image).
        sigmaSpatial - \({\sigma}_H\) parameter in the original article, it's similar to the sigma in the coordinate space into bilateralFilter.
        sigmaColor - \({\sigma}_r\) parameter in the original article, it's similar to the sigma in the color space into bilateralFilter.
        mode - one form three modes DTF_NC, DTF_RF and DTF_IC which corresponds to three modes for filtering 2D signals in the article.
        numIters - optional number of iterations used for filtering, 3 is quite enough. For more details about Domain Transform filter parameters, see the original article CITE: Gastal11 and [Domain Transform filter homepage](http://www.inf.ufrgs.br/~eslgastal/DomainTransform/).
        Returns:
        automatically generated
      • createDTFilter

        public static DTFilter createDTFilter​(Mat guide,
                                              double sigmaSpatial,
                                              double sigmaColor,
                                              int mode)
        Factory method, create instance of DTFilter and produce initialization routines.
        Parameters:
        guide - guided image (used to build transformed distance, which describes edge structure of guided image).
        sigmaSpatial - \({\sigma}_H\) parameter in the original article, it's similar to the sigma in the coordinate space into bilateralFilter.
        sigmaColor - \({\sigma}_r\) parameter in the original article, it's similar to the sigma in the color space into bilateralFilter.
        mode - one form three modes DTF_NC, DTF_RF and DTF_IC which corresponds to three modes for filtering 2D signals in the article. For more details about Domain Transform filter parameters, see the original article CITE: Gastal11 and [Domain Transform filter homepage](http://www.inf.ufrgs.br/~eslgastal/DomainTransform/).
        Returns:
        automatically generated
      • createDTFilter

        public static DTFilter createDTFilter​(Mat guide,
                                              double sigmaSpatial,
                                              double sigmaColor)
        Factory method, create instance of DTFilter and produce initialization routines.
        Parameters:
        guide - guided image (used to build transformed distance, which describes edge structure of guided image).
        sigmaSpatial - \({\sigma}_H\) parameter in the original article, it's similar to the sigma in the coordinate space into bilateralFilter.
        sigmaColor - \({\sigma}_r\) parameter in the original article, it's similar to the sigma in the color space into bilateralFilter. filtering 2D signals in the article. For more details about Domain Transform filter parameters, see the original article CITE: Gastal11 and [Domain Transform filter homepage](http://www.inf.ufrgs.br/~eslgastal/DomainTransform/).
        Returns:
        automatically generated
      • dtFilter

        public static void dtFilter​(Mat guide,
                                    Mat src,
                                    Mat dst,
                                    double sigmaSpatial,
                                    double sigmaColor,
                                    int mode,
                                    int numIters)
        Simple one-line Domain Transform filter call. If you have multiple images to filter with the same guided image then use DTFilter interface to avoid extra computations on initialization stage.
        Parameters:
        guide - guided image (also called as joint image) with unsigned 8-bit or floating-point 32-bit depth and up to 4 channels.
        src - filtering image with unsigned 8-bit or floating-point 32-bit depth and up to 4 channels.
        dst - destination image
        sigmaSpatial - \({\sigma}_H\) parameter in the original article, it's similar to the sigma in the coordinate space into bilateralFilter.
        sigmaColor - \({\sigma}_r\) parameter in the original article, it's similar to the sigma in the color space into bilateralFilter.
        mode - one form three modes DTF_NC, DTF_RF and DTF_IC which corresponds to three modes for filtering 2D signals in the article.
        numIters - optional number of iterations used for filtering, 3 is quite enough. SEE: bilateralFilter, guidedFilter, amFilter
      • dtFilter

        public static void dtFilter​(Mat guide,
                                    Mat src,
                                    Mat dst,
                                    double sigmaSpatial,
                                    double sigmaColor,
                                    int mode)
        Simple one-line Domain Transform filter call. If you have multiple images to filter with the same guided image then use DTFilter interface to avoid extra computations on initialization stage.
        Parameters:
        guide - guided image (also called as joint image) with unsigned 8-bit or floating-point 32-bit depth and up to 4 channels.
        src - filtering image with unsigned 8-bit or floating-point 32-bit depth and up to 4 channels.
        dst - destination image
        sigmaSpatial - \({\sigma}_H\) parameter in the original article, it's similar to the sigma in the coordinate space into bilateralFilter.
        sigmaColor - \({\sigma}_r\) parameter in the original article, it's similar to the sigma in the color space into bilateralFilter.
        mode - one form three modes DTF_NC, DTF_RF and DTF_IC which corresponds to three modes for filtering 2D signals in the article. SEE: bilateralFilter, guidedFilter, amFilter
      • dtFilter

        public static void dtFilter​(Mat guide,
                                    Mat src,
                                    Mat dst,
                                    double sigmaSpatial,
                                    double sigmaColor)
        Simple one-line Domain Transform filter call. If you have multiple images to filter with the same guided image then use DTFilter interface to avoid extra computations on initialization stage.
        Parameters:
        guide - guided image (also called as joint image) with unsigned 8-bit or floating-point 32-bit depth and up to 4 channels.
        src - filtering image with unsigned 8-bit or floating-point 32-bit depth and up to 4 channels.
        dst - destination image
        sigmaSpatial - \({\sigma}_H\) parameter in the original article, it's similar to the sigma in the coordinate space into bilateralFilter.
        sigmaColor - \({\sigma}_r\) parameter in the original article, it's similar to the sigma in the color space into bilateralFilter. filtering 2D signals in the article. SEE: bilateralFilter, guidedFilter, amFilter
      • createGuidedFilter

