Joe Verbout / Mbed 2 deprecated main

opencv on mbed

Dependencies:   mbed

opencv2/photo.hpp@0:ea44dc9ed014, 2016-03-31 (annotated)

Committer:
joeverbout
Date:
Thu Mar 31 21:16:38 2016 +0000
Revision:
0:ea44dc9ed014
OpenCV on mbed attempt

Who changed what in which revision?

UserRevisionLine numberNew contents of line
joeverbout 0:ea44dc9ed014 1 /*M///////////////////////////////////////////////////////////////////////////////////////
joeverbout 0:ea44dc9ed014 2 //
joeverbout 0:ea44dc9ed014 3 // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
joeverbout 0:ea44dc9ed014 4 //
joeverbout 0:ea44dc9ed014 5 // By downloading, copying, installing or using the software you agree to this license.
joeverbout 0:ea44dc9ed014 6 // If you do not agree to this license, do not download, install,
joeverbout 0:ea44dc9ed014 7 // copy or use the software.
joeverbout 0:ea44dc9ed014 8 //
joeverbout 0:ea44dc9ed014 9 //
joeverbout 0:ea44dc9ed014 10 // License Agreement
joeverbout 0:ea44dc9ed014 11 // For Open Source Computer Vision Library
joeverbout 0:ea44dc9ed014 12 //
joeverbout 0:ea44dc9ed014 13 // Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
joeverbout 0:ea44dc9ed014 14 // Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved.
joeverbout 0:ea44dc9ed014 15 // Third party copyrights are property of their respective owners.
joeverbout 0:ea44dc9ed014 16 //
joeverbout 0:ea44dc9ed014 17 // Redistribution and use in source and binary forms, with or without modification,
joeverbout 0:ea44dc9ed014 18 // are permitted provided that the following conditions are met:
joeverbout 0:ea44dc9ed014 19 //
joeverbout 0:ea44dc9ed014 20 // * Redistribution's of source code must retain the above copyright notice,
joeverbout 0:ea44dc9ed014 21 // this list of conditions and the following disclaimer.
joeverbout 0:ea44dc9ed014 22 //
joeverbout 0:ea44dc9ed014 23 // * Redistribution's in binary form must reproduce the above copyright notice,
joeverbout 0:ea44dc9ed014 24 // this list of conditions and the following disclaimer in the documentation
joeverbout 0:ea44dc9ed014 25 // and/or other materials provided with the distribution.
joeverbout 0:ea44dc9ed014 26 //
joeverbout 0:ea44dc9ed014 27 // * The name of the copyright holders may not be used to endorse or promote products
joeverbout 0:ea44dc9ed014 28 // derived from this software without specific prior written permission.
joeverbout 0:ea44dc9ed014 29 //
joeverbout 0:ea44dc9ed014 30 // This software is provided by the copyright holders and contributors "as is" and
joeverbout 0:ea44dc9ed014 31 // any express or implied warranties, including, but not limited to, the implied
joeverbout 0:ea44dc9ed014 32 // warranties of merchantability and fitness for a particular purpose are disclaimed.
joeverbout 0:ea44dc9ed014 33 // In no event shall the Intel Corporation or contributors be liable for any direct,
joeverbout 0:ea44dc9ed014 34 // indirect, incidental, special, exemplary, or consequential damages
joeverbout 0:ea44dc9ed014 35 // (including, but not limited to, procurement of substitute goods or services;
joeverbout 0:ea44dc9ed014 36 // loss of use, data, or profits; or business interruption) however caused
joeverbout 0:ea44dc9ed014 37 // and on any theory of liability, whether in contract, strict liability,
joeverbout 0:ea44dc9ed014 38 // or tort (including negligence or otherwise) arising in any way out of
joeverbout 0:ea44dc9ed014 39 // the use of this software, even if advised of the possibility of such damage.
joeverbout 0:ea44dc9ed014 40 //
joeverbout 0:ea44dc9ed014 41 //M*/
joeverbout 0:ea44dc9ed014 42
joeverbout 0:ea44dc9ed014 43 #ifndef __OPENCV_PHOTO_HPP__
joeverbout 0:ea44dc9ed014 44 #define __OPENCV_PHOTO_HPP__
joeverbout 0:ea44dc9ed014 45
joeverbout 0:ea44dc9ed014 46 #include "opencv2/core.hpp"
joeverbout 0:ea44dc9ed014 47 #include "opencv2/imgproc.hpp"
joeverbout 0:ea44dc9ed014 48
joeverbout 0:ea44dc9ed014 49 /**
joeverbout 0:ea44dc9ed014 50 @defgroup photo Computational Photography
joeverbout 0:ea44dc9ed014 51 @{
joeverbout 0:ea44dc9ed014 52 @defgroup photo_denoise Denoising
joeverbout 0:ea44dc9ed014 53 @defgroup photo_hdr HDR imaging
joeverbout 0:ea44dc9ed014 54
joeverbout 0:ea44dc9ed014 55 This section describes high dynamic range imaging algorithms namely tonemapping, exposure alignment,
joeverbout 0:ea44dc9ed014 56 camera calibration with multiple exposures and exposure fusion.
joeverbout 0:ea44dc9ed014 57
joeverbout 0:ea44dc9ed014 58 @defgroup photo_clone Seamless Cloning
joeverbout 0:ea44dc9ed014 59 @defgroup photo_render Non-Photorealistic Rendering
joeverbout 0:ea44dc9ed014 60 @defgroup photo_c C API
joeverbout 0:ea44dc9ed014 61 @}
joeverbout 0:ea44dc9ed014 62 */
joeverbout 0:ea44dc9ed014 63
joeverbout 0:ea44dc9ed014 64 namespace cv
joeverbout 0:ea44dc9ed014 65 {
joeverbout 0:ea44dc9ed014 66
joeverbout 0:ea44dc9ed014 67 //! @addtogroup photo
joeverbout 0:ea44dc9ed014 68 //! @{
joeverbout 0:ea44dc9ed014 69
joeverbout 0:ea44dc9ed014 70 //! the inpainting algorithm
joeverbout 0:ea44dc9ed014 71 enum
joeverbout 0:ea44dc9ed014 72 {
joeverbout 0:ea44dc9ed014 73 INPAINT_NS = 0, // Navier-Stokes algorithm
joeverbout 0:ea44dc9ed014 74 INPAINT_TELEA = 1 // A. Telea algorithm
joeverbout 0:ea44dc9ed014 75 };
joeverbout 0:ea44dc9ed014 76
joeverbout 0:ea44dc9ed014 77 enum
joeverbout 0:ea44dc9ed014 78 {
joeverbout 0:ea44dc9ed014 79 NORMAL_CLONE = 1,
joeverbout 0:ea44dc9ed014 80 MIXED_CLONE = 2,
joeverbout 0:ea44dc9ed014 81 MONOCHROME_TRANSFER = 3
joeverbout 0:ea44dc9ed014 82 };
joeverbout 0:ea44dc9ed014 83
joeverbout 0:ea44dc9ed014 84 enum
joeverbout 0:ea44dc9ed014 85 {
joeverbout 0:ea44dc9ed014 86 RECURS_FILTER = 1,
joeverbout 0:ea44dc9ed014 87 NORMCONV_FILTER = 2
joeverbout 0:ea44dc9ed014 88 };
joeverbout 0:ea44dc9ed014 89
joeverbout 0:ea44dc9ed014 90 /** @brief Restores the selected region in an image using the region neighborhood.
joeverbout 0:ea44dc9ed014 91
joeverbout 0:ea44dc9ed014 92 @param src Input 8-bit 1-channel or 3-channel image.
joeverbout 0:ea44dc9ed014 93 @param inpaintMask Inpainting mask, 8-bit 1-channel image. Non-zero pixels indicate the area that
joeverbout 0:ea44dc9ed014 94 needs to be inpainted.
joeverbout 0:ea44dc9ed014 95 @param dst Output image with the same size and type as src .
joeverbout 0:ea44dc9ed014 96 @param inpaintRadius Radius of a circular neighborhood of each point inpainted that is considered
joeverbout 0:ea44dc9ed014 97 by the algorithm.
joeverbout 0:ea44dc9ed014 98 @param flags Inpainting method that could be one of the following:
joeverbout 0:ea44dc9ed014 99 - **INPAINT_NS** Navier-Stokes based method [Navier01]
joeverbout 0:ea44dc9ed014 100 - **INPAINT_TELEA** Method by Alexandru Telea @cite Telea04 .
joeverbout 0:ea44dc9ed014 101
joeverbout 0:ea44dc9ed014 102 The function reconstructs the selected image area from the pixel near the area boundary. The
joeverbout 0:ea44dc9ed014 103 function may be used to remove dust and scratches from a scanned photo, or to remove undesirable
joeverbout 0:ea44dc9ed014 104 objects from still images or video. See <http://en.wikipedia.org/wiki/Inpainting> for more details.
