// // This file is auto-generated. Please don't modify it! // #pragma once #ifdef __cplusplus //#import "opencv.hpp" #import "opencv2/xphoto.hpp" #else #define CV_EXPORTS #endif #import @class GrayworldWB; @class LearningBasedWB; @class Mat; @class SimpleWB; @class TonemapDurand; // C++: enum Bm3dSteps (cv.xphoto.Bm3dSteps) typedef NS_ENUM(int, Bm3dSteps) { BM3D_STEPALL = 0, BM3D_STEP1 = 1, BM3D_STEP2 = 2 }; // C++: enum InpaintTypes (cv.xphoto.InpaintTypes) typedef NS_ENUM(int, InpaintTypes) { INPAINT_SHIFTMAP = 0, INPAINT_FSR_BEST = 1, INPAINT_FSR_FAST = 2 }; // C++: enum TransformTypes (cv.xphoto.TransformTypes) typedef NS_ENUM(int, TransformTypes) { HAAR = 0 }; NS_ASSUME_NONNULL_BEGIN // C++: class Xphoto /** * The Xphoto module * * Member classes: `TonemapDurand`, `WhiteBalancer`, `SimpleWB`, `GrayworldWB`, `LearningBasedWB` * * Member enums: `TransformTypes`, `Bm3dSteps`, `InpaintTypes` */ CV_EXPORTS @interface Xphoto : NSObject #pragma mark - Methods // // void cv::xphoto::bm3dDenoising(Mat src, Mat& dstStep1, Mat& dstStep2, float h = 1, int templateWindowSize = 4, int searchWindowSize = 16, int blockMatchingStep1 = 2500, int blockMatchingStep2 = 400, int groupSize = 8, int slidingStep = 1, float beta = 2.0f, int normType = cv::NORM_L2, int step = cv::xphoto::BM3D_STEPALL, int transformType = cv::xphoto::HAAR) // /** * Performs image denoising using the Block-Matching and 3D-filtering algorithm * with several computational * optimizations. Noise expected to be a gaussian white noise. * * @param src Input 8-bit or 16-bit 1-channel image. * @param dstStep1 Output image of the first step of BM3D with the same size and type as src. * @param dstStep2 Output image of the second step of BM3D with the same size and type as src. * @param h Parameter regulating filter strength. Big h value perfectly removes noise but also * removes image details, smaller h value preserves details but also preserves some noise. * @param templateWindowSize Size in pixels of the template patch that is used for block-matching. * Should be power of 2. * @param searchWindowSize Size in pixels of the window that is used to perform block-matching. * Affect performance linearly: greater searchWindowsSize - greater denoising time. * Must be larger than templateWindowSize. * @param blockMatchingStep1 Block matching threshold for the first step of BM3D (hard thresholding), * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * @param blockMatchingStep2 Block matching threshold for the second step of BM3D (Wiener filtering), * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * @param groupSize Maximum size of the 3D group for collaborative filtering. * @param slidingStep Sliding step to process every next reference block. * @param beta Kaiser window parameter that affects the sidelobe attenuation of the transform of the * window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, * set beta to zero. * @param normType Norm used to calculate distance between blocks. L2 is slower than L1 * but yields more accurate results. * @param step Step of BM3D to be executed. Possible variants are: step 1, step 2, both steps. * @param transformType Type of the orthogonal transform used in collaborative filtering step. * Currently only Haar transform is supported. * * This function expected to be applied to grayscale images. Advanced usage of this function * can be manual denoising of colored image in different colorspaces. * * @sa * fastNlMeansDenoising */ + (void)bm3dDenoising:(Mat*)src dstStep1:(Mat*)dstStep1 dstStep2:(Mat*)dstStep2 h:(float)h templateWindowSize:(int)templateWindowSize searchWindowSize:(int)searchWindowSize blockMatchingStep1:(int)blockMatchingStep1 blockMatchingStep2:(int)blockMatchingStep2 groupSize:(int)groupSize slidingStep:(int)slidingStep beta:(float)beta normType:(int)normType step:(int)step transformType:(int)transformType NS_SWIFT_NAME(bm3dDenoising(src:dstStep1:dstStep2:h:templateWindowSize:searchWindowSize:blockMatchingStep1:blockMatchingStep2:groupSize:slidingStep:beta:normType:step:transformType:)); /** * Performs image denoising using the Block-Matching and 3D-filtering algorithm * with several computational * optimizations. Noise expected to be a gaussian white noise. * * @param src Input 8-bit or 16-bit 1-channel image. * @param dstStep1 Output image of the first step of BM3D with the same size and type as src. * @param dstStep2 Output image of the second step of BM3D with the same size and type as src. * @param h Parameter regulating filter strength. Big h value perfectly removes noise but also * removes image details, smaller h value preserves details but also preserves some noise. * @param templateWindowSize Size in pixels of the template patch that is used for block-matching. * Should be power of 2. * @param searchWindowSize Size in pixels of the window that is used to perform block-matching. * Affect performance linearly: greater searchWindowsSize - greater denoising time. * Must be larger than templateWindowSize. * @param blockMatchingStep1 Block matching threshold for the first step of BM3D (hard thresholding), * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * @param blockMatchingStep2 Block matching threshold for the second step of BM3D (Wiener filtering), * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * @param groupSize Maximum size of the 3D group for collaborative filtering. * @param slidingStep Sliding step to process every next reference block. * @param beta Kaiser window parameter that affects the sidelobe attenuation of the transform of the * window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, * set beta to zero. * @param normType Norm used to calculate distance between blocks. L2 is slower than L1 * but yields more accurate results. * @param step Step of BM3D to be executed. Possible variants are: step 1, step 2, both steps. * Currently only Haar transform is supported. * * This function expected to be applied to grayscale images. Advanced usage of this function * can be manual denoising of colored image in different colorspaces. * * @sa * fastNlMeansDenoising */ + (void)bm3dDenoising:(Mat*)src dstStep1:(Mat*)dstStep1 dstStep2:(Mat*)dstStep2 h:(float)h templateWindowSize:(int)templateWindowSize searchWindowSize:(int)searchWindowSize blockMatchingStep1:(int)blockMatchingStep1 blockMatchingStep2:(int)blockMatchingStep2 groupSize:(int)groupSize slidingStep:(int)slidingStep beta:(float)beta normType:(int)normType step:(int)step NS_SWIFT_NAME(bm3dDenoising(src:dstStep1:dstStep2:h:templateWindowSize:searchWindowSize:blockMatchingStep1:blockMatchingStep2:groupSize:slidingStep:beta:normType:step:)); /** * Performs image denoising using the Block-Matching and 3D-filtering algorithm * with several computational * optimizations. Noise expected to be a gaussian white noise. * * @param src Input 8-bit or 16-bit 1-channel image. * @param dstStep1 Output image of the first step of BM3D with the same size and