        public static GuidedFilter createGuidedFilter​(Mat guide,
                                                      int radius,
                                                      double eps)
        Factory method, create instance of GuidedFilter and produce initialization routines.
        Parameters:
        guide - guided image (or array of images) with up to 3 channels, if it have more then 3 channels then only first 3 channels will be used.
        radius - radius of Guided Filter.
        eps - regularization term of Guided Filter. \({eps}^2\) is similar to the sigma in the color space into bilateralFilter. For more details about Guided Filter parameters, see the original article CITE: Kaiming10 .
        Returns:
        automatically generated
      • guidedFilter

        public static void guidedFilter​(Mat guide,
                                        Mat src,
                                        Mat dst,
                                        int radius,
                                        double eps,
                                        int dDepth)
        Simple one-line Guided Filter call. If you have multiple images to filter with the same guided image then use GuidedFilter interface to avoid extra computations on initialization stage.
        Parameters:
        guide - guided image (or array of images) with up to 3 channels, if it have more then 3 channels then only first 3 channels will be used.
        src - filtering image with any numbers of channels.
        dst - output image.
        radius - radius of Guided Filter.
        eps - regularization term of Guided Filter. \({eps}^2\) is similar to the sigma in the color space into bilateralFilter.
        dDepth - optional depth of the output image. SEE: bilateralFilter, dtFilter, amFilter
      • guidedFilter

        public static void guidedFilter​(Mat guide,
                                        Mat src,
                                        Mat dst,
                                        int radius,
                                        double eps)
        Simple one-line Guided Filter call. If you have multiple images to filter with the same guided image then use GuidedFilter interface to avoid extra computations on initialization stage.
        Parameters:
        guide - guided image (or array of images) with up to 3 channels, if it have more then 3 channels then only first 3 channels will be used.
        src - filtering image with any numbers of channels.
        dst - output image.
        radius - radius of Guided Filter.
        eps - regularization term of Guided Filter. \({eps}^2\) is similar to the sigma in the color space into bilateralFilter. SEE: bilateralFilter, dtFilter, amFilter
      • createAMFilter

        public static AdaptiveManifoldFilter createAMFilter​(double sigma_s,
                                                            double sigma_r,
                                                            boolean adjust_outliers)
        Factory method, create instance of AdaptiveManifoldFilter and produce some initialization routines.
        Parameters:
        sigma_s - spatial standard deviation.
        sigma_r - color space standard deviation, it is similar to the sigma in the color space into bilateralFilter.
        adjust_outliers - optional, specify perform outliers adjust operation or not, (Eq. 9) in the original paper. For more details about Adaptive Manifold Filter parameters, see the original article CITE: Gastal12 . Note: Joint images with CV_8U and CV_16U depth converted to images with CV_32F depth and [0; 1] color range before processing. Hence color space sigma sigma_r must be in [0; 1] range, unlike same sigmas in bilateralFilter and dtFilter functions.
        Returns:
        automatically generated
      • createAMFilter

        public static AdaptiveManifoldFilter createAMFilter​(double sigma_s,
                                                            double sigma_r)
        Factory method, create instance of AdaptiveManifoldFilter and produce some initialization routines.
        Parameters:
        sigma_s - spatial standard deviation.
        sigma_r - color space standard deviation, it is similar to the sigma in the color space into bilateralFilter. original paper. For more details about Adaptive Manifold Filter parameters, see the original article CITE: Gastal12 . Note: Joint images with CV_8U and CV_16U depth converted to images with CV_32F depth and [0; 1] color range before processing. Hence color space sigma sigma_r must be in [0; 1] range, unlike same sigmas in bilateralFilter and dtFilter functions.
        Returns:
        automatically generated
      • amFilter

        public static void amFilter​(Mat joint,
                                    Mat src,
                                    Mat dst,
                                    double sigma_s,
                                    double sigma_r,
                                    boolean adjust_outliers)
        Simple one-line Adaptive Manifold Filter call.
        Parameters:
        joint - joint (also called as guided) image or array of images with any numbers of channels.
        src - filtering image with any numbers of channels.
        dst - output image.
        sigma_s - spatial standard deviation.
        sigma_r - color space standard deviation, it is similar to the sigma in the color space into bilateralFilter.
        adjust_outliers - optional, specify perform outliers adjust operation or not, (Eq. 9) in the original paper. Note: Joint images with CV_8U and CV_16U depth converted to images with CV_32F depth and [0; 1] color range before processing. Hence color space sigma sigma_r must be in [0; 1] range, unlike same sigmas in bilateralFilter and dtFilter functions. SEE: bilateralFilter, dtFilter, guidedFilter
      • amFilter

        public static void amFilter​(Mat joint,
                                    Mat src,
                                    Mat dst,
                                    double sigma_s,
                                    double sigma_r)
        Simple one-line Adaptive Manifold Filter call.
        Parameters:
        joint - joint (also called as guided) image or array of images with any numbers of channels.
        src - filtering image with any numbers of channels.
        dst - output image.
        sigma_s - spatial standard deviation.
        sigma_r - color space standard deviation, it is similar to the sigma in the color space into bilateralFilter. original paper. Note: Joint images with CV_8U and CV_16U depth converted to images with CV_32F depth and [0; 1] color range before processing. Hence color space sigma sigma_r must be in [0; 1] range, unlike same sigmas in bilateralFilter and dtFilter functions. SEE: bilateralFilter, dtFilter, guidedFilter
      • jointBilateralFilter

