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Health/Assets/OpenCVForUnity/org/opencv/photo/Photo.cs

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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 <unnamed>
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 &lt;http://en.wikipedia.org/wiki/Inpainting&gt; for more details.
*
* <b>Note:</b>
* <ul>
* <li>
* An example using the inpainting technique can be found at
* opencv_source_code/samples/cpp/inpaint.cpp
* </li>
* <li>
* (Python) An example using the inpainting technique can be found at
* opencv_source_code/samples/python/inpaint.py
* </li>
* </ul>
*/
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
* &lt;http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/&gt; 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
* &lt;http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/&gt; 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
* &lt;http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/&gt; 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
* &lt;http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/&gt; 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
* &lt;http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/&gt; 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
* &lt;http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/&gt; 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
* &lt;http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/&gt; 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
* &lt;http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/&gt; 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<Mat> 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<Mat> 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<Mat> 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<Mat> 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<Mat> 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<Mat> 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<Mat> 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<Mat> 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<Mat> 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<Mat> 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<Mat> 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<Mat> 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<Mat> 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<Mat> 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<Mat> 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<Mat> 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 &gt; 1 brightens the image and gamma &lt; 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 &gt; 1 brightens the image and gamma &lt; 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 &gt; 100.
* param kernel_size The size of the Sobel kernel to be used.
*
* <b>Note:</b>
* 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 &gt; 100.
*
* <b>Note:</b>
* 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.
*
* <b>Note:</b>
* 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.
*
* <b>Note:</b>
* 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);
}
}
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