joeverbout 0:ea44dc9ed014 105
joeverbout 0:ea44dc9ed014 106 @note
joeverbout 0:ea44dc9ed014 107 - An example using the inpainting technique can be found at
joeverbout 0:ea44dc9ed014 108 opencv_source_code/samples/cpp/inpaint.cpp
joeverbout 0:ea44dc9ed014 109 - (Python) An example using the inpainting technique can be found at
joeverbout 0:ea44dc9ed014 110 opencv_source_code/samples/python/inpaint.py
joeverbout 0:ea44dc9ed014 111 */
joeverbout 0:ea44dc9ed014 112 CV_EXPORTS_W void inpaint( InputArray src, InputArray inpaintMask,
joeverbout 0:ea44dc9ed014 113 OutputArray dst, double inpaintRadius, int flags );
joeverbout 0:ea44dc9ed014 114
joeverbout 0:ea44dc9ed014 115 //! @addtogroup photo_denoise
joeverbout 0:ea44dc9ed014 116 //! @{
joeverbout 0:ea44dc9ed014 117
joeverbout 0:ea44dc9ed014 118 /** @brief Perform image denoising using Non-local Means Denoising algorithm
joeverbout 0:ea44dc9ed014 119 <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational
joeverbout 0:ea44dc9ed014 120 optimizations. Noise expected to be a gaussian white noise
joeverbout 0:ea44dc9ed014 121
joeverbout 0:ea44dc9ed014 122 @param src Input 8-bit 1-channel, 2-channel, 3-channel or 4-channel image.
joeverbout 0:ea44dc9ed014 123 @param dst Output image with the same size and type as src .
joeverbout 0:ea44dc9ed014 124 @param templateWindowSize Size in pixels of the template patch that is used to compute weights.
joeverbout 0:ea44dc9ed014 125 Should be odd. Recommended value 7 pixels
joeverbout 0:ea44dc9ed014 126 @param searchWindowSize Size in pixels of the window that is used to compute weighted average for
joeverbout 0:ea44dc9ed014 127 given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
joeverbout 0:ea44dc9ed014 128 denoising time. Recommended value 21 pixels
joeverbout 0:ea44dc9ed014 129 @param h Parameter regulating filter strength. Big h value perfectly removes noise but also
joeverbout 0:ea44dc9ed014 130 removes image details, smaller h value preserves details but also preserves some noise
joeverbout 0:ea44dc9ed014 131
joeverbout 0:ea44dc9ed014 132 This function expected to be applied to grayscale images. For colored images look at
joeverbout 0:ea44dc9ed014 133 fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored
joeverbout 0:ea44dc9ed014 134 image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting
joeverbout 0:ea44dc9ed014 135 image to CIELAB colorspace and then separately denoise L and AB components with different h
joeverbout 0:ea44dc9ed014 136 parameter.
joeverbout 0:ea44dc9ed014 137 */
joeverbout 0:ea44dc9ed014 138 CV_EXPORTS_W void fastNlMeansDenoising( InputArray src, OutputArray dst, float h = 3,
joeverbout 0:ea44dc9ed014 139 int templateWindowSize = 7, int searchWindowSize = 21);
joeverbout 0:ea44dc9ed014 140
joeverbout 0:ea44dc9ed014 141 /** @brief Perform image denoising using Non-local Means Denoising algorithm
joeverbout 0:ea44dc9ed014 142 <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational
joeverbout 0:ea44dc9ed014 143 optimizations. Noise expected to be a gaussian white noise
joeverbout 0:ea44dc9ed014 144
joeverbout 0:ea44dc9ed014 145 @param src Input 8-bit or 16-bit (only with NORM_L1) 1-channel,
joeverbout 0:ea44dc9ed014 146 2-channel, 3-channel or 4-channel image.
joeverbout 0:ea44dc9ed014 147 @param dst Output image with the same size and type as src .
joeverbout 0:ea44dc9ed014 148 @param templateWindowSize Size in pixels of the template patch that is used to compute weights.
joeverbout 0:ea44dc9ed014 149 Should be odd. Recommended value 7 pixels
joeverbout 0:ea44dc9ed014 150 @param searchWindowSize Size in pixels of the window that is used to compute weighted average for
joeverbout 0:ea44dc9ed014 151 given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
joeverbout 0:ea44dc9ed014 152 denoising time. Recommended value 21 pixels
joeverbout 0:ea44dc9ed014 153 @param h Array of parameters regulating filter strength, either one
joeverbout 0:ea44dc9ed014 154 parameter applied to all channels or one per channel in dst. Big h value
joeverbout 0:ea44dc9ed014 155 perfectly removes noise but also removes image details, smaller h
joeverbout 0:ea44dc9ed014 156 value preserves details but also preserves some noise
joeverbout 0:ea44dc9ed014 157 @param normType Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1
joeverbout 0:ea44dc9ed014 158
joeverbout 0:ea44dc9ed014 159 This function expected to be applied to grayscale images. For colored images look at
joeverbout 0:ea44dc9ed014 160 fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored
joeverbout 0:ea44dc9ed014 161 image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting
joeverbout 0:ea44dc9ed014 162 image to CIELAB colorspace and then separately denoise L and AB components with different h
joeverbout 0:ea44dc9ed014 163 parameter.
joeverbout 0:ea44dc9ed014 164 */
joeverbout 0:ea44dc9ed014 165 CV_EXPORTS_W void fastNlMeansDenoising( InputArray src, OutputArray dst,
joeverbout 0:ea44dc9ed014 166 const std::vector<float>& h,
joeverbout 0:ea44dc9ed014 167 int templateWindowSize = 7, int searchWindowSize = 21,
joeverbout 0:ea44dc9ed014 168 int normType = NORM_L2);
joeverbout 0:ea44dc9ed014 169
joeverbout 0:ea44dc9ed014 170 /** @brief Modification of fastNlMeansDenoising function for colored images
joeverbout 0:ea44dc9ed014 171
joeverbout 0:ea44dc9ed014 172 @param src Input 8-bit 3-channel image.
joeverbout 0:ea44dc9ed014 173 @param dst Output image with the same size and type as src .
joeverbout 0:ea44dc9ed014 174 @param templateWindowSize Size in pixels of the template patch that is used to compute weights.
joeverbout 0:ea44dc9ed014 175 Should be odd. Recommended value 7 pixels
joeverbout 0:ea44dc9ed014 176 @param searchWindowSize Size in pixels of the window that is used to compute weighted average for
joeverbout 0:ea44dc9ed014 177 given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
joeverbout 0:ea44dc9ed014 178 denoising time. Recommended value 21 pixels
joeverbout 0:ea44dc9ed014 179 @param h Parameter regulating filter strength for luminance component. Bigger h value perfectly
joeverbout 0:ea44dc9ed014 180 removes noise but also removes image details, smaller h value preserves details but also preserves
joeverbout 0:ea44dc9ed014 181 some noise
joeverbout 0:ea44dc9ed014 182 @param hColor The same as h but for color components. For most images value equals 10
joeverbout 0:ea44dc9ed014 183 will be enough to remove colored noise and do not distort colors
joeverbout 0:ea44dc9ed014 184
joeverbout 0:ea44dc9ed014 185 The function converts image to CIELAB colorspace and then separately denoise L and AB components
joeverbout 0:ea44dc9ed014 186 with given h parameters using fastNlMeansDenoising function.
joeverbout 0:ea44dc9ed014 187 */
joeverbout 0:ea44dc9ed014 188 CV_EXPORTS_W void fastNlMeansDenoisingColored( InputArray src, OutputArray dst,
joeverbout 0:ea44dc9ed014 189 float h = 3, float hColor = 3,
joeverbout 0:ea44dc9ed014 190 int templateWindowSize = 7, int searchWindowSize = 21);
joeverbout 0:ea44dc9ed014 191
joeverbout 0:ea44dc9ed014 192 /** @brief Modification of fastNlMeansDenoising function for images sequence where consequtive images have been
joeverbout 0:ea44dc9ed014 193 captured in small period of time. For example video. This version of the function is for grayscale
joeverbout 0:ea44dc9ed014 194 images or for manual manipulation with colorspaces. For more details see
joeverbout 0:ea44dc9ed014 195 <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.131.6394>
joeverbout 0:ea44dc9ed014 196
joeverbout 0:ea44dc9ed014 197 @param srcImgs Input 8-bit 1-channel, 2-channel, 3-channel or
joeverbout 0:ea44dc9ed014 198 4-channel images sequence. All images should have the same type and
joeverbout 0:ea44dc9ed014 199 size.
joeverbout 0:ea44dc9ed014 200 @param imgToDenoiseIndex Target image to denoise index in srcImgs sequence
joeverbout 0:ea44dc9ed014 201 @param temporalWindowSize Number of surrounding images to use for target image denoising. Should
joeverbout 0:ea44dc9ed014 202 be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to
joeverbout 0:ea44dc9ed014 203 imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise
joeverbout 0:ea44dc9ed014 204 srcImgs[imgToDenoiseIndex] image.
joeverbout 0:ea44dc9ed014 205 @param dst Output image with the same size and type as srcImgs images.
joeverbout 0:ea44dc9ed014 206 @param templateWindowSize Size in pixels of the template patch that is used to compute weights.
joeverbout 0:ea44dc9ed014 207 Should be odd. Recommended value 7 pixels
joeverbout 0:ea44dc9ed014 208 @param searchWindowSize Size in pixels of the window that is used to compute weighted average for
joeverbout 0:ea44dc9ed014 209 given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
joeverbout 0:ea44dc9ed014 210 denoising time. Recommended value 21 pixels
joeverbout 0:ea44dc9ed014 211 @param h Parameter regulating filter strength. Bigger h value
joeverbout 0:ea44dc9ed014 212 perfectly removes noise but also removes image details, smaller h
joeverbout 0:ea44dc9ed014 213 value preserves details but also preserves some noise
joeverbout 0:ea44dc9ed014 214 */
joeverbout 0:ea44dc9ed014 215 CV_EXPORTS_W void fastNlMeansDenoisingMulti( InputArrayOfArrays srcImgs, OutputArray dst,
joeverbout 0:ea44dc9ed014 216 int imgToDenoiseIndex, int temporalWindowSize,
joeverbout 0:ea44dc9ed014 217 float h = 3, int templateWindowSize = 7, int searchWindowSize = 21);
joeverbout 0:ea44dc9ed014 218
joeverbout 0:ea44dc9ed014 219 /** @brief Modification of fastNlMeansDenoising function for images sequence where consequtive images have been
joeverbout 0:ea44dc9ed014 220 captured in small period of time. For example video. This version of the function is for grayscale
joeverbout 0:ea44dc9ed014 221 images or for manual manipulation with colorspaces. For more details see
joeverbout 0:ea44dc9ed014 222 <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.131.6394>
joeverbout 0:ea44dc9ed014 223
joeverbout 0:ea44dc9ed014 224 @param srcImgs Input 8-bit or 16-bit (only with NORM_L1) 1-channel,
joeverbout 0:ea44dc9ed014 225 2-channel, 3-channel or 4-channel images sequence. All images should
joeverbout 0:ea44dc9ed014 226 have the same type and size.