        public static void jointBilateralFilter​(Mat joint,
                                                Mat src,
                                                Mat dst,
                                                int d,
                                                double sigmaColor,
                                                double sigmaSpace,
                                                int borderType)
        Applies the joint bilateral filter to an image.
        Parameters:
        joint - Joint 8-bit or floating-point, 1-channel or 3-channel image.
        src - Source 8-bit or floating-point, 1-channel or 3-channel image with the same depth as joint image.
        dst - Destination image of the same size and type as src .
        d - Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, it is computed from sigmaSpace .
        sigmaColor - Filter sigma in the color space. A larger value of the parameter means that farther colors within the pixel neighborhood (see sigmaSpace ) will be mixed together, resulting in larger areas of semi-equal color.
        sigmaSpace - Filter sigma in the coordinate space. A larger value of the parameter means that farther pixels will influence each other as long as their colors are close enough (see sigmaColor ). When d>0 , it specifies the neighborhood size regardless of sigmaSpace . Otherwise, d is proportional to sigmaSpace .
        borderType - Note: bilateralFilter and jointBilateralFilter use L1 norm to compute difference between colors. SEE: bilateralFilter, amFilter
      • jointBilateralFilter

        public static void jointBilateralFilter​(Mat joint,
                                                Mat src,
                                                Mat dst,
                                                int d,
                                                double sigmaColor,
                                                double sigmaSpace)
        Applies the joint bilateral filter to an image.
        Parameters:
        joint - Joint 8-bit or floating-point, 1-channel or 3-channel image.
        src - Source 8-bit or floating-point, 1-channel or 3-channel image with the same depth as joint image.
        dst - Destination image of the same size and type as src .
        d - Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, it is computed from sigmaSpace .
        sigmaColor - Filter sigma in the color space. A larger value of the parameter means that farther colors within the pixel neighborhood (see sigmaSpace ) will be mixed together, resulting in larger areas of semi-equal color.
        sigmaSpace - Filter sigma in the coordinate space. A larger value of the parameter means that farther pixels will influence each other as long as their colors are close enough (see sigmaColor ). When d>0 , it specifies the neighborhood size regardless of sigmaSpace . Otherwise, d is proportional to sigmaSpace . Note: bilateralFilter and jointBilateralFilter use L1 norm to compute difference between colors. SEE: bilateralFilter, amFilter
      • bilateralTextureFilter

        public static void bilateralTextureFilter​(Mat src,
                                                  Mat dst,
                                                  int fr,
                                                  int numIter,
                                                  double sigmaAlpha,
                                                  double sigmaAvg)
        Applies the bilateral texture filter to an image. It performs structure-preserving texture filter. For more details about this filter see CITE: Cho2014.
        Parameters:
        src - Source image whose depth is 8-bit UINT or 32-bit FLOAT
        dst - Destination image of the same size and type as src.
        fr - Radius of kernel to be used for filtering. It should be positive integer
        numIter - Number of iterations of algorithm, It should be positive integer
        sigmaAlpha - Controls the sharpness of the weight transition from edges to smooth/texture regions, where a bigger value means sharper transition. When the value is negative, it is automatically calculated.
        sigmaAvg - Range blur parameter for texture blurring. Larger value makes result to be more blurred. When the value is negative, it is automatically calculated as described in the paper. SEE: rollingGuidanceFilter, bilateralFilter
      • bilateralTextureFilter

        public static void bilateralTextureFilter​(Mat src,
                                                  Mat dst,
                                                  int fr,
                                                  int numIter,
                                                  double sigmaAlpha)
        Applies the bilateral texture filter to an image. It performs structure-preserving texture filter. For more details about this filter see CITE: Cho2014.
        Parameters:
        src - Source image whose depth is 8-bit UINT or 32-bit FLOAT
        dst - Destination image of the same size and type as src.
        fr - Radius of kernel to be used for filtering. It should be positive integer
        numIter - Number of iterations of algorithm, It should be positive integer
        sigmaAlpha - Controls the sharpness of the weight transition from edges to smooth/texture regions, where a bigger value means sharper transition. When the value is negative, it is automatically calculated. value is negative, it is automatically calculated as described in the paper. SEE: rollingGuidanceFilter, bilateralFilter
      • bilateralTextureFilter

        public static void bilateralTextureFilter​(Mat src,
                                                  Mat dst,
                                                  int fr,
                                                  int numIter)
        Applies the bilateral texture filter to an image. It performs structure-preserving texture filter. For more details about this filter see CITE: Cho2014.
        Parameters:
        src - Source image whose depth is 8-bit UINT or 32-bit FLOAT
        dst - Destination image of the same size and type as src.
        fr - Radius of kernel to be used for filtering. It should be positive integer
        numIter - Number of iterations of algorithm, It should be positive integer a bigger value means sharper transition. When the value is negative, it is automatically calculated. value is negative, it is automatically calculated as described in the paper. SEE: rollingGuidanceFilter, bilateralFilter
      • bilateralTextureFilter

        public static void bilateralTextureFilter​(Mat src,
                                                  Mat dst,
                                                  int fr)
        Applies the bilateral texture filter to an image. It performs structure-preserving texture filter. For more details about this filter see CITE: Cho2014.
        Parameters:
        src - Source image whose depth is 8-bit UINT or 32-bit FLOAT
        dst - Destination image of the same size and type as src.
        fr - Radius of kernel to be used for filtering. It should be positive integer a bigger value means sharper transition. When the value is negative, it is automatically calculated. value is negative, it is automatically calculated as described in the paper. SEE: rollingGuidanceFilter, bilateralFilter
      • bilateralTextureFilter

        public static void bilateralTextureFilter​(Mat src,
                                                  Mat dst)
        Applies the bilateral texture filter to an image. It performs structure-preserving texture filter. For more details about this filter see CITE: Cho2014.
        Parameters:
        src - Source image whose depth is 8-bit UINT or 32-bit FLOAT
        dst - Destination image of the same size and type as src. a bigger value means sharper transition. When the value is negative, it is automatically calculated. value is negative, it is automatically calculated as described in the paper. SEE: rollingGuidanceFilter, bilateralFilter
      • rollingGuidanceFilter

