using OpenCVForUnity.CoreModule; using OpenCVForUnity.UtilsModule; using System; using System.Collections.Generic; using System.Runtime.InteropServices; namespace OpenCVForUnity.PhotoModule { // C++: class Photo public class Photo { // C++: enum public const int INPAINT_NS = 0; public const int INPAINT_TELEA = 1; public const int LDR_SIZE = 256; public const int NORMAL_CLONE = 1; public const int MIXED_CLONE = 2; public const int MONOCHROME_TRANSFER = 3; public const int RECURS_FILTER = 1; public const int NORMCONV_FILTER = 2; // // C++: void cv::inpaint(Mat src, Mat inpaintMask, Mat& dst, double inpaintRadius, int flags) // /** * Restores the selected region in an image using the region neighborhood. * * param src Input 8-bit, 16-bit unsigned or 32-bit float 1-channel or 8-bit 3-channel image. * param inpaintMask Inpainting mask, 8-bit 1-channel image. Non-zero pixels indicate the area that * needs to be inpainted. * param dst Output image with the same size and type as src . * param inpaintRadius Radius of a circular neighborhood of each point inpainted that is considered * by the algorithm. * param flags Inpainting method that could be cv::INPAINT_NS or cv::INPAINT_TELEA * * The function reconstructs the selected image area from the pixel near the area boundary. The * function may be used to remove dust and scratches from a scanned photo, or to remove undesirable * objects from still images or video. See <http://en.wikipedia.org/wiki/Inpainting> for more details. * * Note: * */ public static void inpaint(Mat src, Mat inpaintMask, Mat dst, double inpaintRadius, int flags) { if (src != null) src.ThrowIfDisposed(); if (inpaintMask != null) inpaintMask.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); photo_Photo_inpaint_10(src.nativeObj, inpaintMask.nativeObj, dst.nativeObj, inpaintRadius, flags); } // // C++: void cv::fastNlMeansDenoising(Mat src, Mat& dst, float h = 3, int templateWindowSize = 7, int searchWindowSize = 21) // /** * Perform image denoising using Non-local Means Denoising algorithm * <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational * optimizations. Noise expected to be a gaussian white noise * * param src Input 8-bit 1-channel, 2-channel, 3-channel or 4-channel image. * param dst Output image with the same size and type as src . * param templateWindowSize Size in pixels of the template patch that is used to compute weights. * Should be odd. Recommended value 7 pixels * param searchWindowSize Size in pixels of the window that is used to compute weighted average for * given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater * denoising time. Recommended value 21 pixels * 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 * * This function expected to be applied to grayscale images. For colored images look at * fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored * image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting * image to CIELAB colorspace and then separately denoise L and AB components with different h * parameter. */ public static void fastNlMeansDenoising(Mat src, Mat dst, float h, int templateWindowSize, int searchWindowSize) { if (src != null) src.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); photo_Photo_fastNlMeansDenoising_10(src.nativeObj, dst.nativeObj, h, templateWindowSize, searchWindowSize); } /** * Perform image denoising using Non-local Means Denoising algorithm * <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational * optimizations. Noise expected to be a gaussian white noise * * param src Input 8-bit 1-channel, 2-channel, 3-channel or 4-channel image. * param dst Output image with the same size and type as src . * param templateWindowSize Size in pixels of the template patch that is used to compute weights. * Should be odd. Recommended value 7 pixels * given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater * denoising time. Recommended value 21 pixels * 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 * * This function expected to be applied to grayscale images. For colored images look at * fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored * image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting * image to CIELAB colorspace and then separately denoise L and AB components with different h * parameter. */ public static void fastNlMeansDenoising(Mat src, Mat dst, float h, int templateWindowSize) { if (src != null) src.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); photo_Photo_fastNlMeansDenoising_11(src.nativeObj, dst.nativeObj, h, templateWindowSize); } /** * Perform image denoising using Non-local Means Denoising algorithm * <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational * optimizations. Noise expected to be a gaussian white noise * * param src Input 8-bit 1-channel, 2-channel, 3-channel or 4-channel image. * param dst Output image with the same size and type as src . * Should be odd. Recommended value 7 pixels * given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater * denoising time. Recommended value 21 pixels * 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 * * This function expected to be applied to grayscale images. For colored images look at * fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored * image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting * image to CIELAB colorspace and then separately denoise L and AB components with different h * parameter. */ public static void fastNlMeansDenoising(Mat src, Mat dst, float h) { if (src != null) src.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); photo_Photo_fastNlMeansDenoising_12(src.nativeObj, dst.nativeObj, h); } /** * Perform image denoising using Non-local Means Denoising algorithm * <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational * optimizations. Noise expected to be a gaussian white noise * * param src Input 8-bit 1-channel, 2-channel, 3-channel or 4-channel image. * param dst Output image with the same size and type as src . * Should be odd. Recommended value 7 pixels * given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater * denoising time. Recommended value 21 pixels * removes image details, smaller h value preserves details but also preserves some noise * * This function expected to be applied to grayscale images. For colored images look at * fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored * image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting * image to CIELAB colorspace and then separately denoise L and AB components with different h * parameter. */ public static void fastNlMeansDenoising(Mat src, Mat dst) { if (src != null) src.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); photo_Photo_fastNlMeansDenoising_13(src.nativeObj, dst.nativeObj); } // // C++: void cv::fastNlMeansDenoising(Mat src, Mat& dst, vector_float h, int templateWindowSize = 7, int searchWindowSize = 21, int normType = NORM_L2) // /** * Perform image denoising using Non-local Means Denoising algorithm * <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational * optimizations. Noise expected to be a gaussian white noise * * param src Input 8-bit or 16-bit (only with NORM_L1) 1-channel, * 2-channel, 3-channel or 4-channel image. * param dst Output image with the same size and type as src . * param templateWindowSize Size in pixels of the template patch that is used to compute weights. * Should be odd. Recommended value 7 pixels * param searchWindowSize Size in pixels of the window that is used to compute weighted average for * given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater * denoising time. Recommended value 21 pixels * param h Array of parameters regulating filter strength, either one * parameter applied to all channels or one per channel in dst. Big h value * perfectly removes noise but also removes image details, smaller h * value preserves details but also preserves some noise * param normType Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1 * * This function expected to be applied to grayscale images. For colored images look at * fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored * image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting * image to CIELAB colorspace and then separately denoise L and AB components with different h * parameter. */ public static void fastNlMeansDenoising(Mat src, Mat dst, MatOfFloat h, int templateWindowSize, int searchWindowSize, int normType) { if (src != null) src.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); if (h != null) h.ThrowIfDisposed(); Mat h_mat = h; photo_Photo_fastNlMeansDenoising_14(src.nativeObj, dst.nativeObj, h_mat.nativeObj, templateWindowSize, searchWindowSize, normType); } /** * Perform image denoising using Non-local Means Denoising algorithm * <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational * optimizations. Noise expected to be a gaussian white noise * * param src Input 8-bit or 16-bit (only with NORM_L1) 1-channel, * 2-channel, 3-channel or 4-channel image. * param dst Output image with the same size and type as src . * param templateWindowSize Size in pixels of the template patch that is used to compute weights. * Should be odd. Recommended value 7 pixels * param searchWindowSize Size in pixels of the window that is used to compute weighted average for * given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater * denoising time. Recommended value 21 pixels * param h Array of parameters regulating filter strength, either one * parameter applied to all channels or one per channel in dst. Big h value * perfectly removes noise but also removes image details, smaller h * value preserves details but also preserves some noise * * This function expected to be applied to grayscale images. For colored images look at * fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored * image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting * image to CIELAB colorspace and then separately denoise L and AB components with different h * parameter. */ public static void fastNlMeansDenoising(Mat src, Mat dst, MatOfFloat h, int templateWindowSize, int searchWindowSize) { if (src != null) src.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); if (h != null) h.ThrowIfDisposed(); Mat h_mat = h; photo_Photo_fastNlMeansDenoising_15(src.nativeObj, dst.nativeObj, h_mat.nativeObj, templateWindowSize, searchWindowSize); } /** * Perform image denoising using Non-local Means Denoising algorithm * <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational * optimizations. Noise expected to be a gaussian white noise * * param src Input 8-bit or 16-bit (only with NORM_L1) 1-channel, * 2-channel, 3-channel or 4-channel image. * param dst Output image with the same size and type as src . * param templateWindowSize Size in pixels of the template patch that is used to compute weights. * Should be odd. Recommended value 7 pixels * given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater * denoising time. Recommended value 21 pixels * param h Array of parameters regulating filter strength, either one * parameter applied to all channels or one per channel in dst. Big h value * perfectly removes noise but also removes image details, smaller h * value preserves details but also preserves some noise * * This function expected to be applied to grayscale images. For colored images look at * fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored * image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting * image to CIELAB colorspace and then separately denoise L and AB components with different h * parameter. */ public static void fastNlMeansDenoising(Mat src, Mat dst, MatOfFloat h, int templateWindowSize) { if (src != null) src.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); if (h != null) h.ThrowIfDisposed(); Mat h_mat = h; photo_Photo_fastNlMeansDenoising_16(src.nativeObj, dst.nativeObj, h_mat.nativeObj, templateWindowSize); } /** * Perform image denoising using Non-local Means Denoising algorithm * <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational * optimizations. Noise expected to be a gaussian white noise * * param src Input 8-bit or 16-bit (only with NORM_L1) 1-channel, * 2-channel, 3-channel or 4-channel image. * param dst Output image with the same size and type as src . * Should be odd. Recommended value 7 pixels * given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater * denoising time. Recommended value 21 pixels * param h Array of parameters regulating filter strength, either one * parameter applied to all channels or one per channel in dst. Big h value * perfectly removes noise but also removes image details, smaller h * value preserves details but also preserves some noise * * This function expected to be applied to grayscale images. For colored images look at * fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored * image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting * image to CIELAB colorspace and then separately denoise L and AB components with different h * parameter. */ public static void fastNlMeansDenoising(Mat src, Mat dst, MatOfFloat h) { if (src != null) src.