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#![allow( unused_parens, clippy::excessive_precision, clippy::missing_safety_doc, clippy::not_unsafe_ptr_arg_deref, clippy::should_implement_trait, clippy::too_many_arguments, clippy::unused_unit, )] //! # Image Processing //! //! This module includes image-processing functions. //! # Image Filtering //! //! Functions and classes described in this section are used to perform various linear or non-linear //! filtering operations on 2D images (represented as Mat's). It means that for each pixel location //!  in the source image (normally, rectangular), its neighborhood is considered and used to //! compute the response. In case of a linear filter, it is a weighted sum of pixel values. In case of //! morphological operations, it is the minimum or maximum values, and so on. The computed response is //! stored in the destination image at the same location . It means that the output image //! will be of the same size as the input image. Normally, the functions support multi-channel arrays, //! in which case every channel is processed independently. Therefore, the output image will also have //! the same number of channels as the input one. //! //! Another common feature of the functions and classes described in this section is that, unlike //! simple arithmetic functions, they need to extrapolate values of some non-existing pixels. For //! example, if you want to smooth an image using a Gaussian  filter, then, when //! processing the left-most pixels in each row, you need pixels to the left of them, that is, outside //! of the image. You can let these pixels be the same as the left-most image pixels ("replicated //! border" extrapolation method), or assume that all the non-existing pixels are zeros ("constant //! border" extrapolation method), and so on. OpenCV enables you to specify the extrapolation method. //! For details, see #BorderTypes //! //! @anchor filter_depths //! ### Depth combinations //! Input depth (src.depth()) | Output depth (ddepth) //! --------------------------|---------------------- //! CV_8U | -1/CV_16S/CV_32F/CV_64F //! CV_16U/CV_16S | -1/CV_32F/CV_64F //! CV_32F | -1/CV_32F/CV_64F //! CV_64F | -1/CV_64F //! //! //! Note: when ddepth=-1, the output image will have the same depth as the source. //! //! # Geometric Image Transformations //! //! The functions in this section perform various geometrical transformations of 2D images. They do not //! change the image content but deform the pixel grid and map this deformed grid to the destination //! image. In fact, to avoid sampling artifacts, the mapping is done in the reverse order, from //! destination to the source. That is, for each pixel  of the destination image, the //! functions compute coordinates of the corresponding "donor" pixel in the source image and copy the //! pixel value: //! //!  //! //! In case when you specify the forward mapping , the OpenCV functions first compute the corresponding inverse mapping //!  and then use the above formula. //! //! The actual implementations of the geometrical transformations, from the most generic remap and to //! the simplest and the fastest resize, need to solve two main problems with the above formula: //! //! - Extrapolation of non-existing pixels. Similarly to the filtering functions described in the //! previous section, for some , either one of , or , or both //! of them may fall outside of the image. In this case, an extrapolation method needs to be used. //! OpenCV provides the same selection of extrapolation methods as in the filtering functions. In //! addition, it provides the method #BORDER_TRANSPARENT. This means that the corresponding pixels in //! the destination image will not be modified at all. //! //! - Interpolation of pixel values. Usually  and  are floating-point //! numbers. This means that  can be either an affine or perspective //! transformation, or radial lens distortion correction, and so on. So, a pixel value at fractional //! coordinates needs to be retrieved. In the simplest case, the coordinates can be just rounded to the //! nearest integer coordinates and the corresponding pixel can be used. This is called a //! nearest-neighbor interpolation. However, a better result can be achieved by using more //! sophisticated [interpolation methods](http://en.wikipedia.org/wiki/Multivariate_interpolation) , //! where a polynomial function is fit into some neighborhood of the computed pixel , and then the value of the polynomial at  is taken as the //! interpolated pixel value. In OpenCV, you can choose between several interpolation methods. See //! resize for details. //! //! //! Note: The geometrical transformations do not work with `CV_8S` or `CV_32S` images. //! //! # Miscellaneous Image Transformations //! # Drawing Functions //! //! Drawing functions work with matrices/images of arbitrary depth. The boundaries of the shapes can be //! rendered with antialiasing (implemented only for 8-bit images for now). All the functions include //! the parameter color that uses an RGB value (that may be constructed with the Scalar constructor ) //! for color images and brightness for grayscale images. For color images, the channel ordering is //! normally *Blue, Green, Red*. This is what imshow, imread, and imwrite expect. So, if you form a //! color using the Scalar constructor, it should look like: //! //!  //! //! If you are using your own image rendering and I/O functions, you can use any channel ordering. The //! drawing functions process each channel independently and do not depend on the channel order or even //! on the used color space. The whole image can be converted from BGR to RGB or to a different color //! space using cvtColor . //! //! If a drawn figure is partially or completely outside the image, the drawing functions clip it. Also, //! many drawing functions can handle pixel coordinates specified with sub-pixel accuracy. This means //! that the coordinates can be passed as fixed-point numbers encoded as integers. The number of //! fractional bits is specified by the shift parameter and the real point coordinates are calculated as //!  . This feature is //! especially effective when rendering antialiased shapes. //! //! //! Note: The functions do not support alpha-transparency when the target image is 4-channel. In this //! case, the color[3] is simply copied to the repainted pixels. Thus, if you want to paint //! semi-transparent shapes, you can paint them in a separate buffer and then blend it with the main //! image. //! //! # Color Space Conversions //! # ColorMaps in OpenCV //! //! The human perception isn't built for observing fine changes in grayscale images. Human eyes are more //! sensitive to observing changes between colors, so you often need to recolor your grayscale images to //! get a clue about them. OpenCV now comes with various colormaps to enhance the visualization in your //! computer vision application. //! //! In OpenCV you only need applyColorMap to apply a colormap on a given image. The following sample //! code reads the path to an image from command line, applies a Jet colormap on it and shows the //! result: //! //! @include snippets/imgproc_applyColorMap.cpp //! ## See also //! #ColormapTypes //! //! # Planar Subdivision //! //! The Subdiv2D class described in this section is used to perform various planar subdivision on //! a set of 2D points (represented as vector of Point2f). OpenCV subdivides a plane into triangles //! using the Delaunay's algorithm, which corresponds to the dual graph of the Voronoi diagram. //! In the figure below, the Delaunay's triangulation is marked with black lines and the Voronoi //! diagram with red lines. //! //!  //! //! The subdivisions can be used for the 3D piece-wise transformation of a plane, morphing, fast //! location of points on the plane, building special graphs (such as NNG,RNG), and so forth. //! //! # Histograms //! # Structural Analysis and Shape Descriptors //! # Motion Analysis and Object Tracking //! # Feature Detection //! # Object Detection //! # C API //! # Hardware Acceleration Layer //! # Functions //! # Interface use crate::{mod_prelude::*, core, sys, types}; pub mod prelude { pub use { super::GeneralizedHough, super::GeneralizedHoughBallard, super::GeneralizedHoughGuil, super::CLAHE, super::Subdiv2DTrait, super::LineSegmentDetector, super::LineIteratorTrait }; } /// the threshold value  is a weighted sum (cross-correlation with a Gaussian /// window) of the  neighborhood of  /// minus C . The default sigma (standard deviation) is used for the specified blockSize . See /// #getGaussianKernel pub const ADAPTIVE_THRESH_GAUSSIAN_C: i32 = 1; /// the threshold value  is a mean of the  neighborhood of  minus C pub const ADAPTIVE_THRESH_MEAN_C: i32 = 0; /// BBDT algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity pub const CCL_DEFAULT: i32 = -1; /// BBDT algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity pub const CCL_GRANA: i32 = 1; /// SAUF [Wu2009](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Wu2009) algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity pub const CCL_WU: i32 = 0; /// The total area (in pixels) of the connected component pub const CC_STAT_AREA: i32 = 4; /// The vertical size of the bounding box pub const CC_STAT_HEIGHT: i32 = 3; /// The leftmost (x) coordinate which is the inclusive start of the bounding /// box in the horizontal direction. pub const CC_STAT_LEFT: i32 = 0; /// Max enumeration value. Used internally only for memory allocation pub const CC_STAT_MAX: i32 = 5; /// The topmost (y) coordinate which is the inclusive start of the bounding /// box in the vertical direction. pub const CC_STAT_TOP: i32 = 1; /// The horizontal size of the bounding box pub const CC_STAT_WIDTH: i32 = 2; /// stores absolutely all the contour points. That is, any 2 subsequent points (x1,y1) and /// (x2,y2) of the contour will be either horizontal, vertical or diagonal neighbors, that is, /// max(abs(x1-x2),abs(y2-y1))==1. pub const CHAIN_APPROX_NONE: i32 = 1; /// compresses horizontal, vertical, and diagonal segments and leaves only their end points. /// For example, an up-right rectangular contour is encoded with 4 points. pub const CHAIN_APPROX_SIMPLE: i32 = 2; /// applies one of the flavors of the Teh-Chin chain approximation algorithm [TehChin89](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_TehChin89) pub const CHAIN_APPROX_TC89_KCOS: i32 = 4; /// applies one of the flavors of the Teh-Chin chain approximation algorithm [TehChin89](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_TehChin89) pub const CHAIN_APPROX_TC89_L1: i32 = 3; ///  pub const COLORMAP_AUTUMN: i32 = 0; ///  pub const COLORMAP_BONE: i32 = 1; ///  pub const COLORMAP_CIVIDIS: i32 = 17; ///  pub const COLORMAP_COOL: i32 = 8; ///  pub const COLORMAP_DEEPGREEN: i32 = 21; ///  pub const COLORMAP_HOT: i32 = 11; ///  pub const COLORMAP_HSV: i32 = 9; ///  pub const COLORMAP_INFERNO: i32 = 14; ///  pub const COLORMAP_JET: i32 = 2; ///  pub const COLORMAP_MAGMA: i32 = 13; ///  pub const COLORMAP_OCEAN: i32 = 5; ///  pub const COLORMAP_PARULA: i32 = 12; ///  pub const COLORMAP_PINK: i32 = 10; ///  pub const COLORMAP_PLASMA: i32 = 15; ///  pub const COLORMAP_RAINBOW: i32 = 4; ///  pub const COLORMAP_SPRING: i32 = 7; ///  pub const COLORMAP_SUMMER: i32 = 6; ///  pub const COLORMAP_TURBO: i32 = 20; ///  pub const COLORMAP_TWILIGHT: i32 = 18; ///  pub const COLORMAP_TWILIGHT_SHIFTED: i32 = 19; ///  pub const COLORMAP_VIRIDIS: i32 = 16; ///  pub const COLORMAP_WINTER: i32 = 3; /// convert between RGB/BGR and BGR555 (16-bit images) pub const COLOR_BGR2BGR555: i32 = 22; /// convert between RGB/BGR and BGR565 (16-bit images) pub const COLOR_BGR2BGR565: i32 = 12; /// add alpha channel to RGB or BGR image pub const COLOR_BGR2BGRA: i32 = 0; /// convert between RGB/BGR and grayscale, @ref color_convert_rgb_gray "color conversions" pub const COLOR_BGR2GRAY: i32 = 6; /// convert RGB/BGR to HLS (hue lightness saturation), @ref color_convert_rgb_hls "color conversions" pub const COLOR_BGR2HLS: i32 = 52; pub const COLOR_BGR2HLS_FULL: i32 = 68; /// convert RGB/BGR to HSV (hue saturation value), @ref color_convert_rgb_hsv "color conversions" pub const COLOR_BGR2HSV: i32 = 40; pub const COLOR_BGR2HSV_FULL: i32 = 66; /// convert RGB/BGR to CIE Lab, @ref color_convert_rgb_lab "color conversions" pub const COLOR_BGR2Lab: i32 = 44; /// convert RGB/BGR to CIE Luv, @ref color_convert_rgb_luv "color conversions" pub const COLOR_BGR2Luv: i32 = 50; pub const COLOR_BGR2RGB: i32 = 4; /// convert between RGB and BGR color spaces (with or without alpha channel) pub const COLOR_BGR2RGBA: i32 = 2; /// convert RGB/BGR to CIE XYZ, @ref color_convert_rgb_xyz "color conversions" pub const COLOR_BGR2XYZ: i32 = 32; /// convert RGB/BGR to luma-chroma (aka YCC), @ref color_convert_rgb_ycrcb "color conversions" pub const COLOR_BGR2YCrCb: i32 = 36; /// convert between RGB/BGR and YUV pub const COLOR_BGR2YUV: i32 = 82; /// RGB to YUV 4:2:0 family pub const COLOR_BGR2YUV_I420: i32 = 128; /// RGB to YUV 4:2:0 family pub const COLOR_BGR2YUV_IYUV: i32 = 128; /// RGB to YUV 4:2:0 family pub const COLOR_BGR2YUV_YV12: i32 = 132; pub const COLOR_BGR5552BGR: i32 = 24; pub const COLOR_BGR5552BGRA: i32 = 28; pub const COLOR_BGR5552GRAY: i32 = 31; pub const COLOR_BGR5552RGB: i32 = 25; pub const COLOR_BGR5552RGBA: i32 = 29; pub const COLOR_BGR5652BGR: i32 = 14; pub const COLOR_BGR5652BGRA: i32 = 18; pub const COLOR_BGR5652GRAY: i32 = 21; pub const COLOR_BGR5652RGB: i32 = 15; pub const COLOR_BGR5652RGBA: i32 = 19; /// remove alpha channel from RGB or BGR image pub const COLOR_BGRA2BGR: i32 = 1; pub const COLOR_BGRA2BGR555: i32 = 26; pub const COLOR_BGRA2BGR565: i32 = 16; pub const COLOR_BGRA2GRAY: i32 = 10; pub const COLOR_BGRA2RGB: i32 = 3; pub const COLOR_BGRA2RGBA: i32 = 5; /// RGB to YUV 4:2:0 family pub const COLOR_BGRA2YUV_I420: i32 = 130; /// RGB to YUV 4:2:0 family pub const COLOR_BGRA2YUV_IYUV: i32 = 130; /// RGB to YUV 4:2:0 family pub const COLOR_BGRA2YUV_YV12: i32 = 134; /// Demosaicing pub const COLOR_BayerBG2BGR: i32 = 46; /// Demosaicing with alpha channel pub const COLOR_BayerBG2BGRA: i32 = 139; /// Edge-Aware Demosaicing pub const COLOR_BayerBG2BGR_EA: i32 = 135; /// Demosaicing using Variable Number of Gradients pub const COLOR_BayerBG2BGR_VNG: i32 = 62; /// Demosaicing pub const COLOR_BayerBG2GRAY: i32 = 86; /// Demosaicing pub const COLOR_BayerBG2RGB: i32 = 48; /// Demosaicing with alpha channel pub const COLOR_BayerBG2RGBA: i32 = 141; /// Edge-Aware Demosaicing pub const COLOR_BayerBG2RGB_EA: i32 = 137; /// Demosaicing using Variable Number of Gradients pub const COLOR_BayerBG2RGB_VNG: i32 = 64; /// Demosaicing pub const COLOR_BayerGB2BGR: i32 = 47; /// Demosaicing with alpha channel pub const COLOR_BayerGB2BGRA: i32 = 140; /// Edge-Aware Demosaicing pub const COLOR_BayerGB2BGR_EA: i32 = 136; /// Demosaicing using Variable Number of Gradients pub const COLOR_BayerGB2BGR_VNG: i32 = 63; /// Demosaicing pub const COLOR_BayerGB2GRAY: i32 = 87; /// Demosaicing pub const COLOR_BayerGB2RGB: i32 = 49; /// Demosaicing with alpha channel pub const COLOR_BayerGB2RGBA: i32 = 142; /// Edge-Aware Demosaicing pub const COLOR_BayerGB2RGB_EA: i32 = 138; /// Demosaicing using Variable Number of Gradients pub const COLOR_BayerGB2RGB_VNG: i32 = 65; /// Demosaicing pub const COLOR_BayerGR2BGR: i32 = 49; /// Demosaicing with alpha channel pub const COLOR_BayerGR2BGRA: i32 = 142; /// Edge-Aware Demosaicing pub const COLOR_BayerGR2BGR_EA: i32 = 138; /// Demosaicing using Variable Number of Gradients pub const COLOR_BayerGR2BGR_VNG: i32 = 65; /// Demosaicing pub const COLOR_BayerGR2GRAY: i32 = 89; /// Demosaicing pub const COLOR_BayerGR2RGB: i32 = 47; /// Demosaicing with alpha channel pub const COLOR_BayerGR2RGBA: i32 = 140; /// Edge-Aware Demosaicing pub const COLOR_BayerGR2RGB_EA: i32 = 136; /// Demosaicing using Variable Number of Gradients pub const COLOR_BayerGR2RGB_VNG: i32 = 63; /// Demosaicing pub const COLOR_BayerRG2BGR: i32 = 48; /// Demosaicing with alpha channel pub const COLOR_BayerRG2BGRA: i32 = 141; /// Edge-Aware Demosaicing pub const COLOR_BayerRG2BGR_EA: i32 = 137; /// Demosaicing using Variable Number of Gradients pub const COLOR_BayerRG2BGR_VNG: i32 = 64; /// Demosaicing pub const COLOR_BayerRG2GRAY: i32 = 88; /// Demosaicing pub const COLOR_BayerRG2RGB: i32 = 46; /// Demosaicing with alpha channel pub const COLOR_BayerRG2RGBA: i32 = 139; /// Edge-Aware Demosaicing pub const COLOR_BayerRG2RGB_EA: i32 = 135; /// Demosaicing using Variable Number of Gradients pub const COLOR_BayerRG2RGB_VNG: i32 = 62; /// Demosaicing with alpha channel pub const COLOR_COLORCVT_MAX: i32 = 143; pub const COLOR_GRAY2BGR: i32 = 8; /// convert between grayscale and BGR555 (16-bit images) pub const COLOR_GRAY2BGR555: i32 = 30; /// convert between grayscale to BGR565 (16-bit images) pub const COLOR_GRAY2BGR565: i32 = 20; pub const COLOR_GRAY2BGRA: i32 = 9; pub const COLOR_GRAY2RGB: i32 = 8; pub const COLOR_GRAY2RGBA: i32 = 9; pub const COLOR_HLS2BGR: i32 = 60; pub const COLOR_HLS2BGR_FULL: i32 = 72; pub const COLOR_HLS2RGB: i32 = 61; pub const COLOR_HLS2RGB_FULL: i32 = 73; /// backward conversions to RGB/BGR pub const COLOR_HSV2BGR: i32 = 54; pub const COLOR_HSV2BGR_FULL: i32 = 70; pub const COLOR_HSV2RGB: i32 = 55; pub const COLOR_HSV2RGB_FULL: i32 = 71; pub const COLOR_LBGR2Lab: i32 = 74; pub const COLOR_LBGR2Luv: i32 = 76; pub const COLOR_LRGB2Lab: i32 = 75; pub const COLOR_LRGB2Luv: i32 = 77; pub const COLOR_Lab2BGR: i32 = 56; pub const COLOR_Lab2LBGR: i32 = 78; pub const COLOR_Lab2LRGB: i32 = 79; pub const COLOR_Lab2RGB: i32 = 57; pub const COLOR_Luv2BGR: i32 = 58; pub const COLOR_Luv2LBGR: i32 = 80; pub const COLOR_Luv2LRGB: i32 = 81; pub const COLOR_Luv2RGB: i32 = 59; pub const COLOR_RGB2BGR: i32 = 4; pub const COLOR_RGB2BGR555: i32 = 23; pub const COLOR_RGB2BGR565: i32 = 13; pub const COLOR_RGB2BGRA: i32 = 2; pub const COLOR_RGB2GRAY: i32 = 7; pub const COLOR_RGB2HLS: i32 = 53; pub const COLOR_RGB2HLS_FULL: i32 = 69; pub const COLOR_RGB2HSV: i32 = 41; pub const COLOR_RGB2HSV_FULL: i32 = 67; pub const COLOR_RGB2Lab: i32 = 45; pub const COLOR_RGB2Luv: i32 = 51; pub const COLOR_RGB2RGBA: i32 = 0; pub const COLOR_RGB2XYZ: i32 = 33; pub const COLOR_RGB2YCrCb: i32 = 37; pub const COLOR_RGB2YUV: i32 = 83; /// RGB to YUV 4:2:0 family pub const COLOR_RGB2YUV_I420: i32 = 127; /// RGB to YUV 4:2:0 family pub const COLOR_RGB2YUV_IYUV: i32 = 127; /// RGB to YUV 4:2:0 family pub const COLOR_RGB2YUV_YV12: i32 = 131; pub const COLOR_RGBA2BGR: i32 = 3; pub const COLOR_RGBA2BGR555: i32 = 27; pub const COLOR_RGBA2BGR565: i32 = 17; pub const COLOR_RGBA2BGRA: i32 = 5; pub const COLOR_RGBA2GRAY: i32 = 11; pub const COLOR_RGBA2RGB: i32 = 1; /// RGB to YUV 4:2:0 family pub const COLOR_RGBA2YUV_I420: i32 = 129; /// RGB to YUV 4:2:0 family pub const COLOR_RGBA2YUV_IYUV: i32 = 129; /// RGB to YUV 4:2:0 family pub const COLOR_RGBA2YUV_YV12: i32 = 133; /// alpha premultiplication pub const COLOR_RGBA2mRGBA: i32 = 125; pub const COLOR_XYZ2BGR: i32 = 34; pub const COLOR_XYZ2RGB: i32 = 35; pub const COLOR_YCrCb2BGR: i32 = 38; pub const COLOR_YCrCb2RGB: i32 = 39; pub const COLOR_YUV2BGR: i32 = 84; /// YUV 4:2:0 family to RGB pub const COLOR_YUV2BGRA_I420: i32 = 105; /// YUV 4:2:0 family to RGB pub const COLOR_YUV2BGRA_IYUV: i32 = 105; /// YUV 4:2:0 family to RGB pub const COLOR_YUV2BGRA_NV12: i32 = 95; /// YUV 4:2:0 family to RGB pub const COLOR_YUV2BGRA_NV21: i32 = 97; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2BGRA_UYNV: i32 = 112; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2BGRA_UYVY: i32 = 112; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2BGRA_Y422: i32 = 112; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2BGRA_YUNV: i32 = 120; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2BGRA_YUY2: i32 = 120; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2BGRA_YUYV: i32 = 120; /// YUV 4:2:0 family to RGB pub const COLOR_YUV2BGRA_YV12: i32 = 103; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2BGRA_YVYU: i32 = 122; /// YUV 4:2:0 family to RGB pub const COLOR_YUV2BGR_I420: i32 = 101; /// YUV 4:2:0 family to RGB pub const COLOR_YUV2BGR_IYUV: i32 = 101; /// YUV 4:2:0 family to RGB pub const COLOR_YUV2BGR_NV12: i32 = 91; /// YUV 4:2:0 family to RGB pub const COLOR_YUV2BGR_NV21: i32 = 93; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2BGR_UYNV: i32 = 108; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2BGR_UYVY: i32 = 108; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2BGR_Y422: i32 = 108; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2BGR_YUNV: i32 = 116; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2BGR_YUY2: i32 = 116; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2BGR_YUYV: i32 = 116; /// YUV 4:2:0 family to RGB pub const COLOR_YUV2BGR_YV12: i32 = 99; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2BGR_YVYU: i32 = 118; /// YUV 4:2:0 family to RGB pub const COLOR_YUV2GRAY_420: i32 = 106; /// YUV 4:2:0 family to RGB pub const COLOR_YUV2GRAY_I420: i32 = 106; /// YUV 4:2:0 family to RGB pub const COLOR_YUV2GRAY_IYUV: i32 = 106; /// YUV 4:2:0 family to RGB pub const COLOR_YUV2GRAY_NV12: i32 = 106; /// YUV 4:2:0 family to RGB pub const COLOR_YUV2GRAY_NV21: i32 = 106; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2GRAY_UYNV: i32 = 123; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2GRAY_UYVY: i32 = 123; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2GRAY_Y422: i32 = 123; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2GRAY_YUNV: i32 = 124; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2GRAY_YUY2: i32 = 124; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2GRAY_YUYV: i32 = 124; /// YUV 4:2:0 family to RGB pub const COLOR_YUV2GRAY_YV12: i32 = 106; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2GRAY_YVYU: i32 = 124; pub const COLOR_YUV2RGB: i32 = 85; /// YUV 4:2:0 family to RGB pub const COLOR_YUV2RGBA_I420: i32 = 104; /// YUV 4:2:0 family to RGB pub const COLOR_YUV2RGBA_IYUV: i32 = 104; /// YUV 4:2:0 family to RGB pub const COLOR_YUV2RGBA_NV12: i32 = 94; /// YUV 4:2:0 family to RGB pub const COLOR_YUV2RGBA_NV21: i32 = 96; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2RGBA_UYNV: i32 = 111; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2RGBA_UYVY: i32 = 111; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2RGBA_Y422: i32 = 111; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2RGBA_YUNV: i32 = 119; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2RGBA_YUY2: i32 = 119; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2RGBA_YUYV: i32 = 119; /// YUV 4:2:0 family to RGB pub const COLOR_YUV2RGBA_YV12: i32 = 102; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2RGBA_YVYU: i32 = 121; /// YUV 4:2:0 family to RGB pub const COLOR_YUV2RGB_I420: i32 = 100; /// YUV 4:2:0 family to RGB pub const COLOR_YUV2RGB_IYUV: i32 = 100; /// YUV 4:2:0 family to RGB pub const COLOR_YUV2RGB_NV12: i32 = 90; /// YUV 4:2:0 family to RGB pub const COLOR_YUV2RGB_NV21: i32 = 92; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2RGB_UYNV: i32 = 107; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2RGB_UYVY: i32 = 107; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2RGB_Y422: i32 = 107; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2RGB_YUNV: i32 = 115; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2RGB_YUY2: i32 = 115; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2RGB_YUYV: i32 = 115; /// YUV 4:2:0 family to RGB pub const COLOR_YUV2RGB_YV12: i32 = 98; /// YUV 4:2:2 family to RGB pub const COLOR_YUV2RGB_YVYU: i32 = 117; /// YUV 4:2:0 family to RGB pub const COLOR_YUV420p2BGR: i32 = 99; /// YUV 4:2:0 family to RGB pub const COLOR_YUV420p2BGRA: i32 = 103; /// YUV 4:2:0 family to RGB pub const COLOR_YUV420p2GRAY: i32 = 106; /// YUV 4:2:0 family to RGB pub const COLOR_YUV420p2RGB: i32 = 98; /// YUV 4:2:0 family to RGB pub const COLOR_YUV420p2RGBA: i32 = 102; /// YUV 4:2:0 family to RGB pub const COLOR_YUV420sp2BGR: i32 = 93; /// YUV 4:2:0 family to RGB pub const COLOR_YUV420sp2BGRA: i32 = 97; /// YUV 4:2:0 family to RGB pub const COLOR_YUV420sp2GRAY: i32 = 106; /// YUV 4:2:0 family to RGB pub const COLOR_YUV420sp2RGB: i32 = 92; /// YUV 4:2:0 family to RGB pub const COLOR_YUV420sp2RGBA: i32 = 96; /// alpha premultiplication pub const COLOR_mRGBA2RGBA: i32 = 126; ///  pub const CONTOURS_MATCH_I1: i32 = 1; ///  pub const CONTOURS_MATCH_I2: i32 = 2; ///  pub const CONTOURS_MATCH_I3: i32 = 3; /// distance = max(|x1-x2|,|y1-y2|) pub const DIST_C: i32 = 3; /// distance = c^2(|x|/c-log(1+|x|/c)), c = 1.3998 pub const DIST_FAIR: i32 = 5; /// distance = |x|<c ? x^2/2 : c(|x|-c/2), c=1.345 pub const DIST_HUBER: i32 = 7; /// distance = |x1-x2| + |y1-y2| pub const DIST_L1: i32 = 1; /// L1-L2 metric: distance = 2(sqrt(1+x*x/2) - 1)) pub const DIST_L12: i32 = 4; /// the simple euclidean distance pub const DIST_L2: i32 = 2; /// each connected component of zeros in src (as well as all the non-zero pixels closest to the /// connected component) will be assigned the same label pub const DIST_LABEL_CCOMP: i32 = 0; /// each zero pixel (and all the non-zero pixels closest to it) gets its own label. pub const DIST_LABEL_PIXEL: i32 = 1; /// mask=3 pub const DIST_MASK_3: i32 = 3; /// mask=5 pub const DIST_MASK_5: i32 = 5; pub const DIST_MASK_PRECISE: i32 = 0; /// User defined distance pub const DIST_USER: i32 = -1; /// distance = c^2/2(1-exp(-(x/c)^2)), c = 2.9846 pub const DIST_WELSCH: i32 = 6; pub const FILLED: i32 = -1; pub const FILTER_SCHARR: i32 = -1; /// If set, the difference between the current pixel and seed pixel is considered. Otherwise, /// the difference between neighbor pixels is considered (that is, the range is floating). pub const FLOODFILL_FIXED_RANGE: i32 = 65536; /// If set, the function does not change the image ( newVal is ignored), and only fills the /// mask with the value specified in bits 8-16 of flags as described above. This option only make /// sense in function variants that have the mask parameter. pub const FLOODFILL_MASK_ONLY: i32 = 131072; /// normal size serif font pub const FONT_HERSHEY_COMPLEX: i32 = 3; /// smaller version of FONT_HERSHEY_COMPLEX pub const FONT_HERSHEY_COMPLEX_SMALL: i32 = 5; /// normal size sans-serif font (more complex than FONT_HERSHEY_SIMPLEX) pub const FONT_HERSHEY_DUPLEX: i32 = 2; /// small size sans-serif font pub const FONT_HERSHEY_PLAIN: i32 = 1; /// more complex variant of FONT_HERSHEY_SCRIPT_SIMPLEX pub const FONT_HERSHEY_SCRIPT_COMPLEX: i32 = 7; /// hand-writing style font pub const FONT_HERSHEY_SCRIPT_SIMPLEX: i32 = 6; /// normal size sans-serif font pub const FONT_HERSHEY_SIMPLEX: i32 = 0; /// normal size serif font (more complex than FONT_HERSHEY_COMPLEX) pub const FONT_HERSHEY_TRIPLEX: i32 = 4; /// flag for italic font pub const FONT_ITALIC: i32 = 16; /// an obvious background pixels pub const GC_BGD: i32 = 0; /// The value means that the algorithm should just resume. pub const GC_EVAL: i32 = 2; /// The value means that the algorithm should just run the grabCut algorithm (a single iteration) with the fixed model pub const GC_EVAL_FREEZE_MODEL: i32 = 3; /// an obvious foreground (object) pixel pub const GC_FGD: i32 = 1; /// The function initializes the state using the provided mask. Note that GC_INIT_WITH_RECT /// and GC_INIT_WITH_MASK can be combined. Then, all the pixels outside of the ROI are /// automatically initialized with GC_BGD . pub const GC_INIT_WITH_MASK: i32 = 1; /// The function initializes the state and the mask using the provided rectangle. After that it /// runs iterCount iterations of the algorithm. pub const GC_INIT_WITH_RECT: i32 = 0; /// a possible background pixel pub const GC_PR_BGD: i32 = 2; /// a possible foreground pixel pub const GC_PR_FGD: i32 = 3; /// Bhattacharyya distance /// (In fact, OpenCV computes Hellinger distance, which is related to Bhattacharyya coefficient.) ///  pub const HISTCMP_BHATTACHARYYA: i32 = 3; /// Chi-Square ///  pub const HISTCMP_CHISQR: i32 = 1; /// Alternative Chi-Square ///  /// This alternative formula is regularly used for texture comparison. See e.g. [Puzicha1997](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Puzicha1997) pub const HISTCMP_CHISQR_ALT: i32 = 4; /// Correlation ///  /// where ///  /// and  is a total number of histogram bins. pub const HISTCMP_CORREL: i32 = 0; /// Synonym for HISTCMP_BHATTACHARYYA pub const HISTCMP_HELLINGER: i32 = 3; /// Intersection ///  pub const HISTCMP_INTERSECT: i32 = 2; /// Kullback-Leibler divergence ///  pub const HISTCMP_KL_DIV: i32 = 5; /// basically *21HT*, described in [Yuen90](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Yuen90) pub const HOUGH_GRADIENT: i32 = 3; /// variation of HOUGH_GRADIENT to get better accuracy pub const HOUGH_GRADIENT_ALT: i32 = 4; /// multi-scale variant of the classical Hough transform. The lines are encoded the same way as /// HOUGH_STANDARD. pub const HOUGH_MULTI_SCALE: i32 = 2; /// probabilistic Hough transform (more efficient in case if the picture contains a few long /// linear segments). It returns line segments rather than the whole line. Each segment is /// represented by starting and ending points, and the matrix must be (the created sequence will /// be) of the CV_32SC4 type. pub const HOUGH_PROBABILISTIC: i32 = 1; /// classical or standard Hough transform. Every line is represented by two floating-point /// numbers  , where  is a distance between (0,0) point and the line, /// and  is the angle between x-axis and the normal to the line. Thus, the matrix must /// be (the created sequence will be) of CV_32FC2 type pub const HOUGH_STANDARD: i32 = 0; /// One of the rectangle is fully enclosed in the other pub const INTERSECT_FULL: i32 = 2; /// No intersection pub const INTERSECT_NONE: i32 = 0; /// There is a partial intersection pub const INTERSECT_PARTIAL: i32 = 1; /// resampling using pixel area relation. It may be a preferred method for image decimation, as /// it gives moire'-free results. But when the image is zoomed, it is similar to the INTER_NEAREST /// method. pub const INTER_AREA: i32 = 3; pub const INTER_BITS: i32 = 5; pub const INTER_BITS2: i32 = 10; /// bicubic interpolation pub const INTER_CUBIC: i32 = 2; /// Lanczos interpolation over 8x8 neighborhood pub const INTER_LANCZOS4: i32 = 4; /// bilinear interpolation pub const INTER_LINEAR: i32 = 1; /// Bit exact bilinear interpolation pub const INTER_LINEAR_EXACT: i32 = 5; /// mask for interpolation codes pub const INTER_MAX: i32 = 7; /// nearest neighbor interpolation pub const INTER_NEAREST: i32 = 0; /// Bit exact nearest neighbor interpolation. This will produce same results as /// the nearest neighbor method in PIL, scikit-image or Matlab. pub const INTER_NEAREST_EXACT: i32 = 6; pub const INTER_TAB_SIZE: i32 = 32; pub const INTER_TAB_SIZE2: i32 = 1024; /// 4-connected line pub const LINE_4: i32 = 4; /// 8-connected line pub const LINE_8: i32 = 8; /// antialiased line pub const LINE_AA: i32 = 16; /// Advanced refinement. Number of false alarms is calculated, lines are /// refined through increase of precision, decrement in size, etc. pub const LSD_REFINE_ADV: i32 = 2; /// No refinement applied pub const LSD_REFINE_NONE: i32 = 0; /// Standard refinement is applied. E.g. breaking arches into smaller straighter line approximations. pub const LSD_REFINE_STD: i32 = 1; /// A crosshair marker shape pub const MARKER_CROSS: i32 = 0; /// A diamond marker shape pub const MARKER_DIAMOND: i32 = 3; /// A square marker shape pub const MARKER_SQUARE: i32 = 4; /// A star marker shape, combination of cross and tilted cross pub const MARKER_STAR: i32 = 2; /// A 45 degree tilted crosshair marker shape pub const MARKER_TILTED_CROSS: i32 = 1; /// A downwards pointing triangle marker shape pub const MARKER_TRIANGLE_DOWN: i32 = 6; /// An upwards pointing triangle marker shape pub const MARKER_TRIANGLE_UP: i32 = 5; /// "black hat" ///  pub const MORPH_BLACKHAT: i32 = 6; /// a closing operation ///  pub const MORPH_CLOSE: i32 = 3; /// a cross-shaped structuring element: ///  pub const MORPH_CROSS: i32 = 1; /// see #dilate pub const MORPH_DILATE: i32 = 1; /// an elliptic structuring element, that is, a filled ellipse inscribed /// into the rectangle Rect(0, 0, esize.width, 0.esize.height) pub const MORPH_ELLIPSE: i32 = 2; /// see #erode pub const MORPH_ERODE: i32 = 0; /// a morphological gradient ///  pub const MORPH_GRADIENT: i32 = 4; /// "hit or miss" /// .- Only supported for CV_8UC1 binary images. A tutorial can be found in the documentation pub const MORPH_HITMISS: i32 = 7; /// an opening operation ///  pub const MORPH_OPEN: i32 = 2; /// a rectangular structuring element:  pub const MORPH_RECT: i32 = 0; /// "top hat" ///  pub const MORPH_TOPHAT: i32 = 5; /// retrieves all of the contours and organizes them into a two-level hierarchy. At the top /// level, there are external boundaries of the components. At the second level, there are /// boundaries of the holes. If there is another contour inside a hole of a connected component, it /// is still put at the top level. pub const RETR_CCOMP: i32 = 2; /// retrieves only the extreme outer contours. It sets `hierarchy[i][2]=hierarchy[i][3]=-1` for /// all the contours. pub const RETR_EXTERNAL: i32 = 0; pub const RETR_FLOODFILL: i32 = 4; /// retrieves all of the contours without establishing any hierarchical relationships. pub const RETR_LIST: i32 = 1; /// retrieves all of the contours and reconstructs a full hierarchy of nested contours. pub const RETR_TREE: i32 = 3; pub const Subdiv2D_NEXT_AROUND_DST: i32 = 34; pub const Subdiv2D_NEXT_AROUND_LEFT: i32 = 19; pub const Subdiv2D_NEXT_AROUND_ORG: i32 = 0; pub const Subdiv2D_NEXT_AROUND_RIGHT: i32 = 49; pub const Subdiv2D_PREV_AROUND_DST: i32 = 51; pub const Subdiv2D_PREV_AROUND_LEFT: i32 = 32; pub const Subdiv2D_PREV_AROUND_ORG: i32 = 17; pub const Subdiv2D_PREV_AROUND_RIGHT: i32 = 2; /// Point location error pub const Subdiv2D_PTLOC_ERROR: i32 = -2; /// Point inside some facet pub const Subdiv2D_PTLOC_INSIDE: i32 = 0; /// Point on some edge pub const Subdiv2D_PTLOC_ON_EDGE: i32 = 2; /// Point outside the subdivision bounding rect pub const Subdiv2D_PTLOC_OUTSIDE_RECT: i32 = -1; /// Point coincides with one of the subdivision vertices pub const Subdiv2D_PTLOC_VERTEX: i32 = 1; ///  pub const THRESH_BINARY: i32 = 0; ///  pub const THRESH_BINARY_INV: i32 = 1; pub const THRESH_MASK: i32 = 7; /// flag, use Otsu algorithm to choose the optimal threshold value pub const THRESH_OTSU: i32 = 8; ///  pub const THRESH_TOZERO: i32 = 3; ///  pub const THRESH_TOZERO_INV: i32 = 4; /// flag, use Triangle algorithm to choose the optimal threshold value pub const THRESH_TRIANGLE: i32 = 16; ///  pub const THRESH_TRUNC: i32 = 2; /// !<  /// where ///  /// with mask: ///  pub const TM_CCOEFF: i32 = 4; /// !<  pub const TM_CCOEFF_NORMED: i32 = 5; /// !<  /// with mask: ///  pub const TM_CCORR: i32 = 2; /// !<  /// with mask: ///  pub const TM_CCORR_NORMED: i32 = 3; /// !<  /// with mask: ///  pub const TM_SQDIFF: i32 = 0; /// !