        public static void rollingGuidanceFilter​(Mat src,
                                                 Mat dst,
                                                 int d,
                                                 double sigmaColor,
                                                 double sigmaSpace,
                                                 int numOfIter,
                                                 int borderType)
        Applies the rolling guidance filter to an image. For more details, please see CITE: zhang2014rolling
        Parameters:
        src - Source 8-bit or floating-point, 1-channel or 3-channel image.
        dst - Destination image of the same size and type as src.
        d - Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, it is computed from sigmaSpace .
        sigmaColor - Filter sigma in the color space. A larger value of the parameter means that farther colors within the pixel neighborhood (see sigmaSpace ) will be mixed together, resulting in larger areas of semi-equal color.
        sigmaSpace - Filter sigma in the coordinate space. A larger value of the parameter means that farther pixels will influence each other as long as their colors are close enough (see sigmaColor ). When d>0 , it specifies the neighborhood size regardless of sigmaSpace . Otherwise, d is proportional to sigmaSpace .
        numOfIter - Number of iterations of joint edge-preserving filtering applied on the source image.
        borderType - Note: rollingGuidanceFilter uses jointBilateralFilter as the edge-preserving filter. SEE: jointBilateralFilter, bilateralFilter, amFilter
      • rollingGuidanceFilter

        public static void rollingGuidanceFilter​(Mat src,
                                                 Mat dst,
                                                 int d,
                                                 double sigmaColor,
                                                 double sigmaSpace,
                                                 int numOfIter)
        Applies the rolling guidance filter to an image. For more details, please see CITE: zhang2014rolling
        Parameters:
        src - Source 8-bit or floating-point, 1-channel or 3-channel image.
        dst - Destination image of the same size and type as src.
        d - Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, it is computed from sigmaSpace .
        sigmaColor - Filter sigma in the color space. A larger value of the parameter means that farther colors within the pixel neighborhood (see sigmaSpace ) will be mixed together, resulting in larger areas of semi-equal color.
        sigmaSpace - Filter sigma in the coordinate space. A larger value of the parameter means that farther pixels will influence each other as long as their colors are close enough (see sigmaColor ). When d>0 , it specifies the neighborhood size regardless of sigmaSpace . Otherwise, d is proportional to sigmaSpace .
        numOfIter - Number of iterations of joint edge-preserving filtering applied on the source image. Note: rollingGuidanceFilter uses jointBilateralFilter as the edge-preserving filter. SEE: jointBilateralFilter, bilateralFilter, amFilter
      • rollingGuidanceFilter

        public static void rollingGuidanceFilter​(Mat src,
                                                 Mat dst,
                                                 int d,
                                                 double sigmaColor,
                                                 double sigmaSpace)
        Applies the rolling guidance filter to an image. For more details, please see CITE: zhang2014rolling
        Parameters:
        src - Source 8-bit or floating-point, 1-channel or 3-channel image.
        dst - Destination image of the same size and type as src.
        d - Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, it is computed from sigmaSpace .
        sigmaColor - Filter sigma in the color space. A larger value of the parameter means that farther colors within the pixel neighborhood (see sigmaSpace ) will be mixed together, resulting in larger areas of semi-equal color.
        sigmaSpace - Filter sigma in the coordinate space. A larger value of the parameter means that farther pixels will influence each other as long as their colors are close enough (see sigmaColor ). When d>0 , it specifies the neighborhood size regardless of sigmaSpace . Otherwise, d is proportional to sigmaSpace . Note: rollingGuidanceFilter uses jointBilateralFilter as the edge-preserving filter. SEE: jointBilateralFilter, bilateralFilter, amFilter
      • rollingGuidanceFilter

        public static void rollingGuidanceFilter​(Mat src,
                                                 Mat dst,
                                                 int d,
                                                 double sigmaColor)
        Applies the rolling guidance filter to an image. For more details, please see CITE: zhang2014rolling
        Parameters:
        src - Source 8-bit or floating-point, 1-channel or 3-channel image.
        dst - Destination image of the same size and type as src.
        d - Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, it is computed from sigmaSpace .
        sigmaColor - Filter sigma in the color space. A larger value of the parameter means that farther colors within the pixel neighborhood (see sigmaSpace ) will be mixed together, resulting in larger areas of semi-equal color. farther pixels will influence each other as long as their colors are close enough (see sigmaColor ). When d>0 , it specifies the neighborhood size regardless of sigmaSpace . Otherwise, d is proportional to sigmaSpace . Note: rollingGuidanceFilter uses jointBilateralFilter as the edge-preserving filter. SEE: jointBilateralFilter, bilateralFilter, amFilter
      • rollingGuidanceFilter

        public static void rollingGuidanceFilter​(Mat src,
                                                 Mat dst,
                                                 int d)
        Applies the rolling guidance filter to an image. For more details, please see CITE: zhang2014rolling
        Parameters:
        src - Source 8-bit or floating-point, 1-channel or 3-channel image.
        dst - Destination image of the same size and type as src.
        d - Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, it is computed from sigmaSpace . farther colors within the pixel neighborhood (see sigmaSpace ) will be mixed together, resulting in larger areas of semi-equal color. farther pixels will influence each other as long as their colors are close enough (see sigmaColor ). When d>0 , it specifies the neighborhood size regardless of sigmaSpace . Otherwise, d is proportional to sigmaSpace . Note: rollingGuidanceFilter uses jointBilateralFilter as the edge-preserving filter. SEE: jointBilateralFilter, bilateralFilter, amFilter
      • rollingGuidanceFilter

