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//! # Computational Photography //! //! This module includes photo processing algorithms //! # Inpainting //! # Denoising //! # HDR imaging //! //! This section describes high dynamic range imaging algorithms namely tonemapping, exposure alignment, //! camera calibration with multiple exposures and exposure fusion. //! //! # Contrast Preserving Decolorization //! //! Useful links: //! //! http://www.cse.cuhk.edu.hk/leojia/projects/color2gray/index.html //! //! # Seamless Cloning //! //! Useful links: //! //! https://www.learnopencv.com/seamless-cloning-using-opencv-python-cpp //! //! # Non-Photorealistic Rendering //! //! Useful links: //! //! http://www.inf.ufrgs.br/~eslgastal/DomainTransform //! //! https://www.learnopencv.com/non-photorealistic-rendering-using-opencv-python-c/ use std::os::raw::{c_char, c_void}; use libc::{ptrdiff_t, size_t}; use crate::{Error, Result, core, sys, types}; /// Use Navier-Stokes based method pub const INPAINT_NS: i32 = 0; /// Use the algorithm proposed by Alexandru Telea [Telea04](https://docs.opencv.org/3.4.6/d0/de3/citelist.html#CITEREF_Telea04) pub const INPAINT_TELEA: i32 = 1; pub const LDR_SIZE: i32 = 256; pub const MIXED_CLONE: i32 = 2; pub const MONOCHROME_TRANSFER: i32 = 3; pub const NORMAL_CLONE: i32 = 1; /// Normalized Convolution Filtering pub const NORMCONV_FILTER: i32 = 2; /// Recursive Filtering pub const RECURS_FILTER: i32 = 1; /// Given an original color image, two differently colored versions of this image can be mixed /// seamlessly. /// /// ## Parameters /// * src: Input 8-bit 3-channel image. /// * mask: Input 8-bit 1 or 3-channel image. /// * dst: Output image with the same size and type as src . /// * red_mul: R-channel multiply factor. /// * green_mul: G-channel multiply factor. /// * blue_mul: B-channel multiply factor. /// /// Multiplication factor is between .5 to 2.5. /// /// ## C++ default parameters /// * red_mul: 1.0f /// * green_mul: 1.0f /// * blue_mul: 1.0f pub fn color_change(src: &core::Mat, mask: &core::Mat, dst: &mut core::Mat, red_mul: f32, green_mul: f32, blue_mul: f32) -> Result<()> { unsafe { sys::cv_colorChange_Mat_Mat_Mat_float_float_float(src.as_raw_Mat(), mask.as_raw_Mat(), dst.as_raw_Mat(), red_mul, green_mul, blue_mul) }.into_result() } /// Creates AlignMTB object /// /// ## Parameters /// * 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). /// * exclude_range: range for exclusion bitmap that is constructed to suppress noise around the /// median value. /// * cut: if true cuts images, otherwise fills the new regions with zeros. /// /// ## C++ default parameters /// * max_bits: 6 /// * exclude_range: 4 /// * cut: true pub fn create_align_mtb(max_bits: i32, exclude_range: i32, cut: bool) -> Result<types::PtrOfAlignMTB> { unsafe { sys::cv_createAlignMTB_int_int_bool(max_bits, exclude_range, cut) }.into_result().map(|ptr| types::PtrOfAlignMTB { ptr }) } /// Creates CalibrateDebevec object /// /// ## Parameters /// * samples: number of pixel locations to use /// * lambda: smoothness term weight. Greater values produce smoother results, but can alter the /// response. /// * random: if true sample pixel locations are chosen at random, otherwise they form a /// rectangular grid. /// /// ## C++ default parameters /// * samples: 70 /// * lambda: 10.0f /// * random: false pub fn create_calibrate_debevec(samples: i32, lambda: f32, random: bool) -> Result<types::PtrOfCalibrateDebevec> { unsafe { sys::cv_createCalibrateDebevec_int_float_bool(samples, lambda, random) }.into_result().map(|ptr| types::PtrOfCalibrateDebevec { ptr }) } /// Creates CalibrateRobertson object /// /// ## Parameters /// * max_iter: maximal number of Gauss-Seidel solver iterations. /// * threshold: target difference between results of two successive steps of the minimization. /// /// ## C++ default parameters /// * max_iter: 30 /// * threshold: 0.01f pub fn create_calibrate_robertson(max_iter: i32, threshold: f32) -> Result<types::PtrOfCalibrateRobertson> { unsafe { sys::cv_createCalibrateRobertson_int_float(max_iter, threshold) }.into_result().map(|ptr| types::PtrOfCalibrateRobertson { ptr }) } /// Creates MergeDebevec object pub fn create_merge_debevec() -> Result<types::PtrOfMergeDebevec> { unsafe { sys::cv_createMergeDebevec() }.into_result().map(|ptr| types::PtrOfMergeDebevec { ptr }) } /// Creates MergeMertens object /// /// ## Parameters /// * contrast_weight: contrast measure weight. See MergeMertens. /// * saturation_weight: saturation measure weight /// * exposure_weight: well-exposedness measure weight /// /// ## C++ default parameters /// * contrast_weight: 1.0f /// * saturation_weight: 1.0f /// * exposure_weight: 0.0f pub fn create_merge_mertens(contrast_weight: f32, saturation_weight: f32, exposure_weight: f32) -> Result<types::PtrOfMergeMertens> { unsafe { sys::cv_createMergeMertens_float_float_float(contrast_weight, saturation_weight, exposure_weight) }.into_result().map(|ptr| types::PtrOfMergeMertens { ptr }) } /// Creates MergeRobertson object pub fn create_merge_robertson() -> Result<types::PtrOfMergeRobertson> { unsafe { sys::cv_createMergeRobertson() }.into_result().map(|ptr| types::PtrOfMergeRobertson { ptr }) } /// Creates TonemapDrago object /// /// ## Parameters /// * gamma: gamma value for gamma correction. See createTonemap /// * saturation: positive saturation enhancement value. 