// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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//
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// Copyright (C) 2018 Intel Corporation
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#ifndef OPENCV_GAPI_IMGPROC_HPP
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#define OPENCV_GAPI_IMGPROC_HPP
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#include <opencv2/imgproc.hpp>
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#include <utility> // std::tuple
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#include <opencv2/gapi/gkernel.hpp>
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#include <opencv2/gapi/gmat.hpp>
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#include <opencv2/gapi/gscalar.hpp>
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/** \defgroup gapi_imgproc G-API Image processing functionality
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@{
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@defgroup gapi_filters Graph API: Image filters
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@defgroup gapi_colorconvert Graph API: Converting image from one color space to another
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@}
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*/
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namespace cv { namespace gapi {
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namespace imgproc {
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using GMat2 = std::tuple<GMat,GMat>;
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using GMat3 = std::tuple<GMat,GMat,GMat>; // FIXME: how to avoid this?
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G_TYPED_KERNEL(GFilter2D, <GMat(GMat,int,Mat,Point,Scalar,int,Scalar)>,"org.opencv.imgproc.filters.filter2D") {
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static GMatDesc outMeta(GMatDesc in, int ddepth, Mat, Point, Scalar, int, Scalar) {
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return in.withDepth(ddepth);
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}
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};
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G_TYPED_KERNEL(GSepFilter, <GMat(GMat,int,Mat,Mat,Point,Scalar,int,Scalar)>, "org.opencv.imgproc.filters.sepfilter") {
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static GMatDesc outMeta(GMatDesc in, int ddepth, Mat, Mat, Point, Scalar, int, Scalar) {
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return in.withDepth(ddepth);
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}
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};
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G_TYPED_KERNEL(GBoxFilter, <GMat(GMat,int,Size,Point,bool,int,Scalar)>, "org.opencv.imgproc.filters.boxfilter") {
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static GMatDesc outMeta(GMatDesc in, int ddepth, Size, Point, bool, int, Scalar) {
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return in.withDepth(ddepth);
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}
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};
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G_TYPED_KERNEL(GBlur, <GMat(GMat,Size,Point,int,Scalar)>, "org.opencv.imgproc.filters.blur"){
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static GMatDesc outMeta(GMatDesc in, Size, Point, int, Scalar) {
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return in;
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}
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};
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G_TYPED_KERNEL(GGaussBlur, <GMat(GMat,Size,double,double,int,Scalar)>, "org.opencv.imgproc.filters.gaussianBlur") {
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static GMatDesc outMeta(GMatDesc in, Size, double, double, int, Scalar) {
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return in;
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}
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};
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G_TYPED_KERNEL(GMedianBlur, <GMat(GMat,int)>, "org.opencv.imgproc.filters.medianBlur") {
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static GMatDesc outMeta(GMatDesc in, int) {
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return in;
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}
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};
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G_TYPED_KERNEL(GErode, <GMat(GMat,Mat,Point,int,int,Scalar)>, "org.opencv.imgproc.filters.erode") {
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static GMatDesc outMeta(GMatDesc in, Mat, Point, int, int, Scalar) {
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return in;
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}
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};
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G_TYPED_KERNEL(GDilate, <GMat(GMat,Mat,Point,int,int,Scalar)>, "org.opencv.imgproc.filters.dilate") {
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static GMatDesc outMeta(GMatDesc in, Mat, Point, int, int, Scalar) {
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return in;
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}
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};
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G_TYPED_KERNEL(GSobel, <GMat(GMat,int,int,int,int,double,double,int,Scalar)>, "org.opencv.imgproc.filters.sobel") {
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static GMatDesc outMeta(GMatDesc in, int ddepth, int, int, int, double, double, int, Scalar) {
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return in.withDepth(ddepth);
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}
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};
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G_TYPED_KERNEL_M(GSobelXY, <GMat2(GMat,int,int,int,double,double,int,Scalar)>, "org.opencv.imgproc.filters.sobelxy") {
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static std::tuple<GMatDesc, GMatDesc> outMeta(GMatDesc in, int ddepth, int, int, double, double, int, Scalar) {
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return std::make_tuple(in.withDepth(ddepth), in.withDepth(ddepth));
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}
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};
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G_TYPED_KERNEL(GEqHist, <GMat(GMat)>, "org.opencv.imgproc.equalizeHist"){
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static GMatDesc outMeta(GMatDesc in) {
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return in.withType(CV_8U, 1);
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}
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};
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G_TYPED_KERNEL(GCanny, <GMat(GMat,double,double,int,bool)>, "org.opencv.imgproc.canny"){
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static GMatDesc outMeta(GMatDesc in, double, double, int, bool) {
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return in.withType(CV_8U, 1);