        public static void rollingGuidanceFilter​(Mat src,
                                                 Mat dst)
        Applies the rolling guidance filter to an image. For more details, please see CITE: zhang2014rolling
        Parameters:
        src - Source 8-bit or floating-point, 1-channel or 3-channel image.
        dst - Destination image of the same size and type as src. it is computed from sigmaSpace . farther colors within the pixel neighborhood (see sigmaSpace ) will be mixed together, resulting in larger areas of semi-equal color. farther pixels will influence each other as long as their colors are close enough (see sigmaColor ). When d>0 , it specifies the neighborhood size regardless of sigmaSpace . Otherwise, d is proportional to sigmaSpace . Note: rollingGuidanceFilter uses jointBilateralFilter as the edge-preserving filter. SEE: jointBilateralFilter, bilateralFilter, amFilter
      • createFastBilateralSolverFilter

        public static FastBilateralSolverFilter createFastBilateralSolverFilter​(Mat guide,
                                                                                double sigma_spatial,
                                                                                double sigma_luma,
                                                                                double sigma_chroma,
                                                                                double lambda,
                                                                                int num_iter,
                                                                                double max_tol)
        Factory method, create instance of FastBilateralSolverFilter and execute the initialization routines.
        Parameters:
        guide - image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels.
        sigma_spatial - parameter, that is similar to spatial space sigma (bandwidth) in bilateralFilter.
        sigma_luma - parameter, that is similar to luma space sigma (bandwidth) in bilateralFilter.
        sigma_chroma - parameter, that is similar to chroma space sigma (bandwidth) in bilateralFilter.
        lambda - smoothness strength parameter for solver.
        num_iter - number of iterations used for solver, 25 is usually enough.
        max_tol - convergence tolerance used for solver. For more details about the Fast Bilateral Solver parameters, see the original paper CITE: BarronPoole2016.
        Returns:
        automatically generated
      • createFastBilateralSolverFilter

        public static FastBilateralSolverFilter createFastBilateralSolverFilter​(Mat guide,
                                                                                double sigma_spatial,
                                                                                double sigma_luma,
                                                                                double sigma_chroma,
                                                                                double lambda,
                                                                                int num_iter)
        Factory method, create instance of FastBilateralSolverFilter and execute the initialization routines.
        Parameters:
        guide - image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels.
        sigma_spatial - parameter, that is similar to spatial space sigma (bandwidth) in bilateralFilter.
        sigma_luma - parameter, that is similar to luma space sigma (bandwidth) in bilateralFilter.
        sigma_chroma - parameter, that is similar to chroma space sigma (bandwidth) in bilateralFilter.
        lambda - smoothness strength parameter for solver.
        num_iter - number of iterations used for solver, 25 is usually enough. For more details about the Fast Bilateral Solver parameters, see the original paper CITE: BarronPoole2016.
        Returns:
        automatically generated
      • createFastBilateralSolverFilter

        public static FastBilateralSolverFilter createFastBilateralSolverFilter​(Mat guide,
                                                                                double sigma_spatial,
                                                                                double sigma_luma,
                                                                                double sigma_chroma,
                                                                                double lambda)
        Factory method, create instance of FastBilateralSolverFilter and execute the initialization routines.
        Parameters:
        guide - image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels.
        sigma_spatial - parameter, that is similar to spatial space sigma (bandwidth) in bilateralFilter.
        sigma_luma - parameter, that is similar to luma space sigma (bandwidth) in bilateralFilter.
        sigma_chroma - parameter, that is similar to chroma space sigma (bandwidth) in bilateralFilter.
        lambda - smoothness strength parameter for solver. For more details about the Fast Bilateral Solver parameters, see the original paper CITE: BarronPoole2016.
        Returns:
        automatically generated
      • createFastBilateralSolverFilter

        public static FastBilateralSolverFilter createFastBilateralSolverFilter​(Mat guide,
                                                                                double sigma_spatial,
                                                                                double sigma_luma,
                                                                                double sigma_chroma)
        Factory method, create instance of FastBilateralSolverFilter and execute the initialization routines.
        Parameters:
        guide - image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels.
        sigma_spatial - parameter, that is similar to spatial space sigma (bandwidth) in bilateralFilter.
        sigma_luma - parameter, that is similar to luma space sigma (bandwidth) in bilateralFilter.
        sigma_chroma - parameter, that is similar to chroma space sigma (bandwidth) in bilateralFilter. For more details about the Fast Bilateral Solver parameters, see the original paper CITE: BarronPoole2016.
        Returns:
        automatically generated
      • fastBilateralSolverFilter

        public static void fastBilateralSolverFilter​(Mat guide,
                                                     Mat src,
                                                     Mat confidence,
                                                     Mat dst,
                                                     double sigma_spatial,
                                                     double sigma_luma,
                                                     double sigma_chroma,
                                                     double lambda,
                                                     int num_iter,
                                                     double max_tol)
        Simple one-line Fast Bilateral Solver filter call. If you have multiple images to filter with the same guide then use FastBilateralSolverFilter interface to avoid extra computations.
        Parameters:
        guide - image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels.
        src - source image for filtering with unsigned 8-bit or signed 16-bit or floating-point 32-bit depth and up to 4 channels.
        confidence - confidence image with unsigned 8-bit or floating-point 32-bit confidence and 1 channel.
        dst - destination image.
        sigma_spatial - parameter, that is similar to spatial space sigma (bandwidth) in bilateralFilter.
        sigma_luma - parameter, that is similar to luma space sigma (bandwidth) in bilateralFilter.
        sigma_chroma - parameter, that is similar to chroma space sigma (bandwidth) in bilateralFilter.
        lambda - smoothness strength parameter for solver.
        num_iter - number of iterations used for solver, 25 is usually enough.
        max_tol - convergence tolerance used for solver. For more details about the Fast Bilateral Solver parameters, see the original paper CITE: BarronPoole2016. Note: Confidence images with CV_8U depth are expected to in [0, 255] and CV_32F in [0, 1] range.
      • fastBilateralSolverFilter