joeverbout 0:ea44dc9ed014 227 @param imgToDenoiseIndex Target image to denoise index in srcImgs sequence
joeverbout 0:ea44dc9ed014 228 @param temporalWindowSize Number of surrounding images to use for target image denoising. Should
joeverbout 0:ea44dc9ed014 229 be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to
joeverbout 0:ea44dc9ed014 230 imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise
joeverbout 0:ea44dc9ed014 231 srcImgs[imgToDenoiseIndex] image.
joeverbout 0:ea44dc9ed014 232 @param dst Output image with the same size and type as srcImgs images.
joeverbout 0:ea44dc9ed014 233 @param templateWindowSize Size in pixels of the template patch that is used to compute weights.
joeverbout 0:ea44dc9ed014 234 Should be odd. Recommended value 7 pixels
joeverbout 0:ea44dc9ed014 235 @param searchWindowSize Size in pixels of the window that is used to compute weighted average for
joeverbout 0:ea44dc9ed014 236 given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
joeverbout 0:ea44dc9ed014 237 denoising time. Recommended value 21 pixels
joeverbout 0:ea44dc9ed014 238 @param h Array of parameters regulating filter strength, either one
joeverbout 0:ea44dc9ed014 239 parameter applied to all channels or one per channel in dst. Big h value
joeverbout 0:ea44dc9ed014 240 perfectly removes noise but also removes image details, smaller h
joeverbout 0:ea44dc9ed014 241 value preserves details but also preserves some noise
joeverbout 0:ea44dc9ed014 242 @param normType Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1
joeverbout 0:ea44dc9ed014 243 */
joeverbout 0:ea44dc9ed014 244 CV_EXPORTS_W void fastNlMeansDenoisingMulti( InputArrayOfArrays srcImgs, OutputArray dst,
joeverbout 0:ea44dc9ed014 245 int imgToDenoiseIndex, int temporalWindowSize,
joeverbout 0:ea44dc9ed014 246 const std::vector<float>& h,
joeverbout 0:ea44dc9ed014 247 int templateWindowSize = 7, int searchWindowSize = 21,
joeverbout 0:ea44dc9ed014 248 int normType = NORM_L2);
joeverbout 0:ea44dc9ed014 249
joeverbout 0:ea44dc9ed014 250 /** @brief Modification of fastNlMeansDenoisingMulti function for colored images sequences
joeverbout 0:ea44dc9ed014 251
joeverbout 0:ea44dc9ed014 252 @param srcImgs Input 8-bit 3-channel images sequence. All images should have the same type and
joeverbout 0:ea44dc9ed014 253 size.
joeverbout 0:ea44dc9ed014 254 @param imgToDenoiseIndex Target image to denoise index in srcImgs sequence
joeverbout 0:ea44dc9ed014 255 @param temporalWindowSize Number of surrounding images to use for target image denoising. Should
joeverbout 0:ea44dc9ed014 256 be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to
joeverbout 0:ea44dc9ed014 257 imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise
joeverbout 0:ea44dc9ed014 258 srcImgs[imgToDenoiseIndex] image.
joeverbout 0:ea44dc9ed014 259 @param dst Output image with the same size and type as srcImgs images.
joeverbout 0:ea44dc9ed014 260 @param templateWindowSize Size in pixels of the template patch that is used to compute weights.
joeverbout 0:ea44dc9ed014 261 Should be odd. Recommended value 7 pixels
joeverbout 0:ea44dc9ed014 262 @param searchWindowSize Size in pixels of the window that is used to compute weighted average for
joeverbout 0:ea44dc9ed014 263 given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
joeverbout 0:ea44dc9ed014 264 denoising time. Recommended value 21 pixels
joeverbout 0:ea44dc9ed014 265 @param h Parameter regulating filter strength for luminance component. Bigger h value perfectly
joeverbout 0:ea44dc9ed014 266 removes noise but also removes image details, smaller h value preserves details but also preserves
joeverbout 0:ea44dc9ed014 267 some noise.
joeverbout 0:ea44dc9ed014 268 @param hColor The same as h but for color components.
joeverbout 0:ea44dc9ed014 269
joeverbout 0:ea44dc9ed014 270 The function converts images to CIELAB colorspace and then separately denoise L and AB components
joeverbout 0:ea44dc9ed014 271 with given h parameters using fastNlMeansDenoisingMulti function.
joeverbout 0:ea44dc9ed014 272 */
joeverbout 0:ea44dc9ed014 273 CV_EXPORTS_W void fastNlMeansDenoisingColoredMulti( InputArrayOfArrays srcImgs, OutputArray dst,
joeverbout 0:ea44dc9ed014 274 int imgToDenoiseIndex, int temporalWindowSize,
joeverbout 0:ea44dc9ed014 275 float h = 3, float hColor = 3,
joeverbout 0:ea44dc9ed014 276 int templateWindowSize = 7, int searchWindowSize = 21);
joeverbout 0:ea44dc9ed014 277
joeverbout 0:ea44dc9ed014 278 /** @brief Primal-dual algorithm is an algorithm for solving special types of variational problems (that is,
joeverbout 0:ea44dc9ed014 279 finding a function to minimize some functional). As the image denoising, in particular, may be seen
joeverbout 0:ea44dc9ed014 280 as the variational problem, primal-dual algorithm then can be used to perform denoising and this is
joeverbout 0:ea44dc9ed014 281 exactly what is implemented.
joeverbout 0:ea44dc9ed014 282
joeverbout 0:ea44dc9ed014 283 It should be noted, that this implementation was taken from the July 2013 blog entry
joeverbout 0:ea44dc9ed014 284 @cite MA13 , which also contained (slightly more general) ready-to-use source code on Python.
joeverbout 0:ea44dc9ed014 285 Subsequently, that code was rewritten on C++ with the usage of openCV by Vadim Pisarevsky at the end
joeverbout 0:ea44dc9ed014 286 of July 2013 and finally it was slightly adapted by later authors.
joeverbout 0:ea44dc9ed014 287
joeverbout 0:ea44dc9ed014 288 Although the thorough discussion and justification of the algorithm involved may be found in
joeverbout 0:ea44dc9ed014 289 @cite ChambolleEtAl, it might make sense to skim over it here, following @cite MA13 . To begin
joeverbout 0:ea44dc9ed014 290 with, we consider the 1-byte gray-level images as the functions from the rectangular domain of
joeverbout 0:ea44dc9ed014 291 pixels (it may be seen as set
joeverbout 0:ea44dc9ed014 292 \f$\left\{(x,y)\in\mathbb{N}\times\mathbb{N}\mid 1\leq x\leq n,\;1\leq y\leq m\right\}\f$ for some
joeverbout 0:ea44dc9ed014 293 \f$m,\;n\in\mathbb{N}\f$) into \f$\{0,1,\dots,255\}\f$. We shall denote the noised images as \f$f_i\f$ and with
joeverbout 0:ea44dc9ed014 294 this view, given some image \f$x\f$ of the same size, we may measure how bad it is by the formula
joeverbout 0:ea44dc9ed014 295
joeverbout 0:ea44dc9ed014 296 \f[\left\|\left\|\nabla x\right\|\right\| + \lambda\sum_i\left\|\left\|x-f_i\right\|\right\|\f]
joeverbout 0:ea44dc9ed014 297
joeverbout 0:ea44dc9ed014 298 \f$\|\|\cdot\|\|\f$ here denotes \f$L_2\f$-norm and as you see, the first addend states that we want our
joeverbout 0:ea44dc9ed014 299 image to be smooth (ideally, having zero gradient, thus being constant) and the second states that
joeverbout 0:ea44dc9ed014 300 we want our result to be close to the observations we've got. If we treat \f$x\f$ as a function, this is
joeverbout 0:ea44dc9ed014 301 exactly the functional what we seek to minimize and here the Primal-Dual algorithm comes into play.
joeverbout 0:ea44dc9ed014 302
joeverbout 0:ea44dc9ed014 303 @param observations This array should contain one or more noised versions of the image that is to
joeverbout 0:ea44dc9ed014 304 be restored.
joeverbout 0:ea44dc9ed014 305 @param result Here the denoised image will be stored. There is no need to do pre-allocation of
joeverbout 0:ea44dc9ed014 306 storage space, as it will be automatically allocated, if necessary.
joeverbout 0:ea44dc9ed014 307 @param lambda Corresponds to \f$\lambda\f$ in the formulas above. As it is enlarged, the smooth
joeverbout 0:ea44dc9ed014 308 (blurred) images are treated more favorably than detailed (but maybe more noised) ones. Roughly
joeverbout 0:ea44dc9ed014 309 speaking, as it becomes smaller, the result will be more blur but more sever outliers will be
joeverbout 0:ea44dc9ed014 310 removed.