<  /// with mask: ///  pub const TM_SQDIFF_NORMED: i32 = 1; /// flag, fills all of the destination image pixels. If some of them correspond to outliers in the /// source image, they are set to zero pub const WARP_FILL_OUTLIERS: i32 = 8; /// flag, inverse transformation /// /// For example, #linearPolar or #logPolar transforms: /// - flag is __not__ set:  /// - flag is set:  pub const WARP_INVERSE_MAP: i32 = 16; /// Remaps an image to/from polar space. pub const WARP_POLAR_LINEAR: i32 = 0; /// Remaps an image to/from semilog-polar space. pub const WARP_POLAR_LOG: i32 = 256; /// adaptive threshold algorithm /// ## See also /// adaptiveThreshold #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub enum AdaptiveThresholdTypes { /// the threshold value  is a mean of the  neighborhood of  minus C ADAPTIVE_THRESH_MEAN_C = 0, /// the threshold value  is a weighted sum (cross-correlation with a Gaussian /// window) of the  neighborhood of  /// minus C . The default sigma (standard deviation) is used for the specified blockSize . See /// #getGaussianKernel ADAPTIVE_THRESH_GAUSSIAN_C = 1, } opencv_type_enum! { crate::imgproc::AdaptiveThresholdTypes } /// the color conversion codes /// ## See also /// @ref imgproc_color_conversions /// @ingroup imgproc_color_conversions #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub enum ColorConversionCodes { /// add alpha channel to RGB or BGR image COLOR_BGR2BGRA = 0, // COLOR_RGB2RGBA = 0 as isize, // duplicate discriminant /// remove alpha channel from RGB or BGR image COLOR_BGRA2BGR = 1, // COLOR_RGBA2RGB = 1 as isize, // duplicate discriminant /// convert between RGB and BGR color spaces (with or without alpha channel) COLOR_BGR2RGBA = 2, // COLOR_RGB2BGRA = 2 as isize, // duplicate discriminant COLOR_RGBA2BGR = 3, // COLOR_BGRA2RGB = 3 as isize, // duplicate discriminant COLOR_BGR2RGB = 4, // COLOR_RGB2BGR = 4 as isize, // duplicate discriminant COLOR_BGRA2RGBA = 5, // COLOR_RGBA2BGRA = 5 as isize, // duplicate discriminant /// convert between RGB/BGR and grayscale, @ref color_convert_rgb_gray "color conversions" COLOR_BGR2GRAY = 6, COLOR_RGB2GRAY = 7, COLOR_GRAY2BGR = 8, // COLOR_GRAY2RGB = 8 as isize, // duplicate discriminant COLOR_GRAY2BGRA = 9, // COLOR_GRAY2RGBA = 9 as isize, // duplicate discriminant COLOR_BGRA2GRAY = 10, COLOR_RGBA2GRAY = 11, /// convert between RGB/BGR and BGR565 (16-bit images) COLOR_BGR2BGR565 = 12, COLOR_RGB2BGR565 = 13, COLOR_BGR5652BGR = 14, COLOR_BGR5652RGB = 15, COLOR_BGRA2BGR565 = 16, COLOR_RGBA2BGR565 = 17, COLOR_BGR5652BGRA = 18, COLOR_BGR5652RGBA = 19, /// convert between grayscale to BGR565 (16-bit images) COLOR_GRAY2BGR565 = 20, COLOR_BGR5652GRAY = 21, /// convert between RGB/BGR and BGR555 (16-bit images) COLOR_BGR2BGR555 = 22, COLOR_RGB2BGR555 = 23, COLOR_BGR5552BGR = 24, COLOR_BGR5552RGB = 25, COLOR_BGRA2BGR555 = 26, COLOR_RGBA2BGR555 = 27, COLOR_BGR5552BGRA = 28, COLOR_BGR5552RGBA = 29, /// convert between grayscale and BGR555 (16-bit images) COLOR_GRAY2BGR555 = 30, COLOR_BGR5552GRAY = 31, /// convert RGB/BGR to CIE XYZ, @ref color_convert_rgb_xyz "color conversions" COLOR_BGR2XYZ = 32, COLOR_RGB2XYZ = 33, COLOR_XYZ2BGR = 34, COLOR_XYZ2RGB = 35, /// convert RGB/BGR to luma-chroma (aka YCC), @ref color_convert_rgb_ycrcb "color conversions" COLOR_BGR2YCrCb = 36, COLOR_RGB2YCrCb = 37, COLOR_YCrCb2BGR = 38, COLOR_YCrCb2RGB = 39, /// convert RGB/BGR to HSV (hue saturation value), @ref color_convert_rgb_hsv "color conversions" COLOR_BGR2HSV = 40, COLOR_RGB2HSV = 41, /// convert RGB/BGR to CIE Lab, @ref color_convert_rgb_lab "color conversions" COLOR_BGR2Lab = 44, COLOR_RGB2Lab = 45, /// convert RGB/BGR to CIE Luv, @ref color_convert_rgb_luv "color conversions" COLOR_BGR2Luv = 50, COLOR_RGB2Luv = 51, /// convert RGB/BGR to HLS (hue lightness saturation), @ref color_convert_rgb_hls "color conversions" COLOR_BGR2HLS = 52, COLOR_RGB2HLS = 53, /// backward conversions to RGB/BGR COLOR_HSV2BGR = 54, COLOR_HSV2RGB = 55, COLOR_Lab2BGR = 56, COLOR_Lab2RGB = 57, COLOR_Luv2BGR = 58, COLOR_Luv2RGB = 59, COLOR_HLS2BGR = 60, COLOR_HLS2RGB = 61, COLOR_BGR2HSV_FULL = 66, COLOR_RGB2HSV_FULL = 67, COLOR_BGR2HLS_FULL = 68, COLOR_RGB2HLS_FULL = 69, COLOR_HSV2BGR_FULL = 70, COLOR_HSV2RGB_FULL = 71, COLOR_HLS2BGR_FULL = 72, COLOR_HLS2RGB_FULL = 73, COLOR_LBGR2Lab = 74, COLOR_LRGB2Lab = 75, COLOR_LBGR2Luv = 76, COLOR_LRGB2Luv = 77, COLOR_Lab2LBGR = 78, COLOR_Lab2LRGB = 79, COLOR_Luv2LBGR = 80, COLOR_Luv2LRGB = 81, /// convert between RGB/BGR and YUV COLOR_BGR2YUV = 82, COLOR_RGB2YUV = 83, COLOR_YUV2BGR = 84, COLOR_YUV2RGB = 85, /// YUV 4:2:0 family to RGB COLOR_YUV2RGB_NV12 = 90, /// YUV 4:2:0 family to RGB COLOR_YUV2BGR_NV12 = 91, /// YUV 4:2:0 family to RGB COLOR_YUV2RGB_NV21 = 92, /// YUV 4:2:0 family to RGB COLOR_YUV2BGR_NV21 = 93, // YUV 4:2:0 family to RGB // COLOR_YUV420sp2RGB = 92 as isize, // duplicate discriminant // YUV 4:2:0 family to RGB // COLOR_YUV420sp2BGR = 93 as isize, // duplicate discriminant /// YUV 4:2:0 family to RGB COLOR_YUV2RGBA_NV12 = 94, /// YUV 4:2:0 family to RGB COLOR_YUV2BGRA_NV12 = 95, /// YUV 4:2:0 family to RGB COLOR_YUV2RGBA_NV21 = 96, /// YUV 4:2:0 family to RGB COLOR_YUV2BGRA_NV21 = 97, // YUV 4:2:0 family to RGB // COLOR_YUV420sp2RGBA = 96 as isize, // duplicate discriminant // YUV 4:2:0 family to RGB // COLOR_YUV420sp2BGRA = 97 as isize, // duplicate discriminant /// YUV 4:2:0 family to RGB COLOR_YUV2RGB_YV12 = 98, /// YUV 4:2:0 family to RGB COLOR_YUV2BGR_YV12 = 99, /// YUV 4:2:0 family to RGB COLOR_YUV2RGB_IYUV = 100, /// YUV 4:2:0 family to RGB COLOR_YUV2BGR_IYUV = 101, // YUV 4:2:0 family to RGB // COLOR_YUV2RGB_I420 = 100 as isize, // duplicate discriminant // YUV 4:2:0 family to RGB // COLOR_YUV2BGR_I420 = 101 as isize, // duplicate discriminant // YUV 4:2:0 family to RGB // COLOR_YUV420p2RGB = 98 as isize, // duplicate discriminant // YUV 4:2:0 family to RGB // COLOR_YUV420p2BGR = 99 as isize, // duplicate discriminant /// YUV 4:2:0 family to RGB COLOR_YUV2RGBA_YV12 = 102, /// YUV 4:2:0 family to RGB COLOR_YUV2BGRA_YV12 = 103, /// YUV 4:2:0 family to RGB COLOR_YUV2RGBA_IYUV = 104, /// YUV 4:2:0 family to RGB COLOR_YUV2BGRA_IYUV = 105, // YUV 4:2:0 family to RGB // COLOR_YUV2RGBA_I420 = 104 as isize, // duplicate discriminant // YUV 4:2:0 family to RGB // COLOR_YUV2BGRA_I420 = 105 as isize, // duplicate discriminant // YUV 4:2:0 family to RGB // COLOR_YUV420p2RGBA = 102 as isize, // duplicate discriminant // YUV 4:2:0 family to RGB // COLOR_YUV420p2BGRA = 103 as isize, // duplicate discriminant /// YUV 4:2:0 family to RGB COLOR_YUV2GRAY_420 = 106, // YUV 4:2:0 family to RGB // COLOR_YUV2GRAY_NV21 = 106 as isize, // duplicate discriminant // YUV 4:2:0 family to RGB // COLOR_YUV2GRAY_NV12 = 106 as isize, // duplicate discriminant // YUV 4:2:0 family to RGB // COLOR_YUV2GRAY_YV12 = 106 as isize, // duplicate discriminant // YUV 4:2:0 family to RGB // COLOR_YUV2GRAY_IYUV = 106 as isize, // duplicate discriminant // YUV 4:2:0 family to RGB // COLOR_YUV2GRAY_I420 = 106 as isize, // duplicate discriminant // YUV 4:2:0 family to RGB // COLOR_YUV420sp2GRAY = 106 as isize, // duplicate discriminant // YUV 4:2:0 family to RGB // COLOR_YUV420p2GRAY = 106 as isize, // duplicate discriminant /// YUV 4:2:2 family to RGB COLOR_YUV2RGB_UYVY = 107, /// YUV 4:2:2 family to RGB COLOR_YUV2BGR_UYVY = 108, // YUV 4:2:2 family to RGB // COLOR_YUV2RGB_Y422 = 107 as isize, // duplicate discriminant // YUV 4:2:2 family to RGB // COLOR_YUV2BGR_Y422 = 108 as isize, // duplicate discriminant // YUV 4:2:2 family to RGB // COLOR_YUV2RGB_UYNV = 107 as isize, // duplicate discriminant // YUV 4:2:2 family to RGB // COLOR_YUV2BGR_UYNV = 108 as isize, // duplicate discriminant /// YUV 4:2:2 family to RGB COLOR_YUV2RGBA_UYVY = 111, /// YUV 4:2:2 family to RGB COLOR_YUV2BGRA_UYVY = 112, // YUV 4:2:2 family to RGB // COLOR_YUV2RGBA_Y422 = 111 as isize, // duplicate discriminant // YUV 4:2:2 family to RGB // COLOR_YUV2BGRA_Y422 = 112 as isize, // duplicate discriminant // YUV 4:2:2 family to RGB // COLOR_YUV2RGBA_UYNV = 111 as isize, // duplicate discriminant // YUV 4:2:2 family to RGB // COLOR_YUV2BGRA_UYNV = 112 as isize, // duplicate discriminant /// YUV 4:2:2 family to RGB COLOR_YUV2RGB_YUY2 = 115, /// YUV 4:2:2 family to RGB COLOR_YUV2BGR_YUY2 = 116, /// YUV 4:2:2 family to RGB COLOR_YUV2RGB_YVYU = 117, /// YUV 4:2:2 family to RGB COLOR_YUV2BGR_YVYU = 118, // YUV 4:2:2 family to RGB // COLOR_YUV2RGB_YUYV = 115 as isize, // duplicate discriminant // YUV 4:2:2 family to RGB // COLOR_YUV2BGR_YUYV = 116 as isize, // duplicate discriminant // YUV 4:2:2 family to RGB // COLOR_YUV2RGB_YUNV = 115 as isize, // duplicate discriminant // YUV 4:2:2 family to RGB // COLOR_YUV2BGR_YUNV = 116 as isize, // duplicate discriminant /// YUV 4:2:2 family to RGB COLOR_YUV2RGBA_YUY2 = 119, /// YUV 4:2:2 family to RGB COLOR_YUV2BGRA_YUY2 = 120, /// YUV 4:2:2 family to RGB COLOR_YUV2RGBA_YVYU = 121, /// YUV 4:2:2 family to RGB COLOR_YUV2BGRA_YVYU = 122, // YUV 4:2:2 family to RGB // COLOR_YUV2RGBA_YUYV = 119 as isize, // duplicate discriminant // YUV 4:2:2 family to RGB // COLOR_YUV2BGRA_YUYV = 120 as isize, // duplicate discriminant // YUV 4:2:2 family to RGB // COLOR_YUV2RGBA_YUNV = 119 as isize, // duplicate discriminant // YUV 4:2:2 family to RGB // COLOR_YUV2BGRA_YUNV = 120 as isize, // duplicate discriminant /// YUV 4:2:2 family to RGB COLOR_YUV2GRAY_UYVY = 123, /// YUV 4:2:2 family to RGB COLOR_YUV2GRAY_YUY2 = 124, // YUV 4:2:2 family to RGB // COLOR_YUV2GRAY_Y422 = 123 as isize, // duplicate discriminant // YUV 4:2:2 family to RGB // COLOR_YUV2GRAY_UYNV = 123 as isize, // duplicate discriminant // YUV 4:2:2 family to RGB // COLOR_YUV2GRAY_YVYU = 124 as isize, // duplicate discriminant // YUV 4:2:2 family to RGB // COLOR_YUV2GRAY_YUYV = 124 as isize, // duplicate discriminant // YUV 4:2:2 family to RGB // COLOR_YUV2GRAY_YUNV = 124 as isize, // duplicate discriminant /// alpha premultiplication COLOR_RGBA2mRGBA = 125, /// alpha premultiplication COLOR_mRGBA2RGBA = 126, /// RGB to YUV 4:2:0 family COLOR_RGB2YUV_I420 = 127, /// RGB to YUV 4:2:0 family COLOR_BGR2YUV_I420 = 128, // RGB to YUV 4:2:0 family // COLOR_RGB2YUV_IYUV = 127 as isize, // duplicate discriminant // RGB to YUV 4:2:0 family // COLOR_BGR2YUV_IYUV = 128 as isize, // duplicate discriminant /// RGB to YUV 4:2:0 family COLOR_RGBA2YUV_I420 = 129, /// RGB to YUV 4:2:0 family COLOR_BGRA2YUV_I420 = 130, // RGB to YUV 4:2:0 family // COLOR_RGBA2YUV_IYUV = 129 as isize, // duplicate discriminant // RGB to YUV 4:2:0 family // COLOR_BGRA2YUV_IYUV = 130 as isize, // duplicate discriminant /// RGB to YUV 4:2:0 family COLOR_RGB2YUV_YV12 = 131, /// RGB to YUV 4:2:0 family COLOR_BGR2YUV_YV12 = 132, /// RGB to YUV 4:2:0 family COLOR_RGBA2YUV_YV12 = 133, /// RGB to YUV 4:2:0 family COLOR_BGRA2YUV_YV12 = 134, /// Demosaicing COLOR_BayerBG2BGR = 46, /// Demosaicing COLOR_BayerGB2BGR = 47, /// Demosaicing COLOR_BayerRG2BGR = 48, /// Demosaicing COLOR_BayerGR2BGR = 49, // Demosaicing // COLOR_BayerBG2RGB = 48 as isize, // duplicate discriminant // Demosaicing // COLOR_BayerGB2RGB = 49 as isize, // duplicate discriminant // Demosaicing // COLOR_BayerRG2RGB = 46 as isize, // duplicate discriminant // Demosaicing // COLOR_BayerGR2RGB = 47 as isize, // duplicate discriminant /// Demosaicing COLOR_BayerBG2GRAY = 86, /// Demosaicing COLOR_BayerGB2GRAY = 87, /// Demosaicing COLOR_BayerRG2GRAY = 88, /// Demosaicing COLOR_BayerGR2GRAY = 89, /// Demosaicing using Variable Number of Gradients COLOR_BayerBG2BGR_VNG = 62, /// Demosaicing using Variable Number of Gradients COLOR_BayerGB2BGR_VNG = 63, /// Demosaicing using Variable Number of Gradients COLOR_BayerRG2BGR_VNG = 64, /// Demosaicing using Variable Number of Gradients COLOR_BayerGR2BGR_VNG = 65, // Demosaicing using Variable Number of Gradients // COLOR_BayerBG2RGB_VNG = 64 as isize, // duplicate discriminant // Demosaicing using Variable Number of Gradients // COLOR_BayerGB2RGB_VNG = 65 as isize, // duplicate discriminant // Demosaicing using Variable Number of Gradients // COLOR_BayerRG2RGB_VNG = 62 as isize, // duplicate discriminant // Demosaicing using Variable Number of Gradients // COLOR_BayerGR2RGB_VNG = 63 as isize, // duplicate discriminant /// Edge-Aware Demosaicing COLOR_BayerBG2BGR_EA = 135, /// Edge-Aware Demosaicing COLOR_BayerGB2BGR_EA = 136, /// Edge-Aware Demosaicing COLOR_BayerRG2BGR_EA = 137, /// Edge-Aware Demosaicing COLOR_BayerGR2BGR_EA = 138, // Edge-Aware Demosaicing // COLOR_BayerBG2RGB_EA = 137 as isize, // duplicate discriminant // Edge-Aware Demosaicing // COLOR_BayerGB2RGB_EA = 138 as isize, // duplicate discriminant // Edge-Aware Demosaicing // COLOR_BayerRG2RGB_EA = 135 as isize, // duplicate discriminant // Edge-Aware Demosaicing // COLOR_BayerGR2RGB_EA = 136 as isize, // duplicate discriminant /// Demosaicing with alpha channel COLOR_BayerBG2BGRA = 139, /// Demosaicing with alpha channel COLOR_BayerGB2BGRA = 140, /// Demosaicing with alpha channel COLOR_BayerRG2BGRA = 141, /// Demosaicing with alpha channel COLOR_BayerGR2BGRA = 142, // Demosaicing with alpha channel // COLOR_BayerBG2RGBA = 141 as isize, // duplicate discriminant // Demosaicing with alpha channel // COLOR_BayerGB2RGBA = 142 as isize, // duplicate discriminant // Demosaicing with alpha channel // COLOR_BayerRG2RGBA = 139 as isize, // duplicate discriminant // Demosaicing with alpha channel // COLOR_BayerGR2RGBA = 140 as isize, // duplicate discriminant /// Demosaicing with alpha channel COLOR_COLORCVT_MAX = 143, } opencv_type_enum! { crate::imgproc::ColorConversionCodes } /// GNU Octave/MATLAB equivalent colormaps #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub enum ColormapTypes { ///  COLORMAP_AUTUMN = 0, ///  COLORMAP_BONE = 1, ///  COLORMAP_JET = 2, ///  COLORMAP_WINTER = 3, ///  COLORMAP_RAINBOW = 4, ///  COLORMAP_OCEAN = 5, ///  COLORMAP_SUMMER = 6, ///  COLORMAP_SPRING = 7, ///  COLORMAP_COOL = 8, ///  COLORMAP_HSV = 9, ///  COLORMAP_PINK = 10, ///  COLORMAP_HOT = 11, ///  COLORMAP_PARULA = 12, ///  COLORMAP_MAGMA = 13, ///  COLORMAP_INFERNO = 14, ///  COLORMAP_PLASMA = 15, ///  COLORMAP_VIRIDIS = 16, ///  COLORMAP_CIVIDIS = 17, ///  COLORMAP_TWILIGHT = 18, ///  COLORMAP_TWILIGHT_SHIFTED = 19, ///  COLORMAP_TURBO = 20, ///  COLORMAP_DEEPGREEN = 21, } opencv_type_enum! { crate::imgproc::ColormapTypes } /// connected components algorithm #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub enum ConnectedComponentsAlgorithmsTypes { /// SAUF [Wu2009](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Wu2009) algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity CCL_WU = 0, /// BBDT algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity CCL_DEFAULT = -1, /// BBDT algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity CCL_GRANA = 1, } opencv_type_enum! { crate::imgproc::ConnectedComponentsAlgorithmsTypes } /// connected components statistics #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub enum ConnectedComponentsTypes { /// The leftmost (x) coordinate which is the inclusive start of the bounding /// box in the horizontal direction. CC_STAT_LEFT = 0, /// The topmost (y) coordinate which is the inclusive start of the bounding /// box in the vertical direction. CC_STAT_TOP = 1, /// The horizontal size of the bounding box CC_STAT_WIDTH = 2, /// The vertical size of the bounding box CC_STAT_HEIGHT = 3, /// The total area (in pixels) of the connected component CC_STAT_AREA = 4, /// Max enumeration value. Used internally only for memory allocation CC_STAT_MAX = 5, } opencv_type_enum! { crate::imgproc::ConnectedComponentsTypes } /// the contour approximation algorithm #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub enum ContourApproximationModes { /// stores absolutely all the contour points. That is, any 2 subsequent points (x1,y1) and /// (x2,y2) of the contour will be either horizontal, vertical or diagonal neighbors, that is, /// max(abs(x1-x2),abs(y2-y1))==1. CHAIN_APPROX_NONE = 1, /// compresses horizontal, vertical, and diagonal segments and leaves only their end points. /// For example, an up-right rectangular contour is encoded with 4 points. CHAIN_APPROX_SIMPLE = 2, /// applies one of the flavors of the Teh-Chin chain approximation algorithm [TehChin89](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_TehChin89) CHAIN_APPROX_TC89_L1 = 3, /// applies one of the flavors of the Teh-Chin chain approximation algorithm [TehChin89](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_TehChin89) CHAIN_APPROX_TC89_KCOS = 4, } opencv_type_enum! { crate::imgproc::ContourApproximationModes } /// distanceTransform algorithm flags #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub enum DistanceTransformLabelTypes { /// each connected component of zeros in src (as well as all the non-zero pixels closest to the /// connected component) will be assigned the same label DIST_LABEL_CCOMP = 0, /// each zero pixel (and all the non-zero pixels closest to it) gets its own label. DIST_LABEL_PIXEL = 1, } opencv_type_enum! { crate::imgproc::DistanceTransformLabelTypes } /// Mask size for distance transform #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub enum DistanceTransformMasks { /// mask=3 DIST_MASK_3 = 3, /// mask=5 DIST_MASK_5 = 5, DIST_MASK_PRECISE = 0, } opencv_type_enum! { crate::imgproc::DistanceTransformMasks } /// Distance types for Distance Transform and M-estimators /// ## See also /// distanceTransform, fitLine #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub enum DistanceTypes { /// User defined distance DIST_USER = -1, /// distance = |x1-x2| + |y1-y2| DIST_L1 = 1, /// the simple euclidean distance DIST_L2 = 2, /// distance = max(|x1-x2|,|y1-y2|) DIST_C = 3, /// L1-L2 metric: distance = 2(sqrt(1+x*x/2) - 1)) DIST_L12 = 4, /// distance = c^2(|x|/c-log(1+|x|/c)), c = 1.3998 DIST_FAIR = 5, /// distance = c^2/2(1-exp(-(x/c)^2)), c = 2.9846 DIST_WELSCH = 6, /// distance = |x|<c ? x^2/2 : c(|x|-c/2), c=1.345 DIST_HUBER = 7, } opencv_type_enum! { crate::imgproc::DistanceTypes } /// floodfill algorithm flags #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub enum FloodFillFlags { /// If set, the difference between the current pixel and seed pixel is considered. Otherwise, /// the difference between neighbor pixels is considered (that is, the range is floating). FLOODFILL_FIXED_RANGE = 65536, /// If set, the function does not change the image ( newVal is ignored), and only fills the /// mask with the value specified in bits 8-16 of flags as described above. This option only make /// sense in function variants that have the mask parameter. FLOODFILL_MASK_ONLY = 131072, } opencv_type_enum! { crate::imgproc::FloodFillFlags } /// class of the pixel in GrabCut algorithm #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub enum GrabCutClasses { /// an obvious background pixels GC_BGD = 0, /// an obvious foreground (object) pixel GC_FGD = 1, /// a possible background pixel GC_PR_BGD = 2, /// a possible foreground pixel GC_PR_FGD = 3, } opencv_type_enum! { crate::imgproc::GrabCutClasses } /// GrabCut algorithm flags #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub enum GrabCutModes { /// The function initializes the state and the mask using the provided rectangle. After that it /// runs iterCount iterations of the algorithm. GC_INIT_WITH_RECT = 0, /// The function initializes the state using the provided mask. Note that GC_INIT_WITH_RECT /// and GC_INIT_WITH_MASK can be combined. Then, all the pixels outside of the ROI are /// automatically initialized with GC_BGD . GC_INIT_WITH_MASK = 1, /// The value means that the algorithm should just resume. GC_EVAL = 2, /// The value means that the algorithm should just run the grabCut algorithm (a single iteration) with the fixed model GC_EVAL_FREEZE_MODEL = 3, } opencv_type_enum! { crate::imgproc::GrabCutModes } /// Only a subset of Hershey fonts <https://en.wikipedia.org/wiki/Hershey_fonts> are supported /// @ingroup imgproc_draw #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub enum HersheyFonts { /// normal size sans-serif font FONT_HERSHEY_SIMPLEX = 0, /// small size sans-serif font FONT_HERSHEY_PLAIN = 1, /// normal size sans-serif font (more complex than FONT_HERSHEY_SIMPLEX) FONT_HERSHEY_DUPLEX = 2, /// normal size serif font FONT_HERSHEY_COMPLEX = 3, /// normal size serif font (more complex than FONT_HERSHEY_COMPLEX) FONT_HERSHEY_TRIPLEX = 4, /// smaller version of FONT_HERSHEY_COMPLEX FONT_HERSHEY_COMPLEX_SMALL = 5, /// hand-writing style font FONT_HERSHEY_SCRIPT_SIMPLEX = 6, /// more complex variant of FONT_HERSHEY_SCRIPT_SIMPLEX FONT_HERSHEY_SCRIPT_COMPLEX = 7, /// flag for italic font FONT_ITALIC = 16, } opencv_type_enum! { crate::imgproc::HersheyFonts } /// Histogram comparison methods /// @ingroup imgproc_hist #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub enum HistCompMethods { /// Correlation ///  /// where ///  /// and  is a total number of histogram bins. HISTCMP_CORREL = 0, /// Chi-Square ///  HISTCMP_CHISQR = 1, /// Intersection ///  HISTCMP_INTERSECT = 2, /// Bhattacharyya distance /// (In fact, OpenCV computes Hellinger distance, which is related to Bhattacharyya coefficient.) ///  HISTCMP_BHATTACHARYYA = 3, // Synonym for HISTCMP_BHATTACHARYYA // HISTCMP_HELLINGER = 3 as isize, // duplicate discriminant /// Alternative Chi-Square ///  /// This alternative formula is regularly used for texture comparison. See e.g. [Puzicha1997](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Puzicha1997) HISTCMP_CHISQR_ALT = 4, /// Kullback-Leibler divergence ///  HISTCMP_KL_DIV = 5, } opencv_type_enum! { crate::imgproc::HistCompMethods } /// Variants of a Hough transform #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub enum HoughModes { /// classical or standard Hough transform. Every line is represented by two floating-point /// numbers  , where  is a distance between (0,0) point and the line, /// and  is the angle between x-axis and the normal to the line. Thus, the matrix must /// be (the created sequence will be) of CV_32FC2 type HOUGH_STANDARD = 0, /// probabilistic Hough transform (more efficient in case if the picture contains a few long /// linear segments). It returns line segments rather than the whole line. Each segment is /// represented by starting and ending points, and the matrix must be (the created sequence will /// be) of the CV_32SC4 type. HOUGH_PROBABILISTIC = 1, /// multi-scale variant of the classical Hough transform. The lines are encoded the same way as /// HOUGH_STANDARD. HOUGH_MULTI_SCALE = 2, /// basically *21HT*, described in [Yuen90](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Yuen90) HOUGH_GRADIENT = 3, /// variation of HOUGH_GRADIENT to get better accuracy HOUGH_GRADIENT_ALT = 4, } opencv_type_enum! { crate::imgproc::HoughModes } /// interpolation algorithm #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub enum InterpolationFlags { /// nearest neighbor interpolation INTER_NEAREST = 0, /// bilinear interpolation INTER_LINEAR = 1, /// bicubic interpolation INTER_CUBIC = 2, /// resampling using pixel area relation. It may be a preferred method for image decimation, as /// it gives moire'-free results. But when the image is zoomed, it is similar to the INTER_NEAREST /// method. INTER_AREA = 3, /// Lanczos interpolation over 8x8 neighborhood INTER_LANCZOS4 = 4, /// Bit exact bilinear interpolation INTER_LINEAR_EXACT = 5, /// Bit exact nearest neighbor interpolation. This will produce same results as /// the nearest neighbor method in PIL, scikit-image or Matlab. INTER_NEAREST_EXACT = 6, /// mask for interpolation codes INTER_MAX = 7, /// flag, fills all of the destination image pixels. If some of them correspond to outliers in the /// source image, they are set to zero WARP_FILL_OUTLIERS = 8, /// flag, inverse transformation /// /// For example, #linearPolar or #logPolar transforms: /// - flag is __not__ set:  /// - flag is set:  WARP_INVERSE_MAP = 16, } opencv_type_enum! { crate::imgproc::InterpolationFlags } #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub enum InterpolationMasks { INTER_BITS = 5, INTER_BITS2 = 10, INTER_TAB_SIZE = 32, INTER_TAB_SIZE2 = 1024, } opencv_type_enum! { crate::imgproc::InterpolationMasks } /// Variants of Line Segment %Detector #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub enum LineSegmentDetectorModes { /// No refinement applied LSD_REFINE_NONE = 0, /// Standard refinement is applied. E.g. breaking arches into smaller straighter line approximations. LSD_REFINE_STD = 1, /// Advanced refinement. Number of false alarms is calculated, lines are /// refined through increase of precision, decrement in size, etc. LSD_REFINE_ADV = 2, } opencv_type_enum! { crate::imgproc::LineSegmentDetectorModes } /// types of line /// @ingroup imgproc_draw #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub enum LineTypes { FILLED = -1, /// 4-connected line LINE_4 = 4, /// 8-connected line LINE_8 = 8, /// antialiased line LINE_AA = 16, } opencv_type_enum! { crate::imgproc::LineTypes } /// Possible set of marker types used for the cv::drawMarker function /// @ingroup imgproc_draw #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub enum MarkerTypes { /// A crosshair marker shape MARKER_CROSS = 0, /// A 45 degree tilted crosshair marker shape MARKER_TILTED_CROSS = 1, /// A star marker shape, combination of cross and tilted cross MARKER_STAR = 2, /// A diamond marker shape MARKER_DIAMOND = 3, /// A square marker shape MARKER_SQUARE = 4, /// An upwards pointing triangle marker shape MARKER_TRIANGLE_UP = 5, /// A downwards pointing triangle marker shape MARKER_TRIANGLE_DOWN = 6, } opencv_type_enum! { crate::imgproc::MarkerTypes } /// shape of the structuring element #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub enum MorphShapes { /// a rectangular structuring element:  MORPH_RECT = 0, /// a cross-shaped structuring element: ///  MORPH_CROSS = 1, /// an elliptic structuring element, that is, a filled ellipse inscribed /// into the rectangle Rect(0, 0, esize.width, 0.esize.height) MORPH_ELLIPSE = 2, } opencv_type_enum! { crate::imgproc::MorphShapes } /// type of morphological operation #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub enum MorphTypes { /// see #erode MORPH_ERODE = 0, /// see #dilate MORPH_DILATE = 1, /// an opening operation ///  MORPH_OPEN = 2, /// a closing operation ///  MORPH_CLOSE = 3, /// a morphological gradient ///  MORPH_GRADIENT = 4, /// "top hat" ///  MORPH_TOPHAT = 5, /// "black hat" ///  MORPH_BLACKHAT = 6, /// "hit or miss" /// .- Only supported for CV_8UC1 binary images. A tutorial can be found in the documentation MORPH_HITMISS = 7, } opencv_type_enum! { crate::imgproc::MorphTypes } /// types of intersection between rectangles #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub enum RectanglesIntersectTypes { /// No intersection INTERSECT_NONE = 0, /// There is a partial intersection INTERSECT_PARTIAL = 1, /// One of the rectangle is fully enclosed in the other INTERSECT_FULL = 2, } opencv_type_enum! { crate::imgproc::RectanglesIntersectTypes } /// mode of the contour retrieval algorithm #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub enum RetrievalModes { /// retrieves only the extreme outer contours. It sets `hierarchy[i][2]=hierarchy[i][3]=-1` for /// all the contours. RETR_EXTERNAL = 0, /// retrieves all of the contours without establishing any hierarchical relationships. RETR_LIST = 1, /// retrieves all of the contours and organizes them into a two-level hierarchy. At the top /// level, there are external boundaries of the components. At the second level, there are /// boundaries of the holes. If there is another contour inside a hole of a connected component, it /// is still put at the top level. RETR_CCOMP = 2, /// retrieves all of the contours and reconstructs a full hierarchy of nested contours. RETR_TREE = 3, RETR_FLOODFILL = 4, } opencv_type_enum! { crate::imgproc::RetrievalModes } /// Shape matching methods /// ///  denotes object1, denotes object2 /// ///  /// /// and  are the Hu moments of  and  , respectively. #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub enum ShapeMatchModes { ///  CONTOURS_MATCH_I1 = 1, ///  CONTOURS_MATCH_I2 = 2, ///  CONTOURS_MATCH_I3 = 3, } opencv_type_enum! { crate::imgproc::ShapeMatchModes } #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub enum SpecialFilter { FILTER_SCHARR = -1, } opencv_type_enum! { crate::imgproc::SpecialFilter } /// type of the template matching operation #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub enum TemplateMatchModes { /// !<  /// with mask: ///  TM_SQDIFF = 0, /// !<  /// with mask: ///  TM_SQDIFF_NORMED = 1, /// !<  /// with mask: ///  TM_CCORR = 2, /// !<  /// with mask: ///  TM_CCORR_NORMED = 3, /// !<  /// where ///  /// with mask: ///  TM_CCOEFF = 4, /// !<  TM_CCOEFF_NORMED = 5, } opencv_type_enum! { crate::imgproc::TemplateMatchModes } /// type of the threshold operation ///  #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub enum ThresholdTypes { ///  THRESH_BINARY = 0, ///  THRESH_BINARY_INV = 1, ///  THRESH_TRUNC = 2, ///  THRESH_TOZERO = 3, ///  THRESH_TOZERO_INV = 4, THRESH_MASK = 7, /// flag, use Otsu algorithm to choose the optimal threshold value THRESH_OTSU = 8, /// flag, use Triangle algorithm to choose the optimal threshold value THRESH_TRIANGLE = 16, } opencv_type_enum! { crate::imgproc::ThresholdTypes } /// \brief Specify the polar mapping mode /// ## See also /// warpPolar #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub enum WarpPolarMode { /// Remaps an image to/from polar space. WARP_POLAR_LINEAR = 0, /// Remaps an image to/from semilog-polar space. WARP_POLAR_LOG = 256, } opencv_type_enum! { crate::imgproc::WarpPolarMode } /// \overload /// /// Finds edges in an image using the Canny algorithm with custom image gradient. /// /// ## Parameters /// * dx: 16-bit x derivative of input image (CV_16SC1 or CV_16SC3). /// * dy: 16-bit y derivative of input image (same type as dx). /// * edges: output edge map; single channels 8-bit image, which has the same size as image . /// * threshold1: first threshold for the hysteresis procedure. /// * threshold2: second threshold for the hysteresis procedure. /// * L2gradient: a flag, indicating whether a more accurate  norm ///  should be used to calculate the image gradient magnitude ( /// L2gradient=true ), or whether the default  norm  is enough ( /// L2gradient=false ). /// /// ## C++ default parameters /// * l2gradient: false pub fn canny_derivative(dx: &dyn core::ToInputArray, dy: &dyn core::ToInputArray, edges: &mut dyn core::ToOutputArray, threshold1: f64, threshold2: f64, l2gradient: bool) -> Result<()> { input_array_arg!(dx); input_array_arg!(dy); output_array_arg!(edges); unsafe { sys::cv_Canny_const__InputArrayR_const__InputArrayR_const__OutputArrayR_double_double_bool(dx.as_raw__InputArray(), dy.as_raw__InputArray(), edges.as_raw__OutputArray(), threshold1, threshold2, l2gradient) }.into_result() } /// Finds edges in an image using the Canny algorithm [Canny86](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Canny86) . /// /// The function finds edges in the input image and marks them in the output map edges using the /// Canny algorithm. The smallest value between threshold1 and threshold2 is used for edge linking. The /// largest value is used to find initial segments of strong edges. See /// <http://en.wikipedia.org/wiki/Canny_edge_detector> /// /// ## Parameters /// * image: 8-bit input image. /// * edges: output edge map; single channels 8-bit image, which has the same size as image . /// * threshold1: first threshold for the hysteresis procedure. /// * threshold2: second threshold for the hysteresis procedure. /// * apertureSize: aperture size for the Sobel operator. /// * L2gradient: a flag, indicating whether a more accurate  norm ///  should be used to calculate the image gradient magnitude ( /// L2gradient=true ), or whether the default  norm  is enough ( /// L2gradient=false ). /// /// ## C++ default parameters /// * aperture_size: 3 /// * l2gradient: false pub fn canny(image: &dyn core::ToInputArray, edges: &mut dyn core::ToOutputArray, threshold1: f64, threshold2: f64, aperture_size: i32, l2gradient: bool) -> Result<()> { input_array_arg!(image); output_array_arg!(edges); unsafe { sys::cv_Canny_const__InputArrayR_const__OutputArrayR_double_double_int_bool(image.as_raw__InputArray(), edges.as_raw__OutputArray(), threshold1, threshold2, aperture_size, l2gradient) }.into_result() } /// Computes the "minimal work" distance between two weighted point configurations. /// /// The function computes the earth mover distance and/or a lower boundary of the distance between the /// two weighted point configurations. One of the applications described in [RubnerSept98](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_RubnerSept98), /// [Rubner2000](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Rubner2000) is multi-dimensional histogram comparison for image retrieval. EMD is a transportation /// problem that is solved using some modification of a simplex algorithm, thus the complexity is /// exponential in the worst case, though, on average it is much faster. In the case of a real metric /// the lower boundary can be calculated even faster (using linear-time algorithm) and it can be used /// to determine roughly whether the two signatures are far enough so that they cannot relate to the /// same object. /// /// ## Parameters /// * signature1: First signature, a  floating-point matrix. /// Each row stores the point weight followed by the point coordinates. The matrix is allowed to have /// a single column (weights only) if the user-defined cost matrix is used. The weights must be /// non-negative and have at least one non-zero value. /// * signature2: Second signature of the same format as signature1 , though the number of rows /// may be different. The total weights may be different. In this case an extra "dummy" point is added /// to either signature1 or signature2. The weights must be non-negative and have at least one non-zero /// value. /// * distType: Used metric. See #DistanceTypes. /// * cost: User-defined  cost matrix. Also, if a cost matrix /// is used, lower boundary lowerBound cannot be calculated because it needs a metric function. /// * lowerBound: Optional input/output parameter: lower boundary of a distance between the two /// signatures that is a distance between mass centers. The lower boundary may not be calculated if /// the user-defined cost matrix is used, the total weights of point configurations are not equal, or /// if the signatures consist of weights only (the signature matrices have a single column). You /// **must** initialize \*lowerBound . If the calculated distance between mass centers is greater or /// equal to \*lowerBound (it means that the signatures are far enough), the function does not /// calculate EMD. In any case \*lowerBound is set to the calculated distance between mass centers on /// return. Thus, if you want to calculate both distance between mass centers and EMD, \*lowerBound /// should be set to 0. /// * flow: Resultant  flow matrix:  is /// a flow from  -th point of signature1 to  -th point of signature2 . /// /// ## C++ default parameters /// * cost: noArray() /// * lower_bound: 0 /// * flow: noArray() pub fn emd(signature1: &dyn core::ToInputArray, signature2: &dyn core::ToInputArray, dist_type: i32, cost: &dyn core::ToInputArray, lower_bound: &mut f32, flow: &mut dyn core::ToOutputArray) -> Result<f32> { input_array_arg!(signature1); input_array_arg!(signature2); input_array_arg!(cost); output_array_arg!(flow); unsafe { sys::cv_EMD_const__InputArrayR_const__InputArrayR_int_const__InputArrayR_floatX_const__OutputArrayR(signature1.as_raw__InputArray(), signature2.as_raw__InputArray(), dist_type, cost.as_raw__InputArray(), lower_bound, flow.as_raw__OutputArray()) }.into_result() } /// Blurs an image using a Gaussian filter. /// /// The function convolves the source image with the specified Gaussian kernel. In-place filtering is /// supported. /// /// ## Parameters /// * src: input image; the image can have any number of channels, which are processed /// independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. /// * dst: output image of the same size and type as src. /// * ksize: Gaussian kernel size. ksize.width and ksize.height can differ but they both must be /// positive and odd. Or, they can be zero's and then they are computed from sigma. /// * sigmaX: Gaussian kernel standard deviation in X direction. /// * sigmaY: Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be /// equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height, /// respectively (see #getGaussianKernel for details); to fully control the result regardless of /// possible future modifications of all this semantics, it is recommended to specify all of ksize, /// sigmaX, and sigmaY. /// * borderType: pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. /// ## See also /// sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur /// /// ## C++ default parameters /// * sigma_y: 0 /// * border_type: BORDER_DEFAULT pub fn gaussian_blur(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, ksize: core::Size, sigma_x: f64, sigma_y: f64, border_type: i32) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); unsafe { sys::cv_GaussianBlur_const__InputArrayR_const__OutputArrayR_Size_double_double_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ksize.opencv_as_extern(), sigma_x, sigma_y, border_type) }.into_result() } /// Finds circles in a grayscale image using the Hough transform. /// /// The function finds circles in a grayscale image using a modification of the Hough transform. /// /// Example: : /// @include snippets/imgproc_HoughLinesCircles.cpp /// /// /// Note: Usually the function detects the centers of circles well. However, it may fail to find correct /// radii. You can assist to the function by specifying the radius range ( minRadius and maxRadius ) if /// you know it. Or, in the case of #HOUGH_GRADIENT method you may set maxRadius to a negative number /// to return centers only without radius search, and find the correct radius using an additional procedure. /// /// It also helps to smooth image a bit unless it's already soft. For example, /// GaussianBlur() with 7x7 kernel and 1.5x1.5 sigma or similar blurring may help. /// /// ## Parameters /// * image: 8-bit, single-channel, grayscale input image. /// * circles: Output vector of found circles. Each vector is encoded as 3 or 4 element /// floating-point vector  or  . /// * method: Detection method, see #HoughModes. The available methods are #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT. /// * dp: Inverse ratio of the accumulator resolution to the image resolution. For example, if /// dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has /// half as big width and height. For #HOUGH_GRADIENT_ALT the recommended value is dp=1.5, /// unless some small very circles need to be detected. /// * minDist: Minimum distance between the centers of the detected circles. If the parameter is /// too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is /// too large, some circles may be missed. /// * param1: First method-specific parameter. In case of #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT, /// it is the higher threshold of the two passed to the Canny edge detector (the lower one is twice smaller). /// Note that #HOUGH_GRADIENT_ALT uses #Scharr algorithm to compute image derivatives, so the threshold value /// shough normally be higher, such as 300 or normally exposed and contrasty images. /// * param2: Second method-specific parameter. In case of #HOUGH_GRADIENT, it is the /// accumulator threshold for the circle centers at the detection stage. The smaller it is, the more /// false circles may be detected. Circles, corresponding to the larger accumulator values, will be /// returned first. In the case of #HOUGH_GRADIENT_ALT algorithm, this is the circle "perfectness" measure. /// The closer it to 1, the better shaped circles algorithm selects. In most cases 0.9 should be fine. /// If you want get better detection of small circles, you may decrease it to 0.85, 0.8 or even less. /// But then also try to limit the search range [minRadius, maxRadius] to avoid many false circles. /// * minRadius: Minimum circle radius. /// * maxRadius: Maximum circle radius. If <= 0, uses the maximum image dimension. If < 0, #HOUGH_GRADIENT returns /// centers without finding the radius. #HOUGH_GRADIENT_ALT always computes circle radiuses. /// ## See also /// fitEllipse, minEnclosingCircle /// /// ## C++ default parameters /// * param1: 100 /// * param2: 100 /// * min_radius: 0 /// * max_radius: 0 pub fn hough_circles(image: &dyn core::ToInputArray, circles: &mut dyn core::ToOutputArray, method: i32, dp: f64, min_dist: f64, param1: f64, param2: f64, min_radius: i32, max_radius: i32) -> Result<()> { input_array_arg!(image); output_array_arg!(circles); unsafe { sys::cv_HoughCircles_const__InputArrayR_const__OutputArrayR_int_double_double_double_double_int_int(image.as_raw__InputArray(), circles.as_raw__OutputArray(), method, dp, min_dist, param1, param2, min_radius, max_radius) }.into_result() } /// Finds line segments in a binary image using the probabilistic Hough transform. /// /// The function implements the probabilistic Hough transform algorithm for line detection, described /// in [Matas00](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Matas00) /// /// See the line detection example below: /// @include snippets/imgproc_HoughLinesP.cpp /// This is a sample picture the function parameters have been tuned for: /// ///  /// /// And this is the output of the above program in case of the probabilistic Hough transform: /// ///  /// /// ## Parameters /// * image: 8-bit, single-channel binary source image. The image may be modified by the function. /// * lines: Output vector of lines. Each line is represented by a 4-element vector ///  , where  and  are the ending points of each detected /// line segment. /// * rho: Distance resolution of the accumulator in pixels. /// * theta: Angle resolution of the accumulator in radians. /// * threshold: Accumulator threshold parameter. Only those lines are returned that get enough /// votes (  ). /// * minLineLength: Minimum line length. Line segments shorter than that are rejected. /// * maxLineGap: Maximum allowed gap between points on the same line to link them. /// ## See also /// LineSegmentDetector /// /// ## C++ default parameters /// * min_line_length: 0 /// * max_line_gap: 0 pub fn hough_lines_p(image: &dyn core::ToInputArray, lines: &mut dyn core::ToOutputArray, rho: f64, theta: f64, threshold: i32, min_line_length: f64, max_line_gap: f64) -> Result<()> { input_array_arg!(image); output_array_arg!(lines); unsafe { sys::cv_HoughLinesP_const__InputArrayR_const__OutputArrayR_double_double_int_double_double(image.as_raw__InputArray(), lines.as_raw__OutputArray(), rho, theta, threshold, min_line_length, max_line_gap) }.into_result() } /// Finds lines in a set of points using the standard Hough transform. /// /// The function finds lines in a set of points using a modification of the Hough transform. /// @include snippets/imgproc_HoughLinesPointSet.cpp /// ## Parameters /// * _point: Input vector of points. Each vector must be encoded as a Point vector . Type must be CV_32FC2 or CV_32SC2. /// * _lines: Output vector of found lines. Each vector is encoded as a vector<Vec3d> . /// The larger the value of 'votes', the higher the reliability of the Hough line. /// * lines_max: Max count of hough lines. /// * threshold: Accumulator threshold parameter. Only those lines are returned that get enough /// votes (  ) /// * min_rho: Minimum Distance value of the accumulator in pixels. /// * max_rho: Maximum Distance value of the accumulator in pixels. /// * rho_step: Distance resolution of the accumulator in pixels. /// * min_theta: Minimum angle value of the accumulator in radians. /// * max_theta: Maximum angle value of the accumulator in radians. /// * theta_step: Angle resolution of the accumulator in radians. pub fn hough_lines_point_set(_point: &dyn core::ToInputArray, _lines: &mut dyn core::ToOutputArray, lines_max: i32, threshold: i32, min_rho: f64, max_rho: f64, rho_step: f64, min_theta: f64, max_theta: f64, theta_step: f64) -> Result<()> { input_array_arg!(_point); output_array_arg!