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}
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};
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G_TYPED_KERNEL(GRGB2YUV, <GMat(GMat)>, "org.opencv.imgproc.colorconvert.rgb2yuv") {
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static GMatDesc outMeta(GMatDesc in) {
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return in; // type still remains CV_8UC3;
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}
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};
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G_TYPED_KERNEL(GYUV2RGB, <GMat(GMat)>, "org.opencv.imgproc.colorconvert.yuv2rgb") {
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static GMatDesc outMeta(GMatDesc in) {
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return in; // type still remains CV_8UC3;
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}
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};
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G_TYPED_KERNEL(GNV12toRGB, <GMat(GMat, GMat)>, "org.opencv.imgproc.colorconvert.nv12torgb") {
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static GMatDesc outMeta(GMatDesc in_y, GMatDesc in_uv) {
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GAPI_Assert(in_y.chan == 1);
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GAPI_Assert(in_uv.chan == 2);
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GAPI_Assert(in_y.depth == CV_8U);
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GAPI_Assert(in_uv.depth == CV_8U);
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// UV size should be aligned with Y
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GAPI_Assert(in_y.size.width == 2 * in_uv.size.width);
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GAPI_Assert(in_y.size.height == 2 * in_uv.size.height);
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return in_y.withType(CV_8U, 3); // type will be CV_8UC3;
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}
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};
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G_TYPED_KERNEL(GNV12toBGR, <GMat(GMat, GMat)>, "org.opencv.imgproc.colorconvert.nv12tobgr") {
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static GMatDesc outMeta(GMatDesc in_y, GMatDesc in_uv) {
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GAPI_Assert(in_y.chan == 1);
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GAPI_Assert(in_uv.chan == 2);
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GAPI_Assert(in_y.depth == CV_8U);
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GAPI_Assert(in_uv.depth == CV_8U);
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// UV size should be aligned with Y
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GAPI_Assert(in_y.size.width == 2 * in_uv.size.width);
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GAPI_Assert(in_y.size.height == 2 * in_uv.size.height);
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return in_y.withType(CV_8U, 3); // type will be CV_8UC3;
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}
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};
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G_TYPED_KERNEL(GRGB2Lab, <GMat(GMat)>, "org.opencv.imgproc.colorconvert.rgb2lab") {
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static GMatDesc outMeta(GMatDesc in) {
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return in; // type still remains CV_8UC3;
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}
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};
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G_TYPED_KERNEL(GBGR2LUV, <GMat(GMat)>, "org.opencv.imgproc.colorconvert.bgr2luv") {
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static GMatDesc outMeta(GMatDesc in) {
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return in; // type still remains CV_8UC3;
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}
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};
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G_TYPED_KERNEL(GLUV2BGR, <GMat(GMat)>, "org.opencv.imgproc.colorconvert.luv2bgr") {
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static GMatDesc outMeta(GMatDesc in) {
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return in; // type still remains CV_8UC3;
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}
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};
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G_TYPED_KERNEL(GYUV2BGR, <GMat(GMat)>, "org.opencv.imgproc.colorconvert.yuv2bgr") {
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static GMatDesc outMeta(GMatDesc in) {
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return in; // type still remains CV_8UC3;
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}
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};
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G_TYPED_KERNEL(GBGR2YUV, <GMat(GMat)>, "org.opencv.imgproc.colorconvert.bgr2yuv") {
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static GMatDesc outMeta(GMatDesc in) {
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return in; // type still remains CV_8UC3;
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}
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};
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G_TYPED_KERNEL(GRGB2Gray, <GMat(GMat)>, "org.opencv.imgproc.colorconvert.rgb2gray") {
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static GMatDesc outMeta(GMatDesc in) {
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return in.withType(CV_8U, 1);
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}
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};
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G_TYPED_KERNEL(GRGB2GrayCustom, <GMat(GMat,float,float,float)>, "org.opencv.imgproc.colorconvert.rgb2graycustom") {
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static GMatDesc outMeta(GMatDesc in, float, float, float) {
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return in.withType(CV_8U, 1);
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}
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};
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G_TYPED_KERNEL(GBGR2Gray, <GMat(GMat)>, "org.opencv.imgproc.colorconvert.bgr2gray") {
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static GMatDesc outMeta(GMatDesc in) {
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return in.withType(CV_8U, 1);
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}
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};
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G_TYPED_KERNEL(GBayerGR2RGB, <cv::GMat(cv::GMat)>, "org.opencv.imgproc.colorconvert.bayergr2rgb") {
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static cv::GMatDesc outMeta(cv::GMatDesc in) {
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return in.withType(CV_8U, 3);
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}
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};