1.0 preserves saturation, values greater /// than 1 increase saturation and values less than 1 decrease it. /// * 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. /// /// ## C++ default parameters /// * gamma: 1.0f /// * saturation: 1.0f /// * bias: 0.85f pub fn create_tonemap_drago(gamma: f32, saturation: f32, bias: f32) -> Result<types::PtrOfTonemapDrago> { unsafe { sys::cv_createTonemapDrago_float_float_float(gamma, saturation, bias) }.into_result().map(|ptr| types::PtrOfTonemapDrago { ptr }) } /// Creates TonemapMantiuk object /// /// ## Parameters /// * gamma: gamma value for gamma correction. See createTonemap /// * 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. /// * saturation: saturation enhancement value. See createTonemapDrago /// /// ## C++ default parameters /// * gamma: 1.0f /// * scale: 0.7f /// * saturation: 1.0f pub fn create_tonemap_mantiuk(gamma: f32, scale: f32, saturation: f32) -> Result<types::PtrOfTonemapMantiuk> { unsafe { sys::cv_createTonemapMantiuk_float_float_float(gamma, scale, saturation) }.into_result().map(|ptr| types::PtrOfTonemapMantiuk { ptr }) } /// Creates TonemapReinhard object /// /// ## Parameters /// * gamma: gamma value for gamma correction. See createTonemap /// * intensity: result intensity in [-8, 8] range. Greater intensity produces brighter results. /// * 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. /// * color_adapt: chromatic adaptation in [0, 1] range. If 1 channels are treated independently, /// if 0 adaptation level is the same for each channel. /// /// ## C++ default parameters /// * gamma: 1.0f /// * intensity: 0.0f /// * light_adapt: 1.0f /// * color_adapt: 0.0f pub fn create_tonemap_reinhard(gamma: f32, intensity: f32, light_adapt: f32, color_adapt: f32) -> Result<types::PtrOfTonemapReinhard> { unsafe { sys::cv_createTonemapReinhard_float_float_float_float(gamma, intensity, light_adapt, color_adapt) }.into_result().map(|ptr| types::PtrOfTonemapReinhard { ptr }) } /// Creates simple linear mapper with gamma correction /// /// ## Parameters /// * gamma: positive value for gamma correction. Gamma value of 1.0 implies no correction, gamma /// equal to 2.2f is suitable for most displays. /// Generally gamma \> 1 brightens the image and gamma \< 1 darkens it. /// /// ## C++ default parameters /// * gamma: 1.0f pub fn create_tonemap(gamma: f32) -> Result<types::PtrOfTonemap> { unsafe { sys::cv_createTonemap_float(gamma) }.into_result().map(|ptr| types::PtrOfTonemap { ptr }) } /// 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 /// [CL12](https://docs.opencv.org/3.4.6/d0/de3/citelist.html#CITEREF_CL12) . /// /// ## Parameters /// * src: Input 8-bit 3-channel image. /// * grayscale: Output 8-bit 1-channel image. /// * color_boost: Output 8-bit 3-channel image. /// /// This function is to be applied on color images. pub fn decolor(src: &core::Mat, grayscale: &mut core::Mat, color_boost: &mut core::Mat) -> Result<()> { unsafe { sys::cv_decolor_Mat_Mat_Mat(src.as_raw_Mat(), grayscale.as_raw_Mat(), color_boost.as_raw_Mat()) }.into_result() } /// 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 /// [MA13](https://docs.opencv.org/3.4.6/d0/de3/citelist.html#CITEREF_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 /// [ChambolleEtAl](https://docs.opencv.org/3.4.6/d0/de3/citelist.html#CITEREF_ChambolleEtAl), it might make sense to skim over it here, following [MA13](https://docs.opencv.org/3.4.6/d0/de3/citelist.html#CITEREF_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 /// <span lang='latex'>\left\{(x,y)\in\mathbb{N}\times\mathbb{N}\mid 1\leq x\leq n,\;1\leq y\leq m\right\}</span> for some /// <span lang='latex'>m,\;n\in\mathbb{N}</span>) into <span lang='latex'>\{0,1,\dots,255\}</span>. We shall denote the noised images as <span lang='latex'>f_i</span> and with /// this view, given some image <span lang='latex'>x</span> of the same size, we may measure how bad it is by the formula /// /// <div lang='latex'>\left\|\left\|\nabla x\right\|\right\| + \lambda\sum_i\left\|\left\|x-f_i\right\|\right\|</div> /// /// <span lang='latex'>\|\|\cdot\|\|</span> here denotes <span lang='latex'>L_2</span>-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 <span lang='latex'>x</span> as a function, this is /// exactly the functional what we seek to minimize and here the Primal-Dual algorithm comes into play. /// /// ## Parameters /// * observations: This array should contain one or more noised versions of the image that is to /// be restored. /// * 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. /// * lambda: Corresponds to <span lang='latex'>\lambda</span> 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. /// * 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. /// /// ## C++ default parameters /// * lambda: 1.0 /// * niters: 30 pub fn denoise_tvl1(observations: &types::VectorOfMat, result: &mut core::Mat, lambda: f64, niters: i32) -> Result<()> { unsafe { sys::cv_denoise_TVL1_VectorOfMat_Mat_double_int(observations.as_raw_VectorOfMat(), result.as_raw_Mat(), lambda, niters) }.into_result() } /// This filter enhances the details of a particular image. /// /// ## Parameters /// * src: Input 8-bit 3-channel image. /// * dst: Output image with the same