(_lines); unsafe { sys::cv_HoughLinesPointSet_const__InputArrayR_const__OutputArrayR_int_int_double_double_double_double_double_double(_point.as_raw__InputArray(), _lines.as_raw__OutputArray(), lines_max, threshold, min_rho, max_rho, rho_step, min_theta, max_theta, theta_step) }.into_result() } /// Finds lines in a binary image using the standard Hough transform. /// /// The function implements the standard or standard multi-scale Hough transform algorithm for line /// detection. See <http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm> for a good explanation of Hough /// transform. /// /// ## Parameters /// * image: 8-bit, single-channel binary source image. The image may be modified by the function. /// * lines: Output vector of lines. Each line is represented by a 2 or 3 element vector ///  or  .  is the distance from the coordinate origin  (top-left corner of /// the image).  is the line rotation angle in radians ( ///  ). ///  is the value of accumulator. /// * rho: Distance resolution of the accumulator in pixels. /// * theta: Angle resolution of the accumulator in radians. /// * threshold: Accumulator threshold parameter. Only those lines are returned that get enough /// votes (  ). /// * srn: For the multi-scale Hough transform, it is a divisor for the distance resolution rho . /// The coarse accumulator distance resolution is rho and the accurate accumulator resolution is /// rho/srn . If both srn=0 and stn=0 , the classical Hough transform is used. Otherwise, both these /// parameters should be positive. /// * stn: For the multi-scale Hough transform, it is a divisor for the distance resolution theta. /// * min_theta: For standard and multi-scale Hough transform, minimum angle to check for lines. /// Must fall between 0 and max_theta. /// * max_theta: For standard and multi-scale Hough transform, maximum angle to check for lines. /// Must fall between min_theta and CV_PI. /// /// ## C++ default parameters /// * srn: 0 /// * stn: 0 /// * min_theta: 0 /// * max_theta: CV_PI pub fn hough_lines(image: &dyn core::ToInputArray, lines: &mut dyn core::ToOutputArray, rho: f64, theta: f64, threshold: i32, srn: f64, stn: f64, min_theta: f64, max_theta: f64) -> Result<()> { input_array_arg!(image); output_array_arg!(lines); unsafe { sys::cv_HoughLines_const__InputArrayR_const__OutputArrayR_double_double_int_double_double_double_double(image.as_raw__InputArray(), lines.as_raw__OutputArray(), rho, theta, threshold, srn, stn, min_theta, max_theta) }.into_result() } /// Calculates seven Hu invariants. /// /// The function calculates seven Hu invariants (introduced in [Hu62](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Hu62); see also /// <http://en.wikipedia.org/wiki/Image_moment>) defined as: /// ///  /// /// where  stands for  . /// /// These values are proved to be invariants to the image scale, rotation, and reflection except the /// seventh one, whose sign is changed by reflection. This invariance is proved with the assumption of /// infinite image resolution. In case of raster images, the computed Hu invariants for the original and /// transformed images are a bit different. /// /// ## Parameters /// * moments: Input moments computed with moments . /// * hu: Output Hu invariants. /// ## See also /// matchShapes /// /// ## Overloaded parameters pub fn hu_moments_1(m: core::Moments, hu: &mut dyn core::ToOutputArray) -> Result<()> { output_array_arg!(hu); unsafe { sys::cv_HuMoments_const_MomentsR_const__OutputArrayR(&m, hu.as_raw__OutputArray()) }.into_result() } /// Calculates seven Hu invariants. /// /// The function calculates seven Hu invariants (introduced in [Hu62](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Hu62); see also /// <http://en.wikipedia.org/wiki/Image_moment>) defined as: /// ///  /// /// where  stands for  . /// /// These values are proved to be invariants to the image scale, rotation, and reflection except the /// seventh one, whose sign is changed by reflection. This invariance is proved with the assumption of /// infinite image resolution. In case of raster images, the computed Hu invariants for the original and /// transformed images are a bit different. /// /// ## Parameters /// * moments: Input moments computed with moments . /// * hu: Output Hu invariants. /// ## See also /// matchShapes pub fn hu_moments(moments: core::Moments, hu: &mut [f64; 7]) -> Result<()> { unsafe { sys::cv_HuMoments_const_MomentsR_double_X__7_(&moments, hu) }.into_result() } /// Calculates the Laplacian of an image. /// /// The function calculates the Laplacian of the source image by adding up the second x and y /// derivatives calculated using the Sobel operator: /// ///  /// /// This is done when `ksize > 1`. When `ksize == 1`, the Laplacian is computed by filtering the image /// with the following  aperture: /// ///  /// /// ## Parameters /// * src: Source image. /// * dst: Destination image of the same size and the same number of channels as src . /// * ddepth: Desired depth of the destination image. /// * ksize: Aperture size used to compute the second-derivative filters. See #getDerivKernels for /// details. The size must be positive and odd. /// * scale: Optional scale factor for the computed Laplacian values. By default, no scaling is /// applied. See #getDerivKernels for details. /// * delta: Optional delta value that is added to the results prior to storing them in dst . /// * borderType: Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. /// ## See also /// Sobel, Scharr /// /// ## C++ default parameters /// * ksize: 1 /// * scale: 1 /// * delta: 0 /// * border_type: BORDER_DEFAULT pub fn laplacian(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, ddepth: i32, ksize: i32, scale: f64, delta: f64, border_type: i32) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); unsafe { sys::cv_Laplacian_const__InputArrayR_const__OutputArrayR_int_int_double_double_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ddepth, ksize, scale, delta, border_type) }.into_result() } /// Calculates the first x- or y- image derivative using Scharr operator. /// /// The function computes the first x- or y- spatial image derivative using the Scharr operator. The /// call /// ///  /// /// is equivalent to /// ///  /// /// ## Parameters /// * src: input image. /// * dst: output image of the same size and the same number of channels as src. /// * ddepth: output image depth, see @ref filter_depths "combinations" /// * dx: order of the derivative x. /// * dy: order of the derivative y. /// * scale: optional scale factor for the computed derivative values; by default, no scaling is /// applied (see #getDerivKernels for details). /// * delta: optional delta value that is added to the results prior to storing them in dst. /// * borderType: pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. /// ## See also /// cartToPolar /// /// ## C++ default parameters /// * scale: 1 /// * delta: 0 /// * border_type: BORDER_DEFAULT pub fn scharr(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, ddepth: i32, dx: i32, dy: i32, scale: f64, delta: f64, border_type: i32) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); unsafe { sys::cv_Scharr_const__InputArrayR_const__OutputArrayR_int_int_int_double_double_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ddepth, dx, dy, scale, delta, border_type) }.into_result() } /// Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator. /// /// In all cases except one, the  separable kernel is used to /// calculate the derivative. When , the  or  /// kernel is used (that is, no Gaussian smoothing is done). `ksize = 1` can only be used for the first /// or the second x- or y- derivatives. /// /// There is also the special value `ksize = #FILTER_SCHARR (-1)` that corresponds to the  Scharr /// filter that may give more accurate results than the  Sobel. The Scharr aperture is /// ///  /// /// for the x-derivative, or transposed for the y-derivative. /// /// The function calculates an image derivative by convolving the image with the appropriate kernel: /// ///  /// /// The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less /// resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3) /// or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x- or y- image derivative. The first /// case corresponds to a kernel of: /// ///  /// /// The second case corresponds to a kernel of: /// ///  /// /// ## Parameters /// * src: input image. /// * dst: output image of the same size and the same number of channels as src . /// * ddepth: output image depth, see @ref filter_depths "combinations"; in the case of /// 8-bit input images it will result in truncated derivatives. /// * dx: order of the derivative x. /// * dy: order of the derivative y. /// * ksize: size of the extended Sobel kernel; it must be 1, 3, 5, or 7. /// * scale: optional scale factor for the computed derivative values; by default, no scaling is /// applied (see #getDerivKernels for details). /// * delta: optional delta value that is added to the results prior to storing them in dst. /// * borderType: pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. /// ## See also /// Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar /// /// ## C++ default parameters /// * ksize: 3 /// * scale: 1 /// * delta: 0 /// * border_type: BORDER_DEFAULT pub fn sobel(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, ddepth: i32, dx: i32, dy: i32, ksize: i32, scale: f64, delta: f64, border_type: i32) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); unsafe { sys::cv_Sobel_const__InputArrayR_const__OutputArrayR_int_int_int_int_double_double_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ddepth, dx, dy, ksize, scale, delta, border_type) }.into_result() } /// Adds the per-element product of two input images to the accumulator image. /// /// The function adds the product of two images or their selected regions to the accumulator dst : /// ///  /// /// The function supports multi-channel images. Each channel is processed independently. /// /// ## Parameters /// * src1: First input image, 1- or 3-channel, 8-bit or 32-bit floating point. /// * src2: Second input image of the same type and the same size as src1 . /// * dst: %Accumulator image with the same number of channels as input images, 32-bit or 64-bit /// floating-point. /// * mask: Optional operation mask. /// ## See also /// accumulate, accumulateSquare, accumulateWeighted /// /// ## C++ default parameters /// * mask: noArray() pub fn accumulate_product(src1: &dyn core::ToInputArray, src2: &dyn core::ToInputArray, dst: &mut dyn core::ToInputOutputArray, mask: &dyn core::ToInputArray) -> Result<()> { input_array_arg!(src1); input_array_arg!(src2); input_output_array_arg!(dst); input_array_arg!(mask); unsafe { sys::cv_accumulateProduct_const__InputArrayR_const__InputArrayR_const__InputOutputArrayR_const__InputArrayR(src1.as_raw__InputArray(), src2.as_raw__InputArray(), dst.as_raw__InputOutputArray(), mask.as_raw__InputArray()) }.into_result() } /// Adds the square of a source image to the accumulator image. /// /// The function adds the input image src or its selected region, raised to a power of 2, to the /// accumulator dst : /// ///  /// /// The function supports multi-channel images. Each channel is processed independently. /// /// ## Parameters /// * src: Input image as 1- or 3-channel, 8-bit or 32-bit floating point. /// * dst: %Accumulator image with the same number of channels as input image, 32-bit or 64-bit /// floating-point. /// * mask: Optional operation mask. /// ## See also /// accumulateSquare, accumulateProduct, accumulateWeighted /// /// ## C++ default parameters /// * mask: noArray() pub fn accumulate_square(src: &dyn core::ToInputArray, dst: &mut dyn core::ToInputOutputArray, mask: &dyn core::ToInputArray) -> Result<()> { input_array_arg!(src); input_output_array_arg!(dst); input_array_arg!(mask); unsafe { sys::cv_accumulateSquare_const__InputArrayR_const__InputOutputArrayR_const__InputArrayR(src.as_raw__InputArray(), dst.as_raw__InputOutputArray(), mask.as_raw__InputArray()) }.into_result() } /// Updates a running average. /// /// The function calculates the weighted sum of the input image src and the accumulator dst so that dst /// becomes a running average of a frame sequence: /// ///  /// /// That is, alpha regulates the update speed (how fast the accumulator "forgets" about earlier images). /// The function supports multi-channel images. Each channel is processed independently. /// /// ## Parameters /// * src: Input image as 1- or 3-channel, 8-bit or 32-bit floating point. /// * dst: %Accumulator image with the same number of channels as input image, 32-bit or 64-bit /// floating-point. /// * alpha: Weight of the input image. /// * mask: Optional operation mask. /// ## See also /// accumulate, accumulateSquare, accumulateProduct /// /// ## C++ default parameters /// * mask: noArray() pub fn accumulate_weighted(src: &dyn core::ToInputArray, dst: &mut dyn core::ToInputOutputArray, alpha: f64, mask: &dyn core::ToInputArray) -> Result<()> { input_array_arg!(src); input_output_array_arg!(dst); input_array_arg!(mask); unsafe { sys::cv_accumulateWeighted_const__InputArrayR_const__InputOutputArrayR_double_const__InputArrayR(src.as_raw__InputArray(), dst.as_raw__InputOutputArray(), alpha, mask.as_raw__InputArray()) }.into_result() } /// Adds an image to the accumulator image. /// /// The function adds src or some of its elements to dst : /// ///  /// /// The function supports multi-channel images. Each channel is processed independently. /// /// The function cv::accumulate can be used, for example, to collect statistics of a scene background /// viewed by a still camera and for the further foreground-background segmentation. /// /// ## Parameters /// * src: Input image of type CV_8UC(n), CV_16UC(n), CV_32FC(n) or CV_64FC(n), where n is a positive integer. /// * dst: %Accumulator image with the same number of channels as input image, and a depth of CV_32F or CV_64F. /// * mask: Optional operation mask. /// ## See also /// accumulateSquare, accumulateProduct, accumulateWeighted /// /// ## C++ default parameters /// * mask: noArray() pub fn accumulate(src: &dyn core::ToInputArray, dst: &mut dyn core::ToInputOutputArray, mask: &dyn core::ToInputArray) -> Result<()> { input_array_arg!(src); input_output_array_arg!(dst); input_array_arg!(mask); unsafe { sys::cv_accumulate_const__InputArrayR_const__InputOutputArrayR_const__InputArrayR(src.as_raw__InputArray(), dst.as_raw__InputOutputArray(), mask.as_raw__InputArray()) }.into_result() } /// Applies an adaptive threshold to an array. /// /// The function transforms a grayscale image to a binary image according to the formulae: /// * **THRESH_BINARY** ///  /// * **THRESH_BINARY_INV** ///  /// where  is a threshold calculated individually for each pixel (see adaptiveMethod parameter). /// /// The function can process the image in-place. /// /// ## Parameters /// * src: Source 8-bit single-channel image. /// * dst: Destination image of the same size and the same type as src. /// * maxValue: Non-zero value assigned to the pixels for which the condition is satisfied /// * adaptiveMethod: Adaptive thresholding algorithm to use, see #AdaptiveThresholdTypes. /// The #BORDER_REPLICATE | #BORDER_ISOLATED is used to process boundaries. /// * thresholdType: Thresholding type that must be either #THRESH_BINARY or #THRESH_BINARY_INV, /// see #ThresholdTypes. /// * blockSize: Size of a pixel neighborhood that is used to calculate a threshold value for the /// pixel: 3, 5, 7, and so on. /// * C: Constant subtracted from the mean or weighted mean (see the details below). Normally, it /// is positive but may be zero or negative as well. /// ## See also /// threshold, blur, GaussianBlur pub fn adaptive_threshold(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, max_value: f64, adaptive_method: i32, threshold_type: i32, block_size: i32, c: f64) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); unsafe { sys::cv_adaptiveThreshold_const__InputArrayR_const__OutputArrayR_double_int_int_int_double(src.as_raw__InputArray(), dst.as_raw__OutputArray(), max_value, adaptive_method, threshold_type, block_size, c) }.into_result() } /// Applies a user colormap on a given image. /// /// ## Parameters /// * src: The source image, grayscale or colored of type CV_8UC1 or CV_8UC3. /// * dst: The result is the colormapped source image. Note: Mat::create is called on dst. /// * userColor: The colormap to apply of type CV_8UC1 or CV_8UC3 and size 256 pub fn apply_color_map_user(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, user_color: &dyn core::ToInputArray) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); input_array_arg!(user_color); unsafe { sys::cv_applyColorMap_const__InputArrayR_const__OutputArrayR_const__InputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), user_color.as_raw__InputArray()) }.into_result() } /// Applies a GNU Octave/MATLAB equivalent colormap on a given image. /// /// ## Parameters /// * src: The source image, grayscale or colored of type CV_8UC1 or CV_8UC3. /// * dst: The result is the colormapped source image. Note: Mat::create is called on dst. /// * colormap: The colormap to apply, see #ColormapTypes pub fn apply_color_map(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, colormap: i32) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); unsafe { sys::cv_applyColorMap_const__InputArrayR_const__OutputArrayR_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), colormap) }.into_result() } /// Approximates a polygonal curve(s) with the specified precision. /// /// The function cv::approxPolyDP approximates a curve or a polygon with another curve/polygon with less /// vertices so that the distance between them is less or equal to the specified precision. It uses the /// Douglas-Peucker algorithm <http://en.wikipedia.org/wiki/Ramer-Douglas-Peucker_algorithm> /// /// ## Parameters /// * curve: Input vector of a 2D point stored in std::vector or Mat /// * approxCurve: Result of the approximation. The type should match the type of the input curve. /// * epsilon: Parameter specifying the approximation accuracy. This is the maximum distance /// between the original curve and its approximation. /// * closed: If true, the approximated curve is closed (its first and last vertices are /// connected). Otherwise, it is not closed. pub fn approx_poly_dp(curve: &dyn core::ToInputArray, approx_curve: &mut dyn core::ToOutputArray, epsilon: f64, closed: bool) -> Result<()> { input_array_arg!(curve); output_array_arg!(approx_curve); unsafe { sys::cv_approxPolyDP_const__InputArrayR_const__OutputArrayR_double_bool(curve.as_raw__InputArray(), approx_curve.as_raw__OutputArray(), epsilon, closed) }.into_result() } /// Calculates a contour perimeter or a curve length. /// /// The function computes a curve length or a closed contour perimeter. /// /// ## Parameters /// * curve: Input vector of 2D points, stored in std::vector or Mat. /// * closed: Flag indicating whether the curve is closed or not. pub fn arc_length(curve: &dyn core::ToInputArray, closed: bool) -> Result<f64> { input_array_arg!(curve); unsafe { sys::cv_arcLength_const__InputArrayR_bool(curve.as_raw__InputArray(), closed) }.into_result() } /// Draws a arrow segment pointing from the first point to the second one. /// /// The function cv::arrowedLine draws an arrow between pt1 and pt2 points in the image. See also #line. /// /// ## Parameters /// * img: Image. /// * pt1: The point the arrow starts from. /// * pt2: The point the arrow points to. /// * color: Line color. /// * thickness: Line thickness. /// * line_type: Type of the line. See #LineTypes /// * shift: Number of fractional bits in the point coordinates. /// * tipLength: The length of the arrow tip in relation to the arrow length /// /// ## C++ default parameters /// * thickness: 1 /// * line_type: 8 /// * shift: 0 /// * tip_length: 0.1 pub fn arrowed_line(img: &mut dyn core::ToInputOutputArray, pt1: core::Point, pt2: core::Point, color: core::Scalar, thickness: i32, line_type: i32, shift: i32, tip_length: f64) -> Result<()> { input_output_array_arg!(img); unsafe { sys::cv_arrowedLine_const__InputOutputArrayR_Point_Point_const_ScalarR_int_int_int_double(img.as_raw__InputOutputArray(), pt1.opencv_as_extern(), pt2.opencv_as_extern(), &color, thickness, line_type, shift, tip_length) }.into_result() } /// Applies the bilateral filter to an image. /// /// The function applies bilateral filtering to the input image, as described in /// http://www.dai.ed.ac.uk/CVonline/LOCAL_COPIES/MANDUCHI1/Bilateral_Filtering.html /// bilateralFilter can reduce unwanted noise very well while keeping edges fairly sharp. However, it is /// very slow compared to most filters. /// /// _Sigma values_: For simplicity, you can set the 2 sigma values to be the same. If they are small (\< /// 10), the filter will not have much effect, whereas if they are large (\> 150), they will have a very /// strong effect, making the image look "cartoonish". /// /// _Filter size_: Large filters (d \> 5) are very slow, so it is recommended to use d=5 for real-time /// applications, and perhaps d=9 for offline applications that need heavy noise filtering. /// /// This filter does not work inplace. /// ## Parameters /// * src: Source 8-bit or floating-point, 1-channel or 3-channel image. /// * dst: Destination image of the same size and type as src . /// * d: Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, /// it is computed from sigmaSpace. /// * sigmaColor: Filter sigma in the color space. A larger value of the parameter means that /// farther colors within the pixel neighborhood (see sigmaSpace) will be mixed together, resulting /// in larger areas of semi-equal color. /// * sigmaSpace: Filter sigma in the coordinate space. A larger value of the parameter means that /// farther pixels will influence each other as long as their colors are close enough (see sigmaColor /// ). When d\>0, it specifies the neighborhood size regardless of sigmaSpace. Otherwise, d is /// proportional to sigmaSpace. /// * borderType: border mode used to extrapolate pixels outside of the image, see #BorderTypes /// /// ## C++ default parameters /// * border_type: BORDER_DEFAULT pub fn bilateral_filter(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, d: i32, sigma_color: f64, sigma_space: f64, border_type: i32) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); unsafe { sys::cv_bilateralFilter_const__InputArrayR_const__OutputArrayR_int_double_double_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), d, sigma_color, sigma_space, border_type) }.into_result() } /// Performs linear blending of two images: ///  /// ## Parameters /// * src1: It has a type of CV_8UC(n) or CV_32FC(n), where n is a positive integer. /// * src2: It has the same type and size as src1. /// * weights1: It has a type of CV_32FC1 and the same size with src1. /// * weights2: It has a type of CV_32FC1 and the same size with src1. /// * dst: It is created if it does not have the same size and type with src1. pub fn blend_linear(src1: &dyn core::ToInputArray, src2: &dyn core::ToInputArray, weights1: &dyn core::ToInputArray, weights2: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray) -> Result<()> { input_array_arg!(src1); input_array_arg!(src2); input_array_arg!(weights1); input_array_arg!(weights2); output_array_arg!(dst); unsafe { sys::cv_blendLinear_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src1.as_raw__InputArray(), src2.as_raw__InputArray(), weights1.as_raw__InputArray(), weights2.as_raw__InputArray(), dst.as_raw__OutputArray()) }.into_result() } /// Blurs an image using the normalized box filter. /// /// The function smooths an image using the kernel: /// ///  /// /// The call `blur(src, dst, ksize, anchor, borderType)` is equivalent to `boxFilter(src, dst, src.type(), ksize, /// anchor, true, borderType)`. /// /// ## Parameters /// * src: input image; it can have any number of channels, which are processed independently, but /// the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. /// * dst: output image of the same size and type as src. /// * ksize: blurring kernel size. /// * anchor: anchor point; default value Point(-1,-1) means that the anchor is at the kernel /// center. /// * borderType: border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported. /// ## See also /// boxFilter, bilateralFilter, GaussianBlur, medianBlur /// /// ## C++ default parameters /// * anchor: Point(-1,-1) /// * border_type: BORDER_DEFAULT pub fn blur(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, ksize: core::Size, anchor: core::Point, border_type: i32) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); unsafe { sys::cv_blur_const__InputArrayR_const__OutputArrayR_Size_Point_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ksize.opencv_as_extern(), anchor.opencv_as_extern(), border_type) }.into_result() } /// Calculates the up-right bounding rectangle of a point set or non-zero pixels of gray-scale image. /// /// The function calculates and returns the minimal up-right bounding rectangle for the specified point set or /// non-zero pixels of gray-scale image. /// /// ## Parameters /// * array: Input gray-scale image or 2D point set, stored in std::vector or Mat. pub fn bounding_rect(array: &dyn core::ToInputArray) -> Result<core::Rect> { input_array_arg!(array); unsafe { sys::cv_boundingRect_const__InputArrayR(array.as_raw__InputArray()) }.into_result() } /// Blurs an image using the box filter. /// /// The function smooths an image using the kernel: /// ///  /// /// where /// ///  /// /// Unnormalized box filter is useful for computing various integral characteristics over each pixel /// neighborhood, such as covariance matrices of image derivatives (used in dense optical flow /// algorithms, and so on). If you need to compute pixel sums over variable-size windows, use #integral. /// /// ## Parameters /// * src: input image. /// * dst: output image of the same size and type as src. /// * ddepth: the output image depth (-1 to use src.depth()). /// * ksize: blurring kernel size. /// * anchor: anchor point; default value Point(-1,-1) means that the anchor is at the kernel /// center. /// * normalize: flag, specifying whether the kernel is normalized by its area or not. /// * borderType: border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported. /// ## See also /// blur, bilateralFilter, GaussianBlur, medianBlur, integral /// /// ## C++ default parameters /// * anchor: Point(-1,-1) /// * normalize: true /// * border_type: BORDER_DEFAULT pub fn box_filter(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, ddepth: i32, ksize: core::Size, anchor: core::Point, normalize: bool, border_type: i32) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); unsafe { sys::cv_boxFilter_const__InputArrayR_const__OutputArrayR_int_Size_Point_bool_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ddepth, ksize.opencv_as_extern(), anchor.opencv_as_extern(), normalize, border_type) }.into_result() } /// Finds the four vertices of a rotated rect. Useful to draw the rotated rectangle. /// /// The function finds the four vertices of a rotated rectangle. This function is useful to draw the /// rectangle. In C++, instead of using this function, you can directly use RotatedRect::points method. Please /// visit the @ref tutorial_bounding_rotated_ellipses "tutorial on Creating Bounding rotated boxes and ellipses for contours" for more information. /// /// ## Parameters /// * box: The input rotated rectangle. It may be the output of /// * points: The output array of four vertices of rectangles. pub fn box_points(mut box_: core::RotatedRect, points: &mut dyn core::ToOutputArray) -> Result<()> { output_array_arg!(points); unsafe { sys::cv_boxPoints_RotatedRect_const__OutputArrayR(box_.as_raw_mut_RotatedRect(), points.as_raw__OutputArray()) }.into_result() } /// Constructs the Gaussian pyramid for an image. /// /// The function constructs a vector of images and builds the Gaussian pyramid by recursively applying /// pyrDown to the previously built pyramid layers, starting from `dst[0]==src`. /// /// ## Parameters /// * src: Source image. Check pyrDown for the list of supported types. /// * dst: Destination vector of maxlevel+1 images of the same type as src. dst[0] will be the /// same as src. dst[1] is the next pyramid layer, a smoothed and down-sized src, and so on. /// * maxlevel: 0-based index of the last (the smallest) pyramid layer. It must be non-negative. /// * borderType: Pixel extrapolation method, see #BorderTypes (#BORDER_CONSTANT isn't supported) /// /// ## C++ default parameters /// * border_type: BORDER_DEFAULT pub fn build_pyramid(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, maxlevel: i32, border_type: i32) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); unsafe { sys::cv_buildPyramid_const__InputArrayR_const__OutputArrayR_int_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), maxlevel, border_type) }.into_result() } /// Calculates the back projection of a histogram. /// /// The function cv::calcBackProject calculates the back project of the histogram. That is, similarly to /// #calcHist , at each location (x, y) the function collects the values from the selected channels /// in the input images and finds the corresponding histogram bin. But instead of incrementing it, the /// function reads the bin value, scales it by scale , and stores in backProject(x,y) . In terms of /// statistics, the function computes probability of each element value in respect with the empirical /// probability distribution represented by the histogram. See how, for example, you can find and track /// a bright-colored object in a scene: /// /// - Before tracking, show the object to the camera so that it covers almost the whole frame. /// Calculate a hue histogram. The histogram may have strong maximums, corresponding to the dominant /// colors in the object. /// /// - When tracking, calculate a back projection of a hue plane of each input video frame using that /// pre-computed histogram. Threshold the back projection to suppress weak colors. It may also make /// sense to suppress pixels with non-sufficient color saturation and too dark or too bright pixels. /// /// - Find connected components in the resulting picture and choose, for example, the largest /// component. /// /// This is an approximate algorithm of the CamShift color object tracker. /// /// ## Parameters /// * images: Source arrays. They all should have the same depth, CV_8U, CV_16U or CV_32F , and the same /// size. Each of them can have an arbitrary number of channels. /// * nimages: Number of source images. /// * channels: The list of channels used to compute the back projection. The number of channels /// must match the histogram dimensionality. The first array channels are numerated from 0 to /// images[0].channels()-1 , the second array channels are counted from images[0].channels() to /// images[0].channels() + images[1].channels()-1, and so on. /// * hist: Input histogram that can be dense or sparse. /// * backProject: Destination back projection array that is a single-channel array of the same /// size and depth as images[0] . /// * ranges: Array of arrays of the histogram bin boundaries in each dimension. See #calcHist . /// * scale: Optional scale factor for the output back projection. /// * uniform: Flag indicating whether the histogram is uniform or not (see above). /// ## See also /// calcHist, compareHist /// /// ## Overloaded parameters pub fn calc_back_project(images: &dyn core::ToInputArray, channels: &core::Vector::<i32>, hist: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, ranges: &core::Vector::<f32>, scale: f64) -> Result<()> { input_array_arg!(images); input_array_arg!(hist); output_array_arg!(dst); unsafe { sys::cv_calcBackProject_const__InputArrayR_const_vector_int_R_const__InputArrayR_const__OutputArrayR_const_vector_float_R_double(images.as_raw__InputArray(), channels.as_raw_VectorOfi32(), hist.as_raw__InputArray(), dst.as_raw__OutputArray(), ranges.as_raw_VectorOff32(), scale) }.into_result() } /// Calculates a histogram of a set of arrays. /// /// The function cv::calcHist calculates the histogram of one or more arrays. The elements of a tuple used /// to increment a histogram bin are taken from the corresponding input arrays at the same location. The /// sample below shows how to compute a 2D Hue-Saturation histogram for a color image. : /// @include snippets/imgproc_calcHist.cpp /// /// ## Parameters /// * images: Source arrays. They all should have the same depth, CV_8U, CV_16U or CV_32F , and the same /// size. Each of them can have an arbitrary number of channels. /// * nimages: Number of source images. /// * channels: List of the dims channels used to compute the histogram. The first array channels /// are numerated from 0 to images[0].channels()-1 , the second array channels are counted from /// images[0].channels() to images[0].channels() + images[1].channels()-1, and so on. /// * mask: Optional mask. If the matrix is not empty, it must be an 8-bit array of the same size /// as images[i] . The non-zero mask elements mark the array elements counted in the histogram. /// * hist: Output histogram, which is a dense or sparse dims -dimensional array. /// * dims: Histogram dimensionality that must be positive and not greater than CV_MAX_DIMS /// (equal to 32 in the current OpenCV version). /// * histSize: Array of histogram sizes in each dimension. /// * ranges: Array of the dims arrays of the histogram bin boundaries in each dimension. When the /// histogram is uniform ( uniform =true), then for each dimension i it is enough to specify the lower /// (inclusive) boundary  of the 0-th histogram bin and the upper (exclusive) boundary ///  for the last histogram bin histSize[i]-1 . That is, in case of a /// uniform histogram each of ranges[i] is an array of 2 elements. When the histogram is not uniform ( /// uniform=false ), then each of ranges[i] contains histSize[i]+1 elements: ///  /// . The array elements, that are not between  and  , are not /// counted in the histogram. /// * uniform: Flag indicating whether the histogram is uniform or not (see above). /// * accumulate: Accumulation flag. If it is set, the histogram is not cleared in the beginning /// when it is allocated. This feature enables you to compute a single histogram from several sets of /// arrays, or to update the histogram in time. /// /// ## Overloaded parameters /// /// ## C++ default parameters /// * accumulate: false pub fn calc_hist(images: &dyn core::ToInputArray, channels: &core::Vector::<i32>, mask: &dyn core::ToInputArray, hist: &mut dyn core::ToOutputArray, hist_size: &core::Vector::<i32>, ranges: &core::Vector::<f32>, accumulate: bool) -> Result<()> { input_array_arg!(images); input_array_arg!(mask); output_array_arg!(hist); unsafe { sys::cv_calcHist_const__InputArrayR_const_vector_int_R_const__InputArrayR_const__OutputArrayR_const_vector_int_R_const_vector_float_R_bool(images.as_raw__InputArray(), channels.as_raw_VectorOfi32(), mask.as_raw__InputArray(), hist.as_raw__OutputArray(), hist_size.as_raw_VectorOfi32(), ranges.as_raw_VectorOff32(), accumulate) }.into_result() } /// Draws a circle. /// /// The function cv::circle draws a simple or filled circle with a given center and radius. /// ## Parameters /// * img: Image where the circle is drawn. /// * center: Center of the circle. /// * radius: Radius of the circle. /// * color: Circle color. /// * thickness: Thickness of the circle outline, if positive. Negative values, like #FILLED, /// mean that a filled circle is to be drawn. /// * lineType: Type of the circle boundary. See #LineTypes /// * shift: Number of fractional bits in the coordinates of the center and in the radius value. /// /// ## C++ default parameters /// * thickness: 1 /// * line_type: LINE_8 /// * shift: 0 pub fn circle(img: &mut dyn core::ToInputOutputArray, center: core::Point, radius: i32, color: core::Scalar, thickness: i32, line_type: i32, shift: i32) -> Result<()> { input_output_array_arg!(img); unsafe { sys::cv_circle_const__InputOutputArrayR_Point_int_const_ScalarR_int_int_int(img.as_raw__InputOutputArray(), center.opencv_as_extern(), radius, &color, thickness, line_type, shift) }.into_result() } /// Clips the line against the image rectangle. /// /// The function cv::clipLine calculates a part of the line segment that is entirely within the specified /// rectangle. it returns false if the line segment is completely outside the rectangle. Otherwise, /// it returns true . /// ## Parameters /// * imgSize: Image size. The image rectangle is Rect(0, 0, imgSize.width, imgSize.height) . /// * pt1: First line point. /// * pt2: Second line point. /// /// ## Overloaded parameters /// /// * imgRect: Image rectangle. /// * pt1: First line point. /// * pt2: Second line point. pub fn clip_line(img_rect: core::Rect, pt1: &mut core::Point, pt2: &mut core::Point) -> Result<bool> { unsafe { sys::cv_clipLine_Rect_PointR_PointR(img_rect.opencv_as_extern(), pt1, pt2) }.into_result() } /// Clips the line against the image rectangle. /// /// The function cv::clipLine calculates a part of the line segment that is entirely within the specified /// rectangle. it returns false if the line segment is completely outside the rectangle. Otherwise, /// it returns true . /// ## Parameters /// * imgSize: Image size. The image rectangle is Rect(0, 0, imgSize.width, imgSize.height) . /// * pt1: First line point. /// * pt2: Second line point. /// /// ## Overloaded parameters /// /// * imgSize: Image size. The image rectangle is Rect(0, 0, imgSize.width, imgSize.height) . /// * pt1: First line point. /// * pt2: Second line point. pub fn clip_line_size_i64(img_size: core::Size2l, pt1: &mut core::Point2l, pt2: &mut core::Point2l) -> Result<bool> { unsafe { sys::cv_clipLine_Size2l_Point2lR_Point2lR(img_size.opencv_as_extern(), pt1, pt2) }.into_result() } /// Clips the line against the image rectangle. /// /// The function cv::clipLine calculates a part of the line segment that is entirely within the specified /// rectangle. it returns false if the line segment is completely outside the rectangle. Otherwise, /// it returns true . /// ## Parameters /// * imgSize: Image size. The image rectangle is Rect(0, 0, imgSize.width, imgSize.height) . /// * pt1: First line point. /// * pt2: Second line point. pub fn clip_line_size(img_size: core::Size, pt1: &mut core::Point, pt2: &mut core::Point) -> Result<bool> { unsafe { sys::cv_clipLine_Size_PointR_PointR(img_size.opencv_as_extern(), pt1, pt2) }.into_result() } /// Compares two histograms. /// /// The function cv::compareHist compares two dense or two sparse histograms using the specified method. /// /// The function returns  . /// /// While the function works well with 1-, 2-, 3-dimensional dense histograms, it may not be suitable /// for high-dimensional sparse histograms. In such histograms, because of aliasing and sampling /// problems, the coordinates of non-zero histogram bins can slightly shift. To compare such histograms /// or more general sparse configurations of weighted points, consider using the #EMD function. /// /// ## Parameters /// * H1: First compared histogram. /// * H2: Second compared histogram of the same size as H1 . /// * method: Comparison method, see #HistCompMethods /// /// ## Overloaded parameters pub fn compare_hist_1(h1: &core::SparseMat, h2: &core::SparseMat, method: i32) -> Result<f64> { unsafe { sys::cv_compareHist_const_SparseMatR_const_SparseMatR_int(h1.as_raw_SparseMat(), h2.as_raw_SparseMat(), method) }.into_result() } /// Compares two histograms. /// /// The function cv::compareHist compares two dense or two sparse histograms using the specified method. /// /// The function returns  . /// /// While the function works well with 1-, 2-, 3-dimensional dense histograms, it may not be suitable /// for high-dimensional sparse histograms. In such histograms, because of aliasing and sampling /// problems, the coordinates of non-zero histogram bins can slightly shift. To compare such histograms /// or more general sparse configurations of weighted points, consider using the #EMD function. /// /// ## Parameters /// * H1: First compared histogram. /// * H2: Second compared histogram of the same size as H1 . /// * method: Comparison method, see #HistCompMethods pub fn compare_hist(h1: &dyn core::ToInputArray, h2: &dyn core::ToInputArray, method: i32) -> Result<f64> { input_array_arg!