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); if (h != null) h.ThrowIfDisposed(); Mat h_mat = h; photo_Photo_fastNlMeansDenoising_17(src.nativeObj, dst.nativeObj, h_mat.nativeObj); } // // C++: void cv::fastNlMeansDenoisingColored(Mat src, Mat& dst, float h = 3, float hColor = 3, int templateWindowSize = 7, int searchWindowSize = 21) // /** * Modification of fastNlMeansDenoising function for colored images * * param src Input 8-bit 3-channel image. * param dst Output image with the same size and type as src . * param templateWindowSize Size in pixels of the template patch that is used to compute weights. * Should be odd. Recommended value 7 pixels * param searchWindowSize Size in pixels of the window that is used to compute weighted average for * given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater * denoising time. Recommended value 21 pixels * param h Parameter regulating filter strength for luminance component. Bigger h value perfectly * removes noise but also removes image details, smaller h value preserves details but also preserves * some noise * param hColor The same as h but for color components. For most images value equals 10 * will be enough to remove colored noise and do not distort colors * * The function converts image to CIELAB colorspace and then separately denoise L and AB components * with given h parameters using fastNlMeansDenoising function. */ public static void fastNlMeansDenoisingColored(Mat src, Mat dst, float h, float hColor, int templateWindowSize, int searchWindowSize) { if (src != null) src.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); photo_Photo_fastNlMeansDenoisingColored_10(src.nativeObj, dst.nativeObj, h, hColor, templateWindowSize, searchWindowSize); } /** * Modification of fastNlMeansDenoising function for colored images * * param src Input 8-bit 3-channel image. * param dst Output image with the same size and type as src . * param templateWindowSize Size in pixels of the template patch that is used to compute weights. * Should be odd. Recommended value 7 pixels * given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater * denoising time. Recommended value 21 pixels * param h Parameter regulating filter strength for luminance component. Bigger h value perfectly * removes noise but also removes image details, smaller h value preserves details but also preserves * some noise * param hColor The same as h but for color components. For most images value equals 10 * will be enough to remove colored noise and do not distort colors * * The function converts image to CIELAB colorspace and then separately denoise L and AB components * with given h parameters using fastNlMeansDenoising function. */ public static void fastNlMeansDenoisingColored(Mat src, Mat dst, float h, float hColor, int templateWindowSize) { if (src != null) src.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); photo_Photo_fastNlMeansDenoisingColored_11(src.nativeObj, dst.nativeObj, h, hColor, templateWindowSize); } /** * Modification of fastNlMeansDenoising function for colored images * * param src Input 8-bit 3-channel image. * param dst Output image with the same size and type as src . * Should be odd. Recommended value 7 pixels * given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater * denoising time. Recommended value 21 pixels * param h Parameter regulating filter strength for luminance component. Bigger h value perfectly * removes noise but also removes image details, smaller h value preserves details but also preserves * some noise * param hColor The same as h but for color components. For most images value equals 10 * will be enough to remove colored noise and do not distort colors * * The function converts image to CIELAB colorspace and then separately denoise L and AB components * with given h parameters using fastNlMeansDenoising function. */ public static void fastNlMeansDenoisingColored(Mat src, Mat dst, float h, float hColor) { if (src != null) src.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); photo_Photo_fastNlMeansDenoisingColored_12(src.nativeObj, dst.nativeObj, h, hColor); } /** * Modification of fastNlMeansDenoising function for colored images * * param src Input 8-bit 3-channel image. * param dst Output image with the same size and type as src . * Should be odd. Recommended value 7 pixels * given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater * denoising time. Recommended value 21 pixels * param h Parameter regulating filter strength for luminance component. Bigger h value perfectly * removes noise but also removes image details, smaller h value preserves details but also preserves * some noise * will be enough to remove colored noise and do not distort colors * * The function converts image to CIELAB colorspace and then separately denoise L and AB components * with given h parameters using fastNlMeansDenoising function. */ public static void fastNlMeansDenoisingColored(Mat src, Mat dst, float h) { if (src != null) src.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); photo_Photo_fastNlMeansDenoisingColored_13(src.nativeObj, dst.nativeObj, h); } /** * Modification of fastNlMeansDenoising function for colored images * * param src Input 8-bit 3-channel image. * param dst Output image with the same size and type as src . * Should be odd. Recommended value 7 pixels * given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater * denoising time. Recommended value 21 pixels * removes noise but also removes image details, smaller h value preserves details but also preserves * some noise * will be enough to remove colored noise and do not distort colors * * The function converts image to CIELAB colorspace and then separately denoise L and AB components * with given h parameters using fastNlMeansDenoising function. */ public static void fastNlMeansDenoisingColored(Mat src, Mat dst) { if (src != null) src.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); photo_Photo_fastNlMeansDenoisingColored_14(src.nativeObj, dst.nativeObj); } // // C++: void cv::fastNlMeansDenoisingMulti(vector_Mat srcImgs, Mat& dst, int imgToDenoiseIndex, int temporalWindowSize, float h = 3, int templateWindowSize = 7, int searchWindowSize = 21) // /** * Modification of fastNlMeansDenoising function for images sequence where consecutive images have been * captured in small period of time. For example video. This version of the function is for grayscale * images or for manual manipulation with colorspaces. See CITE: Buades2005DenoisingIS for more details * (open access [here](https://static.aminer.org/pdf/PDF/000/317/196/spatio_temporal_wiener_filtering_of_image_sequences_using_a_parametric.pdf)). * * param srcImgs Input 8-bit 1-channel, 2-channel, 3-channel or * 4-channel images sequence. All images should have the same type and * size. * param imgToDenoiseIndex Target image to denoise index in srcImgs sequence * param temporalWindowSize Number of surrounding images to use for target image denoising. Should * be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to * imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise * srcImgs[imgToDenoiseIndex] image. * param dst Output image with the same size and type as srcImgs images. * param templateWindowSize Size in pixels of the template patch that is used to compute weights. * Should be odd. Recommended value 7 pixels * param searchWindowSize Size in pixels of the window that is used to compute weighted average for * given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater * denoising time. Recommended value 21 pixels * param h Parameter regulating filter strength. Bigger h value * perfectly removes noise but also removes image details, smaller h * value preserves details but also preserves some noise */ public static void fastNlMeansDenoisingMulti(List srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, float h, int templateWindowSize, int searchWindowSize) { if (dst != null) dst.ThrowIfDisposed(); Mat srcImgs_mat = Converters.vector_Mat_to_Mat(srcImgs); photo_Photo_fastNlMeansDenoisingMulti_10(srcImgs_mat.nativeObj, dst.nativeObj, imgToDenoiseIndex, temporalWindowSize, h, templateWindowSize, searchWindowSize); } /** * Modification of fastNlMeansDenoising function for images sequence where consecutive images have been * captured in small period of time. For example video. This version of the function is for grayscale * images or for manual manipulation with colorspaces. See CITE: Buades2005DenoisingIS for more details * (open access [here](https://static.aminer.org/pdf/PDF/000/317/196/spatio_temporal_wiener_filtering_of_image_sequences_using_a_parametric.pdf)). * * param srcImgs Input 8-bit 1-channel, 2-channel, 3-channel or * 4-channel images sequence. All images should have the same type and * size. * param imgToDenoiseIndex Target image to denoise index in srcImgs sequence * param temporalWindowSize Number of surrounding images to use for target image denoising. Should * be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to * imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise * srcImgs[imgToDenoiseIndex] image. * param dst Output image with the same size and type as srcImgs images. * param templateWindowSize Size in pixels of the template patch that is used to compute weights. * Should be odd. Recommended value 7 pixels * given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater * denoising time. Recommended value 21 pixels * param h Parameter regulating filter strength. Bigger h value * perfectly removes noise but also removes image details, smaller h * value preserves details but also preserves some noise */ public static void fastNlMeansDenoisingMulti(List srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, float h, int templateWindowSize) { if (dst != null) dst.ThrowIfDisposed(); Mat srcImgs_mat = Converters.vector_Mat_to_Mat(srcImgs); photo_Photo_fastNlMeansDenoisingMulti_11(srcImgs_mat.nativeObj, dst.nativeObj, imgToDenoiseIndex, temporalWindowSize, h, templateWindowSize); } /** * Modification of fastNlMeansDenoising function for images sequence where consecutive images have been * captured in small period of time. For example video. This version of the function is for grayscale * images or for manual manipulation with colorspaces. See CITE: Buades2005DenoisingIS for more details * (open access [here](https://static.aminer.org/pdf/PDF/000/317/196/spatio_temporal_wiener_filtering_of_image_sequences_using_a_parametric.pdf)). * * param srcImgs Input 8-bit 1-channel, 2-channel, 3-channel or * 4-channel images sequence. All images should have the same type and * size. * param imgToDenoiseIndex Target image to denoise index in srcImgs sequence * param temporalWindowSize Number of surrounding images to use for target image denoising. Should * be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to * imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise * srcImgs[imgToDenoiseIndex] image. * param dst Output image with the same size and type as srcImgs images. * Should be odd. Recommended value 7 pixels * given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater * denoising time. Recommended value 21 pixels * param h Parameter regulating filter strength. Bigger h value * perfectly removes noise but also removes image details, smaller h * value preserves details but also preserves some noise */ public static void fastNlMeansDenoisingMulti(List srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, float h) { if (dst != null) dst.ThrowIfDisposed(); Mat srcImgs_mat = Converters.vector_Mat_to_Mat(srcImgs); photo_Photo_fastNlMeansDenoisingMulti_12(srcImgs_mat.nativeObj, dst.nativeObj, imgToDenoiseIndex, temporalWindowSize, h); } /** * Modification of fastNlMeansDenoising function for images sequence where consecutive images have been * captured in small period of time. For example video. This version of the function is for grayscale * images or for manual manipulation with colorspaces. See CITE: Buades2005DenoisingIS for more details * (open access [here](https://static.aminer.org/pdf/PDF/000/317/196/spatio_temporal_wiener_filtering_of_image_sequences_using_a_parametric.pdf)). * * param srcImgs Input 8-bit 1-channel, 2-channel, 3-channel or * 4-channel images sequence. All images should have the same type and * size. * param imgToDenoiseIndex Target image to denoise index in srcImgs sequence * param temporalWindowSize Number of surrounding images to use for target image denoising. Should * be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to * imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise * srcImgs[imgToDenoiseIndex] image. * param dst Output image with the same size and type as srcImgs images. * Should be odd. Recommended value 7 pixels * given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater * denoising time. Recommended value 21 pixels * perfectly removes noise but also removes image details, smaller h * value preserves details but also preserves some noise */ public static void