joeverbout 0:ea44dc9ed014 311 @param niters Number of iterations that the algorithm will run. Of course, as more iterations as
joeverbout 0:ea44dc9ed014 312 better, but it is hard to quantitatively refine this statement, so just use the default and
joeverbout 0:ea44dc9ed014 313 increase it if the results are poor.
joeverbout 0:ea44dc9ed014 314 */
joeverbout 0:ea44dc9ed014 315 CV_EXPORTS_W void denoise_TVL1(const std::vector<Mat>& observations,Mat& result, double lambda=1.0, int niters=30);
joeverbout 0:ea44dc9ed014 316
joeverbout 0:ea44dc9ed014 317 //! @} photo_denoise
joeverbout 0:ea44dc9ed014 318
joeverbout 0:ea44dc9ed014 319 //! @addtogroup photo_hdr
joeverbout 0:ea44dc9ed014 320 //! @{
joeverbout 0:ea44dc9ed014 321
joeverbout 0:ea44dc9ed014 322 enum { LDR_SIZE = 256 };
joeverbout 0:ea44dc9ed014 323
joeverbout 0:ea44dc9ed014 324 /** @brief Base class for tonemapping algorithms - tools that are used to map HDR image to 8-bit range.
joeverbout 0:ea44dc9ed014 325 */
joeverbout 0:ea44dc9ed014 326 class CV_EXPORTS_W Tonemap : public Algorithm
joeverbout 0:ea44dc9ed014 327 {
joeverbout 0:ea44dc9ed014 328 public:
joeverbout 0:ea44dc9ed014 329 /** @brief Tonemaps image
joeverbout 0:ea44dc9ed014 330
joeverbout 0:ea44dc9ed014 331 @param src source image - 32-bit 3-channel Mat
joeverbout 0:ea44dc9ed014 332 @param dst destination image - 32-bit 3-channel Mat with values in [0, 1] range
joeverbout 0:ea44dc9ed014 333 */
joeverbout 0:ea44dc9ed014 334 CV_WRAP virtual void process(InputArray src, OutputArray dst) = 0;
joeverbout 0:ea44dc9ed014 335
joeverbout 0:ea44dc9ed014 336 CV_WRAP virtual float getGamma() const = 0;
joeverbout 0:ea44dc9ed014 337 CV_WRAP virtual void setGamma(float gamma) = 0;
joeverbout 0:ea44dc9ed014 338 };
joeverbout 0:ea44dc9ed014 339
joeverbout 0:ea44dc9ed014 340 /** @brief Creates simple linear mapper with gamma correction
joeverbout 0:ea44dc9ed014 341
joeverbout 0:ea44dc9ed014 342 @param gamma positive value for gamma correction. Gamma value of 1.0 implies no correction, gamma
joeverbout 0:ea44dc9ed014 343 equal to 2.2f is suitable for most displays.
joeverbout 0:ea44dc9ed014 344 Generally gamma \> 1 brightens the image and gamma \< 1 darkens it.
joeverbout 0:ea44dc9ed014 345 */
joeverbout 0:ea44dc9ed014 346 CV_EXPORTS_W Ptr<Tonemap> createTonemap(float gamma = 1.0f);
joeverbout 0:ea44dc9ed014 347
joeverbout 0:ea44dc9ed014 348 /** @brief Adaptive logarithmic mapping is a fast global tonemapping algorithm that scales the image in
joeverbout 0:ea44dc9ed014 349 logarithmic domain.
joeverbout 0:ea44dc9ed014 350
joeverbout 0:ea44dc9ed014 351 Since it's a global operator the same function is applied to all the pixels, it is controlled by the
joeverbout 0:ea44dc9ed014 352 bias parameter.
joeverbout 0:ea44dc9ed014 353
joeverbout 0:ea44dc9ed014 354 Optional saturation enhancement is possible as described in @cite FL02 .
joeverbout 0:ea44dc9ed014 355
joeverbout 0:ea44dc9ed014 356 For more information see @cite DM03 .
joeverbout 0:ea44dc9ed014 357 */
joeverbout 0:ea44dc9ed014 358 class CV_EXPORTS_W TonemapDrago : public Tonemap
joeverbout 0:ea44dc9ed014 359 {
joeverbout 0:ea44dc9ed014 360 public:
joeverbout 0:ea44dc9ed014 361
joeverbout 0:ea44dc9ed014 362 CV_WRAP virtual float getSaturation() const = 0;
joeverbout 0:ea44dc9ed014 363 CV_WRAP virtual void setSaturation(float saturation) = 0;
joeverbout 0:ea44dc9ed014 364
joeverbout 0:ea44dc9ed014 365 CV_WRAP virtual float getBias() const = 0;
joeverbout 0:ea44dc9ed014 366 CV_WRAP virtual void setBias(float bias) = 0;
joeverbout 0:ea44dc9ed014 367 };
joeverbout 0:ea44dc9ed014 368
joeverbout 0:ea44dc9ed014 369 /** @brief Creates TonemapDrago object
joeverbout 0:ea44dc9ed014 370
joeverbout 0:ea44dc9ed014 371 @param gamma gamma value for gamma correction. See createTonemap
joeverbout 0:ea44dc9ed014 372 @param saturation positive saturation enhancement value. 1.0 preserves saturation, values greater
joeverbout 0:ea44dc9ed014 373 than 1 increase saturation and values less than 1 decrease it.
joeverbout 0:ea44dc9ed014 374 @param bias value for bias function in [0, 1] range. Values from 0.7 to 0.9 usually give best
joeverbout 0:ea44dc9ed014 375 results, default value is 0.85.
joeverbout 0:ea44dc9ed014 376 */
joeverbout 0:ea44dc9ed014 377 CV_EXPORTS_W Ptr<TonemapDrago> createTonemapDrago(float gamma = 1.0f, float saturation = 1.0f, float bias = 0.85f);
joeverbout 0:ea44dc9ed014 378
joeverbout 0:ea44dc9ed014 379 /** @brief This algorithm decomposes image into two layers: base layer and detail layer using bilateral filter
joeverbout 0:ea44dc9ed014 380 and compresses contrast of the base layer thus preserving all the details.
joeverbout 0:ea44dc9ed014 381
joeverbout 0:ea44dc9ed014 382 This implementation uses regular bilateral filter from opencv.
joeverbout 0:ea44dc9ed014 383
joeverbout 0:ea44dc9ed014 384 Saturation enhancement is possible as in ocvTonemapDrago.
joeverbout 0:ea44dc9ed014 385
joeverbout 0:ea44dc9ed014 386 For more information see @cite DD02 .
joeverbout 0:ea44dc9ed014 387 */
joeverbout 0:ea44dc9ed014 388 class CV_EXPORTS_W TonemapDurand : public Tonemap
joeverbout 0:ea44dc9ed014 389 {
joeverbout 0:ea44dc9ed014 390 public:
joeverbout 0:ea44dc9ed014 391
joeverbout 0:ea44dc9ed014 392 CV_WRAP virtual float getSaturation() const = 0;
joeverbout 0:ea44dc9ed014 393 CV_WRAP virtual void setSaturation(float saturation) = 0;
joeverbout 0:ea44dc9ed014 394
joeverbout 0:ea44dc9ed014 395 CV_WRAP virtual float getContrast() const = 0;
joeverbout 0:ea44dc9ed014 396 CV_WRAP virtual void setContrast(float contrast) = 0;
joeverbout 0:ea44dc9ed014 397
joeverbout 0:ea44dc9ed014 398 CV_WRAP virtual float getSigmaSpace() const = 0;
joeverbout 0:ea44dc9ed014 399 CV_WRAP virtual void setSigmaSpace(float sigma_space) = 0;
joeverbout 0:ea44dc9ed014 400
joeverbout 0:ea44dc9ed014 401 CV_WRAP virtual float getSigmaColor() const = 0;
joeverbout 0:ea44dc9ed014 402 CV_WRAP virtual void setSigmaColor(float sigma_color) = 0;
joeverbout 0:ea44dc9ed014 403 };
joeverbout 0:ea44dc9ed014 404
joeverbout 0:ea44dc9ed014 405 /** @brief Creates TonemapDurand object
joeverbout 0:ea44dc9ed014 406
joeverbout 0:ea44dc9ed014 407 @param gamma gamma value for gamma correction. See createTonemap
joeverbout 0:ea44dc9ed014 408 @param contrast resulting contrast on logarithmic scale, i. e. log(max / min), where max and min
joeverbout 0:ea44dc9ed014 409 are maximum and minimum luminance values of the resulting image.
joeverbout 0:ea44dc9ed014 410 @param saturation saturation enhancement value. See createTonemapDrago
joeverbout 0:ea44dc9ed014 411 @param sigma_space bilateral filter sigma in color space
joeverbout 0:ea44dc9ed014 412 @param sigma_color bilateral filter sigma in coordinate space
joeverbout 0:ea44dc9ed014 413 */
joeverbout 0:ea44dc9ed014 414 CV_EXPORTS_W Ptr<TonemapDurand>
joeverbout 0:ea44dc9ed014 415 createTonemapDurand(float gamma = 1.0f, float contrast = 4.0f, float saturation = 1.0f, float sigma_space = 2.0f, float sigma_color = 2.0f);
joeverbout 0:ea44dc9ed014 416
joeverbout 0:ea44dc9ed014 417 /** @brief This is a global tonemapping operator that models human visual system.
joeverbout 0:ea44dc9ed014 418
joeverbout 0:ea44dc9ed014 419 Mapping function is controlled by adaptation parameter, that is computed using light adaptation and
joeverbout 0:ea44dc9ed014 420 color adaptation.
joeverbout 0:ea44dc9ed014 421
joeverbout 0:ea44dc9ed014 422 For more information see @cite RD05 .