type as src. * @param dstStep2 Output image of the second step of BM3D with the same size and type as src. * @param h Parameter regulating filter strength. Big h value perfectly removes noise but also * removes image details, smaller h value preserves details but also preserves some noise. * @param templateWindowSize Size in pixels of the template patch that is used for block-matching. * Should be power of 2. * @param searchWindowSize Size in pixels of the window that is used to perform block-matching. * Affect performance linearly: greater searchWindowsSize - greater denoising time. * Must be larger than templateWindowSize. * @param blockMatchingStep1 Block matching threshold for the first step of BM3D (hard thresholding), * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * @param blockMatchingStep2 Block matching threshold for the second step of BM3D (Wiener filtering), * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * @param groupSize Maximum size of the 3D group for collaborative filtering. * @param slidingStep Sliding step to process every next reference block. * @param beta Kaiser window parameter that affects the sidelobe attenuation of the transform of the * window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, * set beta to zero. * @param normType Norm used to calculate distance between blocks. L2 is slower than L1 * but yields more accurate results. * Currently only Haar transform is supported. * * This function expected to be applied to grayscale images. Advanced usage of this function * can be manual denoising of colored image in different colorspaces. * * @sa * fastNlMeansDenoising */ + (void)bm3dDenoising:(Mat*)src dstStep1:(Mat*)dstStep1 dstStep2:(Mat*)dstStep2 h:(float)h templateWindowSize:(int)templateWindowSize searchWindowSize:(int)searchWindowSize blockMatchingStep1:(int)blockMatchingStep1 blockMatchingStep2:(int)blockMatchingStep2 groupSize:(int)groupSize slidingStep:(int)slidingStep beta:(float)beta normType:(int)normType NS_SWIFT_NAME(bm3dDenoising(src:dstStep1:dstStep2:h:templateWindowSize:searchWindowSize:blockMatchingStep1:blockMatchingStep2:groupSize:slidingStep:beta:normType:)); /** * Performs image denoising using the Block-Matching and 3D-filtering algorithm * with several computational * optimizations. Noise expected to be a gaussian white noise. * * @param src Input 8-bit or 16-bit 1-channel image. * @param dstStep1 Output image of the first step of BM3D with the same size and type as src. * @param dstStep2 Output image of the second step of BM3D with the same size and type as src. * @param h Parameter regulating filter strength. Big h value perfectly removes noise but also * removes image details, smaller h value preserves details but also preserves some noise. * @param templateWindowSize Size in pixels of the template patch that is used for block-matching. * Should be power of 2. * @param searchWindowSize Size in pixels of the window that is used to perform block-matching. * Affect performance linearly: greater searchWindowsSize - greater denoising time. * Must be larger than templateWindowSize. * @param blockMatchingStep1 Block matching threshold for the first step of BM3D (hard thresholding), * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * @param blockMatchingStep2 Block matching threshold for the second step of BM3D (Wiener filtering), * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * @param groupSize Maximum size of the 3D group for collaborative filtering. * @param slidingStep Sliding step to process every next reference block. * @param beta Kaiser window parameter that affects the sidelobe attenuation of the transform of the * window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, * set beta to zero. * but yields more accurate results. * Currently only Haar transform is supported. * * This function expected to be applied to grayscale images. Advanced usage of this function * can be manual denoising of colored image in different colorspaces. * * @sa * fastNlMeansDenoising */ + (void)bm3dDenoising:(Mat*)src dstStep1:(Mat*)dstStep1 dstStep2:(Mat*)dstStep2 h:(float)h templateWindowSize:(int)templateWindowSize searchWindowSize:(int)searchWindowSize blockMatchingStep1:(int)blockMatchingStep1 blockMatchingStep2:(int)blockMatchingStep2 groupSize:(int)groupSize slidingStep:(int)slidingStep beta:(float)beta NS_SWIFT_NAME(bm3dDenoising(src:dstStep1:dstStep2:h:templateWindowSize:searchWindowSize:blockMatchingStep1:blockMatchingStep2:groupSize:slidingStep:beta:)); /** * Performs image denoising using the Block-Matching and 3D-filtering algorithm * with several computational * optimizations. Noise expected to be a gaussian white noise. * * @param src Input 8-bit or 16-bit 1-channel image. * @param dstStep1 Output image of the first step of BM3D with the same size and type as src. * @param dstStep2 Output image of the second step of BM3D with the same size and type as src. * @param h Parameter regulating filter strength. Big h value perfectly removes noise but also * removes image details, smaller h value preserves details but also preserves some noise. * @param templateWindowSize Size in pixels of the template patch that is used for block-matching. * Should be power of 2. * @param searchWindowSize Size in pixels of the window that is used to perform block-matching. * Affect performance linearly: greater searchWindowsSize - greater denoising time. * Must be larger than templateWindowSize. * @param blockMatchingStep1 Block matching threshold for the first step of BM3D (hard thresholding), * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * @param blockMatchingStep2 Block matching threshold for the second step of BM3D (Wiener filtering), * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * @param groupSize Maximum size of the 3D group for collaborative filtering. * @param slidingStep Sliding step to process every next reference block. * window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, * set beta to zero. * but yields more accurate results. * Currently only Haar transform is supported. * * This function expected to be applied to grayscale images. Advanced usage of this function * can be manual denoising of colored image in different colorspaces. * * @sa * fastNlMeansDenoising */ + (void)bm3dDenoising:(Mat*)src dstStep1:(Mat*)dstStep1 dstStep2:(Mat*)dstStep2 h:(float)h templateWindowSize:(int)templateWindowSize searchWindowSize:(int)searchWindowSize blockMatchingStep1:(int)blockMatchingStep1 blockMatchingStep2:(int)blockMatchingStep2 groupSize:(int)groupSize slidingStep:(int)slidingStep NS_SWIFT_NAME(bm3dDenoising(src:dstStep1:dstStep2:h:templateWindowSize:searchWindowSize:blockMatchingStep1:blockMatchingStep2:groupSize:slidingStep:)); /** * Performs image denoising using the Block-Matching and 3D-filtering algorithm * with several computational * optimizations. Noise expected to be a gaussian white noise. * * @param src Input 8-bit or 16-bit 1-channel