fastNlMeansDenoisingMulti(List srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize) { if (dst != null) dst.ThrowIfDisposed(); Mat srcImgs_mat = Converters.vector_Mat_to_Mat(srcImgs); photo_Photo_fastNlMeansDenoisingMulti_13(srcImgs_mat.nativeObj, dst.nativeObj, imgToDenoiseIndex, temporalWindowSize); } // // C++: void cv::fastNlMeansDenoisingMulti(vector_Mat srcImgs, Mat& dst, int imgToDenoiseIndex, int temporalWindowSize, vector_float h, int templateWindowSize = 7, int searchWindowSize = 21, int normType = NORM_L2) // /** * Modification of fastNlMeansDenoising function for images sequence where consecutive images have been * captured in small period of time. For example video. This version of the function is for grayscale * images or for manual manipulation with colorspaces. See CITE: Buades2005DenoisingIS for more details * (open access [here](https://static.aminer.org/pdf/PDF/000/317/196/spatio_temporal_wiener_filtering_of_image_sequences_using_a_parametric.pdf)). * * param srcImgs Input 8-bit or 16-bit (only with NORM_L1) 1-channel, * 2-channel, 3-channel or 4-channel images sequence. All images should * have the same type and size. * param imgToDenoiseIndex Target image to denoise index in srcImgs sequence * param temporalWindowSize Number of surrounding images to use for target image denoising. Should * be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to * imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise * srcImgs[imgToDenoiseIndex] image. * param dst Output image with the same size and type as srcImgs images. * param templateWindowSize Size in pixels of the template patch that is used to compute weights. * Should be odd. Recommended value 7 pixels * param searchWindowSize Size in pixels of the window that is used to compute weighted average for * given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater * denoising time. Recommended value 21 pixels * param h Array of parameters regulating filter strength, either one * parameter applied to all channels or one per channel in dst. Big h value * perfectly removes noise but also removes image details, smaller h * value preserves details but also preserves some noise * param normType Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1 */ public static void fastNlMeansDenoisingMulti(List srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, MatOfFloat h, int templateWindowSize, int searchWindowSize, int normType) { if (dst != null) dst.ThrowIfDisposed(); if (h != null) h.ThrowIfDisposed(); Mat srcImgs_mat = Converters.vector_Mat_to_Mat(srcImgs); Mat h_mat = h; photo_Photo_fastNlMeansDenoisingMulti_14(srcImgs_mat.nativeObj, dst.nativeObj, imgToDenoiseIndex, temporalWindowSize, h_mat.nativeObj, templateWindowSize, searchWindowSize, normType); } /** * Modification of fastNlMeansDenoising function for images sequence where consecutive images have been * captured in small period of time. For example video. This version of the function is for grayscale * images or for manual manipulation with colorspaces. See CITE: Buades2005DenoisingIS for more details * (open access [here](https://static.aminer.org/pdf/PDF/000/317/196/spatio_temporal_wiener_filtering_of_image_sequences_using_a_parametric.pdf)). * * param srcImgs Input 8-bit or 16-bit (only with NORM_L1) 1-channel, * 2-channel, 3-channel or 4-channel images sequence. All images should * have the same type and size. * param imgToDenoiseIndex Target image to denoise index in srcImgs sequence * param temporalWindowSize Number of surrounding images to use for target image denoising. Should * be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to * imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise * srcImgs[imgToDenoiseIndex] image. * param dst Output image with the same size and type as srcImgs images. * param templateWindowSize Size in pixels of the template patch that is used to compute weights. * Should be odd. Recommended value 7 pixels * param searchWindowSize Size in pixels of the window that is used to compute weighted average for * given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater * denoising time. Recommended value 21 pixels * param h Array of parameters regulating filter strength, either one * parameter applied to all channels or one per channel in dst. Big h value * perfectly removes noise but also removes image details, smaller h * value preserves details but also preserves some noise */ public static void fastNlMeansDenoisingMulti(List srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, MatOfFloat h, int templateWindowSize, int searchWindowSize) { if (dst != null) dst.ThrowIfDisposed(); if (h != null) h.ThrowIfDisposed(); Mat srcImgs_mat = Converters.vector_Mat_to_Mat(srcImgs); Mat h_mat = h; photo_Photo_fastNlMeansDenoisingMulti_15(srcImgs_mat.nativeObj, dst.nativeObj, imgToDenoiseIndex, temporalWindowSize, h_mat.nativeObj, templateWindowSize, searchWindowSize); } /** * Modification of fastNlMeansDenoising function for images sequence where consecutive images have been * captured in small period of time. For example video. This version of the function is for grayscale * images or for manual manipulation with colorspaces. See CITE: Buades2005DenoisingIS for more details * (open access [here](https://static.aminer.org/pdf/PDF/000/317/196/spatio_temporal_wiener_filtering_of_image_sequences_using_a_parametric.pdf)). * * param srcImgs Input 8-bit or 16-bit (only with NORM_L1) 1-channel, * 2-channel, 3-channel or 4-channel images sequence. All images should * have the same type and size. * param imgToDenoiseIndex Target image to denoise index in srcImgs sequence * param temporalWindowSize Number of surrounding images to use for target image denoising. Should * be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to * imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise * srcImgs[imgToDenoiseIndex] image. * param dst Output image with the same size and type as srcImgs images. * param templateWindowSize Size in pixels of the template patch that is used to compute weights. * Should be odd. Recommended value 7 pixels * given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater * denoising time. Recommended value 21 pixels * param h Array of parameters regulating filter strength, either one * parameter applied to all channels or one per channel in dst. Big h value * perfectly removes noise but also removes image details, smaller h * value preserves details but also preserves some noise */ public static void fastNlMeansDenoisingMulti(List srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, MatOfFloat h, int templateWindowSize) { if (dst != null) dst.ThrowIfDisposed(); if (h != null) h.ThrowIfDisposed(); Mat srcImgs_mat = Converters.vector_Mat_to_Mat(srcImgs); Mat h_mat = h; photo_Photo_fastNlMeansDenoisingMulti_16(srcImgs_mat.nativeObj, dst.nativeObj, imgToDenoiseIndex, temporalWindowSize, h_mat.nativeObj, templateWindowSize); } /** * Modification of fastNlMeansDenoising function for images sequence where consecutive images have been * captured in small period of time. For example video. This version of the function is for grayscale * images or for manual manipulation with colorspaces. See CITE: Buades2005DenoisingIS for more details * (open access [here](https://static.aminer.org/pdf/PDF/000/317/196/spatio_temporal_wiener_filtering_of_image_sequences_using_a_parametric.pdf)). * * param srcImgs Input 8-bit or 16-bit (only with NORM_L1) 1-channel, * 2-channel, 3-channel or 4-channel images sequence. All images should * have the same type and size. * param imgToDenoiseIndex Target image to denoise index in srcImgs sequence * param temporalWindowSize Number of surrounding images to use for target image denoising. Should * be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to * imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise * srcImgs[imgToDenoiseIndex] image. * param dst Output image with the same size and type as srcImgs images. * Should be odd. Recommended value 7 pixels * given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater * denoising time. Recommended value 21 pixels * param h Array of parameters regulating filter strength, either one * parameter applied to all channels or one per channel in dst. Big h value * perfectly removes noise but also removes image details, smaller h * value preserves details but also preserves some noise */ public static void fastNlMeansDenoisingMulti(List srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, MatOfFloat h) { if (dst != null) dst.ThrowIfDisposed(); if (h != null) h.ThrowIfDisposed(); Mat srcImgs_mat = Converters.vector_Mat_to_Mat(srcImgs); Mat h_mat = h; photo_Photo_fastNlMeansDenoisingMulti_17(srcImgs_mat.nativeObj, dst.nativeObj, imgToDenoiseIndex, temporalWindowSize, h_mat.nativeObj); } // // C++: void cv::fastNlMeansDenoisingColoredMulti(vector_Mat srcImgs, Mat& dst, int imgToDenoiseIndex, int temporalWindowSize, float h = 3, float hColor = 3, int templateWindowSize = 7, int searchWindowSize = 21) // /** * Modification of fastNlMeansDenoisingMulti function for colored images sequences * * param srcImgs Input 8-bit 3-channel images sequence. All images should have the same type and * size. * param imgToDenoiseIndex Target image to denoise index in srcImgs sequence * param temporalWindowSize Number of surrounding images to use for target image denoising. Should * be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to * imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise * srcImgs[imgToDenoiseIndex] image. * param dst Output image with the same size and type as srcImgs images. * param templateWindowSize Size in pixels of the template patch that is used to compute weights. * Should be odd. Recommended value 7 pixels * param searchWindowSize Size in pixels of the window that is used to compute weighted average for * given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater * denoising time. Recommended value 21 pixels * param h Parameter regulating filter strength for luminance component. Bigger h value perfectly * removes noise but also removes image details, smaller h value preserves details but also preserves * some noise. * param hColor The same as h but for color components. * * The function converts images to CIELAB colorspace and then separately denoise L and AB components * with given h parameters using fastNlMeansDenoisingMulti function. */ public static void fastNlMeansDenoisingColoredMulti(List srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, float h, float hColor, int templateWindowSize, int searchWindowSize) { if (dst != null) dst.ThrowIfDisposed(); Mat srcImgs_mat = Converters.vector_Mat_to_Mat(srcImgs); photo_Photo_fastNlMeansDenoisingColoredMulti_10(srcImgs_mat.nativeObj, dst.nativeObj, imgToDenoiseIndex, temporalWindowSize, h, hColor, templateWindowSize, searchWindowSize); } /** * Modification of fastNlMeansDenoisingMulti function for colored images sequences * * param srcImgs Input 8-bit 3-channel images sequence. All images should have the same type and * size. * param imgToDenoiseIndex Target image to denoise index in srcImgs sequence * param temporalWindowSize Number of surrounding images to use for target image denoising. Should * be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to * imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise * srcImgs[imgToDenoiseIndex] image. * param dst Output image with the same size and type as srcImgs images. * param templateWindowSize Size in pixels of the template patch that is used to compute weights. * Should be odd. Recommended value 7 pixels * given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater * denoising time. Recommended value 21 pixels * param h Parameter regulating filter strength for luminance component. Bigger h value perfectly * removes noise but also removes image details, smaller h value preserves details but also preserves * some noise. * param hColor The same as h but for color components. * * The function converts images to CIELAB colorspace and then separately denoise L and AB components * with given h parameters using fastNlMeansDenoisingMulti function. */ public static void fastNlMeansDenoisingColoredMulti(List srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, float h, float hColor, int templateWindowSize) { if (dst != null) dst.ThrowIfDisposed(); Mat srcImgs_mat = Converters.vector_Mat_to_Mat(srcImgs); photo_Photo_fastNlMeansDenoisingColoredMulti_11(srcImgs_mat.nativeObj, dst.nativeObj, imgToDenoiseIndex, temporalWindowSize, h, hColor, templateWindowSize); } /** * Modification of fastNlMeansDenoisingMulti function for colored images sequences * * param srcImgs Input 8-bit 3-channel images sequence. All images should have the same type and * size. * param imgToDenoiseIndex Target image to denoise index in srcImgs sequence * param temporalWindowSize Number of surrounding images to use for target image denoising. Should * be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to * imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise * srcImgs[imgToDenoiseIndex] image. * param dst Output image with the same size and type as srcImgs images. * Should be odd. Recommended value 7 pixels * given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater * denoising time. Recommended value 21 pixels * param h Parameter regulating filter strength for luminance component. Bigger h value perfectly * removes noise but also removes image details, smaller h value preserves details but also preserves * some noise. * param hColor The same as h but for color components. * * The function converts images to CIELAB colorspace and then separately denoise L and AB components * with given h parameters using fastNlMeansDenoisingMulti function. */ public static void fastNlMeansDenoisingColoredMulti(List srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, float h, float hColor) { if (dst != null) dst.ThrowIfDisposed(); Mat srcImgs_mat = Converters.vector_Mat_to_Mat(srcImgs); photo_Photo_fastNlMeansDenoisingColoredMulti_12(srcImgs_mat.nativeObj, dst.nativeObj, imgToDenoiseIndex, temporalWindowSize, h, hColor); } /** * Modification of fastNlMeansDenoisingMulti function for colored images sequences * * param srcImgs Input 8-bit 3-channel images sequence. All images should have the same type and * size. * param imgToDenoiseIndex Target image to denoise index in srcImgs sequence * param temporalWindowSize Number of surrounding images to use for target image denoising. Should * be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to * imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise * srcImgs[imgToDenoiseIndex] image. * param dst Output image with the same size and type as srcImgs images. * Should be odd. Recommended value 7 pixels * given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater * denoising time. Recommended value 21 pixels * param h Parameter regulating filter strength for luminance component. Bigger h value perfectly * removes noise but also removes image details, smaller h value preserves details but also preserves * some noise. * * The function converts images to CIELAB colorspace and then separately denoise L and AB components * with given h parameters using fastNlMeansDenoisingMulti function. */ public static void fastNlMeansDenoisingColoredMulti(List srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, float h) { if (dst != null) dst.ThrowIfDisposed(); Mat srcImgs_mat = Converters.vector_Mat_to_Mat(srcImgs); photo_Photo_fastNlMeansDenoisingColoredMulti_13(srcImgs_mat.nativeObj, dst.nativeObj, imgToDenoiseIndex, temporalWindowSize, h); } /** * Modification of fastNlMeansDenoisingMulti function for colored images sequences * * param srcImgs Input 8-bit 3-channel images sequence. All images should have the same type and * size. * param imgToDenoiseIndex Target image to denoise index in srcImgs sequence * param temporalWindowSize Number of surrounding images to use for target image denoising. Should * be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to * imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise * srcImgs[imgToDenoiseIndex] image. * param dst Output image with the same size and type as srcImgs images. * Should be odd. Recommended value 7 pixels * given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater * denoising time. Recommended value 21 pixels * removes noise but also removes image details, smaller h value preserves details but also preserves * some noise. * * The function converts images to CIELAB colorspace and then separately denoise L and AB components * with given h parameters using fastNlMeansDenoisingMulti function. */ public static void fastNlMeansDenoisingColoredMulti(List srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize) { if (dst != null) dst.ThrowIfDisposed(); Mat srcImgs_mat = Converters.vector_Mat_to_Mat(srcImgs); photo_Photo_fastNlMeansDenoisingColoredMulti_14(srcImgs_mat.nativeObj, dst.nativeObj, imgToDenoiseIndex, temporalWindowSize); } // // C++: void cv::denoise_TVL1(vector_Mat observations, Mat result, double lambda = 1.0, int niters = 30) // /** * Primal-dual algorithm is an algorithm for solving special types of variational problems (that is, * finding a function to minimize some functional). As the image denoising, in particular, may be seen * as the variational problem, primal-dual algorithm then can be used to perform denoising and this is * exactly what is implemented. * * It should be noted, that this implementation was taken from the July 2013 blog entry * CITE: MA13 , which also contained (slightly more general) ready-to-use source code on Python. * Subsequently, that code was rewritten on C++ with the usage of openCV by Vadim Pisarevsky at the end * of July 2013 and finally it was slightly adapted by later authors. * * Although the thorough discussion and justification of the algorithm involved may be found in * CITE: ChambolleEtAl, it might make sense to skim over it here, following CITE: MA13 . To begin * with, we consider the 1-byte gray-level images as the functions from the rectangular domain of * pixels (it may be seen as set * \(\left\{(x,y)\in\mathbb{N}\times\mathbb{N}\mid 1\leq x\leq n,\;1\leq y\leq m\right\}\) for some * \(m,\;n\in\mathbb{N}\)) into \(\{0,1,\dots,255\}\). We shall denote the noised images as \(f_i\) and with * this view, given some image \(x\) of the same size, we may measure how bad it is by the formula * * \(\left\|\left\|\nabla x\right\|\right\| + \lambda\sum_i\left\|\left\|x-f_i\right\|\right\|\) * * \(\|\|\cdot\|\|\) here denotes \(L_2\)-norm and as you see, the first addend states that we want our * image to be smooth (ideally, having zero gradient, thus being constant) and the second states that * we want our result to be close to the observations we've got. If we treat \(x\) as a function, this is * exactly the functional what we seek to minimize and here the Primal-Dual algorithm comes into play. * * param observations This array should contain one or more noised versions of the image that is to * be restored. * param result Here the denoised image will be stored. There is no need to do pre-allocation of * storage space, as it will be automatically allocated, if necessary. * param lambda Corresponds to \(\lambda\) in the formulas above. As it is enlarged, the smooth * (blurred) images are treated more favorably than detailed (but maybe more noised) ones. Roughly * speaking, as it becomes smaller, the result will be more blur but more sever outliers will be * removed. * param niters Number of iterations that the algorithm will run. Of course, as more iterations as * better, but it is hard to quantitatively refine this statement, so just use the default and * increase it if the results are poor. */ public static void denoise_TVL1(List observations, Mat result, double lambda, int niters) { if (result != null) result.ThrowIfDisposed(); Mat observations_mat = Converters.vector_Mat_to_Mat(observations); photo_Photo_denoise_1TVL1_10(observations_mat.nativeObj, result.nativeObj, lambda, niters); } /** * Primal-dual algorithm is an algorithm for solving special types of variational problems (that is, * finding a function to minimize some functional). As the image denoising, in particular, may be seen * as the variational problem, primal-dual algorithm then can be used to perform denoising and this is * exactly what is implemented. * * It should be noted, that this implementation was taken from the July 2013 blog entry * CITE: MA13 , which also contained (slightly more general) ready-to-use source code on Python. * Subsequently, that code was rewritten on C++ with the usage of openCV by Vadim Pisarevsky at the end * of July 2013 and finally it was slightly adapted by later authors. * * Although the thorough discussion and justification of the algorithm involved may be found in * CITE: ChambolleEtAl, it might make sense to skim over it here, following CITE: MA13 . To begin * with, we consider the 1-byte gray-level images as the functions from the rectangular domain of * pixels (it may be seen as set * \(\left\{(x,y)\in\mathbb{N}\times\mathbb{N}\mid 1\leq x\leq n,\;1\leq y\leq m\right\}\) for some * \(m,\;n\in\mathbb{N}\)) into \(\{0,1,\dots,255\}\). We shall denote the noised images as \(f_i\) and with * this view, given some image \(x\) of the same size, we may measure how bad it is by the formula * * \(\left\|\left\|\nabla x\right\|\right\| + \lambda\sum_i\left\|\left\|x-f_i\right\|\right\|\) * * \(\|\|\cdot\|\|\) here denotes \(L_2\)-norm and as you see, the first addend states that we want our * image to be smooth (ideally, having zero gradient, thus being constant) and the second states that * we want our result to be close to the observations we've got. If we treat \(x\) as a function, this is * exactly the functional what we seek to minimize and here the Primal-Dual algorithm comes into play. * * param observations This array should contain one or more noised versions of the image that is to * be restored. * param result Here the denoised image will be stored. There is no need to do pre-allocation of * storage space, as it will be automatically allocated, if necessary. * param lambda Corresponds to \(\lambda\) in the formulas above. As it is enlarged, the smooth * (blurred) images are treated more favorably than detailed (but maybe more noised) ones. Roughly * speaking, as it becomes smaller, the result will be more blur but more sever outliers will be * removed. * better, but it is hard to quantitatively refine this statement, so just use the default and * increase it if the results are poor. */ public static void denoise_TVL1(List observations, Mat result, double lambda) { if (result != null) result.ThrowIfDisposed(); Mat observations_mat = Converters.vector_Mat_to_Mat(observations); photo_Photo_denoise_1TVL1_11(observations_mat.nativeObj, result.nativeObj, lambda); } /** * Primal-dual algorithm is an algorithm for solving special types of variational problems (that is, * finding a function to minimize some functional). As the image denoising, in particular, may be seen * as the variational problem, primal-dual algorithm then can be used to perform denoising and this is * exactly what is implemented. * * It should be noted, that this implementation was taken from the July 2013 blog entry * CITE: MA13 , which also contained (slightly more general) ready-to-use source code on Python. * Subsequently, that code was rewritten on C++ with the usage of openCV by Vadim Pisarevsky at the end * of July 2013 and finally it was slightly adapted by later authors. * * Although the thorough discussion and justification of the algorithm involved may be found in * CITE: ChambolleEtAl, it might make sense to skim over it here, following CITE: MA13 . To begin * with, we consider the 1-byte gray-level images as the functions from the rectangular domain of * pixels (it may be seen as set * \(\left\{(x,y)\in\mathbb{N}\times\mathbb{N}\mid 1\leq x\leq n,\;1\leq y\leq m\right\}\) for some * \(m,\;n\in\mathbb{N}\)) into \(\{0,1,\dots,255\}\). We shall denote the noised images as \(f_i\) and with * this view, given some image \(x\) of the same size, we may measure how bad it is by the formula * * \(\left\|\left\|\nabla x\right\|\right\| + \lambda\sum_i\left\|\left\|x-f_i\right\|\right\|\) * * \(\|\|\cdot\|\|\) here denotes \(L_2\)-norm and as you see, the first addend states that we want our * image to be smooth (ideally, having zero gradient, thus being constant) and the second states that * we want our result to be close to the observations we've got. If we treat \(x\) as a function, this is * exactly the functional what we seek to minimize and here the Primal-Dual algorithm comes into play. * * param observations This array should contain one or more noised versions of the image that is to * be restored. * param result Here the denoised image will be stored. There is no need to do pre-allocation of * storage space, as it will be automatically allocated, if necessary. * (blurred) images are treated more favorably than detailed (but maybe more noised) ones. Roughly * speaking, as it becomes smaller, the result will be more blur but more sever outliers will be * removed. * better, but it is hard to quantitatively refine this statement, so just use the default and * increase it if the results are poor. */ public static void denoise_TVL1(List observations, Mat result) { if (result != null) result.ThrowIfDisposed(); Mat observations_mat = Converters.vector_Mat_to_Mat(observations); photo_Photo_denoise_1TVL1_12(observations_mat.nativeObj, result.nativeObj); } // // C++: Ptr_Tonemap cv::createTonemap(float gamma = 1.0f) // /** * Creates simple linear mapper with gamma correction * * param gamma positive value for gamma correction. Gamma value of 1.0 implies no correction, gamma * equal to 2.2f is suitable for most displays. * Generally gamma > 1 brightens the image and gamma < 1 darkens it. * return automatically generated */ public static Tonemap createTonemap(float gamma) { return Tonemap.