joeverbout 0:ea44dc9ed014 423 */
joeverbout 0:ea44dc9ed014 424 class CV_EXPORTS_W TonemapReinhard : public Tonemap
joeverbout 0:ea44dc9ed014 425 {
joeverbout 0:ea44dc9ed014 426 public:
joeverbout 0:ea44dc9ed014 427 CV_WRAP virtual float getIntensity() const = 0;
joeverbout 0:ea44dc9ed014 428 CV_WRAP virtual void setIntensity(float intensity) = 0;
joeverbout 0:ea44dc9ed014 429
joeverbout 0:ea44dc9ed014 430 CV_WRAP virtual float getLightAdaptation() const = 0;
joeverbout 0:ea44dc9ed014 431 CV_WRAP virtual void setLightAdaptation(float light_adapt) = 0;
joeverbout 0:ea44dc9ed014 432
joeverbout 0:ea44dc9ed014 433 CV_WRAP virtual float getColorAdaptation() const = 0;
joeverbout 0:ea44dc9ed014 434 CV_WRAP virtual void setColorAdaptation(float color_adapt) = 0;
joeverbout 0:ea44dc9ed014 435 };
joeverbout 0:ea44dc9ed014 436
joeverbout 0:ea44dc9ed014 437 /** @brief Creates TonemapReinhard object
joeverbout 0:ea44dc9ed014 438
joeverbout 0:ea44dc9ed014 439 @param gamma gamma value for gamma correction. See createTonemap
joeverbout 0:ea44dc9ed014 440 @param intensity result intensity in [-8, 8] range. Greater intensity produces brighter results.
joeverbout 0:ea44dc9ed014 441 @param light_adapt light adaptation in [0, 1] range. If 1 adaptation is based only on pixel
joeverbout 0:ea44dc9ed014 442 value, if 0 it's global, otherwise it's a weighted mean of this two cases.
joeverbout 0:ea44dc9ed014 443 @param color_adapt chromatic adaptation in [0, 1] range. If 1 channels are treated independently,
joeverbout 0:ea44dc9ed014 444 if 0 adaptation level is the same for each channel.
joeverbout 0:ea44dc9ed014 445 */
joeverbout 0:ea44dc9ed014 446 CV_EXPORTS_W Ptr<TonemapReinhard>
joeverbout 0:ea44dc9ed014 447 createTonemapReinhard(float gamma = 1.0f, float intensity = 0.0f, float light_adapt = 1.0f, float color_adapt = 0.0f);
joeverbout 0:ea44dc9ed014 448
joeverbout 0:ea44dc9ed014 449 /** @brief This algorithm transforms image to contrast using gradients on all levels of gaussian pyramid,
joeverbout 0:ea44dc9ed014 450 transforms contrast values to HVS response and scales the response. After this the image is
joeverbout 0:ea44dc9ed014 451 reconstructed from new contrast values.
joeverbout 0:ea44dc9ed014 452
joeverbout 0:ea44dc9ed014 453 For more information see @cite MM06 .
joeverbout 0:ea44dc9ed014 454 */
joeverbout 0:ea44dc9ed014 455 class CV_EXPORTS_W TonemapMantiuk : public Tonemap
joeverbout 0:ea44dc9ed014 456 {
joeverbout 0:ea44dc9ed014 457 public:
joeverbout 0:ea44dc9ed014 458 CV_WRAP virtual float getScale() const = 0;
joeverbout 0:ea44dc9ed014 459 CV_WRAP virtual void setScale(float scale) = 0;
joeverbout 0:ea44dc9ed014 460
joeverbout 0:ea44dc9ed014 461 CV_WRAP virtual float getSaturation() const = 0;
joeverbout 0:ea44dc9ed014 462 CV_WRAP virtual void setSaturation(float saturation) = 0;
joeverbout 0:ea44dc9ed014 463 };
joeverbout 0:ea44dc9ed014 464
joeverbout 0:ea44dc9ed014 465 /** @brief Creates TonemapMantiuk object
joeverbout 0:ea44dc9ed014 466
joeverbout 0:ea44dc9ed014 467 @param gamma gamma value for gamma correction. See createTonemap
joeverbout 0:ea44dc9ed014 468 @param scale contrast scale factor. HVS response is multiplied by this parameter, thus compressing
joeverbout 0:ea44dc9ed014 469 dynamic range. Values from 0.6 to 0.9 produce best results.
joeverbout 0:ea44dc9ed014 470 @param saturation saturation enhancement value. See createTonemapDrago
joeverbout 0:ea44dc9ed014 471 */
joeverbout 0:ea44dc9ed014 472 CV_EXPORTS_W Ptr<TonemapMantiuk>
joeverbout 0:ea44dc9ed014 473 createTonemapMantiuk(float gamma = 1.0f, float scale = 0.7f, float saturation = 1.0f);
joeverbout 0:ea44dc9ed014 474
joeverbout 0:ea44dc9ed014 475 /** @brief The base class for algorithms that align images of the same scene with different exposures
joeverbout 0:ea44dc9ed014 476 */
joeverbout 0:ea44dc9ed014 477 class CV_EXPORTS_W AlignExposures : public Algorithm
joeverbout 0:ea44dc9ed014 478 {
joeverbout 0:ea44dc9ed014 479 public:
joeverbout 0:ea44dc9ed014 480 /** @brief Aligns images
joeverbout 0:ea44dc9ed014 481
joeverbout 0:ea44dc9ed014 482 @param src vector of input images
joeverbout 0:ea44dc9ed014 483 @param dst vector of aligned images
joeverbout 0:ea44dc9ed014 484 @param times vector of exposure time values for each image
joeverbout 0:ea44dc9ed014 485 @param response 256x1 matrix with inverse camera response function for each pixel value, it should
joeverbout 0:ea44dc9ed014 486 have the same number of channels as images.
joeverbout 0:ea44dc9ed014 487 */
joeverbout 0:ea44dc9ed014 488 CV_WRAP virtual void process(InputArrayOfArrays src, std::vector<Mat>& dst,
joeverbout 0:ea44dc9ed014 489 InputArray times, InputArray response) = 0;
joeverbout 0:ea44dc9ed014 490 };
joeverbout 0:ea44dc9ed014 491
joeverbout 0:ea44dc9ed014 492 /** @brief This algorithm converts images to median threshold bitmaps (1 for pixels brighter than median
joeverbout 0:ea44dc9ed014 493 luminance and 0 otherwise) and than aligns the resulting bitmaps using bit operations.
joeverbout 0:ea44dc9ed014 494
joeverbout 0:ea44dc9ed014 495 It is invariant to exposure, so exposure values and camera response are not necessary.
joeverbout 0:ea44dc9ed014 496
joeverbout 0:ea44dc9ed014 497 In this implementation new image regions are filled with zeros.
joeverbout 0:ea44dc9ed014 498
joeverbout 0:ea44dc9ed014 499 For more information see @cite GW03 .
joeverbout 0:ea44dc9ed014 500 */
joeverbout 0:ea44dc9ed014 501 class CV_EXPORTS_W AlignMTB : public AlignExposures
joeverbout 0:ea44dc9ed014 502 {
joeverbout 0:ea44dc9ed014 503 public:
joeverbout 0:ea44dc9ed014 504 CV_WRAP virtual void process(InputArrayOfArrays src, std::vector<Mat>& dst,
joeverbout 0:ea44dc9ed014 505 InputArray times, InputArray response) = 0;
joeverbout 0:ea44dc9ed014 506
joeverbout 0:ea44dc9ed014 507 /** @brief Short version of process, that doesn't take extra arguments.
joeverbout 0:ea44dc9ed014 508
joeverbout 0:ea44dc9ed014 509 @param src vector of input images
joeverbout 0:ea44dc9ed014 510 @param dst vector of aligned images
joeverbout 0:ea44dc9ed014 511 */
joeverbout 0:ea44dc9ed014 512 CV_WRAP virtual void process(InputArrayOfArrays src, std::vector<Mat>& dst) = 0;
joeverbout 0:ea44dc9ed014 513
joeverbout 0:ea44dc9ed014 514 /** @brief Calculates shift between two images, i. e. how to shift the second image to correspond it with the
joeverbout 0:ea44dc9ed014 515 first.
joeverbout 0:ea44dc9ed014 516
joeverbout 0:ea44dc9ed014 517 @param img0 first image
joeverbout 0:ea44dc9ed014 518 @param img1 second image
joeverbout 0:ea44dc9ed014 519 */
joeverbout 0:ea44dc9ed014 520 CV_WRAP virtual Point calculateShift(InputArray img0, InputArray img1) = 0;
joeverbout 0:ea44dc9ed014 521 /** @brief Helper function, that shift Mat filling new regions with zeros.
joeverbout 0:ea44dc9ed014 522
joeverbout 0:ea44dc9ed014 523 @param src input image
joeverbout 0:ea44dc9ed014 524 @param dst result image
joeverbout 0:ea44dc9ed014 525 @param shift shift value
joeverbout 0:ea44dc9ed014 526 */
joeverbout 0:ea44dc9ed014 527 CV_WRAP virtual void shiftMat(InputArray src, OutputArray dst, const Point shift) = 0;
joeverbout 0:ea44dc9ed014 528 /** @brief Computes median threshold and exclude bitmaps of given image.