image. * @param dstStep1 Output image of the first step of BM3D with the same size and type as src. * @param dstStep2 Output image of the second step of BM3D with the same size and type as src. * @param h Parameter regulating filter strength. Big h value perfectly removes noise but also * removes image details, smaller h value preserves details but also preserves some noise. * @param templateWindowSize Size in pixels of the template patch that is used for block-matching. * Should be power of 2. * @param searchWindowSize Size in pixels of the window that is used to perform block-matching. * Affect performance linearly: greater searchWindowsSize - greater denoising time. * Must be larger than templateWindowSize. * @param blockMatchingStep1 Block matching threshold for the first step of BM3D (hard thresholding), * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * @param blockMatchingStep2 Block matching threshold for the second step of BM3D (Wiener filtering), * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * @param groupSize Maximum size of the 3D group for collaborative filtering. * window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, * set beta to zero. * but yields more accurate results. * Currently only Haar transform is supported. * * This function expected to be applied to grayscale images. Advanced usage of this function * can be manual denoising of colored image in different colorspaces. * * @sa * fastNlMeansDenoising */ + (void)bm3dDenoising:(Mat*)src dstStep1:(Mat*)dstStep1 dstStep2:(Mat*)dstStep2 h:(float)h templateWindowSize:(int)templateWindowSize searchWindowSize:(int)searchWindowSize blockMatchingStep1:(int)blockMatchingStep1 blockMatchingStep2:(int)blockMatchingStep2 groupSize:(int)groupSize NS_SWIFT_NAME(bm3dDenoising(src:dstStep1:dstStep2:h:templateWindowSize:searchWindowSize:blockMatchingStep1:blockMatchingStep2:groupSize:)); /** * Performs image denoising using the Block-Matching and 3D-filtering algorithm * with several computational * optimizations. Noise expected to be a gaussian white noise. * * @param src Input 8-bit or 16-bit 1-channel image. * @param dstStep1 Output image of the first step of BM3D with the same size and type as src. * @param dstStep2 Output image of the second step of BM3D with the same size and type as src. * @param h Parameter regulating filter strength. Big h value perfectly removes noise but also * removes image details, smaller h value preserves details but also preserves some noise. * @param templateWindowSize Size in pixels of the template patch that is used for block-matching. * Should be power of 2. * @param searchWindowSize Size in pixels of the window that is used to perform block-matching. * Affect performance linearly: greater searchWindowsSize - greater denoising time. * Must be larger than templateWindowSize. * @param blockMatchingStep1 Block matching threshold for the first step of BM3D (hard thresholding), * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * @param blockMatchingStep2 Block matching threshold for the second step of BM3D (Wiener filtering), * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, * set beta to zero. * but yields more accurate results. * Currently only Haar transform is supported. * * This function expected to be applied to grayscale images. Advanced usage of this function * can be manual denoising of colored image in different colorspaces. * * @sa * fastNlMeansDenoising */ + (void)bm3dDenoising:(Mat*)src dstStep1:(Mat*)dstStep1 dstStep2:(Mat*)dstStep2 h:(float)h templateWindowSize:(int)templateWindowSize searchWindowSize:(int)searchWindowSize blockMatchingStep1:(int)blockMatchingStep1 blockMatchingStep2:(int)blockMatchingStep2 NS_SWIFT_NAME(bm3dDenoising(src:dstStep1:dstStep2:h:templateWindowSize:searchWindowSize:blockMatchingStep1:blockMatchingStep2:)); /** * Performs image denoising using the Block-Matching and 3D-filtering algorithm * with several computational * optimizations. Noise expected to be a gaussian white noise. * * @param src Input 8-bit or 16-bit 1-channel image. * @param dstStep1 Output image of the first step of BM3D with the same size and type as src. * @param dstStep2 Output image of the second step of BM3D with the same size and type as src. * @param h Parameter regulating filter strength. Big h value perfectly removes noise but also * removes image details, smaller h value preserves details but also preserves some noise. * @param templateWindowSize Size in pixels of the template patch that is used for block-matching. * Should be power of 2. * @param searchWindowSize Size in pixels of the window that is used to perform block-matching. * Affect performance linearly: greater searchWindowsSize - greater denoising time. * Must be larger than templateWindowSize. * @param blockMatchingStep1 Block matching threshold for the first step of BM3D (hard thresholding), * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, * set beta to zero. * but yields more accurate results. * Currently only Haar transform is supported. * * This function expected to be applied to grayscale images. Advanced usage of this function * can be manual denoising of colored image in different colorspaces. * * @sa * fastNlMeansDenoising */ + (void)bm3dDenoising:(Mat*)src dstStep1:(Mat*)dstStep1 dstStep2:(Mat*)dstStep2 h:(float)h templateWindowSize:(int)templateWindowSize searchWindowSize:(int)searchWindowSize blockMatchingStep1:(int)blockMatchingStep1 NS_SWIFT_NAME(bm3dDenoising(src:dstStep1:dstStep2:h:templateWindowSize:searchWindowSize:blockMatchingStep1:)); /** * Performs image denoising using the Block-Matching and 3D-filtering algorithm * with several computational * optimizations. Noise expected to be a gaussian white noise. * * @param src Input 8-bit or 16-bit 1-channel image. * @param dstStep1 Output image of the first step of BM3D with the same size and type as src. * @param dstStep2 Output image of the second step of BM3D with the same size and type as src. * @param h Parameter regulating filter strength. Big h value perfectly removes noise but also * removes image details, smaller h value preserves details but also preserves some noise. * @param templateWindowSize Size in pixels of the template patch that is used for block-matching. * Should be power of 2. * @param searchWindowSize Size in pixels of the window that is used to perform block-matching. * Affect performance linearly: greater searchWindowsSize - greater denoising time. * Must be larger than templateWindowSize. * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, * set beta to zero. * but yields more accurate results. * Currently only Haar transform is supported. * * This function expected to be applied to grayscale images. Advanced usage of this function * can be manual denoising of colored image in different colorspaces. * * @sa * fastNlMeansDenoising */ + (void)bm3dDenoising:(Mat*)src dstStep1:(Mat*)dstStep1 dstStep2:(Mat*)dstStep2 h:(float)h