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(photo_Photo_createTonemap_10(gamma))); } /** * Creates simple linear mapper with gamma correction * * equal to 2.2f is suitable for most displays. * Generally gamma > 1 brightens the image and gamma < 1 darkens it. * return automatically generated */ public static Tonemap createTonemap() { return Tonemap.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(photo_Photo_createTonemap_11())); } // // C++: Ptr_TonemapDrago cv::createTonemapDrago(float gamma = 1.0f, float saturation = 1.0f, float bias = 0.85f) // /** * Creates TonemapDrago object * * param gamma gamma value for gamma correction. See createTonemap * param saturation positive saturation enhancement value. 1.0 preserves saturation, values greater * than 1 increase saturation and values less than 1 decrease it. * param bias value for bias function in [0, 1] range. Values from 0.7 to 0.9 usually give best * results, default value is 0.85. * return automatically generated */ public static TonemapDrago createTonemapDrago(float gamma, float saturation, float bias) { return TonemapDrago.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(photo_Photo_createTonemapDrago_10(gamma, saturation, bias))); } /** * Creates TonemapDrago object * * param gamma gamma value for gamma correction. See createTonemap * param saturation positive saturation enhancement value. 1.0 preserves saturation, values greater * than 1 increase saturation and values less than 1 decrease it. * results, default value is 0.85. * return automatically generated */ public static TonemapDrago createTonemapDrago(float gamma, float saturation) { return TonemapDrago.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(photo_Photo_createTonemapDrago_11(gamma, saturation))); } /** * Creates TonemapDrago object * * param gamma gamma value for gamma correction. See createTonemap * than 1 increase saturation and values less than 1 decrease it. * results, default value is 0.85. * return automatically generated */ public static TonemapDrago createTonemapDrago(float gamma) { return TonemapDrago.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(photo_Photo_createTonemapDrago_12(gamma))); } /** * Creates TonemapDrago object * * than 1 increase saturation and values less than 1 decrease it. * results, default value is 0.85. * return automatically generated */ public static TonemapDrago createTonemapDrago() { return TonemapDrago.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(photo_Photo_createTonemapDrago_13())); } // // C++: Ptr_TonemapReinhard cv::createTonemapReinhard(float gamma = 1.0f, float intensity = 0.0f, float light_adapt = 1.0f, float color_adapt = 0.0f) // /** * Creates TonemapReinhard object * * param gamma gamma value for gamma correction. See createTonemap * param intensity result intensity in [-8, 8] range. Greater intensity produces brighter results. * param light_adapt light adaptation in [0, 1] range. If 1 adaptation is based only on pixel * value, if 0 it's global, otherwise it's a weighted mean of this two cases. * param color_adapt chromatic adaptation in [0, 1] range. If 1 channels are treated independently, * if 0 adaptation level is the same for each channel. * return automatically generated */ public static TonemapReinhard createTonemapReinhard(float gamma, float intensity, float light_adapt, float color_adapt) { return TonemapReinhard.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(photo_Photo_createTonemapReinhard_10(gamma, intensity, light_adapt, color_adapt))); } /** * Creates TonemapReinhard object * * param gamma gamma value for gamma correction. See createTonemap * param intensity result intensity in [-8, 8] range. Greater intensity produces brighter results. * param light_adapt light adaptation in [0, 1] range. If 1 adaptation is based only on pixel * value, if 0 it's global, otherwise it's a weighted mean of this two cases. * if 0 adaptation level is the same for each channel. * return automatically generated */ public static TonemapReinhard createTonemapReinhard(float gamma, float intensity, float light_adapt) { return TonemapReinhard.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(photo_Photo_createTonemapReinhard_11(gamma, intensity, light_adapt))); } /** * Creates TonemapReinhard object * * param gamma gamma value for gamma correction. See createTonemap * param intensity result intensity in [-8, 8] range. Greater intensity produces brighter results. * value, if 0 it's global, otherwise it's a weighted mean of this two cases. * if 0 adaptation level is the same for each channel. * return automatically generated */ public static TonemapReinhard createTonemapReinhard(float gamma, float intensity) { return TonemapReinhard.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(photo_Photo_createTonemapReinhard_12(gamma, intensity))); } /** * Creates TonemapReinhard object * * param gamma gamma value for gamma correction. See createTonemap * value, if 0 it's global, otherwise it's a weighted mean of this two cases. * if 0 adaptation level is the same for each channel. * return automatically generated */ public static TonemapReinhard createTonemapReinhard(float gamma) { return TonemapReinhard.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(photo_Photo_createTonemapReinhard_13(gamma))); } /** * Creates TonemapReinhard object * * value, if 0 it's global, otherwise it's a weighted mean of this two cases. * if 0 adaptation level is the same for each channel. * return automatically generated */ public static TonemapReinhard createTonemapReinhard() { return TonemapReinhard.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(photo_Photo_createTonemapReinhard_14())); } // // C++: Ptr_TonemapMantiuk cv::createTonemapMantiuk(float gamma = 1.0f, float scale = 0.7f, float saturation = 1.0f) // /** * Creates TonemapMantiuk object * * param gamma gamma value for gamma correction. See createTonemap * param scale contrast scale factor. HVS response is multiplied by this parameter, thus compressing * dynamic range. Values from 0.6 to 0.9 produce best results. * param saturation saturation enhancement value. See createTonemapDrago * return automatically generated */ public static TonemapMantiuk createTonemapMantiuk(float gamma, float scale, float saturation) { return TonemapMantiuk.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(photo_Photo_createTonemapMantiuk_10(gamma, scale, saturation))); } /** * Creates TonemapMantiuk object * * param gamma gamma value for gamma correction. See createTonemap * param scale contrast scale factor. HVS response is multiplied by this parameter, thus compressing * dynamic range. Values from 0.6 to 0.9 produce best results. * return automatically generated */ public static TonemapMantiuk createTonemapMantiuk(float gamma, float scale) { return TonemapMantiuk.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(photo_Photo_createTonemapMantiuk_11(gamma, scale))); } /** * Creates TonemapMantiuk object * * param gamma gamma value for gamma correction. See createTonemap * dynamic range. Values from 0.6 to 0.9 produce best results. * return automatically generated */ public static TonemapMantiuk createTonemapMantiuk(float gamma) { return TonemapMantiuk.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(photo_Photo_createTonemapMantiuk_12(gamma))); } /** * Creates TonemapMantiuk object * * dynamic range. Values from 0.6 to 0.9 produce best results. * return automatically generated */ public static TonemapMantiuk createTonemapMantiuk() { return TonemapMantiuk.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(photo_Photo_createTonemapMantiuk_13())); } // // C++: Ptr_AlignMTB cv::createAlignMTB(int max_bits = 6, int exclude_range = 4, bool cut = true) // /** * Creates AlignMTB object * * param max_bits logarithm to the base 2 of maximal shift in each dimension. Values of 5 and 6 are * usually good enough (31 and 63 pixels shift respectively). * param exclude_range range for exclusion bitmap that is constructed to suppress noise around the * median value. * param cut if true cuts images, otherwise fills the new regions with zeros. * return automatically generated */ public static AlignMTB createAlignMTB(int max_bits, int exclude_range, bool cut) { return AlignMTB.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(photo_Photo_createAlignMTB_10(max_bits, exclude_range, cut))); } /** * Creates AlignMTB object * * param max_bits logarithm to the base 2 of maximal shift in each dimension. Values of 5 and 6 are * usually good enough (31 and 63 pixels shift respectively). * param exclude_range range for exclusion bitmap that is constructed to suppress noise around the * median value. * return automatically generated */ public static AlignMTB createAlignMTB(int max_bits, int exclude_range) { return AlignMTB.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(photo_Photo_createAlignMTB_11(max_bits, exclude_range))); } /** * Creates AlignMTB object * * param max_bits logarithm to the base 2 of maximal shift in each dimension. Values of 5 and 6 are * usually good enough (31 and 63 pixels shift respectively). * median value. * return automatically generated */ public static AlignMTB createAlignMTB(int max_bits) { return AlignMTB.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(photo_Photo_createAlignMTB_12(max_bits))); } /** * Creates AlignMTB object * * usually good enough (31 and 63 pixels shift respectively). * median value. * return automatically generated */ public static AlignMTB createAlignMTB() { return AlignMTB.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(photo_Photo_createAlignMTB_13())); } // // C++: Ptr_CalibrateDebevec cv::createCalibrateDebevec(int samples = 70, float lambda = 10.0f, bool random = false) // /** * Creates CalibrateDebevec object * * param samples number of pixel locations to use * param lambda smoothness term weight. Greater values produce smoother results, but can alter the * response. * param random if true sample pixel locations are chosen at random, otherwise they form a * rectangular grid. * return automatically generated */ public static CalibrateDebevec createCalibrateDebevec(int samples, float lambda, bool random) { return CalibrateDebevec.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(photo_Photo_createCalibrateDebevec_10(samples, lambda, random))); } /** * Creates CalibrateDebevec object * * param samples number of pixel locations to use * param lambda smoothness term weight. Greater values produce smoother results, but can alter the * response. * rectangular grid. * return automatically generated */ public static CalibrateDebevec createCalibrateDebevec(int samples, float lambda) { return CalibrateDebevec.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(photo_Photo_createCalibrateDebevec_11(samples, lambda))); } /** * Creates CalibrateDebevec object * * param samples number of pixel locations to use * response. * rectangular grid. * return automatically generated */ public static CalibrateDebevec createCalibrateDebevec(int samples) { return CalibrateDebevec.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(photo_Photo_createCalibrateDebevec_12(samples))); } /** * Creates CalibrateDebevec object * * response. * rectangular grid. * return automatically generated */ public static CalibrateDebevec createCalibrateDebevec() { return CalibrateDebevec.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(photo_Photo_createCalibrateDebevec_13())); } // // C++: Ptr_CalibrateRobertson cv::createCalibrateRobertson(int max_iter = 30, float threshold = 0.01f) // /** * Creates CalibrateRobertson object * * param max_iter maximal number of Gauss-Seidel solver iterations. * param threshold target difference between results of two successive steps of the minimization. * return automatically generated */ public static CalibrateRobertson createCalibrateRobertson(int max_iter, float threshold) { return CalibrateRobertson.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(photo_Photo_createCalibrateRobertson_10(max_iter, threshold))); } /** * Creates CalibrateRobertson object * * param max_iter maximal number of Gauss-Seidel solver iterations. * return automatically generated */ public static CalibrateRobertson createCalibrateRobertson(int max_iter) { return CalibrateRobertson.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(photo_Photo_createCalibrateRobertson_11(max_iter))); } /** * Creates CalibrateRobertson object * * return automatically generated */ public static CalibrateRobertson createCalibrateRobertson() { return CalibrateRobertson.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(photo_Photo_createCalibrateRobertson_12())); } // // C++: Ptr_MergeDebevec cv::createMergeDebevec() // /** * Creates MergeDebevec object * return automatically generated */ public static MergeDebevec createMergeDebevec() { return MergeDebevec.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(photo_Photo_createMergeDebevec_10())); } // // C++: Ptr_MergeMertens cv::createMergeMertens(float contrast_weight = 1.0f, float saturation_weight = 1.0f, float exposure_weight = 0.0f) // /** * Creates MergeMertens object * * param contrast_weight contrast measure weight. See MergeMertens. * param saturation_weight saturation measure weight * param exposure_weight well-exposedness measure weight * return automatically generated */ public static MergeMertens createMergeMertens(float contrast_weight, float saturation_weight, float exposure_weight) { return MergeMertens.