(h1); input_array_arg!(h2); unsafe { sys::cv_compareHist_const__InputArrayR_const__InputArrayR_int(h1.as_raw__InputArray(), h2.as_raw__InputArray(), method) }.into_result() } /// computes the connected components labeled image of boolean image and also produces a statistics output for each label /// /// image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0 /// represents the background label. ltype specifies the output label image type, an important /// consideration based on the total number of labels or alternatively the total number of pixels in /// the source image. ccltype specifies the connected components labeling algorithm to use, currently /// Grana's (BBDT) and Wu's (SAUF) [Wu2009](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Wu2009) algorithms are supported, see the #ConnectedComponentsAlgorithmsTypes /// for details. Note that SAUF algorithm forces a row major ordering of labels while BBDT does not. /// This function uses parallel version of both Grana and Wu's algorithms (statistics included) if at least one allowed /// parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs. /// /// ## Parameters /// * image: the 8-bit single-channel image to be labeled /// * labels: destination labeled image /// * stats: statistics output for each label, including the background label. /// Statistics are accessed via stats(label, COLUMN) where COLUMN is one of /// #ConnectedComponentsTypes, selecting the statistic. The data type is CV_32S. /// * centroids: centroid output for each label, including the background label. Centroids are /// accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F. /// * connectivity: 8 or 4 for 8-way or 4-way connectivity respectively /// * ltype: output image label type. Currently CV_32S and CV_16U are supported. /// * ccltype: connected components algorithm type (see #ConnectedComponentsAlgorithmsTypes). /// /// ## Overloaded parameters /// /// * image: the 8-bit single-channel image to be labeled /// * labels: destination labeled image /// * stats: statistics output for each label, including the background label. /// Statistics are accessed via stats(label, COLUMN) where COLUMN is one of /// #ConnectedComponentsTypes, selecting the statistic. The data type is CV_32S. /// * centroids: centroid output for each label, including the background label. Centroids are /// accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F. /// * connectivity: 8 or 4 for 8-way or 4-way connectivity respectively /// * ltype: output image label type. Currently CV_32S and CV_16U are supported. /// /// ## C++ default parameters /// * connectivity: 8 /// * ltype: CV_32S pub fn connected_components_with_stats(image: &dyn core::ToInputArray, labels: &mut dyn core::ToOutputArray, stats: &mut dyn core::ToOutputArray, centroids: &mut dyn core::ToOutputArray, connectivity: i32, ltype: i32) -> Result<i32> { input_array_arg!(image); output_array_arg!(labels); output_array_arg!(stats); output_array_arg!(centroids); unsafe { sys::cv_connectedComponentsWithStats_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_int_int(image.as_raw__InputArray(), labels.as_raw__OutputArray(), stats.as_raw__OutputArray(), centroids.as_raw__OutputArray(), connectivity, ltype) }.into_result() } /// computes the connected components labeled image of boolean image and also produces a statistics output for each label /// /// image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0 /// represents the background label. ltype specifies the output label image type, an important /// consideration based on the total number of labels or alternatively the total number of pixels in /// the source image. ccltype specifies the connected components labeling algorithm to use, currently /// Grana's (BBDT) and Wu's (SAUF) [Wu2009](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Wu2009) algorithms are supported, see the #ConnectedComponentsAlgorithmsTypes /// for details. Note that SAUF algorithm forces a row major ordering of labels while BBDT does not. /// This function uses parallel version of both Grana and Wu's algorithms (statistics included) if at least one allowed /// parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs. /// /// ## Parameters /// * image: the 8-bit single-channel image to be labeled /// * labels: destination labeled image /// * stats: statistics output for each label, including the background label. /// Statistics are accessed via stats(label, COLUMN) where COLUMN is one of /// #ConnectedComponentsTypes, selecting the statistic. The data type is CV_32S. /// * centroids: centroid output for each label, including the background label. Centroids are /// accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F. /// * connectivity: 8 or 4 for 8-way or 4-way connectivity respectively /// * ltype: output image label type. Currently CV_32S and CV_16U are supported. /// * ccltype: connected components algorithm type (see #ConnectedComponentsAlgorithmsTypes). pub fn connected_components_with_stats_with_algorithm(image: &dyn core::ToInputArray, labels: &mut dyn core::ToOutputArray, stats: &mut dyn core::ToOutputArray, centroids: &mut dyn core::ToOutputArray, connectivity: i32, ltype: i32, ccltype: i32) -> Result<i32> { input_array_arg!(image); output_array_arg!(labels); output_array_arg!(stats); output_array_arg!(centroids); unsafe { sys::cv_connectedComponentsWithStats_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_int_int_int(image.as_raw__InputArray(), labels.as_raw__OutputArray(), stats.as_raw__OutputArray(), centroids.as_raw__OutputArray(), connectivity, ltype, ccltype) }.into_result() } /// computes the connected components labeled image of boolean image /// /// image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0 /// represents the background label. ltype specifies the output label image type, an important /// consideration based on the total number of labels or alternatively the total number of pixels in /// the source image. ccltype specifies the connected components labeling algorithm to use, currently /// Grana (BBDT) and Wu's (SAUF) [Wu2009](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Wu2009) algorithms are supported, see the #ConnectedComponentsAlgorithmsTypes /// for details. Note that SAUF algorithm forces a row major ordering of labels while BBDT does not. /// This function uses parallel version of both Grana and Wu's algorithms if at least one allowed /// parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs. /// /// ## Parameters /// * image: the 8-bit single-channel image to be labeled /// * labels: destination labeled image /// * connectivity: 8 or 4 for 8-way or 4-way connectivity respectively /// * ltype: output image label type. Currently CV_32S and CV_16U are supported. /// * ccltype: connected components algorithm type (see the #ConnectedComponentsAlgorithmsTypes). /// /// ## Overloaded parameters /// /// /// * image: the 8-bit single-channel image to be labeled /// * labels: destination labeled image /// * connectivity: 8 or 4 for 8-way or 4-way connectivity respectively /// * ltype: output image label type. Currently CV_32S and CV_16U are supported. /// /// ## C++ default parameters /// * connectivity: 8 /// * ltype: CV_32S pub fn connected_components(image: &dyn core::ToInputArray, labels: &mut dyn core::ToOutputArray, connectivity: i32, ltype: i32) -> Result<i32> { input_array_arg!(image); output_array_arg!(labels); unsafe { sys::cv_connectedComponents_const__InputArrayR_const__OutputArrayR_int_int(image.as_raw__InputArray(), labels.as_raw__OutputArray(), connectivity, ltype) }.into_result() } /// computes the connected components labeled image of boolean image /// /// image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0 /// represents the background label. ltype specifies the output label image type, an important /// consideration based on the total number of labels or alternatively the total number of pixels in /// the source image. ccltype specifies the connected components labeling algorithm to use, currently /// Grana (BBDT) and Wu's (SAUF) [Wu2009](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Wu2009) algorithms are supported, see the #ConnectedComponentsAlgorithmsTypes /// for details. Note that SAUF algorithm forces a row major ordering of labels while BBDT does not. /// This function uses parallel version of both Grana and Wu's algorithms if at least one allowed /// parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs. /// /// ## Parameters /// * image: the 8-bit single-channel image to be labeled /// * labels: destination labeled image /// * connectivity: 8 or 4 for 8-way or 4-way connectivity respectively /// * ltype: output image label type. Currently CV_32S and CV_16U are supported. /// * ccltype: connected components algorithm type (see the #ConnectedComponentsAlgorithmsTypes). pub fn connected_components_with_algorithm(image: &dyn core::ToInputArray, labels: &mut dyn core::ToOutputArray, connectivity: i32, ltype: i32, ccltype: i32) -> Result<i32> { input_array_arg!(image); output_array_arg!(labels); unsafe { sys::cv_connectedComponents_const__InputArrayR_const__OutputArrayR_int_int_int(image.as_raw__InputArray(), labels.as_raw__OutputArray(), connectivity, ltype, ccltype) }.into_result() } /// Calculates a contour area. /// /// The function computes a contour area. Similarly to moments , the area is computed using the Green /// formula. Thus, the returned area and the number of non-zero pixels, if you draw the contour using /// #drawContours or #fillPoly , can be different. Also, the function will most certainly give a wrong /// results for contours with self-intersections. /// /// Example: /// ```ignore /// vector<Point> contour; /// contour.push_back(Point2f(0, 0)); /// contour.push_back(Point2f(10, 0)); /// contour.push_back(Point2f(10, 10)); /// contour.push_back(Point2f(5, 4)); /// /// double area0 = contourArea(contour); /// vector<Point> approx; /// approxPolyDP(contour, approx, 5, true); /// double area1 = contourArea(approx); /// /// cout << "area0 =" << area0 << endl << /// "area1 =" << area1 << endl << /// "approx poly vertices" << approx.size() << endl; /// ``` /// /// ## Parameters /// * contour: Input vector of 2D points (contour vertices), stored in std::vector or Mat. /// * oriented: Oriented area flag. If it is true, the function returns a signed area value, /// depending on the contour orientation (clockwise or counter-clockwise). Using this feature you can /// determine orientation of a contour by taking the sign of an area. By default, the parameter is /// false, which means that the absolute value is returned. /// /// ## C++ default parameters /// * oriented: false pub fn contour_area(contour: &dyn core::ToInputArray, oriented: bool) -> Result<f64> { input_array_arg!(contour); unsafe { sys::cv_contourArea_const__InputArrayR_bool(contour.as_raw__InputArray(), oriented) }.into_result() } /// Converts image transformation maps from one representation to another. /// /// The function converts a pair of maps for remap from one representation to another. The following /// options ( (map1.type(), map2.type())  (dstmap1.type(), dstmap2.type()) ) are /// supported: /// /// - . This is the /// most frequently used conversion operation, in which the original floating-point maps (see remap ) /// are converted to a more compact and much faster fixed-point representation. The first output array /// contains the rounded coordinates and the second array (created only when nninterpolation=false ) /// contains indices in the interpolation tables. /// /// - . The same as above but /// the original maps are stored in one 2-channel matrix. /// /// - Reverse conversion. Obviously, the reconstructed floating-point maps will not be exactly the same /// as the originals. /// /// ## Parameters /// * map1: The first input map of type CV_16SC2, CV_32FC1, or CV_32FC2 . /// * map2: The second input map of type CV_16UC1, CV_32FC1, or none (empty matrix), /// respectively. /// * dstmap1: The first output map that has the type dstmap1type and the same size as src . /// * dstmap2: The second output map. /// * dstmap1type: Type of the first output map that should be CV_16SC2, CV_32FC1, or /// CV_32FC2 . /// * nninterpolation: Flag indicating whether the fixed-point maps are used for the /// nearest-neighbor or for a more complex interpolation. /// ## See also /// remap, undistort, initUndistortRectifyMap /// /// ## C++ default parameters /// * nninterpolation: false pub fn convert_maps(map1: &dyn core::ToInputArray, map2: &dyn core::ToInputArray, dstmap1: &mut dyn core::ToOutputArray, dstmap2: &mut dyn core::ToOutputArray, dstmap1type: i32, nninterpolation: bool) -> Result<()> { input_array_arg!(map1); input_array_arg!(map2); output_array_arg!(dstmap1); output_array_arg!(dstmap2); unsafe { sys::cv_convertMaps_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_int_bool(map1.as_raw__InputArray(), map2.as_raw__InputArray(), dstmap1.as_raw__OutputArray(), dstmap2.as_raw__OutputArray(), dstmap1type, nninterpolation) }.into_result() } /// Finds the convex hull of a point set. /// /// The function cv::convexHull finds the convex hull of a 2D point set using the Sklansky's algorithm [Sklansky82](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Sklansky82) /// that has *O(N logN)* complexity in the current implementation. /// /// ## Parameters /// * points: Input 2D point set, stored in std::vector or Mat. /// * hull: Output convex hull. It is either an integer vector of indices or vector of points. In /// the first case, the hull elements are 0-based indices of the convex hull points in the original /// array (since the set of convex hull points is a subset of the original point set). In the second /// case, hull elements are the convex hull points themselves. /// * clockwise: Orientation flag. If it is true, the output convex hull is oriented clockwise. /// Otherwise, it is oriented counter-clockwise. The assumed coordinate system has its X axis pointing /// to the right, and its Y axis pointing upwards. /// * returnPoints: Operation flag. In case of a matrix, when the flag is true, the function /// returns convex hull points. Otherwise, it returns indices of the convex hull points. When the /// output array is std::vector, the flag is ignored, and the output depends on the type of the /// vector: std::vector\<int\> implies returnPoints=false, std::vector\<Point\> implies /// returnPoints=true. /// /// /// Note: `points` and `hull` should be different arrays, inplace processing isn't supported. /// /// Check @ref tutorial_hull "the corresponding tutorial" for more details. /// /// useful links: /// /// https://www.learnopencv.com/convex-hull-using-opencv-in-python-and-c/ /// /// ## C++ default parameters /// * clockwise: false /// * return_points: true pub fn convex_hull(points: &dyn core::ToInputArray, hull: &mut dyn core::ToOutputArray, clockwise: bool, return_points: bool) -> Result<()> { input_array_arg!(points); output_array_arg!(hull); unsafe { sys::cv_convexHull_const__InputArrayR_const__OutputArrayR_bool_bool(points.as_raw__InputArray(), hull.as_raw__OutputArray(), clockwise, return_points) }.into_result() } /// Finds the convexity defects of a contour. /// /// The figure below displays convexity defects of a hand contour: /// ///  /// /// ## Parameters /// * contour: Input contour. /// * convexhull: Convex hull obtained using convexHull that should contain indices of the contour /// points that make the hull. /// * convexityDefects: The output vector of convexity defects. In C++ and the new Python/Java /// interface each convexity defect is represented as 4-element integer vector (a.k.a. #Vec4i): /// (start_index, end_index, farthest_pt_index, fixpt_depth), where indices are 0-based indices /// in the original contour of the convexity defect beginning, end and the farthest point, and /// fixpt_depth is fixed-point approximation (with 8 fractional bits) of the distance between the /// farthest contour point and the hull. That is, to get the floating-point value of the depth will be /// fixpt_depth/256.0. pub fn convexity_defects(contour: &dyn core::ToInputArray, convexhull: &dyn core::ToInputArray, convexity_defects: &mut dyn core::ToOutputArray) -> Result<()> { input_array_arg!(contour); input_array_arg!(convexhull); output_array_arg!(convexity_defects); unsafe { sys::cv_convexityDefects_const__InputArrayR_const__InputArrayR_const__OutputArrayR(contour.as_raw__InputArray(), convexhull.as_raw__InputArray(), convexity_defects.as_raw__OutputArray()) }.into_result() } /// Calculates eigenvalues and eigenvectors of image blocks for corner detection. /// /// For every pixel  , the function cornerEigenValsAndVecs considers a blockSize  blockSize /// neighborhood  . It calculates the covariation matrix of derivatives over the neighborhood as: /// ///  /// /// where the derivatives are computed using the Sobel operator. /// /// After that, it finds eigenvectors and eigenvalues of  and stores them in the destination image as ///  where /// /// *  are the non-sorted eigenvalues of  /// *  are the eigenvectors corresponding to  /// *  are the eigenvectors corresponding to  /// /// The output of the function can be used for robust edge or corner detection. /// /// ## Parameters /// * src: Input single-channel 8-bit or floating-point image. /// * dst: Image to store the results. It has the same size as src and the type CV_32FC(6) . /// * blockSize: Neighborhood size (see details below). /// * ksize: Aperture parameter for the Sobel operator. /// * borderType: Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported. /// ## See also /// cornerMinEigenVal, cornerHarris, preCornerDetect /// /// ## C++ default parameters /// * border_type: BORDER_DEFAULT pub fn corner_eigen_vals_and_vecs(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, block_size: i32, ksize: i32, border_type: i32) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); unsafe { sys::cv_cornerEigenValsAndVecs_const__InputArrayR_const__OutputArrayR_int_int_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), block_size, ksize, border_type) }.into_result() } /// Harris corner detector. /// /// The function runs the Harris corner detector on the image. Similarly to cornerMinEigenVal and /// cornerEigenValsAndVecs , for each pixel  it calculates a  gradient covariance /// matrix  over a  neighborhood. Then, it /// computes the following characteristic: /// ///  /// /// Corners in the image can be found as the local maxima of this response map. /// /// ## Parameters /// * src: Input single-channel 8-bit or floating-point image. /// * dst: Image to store the Harris detector responses. It has the type CV_32FC1 and the same /// size as src . /// * blockSize: Neighborhood size (see the details on #cornerEigenValsAndVecs ). /// * ksize: Aperture parameter for the Sobel operator. /// * k: Harris detector free parameter. See the formula above. /// * borderType: Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported. /// /// ## C++ default parameters /// * border_type: BORDER_DEFAULT pub fn corner_harris(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, block_size: i32, ksize: i32, k: f64, border_type: i32) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); unsafe { sys::cv_cornerHarris_const__InputArrayR_const__OutputArrayR_int_int_double_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), block_size, ksize, k, border_type) }.into_result() } /// Calculates the minimal eigenvalue of gradient matrices for corner detection. /// /// The function is similar to cornerEigenValsAndVecs but it calculates and stores only the minimal /// eigenvalue of the covariance matrix of derivatives, that is,  in terms /// of the formulae in the cornerEigenValsAndVecs description. /// /// ## Parameters /// * src: Input single-channel 8-bit or floating-point image. /// * dst: Image to store the minimal eigenvalues. It has the type CV_32FC1 and the same size as /// src . /// * blockSize: Neighborhood size (see the details on #cornerEigenValsAndVecs ). /// * ksize: Aperture parameter for the Sobel operator. /// * borderType: Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported. /// /// ## C++ default parameters /// * ksize: 3 /// * border_type: BORDER_DEFAULT pub fn corner_min_eigen_val(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, block_size: i32, ksize: i32, border_type: i32) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); unsafe { sys::cv_cornerMinEigenVal_const__InputArrayR_const__OutputArrayR_int_int_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), block_size, ksize, border_type) }.into_result() } /// Refines the corner locations. /// /// The function iterates to find the sub-pixel accurate location of corners or radial saddle /// points as described in [forstner1987fast](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_forstner1987fast), and as shown on the figure below. /// ///  /// /// Sub-pixel accurate corner locator is based on the observation that every vector from the center  /// to a point  located within a neighborhood of  is orthogonal to the image gradient at  /// subject to image and measurement noise. Consider the expression: /// ///  /// /// where  is an image gradient at one of the points  in a neighborhood of  . The /// value of  is to be found so that  is minimized. A system of equations may be set up /// with  set to zero: /// ///  /// /// where the gradients are summed within a neighborhood ("search window") of  . Calling the first /// gradient term  and the second gradient term  gives: /// ///  /// /// The algorithm sets the center of the neighborhood window at this new center  and then iterates /// until the center stays within a set threshold. /// /// ## Parameters /// * image: Input single-channel, 8-bit or float image. /// * corners: Initial coordinates of the input corners and refined coordinates provided for /// output. /// * winSize: Half of the side length of the search window. For example, if winSize=Size(5,5) , /// then a  search window is used. /// * zeroZone: Half of the size of the dead region in the middle of the search zone over which /// the summation in the formula below is not done. It is used sometimes to avoid possible /// singularities of the autocorrelation matrix. The value of (-1,-1) indicates that there is no such /// a size. /// * criteria: Criteria for termination of the iterative process of corner refinement. That is, /// the process of corner position refinement stops either after criteria.maxCount iterations or when /// the corner position moves by less than criteria.epsilon on some iteration. pub fn corner_sub_pix(image: &dyn core::ToInputArray, corners: &mut dyn core::ToInputOutputArray, win_size: core::Size, zero_zone: core::Size, criteria: core::TermCriteria) -> Result<()> { input_array_arg!(image); input_output_array_arg!(corners); unsafe { sys::cv_cornerSubPix_const__InputArrayR_const__InputOutputArrayR_Size_Size_TermCriteria(image.as_raw__InputArray(), corners.as_raw__InputOutputArray(), win_size.opencv_as_extern(), zero_zone.opencv_as_extern(), criteria.opencv_as_extern()) }.into_result() } /// Creates a smart pointer to a cv::CLAHE class and initializes it. /// /// ## Parameters /// * clipLimit: Threshold for contrast limiting. /// * tileGridSize: Size of grid for histogram equalization. Input image will be divided into /// equally sized rectangular tiles. tileGridSize defines the number of tiles in row and column. /// /// ## C++ default parameters /// * clip_limit: 40.0 /// * tile_grid_size: Size(8,8) pub fn create_clahe(clip_limit: f64, tile_grid_size: core::Size) -> Result<core::Ptr::<dyn crate::imgproc::CLAHE>> { unsafe { sys::cv_createCLAHE_double_Size(clip_limit, tile_grid_size.opencv_as_extern()) }.into_result().map(|r| unsafe { core::Ptr::<dyn crate::imgproc::CLAHE>::opencv_from_extern(r) } ) } /// Creates a smart pointer to a cv::GeneralizedHoughBallard class and initializes it. pub fn create_generalized_hough_ballard() -> Result<core::Ptr::<dyn crate::imgproc::GeneralizedHoughBallard>> { unsafe { sys::cv_createGeneralizedHoughBallard() }.into_result().map(|r| unsafe { core::Ptr::<dyn crate::imgproc::GeneralizedHoughBallard>::opencv_from_extern(r) } ) } /// Creates a smart pointer to a cv::GeneralizedHoughGuil class and initializes it. pub fn create_generalized_hough_guil() -> Result<core::Ptr::<dyn crate::imgproc::GeneralizedHoughGuil>> { unsafe { sys::cv_createGeneralizedHoughGuil() }.into_result().map(|r| unsafe { core::Ptr::<dyn crate::imgproc::GeneralizedHoughGuil>::opencv_from_extern(r) } ) } /// This function computes a Hanning window coefficients in two dimensions. /// /// See (http://en.wikipedia.org/wiki/Hann_function) and (http://en.wikipedia.org/wiki/Window_function) /// for more information. /// /// An example is shown below: /// ```ignore /// // create hanning window of size 100x100 and type CV_32F /// Mat hann; /// createHanningWindow(hann, Size(100, 100), CV_32F); /// ``` /// /// ## Parameters /// * dst: Destination array to place Hann coefficients in /// * winSize: The window size specifications (both width and height must be > 1) /// * type: Created array type pub fn create_hanning_window(dst: &mut dyn core::ToOutputArray, win_size: core::Size, typ: i32) -> Result<()> { output_array_arg!(dst); unsafe { sys::cv_createHanningWindow_const__OutputArrayR_Size_int(dst.as_raw__OutputArray(), win_size.opencv_as_extern(), typ) }.into_result() } /// Creates a smart pointer to a LineSegmentDetector object and initializes it. /// /// The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want /// to edit those, as to tailor it for their own application. /// /// ## Parameters /// * _refine: The way found lines will be refined, see #LineSegmentDetectorModes /// * _scale: The scale of the image that will be used to find the lines. Range (0..1]. /// * _sigma_scale: Sigma for Gaussian filter. It is computed as sigma = _sigma_scale/_scale. /// * _quant: Bound to the quantization error on the gradient norm. /// * _ang_th: Gradient angle tolerance in degrees. /// * _log_eps: Detection threshold: -log10(NFA) \> log_eps. Used only when advance refinement /// is chosen. /// * _density_th: Minimal density of aligned region points in the enclosing rectangle. /// * _n_bins: Number of bins in pseudo-ordering of gradient modulus. /// /// /// Note: Implementation has been removed due original code license conflict /// /// ## C++ default parameters /// * _refine: LSD_REFINE_STD /// * _scale: 0.8 /// * _sigma_scale: 0.6 /// * _quant: 2.0 /// * _ang_th: 22.5 /// * _log_eps: 0 /// * _density_th: 0.7 /// * _n_bins: 1024 pub fn create_line_segment_detector(_refine: i32, _scale: f64, _sigma_scale: f64, _quant: f64, _ang_th: f64, _log_eps: f64, _density_th: f64, _n_bins: i32) -> Result<core::Ptr::<dyn crate::imgproc::LineSegmentDetector>> { unsafe { sys::cv_createLineSegmentDetector_int_double_double_double_double_double_double_int(_refine, _scale, _sigma_scale, _quant, _ang_th, _log_eps, _density_th, _n_bins) }.into_result().map(|r| unsafe { core::Ptr::<dyn crate::imgproc::LineSegmentDetector>::opencv_from_extern(r) } ) } /// Converts an image from one color space to another where the source image is /// stored in two planes. /// /// This function only supports YUV420 to RGB conversion as of now. /// /// ## Parameters /// * src1: : 8-bit image (#CV_8U) of the Y plane. /// * src2: : image containing interleaved U/V plane. /// * dst: : output image. /// * code: : Specifies the type of conversion. It can take any of the following values: /// - #COLOR_YUV2BGR_NV12 /// - #COLOR_YUV2RGB_NV12 /// - #COLOR_YUV2BGRA_NV12 /// - #COLOR_YUV2RGBA_NV12 /// - #COLOR_YUV2BGR_NV21 /// - #COLOR_YUV2RGB_NV21 /// - #COLOR_YUV2BGRA_NV21 /// - #COLOR_YUV2RGBA_NV21 pub fn cvt_color_two_plane(src1: &dyn core::ToInputArray, src2: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, code: i32) -> Result<()> { input_array_arg!(src1); input_array_arg!(src2); output_array_arg!(dst); unsafe { sys::cv_cvtColorTwoPlane_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int(src1.as_raw__InputArray(), src2.as_raw__InputArray(), dst.as_raw__OutputArray(), code) }.into_result() } /// Converts an image from one color space to another. /// /// The function converts an input image from one color space to another. In case of a transformation /// to-from RGB color space, the order of the channels should be specified explicitly (RGB or BGR). Note /// that the default color format in OpenCV is often referred to as RGB but it is actually BGR (the /// bytes are reversed). So the first byte in a standard (24-bit) color image will be an 8-bit Blue /// component, the second byte will be Green, and the third byte will be Red. The fourth, fifth, and /// sixth bytes would then be the second pixel (Blue, then Green, then Red), and so on. /// /// The conventional ranges for R, G, and B channel values are: /// * 0 to 255 for CV_8U images /// * 0 to 65535 for CV_16U images /// * 0 to 1 for CV_32F images /// /// In case of linear transformations, the range does not matter. But in case of a non-linear /// transformation, an input RGB image should be normalized to the proper value range to get the correct /// results, for example, for RGB  L\*u\*v\* transformation. For example, if you have a /// 32-bit floating-point image directly converted from an 8-bit image without any scaling, then it will /// have the 0..255 value range instead of 0..1 assumed by the function. So, before calling #cvtColor , /// you need first to scale the image down: /// ```ignore /// img *= 1./255; /// cvtColor(img, img, COLOR_BGR2Luv); /// ``` /// /// If you use #cvtColor with 8-bit images, the conversion will have some information lost. For many /// applications, this will not be noticeable but it is recommended to use 32-bit images in applications /// that need the full range of colors or that convert an image before an operation and then convert /// back. /// /// If conversion adds the alpha channel, its value will set to the maximum of corresponding channel /// range: 255 for CV_8U, 65535 for CV_16U, 1 for CV_32F. /// /// ## Parameters /// * src: input image: 8-bit unsigned, 16-bit unsigned ( CV_16UC... ), or single-precision /// floating-point. /// * dst: output image of the same size and depth as src. /// * code: color space conversion code (see #ColorConversionCodes). /// * dstCn: number of channels in the destination image; if the parameter is 0, the number of the /// channels is derived automatically from src and code. /// ## See also /// @ref imgproc_color_conversions /// /// ## C++ default parameters /// * dst_cn: 0 pub fn cvt_color(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, code: i32, dst_cn: i32) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); unsafe { sys::cv_cvtColor_const__InputArrayR_const__OutputArrayR_int_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), code, dst_cn) }.into_result() } /// main function for all demosaicing processes /// /// ## Parameters /// * src: input image: 8-bit unsigned or 16-bit unsigned. /// * dst: output image of the same size and depth as src. /// * code: Color space conversion code (see the description below). /// * dstCn: number of channels in the destination image; if the parameter is 0, the number of the /// channels is derived automatically from src and code. /// /// The function can do the following transformations: /// /// * Demosaicing using bilinear interpolation /// /// #COLOR_BayerBG2BGR , #COLOR_BayerGB2BGR , #COLOR_BayerRG2BGR , #COLOR_BayerGR2BGR /// /// #COLOR_BayerBG2GRAY , #COLOR_BayerGB2GRAY , #COLOR_BayerRG2GRAY , #COLOR_BayerGR2GRAY /// /// * Demosaicing using Variable Number of Gradients. /// /// #COLOR_BayerBG2BGR_VNG , #COLOR_BayerGB2BGR_VNG , #COLOR_BayerRG2BGR_VNG , #COLOR_BayerGR2BGR_VNG /// /// * Edge-Aware Demosaicing. /// /// #COLOR_BayerBG2BGR_EA , #COLOR_BayerGB2BGR_EA , #COLOR_BayerRG2BGR_EA , #COLOR_BayerGR2BGR_EA /// /// * Demosaicing with alpha channel /// /// #COLOR_BayerBG2BGRA , #COLOR_BayerGB2BGRA , #COLOR_BayerRG2BGRA , #COLOR_BayerGR2BGRA /// ## See also /// cvtColor /// /// ## C++ default parameters /// * dst_cn: 0 pub fn demosaicing(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, code: i32, dst_cn: i32) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); unsafe { sys::cv_demosaicing_const__InputArrayR_const__OutputArrayR_int_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), code, dst_cn) }.into_result() } /// Dilates an image by using a specific structuring element. /// /// The function dilates the source image using the specified structuring element that determines the /// shape of a pixel neighborhood over which the maximum is taken: ///  /// /// The function supports the in-place mode. Dilation can be applied several ( iterations ) times. In /// case of multi-channel images, each channel is processed independently. /// /// ## Parameters /// * src: input image; the number of channels can be arbitrary, but the depth should be one of /// CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. /// * dst: output image of the same size and type as src. /// * kernel: structuring element used for dilation; if elemenat=Mat(), a 3 x 3 rectangular /// structuring element is used. Kernel can be created using #getStructuringElement /// * anchor: position of the anchor within the element; default value (-1, -1) means that the /// anchor is at the element center. /// * iterations: number of times dilation is applied. /// * borderType: pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not suported. /// * borderValue: border value in case of a constant border /// ## See also /// erode, morphologyEx, getStructuringElement /// /// ## C++ default parameters /// * anchor: Point(-1,-1) /// * iterations: 1 /// * border_type: BORDER_CONSTANT /// * border_value: morphologyDefaultBorderValue() pub fn dilate(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, kernel: &dyn core::ToInputArray, anchor: core::Point, iterations: i32, border_type: i32, border_value: core::Scalar) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); input_array_arg!(kernel); unsafe { sys::cv_dilate_const__InputArrayR_const__OutputArrayR_const__InputArrayR_Point_int_int_const_ScalarR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), kernel.as_raw__InputArray(), anchor.opencv_as_extern(), iterations, border_type, &border_value) }.into_result() } /// Calculates the distance to the closest zero pixel for each pixel of the source image. /// /// The function cv::distanceTransform calculates the approximate or precise distance from every binary /// image pixel to the nearest zero pixel. For zero image pixels, the distance will obviously be zero. /// /// When maskSize == #DIST_MASK_PRECISE and distanceType == #DIST_L2 , the function runs the /// algorithm described in [Felzenszwalb04](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Felzenszwalb04) . This algorithm is parallelized with the TBB library. /// /// In other cases, the algorithm [Borgefors86](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Borgefors86) is used. This means that for a pixel the function /// finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical, /// diagonal, or knight's move (the latest is available for a  mask). The overall /// distance is calculated as a sum of these basic distances. Since the distance function should be /// symmetric, all of the horizontal and vertical shifts must have the same cost (denoted as a ), all /// the diagonal shifts must have the same cost (denoted as `b`), and all knight's moves must have the /// same cost (denoted as `c`). For the #DIST_C and #DIST_L1 types, the distance is calculated /// precisely, whereas for #DIST_L2 (Euclidean distance) the distance can be calculated only with a /// relative error (a  mask gives more accurate results). For `a`,`b`, and `c`, OpenCV /// uses the values suggested in the original paper: /// - DIST_L1: `a = 1, b = 2` /// - DIST_L2: /// - `3 x 3`: `a=0.955, b=1.3693` /// - `5 x 5`: `a=1, b=1.4, c=2.1969` /// - DIST_C: `a = 1, b = 1` /// /// Typically, for a fast, coarse distance estimation #DIST_L2, a  mask is used. For a /// more accurate distance estimation #DIST_L2, a  mask or the precise algorithm is used. /// Note that both the precise and the approximate algorithms are linear on the number of pixels. /// /// This variant of the function does not only compute the minimum distance for each pixel  /// but also identifies the nearest connected component consisting of zero pixels /// (labelType==#DIST_LABEL_CCOMP) or the nearest zero pixel (labelType==#DIST_LABEL_PIXEL). Index of the /// component/pixel is stored in `labels(x, y)`. When labelType==#DIST_LABEL_CCOMP, the function /// automatically finds connected components of zero pixels in the input image and marks them with /// distinct labels. When labelType==#DIST_LABEL_CCOMP, the function scans through the input image and /// marks all the zero pixels with distinct labels. /// /// In this mode, the complexity is still linear. That is, the function provides a very fast way to /// compute the Voronoi diagram for a binary image. Currently, the second variant can use only the /// approximate distance transform algorithm, i.e. maskSize=#DIST_MASK_PRECISE is not supported /// yet. /// /// ## Parameters /// * src: 8-bit, single-channel (binary) source image. /// * dst: Output image with calculated distances. It is a 8-bit or 32-bit floating-point, /// single-channel image of the same size as src. /// * labels: Output 2D array of labels (the discrete Voronoi diagram). It has the type /// CV_32SC1 and the same size as src. /// * distanceType: Type of distance, see #DistanceTypes /// * maskSize: Size of the distance transform mask, see #DistanceTransformMasks. /// #DIST_MASK_PRECISE is not supported by this variant. In case of the #DIST_L1 or #DIST_C distance type, /// the parameter is forced to 3 because a  mask gives the same result as  or any larger aperture. /// * labelType: Type of the label array to build, see #DistanceTransformLabelTypes. /// /// ## C++ default parameters /// * label_type: DIST_LABEL_CCOMP pub fn distance_transform_with_labels(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, labels: &mut dyn core::ToOutputArray, distance_type: i32, mask_size: i32, label_type: i32) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); output_array_arg!