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G_TYPED_KERNEL(GRGB2HSV, <cv::GMat(cv::GMat)>, "org.opencv.imgproc.colorconvert.rgb2hsv") {
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static cv::GMatDesc outMeta(cv::GMatDesc in) {
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return in;
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}
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};
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G_TYPED_KERNEL(GRGB2YUV422, <cv::GMat(cv::GMat)>, "org.opencv.imgproc.colorconvert.rgb2yuv422") {
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static cv::GMatDesc outMeta(cv::GMatDesc in) {
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GAPI_Assert(in.depth == CV_8U);
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GAPI_Assert(in.chan == 3);
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return in.withType(in.depth, 2);
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}
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};
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G_TYPED_KERNEL(GNV12toRGBp, <GMatP(GMat,GMat)>, "org.opencv.colorconvert.imgproc.nv12torgbp") {
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static GMatDesc outMeta(GMatDesc inY, GMatDesc inUV) {
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GAPI_Assert(inY.depth == CV_8U);
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GAPI_Assert(inUV.depth == CV_8U);
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GAPI_Assert(inY.chan == 1);
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GAPI_Assert(inY.planar == false);
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GAPI_Assert(inUV.chan == 2);
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GAPI_Assert(inUV.planar == false);
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GAPI_Assert(inY.size.width == 2 * inUV.size.width);
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GAPI_Assert(inY.size.height == 2 * inUV.size.height);
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return inY.withType(CV_8U, 3).asPlanar();
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}
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};
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G_TYPED_KERNEL(GNV12toBGRp, <GMatP(GMat,GMat)>, "org.opencv.colorconvert.imgproc.nv12tobgrp") {
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static GMatDesc outMeta(GMatDesc inY, GMatDesc inUV) {
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GAPI_Assert(inY.depth == CV_8U);
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GAPI_Assert(inUV.depth == CV_8U);
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GAPI_Assert(inY.chan == 1);
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GAPI_Assert(inY.planar == false);
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GAPI_Assert(inUV.chan == 2);
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GAPI_Assert(inUV.planar == false);
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GAPI_Assert(inY.size.width == 2 * inUV.size.width);
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GAPI_Assert(inY.size.height == 2 * inUV.size.height);
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return inY.withType(CV_8U, 3).asPlanar();
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}
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};
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}
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//! @addtogroup gapi_filters
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//! @{
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/** @brief Applies a separable linear filter to a matrix(image).
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The function applies a separable linear filter to the matrix. That is, first, every row of src is
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filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D
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kernel kernelY. The final result is returned.
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Supported matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
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Output image must have the same type, size, and number of channels as the input image.
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@note In case of floating-point computation, rounding to nearest even is procedeed
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if hardware supports it (if not - to nearest value).
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@note Function textual ID is "org.opencv.imgproc.filters.sepfilter"
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@param src Source image.
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@param ddepth desired depth of the destination image (the following combinations of src.depth() and ddepth are supported:
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src.depth() = CV_8U, ddepth = -1/CV_16S/CV_32F/CV_64F
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src.depth() = CV_16U/CV_16S, ddepth = -1/CV_32F/CV_64F
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src.depth() = CV_32F, ddepth = -1/CV_32F/CV_64F
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src.depth() = CV_64F, ddepth = -1/CV_64F
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when ddepth=-1, the output image will have the same depth as the source)
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@param kernelX Coefficients for filtering each row.
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@param kernelY Coefficients for filtering each column.
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@param anchor Anchor position within the kernel. The default value \f$(-1,-1)\f$ means that the anchor
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is at the kernel center.
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@param delta Value added to the filtered results before storing them.
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@param borderType Pixel extrapolation method, see cv::BorderTypes
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@param borderValue border value in case of constant border type
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@sa boxFilter, gaussianBlur, medianBlur
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*/
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GAPI_EXPORTS GMat sepFilter(const GMat& src, int ddepth, const Mat& kernelX, const Mat& kernelY, const Point& anchor /*FIXME: = Point(-1,-1)*/,
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const Scalar& delta /*FIXME = GScalar(0)*/, int borderType = BORDER_DEFAULT,
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const Scalar& borderValue = Scalar(0));
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/** @brief Convolves an image with the kernel.
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The function applies an arbitrary linear filter to an image. When
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the aperture is partially outside the image, the function interpolates outlier pixel values
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according to the specified border mode.