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(photo_Photo_createMergeMertens_10(contrast_weight, saturation_weight, exposure_weight))); } /** * Creates MergeMertens object * * param contrast_weight contrast measure weight. See MergeMertens. * param saturation_weight saturation measure weight * return automatically generated */ public static MergeMertens createMergeMertens(float contrast_weight, float saturation_weight) { return MergeMertens.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(photo_Photo_createMergeMertens_11(contrast_weight, saturation_weight))); } /** * Creates MergeMertens object * * param contrast_weight contrast measure weight. See MergeMertens. * return automatically generated */ public static MergeMertens createMergeMertens(float contrast_weight) { return MergeMertens.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(photo_Photo_createMergeMertens_12(contrast_weight))); } /** * Creates MergeMertens object * * return automatically generated */ public static MergeMertens createMergeMertens() { return MergeMertens.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(photo_Photo_createMergeMertens_13())); } // // C++: Ptr_MergeRobertson cv::createMergeRobertson() // /** * Creates MergeRobertson object * return automatically generated */ public static MergeRobertson createMergeRobertson() { return MergeRobertson.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(photo_Photo_createMergeRobertson_10())); } // // C++: void cv::decolor(Mat src, Mat& grayscale, Mat& color_boost) // /** * Transforms a color image to a grayscale image. It is a basic tool in digital printing, stylized * black-and-white photograph rendering, and in many single channel image processing applications * CITE: CL12 . * * param src Input 8-bit 3-channel image. * param grayscale Output 8-bit 1-channel image. * param color_boost Output 8-bit 3-channel image. * * This function is to be applied on color images. */ public static void decolor(Mat src, Mat grayscale, Mat color_boost) { if (src != null) src.ThrowIfDisposed(); if (grayscale != null) grayscale.ThrowIfDisposed(); if (color_boost != null) color_boost.ThrowIfDisposed(); photo_Photo_decolor_10(src.nativeObj, grayscale.nativeObj, color_boost.nativeObj); } // // C++: void cv::seamlessClone(Mat src, Mat dst, Mat mask, Point p, Mat& blend, int flags) // /** * Image editing tasks concern either global changes (color/intensity corrections, filters, * deformations) or local changes concerned to a selection. Here we are interested in achieving local * changes, ones that are restricted to a region manually selected (ROI), in a seamless and effortless * manner. The extent of the changes ranges from slight distortions to complete replacement by novel * content CITE: PM03 . * * param src Input 8-bit 3-channel image. * param dst Input 8-bit 3-channel image. * param mask Input 8-bit 1 or 3-channel image. * param p Point in dst image where object is placed. * param blend Output image with the same size and type as dst. * param flags Cloning method that could be cv::NORMAL_CLONE, cv::MIXED_CLONE or cv::MONOCHROME_TRANSFER */ public static void seamlessClone(Mat src, Mat dst, Mat mask, Point p, Mat blend, int flags) { if (src != null) src.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); if (mask != null) mask.ThrowIfDisposed(); if (blend != null) blend.ThrowIfDisposed(); photo_Photo_seamlessClone_10(src.nativeObj, dst.nativeObj, mask.nativeObj, p.x, p.y, blend.nativeObj, flags); } // // C++: void cv::colorChange(Mat src, Mat mask, Mat& dst, float red_mul = 1.0f, float green_mul = 1.0f, float blue_mul = 1.0f) // /** * Given an original color image, two differently colored versions of this image can be mixed * seamlessly. * * param src Input 8-bit 3-channel image. * param mask Input 8-bit 1 or 3-channel image. * param dst Output image with the same size and type as src . * param red_mul R-channel multiply factor. * param green_mul G-channel multiply factor. * param blue_mul B-channel multiply factor. * * Multiplication factor is between .5 to 2.5. */ public static void colorChange(Mat src, Mat mask, Mat dst, float red_mul, float green_mul, float blue_mul) { if (src != null) src.ThrowIfDisposed(); if (mask != null) mask.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); photo_Photo_colorChange_10(src.nativeObj, mask.nativeObj, dst.nativeObj, red_mul, green_mul, blue_mul); } /** * Given an original color image, two differently colored versions of this image can be mixed * seamlessly. * * param src Input 8-bit 3-channel image. * param mask Input 8-bit 1 or 3-channel image. * param dst Output image with the same size and type as src . * param red_mul R-channel multiply factor. * param green_mul G-channel multiply factor. * * Multiplication factor is between .5 to 2.5. */ public static void colorChange(Mat src, Mat mask, Mat dst, float red_mul, float green_mul) { if (src != null) src.ThrowIfDisposed(); if (mask != null) mask.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); photo_Photo_colorChange_11(src.nativeObj, mask.nativeObj, dst.nativeObj, red_mul, green_mul); } /** * Given an original color image, two differently colored versions of this image can be mixed * seamlessly. * * param src Input 8-bit 3-channel image. * param mask Input 8-bit 1 or 3-channel image. * param dst Output image with the same size and type as src . * param red_mul R-channel multiply factor. * * Multiplication factor is between .5 to 2.5. */ public static void colorChange(Mat src, Mat mask, Mat dst, float red_mul) { if (src != null) src.ThrowIfDisposed(); if (mask != null) mask.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); photo_Photo_colorChange_12(src.nativeObj, mask.nativeObj, dst.nativeObj, red_mul); } /** * Given an original color image, two differently colored versions of this image can be mixed * seamlessly. * * param src Input 8-bit 3-channel image. * param mask Input 8-bit 1 or 3-channel image. * param dst Output image with the same size and type as src . * * Multiplication factor is between .5 to 2.5. */ public static void colorChange(Mat src, Mat mask, Mat dst) { if (src != null) src.ThrowIfDisposed(); if (mask != null) mask.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); photo_Photo_colorChange_13(src.nativeObj, mask.nativeObj, dst.nativeObj); } // // C++: void cv::illuminationChange(Mat src, Mat mask, Mat& dst, float alpha = 0.2f, float beta = 0.4f) // /** * Applying an appropriate non-linear transformation to the gradient field inside the selection and * then integrating back with a Poisson solver, modifies locally the apparent illumination of an image. * * param src Input 8-bit 3-channel image. * param mask Input 8-bit 1 or 3-channel image. * param dst Output image with the same size and type as src. * param alpha Value ranges between 0-2. * param beta Value ranges between 0-2. * * This is useful to highlight under-exposed foreground objects or to reduce specular reflections. */ public static void illuminationChange(Mat src, Mat mask, Mat dst, float alpha, float beta) { if (src != null) src.ThrowIfDisposed(); if (mask != null) mask.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); photo_Photo_illuminationChange_10(src.nativeObj, mask.nativeObj, dst.nativeObj, alpha, beta); } /** * Applying an appropriate non-linear transformation to the gradient field inside the selection and * then integrating back with a Poisson solver, modifies locally the apparent illumination of an image. * * param src Input 8-bit 3-channel image. * param mask Input 8-bit 1 or 3-channel image. * param dst Output image with the same size and type as src. * param alpha Value ranges between 0-2. * * This is useful to highlight under-exposed foreground objects or to reduce specular reflections. */ public static void illuminationChange(Mat src, Mat mask, Mat dst, float alpha) { if (src != null) src.ThrowIfDisposed(); if (mask != null) mask.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); photo_Photo_illuminationChange_11(src.nativeObj, mask.nativeObj, dst.nativeObj, alpha); } /** * Applying an appropriate non-linear transformation to the gradient field inside the selection and * then integrating back with a Poisson solver, modifies locally the apparent illumination of an image. * * param src Input 8-bit 3-channel image. * param mask Input 8-bit 1 or 3-channel image. * param dst Output image with the same size and type as src. * * This is useful to highlight under-exposed foreground objects or to reduce specular reflections. */ public static void illuminationChange(Mat src, Mat mask, Mat dst) { if (src != null) src.ThrowIfDisposed(); if (mask != null) mask.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); photo_Photo_illuminationChange_12(src.nativeObj, mask.nativeObj, dst.nativeObj); } // // C++: void cv::textureFlattening(Mat src, Mat mask, Mat& dst, float low_threshold = 30, float high_threshold = 45, int kernel_size = 3) // /** * By retaining only the gradients at edge locations, before integrating with the Poisson solver, one * washes out the texture of the selected region, giving its contents a flat aspect. Here Canny Edge %Detector is used. * * param src Input 8-bit 3-channel image. * param mask Input 8-bit 1 or 3-channel image. * param dst Output image with the same size and type as src. * param low_threshold %Range from 0 to 100. * param high_threshold Value > 100. * param kernel_size The size of the Sobel kernel to be used. * * Note: * The algorithm assumes that the color of the source image is close to that of the destination. This * assumption means that when the colors don't match, the source image color gets tinted toward the * color of the destination image. */ public static void textureFlattening(Mat src, Mat mask, Mat dst, float low_threshold, float high_threshold, int kernel_size) { if (src != null) src.ThrowIfDisposed(); if (mask != null) mask.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); photo_Photo_textureFlattening_10(src.nativeObj, mask.nativeObj, dst.nativeObj, low_threshold, high_threshold, kernel_size); } /** * By retaining only the gradients at edge locations, before integrating with the Poisson solver, one * washes out the texture of the selected region, giving its contents a flat aspect. Here Canny Edge %Detector is used. * * param src Input 8-bit 3-channel image. * param mask Input 8-bit 1 or 3-channel image. * param dst Output image with the same size and type as src. * param low_threshold %Range from 0 to 100. * param high_threshold Value > 100. * * Note: * The algorithm assumes that the color of the source image is close to that of the destination. This * assumption means that when the colors don't match, the source image color gets tinted toward the * color of the destination image. */ public static void textureFlattening(Mat src, Mat mask, Mat dst, float low_threshold, float high_threshold) { if (src != null) src.ThrowIfDisposed(); if (mask != null) mask.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); photo_Photo_textureFlattening_11(src.nativeObj, mask.nativeObj, dst.nativeObj, low_threshold, high_threshold); } /** * By retaining only the gradients at edge locations, before integrating with the Poisson solver, one * washes out the texture of the selected region, giving its contents a flat aspect. Here Canny Edge %Detector is used. * * param src Input 8-bit 3-channel image. * param mask Input 8-bit 1 or 3-channel image. * param dst Output image with the same size and type as src. * param low_threshold %Range from 0 to 100. * * Note: * The algorithm assumes that the color of the source image is close to that of the destination. This * assumption means that when the colors don't match, the source image color gets tinted toward the * color of the destination image. */ public static void textureFlattening(Mat src, Mat mask, Mat dst, float low_threshold) { if (src != null) src.ThrowIfDisposed(); if (mask != null) mask.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); photo_Photo_textureFlattening_12(src.nativeObj, mask.nativeObj, dst.nativeObj, low_threshold); } /** * By retaining only the gradients at edge locations, before integrating with the Poisson solver, one * washes out the texture of the selected region, giving its contents a flat aspect. Here Canny Edge %Detector is used. * * param src Input 8-bit 3-channel image. * param mask Input 8-bit 1 or 3-channel image. * param dst Output image with the same size and type as src. * * Note: * The algorithm assumes that the color of the source image is close to that of the destination. This * assumption means that when the colors don't match, the source image color gets tinted toward the * color of the destination image. */ public static void textureFlattening(Mat src, Mat mask, Mat dst) { if (src != null) src.ThrowIfDisposed(); if (mask != null) mask.