joeverbout 0:ea44dc9ed014 529
joeverbout 0:ea44dc9ed014 530 @param img input image
joeverbout 0:ea44dc9ed014 531 @param tb median threshold bitmap
joeverbout 0:ea44dc9ed014 532 @param eb exclude bitmap
joeverbout 0:ea44dc9ed014 533 */
joeverbout 0:ea44dc9ed014 534 CV_WRAP virtual void computeBitmaps(InputArray img, OutputArray tb, OutputArray eb) = 0;
joeverbout 0:ea44dc9ed014 535
joeverbout 0:ea44dc9ed014 536 CV_WRAP virtual int getMaxBits() const = 0;
joeverbout 0:ea44dc9ed014 537 CV_WRAP virtual void setMaxBits(int max_bits) = 0;
joeverbout 0:ea44dc9ed014 538
joeverbout 0:ea44dc9ed014 539 CV_WRAP virtual int getExcludeRange() const = 0;
joeverbout 0:ea44dc9ed014 540 CV_WRAP virtual void setExcludeRange(int exclude_range) = 0;
joeverbout 0:ea44dc9ed014 541
joeverbout 0:ea44dc9ed014 542 CV_WRAP virtual bool getCut() const = 0;
joeverbout 0:ea44dc9ed014 543 CV_WRAP virtual void setCut(bool value) = 0;
joeverbout 0:ea44dc9ed014 544 };
joeverbout 0:ea44dc9ed014 545
joeverbout 0:ea44dc9ed014 546 /** @brief Creates AlignMTB object
joeverbout 0:ea44dc9ed014 547
joeverbout 0:ea44dc9ed014 548 @param max_bits logarithm to the base 2 of maximal shift in each dimension. Values of 5 and 6 are
joeverbout 0:ea44dc9ed014 549 usually good enough (31 and 63 pixels shift respectively).
joeverbout 0:ea44dc9ed014 550 @param exclude_range range for exclusion bitmap that is constructed to suppress noise around the
joeverbout 0:ea44dc9ed014 551 median value.
joeverbout 0:ea44dc9ed014 552 @param cut if true cuts images, otherwise fills the new regions with zeros.
joeverbout 0:ea44dc9ed014 553 */
joeverbout 0:ea44dc9ed014 554 CV_EXPORTS_W Ptr<AlignMTB> createAlignMTB(int max_bits = 6, int exclude_range = 4, bool cut = true);
joeverbout 0:ea44dc9ed014 555
joeverbout 0:ea44dc9ed014 556 /** @brief The base class for camera response calibration algorithms.
joeverbout 0:ea44dc9ed014 557 */
joeverbout 0:ea44dc9ed014 558 class CV_EXPORTS_W CalibrateCRF : public Algorithm
joeverbout 0:ea44dc9ed014 559 {
joeverbout 0:ea44dc9ed014 560 public:
joeverbout 0:ea44dc9ed014 561 /** @brief Recovers inverse camera response.
joeverbout 0:ea44dc9ed014 562
joeverbout 0:ea44dc9ed014 563 @param src vector of input images
joeverbout 0:ea44dc9ed014 564 @param dst 256x1 matrix with inverse camera response function
joeverbout 0:ea44dc9ed014 565 @param times vector of exposure time values for each image
joeverbout 0:ea44dc9ed014 566 */
joeverbout 0:ea44dc9ed014 567 CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, InputArray times) = 0;
joeverbout 0:ea44dc9ed014 568 };
joeverbout 0:ea44dc9ed014 569
joeverbout 0:ea44dc9ed014 570 /** @brief Inverse camera response function is extracted for each brightness value by minimizing an objective
joeverbout 0:ea44dc9ed014 571 function as linear system. Objective function is constructed using pixel values on the same position
joeverbout 0:ea44dc9ed014 572 in all images, extra term is added to make the result smoother.
joeverbout 0:ea44dc9ed014 573
joeverbout 0:ea44dc9ed014 574 For more information see @cite DM97 .
joeverbout 0:ea44dc9ed014 575 */
joeverbout 0:ea44dc9ed014 576 class CV_EXPORTS_W CalibrateDebevec : public CalibrateCRF
joeverbout 0:ea44dc9ed014 577 {
joeverbout 0:ea44dc9ed014 578 public:
joeverbout 0:ea44dc9ed014 579 CV_WRAP virtual float getLambda() const = 0;
joeverbout 0:ea44dc9ed014 580 CV_WRAP virtual void setLambda(float lambda) = 0;
joeverbout 0:ea44dc9ed014 581
joeverbout 0:ea44dc9ed014 582 CV_WRAP virtual int getSamples() const = 0;
joeverbout 0:ea44dc9ed014 583 CV_WRAP virtual void setSamples(int samples) = 0;
joeverbout 0:ea44dc9ed014 584
joeverbout 0:ea44dc9ed014 585 CV_WRAP virtual bool getRandom() const = 0;
joeverbout 0:ea44dc9ed014 586 CV_WRAP virtual void setRandom(bool random) = 0;
joeverbout 0:ea44dc9ed014 587 };
joeverbout 0:ea44dc9ed014 588
joeverbout 0:ea44dc9ed014 589 /** @brief Creates CalibrateDebevec object
joeverbout 0:ea44dc9ed014 590
joeverbout 0:ea44dc9ed014 591 @param samples number of pixel locations to use
joeverbout 0:ea44dc9ed014 592 @param lambda smoothness term weight. Greater values produce smoother results, but can alter the
joeverbout 0:ea44dc9ed014 593 response.
joeverbout 0:ea44dc9ed014 594 @param random if true sample pixel locations are chosen at random, otherwise the form a
joeverbout 0:ea44dc9ed014 595 rectangular grid.
joeverbout 0:ea44dc9ed014 596 */
joeverbout 0:ea44dc9ed014 597 CV_EXPORTS_W Ptr<CalibrateDebevec> createCalibrateDebevec(int samples = 70, float lambda = 10.0f, bool random = false);
joeverbout 0:ea44dc9ed014 598
joeverbout 0:ea44dc9ed014 599 /** @brief Inverse camera response function is extracted for each brightness value by minimizing an objective
joeverbout 0:ea44dc9ed014 600 function as linear system. This algorithm uses all image pixels.
joeverbout 0:ea44dc9ed014 601
joeverbout 0:ea44dc9ed014 602 For more information see @cite RB99 .
joeverbout 0:ea44dc9ed014 603 */
joeverbout 0:ea44dc9ed014 604 class CV_EXPORTS_W CalibrateRobertson : public CalibrateCRF
joeverbout 0:ea44dc9ed014 605 {
joeverbout 0:ea44dc9ed014 606 public:
joeverbout 0:ea44dc9ed014 607 CV_WRAP virtual int getMaxIter() const = 0;
joeverbout 0:ea44dc9ed014 608 CV_WRAP virtual void setMaxIter(int max_iter) = 0;
joeverbout 0:ea44dc9ed014 609
joeverbout 0:ea44dc9ed014 610 CV_WRAP virtual float getThreshold() const = 0;
joeverbout 0:ea44dc9ed014 611 CV_WRAP virtual void setThreshold(float threshold) = 0;
joeverbout 0:ea44dc9ed014 612
joeverbout 0:ea44dc9ed014 613 CV_WRAP virtual Mat getRadiance() const = 0;
joeverbout 0:ea44dc9ed014 614 };
joeverbout 0:ea44dc9ed014 615
joeverbout 0:ea44dc9ed014 616 /** @brief Creates CalibrateRobertson object
joeverbout 0:ea44dc9ed014 617
joeverbout 0:ea44dc9ed014 618 @param max_iter maximal number of Gauss-Seidel solver iterations.
joeverbout 0:ea44dc9ed014 619 @param threshold target difference between results of two successive steps of the minimization.
joeverbout 0:ea44dc9ed014 620 */
joeverbout 0:ea44dc9ed014 621 CV_EXPORTS_W Ptr<CalibrateRobertson> createCalibrateRobertson(int max_iter = 30, float threshold = 0.01f);
joeverbout 0:ea44dc9ed014 622
joeverbout 0:ea44dc9ed014 623 /** @brief The base class algorithms that can merge exposure sequence to a single image.
joeverbout 0:ea44dc9ed014 624 */
joeverbout 0:ea44dc9ed014 625 class CV_EXPORTS_W MergeExposures : public Algorithm
joeverbout 0:ea44dc9ed014 626 {
joeverbout 0:ea44dc9ed014 627 public:
joeverbout 0:ea44dc9ed014 628 /** @brief Merges images.
joeverbout 0:ea44dc9ed014 629
joeverbout 0:ea44dc9ed014 630 @param src vector of input images
joeverbout 0:ea44dc9ed014 631 @param dst result image
joeverbout 0:ea44dc9ed014 632 @param times vector of exposure time values for each image
joeverbout 0:ea44dc9ed014 633 @param response 256x1 matrix with inverse camera response function for each pixel value, it should
joeverbout 0:ea44dc9ed014 634 have the same number of channels as images.
joeverbout 0:ea44dc9ed014 635 */
joeverbout 0:ea44dc9ed014 636 CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst,
joeverbout 0:ea44dc9ed014 637 InputArray times, InputArray response) = 0;
joeverbout 0:ea44dc9ed014 638 };
joeverbout 0:ea44dc9ed014 639
joeverbout 0:ea44dc9ed014 640 /** @brief The resulting HDR image is calculated as weighted average of the exposures considering exposure
joeverbout 0:ea44dc9ed014 641 values and camera response.
joeverbout 0:ea44dc9ed014 642
joeverbout 0:ea44dc9ed014 643 For more information see @cite DM97 .