        public static void fastBilateralSolverFilter​(Mat guide,
                                                     Mat src,
                                                     Mat confidence,
                                                     Mat dst,
                                                     double sigma_spatial,
                                                     double sigma_luma,
                                                     double sigma_chroma,
                                                     double lambda,
                                                     int num_iter)
        Simple one-line Fast Bilateral Solver filter call. If you have multiple images to filter with the same guide then use FastBilateralSolverFilter interface to avoid extra computations.
        Parameters:
        guide - image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels.
        src - source image for filtering with unsigned 8-bit or signed 16-bit or floating-point 32-bit depth and up to 4 channels.
        confidence - confidence image with unsigned 8-bit or floating-point 32-bit confidence and 1 channel.
        dst - destination image.
        sigma_spatial - parameter, that is similar to spatial space sigma (bandwidth) in bilateralFilter.
        sigma_luma - parameter, that is similar to luma space sigma (bandwidth) in bilateralFilter.
        sigma_chroma - parameter, that is similar to chroma space sigma (bandwidth) in bilateralFilter.
        lambda - smoothness strength parameter for solver.
        num_iter - number of iterations used for solver, 25 is usually enough. For more details about the Fast Bilateral Solver parameters, see the original paper CITE: BarronPoole2016. Note: Confidence images with CV_8U depth are expected to in [0, 255] and CV_32F in [0, 1] range.
      • fastBilateralSolverFilter

        public static void fastBilateralSolverFilter​(Mat guide,
                                                     Mat src,
                                                     Mat confidence,
                                                     Mat dst,
                                                     double sigma_spatial,
                                                     double sigma_luma,
                                                     double sigma_chroma,
                                                     double lambda)
        Simple one-line Fast Bilateral Solver filter call. If you have multiple images to filter with the same guide then use FastBilateralSolverFilter interface to avoid extra computations.
        Parameters:
        guide - image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels.
        src - source image for filtering with unsigned 8-bit or signed 16-bit or floating-point 32-bit depth and up to 4 channels.
        confidence - confidence image with unsigned 8-bit or floating-point 32-bit confidence and 1 channel.
        dst - destination image.
        sigma_spatial - parameter, that is similar to spatial space sigma (bandwidth) in bilateralFilter.
        sigma_luma - parameter, that is similar to luma space sigma (bandwidth) in bilateralFilter.
        sigma_chroma - parameter, that is similar to chroma space sigma (bandwidth) in bilateralFilter.
        lambda - smoothness strength parameter for solver. For more details about the Fast Bilateral Solver parameters, see the original paper CITE: BarronPoole2016. Note: Confidence images with CV_8U depth are expected to in [0, 255] and CV_32F in [0, 1] range.
      • fastBilateralSolverFilter

        public static void fastBilateralSolverFilter​(Mat guide,
                                                     Mat src,
                                                     Mat confidence,
                                                     Mat dst,
                                                     double sigma_spatial,
                                                     double sigma_luma,
                                                     double sigma_chroma)
        Simple one-line Fast Bilateral Solver filter call. If you have multiple images to filter with the same guide then use FastBilateralSolverFilter interface to avoid extra computations.
        Parameters:
        guide - image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels.
        src - source image for filtering with unsigned 8-bit or signed 16-bit or floating-point 32-bit depth and up to 4 channels.
        confidence - confidence image with unsigned 8-bit or floating-point 32-bit confidence and 1 channel.
        dst - destination image.
        sigma_spatial - parameter, that is similar to spatial space sigma (bandwidth) in bilateralFilter.
        sigma_luma - parameter, that is similar to luma space sigma (bandwidth) in bilateralFilter.
        sigma_chroma - parameter, that is similar to chroma space sigma (bandwidth) in bilateralFilter. For more details about the Fast Bilateral Solver parameters, see the original paper CITE: BarronPoole2016. Note: Confidence images with CV_8U depth are expected to in [0, 255] and CV_32F in [0, 1] range.
      • fastBilateralSolverFilter

        public static void fastBilateralSolverFilter​(Mat guide,
                                                     Mat src,
                                                     Mat confidence,
                                                     Mat dst,
                                                     double sigma_spatial,
                                                     double sigma_luma)
        Simple one-line Fast Bilateral Solver filter call. If you have multiple images to filter with the same guide then use FastBilateralSolverFilter interface to avoid extra computations.
        Parameters:
        guide - image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels.
        src - source image for filtering with unsigned 8-bit or signed 16-bit or floating-point 32-bit depth and up to 4 channels.
        confidence - confidence image with unsigned 8-bit or floating-point 32-bit confidence and 1 channel.
        dst - destination image.
        sigma_spatial - parameter, that is similar to spatial space sigma (bandwidth) in bilateralFilter.
        sigma_luma - parameter, that is similar to luma space sigma (bandwidth) in bilateralFilter. For more details about the Fast Bilateral Solver parameters, see the original paper CITE: BarronPoole2016. Note: Confidence images with CV_8U depth are expected to in [0, 255] and CV_32F in [0, 1] range.
      • fastBilateralSolverFilter

        public static void fastBilateralSolverFilter​(Mat guide,
                                                     Mat src,
                                                     Mat confidence,
                                                     Mat dst,
                                                     double sigma_spatial)
        Simple one-line Fast Bilateral Solver filter call. If you have multiple images to filter with the same guide then use FastBilateralSolverFilter interface to avoid extra computations.
        Parameters:
        guide - image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels.
        src - source image for filtering with unsigned 8-bit or signed 16-bit or floating-point 32-bit depth and up to 4 channels.
        confidence - confidence image with unsigned 8-bit or floating-point 32-bit confidence and 1 channel.
        dst - destination image.
        sigma_spatial - parameter, that is similar to spatial space sigma (bandwidth) in bilateralFilter. For more details about the Fast Bilateral Solver parameters, see the original paper CITE: BarronPoole2016. Note: Confidence images with CV_8U depth are expected to in [0, 255] and CV_32F in [0, 1] range.
      • fastBilateralSolverFilter