templateWindowSize:(int)templateWindowSize searchWindowSize:(int)searchWindowSize NS_SWIFT_NAME(bm3dDenoising(src:dstStep1:dstStep2:h:templateWindowSize:searchWindowSize:)); /** * Performs image denoising using the Block-Matching and 3D-filtering algorithm * with several computational * optimizations. Noise expected to be a gaussian white noise. * * @param src Input 8-bit or 16-bit 1-channel image. * @param dstStep1 Output image of the first step of BM3D with the same size and type as src. * @param dstStep2 Output image of the second step of BM3D with the same size and type as src. * @param h Parameter regulating filter strength. Big h value perfectly removes noise but also * removes image details, smaller h value preserves details but also preserves some noise. * @param templateWindowSize Size in pixels of the template patch that is used for block-matching. * Should be power of 2. * Affect performance linearly: greater searchWindowsSize - greater denoising time. * Must be larger than templateWindowSize. * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, * set beta to zero. * but yields more accurate results. * Currently only Haar transform is supported. * * This function expected to be applied to grayscale images. Advanced usage of this function * can be manual denoising of colored image in different colorspaces. * * @sa * fastNlMeansDenoising */ + (void)bm3dDenoising:(Mat*)src dstStep1:(Mat*)dstStep1 dstStep2:(Mat*)dstStep2 h:(float)h templateWindowSize:(int)templateWindowSize NS_SWIFT_NAME(bm3dDenoising(src:dstStep1:dstStep2:h:templateWindowSize:)); /** * Performs image denoising using the Block-Matching and 3D-filtering algorithm * with several computational * optimizations. Noise expected to be a gaussian white noise. * * @param src Input 8-bit or 16-bit 1-channel image. * @param dstStep1 Output image of the first step of BM3D with the same size and type as src. * @param dstStep2 Output image of the second step of BM3D with the same size and type as src. * @param h Parameter regulating filter strength. Big h value perfectly removes noise but also * removes image details, smaller h value preserves details but also preserves some noise. * Should be power of 2. * Affect performance linearly: greater searchWindowsSize - greater denoising time. * Must be larger than templateWindowSize. * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, * set beta to zero. * but yields more accurate results. * Currently only Haar transform is supported. * * This function expected to be applied to grayscale images. Advanced usage of this function * can be manual denoising of colored image in different colorspaces. * * @sa * fastNlMeansDenoising */ + (void)bm3dDenoising:(Mat*)src dstStep1:(Mat*)dstStep1 dstStep2:(Mat*)dstStep2 h:(float)h NS_SWIFT_NAME(bm3dDenoising(src:dstStep1:dstStep2:h:)); /** * Performs image denoising using the Block-Matching and 3D-filtering algorithm * with several computational * optimizations. Noise expected to be a gaussian white noise. * * @param src Input 8-bit or 16-bit 1-channel image. * @param dstStep1 Output image of the first step of BM3D with the same size and type as src. * @param dstStep2 Output image of the second step of BM3D with the same size and type as src. * removes image details, smaller h value preserves details but also preserves some noise. * Should be power of 2. * Affect performance linearly: greater searchWindowsSize - greater denoising time. * Must be larger than templateWindowSize. * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, * set beta to zero. * but yields more accurate results. * Currently only Haar transform is supported. * * This function expected to be applied to grayscale images. Advanced usage of this function * can be manual denoising of colored image in different colorspaces. * * @sa * fastNlMeansDenoising */ + (void)bm3dDenoising:(Mat*)src dstStep1:(Mat*)dstStep1 dstStep2:(Mat*)dstStep2 NS_SWIFT_NAME(bm3dDenoising(src:dstStep1:dstStep2:)); // // void cv::xphoto::bm3dDenoising(Mat src, Mat& dst, float h = 1, int templateWindowSize = 4, int searchWindowSize = 16, int blockMatchingStep1 = 2500, int blockMatchingStep2 = 400, int groupSize = 8, int slidingStep = 1, float beta = 2.0f, int normType = cv::NORM_L2, int step = cv::xphoto::BM3D_STEPALL, int transformType = cv::xphoto::HAAR) // /** * Performs image denoising using the Block-Matching and 3D-filtering algorithm * with several computational * optimizations. Noise expected to be a gaussian white noise. * * @param src Input 8-bit or 16-bit 1-channel image. * @param dst Output image with the same size and type as src. * @param h Parameter regulating filter strength. Big h value perfectly removes noise but also * removes image details, smaller h value preserves details but also preserves some noise. * @param templateWindowSize Size in pixels of the template patch that is used for block-matching. * Should be power of 2. * @param searchWindowSize Size in pixels of the window that is used to perform block-matching. * Affect performance linearly: greater searchWindowsSize - greater denoising time. * Must be larger than templateWindowSize. * @param blockMatchingStep1 Block matching threshold for the first step of BM3D (hard thresholding), * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * @param blockMatchingStep2 Block matching threshold for the second step of BM3D (Wiener filtering), * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * @param groupSize Maximum size of the 3D group for collaborative filtering. * @param slidingStep Sliding step to process every next reference block. * @param beta Kaiser window parameter that affects the sidelobe attenuation of the transform of the * window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, * set beta to zero. * @param normType Norm used to calculate distance between blocks. L2 is slower than L1 * but yields more accurate results. * @param step Step of BM3D to be executed. Allowed are only BM3D_STEP1 and BM3D_STEPALL. * BM3D_STEP2 is not allowed as it requires basic estimate to be present. * @param transformType Type of the orthogonal transform used in collaborative filtering step. * Currently only Haar transform is supported. * * This function expected to be applied to grayscale images. Advanced usage of this function * can be manual denoising of colored image in different colorspaces. * * @sa * fastNlMeansDenoising */ + (void)bm3dDenoising:(Mat*)src dst:(Mat*)dst h:(float)h templateWindowSize:(int)templateWindowSize searchWindowSize:(int)searchWindowSize blockMatchingStep1:(int)blockMatchingStep1 blockMatchingStep2:(int)blockMatchingStep2 groupSize:(int)groupSize slidingStep:(int)slidingStep beta:(float)beta normType:(int)normType step:(int)step transformType:(int)transformType NS_SWIFT_NAME(bm3dDenoising(src:dst:h:templateWindowSize:searchWindowSize:blockMatchingStep1:blockMatchingStep2:groupSize:slidingStep:beta:normType:step:transformType:)); /** * Performs image