joeverbout 0:ea44dc9ed014 644 */
joeverbout 0:ea44dc9ed014 645 class CV_EXPORTS_W MergeDebevec : public MergeExposures
joeverbout 0:ea44dc9ed014 646 {
joeverbout 0:ea44dc9ed014 647 public:
joeverbout 0:ea44dc9ed014 648 CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst,
joeverbout 0:ea44dc9ed014 649 InputArray times, InputArray response) = 0;
joeverbout 0:ea44dc9ed014 650 CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, InputArray times) = 0;
joeverbout 0:ea44dc9ed014 651 };
joeverbout 0:ea44dc9ed014 652
joeverbout 0:ea44dc9ed014 653 /** @brief Creates MergeDebevec object
joeverbout 0:ea44dc9ed014 654 */
joeverbout 0:ea44dc9ed014 655 CV_EXPORTS_W Ptr<MergeDebevec> createMergeDebevec();
joeverbout 0:ea44dc9ed014 656
joeverbout 0:ea44dc9ed014 657 /** @brief Pixels are weighted using contrast, saturation and well-exposedness measures, than images are
joeverbout 0:ea44dc9ed014 658 combined using laplacian pyramids.
joeverbout 0:ea44dc9ed014 659
joeverbout 0:ea44dc9ed014 660 The resulting image weight is constructed as weighted average of contrast, saturation and
joeverbout 0:ea44dc9ed014 661 well-exposedness measures.
joeverbout 0:ea44dc9ed014 662
joeverbout 0:ea44dc9ed014 663 The resulting image doesn't require tonemapping and can be converted to 8-bit image by multiplying
joeverbout 0:ea44dc9ed014 664 by 255, but it's recommended to apply gamma correction and/or linear tonemapping.
joeverbout 0:ea44dc9ed014 665
joeverbout 0:ea44dc9ed014 666 For more information see @cite MK07 .
joeverbout 0:ea44dc9ed014 667 */
joeverbout 0:ea44dc9ed014 668 class CV_EXPORTS_W MergeMertens : public MergeExposures
joeverbout 0:ea44dc9ed014 669 {
joeverbout 0:ea44dc9ed014 670 public:
joeverbout 0:ea44dc9ed014 671 CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst,
joeverbout 0:ea44dc9ed014 672 InputArray times, InputArray response) = 0;
joeverbout 0:ea44dc9ed014 673 /** @brief Short version of process, that doesn't take extra arguments.
joeverbout 0:ea44dc9ed014 674
joeverbout 0:ea44dc9ed014 675 @param src vector of input images
joeverbout 0:ea44dc9ed014 676 @param dst result image
joeverbout 0:ea44dc9ed014 677 */
joeverbout 0:ea44dc9ed014 678 CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst) = 0;
joeverbout 0:ea44dc9ed014 679
joeverbout 0:ea44dc9ed014 680 CV_WRAP virtual float getContrastWeight() const = 0;
joeverbout 0:ea44dc9ed014 681 CV_WRAP virtual void setContrastWeight(float contrast_weiht) = 0;
joeverbout 0:ea44dc9ed014 682
joeverbout 0:ea44dc9ed014 683 CV_WRAP virtual float getSaturationWeight() const = 0;
joeverbout 0:ea44dc9ed014 684 CV_WRAP virtual void setSaturationWeight(float saturation_weight) = 0;
joeverbout 0:ea44dc9ed014 685
joeverbout 0:ea44dc9ed014 686 CV_WRAP virtual float getExposureWeight() const = 0;
joeverbout 0:ea44dc9ed014 687 CV_WRAP virtual void setExposureWeight(float exposure_weight) = 0;
joeverbout 0:ea44dc9ed014 688 };
joeverbout 0:ea44dc9ed014 689
joeverbout 0:ea44dc9ed014 690 /** @brief Creates MergeMertens object
joeverbout 0:ea44dc9ed014 691
joeverbout 0:ea44dc9ed014 692 @param contrast_weight contrast measure weight. See MergeMertens.
joeverbout 0:ea44dc9ed014 693 @param saturation_weight saturation measure weight
joeverbout 0:ea44dc9ed014 694 @param exposure_weight well-exposedness measure weight
joeverbout 0:ea44dc9ed014 695 */
joeverbout 0:ea44dc9ed014 696 CV_EXPORTS_W Ptr<MergeMertens>
joeverbout 0:ea44dc9ed014 697 createMergeMertens(float contrast_weight = 1.0f, float saturation_weight = 1.0f, float exposure_weight = 0.0f);
joeverbout 0:ea44dc9ed014 698
joeverbout 0:ea44dc9ed014 699 /** @brief The resulting HDR image is calculated as weighted average of the exposures considering exposure
joeverbout 0:ea44dc9ed014 700 values and camera response.
joeverbout 0:ea44dc9ed014 701
joeverbout 0:ea44dc9ed014 702 For more information see @cite RB99 .
joeverbout 0:ea44dc9ed014 703 */
joeverbout 0:ea44dc9ed014 704 class CV_EXPORTS_W MergeRobertson : public MergeExposures
joeverbout 0:ea44dc9ed014 705 {
joeverbout 0:ea44dc9ed014 706 public:
joeverbout 0:ea44dc9ed014 707 CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst,
joeverbout 0:ea44dc9ed014 708 InputArray times, InputArray response) = 0;
joeverbout 0:ea44dc9ed014 709 CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, InputArray times) = 0;
joeverbout 0:ea44dc9ed014 710 };
joeverbout 0:ea44dc9ed014 711
joeverbout 0:ea44dc9ed014 712 /** @brief Creates MergeRobertson object
joeverbout 0:ea44dc9ed014 713 */
joeverbout 0:ea44dc9ed014 714 CV_EXPORTS_W Ptr<MergeRobertson> createMergeRobertson();
joeverbout 0:ea44dc9ed014 715
joeverbout 0:ea44dc9ed014 716 //! @} photo_hdr
joeverbout 0:ea44dc9ed014 717
joeverbout 0:ea44dc9ed014 718 /** @brief Transforms a color image to a grayscale image. It is a basic tool in digital printing, stylized
joeverbout 0:ea44dc9ed014 719 black-and-white photograph rendering, and in many single channel image processing applications
joeverbout 0:ea44dc9ed014 720 @cite CL12 .
joeverbout 0:ea44dc9ed014 721
joeverbout 0:ea44dc9ed014 722 @param src Input 8-bit 3-channel image.
joeverbout 0:ea44dc9ed014 723 @param grayscale Output 8-bit 1-channel image.
joeverbout 0:ea44dc9ed014 724 @param color_boost Output 8-bit 3-channel image.
joeverbout 0:ea44dc9ed014 725
joeverbout 0:ea44dc9ed014 726 This function is to be applied on color images.
joeverbout 0:ea44dc9ed014 727 */
joeverbout 0:ea44dc9ed014 728 CV_EXPORTS_W void decolor( InputArray src, OutputArray grayscale, OutputArray color_boost);
joeverbout 0:ea44dc9ed014 729
joeverbout 0:ea44dc9ed014 730 //! @addtogroup photo_clone
joeverbout 0:ea44dc9ed014 731 //! @{
joeverbout 0:ea44dc9ed014 732
joeverbout 0:ea44dc9ed014 733 /** @brief Image editing tasks concern either global changes (color/intensity corrections, filters,
joeverbout 0:ea44dc9ed014 734 deformations) or local changes concerned to a selection. Here we are interested in achieving local
joeverbout 0:ea44dc9ed014 735 changes, ones that are restricted to a region manually selected (ROI), in a seamless and effortless
joeverbout 0:ea44dc9ed014 736 manner. The extent of the changes ranges from slight distortions to complete replacement by novel
joeverbout 0:ea44dc9ed014 737 content @cite PM03 .
joeverbout 0:ea44dc9ed014 738
joeverbout 0:ea44dc9ed014 739 @param src Input 8-bit 3-channel image.
joeverbout 0:ea44dc9ed014 740 @param dst Input 8-bit 3-channel image.
joeverbout 0:ea44dc9ed014 741 @param mask Input 8-bit 1 or 3-channel image.
joeverbout 0:ea44dc9ed014 742 @param p Point in dst image where object is placed.
joeverbout 0:ea44dc9ed014 743 @param blend Output image with the same size and type as dst.
joeverbout 0:ea44dc9ed014 744 @param flags Cloning method that could be one of the following:
joeverbout 0:ea44dc9ed014 745 - **NORMAL_CLONE** The power of the method is fully expressed when inserting objects with
joeverbout 0:ea44dc9ed014 746 complex outlines into a new background
joeverbout 0:ea44dc9ed014 747 - **MIXED_CLONE** The classic method, color-based selection and alpha masking might be time
joeverbout 0:ea44dc9ed014 748 consuming and often leaves an undesirable halo. Seamless cloning, even averaged with the
joeverbout 0:ea44dc9ed014 749 original image, is not effective. Mixed seamless cloning based on a loose selection proves
joeverbout 0:ea44dc9ed014 750 effective.
joeverbout 0:ea44dc9ed014 751 - **FEATURE_EXCHANGE** Feature exchange allows the user to easily replace certain features of
joeverbout 0:ea44dc9ed014 752 one object by alternative features.
joeverbout 0:ea44dc9ed014 753 */
joeverbout 0:ea44dc9ed014 754 CV_EXPORTS_W void seamlessClone( InputArray src, InputArray dst, InputArray mask, Point p,
joeverbout 0:ea44dc9ed014 755 OutputArray blend, int flags);
joeverbout 0:ea44dc9ed014 756
joeverbout 0:ea44dc9ed014 757 /** @brief Given an original color image, two differently colored versions of this image can be mixed
joeverbout 0:ea44dc9ed014 758 seamlessly.
joeverbout 0:ea44dc9ed014 759
joeverbout 0:ea44dc9ed014 760 @param src Input 8-bit 3-channel image.
joeverbout 0:ea44dc9ed014 761 @param mask Input 8-bit 1 or 3-channel image.