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); photo_Photo_textureFlattening_13(src.nativeObj, mask.nativeObj, dst.nativeObj); } // // C++: void cv::edgePreservingFilter(Mat src, Mat& dst, int flags = 1, float sigma_s = 60, float sigma_r = 0.4f) // /** * Filtering is the fundamental operation in image and video processing. Edge-preserving smoothing * filters are used in many different applications CITE: EM11 . * * param src Input 8-bit 3-channel image. * param dst Output 8-bit 3-channel image. * param flags Edge preserving filters: cv::RECURS_FILTER or cv::NORMCONV_FILTER * param sigma_s %Range between 0 to 200. * param sigma_r %Range between 0 to 1. */ public static void edgePreservingFilter(Mat src, Mat dst, int flags, float sigma_s, float sigma_r) { if (src != null) src.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); photo_Photo_edgePreservingFilter_10(src.nativeObj, dst.nativeObj, flags, sigma_s, sigma_r); } /** * Filtering is the fundamental operation in image and video processing. Edge-preserving smoothing * filters are used in many different applications CITE: EM11 . * * param src Input 8-bit 3-channel image. * param dst Output 8-bit 3-channel image. * param flags Edge preserving filters: cv::RECURS_FILTER or cv::NORMCONV_FILTER * param sigma_s %Range between 0 to 200. */ public static void edgePreservingFilter(Mat src, Mat dst, int flags, float sigma_s) { if (src != null) src.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); photo_Photo_edgePreservingFilter_11(src.nativeObj, dst.nativeObj, flags, sigma_s); } /** * Filtering is the fundamental operation in image and video processing. Edge-preserving smoothing * filters are used in many different applications CITE: EM11 . * * param src Input 8-bit 3-channel image. * param dst Output 8-bit 3-channel image. * param flags Edge preserving filters: cv::RECURS_FILTER or cv::NORMCONV_FILTER */ public static void edgePreservingFilter(Mat src, Mat dst, int flags) { if (src != null) src.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); photo_Photo_edgePreservingFilter_12(src.nativeObj, dst.nativeObj, flags); } /** * Filtering is the fundamental operation in image and video processing. Edge-preserving smoothing * filters are used in many different applications CITE: EM11 . * * param src Input 8-bit 3-channel image. * param dst Output 8-bit 3-channel image. */ public static void edgePreservingFilter(Mat src, Mat dst) { if (src != null) src.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); photo_Photo_edgePreservingFilter_13(src.nativeObj, dst.nativeObj); } // // C++: void cv::detailEnhance(Mat src, Mat& dst, float sigma_s = 10, float sigma_r = 0.15f) // /** * This filter enhances the details of a particular image. * * param src Input 8-bit 3-channel image. * param dst Output image with the same size and type as src. * param sigma_s %Range between 0 to 200. * param sigma_r %Range between 0 to 1. */ public static void detailEnhance(Mat src, Mat dst, float sigma_s, float sigma_r) { if (src != null) src.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); photo_Photo_detailEnhance_10(src.nativeObj, dst.nativeObj, sigma_s, sigma_r); } /** * This filter enhances the details of a particular image. * * param src Input 8-bit 3-channel image. * param dst Output image with the same size and type as src. * param sigma_s %Range between 0 to 200. */ public static void detailEnhance(Mat src, Mat dst, float sigma_s) { if (src != null) src.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); photo_Photo_detailEnhance_11(src.nativeObj, dst.nativeObj, sigma_s); } /** * This filter enhances the details of a particular image. * * param src Input 8-bit 3-channel image. * param dst Output image with the same size and type as src. */ public static void detailEnhance(Mat src, Mat dst) { if (src != null) src.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); photo_Photo_detailEnhance_12(src.nativeObj, dst.nativeObj); } // // C++: void cv::pencilSketch(Mat src, Mat& dst1, Mat& dst2, float sigma_s = 60, float sigma_r = 0.07f, float shade_factor = 0.02f) // /** * Pencil-like non-photorealistic line drawing * * param src Input 8-bit 3-channel image. * param dst1 Output 8-bit 1-channel image. * param dst2 Output image with the same size and type as src. * param sigma_s %Range between 0 to 200. * param sigma_r %Range between 0 to 1. * param shade_factor %Range between 0 to 0.1. */ public static void pencilSketch(Mat src, Mat dst1, Mat dst2, float sigma_s, float sigma_r, float shade_factor) { if (src != null) src.ThrowIfDisposed(); if (dst1 != null) dst1.ThrowIfDisposed(); if (dst2 != null) dst2.ThrowIfDisposed(); photo_Photo_pencilSketch_10(src.nativeObj, dst1.nativeObj, dst2.nativeObj, sigma_s, sigma_r, shade_factor); } /** * Pencil-like non-photorealistic line drawing * * param src Input 8-bit 3-channel image. * param dst1 Output 8-bit 1-channel image. * param dst2 Output image with the same size and type as src. * param sigma_s %Range between 0 to 200. * param sigma_r %Range between 0 to 1. */ public static void pencilSketch(Mat src, Mat dst1, Mat dst2, float sigma_s, float sigma_r) { if (src != null) src.ThrowIfDisposed(); if (dst1 != null) dst1.ThrowIfDisposed(); if (dst2 != null) dst2.ThrowIfDisposed(); photo_Photo_pencilSketch_11(src.nativeObj, dst1.nativeObj, dst2.nativeObj, sigma_s, sigma_r); } /** * Pencil-like non-photorealistic line drawing * * param src Input 8-bit 3-channel image. * param dst1 Output 8-bit 1-channel image. * param dst2 Output image with the same size and type as src. * param sigma_s %Range between 0 to 200. */ public static void pencilSketch(Mat src, Mat dst1, Mat dst2, float sigma_s) { if (src != null) src.ThrowIfDisposed(); if (dst1 != null) dst1.ThrowIfDisposed(); if (dst2 != null) dst2.ThrowIfDisposed(); photo_Photo_pencilSketch_12(src.nativeObj, dst1.nativeObj, dst2.nativeObj, sigma_s); } /** * Pencil-like non-photorealistic line drawing * * param src Input 8-bit 3-channel image. * param dst1 Output 8-bit 1-channel image. * param dst2 Output image with the same size and type as src. */ public static void pencilSketch(Mat src, Mat dst1, Mat dst2) { if (src != null) src.ThrowIfDisposed(); if (dst1 != null) dst1.ThrowIfDisposed(); if (dst2 != null) dst2.ThrowIfDisposed(); photo_Photo_pencilSketch_13(src.nativeObj, dst1.nativeObj, dst2.nativeObj); } // // C++: void cv::stylization(Mat src, Mat& dst, float sigma_s = 60, float sigma_r = 0.45f) // /** * Stylization aims to produce digital imagery with a wide variety of effects not focused on * photorealism. Edge-aware filters are ideal for stylization, as they can abstract regions of low * contrast while preserving, or enhancing, high-contrast features. * * param src Input 8-bit 3-channel image. * param dst Output image with the same size and type as src. * param sigma_s %Range between 0 to 200. * param sigma_r %Range between 0 to 1. */ public static void stylization(Mat src, Mat dst, float sigma_s, float sigma_r) { if (src != null) src.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); photo_Photo_stylization_10(src.nativeObj, dst.nativeObj, sigma_s, sigma_r); } /** * Stylization aims to produce digital imagery with a wide variety of effects not focused on * photorealism. Edge-aware filters are ideal for stylization, as they can abstract regions of low * contrast while preserving, or enhancing, high-contrast features. * * param src Input 8-bit 3-channel image. * param dst Output image with the same size and type as src. * param sigma_s %Range between 0 to 200. */ public static void stylization(Mat src, Mat dst, float sigma_s) { if (src != null) src.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); photo_Photo_stylization_11(src.nativeObj, dst.nativeObj, sigma_s); } /** * Stylization aims to produce digital imagery with a wide variety of effects not focused on * photorealism. Edge-aware filters are ideal for stylization, as they can abstract regions of low * contrast while preserving, or enhancing, high-contrast features. * * param src Input 8-bit 3-channel image. * param dst Output image with the same size and type as src. */ public static void stylization(Mat src, Mat dst) { if (src != null) src.ThrowIfDisposed(); if (dst != null) dst.ThrowIfDisposed(); photo_Photo_stylization_12(src.nativeObj, dst.nativeObj); } // // C++: void cv::cuda::nonLocalMeans(GpuMat src, GpuMat& dst, float h, int search_window = 21, int block_size = 7, int borderMode = BORDER_DEFAULT, Stream stream = Stream::Null()) // // Unknown type 'GpuMat' (I), skipping the function // // C++: void cv::cuda::fastNlMeansDenoising(GpuMat src, GpuMat& dst, float h, int search_window = 21, int block_size = 7, Stream stream = Stream::Null()) // // Unknown type 'GpuMat' (I), skipping the function // // C++: void cv::cuda::fastNlMeansDenoisingColored(GpuMat src, GpuMat& dst, float h_luminance, float photo_render, int search_window = 21, int block_size = 7, Stream stream = Stream::Null()) // // Unknown type 'GpuMat' (I), skipping the function #if (UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR const string LIBNAME = "__Internal"; #else const string LIBNAME = "opencvforunity"; #endif // C++: void cv::inpaint(Mat src, Mat inpaintMask, Mat& dst, double inpaintRadius, int flags) [DllImport(LIBNAME)] private static extern void photo_Photo_inpaint_10(IntPtr src_nativeObj, IntPtr inpaintMask_nativeObj, IntPtr dst_nativeObj, double inpaintRadius, int flags); // C++: void cv::fastNlMeansDenoising(Mat src, Mat& dst, float h = 3, int templateWindowSize = 7, int searchWindowSize = 21) [DllImport(LIBNAME)] private static extern void photo_Photo_fastNlMeansDenoising_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, float h, int templateWindowSize, int searchWindowSize); [DllImport(LIBNAME)] private static extern void photo_Photo_fastNlMeansDenoising_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, float h, int templateWindowSize); [DllImport(LIBNAME)] private static extern void photo_Photo_fastNlMeansDenoising_12(IntPtr src_nativeObj, IntPtr dst_nativeObj, float h); [DllImport(LIBNAME)] private static extern void photo_Photo_fastNlMeansDenoising_13(IntPtr src_nativeObj, IntPtr dst_nativeObj); // C++: void cv::fastNlMeansDenoising(Mat src, Mat& dst, vector_float h, int templateWindowSize = 7, int searchWindowSize = 21, int normType = NORM_L2) [DllImport(LIBNAME)] private static extern void photo_Photo_fastNlMeansDenoising_14(IntPtr src_nativeObj, IntPtr dst_nativeObj, IntPtr h_mat_nativeObj, int templateWindowSize, int searchWindowSize, int normType); [DllImport(LIBNAME)] private static extern void photo_Photo_fastNlMeansDenoising_15(IntPtr src_nativeObj, IntPtr dst_nativeObj, IntPtr h_mat_nativeObj, int templateWindowSize, int searchWindowSize); [DllImport(LIBNAME)] private static extern void photo_Photo_fastNlMeansDenoising_16(IntPtr src_nativeObj, IntPtr dst_nativeObj, IntPtr h_mat_nativeObj, int templateWindowSize); [DllImport(LIBNAME)] private static extern void photo_Photo_fastNlMeansDenoising_17(IntPtr src_nativeObj, IntPtr dst_nativeObj, IntPtr h_mat_nativeObj); // C++: void cv::fastNlMeansDenoisingColored(Mat src, Mat& dst, float h = 3, float hColor = 3, int templateWindowSize = 7, int searchWindowSize = 21) [DllImport(LIBNAME)] private static extern void photo_Photo_fastNlMeansDenoisingColored_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, float h, float hColor, int templateWindowSize, int searchWindowSize); [DllImport(LIBNAME)] private static extern void photo_Photo_fastNlMeansDenoisingColored_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, float h, float hColor, int templateWindowSize); [DllImport(LIBNAME)] private static extern void photo_Photo_fastNlMeansDenoisingColored_12(IntPtr src_nativeObj, IntPtr dst_nativeObj, float h, float hColor); [DllImport(LIBNAME)] private static extern void photo_Photo_fastNlMeansDenoisingColored_13(IntPtr src_nativeObj, IntPtr dst_nativeObj, float h); [DllImport(LIBNAME)] private static extern void photo_Photo_fastNlMeansDenoisingColored_14(IntPtr src_nativeObj, IntPtr dst_nativeObj); // C++: void cv::fastNlMeansDenoisingMulti(vector_Mat srcImgs, Mat& dst, int imgToDenoiseIndex, int temporalWindowSize, float h = 3, int templateWindowSize = 7, int searchWindowSize = 21) [DllImport(LIBNAME)] private static extern void photo_Photo_fastNlMeansDenoisingMulti_10(IntPtr srcImgs_mat_nativeObj, IntPtr dst_nativeObj, int imgToDenoiseIndex, int temporalWindowSize, float h, int templateWindowSize, int searchWindowSize); [DllImport(LIBNAME)] private static extern void photo_Photo_fastNlMeansDenoisingMulti_11(IntPtr srcImgs_mat_nativeObj, IntPtr dst_nativeObj, int imgToDenoiseIndex, int temporalWindowSize, float h, int templateWindowSize); [DllImport(LIBNAME)] private static extern void photo_Photo_fastNlMeansDenoisingMulti_12(IntPtr srcImgs_mat_nativeObj, IntPtr dst_nativeObj, int imgToDenoiseIndex, int temporalWindowSize, float h); [DllImport(LIBNAME)] private static extern void photo_Photo_fastNlMeansDenoisingMulti_13(IntPtr