(labels); unsafe { sys::cv_distanceTransform_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_int_int_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), labels.as_raw__OutputArray(), distance_type, mask_size, label_type) }.into_result() } /// Calculates the distance to the closest zero pixel for each pixel of the source image. /// /// The function cv::distanceTransform calculates the approximate or precise distance from every binary /// image pixel to the nearest zero pixel. For zero image pixels, the distance will obviously be zero. /// /// When maskSize == #DIST_MASK_PRECISE and distanceType == #DIST_L2 , the function runs the /// algorithm described in [Felzenszwalb04](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Felzenszwalb04) . This algorithm is parallelized with the TBB library. /// /// In other cases, the algorithm [Borgefors86](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Borgefors86) is used. This means that for a pixel the function /// finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical, /// diagonal, or knight's move (the latest is available for a  mask). The overall /// distance is calculated as a sum of these basic distances. Since the distance function should be /// symmetric, all of the horizontal and vertical shifts must have the same cost (denoted as a ), all /// the diagonal shifts must have the same cost (denoted as `b`), and all knight's moves must have the /// same cost (denoted as `c`). For the #DIST_C and #DIST_L1 types, the distance is calculated /// precisely, whereas for #DIST_L2 (Euclidean distance) the distance can be calculated only with a /// relative error (a  mask gives more accurate results). For `a`,`b`, and `c`, OpenCV /// uses the values suggested in the original paper: /// - DIST_L1: `a = 1, b = 2` /// - DIST_L2: /// - `3 x 3`: `a=0.955, b=1.3693` /// - `5 x 5`: `a=1, b=1.4, c=2.1969` /// - DIST_C: `a = 1, b = 1` /// /// Typically, for a fast, coarse distance estimation #DIST_L2, a  mask is used. For a /// more accurate distance estimation #DIST_L2, a  mask or the precise algorithm is used. /// Note that both the precise and the approximate algorithms are linear on the number of pixels. /// /// This variant of the function does not only compute the minimum distance for each pixel  /// but also identifies the nearest connected component consisting of zero pixels /// (labelType==#DIST_LABEL_CCOMP) or the nearest zero pixel (labelType==#DIST_LABEL_PIXEL). Index of the /// component/pixel is stored in `labels(x, y)`. When labelType==#DIST_LABEL_CCOMP, the function /// automatically finds connected components of zero pixels in the input image and marks them with /// distinct labels. When labelType==#DIST_LABEL_CCOMP, the function scans through the input image and /// marks all the zero pixels with distinct labels. /// /// In this mode, the complexity is still linear. That is, the function provides a very fast way to /// compute the Voronoi diagram for a binary image. Currently, the second variant can use only the /// approximate distance transform algorithm, i.e. maskSize=#DIST_MASK_PRECISE is not supported /// yet. /// /// ## Parameters /// * src: 8-bit, single-channel (binary) source image. /// * dst: Output image with calculated distances. It is a 8-bit or 32-bit floating-point, /// single-channel image of the same size as src. /// * labels: Output 2D array of labels (the discrete Voronoi diagram). It has the type /// CV_32SC1 and the same size as src. /// * distanceType: Type of distance, see #DistanceTypes /// * maskSize: Size of the distance transform mask, see #DistanceTransformMasks. /// #DIST_MASK_PRECISE is not supported by this variant. In case of the #DIST_L1 or #DIST_C distance type, /// the parameter is forced to 3 because a  mask gives the same result as  or any larger aperture. /// * labelType: Type of the label array to build, see #DistanceTransformLabelTypes. /// /// ## Overloaded parameters /// /// * src: 8-bit, single-channel (binary) source image. /// * dst: Output image with calculated distances. It is a 8-bit or 32-bit floating-point, /// single-channel image of the same size as src . /// * distanceType: Type of distance, see #DistanceTypes /// * maskSize: Size of the distance transform mask, see #DistanceTransformMasks. In case of the /// #DIST_L1 or #DIST_C distance type, the parameter is forced to 3 because a  mask gives /// the same result as  or any larger aperture. /// * dstType: Type of output image. It can be CV_8U or CV_32F. Type CV_8U can be used only for /// the first variant of the function and distanceType == #DIST_L1. /// /// ## C++ default parameters /// * dst_type: CV_32F pub fn distance_transform(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, distance_type: i32, mask_size: i32, dst_type: i32) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); unsafe { sys::cv_distanceTransform_const__InputArrayR_const__OutputArrayR_int_int_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), distance_type, mask_size, dst_type) }.into_result() } /// Draws contours outlines or filled contours. /// /// The function draws contour outlines in the image if  or fills the area /// bounded by the contours if  . The example below shows how to retrieve /// connected components from the binary image and label them: : /// @include snippets/imgproc_drawContours.cpp /// /// ## Parameters /// * image: Destination image. /// * contours: All the input contours. Each contour is stored as a point vector. /// * contourIdx: Parameter indicating a contour to draw. If it is negative, all the contours are drawn. /// * color: Color of the contours. /// * thickness: Thickness of lines the contours are drawn with. If it is negative (for example, /// thickness=#FILLED ), the contour interiors are drawn. /// * lineType: Line connectivity. See #LineTypes /// * hierarchy: Optional information about hierarchy. It is only needed if you want to draw only /// some of the contours (see maxLevel ). /// * maxLevel: Maximal level for drawn contours. If it is 0, only the specified contour is drawn. /// If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function /// draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This /// parameter is only taken into account when there is hierarchy available. /// * offset: Optional contour shift parameter. Shift all the drawn contours by the specified ///  . /// /// Note: When thickness=#FILLED, the function is designed to handle connected components with holes correctly /// even when no hierarchy date is provided. This is done by analyzing all the outlines together /// using even-odd rule. This may give incorrect results if you have a joint collection of separately retrieved /// contours. In order to solve this problem, you need to call #drawContours separately for each sub-group /// of contours, or iterate over the collection using contourIdx parameter. /// /// ## C++ default parameters /// * thickness: 1 /// * line_type: LINE_8 /// * hierarchy: noArray() /// * max_level: INT_MAX /// * offset: Point() pub fn draw_contours(image: &mut dyn core::ToInputOutputArray, contours: &dyn core::ToInputArray, contour_idx: i32, color: core::Scalar, thickness: i32, line_type: i32, hierarchy: &dyn core::ToInputArray, max_level: i32, offset: core::Point) -> Result<()> { input_output_array_arg!(image); input_array_arg!(contours); input_array_arg!(hierarchy); unsafe { sys::cv_drawContours_const__InputOutputArrayR_const__InputArrayR_int_const_ScalarR_int_int_const__InputArrayR_int_Point(image.as_raw__InputOutputArray(), contours.as_raw__InputArray(), contour_idx, &color, thickness, line_type, hierarchy.as_raw__InputArray(), max_level, offset.opencv_as_extern()) }.into_result() } /// Draws a marker on a predefined position in an image. /// /// The function cv::drawMarker draws a marker on a given position in the image. For the moment several /// marker types are supported, see #MarkerTypes for more information. /// /// ## Parameters /// * img: Image. /// * position: The point where the crosshair is positioned. /// * color: Line color. /// * markerType: The specific type of marker you want to use, see #MarkerTypes /// * thickness: Line thickness. /// * line_type: Type of the line, See #LineTypes /// * markerSize: The length of the marker axis [default = 20 pixels] /// /// ## C++ default parameters /// * marker_type: MARKER_CROSS /// * marker_size: 20 /// * thickness: 1 /// * line_type: 8 pub fn draw_marker(img: &mut dyn core::ToInputOutputArray, position: core::Point, color: core::Scalar, marker_type: i32, marker_size: i32, thickness: i32, line_type: i32) -> Result<()> { input_output_array_arg!(img); unsafe { sys::cv_drawMarker_const__InputOutputArrayR_Point_const_ScalarR_int_int_int_int(img.as_raw__InputOutputArray(), position.opencv_as_extern(), &color, marker_type, marker_size, thickness, line_type) }.into_result() } /// Approximates an elliptic arc with a polyline. /// /// The function ellipse2Poly computes the vertices of a polyline that approximates the specified /// elliptic arc. It is used by #ellipse. If `arcStart` is greater than `arcEnd`, they are swapped. /// /// ## Parameters /// * center: Center of the arc. /// * axes: Half of the size of the ellipse main axes. See #ellipse for details. /// * angle: Rotation angle of the ellipse in degrees. See #ellipse for details. /// * arcStart: Starting angle of the elliptic arc in degrees. /// * arcEnd: Ending angle of the elliptic arc in degrees. /// * delta: Angle between the subsequent polyline vertices. It defines the approximation /// accuracy. /// * pts: Output vector of polyline vertices. /// /// ## Overloaded parameters /// /// * center: Center of the arc. /// * axes: Half of the size of the ellipse main axes. See #ellipse for details. /// * angle: Rotation angle of the ellipse in degrees. See #ellipse for details. /// * arcStart: Starting angle of the elliptic arc in degrees. /// * arcEnd: Ending angle of the elliptic arc in degrees. /// * delta: Angle between the subsequent polyline vertices. It defines the approximation accuracy. /// * pts: Output vector of polyline vertices. pub fn ellipse_2_poly_f64(center: core::Point2d, axes: core::Size2d, angle: i32, arc_start: i32, arc_end: i32, delta: i32, pts: &mut core::Vector::<core::Point2d>) -> Result<()> { unsafe { sys::cv_ellipse2Poly_Point2d_Size2d_int_int_int_int_vector_Point2d_R(center.opencv_as_extern(), axes.opencv_as_extern(), angle, arc_start, arc_end, delta, pts.as_raw_mut_VectorOfPoint2d()) }.into_result() } /// Approximates an elliptic arc with a polyline. /// /// The function ellipse2Poly computes the vertices of a polyline that approximates the specified /// elliptic arc. It is used by #ellipse. If `arcStart` is greater than `arcEnd`, they are swapped. /// /// ## Parameters /// * center: Center of the arc. /// * axes: Half of the size of the ellipse main axes. See #ellipse for details. /// * angle: Rotation angle of the ellipse in degrees. See #ellipse for details. /// * arcStart: Starting angle of the elliptic arc in degrees. /// * arcEnd: Ending angle of the elliptic arc in degrees. /// * delta: Angle between the subsequent polyline vertices. It defines the approximation /// accuracy. /// * pts: Output vector of polyline vertices. pub fn ellipse_2_poly(center: core::Point, axes: core::Size, angle: i32, arc_start: i32, arc_end: i32, delta: i32, pts: &mut core::Vector::<core::Point>) -> Result<()> { unsafe { sys::cv_ellipse2Poly_Point_Size_int_int_int_int_vector_Point_R(center.opencv_as_extern(), axes.opencv_as_extern(), angle, arc_start, arc_end, delta, pts.as_raw_mut_VectorOfPoint()) }.into_result() } /// Draws a simple or thick elliptic arc or fills an ellipse sector. /// /// The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic /// arc, or a filled ellipse sector. The drawing code uses general parametric form. /// A piecewise-linear curve is used to approximate the elliptic arc /// boundary. If you need more control of the ellipse rendering, you can retrieve the curve using /// #ellipse2Poly and then render it with #polylines or fill it with #fillPoly. If you use the first /// variant of the function and want to draw the whole ellipse, not an arc, pass `startAngle=0` and /// `endAngle=360`. If `startAngle` is greater than `endAngle`, they are swapped. The figure below explains /// the meaning of the parameters to draw the blue arc. /// ///  /// /// ## Parameters /// * img: Image. /// * center: Center of the ellipse. /// * axes: Half of the size of the ellipse main axes. /// * angle: Ellipse rotation angle in degrees. /// * startAngle: Starting angle of the elliptic arc in degrees. /// * endAngle: Ending angle of the elliptic arc in degrees. /// * color: Ellipse color. /// * thickness: Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that /// a filled ellipse sector is to be drawn. /// * lineType: Type of the ellipse boundary. See #LineTypes /// * shift: Number of fractional bits in the coordinates of the center and values of axes. /// /// ## C++ default parameters /// * thickness: 1 /// * line_type: LINE_8 /// * shift: 0 pub fn ellipse(img: &mut dyn core::ToInputOutputArray, center: core::Point, axes: core::Size, angle: f64, start_angle: f64, end_angle: f64, color: core::Scalar, thickness: i32, line_type: i32, shift: i32) -> Result<()> { input_output_array_arg!(img); unsafe { sys::cv_ellipse_const__InputOutputArrayR_Point_Size_double_double_double_const_ScalarR_int_int_int(img.as_raw__InputOutputArray(), center.opencv_as_extern(), axes.opencv_as_extern(), angle, start_angle, end_angle, &color, thickness, line_type, shift) }.into_result() } /// Draws a simple or thick elliptic arc or fills an ellipse sector. /// /// The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic /// arc, or a filled ellipse sector. The drawing code uses general parametric form. /// A piecewise-linear curve is used to approximate the elliptic arc /// boundary. If you need more control of the ellipse rendering, you can retrieve the curve using /// #ellipse2Poly and then render it with #polylines or fill it with #fillPoly. If you use the first /// variant of the function and want to draw the whole ellipse, not an arc, pass `startAngle=0` and /// `endAngle=360`. If `startAngle` is greater than `endAngle`, they are swapped. The figure below explains /// the meaning of the parameters to draw the blue arc. /// ///  /// /// ## Parameters /// * img: Image. /// * center: Center of the ellipse. /// * axes: Half of the size of the ellipse main axes. /// * angle: Ellipse rotation angle in degrees. /// * startAngle: Starting angle of the elliptic arc in degrees. /// * endAngle: Ending angle of the elliptic arc in degrees. /// * color: Ellipse color. /// * thickness: Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that /// a filled ellipse sector is to be drawn. /// * lineType: Type of the ellipse boundary. See #LineTypes /// * shift: Number of fractional bits in the coordinates of the center and values of axes. /// /// ## Overloaded parameters /// /// * img: Image. /// * box: Alternative ellipse representation via RotatedRect. This means that the function draws /// an ellipse inscribed in the rotated rectangle. /// * color: Ellipse color. /// * thickness: Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that /// a filled ellipse sector is to be drawn. /// * lineType: Type of the ellipse boundary. See #LineTypes /// /// ## C++ default parameters /// * thickness: 1 /// * line_type: LINE_8 pub fn ellipse_rotated_rect(img: &mut dyn core::ToInputOutputArray, box_: &core::RotatedRect, color: core::Scalar, thickness: i32, line_type: i32) -> Result<()> { input_output_array_arg!(img); unsafe { sys::cv_ellipse_const__InputOutputArrayR_const_RotatedRectR_const_ScalarR_int_int(img.as_raw__InputOutputArray(), box_.as_raw_RotatedRect(), &color, thickness, line_type) }.into_result() } /// Equalizes the histogram of a grayscale image. /// /// The function equalizes the histogram of the input image using the following algorithm: /// /// - Calculate the histogram  for src . /// - Normalize the histogram so that the sum of histogram bins is 255. /// - Compute the integral of the histogram: ///  /// - Transform the image using  as a look-up table:  /// /// The algorithm normalizes the brightness and increases the contrast of the image. /// /// ## Parameters /// * src: Source 8-bit single channel image. /// * dst: Destination image of the same size and type as src . pub fn equalize_hist(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); unsafe { sys::cv_equalizeHist_const__InputArrayR_const__OutputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray()) }.into_result() } /// Erodes an image by using a specific structuring element. /// /// The function erodes the source image using the specified structuring element that determines the /// shape of a pixel neighborhood over which the minimum is taken: /// ///  /// /// The function supports the in-place mode. Erosion can be applied several ( iterations ) times. In /// case of multi-channel images, each channel is processed independently. /// /// ## Parameters /// * src: input image; the number of channels can be arbitrary, but the depth should be one of /// CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. /// * dst: output image of the same size and type as src. /// * kernel: structuring element used for erosion; if `element=Mat()`, a `3 x 3` rectangular /// structuring element is used. Kernel can be created using #getStructuringElement. /// * anchor: position of the anchor within the element; default value (-1, -1) means that the /// anchor is at the element center. /// * iterations: number of times erosion is applied. /// * borderType: pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. /// * borderValue: border value in case of a constant border /// ## See also /// dilate, morphologyEx, getStructuringElement /// /// ## C++ default parameters /// * anchor: Point(-1,-1) /// * iterations: 1 /// * border_type: BORDER_CONSTANT /// * border_value: morphologyDefaultBorderValue() pub fn erode(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, kernel: &dyn core::ToInputArray, anchor: core::Point, iterations: i32, border_type: i32, border_value: core::Scalar) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); input_array_arg!(kernel); unsafe { sys::cv_erode_const__InputArrayR_const__OutputArrayR_const__InputArrayR_Point_int_int_const_ScalarR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), kernel.as_raw__InputArray(), anchor.opencv_as_extern(), iterations, border_type, &border_value) }.into_result() } /// Fills a convex polygon. /// /// The function cv::fillConvexPoly draws a filled convex polygon. This function is much faster than the /// function #fillPoly . It can fill not only convex polygons but any monotonic polygon without /// self-intersections, that is, a polygon whose contour intersects every horizontal line (scan line) /// twice at the most (though, its top-most and/or the bottom edge could be horizontal). /// /// ## Parameters /// * img: Image. /// * points: Polygon vertices. /// * color: Polygon color. /// * lineType: Type of the polygon boundaries. See #LineTypes /// * shift: Number of fractional bits in the vertex coordinates. /// /// ## C++ default parameters /// * line_type: LINE_8 /// * shift: 0 pub fn fill_convex_poly(img: &mut dyn core::ToInputOutputArray, points: &dyn core::ToInputArray, color: core::Scalar, line_type: i32, shift: i32) -> Result<()> { input_output_array_arg!(img); input_array_arg!(points); unsafe { sys::cv_fillConvexPoly_const__InputOutputArrayR_const__InputArrayR_const_ScalarR_int_int(img.as_raw__InputOutputArray(), points.as_raw__InputArray(), &color, line_type, shift) }.into_result() } /// Fills the area bounded by one or more polygons. /// /// The function cv::fillPoly fills an area bounded by several polygonal contours. The function can fill /// complex areas, for example, areas with holes, contours with self-intersections (some of their /// parts), and so forth. /// /// ## Parameters /// * img: Image. /// * pts: Array of polygons where each polygon is represented as an array of points. /// * color: Polygon color. /// * lineType: Type of the polygon boundaries. See #LineTypes /// * shift: Number of fractional bits in the vertex coordinates. /// * offset: Optional offset of all points of the contours. /// /// ## C++ default parameters /// * line_type: LINE_8 /// * shift: 0 /// * offset: Point() pub fn fill_poly(img: &mut dyn core::ToInputOutputArray, pts: &dyn core::ToInputArray, color: core::Scalar, line_type: i32, shift: i32, offset: core::Point) -> Result<()> { input_output_array_arg!(img); input_array_arg!(pts); unsafe { sys::cv_fillPoly_const__InputOutputArrayR_const__InputArrayR_const_ScalarR_int_int_Point(img.as_raw__InputOutputArray(), pts.as_raw__InputArray(), &color, line_type, shift, offset.opencv_as_extern()) }.into_result() } /// Convolves an image with the kernel. /// /// The function applies an arbitrary linear filter to an image. In-place operation is supported. When /// the aperture is partially outside the image, the function interpolates outlier pixel values /// according to the specified border mode. /// /// The function does actually compute correlation, not the convolution: /// ///  /// /// That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip /// the kernel using #flip and set the new anchor to `(kernel.cols - anchor.x - 1, kernel.rows - /// anchor.y - 1)`. /// /// The function uses the DFT-based algorithm in case of sufficiently large kernels (~`11 x 11` or /// larger) and the direct algorithm for small kernels. /// /// ## Parameters /// * src: input image. /// * dst: output image of the same size and the same number of channels as src. /// * ddepth: desired depth of the destination image, see @ref filter_depths "combinations" /// * kernel: convolution kernel (or rather a correlation kernel), a single-channel floating point /// matrix; if you want to apply different kernels to different channels, split the image into /// separate color planes using split and process them individually. /// * anchor: anchor of the kernel that indicates the relative position of a filtered point within /// the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor /// is at the kernel center. /// * delta: optional value added to the filtered pixels before storing them in dst. /// * borderType: pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. /// ## See also /// sepFilter2D, dft, matchTemplate /// /// ## C++ default parameters /// * anchor: Point(-1,-1) /// * delta: 0 /// * border_type: BORDER_DEFAULT pub fn filter_2d(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, ddepth: i32, kernel: &dyn core::ToInputArray, anchor: core::Point, delta: f64, border_type: i32) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); input_array_arg!(kernel); unsafe { sys::cv_filter2D_const__InputArrayR_const__OutputArrayR_int_const__InputArrayR_Point_double_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ddepth, kernel.as_raw__InputArray(), anchor.opencv_as_extern(), delta, border_type) }.into_result() } /// Finds contours in a binary image. /// /// The function retrieves contours from the binary image using the algorithm [Suzuki85](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Suzuki85) . The contours /// are a useful tool for shape analysis and object detection and recognition. See squares.cpp in the /// OpenCV sample directory. /// /// Note: Since opencv 3.2 source image is not modified by this function. /// /// ## Parameters /// * image: Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero /// pixels remain 0's, so the image is treated as binary . You can use #compare, #inRange, #threshold , /// #adaptiveThreshold, #Canny, and others to create a binary image out of a grayscale or color one. /// If mode equals to #RETR_CCOMP or #RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1). /// * contours: Detected contours. Each contour is stored as a vector of points (e.g. /// std::vector<std::vector<cv::Point> >). /// * hierarchy: Optional output vector (e.g. std::vector<cv::Vec4i>), containing information about the image topology. It has /// as many elements as the number of contours. For each i-th contour contours[i], the elements /// hierarchy[i][0] , hierarchy[i][1] , hierarchy[i][2] , and hierarchy[i][3] are set to 0-based indices /// in contours of the next and previous contours at the same hierarchical level, the first child /// contour and the parent contour, respectively. If for the contour i there are no next, previous, /// parent, or nested contours, the corresponding elements of hierarchy[i] will be negative. /// * mode: Contour retrieval mode, see #RetrievalModes /// * method: Contour approximation method, see #ContourApproximationModes /// * offset: Optional offset by which every contour point is shifted. This is useful if the /// contours are extracted from the image ROI and then they should be analyzed in the whole image /// context. /// /// ## C++ default parameters /// * offset: Point() pub fn find_contours_with_hierarchy(image: &dyn core::ToInputArray, contours: &mut dyn core::ToOutputArray, hierarchy: &mut dyn core::ToOutputArray, mode: i32, method: i32, offset: core::Point) -> Result<()> { input_array_arg!(image); output_array_arg!(contours); output_array_arg!(hierarchy); unsafe { sys::cv_findContours_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_int_int_Point(image.as_raw__InputArray(), contours.as_raw__OutputArray(), hierarchy.as_raw__OutputArray(), mode, method, offset.opencv_as_extern()) }.into_result() } /// Finds contours in a binary image. /// /// The function retrieves contours from the binary image using the algorithm [Suzuki85](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Suzuki85) . The contours /// are a useful tool for shape analysis and object detection and recognition. See squares.cpp in the /// OpenCV sample directory. /// /// Note: Since opencv 3.2 source image is not modified by this function. /// /// ## Parameters /// * image: Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero /// pixels remain 0's, so the image is treated as binary . You can use #compare, #inRange, #threshold , /// #adaptiveThreshold, #Canny, and others to create a binary image out of a grayscale or color one. /// If mode equals to #RETR_CCOMP or #RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1). /// * contours: Detected contours. Each contour is stored as a vector of points (e.g. /// std::vector<std::vector<cv::Point> >). /// * hierarchy: Optional output vector (e.g. std::vector<cv::Vec4i>), containing information about the image topology. It has /// as many elements as the number of contours. For each i-th contour contours[i], the elements /// hierarchy[i][0] , hierarchy[i][1] , hierarchy[i][2] , and hierarchy[i][3] are set to 0-based indices /// in contours of the next and previous contours at the same hierarchical level, the first child /// contour and the parent contour, respectively. If for the contour i there are no next, previous, /// parent, or nested contours, the corresponding elements of hierarchy[i] will be negative. /// * mode: Contour retrieval mode, see #RetrievalModes /// * method: Contour approximation method, see #ContourApproximationModes /// * offset: Optional offset by which every contour point is shifted. This is useful if the /// contours are extracted from the image ROI and then they should be analyzed in the whole image /// context. /// /// ## Overloaded parameters /// /// ## C++ default parameters /// * offset: Point() pub fn find_contours(image: &dyn core::ToInputArray, contours: &mut dyn core::ToOutputArray, mode: i32, method: i32, offset: core::Point) -> Result<()> { input_array_arg!(image); output_array_arg!(contours); unsafe { sys::cv_findContours_const__InputArrayR_const__OutputArrayR_int_int_Point(image.as_raw__InputArray(), contours.as_raw__OutputArray(), mode, method, offset.opencv_as_extern()) }.into_result() } /// Fits an ellipse around a set of 2D points. /// /// The function calculates the ellipse that fits a set of 2D points. /// It returns the rotated rectangle in which the ellipse is inscribed. /// The Approximate Mean Square (AMS) proposed by [Taubin1991](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Taubin1991) is used. /// /// For an ellipse, this basis set is , /// which is a set of six free coefficients . /// However, to specify an ellipse, all that is needed is five numbers; the major and minor axes lengths , /// the position , and the orientation . This is because the basis set includes lines, /// quadratics, parabolic and hyperbolic functions as well as elliptical functions as possible fits. /// If the fit is found to be a parabolic or hyperbolic function then the standard #fitEllipse method is used. /// The AMS method restricts the fit to parabolic, hyperbolic and elliptical curves /// by imposing the condition that  where /// the matrices  and  are the partial derivatives of the design matrix  with /// respect to x and y. The matrices are formed row by row applying the following to /// each of the points in the set: /// \f{align*}{ /// D(i,:)&=\left\{x_i^2, x_i y_i, y_i^2, x_i, y_i, 1\right\} & /// D_x(i,:)&=\left\{2 x_i,y_i,0,1,0,0\right\} & /// D_y(i,:)&=\left\{0,x_i,2 y_i,0,1,0\right\} /// \f} /// The AMS method minimizes the cost function /// \f{equation*}{ /// \epsilon ^2=\frac{ A^T D^T D A }{ A^T (D_x^T D_x + D_y^T D_y) A^T } /// \f} /// /// The minimum cost is found by solving the generalized eigenvalue problem. /// /// \f{equation*}{ /// D^T D A = \lambda \left( D_x^T D_x + D_y^T D_y\right) A /// \f} /// /// ## Parameters /// * points: Input 2D point set, stored in std::vector\<\> or Mat pub fn fit_ellipse_ams(points: &dyn core::ToInputArray) -> Result<core::RotatedRect> { input_array_arg!(points); unsafe { sys::cv_fitEllipseAMS_const__InputArrayR(points.as_raw__InputArray()) }.into_result().map(|r| unsafe { core::RotatedRect::opencv_from_extern(r) } ) } /// Fits an ellipse around a set of 2D points. /// /// The function calculates the ellipse that fits a set of 2D points. /// It returns the rotated rectangle in which the ellipse is inscribed. /// The Direct least square (Direct) method by [Fitzgibbon1999](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Fitzgibbon1999) is used. /// /// For an ellipse, this basis set is , /// which is a set of six free coefficients . /// However, to specify an ellipse, all that is needed is five numbers; the major and minor axes lengths , /// the position , and the orientation . This is because the basis set includes lines, /// quadratics, parabolic and hyperbolic functions as well as elliptical functions as possible fits. /// The Direct method confines the fit to ellipses by ensuring that . /// The condition imposed is that  which satisfies the inequality /// and as the coefficients can be arbitrarily scaled is not overly restrictive. /// /// \f{equation*}{ /// \epsilon ^2= A^T D^T D A \quad \text{with} \quad A^T C A =1 \quad \text{and} \quad C=\left(\begin{matrix} /// 0 & 0 & 2 & 0 & 0 & 0 \\ /// 0 & -1 & 0 & 0 & 0 & 0 \\ /// 2 & 0 & 0 & 0 & 0 & 0 \\ /// 0 & 0 & 0 & 0 & 0 & 0 \\ /// 0 & 0 & 0 & 0 & 0 & 0 \\ /// 0 & 0 & 0 & 0 & 0 & 0 /// \end{matrix} \right) /// \f} /// /// The minimum cost is found by solving the generalized eigenvalue problem. /// /// \f{equation*}{ /// D^T D A = \lambda \left( C\right) A /// \f} /// /// The system produces only one positive eigenvalue  which is chosen as the solution /// with its eigenvector . These are used to find the coefficients /// /// \f{equation*}{ /// A = \sqrt{\frac{1}{\mathbf{u}^T C \mathbf{u}}} \mathbf{u} /// \f} /// The scaling factor guarantees that . /// /// ## Parameters /// * points: Input 2D point set, stored in std::vector\<\> or Mat pub fn fit_ellipse_direct(points: &dyn core::ToInputArray) -> Result<core::RotatedRect> { input_array_arg!(points); unsafe { sys::cv_fitEllipseDirect_const__InputArrayR(points.as_raw__InputArray()) }.into_result().map(|r| unsafe { core::RotatedRect::opencv_from_extern(r) } ) } /// Fits an ellipse around a set of 2D points. /// /// The function calculates the ellipse that fits (in a least-squares sense) a set of 2D points best of /// all. It returns the rotated rectangle in which the ellipse is inscribed. The first algorithm described by [Fitzgibbon95](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Fitzgibbon95) /// is used. Developer should keep in mind that it is possible that the returned /// ellipse/rotatedRect data contains negative indices, due to the data points being close to the /// border of the containing Mat element. /// /// ## Parameters /// * points: Input 2D point set, stored in std::vector\<\> or Mat pub fn fit_ellipse(points: &dyn core::ToInputArray) -> Result<core::RotatedRect> { input_array_arg!(points); unsafe { sys::cv_fitEllipse_const__InputArrayR(points.as_raw__InputArray()) }.into_result().map(|r| unsafe { core::RotatedRect::opencv_from_extern(r) } ) } /// Fits a line to a 2D or 3D point set. /// /// The function fitLine fits a line to a 2D or 3D point set by minimizing  where ///  is a distance between the  point, the line and  is a distance function, one /// of the following: /// * DIST_L2 ///  /// - DIST_L1 ///  /// - DIST_L12 ///  /// - DIST_FAIR ///  /// - DIST_WELSCH ///  /// - DIST_HUBER ///  /// /// The algorithm is based on the M-estimator ( <http://en.wikipedia.org/wiki/M-estimator> ) technique /// that iteratively fits the line using the weighted least-squares algorithm. After each iteration the /// weights  are adjusted to be inversely proportional to  . /// /// ## Parameters /// * points: Input vector of 2D or 3D points, stored in std::vector\<\> or Mat. /// * line: Output line parameters. In case of 2D fitting, it should be a vector of 4 elements /// (like Vec4f) - (vx, vy, x0, y0), where (vx, vy) is a normalized vector collinear to the line and /// (x0, y0) is a point on the line. In case of 3D fitting, it should be a vector of 6 elements (like /// Vec6f) - (vx, vy, vz, x0, y0, z0), where (vx, vy, vz) is a normalized vector collinear to the line /// and (x0, y0, z0) is a point on the line. /// * distType: Distance used by the M-estimator, see #DistanceTypes /// * param: Numerical parameter ( C ) for some types of distances. If it is 0, an optimal value /// is chosen. /// * reps: Sufficient accuracy for the radius (distance between the coordinate origin and the line). /// * aeps: Sufficient accuracy for the angle. 0.01 would be a good default value for reps and aeps. pub fn fit_line(points: &dyn core::ToInputArray, line: &mut dyn core::ToOutputArray, dist_type: i32, param: f64, reps: f64, aeps: f64) -> Result<()> { input_array_arg!(points); output_array_arg!(line); unsafe { sys::cv_fitLine_const__InputArrayR_const__OutputArrayR_int_double_double_double(points.as_raw__InputArray(), line.as_raw__OutputArray(), dist_type, param, reps, aeps) }.into_result() } /// Fills a connected component with the given color. /// /// The function cv::floodFill fills a connected component starting from the seed point with the specified /// color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The /// pixel at  is considered to belong to the repainted domain if: /// /// - in case of a grayscale image and floating range ///  /// /// /// - in case of a grayscale image and fixed range ///  /// /// /// - in case of a color image and floating range ///  ///  /// and ///  /// /// /// - in case of a color image and fixed range ///  ///  /// and ///  /// /// /// where  is the value of one of pixel neighbors that is already known to belong to the /// component. That is, to be added to the connected component, a color/brightness of the pixel should /// be close enough to: /// - Color/brightness of one of its neighbors that already belong to the connected component in case /// of a floating range. /// - Color/brightness of the seed point in case of a fixed range. /// /// Use these functions to either mark a connected component with the specified color in-place, or build /// a mask and then extract the contour, or copy the region to another image, and so on. /// /// ## Parameters /// * image: Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the /// function unless the #FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See /// the details below. /// * mask: Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels /// taller than image. Since this is both an input and output parameter, you must take responsibility /// of initializing it. Flood-filling cannot go across non-zero pixels in the input mask. For example, /// an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the /// mask corresponding to filled pixels in the image are set to 1 or to the a value specified in flags /// as described below. Additionally, the function fills the border of the mask with ones to simplify /// internal processing. It is therefore possible to use the same mask in multiple calls to the function /// to make sure the filled areas do not overlap. /// * seedPoint: Starting point. /// * newVal: New value of the repainted domain pixels. /// * loDiff: Maximal lower brightness/color difference between the currently observed pixel and /// one of its neighbors belonging to the component, or a seed pixel being added to the component. /// * upDiff: Maximal upper brightness/color difference between the currently observed pixel and /// one of its neighbors belonging to the component, or a seed pixel being added to the component. /// * rect: Optional output parameter set by the function to the minimum bounding rectangle of the /// repainted domain. /// * flags: Operation flags. The first 8 bits contain a connectivity value. The default value of /// 4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A /// connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner) /// will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill /// the mask (the default value is 1). For example, 4 | ( 255 \<\< 8 ) will consider 4 nearest /// neighbours and fill the mask with a value of 255. The following additional options occupy higher /// bits and therefore may be further combined with the connectivity and mask fill values using /// bit-wise or (|), see #FloodFillFlags. /// /// /// Note: Since the mask is larger than the filled image, a pixel  in image corresponds to the /// pixel  in the mask . /// ## See also /// findContours /// /// ## Overloaded parameters /// /// /// variant without `mask` parameter /// /// ## C++ default parameters /// * rect: 0 /// * lo_diff: Scalar() /// * up_diff: Scalar() /// * flags: 4 pub fn flood_fill(image: &mut dyn core::ToInputOutputArray, seed_point: core::Point, new_val: core::Scalar, rect: &mut core::Rect, lo_diff: core::Scalar, up_diff: core::Scalar, flags: i32) -> Result<i32> { input_output_array_arg!