denoising using the Block-Matching and 3D-filtering algorithm * with several computational * optimizations. Noise expected to be a gaussian white noise. * * @param src Input 8-bit or 16-bit 1-channel image. * @param dst Output image with the same size and type as src. * @param h Parameter regulating filter strength. Big h value perfectly removes noise but also * removes image details, smaller h value preserves details but also preserves some noise. * @param templateWindowSize Size in pixels of the template patch that is used for block-matching. * Should be power of 2. * @param searchWindowSize Size in pixels of the window that is used to perform block-matching. * Affect performance linearly: greater searchWindowsSize - greater denoising time. * Must be larger than templateWindowSize. * @param blockMatchingStep1 Block matching threshold for the first step of BM3D (hard thresholding), * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * @param blockMatchingStep2 Block matching threshold for the second step of BM3D (Wiener filtering), * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * @param groupSize Maximum size of the 3D group for collaborative filtering. * @param slidingStep Sliding step to process every next reference block. * @param beta Kaiser window parameter that affects the sidelobe attenuation of the transform of the * window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, * set beta to zero. * @param normType Norm used to calculate distance between blocks. L2 is slower than L1 * but yields more accurate results. * @param step Step of BM3D to be executed. Allowed are only BM3D_STEP1 and BM3D_STEPALL. * BM3D_STEP2 is not allowed as it requires basic estimate to be present. * Currently only Haar transform is supported. * * This function expected to be applied to grayscale images. Advanced usage of this function * can be manual denoising of colored image in different colorspaces. * * @sa * fastNlMeansDenoising */ + (void)bm3dDenoising:(Mat*)src dst:(Mat*)dst h:(float)h templateWindowSize:(int)templateWindowSize searchWindowSize:(int)searchWindowSize blockMatchingStep1:(int)blockMatchingStep1 blockMatchingStep2:(int)blockMatchingStep2 groupSize:(int)groupSize slidingStep:(int)slidingStep beta:(float)beta normType:(int)normType step:(int)step NS_SWIFT_NAME(bm3dDenoising(src:dst:h:templateWindowSize:searchWindowSize:blockMatchingStep1:blockMatchingStep2:groupSize:slidingStep:beta:normType:step:)); /** * Performs image denoising using the Block-Matching and 3D-filtering algorithm * with several computational * optimizations. Noise expected to be a gaussian white noise. * * @param src Input 8-bit or 16-bit 1-channel image. * @param dst Output image with the same size and type as src. * @param h Parameter regulating filter strength. Big h value perfectly removes noise but also * removes image details, smaller h value preserves details but also preserves some noise. * @param templateWindowSize Size in pixels of the template patch that is used for block-matching. * Should be power of 2. * @param searchWindowSize Size in pixels of the window that is used to perform block-matching. * Affect performance linearly: greater searchWindowsSize - greater denoising time. * Must be larger than templateWindowSize. * @param blockMatchingStep1 Block matching threshold for the first step of BM3D (hard thresholding), * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * @param blockMatchingStep2 Block matching threshold for the second step of BM3D (Wiener filtering), * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * @param groupSize Maximum size of the 3D group for collaborative filtering. * @param slidingStep Sliding step to process every next reference block. * @param beta Kaiser window parameter that affects the sidelobe attenuation of the transform of the * window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, * set beta to zero. * @param normType Norm used to calculate distance between blocks. L2 is slower than L1 * but yields more accurate results. * BM3D_STEP2 is not allowed as it requires basic estimate to be present. * Currently only Haar transform is supported. * * This function expected to be applied to grayscale images. Advanced usage of this function * can be manual denoising of colored image in different colorspaces. * * @sa * fastNlMeansDenoising */ + (void)bm3dDenoising:(Mat*)src dst:(Mat*)dst h:(float)h templateWindowSize:(int)templateWindowSize searchWindowSize:(int)searchWindowSize blockMatchingStep1:(int)blockMatchingStep1 blockMatchingStep2:(int)blockMatchingStep2 groupSize:(int)groupSize slidingStep:(int)slidingStep beta:(float)beta normType:(int)normType NS_SWIFT_NAME(bm3dDenoising(src:dst:h:templateWindowSize:searchWindowSize:blockMatchingStep1:blockMatchingStep2:groupSize:slidingStep:beta:normType:)); /** * Performs image denoising using the Block-Matching and 3D-filtering algorithm * with several computational * optimizations. Noise expected to be a gaussian white noise. * * @param src Input 8-bit or 16-bit 1-channel image. * @param dst Output image with the same size and type as src. * @param h Parameter regulating filter strength. Big h value perfectly removes noise but also * removes image details, smaller h value preserves details but also preserves some noise. * @param templateWindowSize Size in pixels of the template patch that is used for block-matching. * Should be power of 2. * @param searchWindowSize Size in pixels of the window that is used to perform block-matching. * Affect performance linearly: greater searchWindowsSize - greater denoising time. * Must be larger than templateWindowSize. * @param blockMatchingStep1 Block matching threshold for the first step of BM3D (hard thresholding), * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * @param blockMatchingStep2 Block matching threshold for the second step of BM3D (Wiener filtering), * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * @param groupSize Maximum size of the 3D group for collaborative filtering. * @param slidingStep Sliding step to process every next reference block. * @param beta Kaiser window parameter that affects the sidelobe attenuation of the transform of the * window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, * set beta to zero. * but yields more accurate results. * BM3D_STEP2 is not allowed as it requires basic estimate to be present. * Currently only Haar transform is supported. * * This function expected to be applied to grayscale images. Advanced usage of this function * can be manual denoising of colored image in different colorspaces. * * @sa * fastNlMeansDenoising */ + (void)bm3dDenoising:(Mat*)src dst:(Mat*)dst h:(float)h templateWindowSize:(int)templateWindowSize searchWindowSize:(int)searchWindowSize blockMatchingStep1:(int)blockMatchingStep1 blockMatchingStep2:(int)blockMatchingStep2 groupSize:(int)groupSize slidingStep:(int)slidingStep beta:(float)beta