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The function does actually compute correlation, not the convolution:
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\f[\texttt{dst} (x,y) = \sum _{ \stackrel{0\leq x' < \texttt{kernel.cols},}{0\leq y' < \texttt{kernel.rows}} } \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )\f]
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That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip
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the kernel using flip and set the new anchor to `(kernel.cols - anchor.x - 1, kernel.rows -
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anchor.y - 1)`.
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Supported matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
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Output image must have the same size and number of channels an input image.
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@note Rounding to nearest even is procedeed if hardware supports it, if not - to nearest.
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@note Function textual ID is "org.opencv.imgproc.filters.filter2D"
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@param src input image.
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@param ddepth desired depth of the destination image
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@param kernel convolution kernel (or rather a correlation kernel), a single-channel floating point
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matrix; if you want to apply different kernels to different channels, split the image into
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separate color planes using split and process them individually.
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@param anchor anchor of the kernel that indicates the relative position of a filtered point within
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the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor
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is at the kernel center.
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@param delta optional value added to the filtered pixels before storing them in dst.
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@param borderType pixel extrapolation method, see cv::BorderTypes
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@param borderValue border value in case of constant border type
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@sa sepFilter
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*/
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GAPI_EXPORTS GMat filter2D(const GMat& src, int ddepth, const Mat& kernel, const Point& anchor = Point(-1,-1), const Scalar& delta = Scalar(0),
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int borderType = BORDER_DEFAULT, const Scalar& borderValue = Scalar(0));
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/** @brief Blurs an image using the box filter.
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The function smooths an image using the kernel:
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\f[\texttt{K} = \alpha \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \end{bmatrix}\f]
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where
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\f[\alpha = \fork{\frac{1}{\texttt{ksize.width*ksize.height}}}{when \texttt{normalize=true}}{1}{otherwise}\f]
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Unnormalized box filter is useful for computing various integral characteristics over each pixel
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neighborhood, such as covariance matrices of image derivatives (used in dense optical flow
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algorithms, and so on). If you need to compute pixel sums over variable-size windows, use cv::integral.
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Supported input matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
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Output image must have the same type, size, and number of channels as the input image.
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@note Rounding to nearest even is procedeed if hardware supports it, if not - to nearest.
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@note Function textual ID is "org.opencv.imgproc.filters.boxfilter"
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@param src Source image.
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@param dtype the output image depth (-1 to set the input image data type).
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@param ksize blurring kernel size.
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@param anchor Anchor position within the kernel. The default value \f$(-1,-1)\f$ means that the anchor
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is at the kernel center.
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@param normalize flag, specifying whether the kernel is normalized by its area or not.
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@param borderType Pixel extrapolation method, see cv::BorderTypes
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@param borderValue border value in case of constant border type
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@sa sepFilter, gaussianBlur, medianBlur, integral
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*/
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GAPI_EXPORTS GMat boxFilter(const GMat& src, int dtype, const Size& ksize, const Point& anchor = Point(-1,-1),
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bool normalize = true, int borderType = BORDER_DEFAULT,
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const Scalar& borderValue = Scalar(0));
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/** @brief Blurs an image using the normalized box filter.
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The function smooths an image using the kernel:
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\f[\texttt{K} = \frac{1}{\texttt{ksize.width*ksize.height}} \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \end{bmatrix}\f]
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The call `blur(src, dst, ksize, anchor, borderType)` is equivalent to `boxFilter(src, dst, src.type(),
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anchor, true, borderType)`.
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Supported input matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
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Output image must have the same type, size, and number of channels as the input image.
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@note Rounding to nearest even is procedeed if hardware supports it, if not - to nearest.
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@note Function textual ID is "org.opencv.imgproc.filters.blur"
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@param src Source image.
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@param ksize blurring kernel size.
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@param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel
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center.
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@param borderType border mode used to extrapolate pixels outside of the image, see cv::BorderTypes
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@param borderValue border value in case of constant border type
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@sa boxFilter, bilateralFilter, GaussianBlur, medianBlur
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*/
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GAPI_EXPORTS GMat blur(const GMat& src, const Size& ksize, const Point& anchor = Point(-1,-1),
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int borderType = BORDER_DEFAULT, const Scalar& borderValue = Scalar(0));
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//GAPI_EXPORTS_W void blur( InputArray src, OutputArray dst,
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// Size ksize, Point anchor = Point(-1,-1),
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// int borderType = BORDER_DEFAULT );
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/** @brief Blurs an image using a Gaussian filter.
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The function filter2Ds the source image with the specified Gaussian kernel.
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Output image must have the same type and number of channels an input image.
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Supported input matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
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Output image must have the same type, size, and number of channels as the input image.