joeverbout 0:ea44dc9ed014 762 @param dst Output image with the same size and type as src .
joeverbout 0:ea44dc9ed014 763 @param red_mul R-channel multiply factor.
joeverbout 0:ea44dc9ed014 764 @param green_mul G-channel multiply factor.
joeverbout 0:ea44dc9ed014 765 @param blue_mul B-channel multiply factor.
joeverbout 0:ea44dc9ed014 766
joeverbout 0:ea44dc9ed014 767 Multiplication factor is between .5 to 2.5.
joeverbout 0:ea44dc9ed014 768 */
joeverbout 0:ea44dc9ed014 769 CV_EXPORTS_W void colorChange(InputArray src, InputArray mask, OutputArray dst, float red_mul = 1.0f,
joeverbout 0:ea44dc9ed014 770 float green_mul = 1.0f, float blue_mul = 1.0f);
joeverbout 0:ea44dc9ed014 771
joeverbout 0:ea44dc9ed014 772 /** @brief Applying an appropriate non-linear transformation to the gradient field inside the selection and
joeverbout 0:ea44dc9ed014 773 then integrating back with a Poisson solver, modifies locally the apparent illumination of an image.
joeverbout 0:ea44dc9ed014 774
joeverbout 0:ea44dc9ed014 775 @param src Input 8-bit 3-channel image.
joeverbout 0:ea44dc9ed014 776 @param mask Input 8-bit 1 or 3-channel image.
joeverbout 0:ea44dc9ed014 777 @param dst Output image with the same size and type as src.
joeverbout 0:ea44dc9ed014 778 @param alpha Value ranges between 0-2.
joeverbout 0:ea44dc9ed014 779 @param beta Value ranges between 0-2.
joeverbout 0:ea44dc9ed014 780
joeverbout 0:ea44dc9ed014 781 This is useful to highlight under-exposed foreground objects or to reduce specular reflections.
joeverbout 0:ea44dc9ed014 782 */
joeverbout 0:ea44dc9ed014 783 CV_EXPORTS_W void illuminationChange(InputArray src, InputArray mask, OutputArray dst,
joeverbout 0:ea44dc9ed014 784 float alpha = 0.2f, float beta = 0.4f);
joeverbout 0:ea44dc9ed014 785
joeverbout 0:ea44dc9ed014 786 /** @brief By retaining only the gradients at edge locations, before integrating with the Poisson solver, one
joeverbout 0:ea44dc9ed014 787 washes out the texture of the selected region, giving its contents a flat aspect. Here Canny Edge
joeverbout 0:ea44dc9ed014 788 Detector is used.
joeverbout 0:ea44dc9ed014 789
joeverbout 0:ea44dc9ed014 790 @param src Input 8-bit 3-channel image.
joeverbout 0:ea44dc9ed014 791 @param mask Input 8-bit 1 or 3-channel image.
joeverbout 0:ea44dc9ed014 792 @param dst Output image with the same size and type as src.
joeverbout 0:ea44dc9ed014 793 @param low_threshold Range from 0 to 100.
joeverbout 0:ea44dc9ed014 794 @param high_threshold Value \> 100.
joeverbout 0:ea44dc9ed014 795 @param kernel_size The size of the Sobel kernel to be used.
joeverbout 0:ea44dc9ed014 796
joeverbout 0:ea44dc9ed014 797 **NOTE:**
joeverbout 0:ea44dc9ed014 798
joeverbout 0:ea44dc9ed014 799 The algorithm assumes that the color of the source image is close to that of the destination. This
joeverbout 0:ea44dc9ed014 800 assumption means that when the colors don't match, the source image color gets tinted toward the
joeverbout 0:ea44dc9ed014 801 color of the destination image.
joeverbout 0:ea44dc9ed014 802 */
joeverbout 0:ea44dc9ed014 803 CV_EXPORTS_W void textureFlattening(InputArray src, InputArray mask, OutputArray dst,
joeverbout 0:ea44dc9ed014 804 float low_threshold = 30, float high_threshold = 45,
joeverbout 0:ea44dc9ed014 805 int kernel_size = 3);
joeverbout 0:ea44dc9ed014 806
joeverbout 0:ea44dc9ed014 807 //! @} photo_clone
joeverbout 0:ea44dc9ed014 808
joeverbout 0:ea44dc9ed014 809 //! @addtogroup photo_render
joeverbout 0:ea44dc9ed014 810 //! @{
joeverbout 0:ea44dc9ed014 811
joeverbout 0:ea44dc9ed014 812 /** @brief Filtering is the fundamental operation in image and video processing. Edge-preserving smoothing
joeverbout 0:ea44dc9ed014 813 filters are used in many different applications @cite EM11 .
joeverbout 0:ea44dc9ed014 814
joeverbout 0:ea44dc9ed014 815 @param src Input 8-bit 3-channel image.
joeverbout 0:ea44dc9ed014 816 @param dst Output 8-bit 3-channel image.
joeverbout 0:ea44dc9ed014 817 @param flags Edge preserving filters:
joeverbout 0:ea44dc9ed014 818 - **RECURS_FILTER** = 1
joeverbout 0:ea44dc9ed014 819 - **NORMCONV_FILTER** = 2
joeverbout 0:ea44dc9ed014 820 @param sigma_s Range between 0 to 200.
joeverbout 0:ea44dc9ed014 821 @param sigma_r Range between 0 to 1.
joeverbout 0:ea44dc9ed014 822 */
joeverbout 0:ea44dc9ed014 823 CV_EXPORTS_W void edgePreservingFilter(InputArray src, OutputArray dst, int flags = 1,
joeverbout 0:ea44dc9ed014 824 float sigma_s = 60, float sigma_r = 0.4f);
joeverbout 0:ea44dc9ed014 825
joeverbout 0:ea44dc9ed014 826 /** @brief This filter enhances the details of a particular image.
joeverbout 0:ea44dc9ed014 827
joeverbout 0:ea44dc9ed014 828 @param src Input 8-bit 3-channel image.
joeverbout 0:ea44dc9ed014 829 @param dst Output image with the same size and type as src.
joeverbout 0:ea44dc9ed014 830 @param sigma_s Range between 0 to 200.
joeverbout 0:ea44dc9ed014 831 @param sigma_r Range between 0 to 1.
joeverbout 0:ea44dc9ed014 832 */
joeverbout 0:ea44dc9ed014 833 CV_EXPORTS_W void detailEnhance(InputArray src, OutputArray dst, float sigma_s = 10,
joeverbout 0:ea44dc9ed014 834 float sigma_r = 0.15f);
joeverbout 0:ea44dc9ed014 835
joeverbout 0:ea44dc9ed014 836 /** @brief Pencil-like non-photorealistic line drawing
joeverbout 0:ea44dc9ed014 837
joeverbout 0:ea44dc9ed014 838 @param src Input 8-bit 3-channel image.
joeverbout 0:ea44dc9ed014 839 @param dst1 Output 8-bit 1-channel image.
joeverbout 0:ea44dc9ed014 840 @param dst2 Output image with the same size and type as src.
joeverbout 0:ea44dc9ed014 841 @param sigma_s Range between 0 to 200.
joeverbout 0:ea44dc9ed014 842 @param sigma_r Range between 0 to 1.
joeverbout 0:ea44dc9ed014 843 @param shade_factor Range between 0 to 0.1.
joeverbout 0:ea44dc9ed014 844 */
joeverbout 0:ea44dc9ed014 845 CV_EXPORTS_W void pencilSketch(InputArray src, OutputArray dst1, OutputArray dst2,
joeverbout 0:ea44dc9ed014 846 float sigma_s = 60, float sigma_r = 0.07f, float shade_factor = 0.02f);
joeverbout 0:ea44dc9ed014 847
joeverbout 0:ea44dc9ed014 848 /** @brief Stylization aims to produce digital imagery with a wide variety of effects not focused on
joeverbout 0:ea44dc9ed014 849 photorealism. Edge-aware filters are ideal for stylization, as they can abstract regions of low
joeverbout 0:ea44dc9ed014 850 contrast while preserving, or enhancing, high-contrast features.
joeverbout 0:ea44dc9ed014 851
joeverbout 0:ea44dc9ed014 852 @param src Input 8-bit 3-channel image.
joeverbout 0:ea44dc9ed014 853 @param dst Output image with the same size and type as src.
joeverbout 0:ea44dc9ed014 854 @param sigma_s Range between 0 to 200.
joeverbout 0:ea44dc9ed014 855 @param sigma_r Range between 0 to 1.
joeverbout 0:ea44dc9ed014 856 */
joeverbout 0:ea44dc9ed014 857 CV_EXPORTS_W void stylization(InputArray src, OutputArray dst, float sigma_s = 60,
joeverbout 0:ea44dc9ed014 858 float sigma_r = 0.45f);
joeverbout 0:ea44dc9ed014 859
joeverbout 0:ea44dc9ed014 860 //! @} photo_render
joeverbout 0:ea44dc9ed014 861
joeverbout 0:ea44dc9ed014 862 //! @} photo
joeverbout 0:ea44dc9ed014 863
joeverbout 0:ea44dc9ed014 864 } // cv
joeverbout 0:ea44dc9ed014 865
joeverbout 0:ea44dc9ed014 866 #ifndef DISABLE_OPENCV_24_COMPATIBILITY
joeverbout 0:ea44dc9ed014 867 #include "opencv2/photo/photo_c.h"
joeverbout 0:ea44dc9ed014 868 #endif
joeverbout 0:ea44dc9ed014 869
joeverbout 0:ea44dc9ed014 870 #endif
joeverbout 0:ea44dc9ed014 871