(image); unsafe { sys::cv_floodFill_const__InputOutputArrayR_Point_Scalar_RectX_Scalar_Scalar_int(image.as_raw__InputOutputArray(), seed_point.opencv_as_extern(), new_val.opencv_as_extern(), rect, lo_diff.opencv_as_extern(), up_diff.opencv_as_extern(), flags) }.into_result() } /// Fills a connected component with the given color. /// /// The function cv::floodFill fills a connected component starting from the seed point with the specified /// color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The /// pixel at  is considered to belong to the repainted domain if: /// /// - in case of a grayscale image and floating range ///  /// /// /// - in case of a grayscale image and fixed range ///  /// /// /// - in case of a color image and floating range ///  ///  /// and ///  /// /// /// - in case of a color image and fixed range ///  ///  /// and ///  /// /// /// where  is the value of one of pixel neighbors that is already known to belong to the /// component. That is, to be added to the connected component, a color/brightness of the pixel should /// be close enough to: /// - Color/brightness of one of its neighbors that already belong to the connected component in case /// of a floating range. /// - Color/brightness of the seed point in case of a fixed range. /// /// Use these functions to either mark a connected component with the specified color in-place, or build /// a mask and then extract the contour, or copy the region to another image, and so on. /// /// ## Parameters /// * image: Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the /// function unless the #FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See /// the details below. /// * mask: Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels /// taller than image. Since this is both an input and output parameter, you must take responsibility /// of initializing it. Flood-filling cannot go across non-zero pixels in the input mask. For example, /// an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the /// mask corresponding to filled pixels in the image are set to 1 or to the a value specified in flags /// as described below. Additionally, the function fills the border of the mask with ones to simplify /// internal processing. It is therefore possible to use the same mask in multiple calls to the function /// to make sure the filled areas do not overlap. /// * seedPoint: Starting point. /// * newVal: New value of the repainted domain pixels. /// * loDiff: Maximal lower brightness/color difference between the currently observed pixel and /// one of its neighbors belonging to the component, or a seed pixel being added to the component. /// * upDiff: Maximal upper brightness/color difference between the currently observed pixel and /// one of its neighbors belonging to the component, or a seed pixel being added to the component. /// * rect: Optional output parameter set by the function to the minimum bounding rectangle of the /// repainted domain. /// * flags: Operation flags. The first 8 bits contain a connectivity value. The default value of /// 4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A /// connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner) /// will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill /// the mask (the default value is 1). For example, 4 | ( 255 \<\< 8 ) will consider 4 nearest /// neighbours and fill the mask with a value of 255. The following additional options occupy higher /// bits and therefore may be further combined with the connectivity and mask fill values using /// bit-wise or (|), see #FloodFillFlags. /// /// /// Note: Since the mask is larger than the filled image, a pixel  in image corresponds to the /// pixel  in the mask . /// ## See also /// findContours /// /// ## C++ default parameters /// * rect: 0 /// * lo_diff: Scalar() /// * up_diff: Scalar() /// * flags: 4 pub fn flood_fill_mask(image: &mut dyn core::ToInputOutputArray, mask: &mut dyn core::ToInputOutputArray, seed_point: core::Point, new_val: core::Scalar, rect: &mut core::Rect, lo_diff: core::Scalar, up_diff: core::Scalar, flags: i32) -> Result<i32> { input_output_array_arg!(image); input_output_array_arg!(mask); unsafe { sys::cv_floodFill_const__InputOutputArrayR_const__InputOutputArrayR_Point_Scalar_RectX_Scalar_Scalar_int(image.as_raw__InputOutputArray(), mask.as_raw__InputOutputArray(), seed_point.opencv_as_extern(), new_val.opencv_as_extern(), rect, lo_diff.opencv_as_extern(), up_diff.opencv_as_extern(), flags) }.into_result() } /// Calculates an affine transform from three pairs of the corresponding points. /// /// The function calculates the  matrix of an affine transform so that: /// ///  /// /// where /// ///  /// /// ## Parameters /// * src: Coordinates of triangle vertices in the source image. /// * dst: Coordinates of the corresponding triangle vertices in the destination image. /// ## See also /// warpAffine, transform pub fn get_affine_transform_slice(src: &[core::Point2f], dst: &[core::Point2f]) -> Result<core::Mat> { unsafe { sys::cv_getAffineTransform_const_Point2fX_const_Point2fX(src.as_ptr(), dst.as_ptr()) }.into_result().map(|r| unsafe { core::Mat::opencv_from_extern(r) } ) } pub fn get_affine_transform(src: &dyn core::ToInputArray, dst: &dyn core::ToInputArray) -> Result<core::Mat> { input_array_arg!(src); input_array_arg!(dst); unsafe { sys::cv_getAffineTransform_const__InputArrayR_const__InputArrayR(src.as_raw__InputArray(), dst.as_raw__InputArray()) }.into_result().map(|r| unsafe { core::Mat::opencv_from_extern(r) } ) } /// Returns filter coefficients for computing spatial image derivatives. /// /// The function computes and returns the filter coefficients for spatial image derivatives. When /// `ksize=FILTER_SCHARR`, the Scharr  kernels are generated (see #Scharr). Otherwise, Sobel /// kernels are generated (see #Sobel). The filters are normally passed to #sepFilter2D or to /// /// ## Parameters /// * kx: Output matrix of row filter coefficients. It has the type ktype . /// * ky: Output matrix of column filter coefficients. It has the type ktype . /// * dx: Derivative order in respect of x. /// * dy: Derivative order in respect of y. /// * ksize: Aperture size. It can be FILTER_SCHARR, 1, 3, 5, or 7. /// * normalize: Flag indicating whether to normalize (scale down) the filter coefficients or not. /// Theoretically, the coefficients should have the denominator . If you are /// going to filter floating-point images, you are likely to use the normalized kernels. But if you /// compute derivatives of an 8-bit image, store the results in a 16-bit image, and wish to preserve /// all the fractional bits, you may want to set normalize=false . /// * ktype: Type of filter coefficients. It can be CV_32f or CV_64F . /// /// ## C++ default parameters /// * normalize: false /// * ktype: CV_32F pub fn get_deriv_kernels(kx: &mut dyn core::ToOutputArray, ky: &mut dyn core::ToOutputArray, dx: i32, dy: i32, ksize: i32, normalize: bool, ktype: i32) -> Result<()> { output_array_arg!(kx); output_array_arg!(ky); unsafe { sys::cv_getDerivKernels_const__OutputArrayR_const__OutputArrayR_int_int_int_bool_int(kx.as_raw__OutputArray(), ky.as_raw__OutputArray(), dx, dy, ksize, normalize, ktype) }.into_result() } /// Calculates the font-specific size to use to achieve a given height in pixels. /// /// ## Parameters /// * fontFace: Font to use, see cv::HersheyFonts. /// * pixelHeight: Pixel height to compute the fontScale for /// * thickness: Thickness of lines used to render the text.See putText for details. /// ## Returns /// The fontSize to use for cv::putText /// ## See also /// cv::putText /// /// ## C++ default parameters /// * thickness: 1 pub fn get_font_scale_from_height(font_face: i32, pixel_height: i32, thickness: i32) -> Result<f64> { unsafe { sys::cv_getFontScaleFromHeight_const_int_const_int_const_int(font_face, pixel_height, thickness) }.into_result() } /// Returns Gabor filter coefficients. /// /// For more details about gabor filter equations and parameters, see: [Gabor /// Filter](http://en.wikipedia.org/wiki/Gabor_filter). /// /// ## Parameters /// * ksize: Size of the filter returned. /// * sigma: Standard deviation of the gaussian envelope. /// * theta: Orientation of the normal to the parallel stripes of a Gabor function. /// * lambd: Wavelength of the sinusoidal factor. /// * gamma: Spatial aspect ratio. /// * psi: Phase offset. /// * ktype: Type of filter coefficients. It can be CV_32F or CV_64F . /// /// ## C++ default parameters /// * psi: CV_PI*0.5 /// * ktype: CV_64F pub fn get_gabor_kernel(ksize: core::Size, sigma: f64, theta: f64, lambd: f64, gamma: f64, psi: f64, ktype: i32) -> Result<core::Mat> { unsafe { sys::cv_getGaborKernel_Size_double_double_double_double_double_int(ksize.opencv_as_extern(), sigma, theta, lambd, gamma, psi, ktype) }.into_result().map(|r| unsafe { core::Mat::opencv_from_extern(r) } ) } /// Returns Gaussian filter coefficients. /// /// The function computes and returns the  matrix of Gaussian filter /// coefficients: /// ///  /// /// where  and  is the scale factor chosen so that . /// /// Two of such generated kernels can be passed to sepFilter2D. Those functions automatically recognize /// smoothing kernels (a symmetrical kernel with sum of weights equal to 1) and handle them accordingly. /// You may also use the higher-level GaussianBlur. /// ## Parameters /// * ksize: Aperture size. It should be odd (  ) and positive. /// * sigma: Gaussian standard deviation. If it is non-positive, it is computed from ksize as /// `sigma = 0.3*((ksize-1)*0.5 - 1) + 0.8`. /// * ktype: Type of filter coefficients. It can be CV_32F or CV_64F . /// ## See also /// sepFilter2D, getDerivKernels, getStructuringElement, GaussianBlur /// /// ## C++ default parameters /// * ktype: CV_64F pub fn get_gaussian_kernel(ksize: i32, sigma: f64, ktype: i32) -> Result<core::Mat> { unsafe { sys::cv_getGaussianKernel_int_double_int(ksize, sigma, ktype) }.into_result().map(|r| unsafe { core::Mat::opencv_from_extern(r) } ) } /// Calculates a perspective transform from four pairs of the corresponding points. /// /// The function calculates the  matrix of a perspective transform so that: /// ///  /// /// where /// ///  /// /// ## Parameters /// * src: Coordinates of quadrangle vertices in the source image. /// * dst: Coordinates of the corresponding quadrangle vertices in the destination image. /// * solveMethod: method passed to cv::solve (#DecompTypes) /// ## See also /// findHomography, warpPerspective, perspectiveTransform /// /// ## Overloaded parameters /// /// ## C++ default parameters /// * solve_method: DECOMP_LU pub fn get_perspective_transform_slice(src: &[core::Point2f], dst: &[core::Point2f], solve_method: i32) -> Result<core::Mat> { unsafe { sys::cv_getPerspectiveTransform_const_Point2fX_const_Point2fX_int(src.as_ptr(), dst.as_ptr(), solve_method) }.into_result().map(|r| unsafe { core::Mat::opencv_from_extern(r) } ) } /// Calculates a perspective transform from four pairs of the corresponding points. /// /// The function calculates the  matrix of a perspective transform so that: /// ///  /// /// where /// ///  /// /// ## Parameters /// * src: Coordinates of quadrangle vertices in the source image. /// * dst: Coordinates of the corresponding quadrangle vertices in the destination image. /// * solveMethod: method passed to cv::solve (#DecompTypes) /// ## See also /// findHomography, warpPerspective, perspectiveTransform /// /// ## C++ default parameters /// * solve_method: DECOMP_LU pub fn get_perspective_transform(src: &dyn core::ToInputArray, dst: &dyn core::ToInputArray, solve_method: i32) -> Result<core::Mat> { input_array_arg!(src); input_array_arg!(dst); unsafe { sys::cv_getPerspectiveTransform_const__InputArrayR_const__InputArrayR_int(src.as_raw__InputArray(), dst.as_raw__InputArray(), solve_method) }.into_result().map(|r| unsafe { core::Mat::opencv_from_extern(r) } ) } /// Retrieves a pixel rectangle from an image with sub-pixel accuracy. /// /// The function getRectSubPix extracts pixels from src: /// ///  /// /// where the values of the pixels at non-integer coordinates are retrieved using bilinear /// interpolation. Every channel of multi-channel images is processed independently. Also /// the image should be a single channel or three channel image. While the center of the /// rectangle must be inside the image, parts of the rectangle may be outside. /// /// ## Parameters /// * image: Source image. /// * patchSize: Size of the extracted patch. /// * center: Floating point coordinates of the center of the extracted rectangle within the /// source image. The center must be inside the image. /// * patch: Extracted patch that has the size patchSize and the same number of channels as src . /// * patchType: Depth of the extracted pixels. By default, they have the same depth as src . /// ## See also /// warpAffine, warpPerspective /// /// ## C++ default parameters /// * patch_type: -1 pub fn get_rect_sub_pix(image: &dyn core::ToInputArray, patch_size: core::Size, center: core::Point2f, patch: &mut dyn core::ToOutputArray, patch_type: i32) -> Result<()> { input_array_arg!(image); output_array_arg!(patch); unsafe { sys::cv_getRectSubPix_const__InputArrayR_Size_Point2f_const__OutputArrayR_int(image.as_raw__InputArray(), patch_size.opencv_as_extern(), center.opencv_as_extern(), patch.as_raw__OutputArray(), patch_type) }.into_result() } /// Calculates an affine matrix of 2D rotation. /// /// The function calculates the following matrix: /// ///  /// /// where /// ///  /// /// The transformation maps the rotation center to itself. If this is not the target, adjust the shift. /// /// ## Parameters /// * center: Center of the rotation in the source image. /// * angle: Rotation angle in degrees. Positive values mean counter-clockwise rotation (the /// coordinate origin is assumed to be the top-left corner). /// * scale: Isotropic scale factor. /// ## See also /// getAffineTransform, warpAffine, transform pub fn get_rotation_matrix_2d(center: core::Point2f, angle: f64, scale: f64) -> Result<core::Mat> { unsafe { sys::cv_getRotationMatrix2D_Point2f_double_double(center.opencv_as_extern(), angle, scale) }.into_result().map(|r| unsafe { core::Mat::opencv_from_extern(r) } ) } #[cfg(not(target_os = "windows"))] /// ## See also /// getRotationMatrix2D pub fn get_rotation_matrix_2d_matx(center: core::Point2f, angle: f64, scale: f64) -> Result<core::Matx23d> { unsafe { sys::cv_getRotationMatrix2D__Point2f_double_double(center.opencv_as_extern(), angle, scale) }.into_result() } /// Returns a structuring element of the specified size and shape for morphological operations. /// /// The function constructs and returns the structuring element that can be further passed to #erode, /// #dilate or #morphologyEx. But you can also construct an arbitrary binary mask yourself and use it as /// the structuring element. /// /// ## Parameters /// * shape: Element shape that could be one of #MorphShapes /// * ksize: Size of the structuring element. /// * anchor: Anchor position within the element. The default value  means that the /// anchor is at the center. Note that only the shape of a cross-shaped element depends on the anchor /// position. In other cases the anchor just regulates how much the result of the morphological /// operation is shifted. /// /// ## C++ default parameters /// * anchor: Point(-1,-1) pub fn get_structuring_element(shape: i32, ksize: core::Size, anchor: core::Point) -> Result<core::Mat> { unsafe { sys::cv_getStructuringElement_int_Size_Point(shape, ksize.opencv_as_extern(), anchor.opencv_as_extern()) }.into_result().map(|r| unsafe { core::Mat::opencv_from_extern(r) } ) } /// Calculates the width and height of a text string. /// /// The function cv::getTextSize calculates and returns the size of a box that contains the specified text. /// That is, the following code renders some text, the tight box surrounding it, and the baseline: : /// ```ignore /// String text = "Funny text inside the box"; /// int fontFace = FONT_HERSHEY_SCRIPT_SIMPLEX; /// double fontScale = 2; /// int thickness = 3; /// /// Mat img(600, 800, CV_8UC3, Scalar::all(0)); /// /// int baseline=0; /// Size textSize = getTextSize(text, fontFace, /// fontScale, thickness, &baseline); /// baseline += thickness; /// /// // center the text /// Point textOrg((img.cols - textSize.width)/2, /// (img.rows + textSize.height)/2); /// /// // draw the box /// rectangle(img, textOrg + Point(0, baseline), /// textOrg + Point(textSize.width, -textSize.height), /// Scalar(0,0,255)); /// // ... and the baseline first /// line(img, textOrg + Point(0, thickness), /// textOrg + Point(textSize.width, thickness), /// Scalar(0, 0, 255)); /// /// // then put the text itself /// putText(img, text, textOrg, fontFace, fontScale, /// Scalar::all(255), thickness, 8); /// ``` /// /// /// ## Parameters /// * text: Input text string. /// * fontFace: Font to use, see #HersheyFonts. /// * fontScale: Font scale factor that is multiplied by the font-specific base size. /// * thickness: Thickness of lines used to render the text. See #putText for details. /// * baseLine:[out] y-coordinate of the baseline relative to the bottom-most text /// point. /// ## Returns /// The size of a box that contains the specified text. /// ## See also /// putText pub fn get_text_size(text: &str, font_face: i32, font_scale: f64, thickness: i32, base_line: &mut i32) -> Result<core::Size> { extern_container_arg!(text); unsafe { sys::cv_getTextSize_const_StringR_int_double_int_intX(text.opencv_as_extern(), font_face, font_scale, thickness, base_line) }.into_result() } /// Determines strong corners on an image. /// /// The function finds the most prominent corners in the image or in the specified image region, as /// described in [Shi94](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Shi94) /// /// * Function calculates the corner quality measure at every source image pixel using the /// #cornerMinEigenVal or #cornerHarris . /// * Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are /// retained). /// * The corners with the minimal eigenvalue less than ///  are rejected. /// * The remaining corners are sorted by the quality measure in the descending order. /// * Function throws away each corner for which there is a stronger corner at a distance less than /// maxDistance. /// /// The function can be used to initialize a point-based tracker of an object. /// /// /// Note: If the function is called with different values A and B of the parameter qualityLevel , and /// A \> B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector /// with qualityLevel=B . /// /// ## Parameters /// * image: Input 8-bit or floating-point 32-bit, single-channel image. /// * corners: Output vector of detected corners. /// * maxCorners: Maximum number of corners to return. If there are more corners than are found, /// the strongest of them is returned. `maxCorners <= 0` implies that no limit on the maximum is set /// and all detected corners are returned. /// * qualityLevel: Parameter characterizing the minimal accepted quality of image corners. The /// parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue /// (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the /// quality measure less than the product are rejected. For example, if the best corner has the /// quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure /// less than 15 are rejected. /// * minDistance: Minimum possible Euclidean distance between the returned corners. /// * mask: Optional region of interest. If the image is not empty (it needs to have the type /// CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected. /// * blockSize: Size of an average block for computing a derivative covariation matrix over each /// pixel neighborhood. See cornerEigenValsAndVecs . /// * useHarrisDetector: Parameter indicating whether to use a Harris detector (see #cornerHarris) /// or #cornerMinEigenVal. /// * k: Free parameter of the Harris detector. /// ## See also /// cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform, /// /// ## C++ default parameters /// * mask: noArray() /// * block_size: 3 /// * use_harris_detector: false /// * k: 0.04 pub fn good_features_to_track(image: &dyn core::ToInputArray, corners: &mut dyn core::ToOutputArray, max_corners: i32, quality_level: f64, min_distance: f64, mask: &dyn core::ToInputArray, block_size: i32, use_harris_detector: bool, k: f64) -> Result<()> { input_array_arg!(image); output_array_arg!(corners); input_array_arg!(mask); unsafe { sys::cv_goodFeaturesToTrack_const__InputArrayR_const__OutputArrayR_int_double_double_const__InputArrayR_int_bool_double(image.as_raw__InputArray(), corners.as_raw__OutputArray(), max_corners, quality_level, min_distance, mask.as_raw__InputArray(), block_size, use_harris_detector, k) }.into_result() } /// ## C++ default parameters /// * use_harris_detector: false /// * k: 0.04 pub fn good_features_to_track_with_gradient(image: &dyn core::ToInputArray, corners: &mut dyn core::ToOutputArray, max_corners: i32, quality_level: f64, min_distance: f64, mask: &dyn core::ToInputArray, block_size: i32, gradient_size: i32, use_harris_detector: bool, k: f64) -> Result<()> { input_array_arg!(image); output_array_arg!(corners); input_array_arg!(mask); unsafe { sys::cv_goodFeaturesToTrack_const__InputArrayR_const__OutputArrayR_int_double_double_const__InputArrayR_int_int_bool_double(image.as_raw__InputArray(), corners.as_raw__OutputArray(), max_corners, quality_level, min_distance, mask.as_raw__InputArray(), block_size, gradient_size, use_harris_detector, k) }.into_result() } /// Runs the GrabCut algorithm. /// /// The function implements the [GrabCut image segmentation algorithm](http://en.wikipedia.org/wiki/GrabCut). /// /// ## Parameters /// * img: Input 8-bit 3-channel image. /// * mask: Input/output 8-bit single-channel mask. The mask is initialized by the function when /// mode is set to #GC_INIT_WITH_RECT. Its elements may have one of the #GrabCutClasses. /// * rect: ROI containing a segmented object. The pixels outside of the ROI are marked as /// "obvious background". The parameter is only used when mode==#GC_INIT_WITH_RECT . /// * bgdModel: Temporary array for the background model. Do not modify it while you are /// processing the same image. /// * fgdModel: Temporary arrays for the foreground model. Do not modify it while you are /// processing the same image. /// * iterCount: Number of iterations the algorithm should make before returning the result. Note /// that the result can be refined with further calls with mode==#GC_INIT_WITH_MASK or /// mode==GC_EVAL . /// * mode: Operation mode that could be one of the #GrabCutModes /// /// ## C++ default parameters /// * mode: GC_EVAL pub fn grab_cut(img: &dyn core::ToInputArray, mask: &mut dyn core::ToInputOutputArray, rect: core::Rect, bgd_model: &mut dyn core::ToInputOutputArray, fgd_model: &mut dyn core::ToInputOutputArray, iter_count: i32, mode: i32) -> Result<()> { input_array_arg!(img); input_output_array_arg!(mask); input_output_array_arg!(bgd_model); input_output_array_arg!(fgd_model); unsafe { sys::cv_grabCut_const__InputArrayR_const__InputOutputArrayR_Rect_const__InputOutputArrayR_const__InputOutputArrayR_int_int(img.as_raw__InputArray(), mask.as_raw__InputOutputArray(), rect.opencv_as_extern(), bgd_model.as_raw__InputOutputArray(), fgd_model.as_raw__InputOutputArray(), iter_count, mode) }.into_result() } /// Calculates the integral of an image. /// /// The function calculates one or more integral images for the source image as follows: /// ///  /// ///  /// ///  /// /// Using these integral images, you can calculate sum, mean, and standard deviation over a specific /// up-right or rotated rectangular region of the image in a constant time, for example: /// ///  /// /// It makes possible to do a fast blurring or fast block correlation with a variable window size, for /// example. In case of multi-channel images, sums for each channel are accumulated independently. /// /// As a practical example, the next figure shows the calculation of the integral of a straight /// rectangle Rect(3,3,3,2) and of a tilted rectangle Rect(5,1,2,3) . The selected pixels in the /// original image are shown, as well as the relative pixels in the integral images sum and tilted . /// ///  /// /// ## Parameters /// * src: input image as , 8-bit or floating-point (32f or 64f). /// * sum: integral image as  , 32-bit integer or floating-point (32f or 64f). /// * sqsum: integral image for squared pixel values; it is , double-precision /// floating-point (64f) array. /// * tilted: integral for the image rotated by 45 degrees; it is  array with /// the same data type as sum. /// * sdepth: desired depth of the integral and the tilted integral images, CV_32S, CV_32F, or /// CV_64F. /// * sqdepth: desired depth of the integral image of squared pixel values, CV_32F or CV_64F. /// /// ## C++ default parameters /// * sdepth: -1 /// * sqdepth: -1 pub fn integral3(src: &dyn core::ToInputArray, sum: &mut dyn core::ToOutputArray, sqsum: &mut dyn core::ToOutputArray, tilted: &mut dyn core::ToOutputArray, sdepth: i32, sqdepth: i32) -> Result<()> { input_array_arg!(src); output_array_arg!(sum); output_array_arg!(sqsum); output_array_arg!(tilted); unsafe { sys::cv_integral_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_int_int(src.as_raw__InputArray(), sum.as_raw__OutputArray(), sqsum.as_raw__OutputArray(), tilted.as_raw__OutputArray(), sdepth, sqdepth) }.into_result() } /// Calculates the integral of an image. /// /// The function calculates one or more integral images for the source image as follows: /// ///  /// ///  /// ///  /// /// Using these integral images, you can calculate sum, mean, and standard deviation over a specific /// up-right or rotated rectangular region of the image in a constant time, for example: /// ///  /// /// It makes possible to do a fast blurring or fast block correlation with a variable window size, for /// example. In case of multi-channel images, sums for each channel are accumulated independently. /// /// As a practical example, the next figure shows the calculation of the integral of a straight /// rectangle Rect(3,3,3,2) and of a tilted rectangle Rect(5,1,2,3) . The selected pixels in the /// original image are shown, as well as the relative pixels in the integral images sum and tilted . /// ///  /// /// ## Parameters /// * src: input image as , 8-bit or floating-point (32f or 64f). /// * sum: integral image as  , 32-bit integer or floating-point (32f or 64f). /// * sqsum: integral image for squared pixel values; it is , double-precision /// floating-point (64f) array. /// * tilted: integral for the image rotated by 45 degrees; it is  array with /// the same data type as sum. /// * sdepth: desired depth of the integral and the tilted integral images, CV_32S, CV_32F, or /// CV_64F. /// * sqdepth: desired depth of the integral image of squared pixel values, CV_32F or CV_64F. /// /// ## Overloaded parameters /// /// ## C++ default parameters /// * sdepth: -1 /// * sqdepth: -1 pub fn integral2(src: &dyn core::ToInputArray, sum: &mut dyn core::ToOutputArray, sqsum: &mut dyn core::ToOutputArray, sdepth: i32, sqdepth: i32) -> Result<()> { input_array_arg!(src); output_array_arg!(sum); output_array_arg!(sqsum); unsafe { sys::cv_integral_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_int_int(src.as_raw__InputArray(), sum.as_raw__OutputArray(), sqsum.as_raw__OutputArray(), sdepth, sqdepth) }.into_result() } /// Calculates the integral of an image. /// /// The function calculates one or more integral images for the source image as follows: /// ///  /// ///  /// ///  /// /// Using these integral images, you can calculate sum, mean, and standard deviation over a specific /// up-right or rotated rectangular region of the image in a constant time, for example: /// ///  /// /// It makes possible to do a fast blurring or fast block correlation with a variable window size, for /// example. In case of multi-channel images, sums for each channel are accumulated independently. /// /// As a practical example, the next figure shows the calculation of the integral of a straight /// rectangle Rect(3,3,3,2) and of a tilted rectangle Rect(5,1,2,3) . The selected pixels in the /// original image are shown, as well as the relative pixels in the integral images sum and tilted . /// ///  /// /// ## Parameters /// * src: input image as , 8-bit or floating-point (32f or 64f). /// * sum: integral image as  , 32-bit integer or floating-point (32f or 64f). /// * sqsum: integral image for squared pixel values; it is , double-precision /// floating-point (64f) array. /// * tilted: integral for the image rotated by 45 degrees; it is  array with /// the same data type as sum. /// * sdepth: desired depth of the integral and the tilted integral images, CV_32S, CV_32F, or /// CV_64F. /// * sqdepth: desired depth of the integral image of squared pixel values, CV_32F or CV_64F. /// /// ## Overloaded parameters /// /// ## C++ default parameters /// * sdepth: -1 pub fn integral(src: &dyn core::ToInputArray, sum: &mut dyn core::ToOutputArray, sdepth: i32) -> Result<()> { input_array_arg!(src); output_array_arg!(sum); unsafe { sys::cv_integral_const__InputArrayR_const__OutputArrayR_int(src.as_raw__InputArray(), sum.as_raw__OutputArray(), sdepth) }.into_result() } /// Finds intersection of two convex polygons /// /// ## Parameters /// * _p1: First polygon /// * _p2: Second polygon /// * _p12: Output polygon describing the intersecting area /// * handleNested: When true, an intersection is found if one of the polygons is fully enclosed in the other. /// When false, no intersection is found. If the polygons share a side or the vertex of one polygon lies on an edge /// of the other, they are not considered nested and an intersection will be found regardless of the value of handleNested. /// /// ## Returns /// Absolute value of area of intersecting polygon /// /// /// Note: intersectConvexConvex doesn't confirm that both polygons are convex and will return invalid results if they aren't. /// /// ## C++ default parameters /// * handle_nested: true pub fn intersect_convex_convex(_p1: &dyn core::ToInputArray, _p2: &dyn core::ToInputArray, _p12: &mut dyn core::ToOutputArray, handle_nested: bool) -> Result<f32> { input_array_arg!(_p1); input_array_arg!(_p2); output_array_arg!(_p12); unsafe { sys::cv_intersectConvexConvex_const__InputArrayR_const__InputArrayR_const__OutputArrayR_bool(_p1.as_raw__InputArray(), _p2.as_raw__InputArray(), _p12.as_raw__OutputArray(), handle_nested) }.into_result() } /// Inverts an affine transformation. /// /// The function computes an inverse affine transformation represented by  matrix M: /// ///  /// /// The result is also a  matrix of the same type as M. /// /// ## Parameters /// * M: Original affine transformation. /// * iM: Output reverse affine transformation. pub fn invert_affine_transform(m: &dyn core::ToInputArray, i_m: &mut dyn core::ToOutputArray) -> Result<()> { input_array_arg!(m); output_array_arg!(i_m); unsafe { sys::cv_invertAffineTransform_const__InputArrayR_const__OutputArrayR(m.as_raw__InputArray(), i_m.as_raw__OutputArray()) }.into_result() } /// Tests a contour convexity. /// /// The function tests whether the input contour is convex or not. The contour must be simple, that is, /// without self-intersections. Otherwise, the function output is undefined. /// /// ## Parameters /// * contour: Input vector of 2D points, stored in std::vector\<\> or Mat pub fn is_contour_convex(contour: &dyn core::ToInputArray) -> Result<bool> { input_array_arg!(contour); unsafe { sys::cv_isContourConvex_const__InputArrayR(contour.as_raw__InputArray()) }.into_result() } /// Draws a line segment connecting two points. /// /// The function line draws the line segment between pt1 and pt2 points in the image. The line is /// clipped by the image boundaries. For non-antialiased lines with integer coordinates, the 8-connected /// or 4-connected Bresenham algorithm is used. Thick lines are drawn with rounding endings. Antialiased /// lines are drawn using Gaussian filtering. /// /// ## Parameters /// * img: Image. /// * pt1: First point of the line segment. /// * pt2: Second point of the line segment. /// * color: Line color. /// * thickness: Line thickness. /// * lineType: Type of the line. See #LineTypes. /// * shift: Number of fractional bits in the point coordinates. /// /// ## C++ default parameters /// * thickness: 1 /// * line_type: LINE_8 /// * shift: 0 pub fn line(img: &mut dyn core::ToInputOutputArray, pt1: core::Point, pt2: core::Point, color: core::Scalar, thickness: i32, line_type: i32, shift: i32) -> Result<()> { input_output_array_arg!(img); unsafe { sys::cv_line_const__InputOutputArrayR_Point_Point_const_ScalarR_int_int_int(img.as_raw__InputOutputArray(), pt1.opencv_as_extern(), pt2.opencv_as_extern(), &color, thickness, line_type, shift) }.into_result() } /// Remaps an image to polar coordinates space. /// /// /// **Deprecated**: This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags) /// /// @internal /// Transform the source image using the following transformation (See @ref polar_remaps_reference_image "Polar remaps reference image c)"): ///  /// /// where ///  /// /// and ///  /// /// /// ## Parameters /// * src: Source image /// * dst: Destination image. It will have same size and type as src. /// * center: The transformation center; /// * maxRadius: The radius of the bounding circle to transform. It determines the inverse magnitude scale parameter too. /// * flags: A combination of interpolation methods, see #InterpolationFlags /// /// /// Note: /// * The function can not operate in-place. /// * To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees. /// ## See also /// cv::logPolar /// @endinternal #[deprecated = "This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags)"] pub fn linear_polar(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, center: core::Point2f, max_radius: f64, flags: i32) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); unsafe { sys::cv_linearPolar_const__InputArrayR_const__OutputArrayR_Point2f_double_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), center.opencv_as_extern(), max_radius, flags) }.into_result() } /// Remaps an image to semilog-polar coordinates space. /// /// /// **Deprecated**: This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags+WARP_POLAR_LOG); /// /// @internal /// Transform the source image using the following transformation (See @ref polar_remaps_reference_image "Polar remaps reference image d)"): ///  /// /// where ///  /// /// and ///  /// /// The function emulates the human "foveal" vision and can be used for fast scale and /// rotation-invariant template matching, for object tracking and so forth. /// ## Parameters /// * src: Source image /// * dst: Destination image. It will have same size and type as src. /// * center: The transformation center; where the output precision is maximal /// * M: Magnitude scale parameter. It determines the radius of the bounding circle to transform too. /// * flags: A combination of interpolation methods, see #InterpolationFlags /// /// /// Note: /// * The function can not operate in-place. /// * To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees. /// ## See also /// cv::linearPolar /// @endinternal #[deprecated = "This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags+WARP_POLAR_LOG);"] pub fn log_polar(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, center: core::Point2f, m: f64, flags: i32) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); unsafe { sys::cv_logPolar_const__InputArrayR_const__OutputArrayR_Point2f_double_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), center.opencv_as_extern(), m, flags) }.into_result() } /// Compares two shapes. /// /// The function compares two shapes. All three implemented methods use the Hu invariants (see #HuMoments) /// /// ## Parameters /// * contour1: First contour or grayscale image. /// * contour2: Second contour or grayscale image. /// * method: Comparison method, see #ShapeMatchModes /// * parameter: Method-specific parameter (not supported now). pub fn match_shapes(contour1: &dyn core::ToInputArray, contour2: &dyn core::ToInputArray, method: i32, parameter: f64) -> Result<f64> { input_array_arg!(contour1); input_array_arg!(contour2); unsafe { sys::cv_matchShapes_const__InputArrayR_const__InputArrayR_int_double(contour1.as_raw__InputArray(), contour2.as_raw__InputArray(), method, parameter) }.into_result() } /// Compares a template against overlapped image regions. /// /// The function slides through image , compares the overlapped patches of size  against /// templ using the specified method and stores the comparison results in result . #TemplateMatchModes /// describes the formulae for the available comparison methods (  denotes image,  /// template,  result,  the optional mask ). The summation is done over template and/or /// the image patch:  /// /// After the function finishes the comparison, the best matches can be found as global minimums (when /// #TM_SQDIFF was used) or maximums (when #TM_CCORR or #TM_CCOEFF was used) using the /// #minMaxLoc function. In case of a color image, template summation in the numerator and each sum in /// the denominator is done over all of the channels and separate mean values are used for each channel. /// That is, the function can take a color template and a color image. The result will still be a /// single-channel image, which is easier to analyze. /// /// ## Parameters /// * image: Image where the search is running. It must be 8-bit or 32-bit floating-point. /// * templ: Searched template. It must be not greater than the source image and have the same /// data type. /// * result: Map of comparison results. It must be single-channel 32-bit floating-point. If image /// is  and templ is  , then result is  . /// * method: Parameter specifying the comparison method, see #TemplateMatchModes /// * mask: Optional mask. It must have the same size as templ. It must either have the same number /// of channels as template or only one channel, which is then used for all template and /// image channels. If the data type is #CV_8U, the mask is interpreted as a binary mask, /// meaning only elements where mask is nonzero are used and are kept unchanged independent /// of the actual mask value (weight equals 1). For data tpye #CV_32F, the mask values are /// used as weights. The exact formulas are documented in #TemplateMatchModes. /// /// ## C++ default parameters /// * mask: noArray() pub fn match_template(image: &dyn core::ToInputArray, templ: &dyn core::ToInputArray, result: &mut dyn core::ToOutputArray, method: i32, mask: &dyn core::ToInputArray) -> Result<()> { input_array_arg!(image); input_array_arg!(templ); output_array_arg!(result); input_array_arg!(mask); unsafe { sys::cv_matchTemplate_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int_const__InputArrayR(image.as_raw__InputArray(), templ.as_raw__InputArray(), result.as_raw__OutputArray(), method, mask.as_raw__InputArray()) }.into_result() } /// Blurs an image using the median filter. /// /// The function smoothes an image using the median filter with the  aperture. Each channel of a multi-channel image is processed independently. /// In-place operation is supported. /// /// /// Note: The median filter uses #BORDER_REPLICATE internally to cope with border pixels, see #BorderTypes /// /// ## Parameters /// * src: input 1-, 3-, or 4-channel image; when ksize is 3 or 5, the image depth should be /// CV_8U, CV_16U, or CV_32F, for larger aperture sizes, it can only be CV_8U. /// * dst: destination array of the same size and type as src. /// * ksize: aperture linear size; it must be odd and greater than 1, for example: 3, 5, 7 ... /// ## See also /// bilateralFilter, blur, boxFilter, GaussianBlur pub fn median_blur(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, ksize: i32) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); unsafe { sys::cv_medianBlur_const__InputArrayR_const__OutputArrayR_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ksize) }.into_result() } /// Finds a rotated rectangle of the minimum area enclosing the input 2D point set. /// /// The function calculates and returns the minimum-area bounding rectangle (possibly rotated) for a /// specified point set. Developer should keep in mind that the returned RotatedRect can contain negative /// indices when data is close to the containing Mat element boundary. /// /// ## Parameters /// * points: Input vector of 2D points, stored in std::vector\<\> or Mat pub fn min_area_rect(points: &dyn core::ToInputArray) -> Result<core::RotatedRect> { input_array_arg!(points); unsafe { sys::cv_minAreaRect_const__InputArrayR(points.as_raw__InputArray()) }.into_result().map(|r| unsafe { core::RotatedRect::opencv_from_extern(r) } ) } /// Finds a circle of the minimum area enclosing a 2D point set. /// /// The function finds the minimal enclosing circle of a 2D point set using an iterative algorithm. /// /// ## Parameters /// * points: Input vector of 2D points, stored in std::vector\<\> or Mat /// * center: Output center of the circle. /// * radius: Output radius of the circle. pub fn min_enclosing_circle(points: &dyn core::ToInputArray, center: &mut core::Point2f, radius: &mut f32) -> Result<()> { input_array_arg!