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@note Rounding to nearest even is procedeed if hardware supports it, if not - to nearest.
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@note Function textual ID is "org.opencv.imgproc.filters.gaussianBlur"
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@param src input image;
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@param ksize Gaussian kernel size. ksize.width and ksize.height can differ but they both must be
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positive and odd. Or, they can be zero's and then they are computed from sigma.
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@param sigmaX Gaussian kernel standard deviation in X direction.
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@param sigmaY Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be
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equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height,
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respectively (see cv::getGaussianKernel for details); to fully control the result regardless of
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possible future modifications of all this semantics, it is recommended to specify all of ksize,
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sigmaX, and sigmaY.
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@param borderType pixel extrapolation method, see cv::BorderTypes
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@param borderValue border value in case of constant border type
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@sa sepFilter, boxFilter, medianBlur
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*/
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GAPI_EXPORTS GMat gaussianBlur(const GMat& src, const Size& ksize, double sigmaX, double sigmaY = 0,
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int borderType = BORDER_DEFAULT, const Scalar& borderValue = Scalar(0));
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/** @brief Blurs an image using the median filter.
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The function smoothes an image using the median filter with the \f$\texttt{ksize} \times
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\texttt{ksize}\f$ aperture. Each channel of a multi-channel image is processed independently.
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Output image must have the same type, size, and number of channels as the input image.
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@note Rounding to nearest even is procedeed if hardware supports it, if not - to nearest.
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The median filter uses cv::BORDER_REPLICATE internally to cope with border pixels, see cv::BorderTypes
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@note Function textual ID is "org.opencv.imgproc.filters.medianBlur"
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@param src input matrix (image)
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@param ksize aperture linear size; it must be odd and greater than 1, for example: 3, 5, 7 ...
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@sa boxFilter, gaussianBlur
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*/
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GAPI_EXPORTS GMat medianBlur(const GMat& src, int ksize);
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/** @brief Erodes an image by using a specific structuring element.
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The function erodes the source image using the specified structuring element that determines the
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shape of a pixel neighborhood over which the minimum is taken:
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\f[\texttt{dst} (x,y) = \min _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f]
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Erosion can be applied several (iterations) times. In case of multi-channel images, each channel is processed independently.
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Supported input matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, and @ref CV_32FC1.
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Output image must have the same type, size, and number of channels as the input image.
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@note Rounding to nearest even is procedeed if hardware supports it, if not - to nearest.
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@note Function textual ID is "org.opencv.imgproc.filters.erode"
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@param src input image
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@param kernel structuring element used for erosion; if `element=Mat()`, a `3 x 3` rectangular
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structuring element is used. Kernel can be created using getStructuringElement.
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@param anchor position of the anchor within the element; default value (-1, -1) means that the
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anchor is at the element center.
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@param iterations number of times erosion is applied.
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@param borderType pixel extrapolation method, see cv::BorderTypes
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@param borderValue border value in case of a constant border
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@sa dilate
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*/
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GAPI_EXPORTS GMat erode(const GMat& src, const Mat& kernel, const Point& anchor = Point(-1,-1), int iterations = 1,
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int borderType = BORDER_CONSTANT,
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const Scalar& borderValue = morphologyDefaultBorderValue());
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/** @brief Erodes an image by using 3 by 3 rectangular structuring element.
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The function erodes the source image using the rectangular structuring element with rectangle center as an anchor.
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Erosion can be applied several (iterations) times. In case of multi-channel images, each channel is processed independently.
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Supported input matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, and @ref CV_32FC1.
|
Output image must have the same type, size, and number of channels as the input image.
|
@note Rounding to nearest even is procedeed if hardware supports it, if not - to nearest.
|
|
@param src input image
|
@param iterations number of times erosion is applied.
|
@param borderType pixel extrapolation method, see cv::BorderTypes
|
@param borderValue border value in case of a constant border
|
@sa erode, dilate3x3
|
*/
|
GAPI_EXPORTS GMat erode3x3(const GMat& src, int iterations = 1,
|
int borderType = BORDER_CONSTANT,
|
const Scalar& borderValue = morphologyDefaultBorderValue());
|
|
/** @brief 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:
|
\f[\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f]
|
|
Dilation can be applied several (iterations) times. In case of multi-channel images, each channel is processed independently.
|
Supported input matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, and @ref CV_32FC1.
|
Output image must have the same type, size, and number of channels as the input image.
|
@note Rounding to nearest even is procedeed if hardware supports it, if not - to nearest.
|
|
@note Function textual ID is "org.opencv.imgproc.filters.dilate"
|
|
@param src input image.