srcImgs_mat_nativeObj, IntPtr dst_nativeObj, int imgToDenoiseIndex, int temporalWindowSize); // C++: void cv::fastNlMeansDenoisingMulti(vector_Mat srcImgs, Mat& dst, int imgToDenoiseIndex, int temporalWindowSize, vector_float h, int templateWindowSize = 7, int searchWindowSize = 21, int normType = NORM_L2) [DllImport(LIBNAME)] private static extern void photo_Photo_fastNlMeansDenoisingMulti_14(IntPtr srcImgs_mat_nativeObj, IntPtr dst_nativeObj, int imgToDenoiseIndex, int temporalWindowSize, IntPtr h_mat_nativeObj, int templateWindowSize, int searchWindowSize, int normType); [DllImport(LIBNAME)] private static extern void photo_Photo_fastNlMeansDenoisingMulti_15(IntPtr srcImgs_mat_nativeObj, IntPtr dst_nativeObj, int imgToDenoiseIndex, int temporalWindowSize, IntPtr h_mat_nativeObj, int templateWindowSize, int searchWindowSize); [DllImport(LIBNAME)] private static extern void photo_Photo_fastNlMeansDenoisingMulti_16(IntPtr srcImgs_mat_nativeObj, IntPtr dst_nativeObj, int imgToDenoiseIndex, int temporalWindowSize, IntPtr h_mat_nativeObj, int templateWindowSize); [DllImport(LIBNAME)] private static extern void photo_Photo_fastNlMeansDenoisingMulti_17(IntPtr srcImgs_mat_nativeObj, IntPtr dst_nativeObj, int imgToDenoiseIndex, int temporalWindowSize, IntPtr h_mat_nativeObj); // C++: void cv::fastNlMeansDenoisingColoredMulti(vector_Mat srcImgs, Mat& dst, int imgToDenoiseIndex, int temporalWindowSize, float h = 3, float hColor = 3, int templateWindowSize = 7, int searchWindowSize = 21) [DllImport(LIBNAME)] private static extern void photo_Photo_fastNlMeansDenoisingColoredMulti_10(IntPtr srcImgs_mat_nativeObj, IntPtr dst_nativeObj, int imgToDenoiseIndex, int temporalWindowSize, float h, float hColor, int templateWindowSize, int searchWindowSize); [DllImport(LIBNAME)] private static extern void photo_Photo_fastNlMeansDenoisingColoredMulti_11(IntPtr srcImgs_mat_nativeObj, IntPtr dst_nativeObj, int imgToDenoiseIndex, int temporalWindowSize, float h, float hColor, int templateWindowSize); [DllImport(LIBNAME)] private static extern void photo_Photo_fastNlMeansDenoisingColoredMulti_12(IntPtr srcImgs_mat_nativeObj, IntPtr dst_nativeObj, int imgToDenoiseIndex, int temporalWindowSize, float h, float hColor); [DllImport(LIBNAME)] private static extern void photo_Photo_fastNlMeansDenoisingColoredMulti_13(IntPtr srcImgs_mat_nativeObj, IntPtr dst_nativeObj, int imgToDenoiseIndex, int temporalWindowSize, float h); [DllImport(LIBNAME)] private static extern void photo_Photo_fastNlMeansDenoisingColoredMulti_14(IntPtr srcImgs_mat_nativeObj, IntPtr dst_nativeObj, int imgToDenoiseIndex, int temporalWindowSize); // C++: void cv::denoise_TVL1(vector_Mat observations, Mat result, double lambda = 1.0, int niters = 30) [DllImport(LIBNAME)] private static extern void photo_Photo_denoise_1TVL1_10(IntPtr observations_mat_nativeObj, IntPtr result_nativeObj, double lambda, int niters); [DllImport(LIBNAME)] private static extern void photo_Photo_denoise_1TVL1_11(IntPtr observations_mat_nativeObj, IntPtr result_nativeObj, double lambda); [DllImport(LIBNAME)] private static extern void photo_Photo_denoise_1TVL1_12(IntPtr observations_mat_nativeObj, IntPtr result_nativeObj); // C++: Ptr_Tonemap cv::createTonemap(float gamma = 1.0f) [DllImport(LIBNAME)] private static extern IntPtr photo_Photo_createTonemap_10(float gamma); [DllImport(LIBNAME)] private static extern IntPtr photo_Photo_createTonemap_11(); // C++: Ptr_TonemapDrago cv::createTonemapDrago(float gamma = 1.0f, float saturation = 1.0f, float bias = 0.85f) [DllImport(LIBNAME)] private static extern IntPtr photo_Photo_createTonemapDrago_10(float gamma, float saturation, float bias); [DllImport(LIBNAME)] private static extern IntPtr photo_Photo_createTonemapDrago_11(float gamma, float saturation); [DllImport(LIBNAME)] private static extern IntPtr photo_Photo_createTonemapDrago_12(float gamma); [DllImport(LIBNAME)] private static extern IntPtr photo_Photo_createTonemapDrago_13(); // C++: Ptr_TonemapReinhard cv::createTonemapReinhard(float gamma = 1.0f, float intensity = 0.0f, float light_adapt = 1.0f, float color_adapt = 0.0f) [DllImport(LIBNAME)] private static extern IntPtr photo_Photo_createTonemapReinhard_10(float gamma, float intensity, float light_adapt, float color_adapt); [DllImport(LIBNAME)] private static extern IntPtr photo_Photo_createTonemapReinhard_11(float gamma, float intensity, float light_adapt); [DllImport(LIBNAME)] private static extern IntPtr photo_Photo_createTonemapReinhard_12(float gamma, float intensity); [DllImport(LIBNAME)] private static extern IntPtr photo_Photo_createTonemapReinhard_13(float gamma); [DllImport(LIBNAME)] private static extern IntPtr photo_Photo_createTonemapReinhard_14(); // C++: Ptr_TonemapMantiuk cv::createTonemapMantiuk(float gamma = 1.0f, float scale = 0.7f, float saturation = 1.0f) [DllImport(LIBNAME)] private static extern IntPtr photo_Photo_createTonemapMantiuk_10(float gamma, float scale, float saturation); [DllImport(LIBNAME)] private static extern IntPtr photo_Photo_createTonemapMantiuk_11(float gamma, float scale); [DllImport(LIBNAME)] private static extern IntPtr photo_Photo_createTonemapMantiuk_12(float gamma); [DllImport(LIBNAME)] private static extern IntPtr photo_Photo_createTonemapMantiuk_13(); // C++: Ptr_AlignMTB cv::createAlignMTB(int max_bits = 6, int exclude_range = 4, bool cut = true) [DllImport(LIBNAME)] private static extern IntPtr photo_Photo_createAlignMTB_10(int max_bits, int exclude_range, [MarshalAs(UnmanagedType.U1)] bool cut); [DllImport(LIBNAME)] private static extern IntPtr photo_Photo_createAlignMTB_11(int max_bits, int exclude_range); [DllImport(LIBNAME)] private static extern IntPtr photo_Photo_createAlignMTB_12(int max_bits); [DllImport(LIBNAME)] private static extern IntPtr photo_Photo_createAlignMTB_13(); // C++: Ptr_CalibrateDebevec cv::createCalibrateDebevec(int samples = 70, float lambda = 10.0f, bool random = false) [DllImport(LIBNAME)] private static extern IntPtr photo_Photo_createCalibrateDebevec_10(int samples, float lambda, [MarshalAs(UnmanagedType.U1)] bool random); [DllImport(LIBNAME)] private static extern IntPtr photo_Photo_createCalibrateDebevec_11(int samples, float lambda); [DllImport(LIBNAME)] private static extern IntPtr photo_Photo_createCalibrateDebevec_12(int samples); [DllImport(LIBNAME)] private static extern IntPtr photo_Photo_createCalibrateDebevec_13(); // C++: Ptr_CalibrateRobertson cv::createCalibrateRobertson(int max_iter = 30, float threshold = 0.01f) [DllImport(LIBNAME)] private static extern IntPtr photo_Photo_createCalibrateRobertson_10(int max_iter, float threshold); [DllImport(LIBNAME)] private static extern IntPtr photo_Photo_createCalibrateRobertson_11(int max_iter); [DllImport(LIBNAME)] private static extern IntPtr photo_Photo_createCalibrateRobertson_12(); // C++: Ptr_MergeDebevec cv::createMergeDebevec() [DllImport(LIBNAME)] private static extern IntPtr photo_Photo_createMergeDebevec_10(); // C++: Ptr_MergeMertens cv::createMergeMertens(float contrast_weight = 1.0f, float saturation_weight = 1.0f, float exposure_weight = 0.0f) [DllImport(LIBNAME)] private static extern IntPtr photo_Photo_createMergeMertens_10(float contrast_weight, float saturation_weight, float exposure_weight); [DllImport(LIBNAME)] private static extern IntPtr photo_Photo_createMergeMertens_11(float contrast_weight, float saturation_weight); [DllImport(LIBNAME)] private static extern IntPtr photo_Photo_createMergeMertens_12(float contrast_weight); [DllImport(LIBNAME)] private static extern IntPtr photo_Photo_createMergeMertens_13(); // C++: Ptr_MergeRobertson cv::createMergeRobertson() [DllImport(LIBNAME)] private static extern IntPtr photo_Photo_createMergeRobertson_10(); // C++: void cv::decolor(Mat src, Mat& grayscale, Mat& color_boost) [DllImport(LIBNAME)] private static extern void photo_Photo_decolor_10(IntPtr src_nativeObj, IntPtr grayscale_nativeObj, IntPtr color_boost_nativeObj); // C++: void cv::seamlessClone(Mat src, Mat dst, Mat mask, Point p, Mat& blend, int flags) [DllImport(LIBNAME)] private static extern void photo_Photo_seamlessClone_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, IntPtr mask_nativeObj, double p_x, double p_y, IntPtr blend_nativeObj, int flags); // C++: void cv::colorChange(Mat src, Mat mask, Mat& dst, float red_mul = 1.0f, float green_mul = 1.0f, float blue_mul = 1.0f) [DllImport(LIBNAME)] private static extern void photo_Photo_colorChange_10(IntPtr src_nativeObj, IntPtr mask_nativeObj, IntPtr dst_nativeObj, float red_mul, float green_mul, float blue_mul); [DllImport(LIBNAME)] private static extern void photo_Photo_colorChange_11(IntPtr src_nativeObj, IntPtr mask_nativeObj, IntPtr dst_nativeObj, float red_mul, float green_mul); [DllImport(LIBNAME)] private static extern void photo_Photo_colorChange_12(IntPtr src_nativeObj, IntPtr mask_nativeObj, IntPtr dst_nativeObj, float red_mul); [DllImport(LIBNAME)] private static extern void photo_Photo_colorChange_13(IntPtr src_nativeObj, IntPtr mask_nativeObj, IntPtr dst_nativeObj); // C++: void cv::illuminationChange(Mat src, Mat mask, Mat& dst, float alpha = 0.2f, float beta = 0.4f) [DllImport(LIBNAME)] private static extern void photo_Photo_illuminationChange_10(IntPtr src_nativeObj, IntPtr mask_nativeObj, IntPtr dst_nativeObj, float alpha, float beta); [DllImport(LIBNAME)] private static extern void photo_Photo_illuminationChange_11(IntPtr src_nativeObj, IntPtr mask_nativeObj, IntPtr dst_nativeObj, float alpha); [DllImport(LIBNAME)] private static extern void photo_Photo_illuminationChange_12(IntPtr src_nativeObj, IntPtr mask_nativeObj, IntPtr dst_nativeObj); // C++: void cv::textureFlattening(Mat src, Mat mask, Mat& dst, float low_threshold = 30, float high_threshold = 45, int kernel_size = 3) [DllImport(LIBNAME)] private static extern void photo_Photo_textureFlattening_10(IntPtr src_nativeObj, IntPtr mask_nativeObj, IntPtr dst_nativeObj, float low_threshold, float high_threshold, int kernel_size); [DllImport(LIBNAME)] private static extern void photo_Photo_textureFlattening_11(IntPtr src_nativeObj, IntPtr mask_nativeObj, IntPtr dst_nativeObj, float low_threshold, float high_threshold); [DllImport(LIBNAME)] private static extern void photo_Photo_textureFlattening_12(IntPtr src_nativeObj, IntPtr mask_nativeObj, IntPtr dst_nativeObj, float low_threshold); [DllImport(LIBNAME)] private static extern void photo_Photo_textureFlattening_13(IntPtr src_nativeObj, IntPtr mask_nativeObj, IntPtr dst_nativeObj); // C++: void cv::edgePreservingFilter(Mat src, Mat& dst, int flags = 1, float sigma_s = 60, float sigma_r = 0.4f) [DllImport(LIBNAME)] private static extern void photo_Photo_edgePreservingFilter_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, int flags, float sigma_s, float sigma_r); [DllImport(LIBNAME)] private static extern void photo_Photo_edgePreservingFilter_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, int flags, float sigma_s); [DllImport(LIBNAME)] private static extern void photo_Photo_edgePreservingFilter_12(IntPtr src_nativeObj, IntPtr dst_nativeObj, int flags); [DllImport(LIBNAME)] private static extern void photo_Photo_edgePreservingFilter_13(IntPtr src_nativeObj, IntPtr dst_nativeObj); // C++: void cv::detailEnhance(Mat src, Mat& dst, float sigma_s = 10, float sigma_r = 0.15f) [DllImport(LIBNAME)] private static extern void photo_Photo_detailEnhance_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, float sigma_s, float sigma_r); [DllImport(LIBNAME)] private static extern void photo_Photo_detailEnhance_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, float sigma_s); [DllImport(LIBNAME)] private static extern void photo_Photo_detailEnhance_12(IntPtr src_nativeObj, IntPtr dst_nativeObj); // C++: void cv::pencilSketch(Mat src, Mat& dst1, Mat& dst2, float sigma_s = 60, float sigma_r = 0.07f, float shade_factor = 0.02f) [DllImport(LIBNAME)] private static extern void photo_Photo_pencilSketch_10(IntPtr src_nativeObj, IntPtr dst1_nativeObj, IntPtr dst2_nativeObj, float sigma_s, float sigma_r, float shade_factor); [DllImport(LIBNAME)] private static extern void photo_Photo_pencilSketch_11(IntPtr src_nativeObj, IntPtr dst1_nativeObj, IntPtr dst2_nativeObj, float sigma_s, float sigma_r); [DllImport(LIBNAME)] private static extern void photo_Photo_pencilSketch_12(IntPtr src_nativeObj, IntPtr dst1_nativeObj, IntPtr dst2_nativeObj, float sigma_s); [DllImport(LIBNAME)] private static extern void photo_Photo_pencilSketch_13(IntPtr src_nativeObj, IntPtr dst1_nativeObj, IntPtr dst2_nativeObj); // C++: void cv::stylization(Mat src, Mat& dst, float sigma_s = 60, float sigma_r = 0.45f) [DllImport(LIBNAME)] private static extern void photo_Photo_stylization_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, float sigma_s, float sigma_r); [DllImport(LIBNAME)] private static extern void photo_Photo_stylization_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, float sigma_s); [DllImport(LIBNAME)] private static extern void photo_Photo_stylization_12(IntPtr src_nativeObj, IntPtr dst_nativeObj); } }