        public static void fastBilateralSolverFilter​(Mat guide,
                                                     Mat src,
                                                     Mat confidence,
                                                     Mat dst)
        Simple one-line Fast Bilateral Solver filter call. If you have multiple images to filter with the same guide then use FastBilateralSolverFilter interface to avoid extra computations.
        Parameters:
        guide - image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels.
        src - source image for filtering with unsigned 8-bit or signed 16-bit or floating-point 32-bit depth and up to 4 channels.
        confidence - confidence image with unsigned 8-bit or floating-point 32-bit confidence and 1 channel.
        dst - destination image. For more details about the Fast Bilateral Solver parameters, see the original paper CITE: BarronPoole2016. Note: Confidence images with CV_8U depth are expected to in [0, 255] and CV_32F in [0, 1] range.
      • createFastGlobalSmootherFilter

        public static FastGlobalSmootherFilter createFastGlobalSmootherFilter​(Mat guide,
                                                                              double lambda,
                                                                              double sigma_color,
                                                                              double lambda_attenuation,
                                                                              int num_iter)
        Factory method, create instance of FastGlobalSmootherFilter and execute the initialization routines.
        Parameters:
        guide - image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels.
        lambda - parameter defining the amount of regularization
        sigma_color - parameter, that is similar to color space sigma in bilateralFilter.
        lambda_attenuation - internal parameter, defining how much lambda decreases after each iteration. Normally, it should be 0.25. Setting it to 1.0 may lead to streaking artifacts.
        num_iter - number of iterations used for filtering, 3 is usually enough. For more details about Fast Global Smoother parameters, see the original paper CITE: Min2014. However, please note that there are several differences. Lambda attenuation described in the paper is implemented a bit differently so do not expect the results to be identical to those from the paper; sigma_color values from the paper should be multiplied by 255.0 to achieve the same effect. Also, in case of image filtering where source and guide image are the same, authors propose to dynamically update the guide image after each iteration. To maximize the performance this feature was not implemented here.
        Returns:
        automatically generated
      • createFastGlobalSmootherFilter

        public static FastGlobalSmootherFilter createFastGlobalSmootherFilter​(Mat guide,
                                                                              double lambda,
                                                                              double sigma_color,
                                                                              double lambda_attenuation)
        Factory method, create instance of FastGlobalSmootherFilter and execute the initialization routines.
        Parameters:
        guide - image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels.
        lambda - parameter defining the amount of regularization
        sigma_color - parameter, that is similar to color space sigma in bilateralFilter.
        lambda_attenuation - internal parameter, defining how much lambda decreases after each iteration. Normally, it should be 0.25. Setting it to 1.0 may lead to streaking artifacts. For more details about Fast Global Smoother parameters, see the original paper CITE: Min2014. However, please note that there are several differences. Lambda attenuation described in the paper is implemented a bit differently so do not expect the results to be identical to those from the paper; sigma_color values from the paper should be multiplied by 255.0 to achieve the same effect. Also, in case of image filtering where source and guide image are the same, authors propose to dynamically update the guide image after each iteration. To maximize the performance this feature was not implemented here.
        Returns:
        automatically generated
      • createFastGlobalSmootherFilter

        public static FastGlobalSmootherFilter createFastGlobalSmootherFilter​(Mat guide,
                                                                              double lambda,
                                                                              double sigma_color)
        Factory method, create instance of FastGlobalSmootherFilter and execute the initialization routines.
        Parameters:
        guide - image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels.
        lambda - parameter defining the amount of regularization
        sigma_color - parameter, that is similar to color space sigma in bilateralFilter. it should be 0.25. Setting it to 1.0 may lead to streaking artifacts. For more details about Fast Global Smoother parameters, see the original paper CITE: Min2014. However, please note that there are several differences. Lambda attenuation described in the paper is implemented a bit differently so do not expect the results to be identical to those from the paper; sigma_color values from the paper should be multiplied by 255.0 to achieve the same effect. Also, in case of image filtering where source and guide image are the same, authors propose to dynamically update the guide image after each iteration. To maximize the performance this feature was not implemented here.
        Returns:
        automatically generated
      • fastGlobalSmootherFilter

        public static void fastGlobalSmootherFilter​(Mat guide,
                                                    Mat src,
                                                    Mat dst,
                                                    double lambda,
                                                    double sigma_color,
                                                    double lambda_attenuation,
                                                    int num_iter)
        Simple one-line Fast Global Smoother filter call. If you have multiple images to filter with the same guide then use FastGlobalSmootherFilter interface to avoid extra computations.
        Parameters:
        guide - image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels.
        src - source image for filtering with unsigned 8-bit or signed 16-bit or floating-point 32-bit depth and up to 4 channels.
        dst - destination image.
        lambda - parameter defining the amount of regularization
        sigma_color - parameter, that is similar to color space sigma in bilateralFilter.
        lambda_attenuation - internal parameter, defining how much lambda decreases after each iteration. Normally, it should be 0.25. Setting it to 1.0 may lead to streaking artifacts.
        num_iter - number of iterations used for filtering, 3 is usually enough.
      • fastGlobalSmootherFilter

        public static void fastGlobalSmootherFilter​(Mat guide,
                                                    Mat src,
                                                    Mat dst,
                                                    double lambda,
                                                    double sigma_color,
                                                    double lambda_attenuation)
        Simple one-line Fast Global Smoother filter call. If you have multiple images to filter with the same guide then use FastGlobalSmootherFilter interface to avoid extra computations.
        Parameters:
        guide - image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels.
        src - source image for filtering with unsigned 8-bit or signed 16-bit or floating-point 32-bit depth and up to 4 channels.
        dst - destination image.
        lambda - parameter defining the amount of regularization
        sigma_color - parameter, that is similar to color space sigma in bilateralFilter.
        lambda_attenuation - internal parameter, defining how much lambda decreases after each iteration. Normally, it should be 0.25. Setting it to 1.0 may lead to streaking artifacts.
      • fastGlobalSmootherFilter