NS_SWIFT_NAME(bm3dDenoising(src:dst:h:templateWindowSize:searchWindowSize:blockMatchingStep1:blockMatchingStep2:groupSize:slidingStep:beta:)); /** * Performs image denoising using the Block-Matching and 3D-filtering algorithm * with several computational * optimizations. Noise expected to be a gaussian white noise. * * @param src Input 8-bit or 16-bit 1-channel image. * @param dst Output image with the same size and type as src. * @param h Parameter regulating filter strength. Big h value perfectly removes noise but also * removes image details, smaller h value preserves details but also preserves some noise. * @param templateWindowSize Size in pixels of the template patch that is used for block-matching. * Should be power of 2. * @param searchWindowSize Size in pixels of the window that is used to perform block-matching. * Affect performance linearly: greater searchWindowsSize - greater denoising time. * Must be larger than templateWindowSize. * @param blockMatchingStep1 Block matching threshold for the first step of BM3D (hard thresholding), * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * @param blockMatchingStep2 Block matching threshold for the second step of BM3D (Wiener filtering), * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * @param groupSize Maximum size of the 3D group for collaborative filtering. * @param slidingStep Sliding step to process every next reference block. * window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, * set beta to zero. * but yields more accurate results. * BM3D_STEP2 is not allowed as it requires basic estimate to be present. * Currently only Haar transform is supported. * * This function expected to be applied to grayscale images. Advanced usage of this function * can be manual denoising of colored image in different colorspaces. * * @sa * fastNlMeansDenoising */ + (void)bm3dDenoising:(Mat*)src dst:(Mat*)dst h:(float)h templateWindowSize:(int)templateWindowSize searchWindowSize:(int)searchWindowSize blockMatchingStep1:(int)blockMatchingStep1 blockMatchingStep2:(int)blockMatchingStep2 groupSize:(int)groupSize slidingStep:(int)slidingStep NS_SWIFT_NAME(bm3dDenoising(src:dst:h:templateWindowSize:searchWindowSize:blockMatchingStep1:blockMatchingStep2:groupSize:slidingStep:)); /** * Performs image denoising using the Block-Matching and 3D-filtering algorithm * with several computational * optimizations. Noise expected to be a gaussian white noise. * * @param src Input 8-bit or 16-bit 1-channel image. * @param dst Output image with the same size and type as src. * @param h Parameter regulating filter strength. Big h value perfectly removes noise but also * removes image details, smaller h value preserves details but also preserves some noise. * @param templateWindowSize Size in pixels of the template patch that is used for block-matching. * Should be power of 2. * @param searchWindowSize Size in pixels of the window that is used to perform block-matching. * Affect performance linearly: greater searchWindowsSize - greater denoising time. * Must be larger than templateWindowSize. * @param blockMatchingStep1 Block matching threshold for the first step of BM3D (hard thresholding), * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * @param blockMatchingStep2 Block matching threshold for the second step of BM3D (Wiener filtering), * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * @param groupSize Maximum size of the 3D group for collaborative filtering. * window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, * set beta to zero. * but yields more accurate results. * BM3D_STEP2 is not allowed as it requires basic estimate to be present. * Currently only Haar transform is supported. * * This function expected to be applied to grayscale images. Advanced usage of this function * can be manual denoising of colored image in different colorspaces. * * @sa * fastNlMeansDenoising */ + (void)bm3dDenoising:(Mat*)src dst:(Mat*)dst h:(float)h templateWindowSize:(int)templateWindowSize searchWindowSize:(int)searchWindowSize blockMatchingStep1:(int)blockMatchingStep1 blockMatchingStep2:(int)blockMatchingStep2 groupSize:(int)groupSize NS_SWIFT_NAME(bm3dDenoising(src:dst:h:templateWindowSize:searchWindowSize:blockMatchingStep1:blockMatchingStep2:groupSize:)); /** * Performs image denoising using the Block-Matching and 3D-filtering algorithm * with several computational * optimizations. Noise expected to be a gaussian white noise. * * @param src Input 8-bit or 16-bit 1-channel image. * @param dst Output image with the same size and type as src. * @param h Parameter regulating filter strength. Big h value perfectly removes noise but also * removes image details, smaller h value preserves details but also preserves some noise. * @param templateWindowSize Size in pixels of the template patch that is used for block-matching. * Should be power of 2. * @param searchWindowSize Size in pixels of the window that is used to perform block-matching. * Affect performance linearly: greater searchWindowsSize - greater denoising time. * Must be larger than templateWindowSize. * @param blockMatchingStep1 Block matching threshold for the first step of BM3D (hard thresholding), * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * @param blockMatchingStep2 Block matching threshold for the second step of BM3D (Wiener filtering), * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, * set beta to zero. * but yields more accurate results. * BM3D_STEP2 is not allowed as it requires basic estimate to be present. * Currently only Haar transform is supported. * * This function expected to be applied to grayscale images. Advanced usage of this function * can be manual denoising of colored image in different colorspaces. * * @sa * fastNlMeansDenoising */ + (void)bm3dDenoising:(Mat*)src dst:(Mat*)dst h:(float)h templateWindowSize:(int)templateWindowSize searchWindowSize:(int)searchWindowSize blockMatchingStep1:(int)blockMatchingStep1 blockMatchingStep2:(int)blockMatchingStep2 NS_SWIFT_NAME(bm3dDenoising(src:dst:h:templateWindowSize:searchWindowSize:blockMatchingStep1:blockMatchingStep2:)); /** * Performs image denoising using the Block-Matching and 3D-filtering algorithm * with several computational * optimizations. Noise expected to be a gaussian white noise. * * @param src Input 8-bit or 16-bit 1-channel image. * @param dst Output image with the same size and type as src. * @param h Parameter regulating filter strength. Big h value perfectly removes noise but also * removes image details, smaller h value preserves details but also preserves some noise. * @param templateWindowSize Size in pixels of the template patch that is used for block-matching. * Should be power of 2. * @param searchWindowSize Size in pixels of the window that is used to perform block-matching. * Affect performance linearly: greater searchWindowsSize - greater denoising time. * Must be larger than templateWindowSize. * @param blockMatchingStep1 Block matching