|
@param kernel structuring element used for dilation; if elemenat=Mat(), a 3 x 3 rectangular
|
structuring element is used. Kernel can be created using getStructuringElement
|
@param anchor position of the anchor within the element; default value (-1, -1) means that the
|
anchor is at the element center.
|
@param iterations number of times dilation is applied.
|
@param borderType pixel extrapolation method, see cv::BorderTypes
|
@param borderValue border value in case of a constant border
|
@sa erode, morphologyEx, getStructuringElement
|
*/
|
GAPI_EXPORTS GMat dilate(const GMat& src, const Mat& kernel, const Point& anchor = Point(-1,-1), int iterations = 1,
|
int borderType = BORDER_CONSTANT,
|
const Scalar& borderValue = morphologyDefaultBorderValue());
|
|
/** @brief Dilates an image by using 3 by 3 rectangular 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:
|
\f[\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f]
|
|
Dilation can be applied several (iterations) times. In case of multi-channel images, each channel is processed independently.
|
Supported input matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, and @ref CV_32FC1.
|
Output image must have the same type, size, and number of channels as the input image.
|
@note Rounding to nearest even is procedeed if hardware supports it, if not - to nearest.
|
|
@note Function textual ID is "org.opencv.imgproc.filters.dilate"
|
|
@param src input image.
|
@param iterations number of times dilation is applied.
|
@param borderType pixel extrapolation method, see cv::BorderTypes
|
@param borderValue border value in case of a constant border
|
@sa dilate, erode3x3
|
*/
|
|
GAPI_EXPORTS GMat dilate3x3(const GMat& src, int iterations = 1,
|
int borderType = BORDER_CONSTANT,
|
const Scalar& borderValue = morphologyDefaultBorderValue());
|
|
/** @brief Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
|
|
In all cases except one, the \f$\texttt{ksize} \times \texttt{ksize}\f$ separable kernel is used to
|
calculate the derivative. When \f$\texttt{ksize = 1}\f$, the \f$3 \times 1\f$ or \f$1 \times 3\f$
|
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 \f$3\times3\f$ Scharr
|
filter that may give more accurate results than the \f$3\times3\f$ Sobel. The Scharr aperture is
|
|
\f[\vecthreethree{-3}{0}{3}{-10}{0}{10}{-3}{0}{3}\f]
|
|
for the x-derivative, or transposed for the y-derivative.
|
|
The function calculates an image derivative by convolving the image with the appropriate kernel:
|
|
\f[\texttt{dst} = \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\f]
|
|
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:
|
|
\f[\vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}\f]
|
|
The second case corresponds to a kernel of:
|
|
\f[\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\f]
|
|
@note Rounding to nearest even is procedeed if hardware supports it, if not - to nearest.
|
|
@note Function textual ID is "org.opencv.imgproc.filters.sobel"
|
|
@param src input image.
|
@param ddepth output image depth, see @ref filter_depths "combinations"; in the case of
|
8-bit input images it will result in truncated derivatives.
|
@param dx order of the derivative x.
|
@param dy order of the derivative y.
|
@param ksize size of the extended Sobel kernel; it must be odd.
|
@param scale optional scale factor for the computed derivative values; by default, no scaling is
|
applied (see cv::getDerivKernels for details).
|
@param delta optional delta value that is added to the results prior to storing them in dst.
|
@param borderType pixel extrapolation method, see cv::BorderTypes
|
@param borderValue border value in case of constant border type
|
@sa filter2D, gaussianBlur, cartToPolar
|
*/
|
GAPI_EXPORTS GMat Sobel(const GMat& src, int ddepth, int dx, int dy, int ksize = 3,
|
double scale = 1, double delta = 0,
|
int borderType = BORDER_DEFAULT,
|
const Scalar& borderValue = Scalar(0));
|
|
/** @brief Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
|
|
In all cases except one, the \f$\texttt{ksize} \times \texttt{ksize}\f$ separable kernel is used to
|
calculate the derivative. When \f$\texttt{ksize = 1}\f$, the \f$3 \times 1\f$ or \f$1 \times 3\f$
|
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 \f$3\times3\f$ Scharr
|
filter that may give more accurate results than the \f$3\times3\f$ Sobel. The Scharr aperture is
|
|
\f[\vecthreethree{-3}{0}{3}{-10}{0}{10}{-3}{0}{3}\f]
|
|
for the x-derivative, or transposed for the y-derivative.
|
|
The function calculates an image derivative by convolving the image with the appropriate kernel:
|
|
\f[\texttt{dst} = \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\f]
|
|
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:
|
|
\f[\vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}\f]
|
|
The second case corresponds to a kernel of:
|
|
\f[\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\f]
|
|
@note First returned matrix correspons to dx derivative while the second one to dy.