(points); unsafe { sys::cv_minEnclosingCircle_const__InputArrayR_Point2fR_floatR(points.as_raw__InputArray(), center, radius) }.into_result() } /// Finds a triangle of minimum area enclosing a 2D point set and returns its area. /// /// The function finds a triangle of minimum area enclosing the given set of 2D points and returns its /// area. The output for a given 2D point set is shown in the image below. 2D points are depicted in /// *red* and the enclosing triangle in *yellow*. /// ///  /// /// The implementation of the algorithm is based on O'Rourke's [ORourke86](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_ORourke86) and Klee and Laskowski's /// [KleeLaskowski85](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_KleeLaskowski85) papers. O'Rourke provides a  algorithm for finding the minimal /// enclosing triangle of a 2D convex polygon with n vertices. Since the #minEnclosingTriangle function /// takes a 2D point set as input an additional preprocessing step of computing the convex hull of the /// 2D point set is required. The complexity of the #convexHull function is  which is higher /// than . Thus the overall complexity of the function is . /// /// ## Parameters /// * points: Input vector of 2D points with depth CV_32S or CV_32F, stored in std::vector\<\> or Mat /// * triangle: Output vector of three 2D points defining the vertices of the triangle. The depth /// of the OutputArray must be CV_32F. pub fn min_enclosing_triangle(points: &dyn core::ToInputArray, triangle: &mut dyn core::ToOutputArray) -> Result<f64> { input_array_arg!(points); output_array_arg!(triangle); unsafe { sys::cv_minEnclosingTriangle_const__InputArrayR_const__OutputArrayR(points.as_raw__InputArray(), triangle.as_raw__OutputArray()) }.into_result() } /// Calculates all of the moments up to the third order of a polygon or rasterized shape. /// /// The function computes moments, up to the 3rd order, of a vector shape or a rasterized shape. The /// results are returned in the structure cv::Moments. /// /// ## Parameters /// * array: Raster image (single-channel, 8-bit or floating-point 2D array) or an array ( ///  or  ) of 2D points (Point or Point2f ). /// * binaryImage: If it is true, all non-zero image pixels are treated as 1's. The parameter is /// used for images only. /// ## Returns /// moments. /// /// /// Note: Only applicable to contour moments calculations from Python bindings: Note that the numpy /// type for the input array should be either np.int32 or np.float32. /// ## See also /// contourArea, arcLength /// /// ## C++ default parameters /// * binary_image: false pub fn moments(array: &dyn core::ToInputArray, binary_image: bool) -> Result<core::Moments> { input_array_arg!(array); unsafe { sys::cv_moments_const__InputArrayR_bool(array.as_raw__InputArray(), binary_image) }.into_result() } /// returns "magic" border value for erosion and dilation. It is automatically transformed to Scalar::all(-DBL_MAX) for dilation. pub fn morphology_default_border_value() -> Result<core::Scalar> { unsafe { sys::cv_morphologyDefaultBorderValue() }.into_result() } /// Performs advanced morphological transformations. /// /// The function cv::morphologyEx can perform advanced morphological transformations using an erosion and dilation as /// basic operations. /// /// Any of the operations can be done in-place. In case of multi-channel images, each channel is /// processed independently. /// /// ## Parameters /// * src: Source image. The number of channels can be arbitrary. The depth should be one of /// CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. /// * dst: Destination image of the same size and type as source image. /// * op: Type of a morphological operation, see #MorphTypes /// * kernel: Structuring element. It can be created using #getStructuringElement. /// * anchor: Anchor position with the kernel. Negative values mean that the anchor is at the /// kernel center. /// * iterations: Number of times erosion and dilation are applied. /// * borderType: Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. /// * borderValue: Border value in case of a constant border. The default value has a special /// meaning. /// ## See also /// dilate, erode, getStructuringElement /// /// Note: The number of iterations is the number of times erosion or dilatation operation will be applied. /// For instance, an opening operation (#MORPH_OPEN) with two iterations is equivalent to apply /// successively: erode -> erode -> dilate -> dilate (and not erode -> dilate -> erode -> dilate). /// /// ## C++ default parameters /// * anchor: Point(-1,-1) /// * iterations: 1 /// * border_type: BORDER_CONSTANT /// * border_value: morphologyDefaultBorderValue() pub fn morphology_ex(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, op: i32, kernel: &dyn core::ToInputArray, anchor: core::Point, iterations: i32, border_type: i32, border_value: core::Scalar) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); input_array_arg!(kernel); unsafe { sys::cv_morphologyEx_const__InputArrayR_const__OutputArrayR_int_const__InputArrayR_Point_int_int_const_ScalarR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), op, kernel.as_raw__InputArray(), anchor.opencv_as_extern(), iterations, border_type, &border_value) }.into_result() } /// The function is used to detect translational shifts that occur between two images. /// /// The operation takes advantage of the Fourier shift theorem for detecting the translational shift in /// the frequency domain. It can be used for fast image registration as well as motion estimation. For /// more information please see <http://en.wikipedia.org/wiki/Phase_correlation> /// /// Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed /// with getOptimalDFTSize. /// /// The function performs the following equations: /// - First it applies a Hanning window (see <http://en.wikipedia.org/wiki/Hann_function>) to each /// image to remove possible edge effects. This window is cached until the array size changes to speed /// up processing time. /// - Next it computes the forward DFTs of each source array: ///  /// where  is the forward DFT. /// - It then computes the cross-power spectrum of each frequency domain array: ///  /// - Next the cross-correlation is converted back into the time domain via the inverse DFT: ///  /// - Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to /// achieve sub-pixel accuracy. ///  /// - If non-zero, the response parameter is computed as the sum of the elements of r within the 5x5 /// centroid around the peak location. It is normalized to a maximum of 1 (meaning there is a single /// peak) and will be smaller when there are multiple peaks. /// /// ## Parameters /// * src1: Source floating point array (CV_32FC1 or CV_64FC1) /// * src2: Source floating point array (CV_32FC1 or CV_64FC1) /// * window: Floating point array with windowing coefficients to reduce edge effects (optional). /// * response: Signal power within the 5x5 centroid around the peak, between 0 and 1 (optional). /// ## Returns /// detected phase shift (sub-pixel) between the two arrays. /// ## See also /// dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow /// /// ## C++ default parameters /// * window: noArray() /// * response: 0 pub fn phase_correlate(src1: &dyn core::ToInputArray, src2: &dyn core::ToInputArray, window: &dyn core::ToInputArray, response: &mut f64) -> Result<core::Point2d> { input_array_arg!(src1); input_array_arg!(src2); input_array_arg!(window); unsafe { sys::cv_phaseCorrelate_const__InputArrayR_const__InputArrayR_const__InputArrayR_doubleX(src1.as_raw__InputArray(), src2.as_raw__InputArray(), window.as_raw__InputArray(), response) }.into_result() } /// Performs a point-in-contour test. /// /// The function determines whether the point is inside a contour, outside, or lies on an edge (or /// coincides with a vertex). It returns positive (inside), negative (outside), or zero (on an edge) /// value, correspondingly. When measureDist=false , the return value is +1, -1, and 0, respectively. /// Otherwise, the return value is a signed distance between the point and the nearest contour edge. /// /// See below a sample output of the function where each image pixel is tested against the contour: /// ///  /// /// ## Parameters /// * contour: Input contour. /// * pt: Point tested against the contour. /// * measureDist: If true, the function estimates the signed distance from the point to the /// nearest contour edge. Otherwise, the function only checks if the point is inside a contour or not. pub fn point_polygon_test(contour: &dyn core::ToInputArray, pt: core::Point2f, measure_dist: bool) -> Result<f64> { input_array_arg!(contour); unsafe { sys::cv_pointPolygonTest_const__InputArrayR_Point2f_bool(contour.as_raw__InputArray(), pt.opencv_as_extern(), measure_dist) }.into_result() } /// Draws several polygonal curves. /// /// ## Parameters /// * img: Image. /// * pts: Array of polygonal curves. /// * isClosed: Flag indicating whether the drawn polylines are closed or not. If they are closed, /// the function draws a line from the last vertex of each curve to its first vertex. /// * color: Polyline color. /// * thickness: Thickness of the polyline edges. /// * lineType: Type of the line segments. See #LineTypes /// * shift: Number of fractional bits in the vertex coordinates. /// /// The function cv::polylines draws one or more polygonal curves. /// /// ## C++ default parameters /// * thickness: 1 /// * line_type: LINE_8 /// * shift: 0 pub fn polylines(img: &mut dyn core::ToInputOutputArray, pts: &dyn core::ToInputArray, is_closed: bool, color: core::Scalar, thickness: i32, line_type: i32, shift: i32) -> Result<()> { input_output_array_arg!(img); input_array_arg!(pts); unsafe { sys::cv_polylines_const__InputOutputArrayR_const__InputArrayR_bool_const_ScalarR_int_int_int(img.as_raw__InputOutputArray(), pts.as_raw__InputArray(), is_closed, &color, thickness, line_type, shift) }.into_result() } /// Calculates a feature map for corner detection. /// /// The function calculates the complex spatial derivative-based function of the source image /// ///  /// /// where , are the first image derivatives, , are the second image /// derivatives, and  is the mixed derivative. /// /// The corners can be found as local maximums of the functions, as shown below: /// ```ignore /// Mat corners, dilated_corners; /// preCornerDetect(image, corners, 3); /// // dilation with 3x3 rectangular structuring element /// dilate(corners, dilated_corners, Mat(), 1); /// Mat corner_mask = corners == dilated_corners; /// ``` /// /// /// ## Parameters /// * src: Source single-channel 8-bit of floating-point image. /// * dst: Output image that has the type CV_32F and the same size as src . /// * ksize: %Aperture size of the Sobel . /// * borderType: Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported. /// /// ## C++ default parameters /// * border_type: BORDER_DEFAULT pub fn pre_corner_detect(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, ksize: i32, border_type: i32) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); unsafe { sys::cv_preCornerDetect_const__InputArrayR_const__OutputArrayR_int_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ksize, border_type) }.into_result() } /// Draws a text string. /// /// The function cv::putText renders the specified text string in the image. Symbols that cannot be rendered /// using the specified font are replaced by question marks. See #getTextSize for a text rendering code /// example. /// /// ## Parameters /// * img: Image. /// * text: Text string to be drawn. /// * org: Bottom-left corner of the text string in the image. /// * fontFace: Font type, see #HersheyFonts. /// * fontScale: Font scale factor that is multiplied by the font-specific base size. /// * color: Text color. /// * thickness: Thickness of the lines used to draw a text. /// * lineType: Line type. See #LineTypes /// * bottomLeftOrigin: When true, the image data origin is at the bottom-left corner. Otherwise, /// it is at the top-left corner. /// /// ## C++ default parameters /// * thickness: 1 /// * line_type: LINE_8 /// * bottom_left_origin: false pub fn put_text(img: &mut dyn core::ToInputOutputArray, text: &str, org: core::Point, font_face: i32, font_scale: f64, color: core::Scalar, thickness: i32, line_type: i32, bottom_left_origin: bool) -> Result<()> { input_output_array_arg!(img); extern_container_arg!(text); unsafe { sys::cv_putText_const__InputOutputArrayR_const_StringR_Point_int_double_Scalar_int_int_bool(img.as_raw__InputOutputArray(), text.opencv_as_extern(), org.opencv_as_extern(), font_face, font_scale, color.opencv_as_extern(), thickness, line_type, bottom_left_origin) }.into_result() } /// Blurs an image and downsamples it. /// /// By default, size of the output image is computed as `Size((src.cols+1)/2, (src.rows+1)/2)`, but in /// any case, the following conditions should be satisfied: /// ///  /// /// The function performs the downsampling step of the Gaussian pyramid construction. First, it /// convolves the source image with the kernel: /// ///  /// /// Then, it downsamples the image by rejecting even rows and columns. /// /// ## Parameters /// * src: input image. /// * dst: output image; it has the specified size and the same type as src. /// * dstsize: size of the output image. /// * borderType: Pixel extrapolation method, see #BorderTypes (#BORDER_CONSTANT isn't supported) /// /// ## C++ default parameters /// * dstsize: Size() /// * border_type: BORDER_DEFAULT pub fn pyr_down(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, dstsize: core::Size, border_type: i32) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); unsafe { sys::cv_pyrDown_const__InputArrayR_const__OutputArrayR_const_SizeR_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), &dstsize, border_type) }.into_result() } /// Performs initial step of meanshift segmentation of an image. /// /// The function implements the filtering stage of meanshift segmentation, that is, the output of the /// function is the filtered "posterized" image with color gradients and fine-grain texture flattened. /// At every pixel (X,Y) of the input image (or down-sized input image, see below) the function executes /// meanshift iterations, that is, the pixel (X,Y) neighborhood in the joint space-color hyperspace is /// considered: /// ///  /// /// where (R,G,B) and (r,g,b) are the vectors of color components at (X,Y) and (x,y), respectively /// (though, the algorithm does not depend on the color space used, so any 3-component color space can /// be used instead). Over the neighborhood the average spatial value (X',Y') and average color vector /// (R',G',B') are found and they act as the neighborhood center on the next iteration: /// ///  /// /// After the iterations over, the color components of the initial pixel (that is, the pixel from where /// the iterations started) are set to the final value (average color at the last iteration): /// ///  /// /// When maxLevel \> 0, the gaussian pyramid of maxLevel+1 levels is built, and the above procedure is /// run on the smallest layer first. After that, the results are propagated to the larger layer and the /// iterations are run again only on those pixels where the layer colors differ by more than sr from the /// lower-resolution layer of the pyramid. That makes boundaries of color regions sharper. Note that the /// results will be actually different from the ones obtained by running the meanshift procedure on the /// whole original image (i.e. when maxLevel==0). /// /// ## Parameters /// * src: The source 8-bit, 3-channel image. /// * dst: The destination image of the same format and the same size as the source. /// * sp: The spatial window radius. /// * sr: The color window radius. /// * maxLevel: Maximum level of the pyramid for the segmentation. /// * termcrit: Termination criteria: when to stop meanshift iterations. /// /// ## C++ default parameters /// * max_level: 1 /// * termcrit: TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5,1) pub fn pyr_mean_shift_filtering(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, sp: f64, sr: f64, max_level: i32, termcrit: core::TermCriteria) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); unsafe { sys::cv_pyrMeanShiftFiltering_const__InputArrayR_const__OutputArrayR_double_double_int_TermCriteria(src.as_raw__InputArray(), dst.as_raw__OutputArray(), sp, sr, max_level, termcrit.opencv_as_extern()) }.into_result() } /// Upsamples an image and then blurs it. /// /// By default, size of the output image is computed as `Size(src.cols\*2, (src.rows\*2)`, but in any /// case, the following conditions should be satisfied: /// ///  /// /// The function performs the upsampling step of the Gaussian pyramid construction, though it can /// actually be used to construct the Laplacian pyramid. First, it upsamples the source image by /// injecting even zero rows and columns and then convolves the result with the same kernel as in /// pyrDown multiplied by 4. /// /// ## Parameters /// * src: input image. /// * dst: output image. It has the specified size and the same type as src . /// * dstsize: size of the output image. /// * borderType: Pixel extrapolation method, see #BorderTypes (only #BORDER_DEFAULT is supported) /// /// ## C++ default parameters /// * dstsize: Size() /// * border_type: BORDER_DEFAULT pub fn pyr_up(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, dstsize: core::Size, border_type: i32) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); unsafe { sys::cv_pyrUp_const__InputArrayR_const__OutputArrayR_const_SizeR_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), &dstsize, border_type) }.into_result() } /// Draws a simple, thick, or filled up-right rectangle. /// /// The function cv::rectangle draws a rectangle outline or a filled rectangle whose two opposite corners /// are pt1 and pt2. /// /// ## Parameters /// * img: Image. /// * pt1: Vertex of the rectangle. /// * pt2: Vertex of the rectangle opposite to pt1 . /// * color: Rectangle color or brightness (grayscale image). /// * thickness: Thickness of lines that make up the rectangle. Negative values, like #FILLED, /// mean that the function has to draw a filled rectangle. /// * lineType: Type of the line. See #LineTypes /// * shift: Number of fractional bits in the point coordinates. /// /// ## C++ default parameters /// * thickness: 1 /// * line_type: LINE_8 /// * shift: 0 pub fn rectangle_points(img: &mut dyn core::ToInputOutputArray, pt1: core::Point, pt2: core::Point, color: core::Scalar, thickness: i32, line_type: i32, shift: i32) -> Result<()> { input_output_array_arg!(img); unsafe { sys::cv_rectangle_const__InputOutputArrayR_Point_Point_const_ScalarR_int_int_int(img.as_raw__InputOutputArray(), pt1.opencv_as_extern(), pt2.opencv_as_extern(), &color, thickness, line_type, shift) }.into_result() } /// Draws a simple, thick, or filled up-right rectangle. /// /// The function cv::rectangle draws a rectangle outline or a filled rectangle whose two opposite corners /// are pt1 and pt2. /// /// ## Parameters /// * img: Image. /// * pt1: Vertex of the rectangle. /// * pt2: Vertex of the rectangle opposite to pt1 . /// * color: Rectangle color or brightness (grayscale image). /// * thickness: Thickness of lines that make up the rectangle. Negative values, like #FILLED, /// mean that the function has to draw a filled rectangle. /// * lineType: Type of the line. See #LineTypes /// * shift: Number of fractional bits in the point coordinates. /// /// ## Overloaded parameters /// /// /// use `rec` parameter as alternative specification of the drawn rectangle: `r.tl() and /// r.br()-Point(1,1)` are opposite corners /// /// ## C++ default parameters /// * thickness: 1 /// * line_type: LINE_8 /// * shift: 0 pub fn rectangle(img: &mut dyn core::ToInputOutputArray, rec: core::Rect, color: core::Scalar, thickness: i32, line_type: i32, shift: i32) -> Result<()> { input_output_array_arg!(img); unsafe { sys::cv_rectangle_const__InputOutputArrayR_Rect_const_ScalarR_int_int_int(img.as_raw__InputOutputArray(), rec.opencv_as_extern(), &color, thickness, line_type, shift) }.into_result() } /// Applies a generic geometrical transformation to an image. /// /// The function remap transforms the source image using the specified map: /// ///  /// /// where values of pixels with non-integer coordinates are computed using one of available /// interpolation methods.  and  can be encoded as separate floating-point maps /// in  and  respectively, or interleaved floating-point maps of  in /// , or fixed-point maps created by using convertMaps. The reason you might want to /// convert from floating to fixed-point representations of a map is that they can yield much faster /// (\~2x) remapping operations. In the converted case,  contains pairs (cvFloor(x), /// cvFloor(y)) and  contains indices in a table of interpolation coefficients. /// /// This function cannot operate in-place. /// /// ## Parameters /// * src: Source image. /// * dst: Destination image. It has the same size as map1 and the same type as src . /// * map1: The first map of either (x,y) points or just x values having the type CV_16SC2 , /// CV_32FC1, or CV_32FC2. See convertMaps for details on converting a floating point /// representation to fixed-point for speed. /// * map2: The second map of y values having the type CV_16UC1, CV_32FC1, or none (empty map /// if map1 is (x,y) points), respectively. /// * interpolation: Interpolation method (see #InterpolationFlags). The method #INTER_AREA is /// not supported by this function. /// * borderMode: Pixel extrapolation method (see #BorderTypes). When /// borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image that /// corresponds to the "outliers" in the source image are not modified by the function. /// * borderValue: Value used in case of a constant border. By default, it is 0. /// /// Note: /// Due to current implementation limitations the size of an input and output images should be less than 32767x32767. /// /// ## C++ default parameters /// * border_mode: BORDER_CONSTANT /// * border_value: Scalar() pub fn remap(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, map1: &dyn core::ToInputArray, map2: &dyn core::ToInputArray, interpolation: i32, border_mode: i32, border_value: core::Scalar) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); input_array_arg!(map1); input_array_arg!(map2); unsafe { sys::cv_remap_const__InputArrayR_const__OutputArrayR_const__InputArrayR_const__InputArrayR_int_int_const_ScalarR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), map1.as_raw__InputArray(), map2.as_raw__InputArray(), interpolation, border_mode, &border_value) }.into_result() } /// Resizes an image. /// /// The function resize resizes the image src down to or up to the specified size. Note that the /// initial dst type or size are not taken into account. Instead, the size and type are derived from /// the `src`,`dsize`,`fx`, and `fy`. If you want to resize src so that it fits the pre-created dst, /// you may call the function as follows: /// ```ignore /// // explicitly specify dsize=dst.size(); fx and fy will be computed from that. /// resize(src, dst, dst.size(), 0, 0, interpolation); /// ``` /// /// If you want to decimate the image by factor of 2 in each direction, you can call the function this /// way: /// ```ignore /// // specify fx and fy and let the function compute the destination image size. /// resize(src, dst, Size(), 0.5, 0.5, interpolation); /// ``` /// /// To shrink an image, it will generally look best with #INTER_AREA interpolation, whereas to /// enlarge an image, it will generally look best with c#INTER_CUBIC (slow) or #INTER_LINEAR /// (faster but still looks OK). /// /// ## Parameters /// * src: input image. /// * dst: output image; it has the size dsize (when it is non-zero) or the size computed from /// src.size(), fx, and fy; the type of dst is the same as of src. /// * dsize: output image size; if it equals zero, it is computed as: ///  /// Either dsize or both fx and fy must be non-zero. /// * fx: scale factor along the horizontal axis; when it equals 0, it is computed as ///  /// * fy: scale factor along the vertical axis; when it equals 0, it is computed as ///  /// * interpolation: interpolation method, see #InterpolationFlags /// ## See also /// warpAffine, warpPerspective, remap /// /// ## C++ default parameters /// * fx: 0 /// * fy: 0 /// * interpolation: INTER_LINEAR pub fn resize(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, dsize: core::Size, fx: f64, fy: f64, interpolation: i32) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); unsafe { sys::cv_resize_const__InputArrayR_const__OutputArrayR_Size_double_double_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), dsize.opencv_as_extern(), fx, fy, interpolation) }.into_result() } /// Finds out if there is any intersection between two rotated rectangles. /// /// If there is then the vertices of the intersecting region are returned as well. /// /// Below are some examples of intersection configurations. The hatched pattern indicates the /// intersecting region and the red vertices are returned by the function. /// ///  /// /// ## Parameters /// * rect1: First rectangle /// * rect2: Second rectangle /// * intersectingRegion: The output array of the vertices of the intersecting region. It returns /// at most 8 vertices. Stored as std::vector\<cv::Point2f\> or cv::Mat as Mx1 of type CV_32FC2. /// ## Returns /// One of #RectanglesIntersectTypes pub fn rotated_rectangle_intersection(rect1: &core::RotatedRect, rect2: &core::RotatedRect, intersecting_region: &mut dyn core::ToOutputArray) -> Result<i32> { output_array_arg!(intersecting_region); unsafe { sys::cv_rotatedRectangleIntersection_const_RotatedRectR_const_RotatedRectR_const__OutputArrayR(rect1.as_raw_RotatedRect(), rect2.as_raw_RotatedRect(), intersecting_region.as_raw__OutputArray()) }.into_result() } /// Applies a separable linear filter to an image. /// /// The function applies a separable linear filter to the image. That is, first, every row of src is /// filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D /// kernel kernelY. The final result shifted by delta is stored in dst . /// /// ## Parameters /// * src: Source image. /// * dst: Destination image of the same size and the same number of channels as src . /// * ddepth: Destination image depth, see @ref filter_depths "combinations" /// * kernelX: Coefficients for filtering each row. /// * kernelY: Coefficients for filtering each column. /// * anchor: Anchor position within the kernel. The default value  means that the anchor /// is at the kernel center. /// * delta: Value added to the filtered results before storing them. /// * borderType: Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. /// ## See also /// filter2D, Sobel, GaussianBlur, boxFilter, blur /// /// ## C++ default parameters /// * anchor: Point(-1,-1) /// * delta: 0 /// * border_type: BORDER_DEFAULT pub fn sep_filter_2d(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, ddepth: i32, kernel_x: &dyn core::ToInputArray, kernel_y: &dyn core::ToInputArray, anchor: core::Point, delta: f64, border_type: i32) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); input_array_arg!(kernel_x); input_array_arg!(kernel_y); unsafe { sys::cv_sepFilter2D_const__InputArrayR_const__OutputArrayR_int_const__InputArrayR_const__InputArrayR_Point_double_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ddepth, kernel_x.as_raw__InputArray(), kernel_y.as_raw__InputArray(), anchor.opencv_as_extern(), delta, border_type) }.into_result() } /// Calculates the first order image derivative in both x and y using a Sobel operator /// /// Equivalent to calling: /// /// ```ignore /// Sobel( src, dx, CV_16SC1, 1, 0, 3 ); /// Sobel( src, dy, CV_16SC1, 0, 1, 3 ); /// ``` /// /// /// ## Parameters /// * src: input image. /// * dx: output image with first-order derivative in x. /// * dy: output image with first-order derivative in y. /// * ksize: size of Sobel kernel. It must be 3. /// * borderType: pixel extrapolation method, see #BorderTypes. /// Only #BORDER_DEFAULT=#BORDER_REFLECT_101 and #BORDER_REPLICATE are supported. /// ## See also /// Sobel /// /// ## C++ default parameters /// * ksize: 3 /// * border_type: BORDER_DEFAULT pub fn spatial_gradient(src: &dyn core::ToInputArray, dx: &mut dyn core::ToOutputArray, dy: &mut dyn core::ToOutputArray, ksize: i32, border_type: i32) -> Result<()> { input_array_arg!(src); output_array_arg!(dx); output_array_arg!(dy); unsafe { sys::cv_spatialGradient_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_int_int(src.as_raw__InputArray(), dx.as_raw__OutputArray(), dy.as_raw__OutputArray(), ksize, border_type) }.into_result() } /// Calculates the normalized sum of squares of the pixel values overlapping the filter. /// /// For every pixel  in the source image, the function calculates the sum of squares of those neighboring /// pixel values which overlap the filter placed over the pixel . /// /// The unnormalized square box filter can be useful in computing local image statistics such as the the local /// variance and standard deviation around the neighborhood of a pixel. /// /// ## Parameters /// * src: input image /// * dst: output image of the same size and type as _src /// * ddepth: the output image depth (-1 to use src.depth()) /// * ksize: kernel size /// * anchor: kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel /// center. /// * normalize: flag, specifying whether the kernel is to be normalized by it's area or not. /// * borderType: border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported. /// ## See also /// boxFilter /// /// ## C++ default parameters /// * anchor: Point(-1,-1) /// * normalize: true /// * border_type: BORDER_DEFAULT pub fn sqr_box_filter(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, ddepth: i32, ksize: core::Size, anchor: core::Point, normalize: bool, border_type: i32) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); unsafe { sys::cv_sqrBoxFilter_const__InputArrayR_const__OutputArrayR_int_Size_Point_bool_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ddepth, ksize.opencv_as_extern(), anchor.opencv_as_extern(), normalize, border_type) }.into_result() } /// Applies a fixed-level threshold to each array element. /// /// The function applies fixed-level thresholding to a multiple-channel array. The function is typically /// used to get a bi-level (binary) image out of a grayscale image ( #compare could be also used for /// this purpose) or for removing a noise, that is, filtering out pixels with too small or too large /// values. There are several types of thresholding supported by the function. They are determined by /// type parameter. /// /// Also, the special values #THRESH_OTSU or #THRESH_TRIANGLE may be combined with one of the /// above values. In these cases, the function determines the optimal threshold value using the Otsu's /// or Triangle algorithm and uses it instead of the specified thresh. /// /// /// Note: Currently, the Otsu's and Triangle methods are implemented only for 8-bit single-channel images. /// /// ## Parameters /// * src: input array (multiple-channel, 8-bit or 32-bit floating point). /// * dst: output array of the same size and type and the same number of channels as src. /// * thresh: threshold value. /// * maxval: maximum value to use with the #THRESH_BINARY and #THRESH_BINARY_INV thresholding /// types. /// * type: thresholding type (see #ThresholdTypes). /// ## Returns /// the computed threshold value if Otsu's or Triangle methods used. /// ## See also /// adaptiveThreshold, findContours, compare, min, max pub fn threshold(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, thresh: f64, maxval: f64, typ: i32) -> Result<f64> { input_array_arg!(src); output_array_arg!(dst); unsafe { sys::cv_threshold_const__InputArrayR_const__OutputArrayR_double_double_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), thresh, maxval, typ) }.into_result() } /// Applies an affine transformation to an image. /// /// The function warpAffine transforms the source image using the specified matrix: /// ///  /// /// when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted /// with #invertAffineTransform and then put in the formula above instead of M. The function cannot /// operate in-place. /// /// ## Parameters /// * src: input image. /// * dst: output image that has the size dsize and the same type as src . /// * M:  transformation matrix. /// * dsize: size of the output image. /// * flags: combination of interpolation methods (see #InterpolationFlags) and the optional /// flag #WARP_INVERSE_MAP that means that M is the inverse transformation ( ///  ). /// * borderMode: pixel extrapolation method (see #BorderTypes); when /// borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image corresponding to /// the "outliers" in the source image are not modified by the function. /// * borderValue: value used in case of a constant border; by default, it is 0. /// ## See also /// warpPerspective, resize, remap, getRectSubPix, transform /// /// ## C++ default parameters /// * flags: INTER_LINEAR /// * border_mode: BORDER_CONSTANT /// * border_value: Scalar() pub fn warp_affine(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, m: &dyn core::ToInputArray, dsize: core::Size, flags: i32, border_mode: i32, border_value: core::Scalar) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); input_array_arg!(m); unsafe { sys::cv_warpAffine_const__InputArrayR_const__OutputArrayR_const__InputArrayR_Size_int_int_const_ScalarR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), m.as_raw__InputArray(), dsize.opencv_as_extern(), flags, border_mode, &border_value) }.into_result() } /// Applies a perspective transformation to an image. /// /// The function warpPerspective transforms the source image using the specified matrix: /// ///  /// /// when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invert /// and then put in the formula above instead of M. The function cannot operate in-place. /// /// ## Parameters /// * src: input image. /// * dst: output image that has the size dsize and the same type as src . /// * M:  transformation matrix. /// * dsize: size of the output image. /// * flags: combination of interpolation methods (#INTER_LINEAR or #INTER_NEAREST) and the /// optional flag #WARP_INVERSE_MAP, that sets M as the inverse transformation ( ///  ). /// * borderMode: pixel extrapolation method (#BORDER_CONSTANT or #BORDER_REPLICATE). /// * borderValue: value used in case of a constant border; by default, it equals 0. /// ## See also /// warpAffine, resize, remap, getRectSubPix, perspectiveTransform /// /// ## C++ default parameters /// * flags: INTER_LINEAR /// * border_mode: BORDER_CONSTANT /// * border_value: Scalar() pub fn warp_perspective(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, m: &dyn core::ToInputArray, dsize: core::Size, flags: i32, border_mode: i32, border_value: core::Scalar) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); input_array_arg!(m); unsafe { sys::cv_warpPerspective_const__InputArrayR_const__OutputArrayR_const__InputArrayR_Size_int_int_const_ScalarR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), m.as_raw__InputArray(), dsize.opencv_as_extern(), flags, border_mode, &border_value) }.into_result() } /// \brief Remaps an image to polar or semilog-polar coordinates space /// /// @anchor polar_remaps_reference_image ///  /// /// Transform the source image using the following transformation: ///  /// /// where ///  /// /// and ///  /// /// /// \par Linear vs semilog mapping /// /// Polar mapping can be linear or semi-log. Add one of #WarpPolarMode to `flags` to specify the polar mapping mode. /// /// Linear is the default mode. /// /// The semilog mapping emulates the human "foveal" vision that permit very high acuity on the line of sight (central vision) /// in contrast to peripheral vision where acuity is minor. /// /// \par Option on `dsize`: /// /// - if both values in `dsize <=0 ` (default), /// the destination image will have (almost) same area of source bounding circle: ///  /// /// /// - if only `dsize.height <= 0`, /// the destination image area will be proportional to the bounding circle area but scaled by `Kx * Kx`: ///  /// /// - if both values in `dsize > 0 `, /// the destination image will have the given size therefore the area of the bounding circle will be scaled to `dsize`. /// /// /// \par Reverse mapping /// /// You can get reverse mapping adding #WARP_INVERSE_MAP to `flags` /// \snippet polar_transforms.cpp InverseMap /// /// In addiction, to calculate the original coordinate from a polar mapped coordinate : /// \snippet polar_transforms.cpp InverseCoordinate /// /// ## Parameters /// * src: Source image. /// * dst: Destination image. It will have same type as src. /// * dsize: The destination image size (see description for valid options). /// * center: The transformation center. /// * maxRadius: The radius of the bounding circle to transform. It determines the inverse magnitude scale parameter too. /// * flags: A combination of interpolation methods, #InterpolationFlags + #WarpPolarMode. /// - Add #WARP_POLAR_LINEAR to select linear polar mapping (default) /// - Add #WARP_POLAR_LOG to select semilog polar mapping /// - Add #WARP_INVERSE_MAP for reverse mapping. /// /// Note: /// * The function can not operate in-place. /// * To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees. /// * This function uses #remap. Due to current implementation limitations the size of an input and output images should be less than 32767x32767. /// ## See also /// cv::remap pub fn warp_polar(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, dsize: core::Size, center: core::Point2f, max_radius: f64, flags: i32) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); unsafe { sys::cv_warpPolar_const__InputArrayR_const__OutputArrayR_Size_Point2f_double_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), dsize.opencv_as_extern(), center.opencv_as_extern(), max_radius, flags) }.into_result() } /// Performs a marker-based image segmentation using the watershed algorithm. /// /// The function implements one of the variants of watershed, non-parametric marker-based segmentation /// algorithm, described in [Meyer92](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Meyer92) . /// /// Before passing the image to the function, you have to roughly outline the desired regions in the /// image markers with positive (\>0) indices. So, every region is represented as one or more connected /// components with the pixel values 1, 2, 3, and so on. Such markers can be retrieved from a binary /// mask using #findContours and #drawContours (see the watershed.cpp demo). The markers are "seeds" of /// the future image regions. All the other pixels in markers , whose relation to the outlined regions /// is not known and should be defined by the algorithm, should be set to 0's. In the function output, /// each pixel in markers is set to a value of the "seed" components or to -1 at boundaries between the /// regions. /// /// /// Note: Any two neighbor connected components are not necessarily separated by a watershed boundary /// (-1's pixels); for example, they can touch each other in the initial marker image passed to the /// function. /// /// ## Parameters /// * image: Input 8-bit 3-channel image. /// * markers: Input/output 32-bit single-channel image (map) of markers. It should have the same /// size as image . /// ## See also /// findContours /// /// @ingroup imgproc_misc pub fn watershed(image: &dyn core::ToInputArray, markers: &mut dyn core::ToInputOutputArray) -> Result<()> { input_array_arg!(image); input_output_array_arg!