threshold for the first step of BM3D (hard thresholding), * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, * set beta to zero. * but yields more accurate results. * BM3D_STEP2 is not allowed as it requires basic estimate to be present. * Currently only Haar transform is supported. * * This function expected to be applied to grayscale images. Advanced usage of this function * can be manual denoising of colored image in different colorspaces. * * @sa * fastNlMeansDenoising */ + (void)bm3dDenoising:(Mat*)src dst:(Mat*)dst h:(float)h templateWindowSize:(int)templateWindowSize searchWindowSize:(int)searchWindowSize blockMatchingStep1:(int)blockMatchingStep1 NS_SWIFT_NAME(bm3dDenoising(src:dst:h:templateWindowSize:searchWindowSize:blockMatchingStep1:)); /** * Performs image denoising using the Block-Matching and 3D-filtering algorithm * with several computational * optimizations. Noise expected to be a gaussian white noise. * * @param src Input 8-bit or 16-bit 1-channel image. * @param dst Output image with the same size and type as src. * @param h Parameter regulating filter strength. Big h value perfectly removes noise but also * removes image details, smaller h value preserves details but also preserves some noise. * @param templateWindowSize Size in pixels of the template patch that is used for block-matching. * Should be power of 2. * @param searchWindowSize Size in pixels of the window that is used to perform block-matching. * Affect performance linearly: greater searchWindowsSize - greater denoising time. * Must be larger than templateWindowSize. * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, * set beta to zero. * but yields more accurate results. * BM3D_STEP2 is not allowed as it requires basic estimate to be present. * Currently only Haar transform is supported. * * This function expected to be applied to grayscale images. Advanced usage of this function * can be manual denoising of colored image in different colorspaces. * * @sa * fastNlMeansDenoising */ + (void)bm3dDenoising:(Mat*)src dst:(Mat*)dst h:(float)h templateWindowSize:(int)templateWindowSize searchWindowSize:(int)searchWindowSize NS_SWIFT_NAME(bm3dDenoising(src:dst:h:templateWindowSize:searchWindowSize:)); /** * Performs image denoising using the Block-Matching and 3D-filtering algorithm * with several computational * optimizations. Noise expected to be a gaussian white noise. * * @param src Input 8-bit or 16-bit 1-channel image. * @param dst Output image with the same size and type as src. * @param h Parameter regulating filter strength. Big h value perfectly removes noise but also * removes image details, smaller h value preserves details but also preserves some noise. * @param templateWindowSize Size in pixels of the template patch that is used for block-matching. * Should be power of 2. * Affect performance linearly: greater searchWindowsSize - greater denoising time. * Must be larger than templateWindowSize. * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, * set beta to zero. * but yields more accurate results. * BM3D_STEP2 is not allowed as it requires basic estimate to be present. * Currently only Haar transform is supported. * * This function expected to be applied to grayscale images. Advanced usage of this function * can be manual denoising of colored image in different colorspaces. * * @sa * fastNlMeansDenoising */ + (void)bm3dDenoising:(Mat*)src dst:(Mat*)dst h:(float)h templateWindowSize:(int)templateWindowSize NS_SWIFT_NAME(bm3dDenoising(src:dst:h:templateWindowSize:)); /** * Performs image denoising using the Block-Matching and 3D-filtering algorithm * with several computational * optimizations. Noise expected to be a gaussian white noise. * * @param src Input 8-bit or 16-bit 1-channel image. * @param dst Output image with the same size and type as src. * @param h Parameter regulating filter strength. Big h value perfectly removes noise but also * removes image details, smaller h value preserves details but also preserves some noise. * Should be power of 2. * Affect performance linearly: greater searchWindowsSize - greater denoising time. * Must be larger than templateWindowSize. * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, * set beta to zero. * but yields more accurate results. * BM3D_STEP2 is not allowed as it requires basic estimate to be present. * Currently only Haar transform is supported. * * This function expected to be applied to grayscale images. Advanced usage of this function * can be manual denoising of colored image in different colorspaces. * * @sa * fastNlMeansDenoising */ + (void)bm3dDenoising:(Mat*)src dst:(Mat*)dst h:(float)h NS_SWIFT_NAME(bm3dDenoising(src:dst:h:)); /** * Performs image denoising using the Block-Matching and 3D-filtering algorithm * with several computational * optimizations. Noise expected to be a gaussian white noise. * * @param src Input 8-bit or 16-bit 1-channel image. * @param dst Output image with the same size and type as src. * removes image details, smaller h value preserves details but also preserves some noise. * Should be power of 2. * Affect performance linearly: greater searchWindowsSize - greater denoising time. * Must be larger than templateWindowSize. * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * i.e. maximum distance for which two blocks are considered similar. * Value expressed in euclidean distance. * window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, * set beta to zero. * but yields more accurate results. * BM3D_STEP2 is not allowed as it requires basic estimate to be present. * Currently only Haar transform is supported. * * This function expected to be applied to grayscale images. Advanced usage of this function * can be manual denoising of colored image in different colorspaces. * * @sa * fastNlMeansDenoising */ + (void)bm3dDenoising:(Mat*)src dst:(Mat*)dst NS_SWIFT_NAME(bm3dDenoising(src:dst:)); // // Ptr_TonemapDurand cv::xphoto::createTonemapDurand(float gamma = 1.0f, float contrast = 4.0f, float saturation = 1.0f, float sigma_color = 2.0f, float sigma_space = 2.0f) // /** * Creates TonemapDurand object * * You need to set the OPENCV_ENABLE_NONFREE option in cmake to use those. Use them at your own risk. * * @param gamma gamma value for gamma correction. See createTonemap * @param contrast resulting contrast on logarithmic scale, i. e. log(max / min), where max and min * are maximum and minimum luminance values of the resulting image. * @param saturation saturation enhancement value. See createTonemapDrago * @param sigma_color bilateral filter sigma in color space * @param sigma_space bilateral filter sigma in coordinate space */ + (TonemapDurand*)createTonemapDurand:(float)gamma contrast:(float)contrast saturation:(float)saturation sigma_color:(float)sigma_color sigma_space:(float)sigma_space NS_SWIFT_NAME(createTonemapDurand(gamma:contrast:saturation:sigma_color:sigma_space:)); /** * Creates TonemapDurand object * * You need to