|
|
@note Rounding to nearest even is procedeed if hardware supports it, if not - to nearest.
|
|
@note Function textual ID is "org.opencv.imgproc.filters.sobelxy"
|
|
@param src input image.
|
@param ddepth output image depth, see @ref filter_depths "combinations"; in the case of
|
8-bit input images it will result in truncated derivatives.
|
@param order order of the derivatives.
|
@param ksize size of the extended Sobel kernel; it must be odd.
|
@param scale optional scale factor for the computed derivative values; by default, no scaling is
|
applied (see cv::getDerivKernels for details).
|
@param delta optional delta value that is added to the results prior to storing them in dst.
|
@param borderType pixel extrapolation method, see cv::BorderTypes
|
@param borderValue border value in case of constant border type
|
@sa filter2D, gaussianBlur, cartToPolar
|
*/
|
GAPI_EXPORTS std::tuple<GMat, GMat> SobelXY(const GMat& src, int ddepth, int order, int ksize = 3,
|
double scale = 1, double delta = 0,
|
int borderType = BORDER_DEFAULT,
|
const Scalar& borderValue = Scalar(0));
|
|
/** @brief Finds edges in an image using the Canny algorithm.
|
|
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>
|
|
@note Function textual ID is "org.opencv.imgproc.filters.canny"
|
|
@param image 8-bit input image.
|
@param threshold1 first threshold for the hysteresis procedure.
|
@param threshold2 second threshold for the hysteresis procedure.
|
@param apertureSize aperture size for the Sobel operator.
|
@param L2gradient a flag, indicating whether a more accurate \f$L_2\f$ norm
|
\f$=\sqrt{(dI/dx)^2 + (dI/dy)^2}\f$ should be used to calculate the image gradient magnitude (
|
L2gradient=true ), or whether the default \f$L_1\f$ norm \f$=|dI/dx|+|dI/dy|\f$ is enough (
|
L2gradient=false ).
|
*/
|
GAPI_EXPORTS GMat Canny(const GMat& image, double threshold1, double threshold2,
|
int apertureSize = 3, bool L2gradient = false);
|
|
/** @brief Equalizes the histogram of a grayscale image.
|
|
The function equalizes the histogram of the input image using the following algorithm:
|
|
- Calculate the histogram \f$H\f$ for src .
|
- Normalize the histogram so that the sum of histogram bins is 255.
|
- Compute the integral of the histogram:
|
\f[H'_i = \sum _{0 \le j < i} H(j)\f]
|
- Transform the image using \f$H'\f$ as a look-up table: \f$\texttt{dst}(x,y) = H'(\texttt{src}(x,y))\f$
|
|
The algorithm normalizes the brightness and increases the contrast of the image.
|
@note The returned image is of the same size and type as input.
|
|
@note Function textual ID is "org.opencv.imgproc.equalizeHist"
|
|
@param src Source 8-bit single channel image.
|
*/
|
GAPI_EXPORTS GMat equalizeHist(const GMat& src);
|
|
//! @} gapi_filters
|
|
//! @addtogroup gapi_colorconvert
|
//! @{
|
/** @brief Converts an image from RGB color space to gray-scaled.
|
The conventional ranges for R, G, and B channel values are 0 to 255.
|
Resulting gray color value computed as
|
\f[\texttt{dst} (I)= \texttt{0.299} * \texttt{src}(I).R + \texttt{0.587} * \texttt{src}(I).G + \texttt{0.114} * \texttt{src}(I).B \f]
|
|
@note Function textual ID is "org.opencv.imgproc.colorconvert.rgb2gray"
|
|
@param src input image: 8-bit unsigned 3-channel image @ref CV_8UC1.
|
@sa RGB2YUV
|
*/
|
GAPI_EXPORTS GMat RGB2Gray(const GMat& src);
|
|
/** @overload
|
Resulting gray color value computed as
|
\f[\texttt{dst} (I)= \texttt{rY} * \texttt{src}(I).R + \texttt{gY} * \texttt{src}(I).G + \texttt{bY} * \texttt{src}(I).B \f]
|
|
@note Function textual ID is "org.opencv.imgproc.colorconvert.rgb2graycustom"
|
|
@param src input image: 8-bit unsigned 3-channel image @ref CV_8UC1.
|
@param rY float multiplier for R channel.
|
@param gY float multiplier for G channel.
|
@param bY float multiplier for B channel.