size and type as src. /// * sigma_s: %Range between 0 to 200. /// * sigma_r: %Range between 0 to 1. /// /// ## C++ default parameters /// * sigma_s: 10 /// * sigma_r: 0.15f pub fn detail_enhance(src: &core::Mat, dst: &mut core::Mat, sigma_s: f32, sigma_r: f32) -> Result<()> { unsafe { sys::cv_detailEnhance_Mat_Mat_float_float(src.as_raw_Mat(), dst.as_raw_Mat(), sigma_s, sigma_r) }.into_result() } /// Filtering is the fundamental operation in image and video processing. Edge-preserving smoothing /// filters are used in many different applications [EM11](https://docs.opencv.org/3.4.6/d0/de3/citelist.html#CITEREF_EM11) . /// /// ## Parameters /// * src: Input 8-bit 3-channel image. /// * dst: Output 8-bit 3-channel image. /// * flags: Edge preserving filters: cv::RECURS_FILTER or cv::NORMCONV_FILTER /// * sigma_s: %Range between 0 to 200. /// * sigma_r: %Range between 0 to 1. /// /// ## C++ default parameters /// * flags: 1 /// * sigma_s: 60 /// * sigma_r: 0.4f pub fn edge_preserving_filter(src: &core::Mat, dst: &mut core::Mat, flags: i32, sigma_s: f32, sigma_r: f32) -> Result<()> { unsafe { sys::cv_edgePreservingFilter_Mat_Mat_int_float_float(src.as_raw_Mat(), dst.as_raw_Mat(), flags, sigma_s, sigma_r) }.into_result() } /// Modification of fastNlMeansDenoisingMulti function for colored images sequences /// /// ## Parameters /// * srcImgs: Input 8-bit 3-channel images sequence. All images should have the same type and /// size. /// * imgToDenoiseIndex: Target image to denoise index in srcImgs sequence /// * 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. /// * dst: Output image with the same size and type as srcImgs images. /// * templateWindowSize: Size in pixels of the template patch that is used to compute weights. /// Should be odd. Recommended value 7 pixels /// * 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 /// * 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. /// * 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. /// /// ## C++ default parameters /// * h: 3 /// * h_color: 3 /// * template_window_size: 7 /// * search_window_size: 21 pub fn fast_nl_means_denoising_colored_multi(src_imgs: &types::VectorOfMat, dst: &mut core::Mat, img_to_denoise_index: i32, temporal_window_size: i32, h: f32, h_color: f32, template_window_size: i32, search_window_size: i32) -> Result<()> { unsafe { sys::cv_fastNlMeansDenoisingColoredMulti_VectorOfMat_Mat_int_int_float_float_int_int(src_imgs.as_raw_VectorOfMat(), dst.as_raw_Mat(), img_to_denoise_index, temporal_window_size, h, h_color, template_window_size, search_window_size) }.into_result() } /// Modification of fastNlMeansDenoising function for colored images /// /// ## Parameters /// * src: Input 8-bit 3-channel image. /// * dst: Output image with the same size and type as src . /// * templateWindowSize: Size in pixels of the template patch that is used to compute weights. /// Should be odd. Recommended value 7 pixels /// * 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 /// * 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 /// * 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. /// /// ## C++ default parameters /// * h: 3 /// * h_color: 3 /// * template_window_size: 7 /// * search_window_size: 21 pub fn fast_nl_means_denoising_color(src: &core::Mat, dst: &mut core::Mat, h: f32, h_color: f32, template_window_size: i32, search_window_size: i32) -> Result<()> { unsafe { sys::cv_fastNlMeansDenoisingColored_Mat_Mat_float_float_int_int(src.as_raw_Mat(), dst.as_raw_Mat(), h, h_color, template_window_size, search_window_size) }.into_result() } /// 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. For more details see /// <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.131.6394> /// /// ## Parameters /// * 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. /// * imgToDenoiseIndex: Target image to denoise index in srcImgs sequence /// * 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. /// * dst: Output image with the same size and type as srcImgs images. /// * templateWindowSize: Size in pixels of the template patch that is used to compute weights. /// Should be odd. Recommended value 7 pixels /// * 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 /// * 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 /// * normType: Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1 /// /// ## C++ default parameters /// * template_window_size: 7 /// * search_window_size: 21 /// * norm_type: NORM_L2 pub fn fast_nl_means_denoising_multi(src_imgs: &types::VectorOfMat, dst: &mut core::Mat, img_to_denoise_index: i32, temporal_window_size: i32, h: &types::VectorOffloat, template_window_size: i32, search_window_size: i32, norm_type: i32) -> Result<()> { unsafe { sys::cv_fastNlMeansDenoisingMulti_VectorOfMat_Mat_int_int_VectorOffloat_int_int_int(src_imgs.as_raw_VectorOfMat(), dst.as_raw_Mat(), img_to_denoise_index, temporal_window_size, h.as_raw_VectorOffloat(), template_window_size, search_window_size, norm_type) }.into_result() } /// 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. For more details see /// <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.131.6394> /// /// ## Parameters /// * srcImgs: Input 8-bit 1-channel, 2-channel, 3-channel or /// 4-channel images sequence. All images should have the same type and /// size. /// * imgToDenoiseIndex: Target image to denoise index in srcImgs sequence /// * 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. /// * dst: Output image with the same size and type as srcImgs images. /// * templateWindowSize: Size in pixels of the template patch that is used to compute weights. /// Should be odd. Recommended value 7 pixels /// * 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 /// * 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 /// /// ## C++ default parameters /// * h: 3 /// * template_window_size: 7 /// * search_window_size: 21 pub fn fast_nl_means_denoising_multi_1(src_imgs: &types::VectorOfMat, dst: &mut core::Mat, img_to_denoise_index: i32, temporal_window_size: i32, h: f32, template_window_size: i32, search_window_size: i32) -> Result<()> { unsafe { sys::cv_fastNlMeansDenoisingMulti_VectorOfMat_Mat_int_int_float_int_int(src_imgs.as_raw_VectorOfMat(), dst.as_raw_Mat(), img_to_denoise_index, temporal_window_size, h, template_window_size, search_window_size) }.into_result() } /// Perform image denoising using Non-local Means Denoising algorithm /// <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational /// optimizations. Noise expected to be a gaussian white noise /// /// ## Parameters /// * src: Input 8-bit or 16-bit (only with NORM_L1) 1-channel, /// 2-channel, 3-channel or 4-channel image. /// * dst: Output image with the same size and type as src . /// * templateWindowSize: Size in pixels of the template patch that is used to compute weights. /// Should be odd. Recommended value 7 pixels /// * 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 /// * 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 /// * 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. /// /// ## C++ default parameters /// * template_window_size: 7 /// * search_window_size: 21 /// * norm_type: NORM_L2 pub fn fast_nl_means_denoising_vec(src: &core::Mat, dst: &mut core::Mat, h: &types::VectorOffloat, template_window_size: i32, search_window_size: i32, norm_type: i32) -> Result<()> { unsafe { sys::cv_fastNlMeansDenoising_Mat_Mat_VectorOffloat_int_int_int(src.as_raw_Mat(), dst.as_raw_Mat(), h.as_raw_VectorOffloat(), template_window_size, search_window_size, norm_type) }.into_result() } /// Perform image denoising using Non-local Means Denoising algorithm /// <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational /// optimizations. Noise expected to be a gaussian white noise /// /// ## Parameters /// * src: Input 8-bit 1-channel, 2-channel, 3-channel or 4-channel image. /// * dst: Output image with the same size and type as src . /// * templateWindowSize: Size in pixels of the template patch that is used to compute weights. /// Should be odd. Recommended value 7 pixels /// * 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 /// * 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. /// /// ## C++ default parameters /// * h: 3 /// * template_window_size: 7 /// * search_window_size: 21 pub fn fast_nl_means_denoising_window(src: &core::Mat, dst: &mut core::Mat, h: f32, template_window_size: i32, search_window_size: i32) -> Result<()> { unsafe { sys::cv_fastNlMeansDenoising_Mat_Mat_float_int_int(src.as_raw_Mat(), dst.as_raw_Mat(), h, template_window_size, search_window_size) }.into_result() } /// 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. /// /// ## Parameters /// * src: Input 8-bit 3-channel image. /// * mask: Input 8-bit 1 or 3-channel image. /// * dst: Output image with the same size and type as src. /// * alpha: Value ranges between 0-2. /// * beta: Value ranges between 0-2. /// /// This is useful to highlight under-exposed foreground objects or to reduce specular reflections. /// /// ## C++ default parameters /// * alpha: 0.2f /// * beta: 0.4f pub fn illumination_change(src: &core::Mat, mask: &core::Mat, dst: &mut core::Mat, alpha: f32, beta: f32) -> Result<()> { unsafe { sys::cv_illuminationChange_Mat_Mat_Mat_float_float(src.as_raw_Mat(), mask.as_raw_Mat(), dst.as_raw_Mat(), alpha, beta) }.into_result() } /// Restores the selected region in an image using the region neighborhood. /// /// ## Parameters /// * src: Input 8-bit, 16-bit unsigned or 32-bit float 1-channel or 8-bit 3-channel image. /// * inpaintMask: Inpainting mask, 8-bit 1-channel image. Non-zero pixels indicate the area that /// needs to be inpainted. /// * dst: Output image with the same size and type as src . /// * inpaintRadius: Radius of a circular neighborhood of each point inpainted that is considered /// by the algorithm. /// * flags: Inpainting method that could be cv::INPAINT_NS or cv::INPAINT_TELEA /// /// The function reconstructs the selected image area from the pixel near the area boundary. The /// function may be used to remove dust and scratches from a scanned photo, or to remove undesirable /// objects from still images or video. See <http://en.wikipedia.org/wiki/Inpainting> for more details. /// /// /// Note: /// * An example using the inpainting technique can be found at /// opencv_source_code/samples/cpp/inpaint.cpp /// * (Python) An example using the inpainting technique can be found at /// opencv_source_code/samples/python/inpaint.py pub fn inpaint(src: &core::Mat, inpaint_mask: &core::Mat, dst: &mut core::Mat, inpaint_radius: f64, flags: i32) -> Result<()> { unsafe { sys::cv_inpaint_Mat_Mat_Mat_double_int(src.as_raw_Mat(), inpaint_mask.as_raw_Mat(), dst.as_raw_Mat(), inpaint_radius, flags) }.into_result() } /// Pencil-like non-photorealistic line drawing /// /// ## Parameters /// * src: Input 8-bit 3-channel image. /// * dst1: Output 8-bit 1-channel image. /// * dst2: Output image with the same size and type as src. /// * sigma_s: %Range between 0 to 200. /// * sigma_r: %Range between 0 to 1. /// * shade_factor: %Range between 0 to 0.1. /// /// ## C++ default parameters /// * sigma_s: 60 /// * sigma_r: 0.07f /// * shade_factor: 0.02f pub fn pencil_sketch(src: &core::Mat, dst1: &mut core::Mat, dst2: &mut core::Mat, sigma_s: f32, sigma_r: f32, shade_factor: f32) -> Result<()> { unsafe { sys::cv_pencilSketch_Mat_Mat_Mat_float_float_float(src.as_raw_Mat(), dst1.as_raw_Mat(), dst2.as_raw_Mat(), sigma_s, sigma_r, shade_factor) }.into_result() } /// 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 [PM03](https://docs.opencv.org/3.4.6/d0/de3/citelist.html#CITEREF_PM03) . /// /// ## Parameters /// * src: Input 8-bit 3-channel image. /// * dst: Input 8-bit 3-channel image. /// * mask: Input 8-bit 1 or 3-channel image. /// * p: Point in dst image where object is placed. /// * blend: Output image with the same size and type as dst. /// * flags: Cloning method that could be cv::NORMAL_CLONE, cv::MIXED_CLONE or cv::MONOCHROME_TRANSFER pub fn seamless_clone(src: &core::Mat, dst: &core::Mat, mask: &core::Mat, p: core::Point, blend: &mut core::Mat, flags: i32) -> Result<()> { unsafe { sys::cv_seamlessClone_Mat_Mat_Mat_Point_Mat_int(src.as_raw_Mat(), dst.as_raw_Mat(), mask.as_raw_Mat(), p, blend.as_raw_Mat(), flags) }.into_result() } /// 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. /// /// ## Parameters /// * src: Input 8-bit 3-channel image. /// * dst: Output image with the same size and type as src. /// * sigma_s: %Range between 0 to 200. /// * sigma_r: %Range between 0 to 1. /// /// ## C++ default parameters /// * sigma_s: 60 /// * sigma_r: 0.45f pub fn stylization(src: &core::Mat, dst: &mut core::Mat, sigma_s: f32, sigma_r: f32) -> Result<()> { unsafe { sys::cv_stylization_Mat_Mat_float_float(src.as_raw_Mat(), dst.as_raw_Mat(), sigma_s, sigma_r) }.into_result() } /// 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. /// /// ## Parameters /// * src: Input 8-bit 3-channel image. /// * mask: Input 8-bit 1 or 3-channel image. /// * dst: Output image with the same size and type as src. /// * low_threshold: %Range from 0 to 100. /// * high_threshold: Value \> 100. /// * kernel_size: The size of the Sobel kernel to be used. /// /// /// Note: /// The algorithm assumes that the color of the source image is close to that of the destination. This /// assumption means that when the colors don't match, the source image color gets tinted toward the /// color of the destination image. /// /// ## C++ default parameters /// * low_threshold: 30 /// * high_threshold: 45 /// * kernel_size: 3 pub fn texture_flattening(src: &core::Mat, mask: &core::Mat, dst: &mut core::Mat, low_threshold: f32, high_threshold: f32, kernel_size: i32) -> Result<()> { unsafe { sys::cv_textureFlattening_Mat_Mat_Mat_float_float_int(src.as_raw_Mat(), mask.as_raw_Mat(), dst.as_raw_Mat(), low_threshold, high_threshold, kernel_size) }.into_result() } // Generating impl for trait cv::AlignExposures (trait) /// The base class for algorithms that align images of the same scene with different exposures pub trait AlignExposures: core::Algorithm { #[inline(always)] fn as_raw_AlignExposures(&self) -> *mut c_void; /// Aligns images /// /// ## Parameters /// * src: vector of input images /// * dst: vector of aligned images /// * times: vector of exposure time values for each image /// * response: 256x1 matrix with inverse camera response function for each pixel value, it should /// have the same number of channels as images. fn process(&mut self, src: &types::VectorOfMat, dst: &mut types::VectorOfMat, times: &core::Mat, response: &core::Mat) -> Result<()> { unsafe { sys::cv_AlignExposures_process_VectorOfMat_VectorOfMat_Mat_Mat(self.as_raw_AlignExposures(), src.as_raw_VectorOfMat(), dst.as_raw_VectorOfMat(), times.as_raw_Mat(), response.as_raw_Mat()) }.into_result() } } // Generating impl for trait cv::AlignMTB (trait) /// This algorithm converts images to median threshold bitmaps (1 for pixels brighter than median /// luminance and 0 otherwise) and than aligns the resulting bitmaps using bit operations. /// /// It is invariant to exposure, so exposure values and camera response are not necessary. /// /// In this implementation new image regions are filled with zeros. /// /// For more information see [GW03](https://docs.opencv.org/3.4.6/d0/de3/citelist.html#CITEREF_GW03) . pub trait AlignMTB: crate::photo::AlignExposures { #[inline(always)] fn as_raw_AlignMTB(&self) -> *mut c_void; fn process_with_response(&mut self, src: &types::VectorOfMat, dst: &mut types::VectorOfMat, times: &core::Mat, response: &core::Mat) -> Result<()> { unsafe { sys::cv_AlignMTB_process_VectorOfMat_VectorOfMat_Mat_Mat(self.as_raw_AlignMTB(), src.as_raw_VectorOfMat(), dst.as_raw_VectorOfMat(), times.as_raw_Mat(), response.as_raw_Mat()) }.into_result() } /// Short version of process, that doesn't take extra arguments. /// /// ## Parameters /// * src: vector of input images /// * dst: vector of aligned images fn process(&mut self, src: &types::VectorOfMat, dst: &mut types::VectorOfMat) -> Result<()> { unsafe { sys::cv_AlignMTB_process_VectorOfMat_VectorOfMat(self.as_raw_AlignMTB(), src.as_raw_VectorOfMat(), dst.as_raw_VectorOfMat()) }.into_result() } /// Calculates shift between two images, i. e. how to shift the second image to correspond it with the /// first. /// /// ## Parameters /// * img0: first image /// * img1: second image fn calculate_shift(&mut self, img0: &core::Mat, img1: &core::Mat) -> Result<core::Point> { unsafe { sys::cv_AlignMTB_calculateShift_Mat_Mat(self.as_raw_AlignMTB(), img0.as_raw_Mat(), img1.as_raw_Mat()) }.into_result() } /// Helper function, that shift Mat filling new regions with zeros. /// /// ## Parameters /// * src: input image /// * dst: result image /// * shift: shift value fn shift_mat(&mut self, src: &core::Mat, dst: &mut core::Mat, shift: core::Point) -> Result<()> { unsafe { sys::cv_AlignMTB_shiftMat_Mat_Mat_Point(self.as_raw_AlignMTB(), src.as_raw_Mat(), dst.as_raw_Mat(), shift) }.into_result() } /// Computes median threshold and exclude bitmaps of given image. /// /// ## Parameters /// * img: input image /// * tb: median threshold bitmap /// * eb: exclude bitmap fn compute_bitmaps(&mut self, img: &core::Mat, tb: &mut core::Mat, eb: &mut core::Mat) -> Result<()> { unsafe { sys::cv_AlignMTB_computeBitmaps_Mat_Mat_Mat(self.as_raw_AlignMTB(), img.as_raw_Mat(), tb.as_raw_Mat(), eb.as_raw_Mat()) }.into_result() } fn get_max_bits(&self) -> Result<i32> { unsafe { sys::cv_AlignMTB_getMaxBits_const(self.as_raw_AlignMTB()) }.into_result() } fn set_max_bits(&mut self, max_bits: i32) -> Result<()> { unsafe { sys::cv_AlignMTB_setMaxBits_int(self.as_raw_AlignMTB(), max_bits) }.into_result() } fn get_exclude_range(&self) -> Result<i32> { unsafe { sys::cv_AlignMTB_getExcludeRange_const(self.as_raw_AlignMTB()) }.into_result() } fn set_exclude_range(&mut self, exclude_range: i32) -> Result<()> { unsafe { sys::cv_AlignMTB_setExcludeRange_int(self.as_raw_AlignMTB(), exclude_range) }.into_result() } fn get_cut(&self) -> Result<bool> { unsafe { sys::cv_AlignMTB_getCut_const(self.as_raw_AlignMTB()) }.into_result() } fn set_cut(&mut self, value: bool) -> Result<()> { unsafe { sys::cv_AlignMTB_setCut_bool(self.as_raw_AlignMTB(), value) }.into_result() } } // Generating impl for trait cv::CalibrateCRF (trait) /// The base class for camera response calibration algorithms. pub trait CalibrateCRF: core::Algorithm { #[inline(always)] fn as_raw_CalibrateCRF(&self) -> *mut c_void; /// Recovers inverse camera response. /// /// ## Parameters /// * src: vector of input images /// * dst: 256x1 matrix with inverse camera response function /// * times: vector of exposure time values for each image fn process(&mut self, src: &types::VectorOfMat, dst: &mut core::Mat, times: &core::Mat) -> Result<()> { unsafe { sys::cv_CalibrateCRF_process_VectorOfMat_Mat_Mat(self.as_raw_CalibrateCRF(), src.as_raw_VectorOfMat(), dst.as_raw_Mat(), times.as_raw_Mat()) }.into_result() } } // Generating impl for trait cv::CalibrateDebevec (trait) /// Inverse camera response function is extracted for each brightness value by minimizing an objective /// function as linear system. Objective function is constructed using pixel values on the same position /// in all images, extra term is added to make the result smoother. /// /// For more information see [DM97](https://docs.opencv.org/3.4.6/d0/de3/citelist.html#CITEREF_DM97) . pub trait CalibrateDebevec: crate::photo::CalibrateCRF { #[inline(always)] fn as_raw_CalibrateDebevec(&self) -> *mut c_void; fn get_lambda(&self) -> Result<f32> { unsafe { sys::cv_CalibrateDebevec_getLambda_const(self.as_raw_CalibrateDebevec()) }.into_result() } fn set_lambda(&mut self, lambda: f32) -> Result<()> { unsafe { sys::cv_CalibrateDebevec_setLambda_float(self.as_raw_CalibrateDebevec(), lambda) }.into_result() } fn get_samples(&self) -> Result<i32> { unsafe { sys::cv_CalibrateDebevec_getSamples_const(self.as_raw_CalibrateDebevec()) }.into_result() } fn set_samples(&mut self, samples: i32) -> Result<()> { unsafe { sys::cv_CalibrateDebevec_setSamples_int(self.as_raw_CalibrateDebevec(), samples) }.into_result() } fn get_random(&self) -> Result<bool> { unsafe { sys::cv_CalibrateDebevec_getRandom_const(self.as_raw_CalibrateDebevec()) }.into_result() } fn set_random(&mut self, random: bool) -> Result<()> { unsafe { sys::cv_CalibrateDebevec_setRandom_bool(self.as_raw_CalibrateDebevec(), random) }.into_result() } } // Generating impl for trait cv::CalibrateRobertson (trait) /// Inverse camera response function is extracted for each brightness value by minimizing an objective /// function as linear system. This algorithm uses all image pixels. /// /// For more information see [RB99](https://docs.opencv.org/3.4.6/d0/de3/citelist.html#CITEREF_RB99) . pub trait CalibrateRobertson: crate::photo::CalibrateCRF { #[inline(always)] fn as_raw_CalibrateRobertson(&self) -> *mut c_void; fn get_max_iter(&self) -> Result<i32> { unsafe { sys::cv_CalibrateRobertson_getMaxIter_const(self.as_raw_CalibrateRobertson()) }.into_result() } fn set_max_iter(&mut self, max_iter: i32) -> Result<()> { unsafe { sys::cv_CalibrateRobertson_setMaxIter_int(self.as_raw_CalibrateRobertson(), max_iter) }.into_result() } fn