set the OPENCV_ENABLE_NONFREE option in cmake to use those. Use them at your own risk. * * @param gamma gamma value for gamma correction. See createTonemap * @param contrast resulting contrast on logarithmic scale, i. e. log(max / min), where max and min * are maximum and minimum luminance values of the resulting image. * @param saturation saturation enhancement value. See createTonemapDrago * @param sigma_color bilateral filter sigma in color space */ + (TonemapDurand*)createTonemapDurand:(float)gamma contrast:(float)contrast saturation:(float)saturation sigma_color:(float)sigma_color NS_SWIFT_NAME(createTonemapDurand(gamma:contrast:saturation:sigma_color:)); /** * Creates TonemapDurand object * * You need to set the OPENCV_ENABLE_NONFREE option in cmake to use those. Use them at your own risk. * * @param gamma gamma value for gamma correction. See createTonemap * @param contrast resulting contrast on logarithmic scale, i. e. log(max / min), where max and min * are maximum and minimum luminance values of the resulting image. * @param saturation saturation enhancement value. See createTonemapDrago */ + (TonemapDurand*)createTonemapDurand:(float)gamma contrast:(float)contrast saturation:(float)saturation NS_SWIFT_NAME(createTonemapDurand(gamma:contrast:saturation:)); /** * Creates TonemapDurand object * * You need to set the OPENCV_ENABLE_NONFREE option in cmake to use those. Use them at your own risk. * * @param gamma gamma value for gamma correction. See createTonemap * @param contrast resulting contrast on logarithmic scale, i. e. log(max / min), where max and min * are maximum and minimum luminance values of the resulting image. */ + (TonemapDurand*)createTonemapDurand:(float)gamma contrast:(float)contrast NS_SWIFT_NAME(createTonemapDurand(gamma:contrast:)); /** * Creates TonemapDurand object * * You need to set the OPENCV_ENABLE_NONFREE option in cmake to use those. Use them at your own risk. * * @param gamma gamma value for gamma correction. See createTonemap * are maximum and minimum luminance values of the resulting image. */ + (TonemapDurand*)createTonemapDurand:(float)gamma NS_SWIFT_NAME(createTonemapDurand(gamma:)); /** * Creates TonemapDurand object * * You need to set the OPENCV_ENABLE_NONFREE option in cmake to use those. Use them at your own risk. * * are maximum and minimum luminance values of the resulting image. */ + (TonemapDurand*)createTonemapDurand NS_SWIFT_NAME(createTonemapDurand()); // // void cv::xphoto::inpaint(Mat src, Mat mask, Mat dst, int algorithmType) // /** * The function implements different single-image inpainting algorithms. * * See the original papers CITE: He2012 (Shiftmap) or CITE: GenserPCS2018 and CITE: SeilerTIP2015 (FSR) for details. * * @param src source image * - #INPAINT_SHIFTMAP: it could be of any type and any number of channels from 1 to 4. In case of * 3- and 4-channels images the function expect them in CIELab colorspace or similar one, where first * color component shows intensity, while second and third shows colors. Nonetheless you can try any * colorspaces. * - #INPAINT_FSR_BEST or #INPAINT_FSR_FAST: 1-channel grayscale or 3-channel BGR image. * @param mask mask (#CV_8UC1), where non-zero pixels indicate valid image area, while zero pixels * indicate area to be inpainted * @param dst destination image * @param algorithmType see xphoto::InpaintTypes */ + (void)inpaint:(Mat*)src mask:(Mat*)mask dst:(Mat*)dst algorithmType:(int)algorithmType NS_SWIFT_NAME(inpaint(src:mask:dst:algorithmType:)); // // void cv::xphoto::dctDenoising(Mat src, Mat dst, double sigma, int psize = 16) // /** * The function implements simple dct-based denoising * * . * @param src source image * @param dst destination image * @param sigma expected noise standard deviation * @param psize size of block side where dct is computed * * @sa * fastNlMeansDenoising */ + (void)dctDenoising:(Mat*)src dst:(Mat*)dst sigma:(double)sigma psize:(int)psize NS_SWIFT_NAME(dctDenoising(src:dst:sigma:psize:)); /** * The function implements simple dct-based denoising * * . * @param src source image * @param dst destination image * @param sigma expected noise standard deviation * * @sa * fastNlMeansDenoising */ + (void)dctDenoising:(Mat*)src dst:(Mat*)dst sigma:(double)sigma NS_SWIFT_NAME(dctDenoising(src:dst:sigma:)); // // void cv::xphoto::oilPainting(Mat src, Mat& dst, int size, int dynRatio, int code) // /** * oilPainting * See the book CITE: Holzmann1988 for details. * @param src Input three-channel or one channel image (either CV_8UC3 or CV_8UC1) * @param dst Output image of the same size and type as src. * @param size neighbouring size is 2-size+1 * @param dynRatio image is divided by dynRatio before histogram processing */ + (void)oilPainting:(Mat*)src dst:(Mat*)dst size:(int)size dynRatio:(int)dynRatio code:(int)code NS_SWIFT_NAME(oilPainting(src:dst:size:dynRatio:code:)); // // void cv::xphoto::oilPainting(Mat src, Mat& dst, int size, int dynRatio) // /** * oilPainting * See the book CITE: Holzmann1988 for details. * @param src Input three-channel or one channel image (either CV_8UC3 or CV_8UC1) * @param dst Output image of the same size and type as src. * @param size neighbouring size is 2-size+1 * @param dynRatio image is divided by dynRatio before histogram processing */ + (void)oilPainting:(Mat*)src dst:(Mat*)dst size:(int)size dynRatio:(int)dynRatio NS_SWIFT_NAME(oilPainting(src:dst:size:dynRatio:)); // // Ptr_SimpleWB cv::xphoto::createSimpleWB() // /** * Creates an instance of SimpleWB */ + (SimpleWB*)createSimpleWB NS_SWIFT_NAME(createSimpleWB()); // // Ptr_GrayworldWB cv::xphoto::createGrayworldWB() // /** * Creates an instance of GrayworldWB */ + (GrayworldWB*)createGrayworldWB NS_SWIFT_NAME(createGrayworldWB()); // // Ptr_LearningBasedWB cv::xphoto::createLearningBasedWB(String path_to_model = String()) // /** * Creates an instance of LearningBasedWB * * @param path_to_model Path to a .yml file with the model. If not specified, the default model is used */ + (LearningBasedWB*)createLearningBasedWB:(NSString*)path_to_model NS_SWIFT_NAME(createLearningBasedWB(path_to_model:)); /** * Creates an instance of LearningBasedWB * */ + (LearningBasedWB*)createLearningBasedWB NS_SWIFT_NAME(createLearningBasedWB()); // // void cv::xphoto::applyChannelGains(Mat src, Mat& dst, float gainB, float gainG, float gainR) // /** * Implements an efficient fixed-point approximation for applying channel gains, which is * the last step of multiple white balance algorithms. * * @param src Input three-channel image in the BGR color space (either CV_8UC3 or CV_16UC3) * @param dst Output image of the same size and type as src. * @param gainB gain for the B channel * @param gainG gain for the G channel * @param gainR gain for the R channel */ + (void)applyChannelGains:(Mat*)src dst:(Mat*)dst gainB:(float)gainB gainG:(float)gainG gainR:(float)gainR NS_SWIFT_NAME(applyChannelGains(src:dst:gainB:gainG:gainR:)); @end NS_ASSUME_NONNULL_END