(markers); unsafe { sys::cv_watershed_const__InputArrayR_const__InputOutputArrayR(image.as_raw__InputArray(), markers.as_raw__InputOutputArray()) }.into_result() } /// ## C++ default parameters /// * cost: noArray() /// * lower_bound: Ptr<float>() /// * flow: noArray() pub fn emd_1(signature1: &dyn core::ToInputArray, signature2: &dyn core::ToInputArray, dist_type: i32, cost: &dyn core::ToInputArray, mut lower_bound: core::Ptr::<f32>, flow: &mut dyn core::ToOutputArray) -> Result<f32> { input_array_arg!(signature1); input_array_arg!(signature2); input_array_arg!(cost); output_array_arg!(flow); unsafe { sys::cv_wrapperEMD_const__InputArrayR_const__InputArrayR_int_const__InputArrayR_Ptr_float__const__OutputArrayR(signature1.as_raw__InputArray(), signature2.as_raw__InputArray(), dist_type, cost.as_raw__InputArray(), lower_bound.as_raw_mut_PtrOff32(), flow.as_raw__OutputArray()) }.into_result() } /// Base class for Contrast Limited Adaptive Histogram Equalization. pub trait CLAHE: core::AlgorithmTrait { fn as_raw_CLAHE(&self) -> *const c_void; fn as_raw_mut_CLAHE(&mut self) -> *mut c_void; /// Equalizes the histogram of a grayscale image using Contrast Limited Adaptive Histogram Equalization. /// /// ## Parameters /// * src: Source image of type CV_8UC1 or CV_16UC1. /// * dst: Destination image. fn apply(&mut self, src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); unsafe { sys::cv_CLAHE_apply_const__InputArrayR_const__OutputArrayR(self.as_raw_mut_CLAHE(), src.as_raw__InputArray(), dst.as_raw__OutputArray()) }.into_result() } /// Sets threshold for contrast limiting. /// /// ## Parameters /// * clipLimit: threshold value. fn set_clip_limit(&mut self, clip_limit: f64) -> Result<()> { unsafe { sys::cv_CLAHE_setClipLimit_double(self.as_raw_mut_CLAHE(), clip_limit) }.into_result() } /// Returns threshold value for contrast limiting. fn get_clip_limit(&self) -> Result<f64> { unsafe { sys::cv_CLAHE_getClipLimit_const(self.as_raw_CLAHE()) }.into_result() } /// Sets size of grid for histogram equalization. Input image will be divided into /// equally sized rectangular tiles. /// /// ## Parameters /// * tileGridSize: defines the number of tiles in row and column. fn set_tiles_grid_size(&mut self, tile_grid_size: core::Size) -> Result<()> { unsafe { sys::cv_CLAHE_setTilesGridSize_Size(self.as_raw_mut_CLAHE(), tile_grid_size.opencv_as_extern()) }.into_result() } /// Returns Size defines the number of tiles in row and column. fn get_tiles_grid_size(&self) -> Result<core::Size> { unsafe { sys::cv_CLAHE_getTilesGridSize_const(self.as_raw_CLAHE()) }.into_result() } fn collect_garbage(&mut self) -> Result<()> { unsafe { sys::cv_CLAHE_collectGarbage(self.as_raw_mut_CLAHE()) }.into_result() } } /// finds arbitrary template in the grayscale image using Generalized Hough Transform pub trait GeneralizedHough: core::AlgorithmTrait { fn as_raw_GeneralizedHough(&self) -> *const c_void; fn as_raw_mut_GeneralizedHough(&mut self) -> *mut c_void; /// set template to search /// /// ## C++ default parameters /// * templ_center: Point(-1,-1) fn set_template(&mut self, templ: &dyn core::ToInputArray, templ_center: core::Point) -> Result<()> { input_array_arg!(templ); unsafe { sys::cv_GeneralizedHough_setTemplate_const__InputArrayR_Point(self.as_raw_mut_GeneralizedHough(), templ.as_raw__InputArray(), templ_center.opencv_as_extern()) }.into_result() } /// ## C++ default parameters /// * templ_center: Point(-1,-1) fn set_template_1(&mut self, edges: &dyn core::ToInputArray, dx: &dyn core::ToInputArray, dy: &dyn core::ToInputArray, templ_center: core::Point) -> Result<()> { input_array_arg!(edges); input_array_arg!(dx); input_array_arg!(dy); unsafe { sys::cv_GeneralizedHough_setTemplate_const__InputArrayR_const__InputArrayR_const__InputArrayR_Point(self.as_raw_mut_GeneralizedHough(), edges.as_raw__InputArray(), dx.as_raw__InputArray(), dy.as_raw__InputArray(), templ_center.opencv_as_extern()) }.into_result() } /// find template on image /// /// ## C++ default parameters /// * votes: noArray() fn detect(&mut self, image: &dyn core::ToInputArray, positions: &mut dyn core::ToOutputArray, votes: &mut dyn core::ToOutputArray) -> Result<()> { input_array_arg!(image); output_array_arg!(positions); output_array_arg!(votes); unsafe { sys::cv_GeneralizedHough_detect_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(self.as_raw_mut_GeneralizedHough(), image.as_raw__InputArray(), positions.as_raw__OutputArray(), votes.as_raw__OutputArray()) }.into_result() } /// ## C++ default parameters /// * votes: noArray() fn detect_with_edges(&mut self, edges: &dyn core::ToInputArray, dx: &dyn core::ToInputArray, dy: &dyn core::ToInputArray, positions: &mut dyn core::ToOutputArray, votes: &mut dyn core::ToOutputArray) -> Result<()> { input_array_arg!(edges); input_array_arg!(dx); input_array_arg!(dy); output_array_arg!(positions); output_array_arg!(votes); unsafe { sys::cv_GeneralizedHough_detect_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(self.as_raw_mut_GeneralizedHough(), edges.as_raw__InputArray(), dx.as_raw__InputArray(), dy.as_raw__InputArray(), positions.as_raw__OutputArray(), votes.as_raw__OutputArray()) }.into_result() } /// Canny low threshold. fn set_canny_low_thresh(&mut self, canny_low_thresh: i32) -> Result<()> { unsafe { sys::cv_GeneralizedHough_setCannyLowThresh_int(self.as_raw_mut_GeneralizedHough(), canny_low_thresh) }.into_result() } fn get_canny_low_thresh(&self) -> Result<i32> { unsafe { sys::cv_GeneralizedHough_getCannyLowThresh_const(self.as_raw_GeneralizedHough()) }.into_result() } /// Canny high threshold. fn set_canny_high_thresh(&mut self, canny_high_thresh: i32) -> Result<()> { unsafe { sys::cv_GeneralizedHough_setCannyHighThresh_int(self.as_raw_mut_GeneralizedHough(), canny_high_thresh) }.into_result() } fn get_canny_high_thresh(&self) -> Result<i32> { unsafe { sys::cv_GeneralizedHough_getCannyHighThresh_const(self.as_raw_GeneralizedHough()) }.into_result() } /// Minimum distance between the centers of the detected objects. fn set_min_dist(&mut self, min_dist: f64) -> Result<()> { unsafe { sys::cv_GeneralizedHough_setMinDist_double(self.as_raw_mut_GeneralizedHough(), min_dist) }.into_result() } fn get_min_dist(&self) -> Result<f64> { unsafe { sys::cv_GeneralizedHough_getMinDist_const(self.as_raw_GeneralizedHough()) }.into_result() } /// Inverse ratio of the accumulator resolution to the image resolution. fn set_dp(&mut self, dp: f64) -> Result<()> { unsafe { sys::cv_GeneralizedHough_setDp_double(self.as_raw_mut_GeneralizedHough(), dp) }.into_result() } fn get_dp(&self) -> Result<f64> { unsafe { sys::cv_GeneralizedHough_getDp_const(self.as_raw_GeneralizedHough()) }.into_result() } /// Maximal size of inner buffers. fn set_max_buffer_size(&mut self, max_buffer_size: i32) -> Result<()> { unsafe { sys::cv_GeneralizedHough_setMaxBufferSize_int(self.as_raw_mut_GeneralizedHough(), max_buffer_size) }.into_result() } fn get_max_buffer_size(&self) -> Result<i32> { unsafe { sys::cv_GeneralizedHough_getMaxBufferSize_const(self.as_raw_GeneralizedHough()) }.into_result() } } /// finds arbitrary template in the grayscale image using Generalized Hough Transform /// /// Detects position only without translation and rotation [Ballard1981](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Ballard1981) . pub trait GeneralizedHoughBallard: crate::imgproc::GeneralizedHough { fn as_raw_GeneralizedHoughBallard(&self) -> *const c_void; fn as_raw_mut_GeneralizedHoughBallard(&mut self) -> *mut c_void; /// R-Table levels. fn set_levels(&mut self, levels: i32) -> Result<()> { unsafe { sys::cv_GeneralizedHoughBallard_setLevels_int(self.as_raw_mut_GeneralizedHoughBallard(), levels) }.into_result() } fn get_levels(&self) -> Result<i32> { unsafe { sys::cv_GeneralizedHoughBallard_getLevels_const(self.as_raw_GeneralizedHoughBallard()) }.into_result() } /// The accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected. fn set_votes_threshold(&mut self, votes_threshold: i32) -> Result<()> { unsafe { sys::cv_GeneralizedHoughBallard_setVotesThreshold_int(self.as_raw_mut_GeneralizedHoughBallard(), votes_threshold) }.into_result() } fn get_votes_threshold(&self) -> Result<i32> { unsafe { sys::cv_GeneralizedHoughBallard_getVotesThreshold_const(self.as_raw_GeneralizedHoughBallard()) }.into_result() } } /// finds arbitrary template in the grayscale image using Generalized Hough Transform /// /// Detects position, translation and rotation [Guil1999](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Guil1999) . pub trait GeneralizedHoughGuil: crate::imgproc::GeneralizedHough { fn as_raw_GeneralizedHoughGuil(&self) -> *const c_void; fn as_raw_mut_GeneralizedHoughGuil(&mut self) -> *mut c_void; /// Angle difference in degrees between two points in feature. fn set_xi(&mut self, xi: f64) -> Result<()> { unsafe { sys::cv_GeneralizedHoughGuil_setXi_double(self.as_raw_mut_GeneralizedHoughGuil(), xi) }.into_result() } fn get_xi(&self) -> Result<f64> { unsafe { sys::cv_GeneralizedHoughGuil_getXi_const(self.as_raw_GeneralizedHoughGuil()) }.into_result() } /// Feature table levels. fn set_levels(&mut self, levels: i32) -> Result<()> { unsafe { sys::cv_GeneralizedHoughGuil_setLevels_int(self.as_raw_mut_GeneralizedHoughGuil(), levels) }.into_result() } fn get_levels(&self) -> Result<i32> { unsafe { sys::cv_GeneralizedHoughGuil_getLevels_const(self.as_raw_GeneralizedHoughGuil()) }.into_result() } /// Maximal difference between angles that treated as equal. fn set_angle_epsilon(&mut self, angle_epsilon: f64) -> Result<()> { unsafe { sys::cv_GeneralizedHoughGuil_setAngleEpsilon_double(self.as_raw_mut_GeneralizedHoughGuil(), angle_epsilon) }.into_result() } fn get_angle_epsilon(&self) -> Result<f64> { unsafe { sys::cv_GeneralizedHoughGuil_getAngleEpsilon_const(self.as_raw_GeneralizedHoughGuil()) }.into_result() } /// Minimal rotation angle to detect in degrees. fn set_min_angle(&mut self, min_angle: f64) -> Result<()> { unsafe { sys::cv_GeneralizedHoughGuil_setMinAngle_double(self.as_raw_mut_GeneralizedHoughGuil(), min_angle) }.into_result() } fn get_min_angle(&self) -> Result<f64> { unsafe { sys::cv_GeneralizedHoughGuil_getMinAngle_const(self.as_raw_GeneralizedHoughGuil()) }.into_result() } /// Maximal rotation angle to detect in degrees. fn set_max_angle(&mut self, max_angle: f64) -> Result<()> { unsafe { sys::cv_GeneralizedHoughGuil_setMaxAngle_double(self.as_raw_mut_GeneralizedHoughGuil(), max_angle) }.into_result() } fn get_max_angle(&self) -> Result<f64> { unsafe { sys::cv_GeneralizedHoughGuil_getMaxAngle_const(self.as_raw_GeneralizedHoughGuil()) }.into_result() } /// Angle step in degrees. fn set_angle_step(&mut self, angle_step: f64) -> Result<()> { unsafe { sys::cv_GeneralizedHoughGuil_setAngleStep_double(self.as_raw_mut_GeneralizedHoughGuil(), angle_step) }.into_result() } fn get_angle_step(&self) -> Result<f64> { unsafe { sys::cv_GeneralizedHoughGuil_getAngleStep_const(self.as_raw_GeneralizedHoughGuil()) }.into_result() } /// Angle votes threshold. fn set_angle_thresh(&mut self, angle_thresh: i32) -> Result<()> { unsafe { sys::cv_GeneralizedHoughGuil_setAngleThresh_int(self.as_raw_mut_GeneralizedHoughGuil(), angle_thresh) }.into_result() } fn get_angle_thresh(&self) -> Result<i32> { unsafe { sys::cv_GeneralizedHoughGuil_getAngleThresh_const(self.as_raw_GeneralizedHoughGuil()) }.into_result() } /// Minimal scale to detect. fn set_min_scale(&mut self, min_scale: f64) -> Result<()> { unsafe { sys::cv_GeneralizedHoughGuil_setMinScale_double(self.as_raw_mut_GeneralizedHoughGuil(), min_scale) }.into_result() } fn get_min_scale(&self) -> Result<f64> { unsafe { sys::cv_GeneralizedHoughGuil_getMinScale_const(self.as_raw_GeneralizedHoughGuil()) }.into_result() } /// Maximal scale to detect. fn set_max_scale(&mut self, max_scale: f64) -> Result<()> { unsafe { sys::cv_GeneralizedHoughGuil_setMaxScale_double(self.as_raw_mut_GeneralizedHoughGuil(), max_scale) }.into_result() } fn get_max_scale(&self) -> Result<f64> { unsafe { sys::cv_GeneralizedHoughGuil_getMaxScale_const(self.as_raw_GeneralizedHoughGuil()) }.into_result() } /// Scale step. fn set_scale_step(&mut self, scale_step: f64) -> Result<()> { unsafe { sys::cv_GeneralizedHoughGuil_setScaleStep_double(self.as_raw_mut_GeneralizedHoughGuil(), scale_step) }.into_result() } fn get_scale_step(&self) -> Result<f64> { unsafe { sys::cv_GeneralizedHoughGuil_getScaleStep_const(self.as_raw_GeneralizedHoughGuil()) }.into_result() } /// Scale votes threshold. fn set_scale_thresh(&mut self, scale_thresh: i32) -> Result<()> { unsafe { sys::cv_GeneralizedHoughGuil_setScaleThresh_int(self.as_raw_mut_GeneralizedHoughGuil(), scale_thresh) }.into_result() } fn get_scale_thresh(&self) -> Result<i32> { unsafe { sys::cv_GeneralizedHoughGuil_getScaleThresh_const(self.as_raw_GeneralizedHoughGuil()) }.into_result() } /// Position votes threshold. fn set_pos_thresh(&mut self, pos_thresh: i32) -> Result<()> { unsafe { sys::cv_GeneralizedHoughGuil_setPosThresh_int(self.as_raw_mut_GeneralizedHoughGuil(), pos_thresh) }.into_result() } fn get_pos_thresh(&self) -> Result<i32> { unsafe { sys::cv_GeneralizedHoughGuil_getPosThresh_const(self.as_raw_GeneralizedHoughGuil()) }.into_result() } } /// Line iterator /// /// The class is used to iterate over all the pixels on the raster line /// segment connecting two specified points. /// /// The class LineIterator is used to get each pixel of a raster line. It /// can be treated as versatile implementation of the Bresenham algorithm /// where you can stop at each pixel and do some extra processing, for /// example, grab pixel values along the line or draw a line with an effect /// (for example, with XOR operation). /// /// The number of pixels along the line is stored in LineIterator::count. /// The method LineIterator::pos returns the current position in the image: /// /// ```ignore /// // grabs pixels along the line (pt1, pt2) /// // from 8-bit 3-channel image to the buffer /// LineIterator it(img, pt1, pt2, 8); /// LineIterator it2 = it; /// vector<Vec3b> buf(it.count); /// /// for(int i = 0; i < it.count; i++, ++it) /// buf[i] = *(const Vec3b*)*it; /// /// // alternative way of iterating through the line /// for(int i = 0; i < it2.count; i++, ++it2) /// { /// Vec3b val = img.at<Vec3b>(it2.pos()); /// CV_Assert(buf[i] == val); /// } /// ``` /// pub trait LineIteratorTrait { fn as_raw_LineIterator(&self) -> *const c_void; fn as_raw_mut_LineIterator(&mut self) -> *mut c_void; fn ptr(&mut self) -> &mut u8 { unsafe { sys::cv_LineIterator_getPropPtr(self.as_raw_mut_LineIterator()) }.into_result().and_then(|x| unsafe { x.as_mut() }.ok_or_else(|| Error::new(core::StsNullPtr, "Function returned Null pointer".to_string()))).expect("Infallible function failed: ptr") } fn set_ptr(&mut self, val: &mut u8) -> () { unsafe { sys::cv_LineIterator_setPropPtr_unsigned_charX(self.as_raw_mut_LineIterator(), val) }.into_result().expect("Infallible function failed: set_ptr") } fn ptr0(&self) -> &u8 { unsafe { sys::cv_LineIterator_getPropPtr0_const(self.as_raw_LineIterator()) }.into_result().and_then(|x| unsafe { x.as_ref() }.ok_or_else(|| Error::new(core::StsNullPtr, "Function returned Null pointer".to_string()))).expect("Infallible function failed: ptr0") } fn step(&self) -> i32 { unsafe { sys::cv_LineIterator_getPropStep_const(self.as_raw_LineIterator()) }.into_result().expect("Infallible function failed: step") } fn set_step(&mut self, val: i32) -> () { unsafe { sys::cv_LineIterator_setPropStep_int(self.as_raw_mut_LineIterator(), val) }.into_result().expect("Infallible function failed: set_step") } fn elem_size(&self) -> i32 { unsafe { sys::cv_LineIterator_getPropElemSize_const(self.as_raw_LineIterator()) }.into_result().expect("Infallible function failed: elem_size") } fn set_elem_size(&mut self, val: i32) -> () { unsafe { sys::cv_LineIterator_setPropElemSize_int(self.as_raw_mut_LineIterator(), val) }.into_result().expect("Infallible function failed: set_elem_size") } fn err(&self) -> i32 { unsafe { sys::cv_LineIterator_getPropErr_const(self.as_raw_LineIterator()) }.into_result().expect("Infallible function failed: err") } fn set_err(&mut self, val: i32) -> () { unsafe { sys::cv_LineIterator_setPropErr_int(self.as_raw_mut_LineIterator(), val) }.into_result().expect("Infallible function failed: set_err") } fn count(&self) -> i32 { unsafe { sys::cv_LineIterator_getPropCount_const(self.as_raw_LineIterator()) }.into_result().expect("Infallible function failed: count") } fn set_count(&mut self, val: i32) -> () { unsafe { sys::cv_LineIterator_setPropCount_int(self.as_raw_mut_LineIterator(), val) }.into_result().expect("Infallible function failed: set_count") } fn minus_delta(&self) -> i32 { unsafe { sys::cv_LineIterator_getPropMinusDelta_const(self.as_raw_LineIterator()) }.into_result().expect("Infallible function failed: minus_delta") } fn set_minus_delta(&mut self, val: i32) -> () { unsafe { sys::cv_LineIterator_setPropMinusDelta_int(self.as_raw_mut_LineIterator(), val) }.into_result().expect("Infallible function failed: set_minus_delta") } fn plus_delta(&self) -> i32 { unsafe { sys::cv_LineIterator_getPropPlusDelta_const(self.as_raw_LineIterator()) }.into_result().expect("Infallible function failed: plus_delta") } fn set_plus_delta(&mut self, val: i32) -> () { unsafe { sys::cv_LineIterator_setPropPlusDelta_int(self.as_raw_mut_LineIterator(), val) }.into_result().expect("Infallible function failed: set_plus_delta") } fn minus_step(&self) -> i32 { unsafe { sys::cv_LineIterator_getPropMinusStep_const(self.as_raw_LineIterator()) }.into_result().expect("Infallible function failed: minus_step") } fn set_minus_step(&mut self, val: i32) -> () { unsafe { sys::cv_LineIterator_setPropMinusStep_int(self.as_raw_mut_LineIterator(), val) }.into_result().expect("Infallible function failed: set_minus_step") } fn plus_step(&self) -> i32 { unsafe { sys::cv_LineIterator_getPropPlusStep_const(self.as_raw_LineIterator()) }.into_result().expect("Infallible function failed: plus_step") } fn set_plus_step(&mut self, val: i32) -> () { unsafe { sys::cv_LineIterator_setPropPlusStep_int(self.as_raw_mut_LineIterator(), val) }.into_result().expect("Infallible function failed: set_plus_step") } fn minus_shift(&self) -> i32 { unsafe { sys::cv_LineIterator_getPropMinusShift_const(self.as_raw_LineIterator()) }.into_result().expect("Infallible function failed: minus_shift") } fn set_minus_shift(&mut self, val: i32) -> () { unsafe { sys::cv_LineIterator_setPropMinusShift_int(self.as_raw_mut_LineIterator(), val) }.into_result().expect("Infallible function failed: set_minus_shift") } fn plus_shift(&self) -> i32 { unsafe { sys::cv_LineIterator_getPropPlusShift_const(self.as_raw_LineIterator()) }.into_result().expect("Infallible function failed: plus_shift") } fn set_plus_shift(&mut self, val: i32) -> () { unsafe { sys::cv_LineIterator_setPropPlusShift_int(self.as_raw_mut_LineIterator(), val) }.into_result().expect("Infallible function failed: set_plus_shift") } fn p(&self) -> core::Point { unsafe { sys::cv_LineIterator_getPropP_const(self.as_raw_LineIterator()) }.into_result().expect("Infallible function failed: p") } fn set_p(&mut self, val: core::Point) -> () { unsafe { sys::cv_LineIterator_setPropP_Point(self.as_raw_mut_LineIterator(), val.opencv_as_extern()) }.into_result().expect("Infallible function failed: set_p") } fn ptmode(&self) -> bool { unsafe { sys::cv_LineIterator_getPropPtmode_const(self.as_raw_LineIterator()) }.into_result().expect("Infallible function failed: ptmode") } fn set_ptmode(&mut self, val: bool) -> () { unsafe { sys::cv_LineIterator_setPropPtmode_bool(self.as_raw_mut_LineIterator(), val) }.into_result().expect("Infallible function failed: set_ptmode") } fn init(&mut self, img: &core::Mat, bounding_area_rect: core::Rect, pt1: core::Point, pt2: core::Point, connectivity: i32, left_to_right: bool) -> Result<()> { unsafe { sys::cv_LineIterator_init_const_MatX_Rect_Point_Point_int_bool(self.as_raw_mut_LineIterator(), img.as_raw_Mat(), bounding_area_rect.opencv_as_extern(), pt1.opencv_as_extern(), pt2.opencv_as_extern(), connectivity, left_to_right) }.into_result() } /// returns pointer to the current pixel fn try_deref_mut(&mut self) -> Result<&mut u8> { unsafe { sys::cv_LineIterator_operatorX(self.as_raw_mut_LineIterator()) }.into_result().and_then(|x| unsafe { x.as_mut() }.ok_or_else(|| Error::new(core::StsNullPtr, "Function returned Null pointer".to_string()))) } /// returns coordinates of the current pixel fn pos(&self) -> Result<core::Point> { unsafe { sys::cv_LineIterator_pos_const(self.as_raw_LineIterator()) }.into_result() } } /// Line iterator /// /// The class is used to iterate over all the pixels on the raster line /// segment connecting two specified points. /// /// The class LineIterator is used to get each pixel of a raster line. It /// can be treated as versatile implementation of the Bresenham algorithm /// where you can stop at each pixel and do some extra processing, for /// example, grab pixel values along the line or draw a line with an effect /// (for example, with XOR operation). /// /// The number of pixels along the line is stored in LineIterator::count. /// The method LineIterator::pos returns the current position in the image: /// /// ```ignore /// // grabs pixels along the line (pt1, pt2) /// // from 8-bit 3-channel image to the buffer /// LineIterator it(img, pt1, pt2, 8); /// LineIterator it2 = it; /// vector<Vec3b> buf(it.count); /// /// for(int i = 0; i < it.count; i++, ++it) /// buf[i] = *(const Vec3b*)*it; /// /// // alternative way of iterating through the line /// for(int i = 0; i < it2.count; i++, ++it2) /// { /// Vec3b val = img.at<Vec3b>(it2.pos()); /// CV_Assert(buf[i] == val); /// } /// ``` /// pub struct LineIterator { ptr: *mut c_void } opencv_type_boxed! { LineIterator } impl Drop for LineIterator { fn drop(&mut self) { extern "C" { fn cv_LineIterator_delete(instance: *mut c_void); } unsafe { cv_LineIterator_delete(self.as_raw_mut_LineIterator()) }; } } impl LineIterator { #[inline] pub fn as_raw_LineIterator(&self) -> *const c_void { self.as_raw() } #[inline] pub fn as_raw_mut_LineIterator(&mut self) -> *mut c_void { self.as_raw_mut() } } unsafe impl Send for LineIterator {} impl crate::imgproc::LineIteratorTrait for LineIterator { #[inline] fn as_raw_LineIterator(&self) -> *const c_void { self.as_raw() } #[inline] fn as_raw_mut_LineIterator(&mut self) -> *mut c_void { self.as_raw_mut() } } impl LineIterator { /// initializes the iterator /// /// creates iterators for the line connecting pt1 and pt2 /// the line will be clipped on the image boundaries /// the line is 8-connected or 4-connected /// If leftToRight=true, then the iteration is always done /// from the left-most point to the right most, /// not to depend on the ordering of pt1 and pt2 parameters; /// /// ## C++ default parameters /// * connectivity: 8 /// * left_to_right: false pub fn new(img: &core::Mat, pt1: core::Point, pt2: core::Point, connectivity: i32, left_to_right: bool) -> Result<crate::imgproc::LineIterator> { unsafe { sys::cv_LineIterator_LineIterator_const_MatR_Point_Point_int_bool(img.as_raw_Mat(), pt1.opencv_as_extern(), pt2.opencv_as_extern(), connectivity, left_to_right) }.into_result().map(|r| unsafe { crate::imgproc::LineIterator::opencv_from_extern(r) } ) } /// ## C++ default parameters /// * connectivity: 8 /// * left_to_right: false pub fn new_1(pt1: core::Point, pt2: core::Point, connectivity: i32, left_to_right: bool) -> Result<crate::imgproc::LineIterator> { unsafe { sys::cv_LineIterator_LineIterator_Point_Point_int_bool(pt1.opencv_as_extern(), pt2.opencv_as_extern(), connectivity, left_to_right) }.into_result().map(|r| unsafe { crate::imgproc::LineIterator::opencv_from_extern(r) } ) } /// ## C++ default parameters /// * connectivity: 8 /// * left_to_right: false pub fn new_2(bounding_area_size: core::Size, pt1: core::Point, pt2: core::Point, connectivity: i32, left_to_right: bool) -> Result<crate::imgproc::LineIterator> { unsafe { sys::cv_LineIterator_LineIterator_Size_Point_Point_int_bool(bounding_area_size.opencv_as_extern(), pt1.opencv_as_extern(), pt2.opencv_as_extern(), connectivity, left_to_right) }.into_result().map(|r| unsafe { crate::imgproc::LineIterator::opencv_from_extern(r) } ) } /// ## C++ default parameters /// * connectivity: 8 /// * left_to_right: false pub fn new_3(bounding_area_rect: core::Rect, pt1: core::Point, pt2: core::Point, connectivity: i32, left_to_right: bool) -> Result<crate::imgproc::LineIterator> { unsafe { sys::cv_LineIterator_LineIterator_Rect_Point_Point_int_bool(bounding_area_rect.opencv_as_extern(), pt1.opencv_as_extern(), pt2.opencv_as_extern(), connectivity, left_to_right) }.into_result().map(|r| unsafe { crate::imgproc::LineIterator::opencv_from_extern(r) } ) } } /// Line segment detector class /// /// following the algorithm described at [Rafael12](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Rafael12) . /// /// /// Note: Implementation has been removed due original code license conflict pub trait LineSegmentDetector: core::AlgorithmTrait { fn as_raw_LineSegmentDetector(&self) -> *const c_void; fn as_raw_mut_LineSegmentDetector(&mut self) -> *mut c_void; /// Finds lines in the input image. /// /// This is the output of the default parameters of the algorithm on the above shown image. /// ///  /// /// ## Parameters /// * _image: A grayscale (CV_8UC1) input image. If only a roi needs to be selected, use: /// `lsd_ptr-\>detect(image(roi), lines, ...); lines += Scalar(roi.x, roi.y, roi.x, roi.y);` /// * _lines: A vector of Vec4i or Vec4f elements specifying the beginning and ending point of a line. Where /// Vec4i/Vec4f is (x1, y1, x2, y2), point 1 is the start, point 2 - end. Returned lines are strictly /// oriented depending on the gradient. /// * width: Vector of widths of the regions, where the lines are found. E.g. Width of line. /// * prec: Vector of precisions with which the lines are found. /// * nfa: Vector containing number of false alarms in the line region, with precision of 10%. The /// bigger the value, logarithmically better the detection. /// - -1 corresponds to 10 mean false alarms /// - 0 corresponds to 1 mean false alarm /// - 1 corresponds to 0.1 mean false alarms /// This vector will be calculated only when the objects type is #LSD_REFINE_ADV. /// /// ## C++ default parameters /// * width: noArray() /// * prec: noArray() /// * nfa: noArray() fn detect(&mut self, _image: &dyn core::ToInputArray, _lines: &mut dyn core::ToOutputArray, width: &mut dyn core::ToOutputArray, prec: &mut dyn core::ToOutputArray, nfa: &mut dyn core::ToOutputArray) -> Result<()> { input_array_arg!(_image); output_array_arg!(_lines); output_array_arg!(width); output_array_arg!(prec); output_array_arg!(nfa); unsafe { sys::cv_LineSegmentDetector_detect_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(self.as_raw_mut_LineSegmentDetector(), _image.as_raw__InputArray(), _lines.as_raw__OutputArray(), width.as_raw__OutputArray(), prec.as_raw__OutputArray(), nfa.as_raw__OutputArray()) }.into_result() } /// Draws the line segments on a given image. /// ## Parameters /// * _image: The image, where the lines will be drawn. Should be bigger or equal to the image, /// where the lines were found. /// * lines: A vector of the lines that needed to be drawn. fn draw_segments(&mut self, _image: &mut dyn core::ToInputOutputArray, lines: &dyn core::ToInputArray) -> Result<()> { input_output_array_arg!(_image); input_array_arg!(lines); unsafe { sys::cv_LineSegmentDetector_drawSegments_const__InputOutputArrayR_const__InputArrayR(self.as_raw_mut_LineSegmentDetector(), _image.as_raw__InputOutputArray(), lines.as_raw__InputArray()) }.into_result() } /// Draws two groups of lines in blue and red, counting the non overlapping (mismatching) pixels. /// /// ## Parameters /// * size: The size of the image, where lines1 and lines2 were found. /// * lines1: The first group of lines that needs to be drawn. It is visualized in blue color. /// * lines2: The second group of lines. They visualized in red color. /// * _image: Optional image, where the lines will be drawn. The image should be color(3-channel) /// in order for lines1 and lines2 to be drawn in the above mentioned colors. /// /// ## C++ default parameters /// * _image: noArray() fn compare_segments(&mut self, size: core::Size, lines1: &dyn core::ToInputArray, lines2: &dyn core::ToInputArray, _image: &mut dyn core::ToInputOutputArray) -> Result<i32> { input_array_arg!(lines1); input_array_arg!(lines2); input_output_array_arg!(_image); unsafe { sys::cv_LineSegmentDetector_compareSegments_const_SizeR_const__InputArrayR_const__InputArrayR_const__InputOutputArrayR(self.as_raw_mut_LineSegmentDetector(), &size, lines1.as_raw__InputArray(), lines2.as_raw__InputArray(), _image.as_raw__InputOutputArray()) }.into_result() } } pub trait Subdiv2DTrait { fn as_raw_Subdiv2D(&self) -> *const c_void; fn as_raw_mut_Subdiv2D(&mut self) -> *mut c_void; /// Creates a new empty Delaunay subdivision /// /// ## Parameters /// * rect: Rectangle that includes all of the 2D points that are to be added to the subdivision. fn init_delaunay(&mut self, rect: core::Rect) -> Result<()> { unsafe { sys::cv_Subdiv2D_initDelaunay_Rect(self.as_raw_mut_Subdiv2D(), rect.opencv_as_extern()) }.into_result() } /// Insert a single point into a Delaunay triangulation. /// /// ## Parameters /// * pt: Point to insert. /// /// The function inserts a single point into a subdivision and modifies the subdivision topology /// appropriately. If a point with the same coordinates exists already, no new point is added. /// ## Returns /// the ID of the point. /// /// /// Note: If the point is outside of the triangulation specified rect a runtime error is raised. fn insert(&mut self, pt: core::Point2f) -> Result<i32> { unsafe { sys::cv_Subdiv2D_insert_Point2f(self.as_raw_mut_Subdiv2D(), pt.opencv_as_extern()) }.into_result() } /// Insert multiple points into a Delaunay triangulation. /// /// ## Parameters /// * ptvec: Points to insert. /// /// The function inserts a vector of points into a subdivision and modifies the subdivision topology /// appropriately. fn insert_multiple(&mut self, ptvec: &core::Vector::<core::Point2f>) -> Result<()> { unsafe { sys::cv_Subdiv2D_insert_const_vector_Point2f_R(self.as_raw_mut_Subdiv2D(), ptvec.as_raw_VectorOfPoint2f()) }.into_result() } /// Returns the location of a point within a Delaunay triangulation. /// /// ## Parameters /// * pt: Point to locate. /// * edge: Output edge that the point belongs to or is located to the right of it. /// * vertex: Optional output vertex the input point coincides with. /// /// The function locates the input point within the subdivision and gives one of the triangle edges /// or vertices. /// /// ## Returns /// an integer which specify one of the following five cases for point location: /// * The point falls into some facet. The function returns #PTLOC_INSIDE and edge will contain one of /// edges of the facet. /// * The point falls onto the edge. The function returns #PTLOC_ON_EDGE and edge will contain this edge. /// * The point coincides with one of the subdivision vertices. The function returns #PTLOC_VERTEX and /// vertex will contain a pointer to the vertex. /// * The point is outside the subdivision reference rectangle. The function returns #PTLOC_OUTSIDE_RECT /// and no pointers are filled. /// * One of input arguments is invalid. A runtime error is raised or, if silent or "parent" error /// processing mode is selected, #PTLOC_ERROR is returned. fn locate(&mut self, pt: core::Point2f, edge: &mut i32, vertex: &mut i32) -> Result<i32> { unsafe { sys::cv_Subdiv2D_locate_Point2f_intR_intR(self.as_raw_mut_Subdiv2D(), pt.opencv_as_extern(), edge, vertex) }.into_result() } /// Finds the subdivision vertex closest to the given point. /// /// ## Parameters /// * pt: Input point. /// * nearestPt: Output subdivision vertex point. /// /// The function is another function that locates the input point within the subdivision. It finds the /// subdivision vertex that is the closest to the input point. It is not necessarily one of vertices /// of the facet containing the input point, though the facet (located using locate() ) is used as a /// starting point. /// /// ## Returns /// vertex ID. /// /// ## C++ default parameters /// * nearest_pt: 0 fn find_nearest(&mut self, pt: core::Point2f, nearest_pt: &mut core::Point2f) -> Result<i32> { unsafe { sys::cv_Subdiv2D_findNearest_Point2f_Point2fX(self.as_raw_mut_Subdiv2D(), pt.opencv_as_extern(), nearest_pt) }.into_result() } /// Returns a list of all edges. /// /// ## Parameters /// * edgeList: Output vector. /// /// The function gives each edge as a 4 numbers vector, where each two are one of the edge /// vertices. i.e. org_x = v[0], org_y = v[1], dst_x = v[2], dst_y = v[3]. fn get_edge_list(&self, edge_list: &mut core::Vector::<core::Vec4f>) -> Result<()> { unsafe { sys::cv_Subdiv2D_getEdgeList_const_vector_Vec4f_R(self.as_raw_Subdiv2D(), edge_list.as_raw_mut_VectorOfVec4f()) }.into_result() } /// Returns a list of the leading edge ID connected to each triangle. /// /// ## Parameters /// * leadingEdgeList: Output vector. /// /// The function gives one edge ID for each triangle. fn get_leading_edge_list(&self, leading_edge_list: &mut core::Vector::<i32>) -> Result<()> { unsafe { sys::cv_Subdiv2D_getLeadingEdgeList_const_vector_int_R(self.as_raw_Subdiv2D(), leading_edge_list.as_raw_mut_VectorOfi32()) }.into_result() } /// Returns a list of all triangles. /// /// ## Parameters /// * triangleList: Output vector. /// /// The function gives each triangle as a 6 numbers vector, where each two are one of the triangle /// vertices. i.e. p1_x = v[0], p1_y = v[1], p2_x = v[2], p2_y = v[3], p3_x = v[4], p3_y = v[5]. fn get_triangle_list(&self, triangle_list: &mut core::Vector::<core::Vec6f>) -> Result<()> { unsafe { sys::cv_Subdiv2D_getTriangleList_const_vector_Vec6f_R(self.as_raw_Subdiv2D(), triangle_list.as_raw_mut_VectorOfVec6f()) }.into_result() } /// Returns a list of all Voronoi facets. /// /// ## Parameters /// * idx: Vector of vertices IDs to consider. For all vertices you can pass empty vector. /// * facetList: Output vector of the Voronoi facets. /// * facetCenters: Output vector of the Voronoi facets center points. fn get_voronoi_facet_list(&mut self, idx: &core::Vector::<i32>, facet_list: &mut core::Vector::<core::Vector::<core::Point2f>>, facet_centers: &mut core::Vector::<core::Point2f>) -> Result<()> { unsafe { sys::cv_Subdiv2D_getVoronoiFacetList_const_vector_int_R_vector_vector_Point2f__R_vector_Point2f_R(self.as_raw_mut_Subdiv2D(), idx.as_raw_VectorOfi32(), facet_list.as_raw_mut_VectorOfVectorOfPoint2f(), facet_centers.as_raw_mut_VectorOfPoint2f()) }.into_result() } /// Returns vertex location from vertex ID. /// /// ## Parameters /// * vertex: vertex ID. /// * firstEdge: Optional. The first edge ID which is connected to the vertex. /// ## Returns /// vertex (x,y) /// /// ## C++ default parameters /// * first_edge: 0 fn get_vertex(&self, vertex: i32, first_edge: &mut i32) -> Result<core::Point2f> { unsafe { sys::cv_Subdiv2D_getVertex_const_int_intX(self.as_raw_Subdiv2D(), vertex, first_edge) }.into_result() } /// Returns one of the edges related to the given edge. /// /// ## Parameters /// * edge: Subdivision edge ID. /// * nextEdgeType: Parameter specifying which of the related edges to return. /// The following values are possible: /// * NEXT_AROUND_ORG next around the edge origin ( eOnext on the picture below if e is the input edge) /// * NEXT_AROUND_DST next around the edge vertex ( eDnext ) /// * PREV_AROUND_ORG previous around the edge origin (reversed eRnext ) /// * PREV_AROUND_DST previous around the edge destination (reversed eLnext ) /// * NEXT_AROUND_LEFT next around the left facet ( eLnext ) /// * NEXT_AROUND_RIGHT next around the right facet ( eRnext ) /// * PREV_AROUND_LEFT previous around the left facet (reversed eOnext ) /// * PREV_AROUND_RIGHT previous around the right facet (reversed eDnext ) /// ///  /// /// ## Returns /// edge ID related to the input edge. fn get_edge(&self, edge: i32, next_edge_type: i32) -> Result<i32> { unsafe { sys::cv_Subdiv2D_getEdge_const_int_int(self.as_raw_Subdiv2D(), edge, next_edge_type) }.into_result() } /// Returns next edge around the edge origin. /// /// ## Parameters /// * edge: Subdivision edge ID. /// /// ## Returns /// an integer which is next edge ID around the edge origin: eOnext on the /// picture above if e is the input edge). fn next_edge(&self, edge: i32) -> Result<i32> { unsafe { sys::cv_Subdiv2D_nextEdge_const_int(self.as_raw_Subdiv2D(), edge) }.into_result() } /// Returns another edge of the same quad-edge. /// /// ## Parameters /// * edge: Subdivision edge ID. /// * rotate: Parameter specifying which of the edges of the same quad-edge as the input /// one to return. The following values are possible: /// * 0 - the input edge ( e on the picture below if e is the input edge) /// * 1 - the rotated edge ( eRot ) /// * 2 - the reversed edge (reversed e (in green)) /// * 3 - the reversed rotated edge (reversed eRot (in green)) /// /// ## Returns /// one of the edges ID of the same quad-edge as the input edge. fn rotate_edge(&self, edge: i32, rotate: i32) -> Result<i32> { unsafe { sys::cv_Subdiv2D_rotateEdge_const_int_int(self.as_raw_Subdiv2D(), edge, rotate) }.into_result() } fn sym_edge(&self, edge: i32) -> Result<i32> { unsafe { sys::cv_Subdiv2D_symEdge_const_int(self.as_raw_Subdiv2D(), edge) }.into_result() } /// Returns the edge origin. /// /// ## Parameters /// * edge: Subdivision edge ID. /// * orgpt: Output vertex location. /// /// ## Returns /// vertex ID. /// /// ## C++ default parameters /// * orgpt: 0 fn edge_org(&self, edge: i32, orgpt: &mut core::Point2f) -> Result<i32> { unsafe { sys::cv_Subdiv2D_edgeOrg_const_int_Point2fX(self.as_raw_Subdiv2D(), edge, orgpt) }.into_result() } /// Returns the edge destination. /// /// ## Parameters /// * edge: Subdivision edge ID. /// * dstpt: Output vertex location. /// /// ## Returns /// vertex ID. /// /// ## C++ default parameters /// * dstpt: 0 fn edge_dst(&self, edge: i32, dstpt: &mut core::Point2f) -> Result<i32> { unsafe { sys::cv_Subdiv2D_edgeDst_const_int_Point2fX(self.as_raw_Subdiv2D(), edge, dstpt) }.into_result() } } pub struct Subdiv2D { ptr: *mut c_void } opencv_type_boxed! { Subdiv2D } impl Drop for Subdiv2D { fn drop(&mut self) { extern "C" { fn cv_Subdiv2D_delete(instance: *mut c_void); } unsafe { cv_Subdiv2D_delete(self.as_raw_mut_Subdiv2D()) }; } } impl Subdiv2D { #[inline] pub fn as_raw_Subdiv2D(&self) -> *const c_void { self.as_raw() } #[inline] pub fn as_raw_mut_Subdiv2D(&mut self) -> *mut c_void { self.as_raw_mut() } } unsafe impl Send for Subdiv2D {} impl crate::imgproc::Subdiv2DTrait for Subdiv2D { #[inline] fn as_raw_Subdiv2D(&self) -> *const c_void { self.as_raw() } #[inline] fn as_raw_mut_Subdiv2D(&mut self) -> *mut c_void { self.as_raw_mut() } } impl Subdiv2D { /// creates an empty Subdiv2D object. /// To create a new empty Delaunay subdivision you need to use the #initDelaunay function. pub fn default() -> Result<crate::imgproc::Subdiv2D> { unsafe { sys::cv_Subdiv2D_Subdiv2D() }.into_result().map(|r| unsafe { crate::imgproc::Subdiv2D::opencv_from_extern(r) } ) } /// creates an empty Subdiv2D object. /// To create a new empty Delaunay subdivision you need to use the #initDelaunay function. /// /// ## Overloaded parameters /// /// /// ## Parameters /// * rect: Rectangle that includes all of the 2D points that are to be added to the subdivision. /// /// The function creates an empty Delaunay subdivision where 2D points can be added using the function /// insert() . All of the points to be added must be within the specified rectangle, otherwise a runtime /// error is raised. pub fn new(rect: core::Rect) -> Result<crate::imgproc::Subdiv2D> { unsafe { sys::cv_Subdiv2D_Subdiv2D_Rect(rect.opencv_as_extern()) }.into_result().map(|r| unsafe { crate::imgproc::Subdiv2D::opencv_from_extern(r) } ) } }