get_threshold(&self) -> Result<f32> { unsafe { sys::cv_CalibrateRobertson_getThreshold_const(self.as_raw_CalibrateRobertson()) }.into_result() } fn set_threshold(&mut self, threshold: f32) -> Result<()> { unsafe { sys::cv_CalibrateRobertson_setThreshold_float(self.as_raw_CalibrateRobertson(), threshold) }.into_result() } fn get_radiance(&self) -> Result<core::Mat> { unsafe { sys::cv_CalibrateRobertson_getRadiance_const(self.as_raw_CalibrateRobertson()) }.into_result().map(|ptr| core::Mat { ptr }) } } // Generating impl for trait cv::MergeDebevec (trait) /// The resulting HDR image is calculated as weighted average of the exposures considering exposure /// values and camera response. /// /// For more information see [DM97](https://docs.opencv.org/3.4.6/d0/de3/citelist.html#CITEREF_DM97) . pub trait MergeDebevec: crate::photo::MergeExposures { #[inline(always)] fn as_raw_MergeDebevec(&self) -> *mut c_void; fn process_with_response(&mut self, src: &types::VectorOfMat, dst: &mut core::Mat, times: &core::Mat, response: &core::Mat) -> Result<()> { unsafe { sys::cv_MergeDebevec_process_VectorOfMat_Mat_Mat_Mat(self.as_raw_MergeDebevec(), src.as_raw_VectorOfMat(), dst.as_raw_Mat(), times.as_raw_Mat(), response.as_raw_Mat()) }.into_result() } fn process(&mut self, src: &types::VectorOfMat, dst: &mut core::Mat, times: &core::Mat) -> Result<()> { unsafe { sys::cv_MergeDebevec_process_VectorOfMat_Mat_Mat(self.as_raw_MergeDebevec(), src.as_raw_VectorOfMat(), dst.as_raw_Mat(), times.as_raw_Mat()) }.into_result() } } // Generating impl for trait cv::MergeExposures (trait) /// The base class algorithms that can merge exposure sequence to a single image. pub trait MergeExposures: core::Algorithm { #[inline(always)] fn as_raw_MergeExposures(&self) -> *mut c_void; /// Merges images. /// /// ## Parameters /// * src: vector of input images /// * dst: result image /// * times: vector of exposure time values for each image /// * response: 256x1 matrix with inverse camera response function for each pixel value, it should /// have the same number of channels as images. fn process(&mut self, src: &types::VectorOfMat, dst: &mut core::Mat, times: &core::Mat, response: &core::Mat) -> Result<()> { unsafe { sys::cv_MergeExposures_process_VectorOfMat_Mat_Mat_Mat(self.as_raw_MergeExposures(), src.as_raw_VectorOfMat(), dst.as_raw_Mat(), times.as_raw_Mat(), response.as_raw_Mat()) }.into_result() } } // Generating impl for trait cv::MergeMertens (trait) /// Pixels are weighted using contrast, saturation and well-exposedness measures, than images are /// combined using laplacian pyramids. /// /// The resulting image weight is constructed as weighted average of contrast, saturation and /// well-exposedness measures. /// /// The resulting image doesn't require tonemapping and can be converted to 8-bit image by multiplying /// by 255, but it's recommended to apply gamma correction and/or linear tonemapping. /// /// For more information see [MK07](https://docs.opencv.org/3.4.6/d0/de3/citelist.html#CITEREF_MK07) . pub trait MergeMertens: crate::photo::MergeExposures { #[inline(always)] fn as_raw_MergeMertens(&self) -> *mut c_void; fn process_with_response(&mut self, src: &types::VectorOfMat, dst: &mut core::Mat, times: &core::Mat, response: &core::Mat) -> Result<()> { unsafe { sys::cv_MergeMertens_process_VectorOfMat_Mat_Mat_Mat(self.as_raw_MergeMertens(), src.as_raw_VectorOfMat(), dst.as_raw_Mat(), times.as_raw_Mat(), response.as_raw_Mat()) }.into_result() } /// Short version of process, that doesn't take extra arguments. /// /// ## Parameters /// * src: vector of input images /// * dst: result image fn process(&mut self, src: &types::VectorOfMat, dst: &mut core::Mat) -> Result<()> { unsafe { sys::cv_MergeMertens_process_VectorOfMat_Mat(self.as_raw_MergeMertens(), src.as_raw_VectorOfMat(), dst.as_raw_Mat()) }.into_result() } fn get_contrast_weight(&self) -> Result<f32> { unsafe { sys::cv_MergeMertens_getContrastWeight_const(self.as_raw_MergeMertens()) }.into_result() } fn set_contrast_weight(&mut self, contrast_weiht: f32) -> Result<()> { unsafe { sys::cv_MergeMertens_setContrastWeight_float(self.as_raw_MergeMertens(), contrast_weiht) }.into_result() } fn get_saturation_weight(&self) -> Result<f32> { unsafe { sys::cv_MergeMertens_getSaturationWeight_const(self.as_raw_MergeMertens()) }.into_result() } fn set_saturation_weight(&mut self, saturation_weight: f32) -> Result<()> { unsafe { sys::cv_MergeMertens_setSaturationWeight_float(self.as_raw_MergeMertens(), saturation_weight) }.into_result() } fn get_exposure_weight(&self) -> Result<f32> { unsafe { sys::cv_MergeMertens_getExposureWeight_const(self.as_raw_MergeMertens()) }.into_result() } fn set_exposure_weight(&mut self, exposure_weight: f32) -> Result<()> { unsafe { sys::cv_MergeMertens_setExposureWeight_float(self.as_raw_MergeMertens(), exposure_weight) }.into_result() } } // Generating impl for trait cv::MergeRobertson (trait) /// The resulting HDR image is calculated as weighted average of the exposures considering exposure /// values and camera response. /// /// For more information see [RB99](https://docs.opencv.org/3.4.6/d0/de3/citelist.html#CITEREF_RB99) . pub trait MergeRobertson: crate::photo::MergeExposures { #[inline(always)] fn as_raw_MergeRobertson(&self) -> *mut c_void; fn process_with_response(&mut