        public static void fastGlobalSmootherFilter​(Mat guide,
                                                    Mat src,
                                                    Mat dst,
                                                    double lambda,
                                                    double sigma_color)
        Simple one-line Fast Global Smoother filter call. If you have multiple images to filter with the same guide then use FastGlobalSmootherFilter interface to avoid extra computations.
        Parameters:
        guide - image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels.
        src - source image for filtering with unsigned 8-bit or signed 16-bit or floating-point 32-bit depth and up to 4 channels.
        dst - destination image.
        lambda - parameter defining the amount of regularization
        sigma_color - parameter, that is similar to color space sigma in bilateralFilter. it should be 0.25. Setting it to 1.0 may lead to streaking artifacts.
      • l0Smooth

        public static void l0Smooth​(Mat src,
                                    Mat dst,
                                    double lambda,
                                    double kappa)
        Global image smoothing via L0 gradient minimization.
        Parameters:
        src - source image for filtering with unsigned 8-bit or signed 16-bit or floating-point depth.
        dst - destination image.
        lambda - parameter defining the smooth term weight.
        kappa - parameter defining the increasing factor of the weight of the gradient data term. For more details about L0 Smoother, see the original paper CITE: xu2011image.
      • l0Smooth

        public static void l0Smooth​(Mat src,
                                    Mat dst,
                                    double lambda)
        Global image smoothing via L0 gradient minimization.
        Parameters:
        src - source image for filtering with unsigned 8-bit or signed 16-bit or floating-point depth.
        dst - destination image.
        lambda - parameter defining the smooth term weight. For more details about L0 Smoother, see the original paper CITE: xu2011image.
      • l0Smooth

        public static void l0Smooth​(Mat src,
                                    Mat dst)
        Global image smoothing via L0 gradient minimization.
        Parameters:
        src - source image for filtering with unsigned 8-bit or signed 16-bit or floating-point depth.
        dst - destination image. For more details about L0 Smoother, see the original paper CITE: xu2011image.
      • weightedMedianFilter

        public static void weightedMedianFilter​(Mat joint,
                                                Mat src,
                                                Mat dst,
                                                int r,
                                                double sigma,
                                                int weightType,
                                                Mat mask)
        Applies weighted median filter to an image. For more details about this implementation, please see CITE: zhang2014100+ the pixel will be ignored when maintaining the joint-histogram. This is useful for applications like optical flow occlusion handling. SEE: medianBlur, jointBilateralFilter
        Parameters:
        joint - automatically generated
        src - automatically generated
        dst - automatically generated
        r - automatically generated
        sigma - automatically generated
        weightType - automatically generated
        mask - automatically generated
      • weightedMedianFilter

        public static void weightedMedianFilter​(Mat joint,
                                                Mat src,
                                                Mat dst,
                                                int r,
                                                double sigma,
                                                int weightType)
        Applies weighted median filter to an image. For more details about this implementation, please see CITE: zhang2014100+ the pixel will be ignored when maintaining the joint-histogram. This is useful for applications like optical flow occlusion handling. SEE: medianBlur, jointBilateralFilter
        Parameters:
        joint - automatically generated
        src - automatically generated
        dst - automatically generated
        r - automatically generated
        sigma - automatically generated
        weightType - automatically generated
      • weightedMedianFilter

        public static void weightedMedianFilter​(Mat joint,
                                                Mat src,
                                                Mat dst,
                                                int r,
                                                double sigma)
        Applies weighted median filter to an image. For more details about this implementation, please see CITE: zhang2014100+ the pixel will be ignored when maintaining the joint-histogram. This is useful for applications like optical flow occlusion handling. SEE: medianBlur, jointBilateralFilter
        Parameters:
        joint - automatically generated
        src - automatically generated
        dst - automatically generated
        r - automatically generated
        sigma - automatically generated
      • weightedMedianFilter

        public static void weightedMedianFilter​(Mat joint,
                                                Mat src,
                                                Mat dst,
                                                int r)
        Applies weighted median filter to an image. For more details about this implementation, please see CITE: zhang2014100+ the pixel will be ignored when maintaining the joint-histogram. This is useful for applications like optical flow occlusion handling. SEE: medianBlur, jointBilateralFilter
        Parameters:
        joint - automatically generated
        src - automatically generated
        dst - automatically generated
        r - automatically generated
      • edgePreservingFilter

        public static void edgePreservingFilter​(Mat src,
                                                Mat dst,
                                                int d,
                                                double threshold)
        Smoothes an image using the Edge-Preserving filter. The function smoothes Gaussian noise as well as salt & pepper noise. For more details about this implementation, please see [ReiWoe18] Reich, S. and Wörgötter, F. and Dellen, B. (2018). A Real-Time Edge-Preserving Denoising Filter. Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP): Visapp, 85-94, 4. DOI: 10.5220/0006509000850094.
        Parameters:
        src - Source 8-bit 3-channel image.
        dst - Destination image of the same size and type as src.
        d - Diameter of each pixel neighborhood that is used during filtering. Must be greater or equal 3.
        threshold - Threshold, which distinguishes between noise, outliers, and data.
      • PeiLinNormalization

        public static void PeiLinNormalization​(Mat I,
                                               Mat T)
      • createRFFeatureGetter

        public static RFFeatureGetter createRFFeatureGetter()
      • createStructuredEdgeDetection

        public static StructuredEdgeDetection createStructuredEdgeDetection​(java.lang.String model)