self, src: &types::VectorOfMat, dst: &mut core::Mat, times: &core::Mat, response: &core::Mat) -> Result<()> { unsafe { sys::cv_MergeRobertson_process_VectorOfMat_Mat_Mat_Mat(self.as_raw_MergeRobertson(), src.as_raw_VectorOfMat(), dst.as_raw_Mat(), times.as_raw_Mat(), response.as_raw_Mat()) }.into_result() } fn process(&mut self, src: &types::VectorOfMat, dst: &mut core::Mat, times: &core::Mat) -> Result<()> { unsafe { sys::cv_MergeRobertson_process_VectorOfMat_Mat_Mat(self.as_raw_MergeRobertson(), src.as_raw_VectorOfMat(), dst.as_raw_Mat(), times.as_raw_Mat()) }.into_result() } } // Generating impl for trait cv::Tonemap (trait) /// Base class for tonemapping algorithms - tools that are used to map HDR image to 8-bit range. pub trait Tonemap: core::Algorithm { #[inline(always)] fn as_raw_Tonemap(&self) -> *mut c_void; /// Tonemaps image /// /// ## Parameters /// * src: source image - CV_32FC3 Mat (float 32 bits 3 channels) /// * dst: destination image - CV_32FC3 Mat with values in [0, 1] range fn process(&mut self, src: &core::Mat, dst: &mut core::Mat) -> Result<()> { unsafe { sys::cv_Tonemap_process_Mat_Mat(self.as_raw_Tonemap(), src.as_raw_Mat(), dst.as_raw_Mat()) }.into_result() } fn get_gamma(&self) -> Result<f32> { unsafe { sys::cv_Tonemap_getGamma_const(self.as_raw_Tonemap()) }.into_result() } fn set_gamma(&mut self, gamma: f32) -> Result<()> { unsafe { sys::cv_Tonemap_setGamma_float(self.as_raw_Tonemap(), gamma) }.into_result() } } // Generating impl for trait cv::TonemapDrago (trait) /// Adaptive logarithmic mapping is a fast global tonemapping algorithm that scales the image in /// logarithmic domain. /// /// Since it's a global operator the same function is applied to all the pixels, it is controlled by the /// bias parameter. /// /// Optional saturation enhancement is possible as described in [FL02](https://docs.opencv.org/3.4.6/d0/de3/citelist.html#CITEREF_FL02) . /// /// For more information see [DM03](https://docs.opencv.org/3.4.6/d0/de3/citelist.html#CITEREF_DM03) . pub trait TonemapDrago: crate::photo::Tonemap { #[inline(always)] fn as_raw_TonemapDrago(&self) -> *mut c_void; fn get_saturation(&self) -> Result<f32> { unsafe { sys::cv_TonemapDrago_getSaturation_const(self.as_raw_TonemapDrago()) }.into_result() } fn set_saturation(&mut self, saturation: f32) -> Result<()> { unsafe { sys::cv_TonemapDrago_setSaturation_float(self.as_raw_TonemapDrago(), saturation) }.into_result() } fn get_bias(&self) -> Result<f32> { unsafe { sys::cv_TonemapDrago_getBias_const(self.as_raw_TonemapDrago()) }.into_result() } fn set_bias(&mut self, bias: f32) -> Result<()> { unsafe { sys::cv_TonemapDrago_setBias_float(self.as_raw_TonemapDrago(), bias) }.into_result() } } // Generating impl for trait cv::TonemapMantiuk (trait) /// This algorithm transforms image to contrast using gradients on all levels of gaussian pyramid, /// transforms contrast values to HVS response and scales the response. After this the image is /// reconstructed from new contrast values. /// /// For more information see [MM06](https://docs.opencv.org/3.4.6/d0/de3/citelist.html#CITEREF_MM06) . pub trait TonemapMantiuk: crate::photo::Tonemap { #[inline(always)] fn as_raw_TonemapMantiuk(&self) -> *mut c_void; fn get_scale(&self) -> Result<f32> { unsafe { sys::cv_TonemapMantiuk_getScale_const(self.as_raw_TonemapMantiuk()) }.into_result() } fn set_scale(&mut self, scale: f32) -> Result<()> { unsafe { sys::cv_TonemapMantiuk_setScale_float(self.as_raw_TonemapMantiuk(), scale) }.into_result() } fn get_saturation(&self) -> Result<f32> { unsafe { sys::cv_TonemapMantiuk_getSaturation_const(self.as_raw_TonemapMantiuk()) }.into_result() } fn set_saturation(&mut self, saturation: f32) -> Result<()> { unsafe { sys::cv_TonemapMantiuk_setSaturation_float(self.as_raw_TonemapMantiuk(), saturation) }.into_result() } } // Generating impl for trait cv::TonemapReinhard (trait) /// This is a global tonemapping operator that models human visual system. /// /// Mapping function is controlled by adaptation parameter, that is computed using light adaptation and /// color adaptation. /// /// For more information see [RD05](https://docs.opencv.org/3.4.6/d0/de3/citelist.html#CITEREF_RD05) . pub trait TonemapReinhard: crate::photo::Tonemap { #[inline(always)] fn as_raw_TonemapReinhard(&self) -> *mut c_void; fn get_intensity(&self) -> Result<f32> { unsafe { sys::cv_TonemapReinhard_getIntensity_const(self.as_raw_TonemapReinhard()) }.into_result() } fn set_intensity(&mut self, intensity: f32) -> Result<()> { unsafe { sys::cv_TonemapReinhard_setIntensity_float(self.as_raw_TonemapReinhard(), intensity) }.into_result() } fn get_light_adaptation(&self) -> Result<f32> { unsafe { sys::cv_TonemapReinhard_getLightAdaptation_const(self.as_raw_TonemapReinhard()) }.into_result() } fn set_light_adaptation(&mut self, light_adapt: f32) -> Result<()> { unsafe { sys::cv_TonemapReinhard_setLightAdaptation_float(self.as_raw_TonemapReinhard(), light_adapt) }.into_result() } fn get_color_adaptation(&self) -> Result<f32> { unsafe { sys::cv_TonemapReinhard_getColorAdaptation_const(self.as_raw_TonemapReinhard()) }.into_result() } fn set_color_adaptation(&mut self, color_adapt: f32) -> Result<()> { unsafe { sys::cv_TonemapReinhard_setColorAdaptation_float(self.as_raw_TonemapReinhard(), color_adapt) }.into_result() } }