|
@sa RGB2YUV
|
*/
|
GAPI_EXPORTS GMat RGB2Gray(const GMat& src, float rY, float gY, float bY);
|
|
/** @brief Converts an image from BGR color space to gray-scaled.
|
The conventional ranges for B, G, and R channel values are 0 to 255.
|
Resulting gray color value computed as
|
\f[\texttt{dst} (I)= \texttt{0.114} * \texttt{src}(I).B + \texttt{0.587} * \texttt{src}(I).G + \texttt{0.299} * \texttt{src}(I).R \f]
|
|
@note Function textual ID is "org.opencv.imgproc.colorconvert.bgr2gray"
|
|
@param src input image: 8-bit unsigned 3-channel image @ref CV_8UC1.
|
@sa BGR2LUV
|
*/
|
GAPI_EXPORTS GMat BGR2Gray(const GMat& src);
|
|
/** @brief Converts an image from RGB color space to YUV color space.
|
|
The function converts an input image from RGB color space to YUV.
|
The conventional ranges for R, G, and B channel values are 0 to 255.
|
|
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, like here, at RGB \f$\rightarrow\f$ Y\*u\*v\* transformation.
|
Output image must be 8-bit unsigned 3-channel image @ref CV_8UC3.
|
|
@note Function textual ID is "org.opencv.imgproc.colorconvert.rgb2yuv"
|
|
@param src input image: 8-bit unsigned 3-channel image @ref CV_8UC3.
|
@sa YUV2RGB, RGB2Lab
|
*/
|
GAPI_EXPORTS GMat RGB2YUV(const GMat& src);
|
|
/** @brief Converts an image from BGR color space to LUV color space.
|
|
The function converts an input image from BGR color space to LUV.
|
The conventional ranges for B, G, and R channel values are 0 to 255.
|
|
Output image must be 8-bit unsigned 3-channel image @ref CV_8UC3.
|
|
@note Function textual ID is "org.opencv.imgproc.colorconvert.bgr2luv"
|
|
@param src input image: 8-bit unsigned 3-channel image @ref CV_8UC3.
|
@sa RGB2Lab, RGB2LUV
|
*/
|
GAPI_EXPORTS GMat BGR2LUV(const GMat& src);
|
|
/** @brief Converts an image from LUV color space to BGR color space.
|
|
The function converts an input image from LUV color space to BGR.
|
The conventional ranges for B, G, and R channel values are 0 to 255.
|
|
Output image must be 8-bit unsigned 3-channel image @ref CV_8UC3.
|
|
@note Function textual ID is "org.opencv.imgproc.colorconvert.luv2bgr"
|
|
@param src input image: 8-bit unsigned 3-channel image @ref CV_8UC3.
|
@sa BGR2LUV
|
*/
|
GAPI_EXPORTS GMat LUV2BGR(const GMat& src);
|
|
/** @brief Converts an image from YUV color space to BGR color space.
|
|
The function converts an input image from YUV color space to BGR.
|
The conventional ranges for B, G, and R channel values are 0 to 255.
|
|
Output image must be 8-bit unsigned 3-channel image @ref CV_8UC3.
|
|
@note Function textual ID is "org.opencv.imgproc.colorconvert.yuv2bgr"
|
|
@param src input image: 8-bit unsigned 3-channel image @ref CV_8UC3.
|
@sa BGR2YUV
|
*/
|
GAPI_EXPORTS GMat YUV2BGR(const GMat& src);
|
|
/** @brief Converts an image from BGR color space to YUV color space.
|
|
The function converts an input image from BGR color space to YUV.
|
The conventional ranges for B, G, and R channel values are 0 to 255.
|
|
Output image must be 8-bit unsigned 3-channel image @ref CV_8UC3.
|
|
@note Function textual ID is "org.opencv.imgproc.colorconvert.bgr2yuv"
|
|
@param src input image: 8-bit unsigned 3-channel image @ref CV_8UC3.
|
@sa YUV2BGR
|
*/
|
GAPI_EXPORTS GMat BGR2YUV(const GMat& src);
|
|
/** @brief Converts an image from RGB color space to Lab color space.
|
|
The function converts an input image from BGR color space to Lab.
|
The conventional ranges for R, G, and B channel values are 0 to 255.
|
|
Output image must be 8-bit unsigned 3-channel image @ref CV_8UC1.
|
|
@note Function textual ID is "org.opencv.imgproc.colorconvert.rgb2lab"
|
|
@param src input image: 8-bit unsigned 3-channel image @ref CV_8UC1.
|
@sa RGB2YUV, RGB2LUV
|
*/
|
GAPI_EXPORTS GMat RGB2Lab(const GMat& src);
|
|
/** @brief Converts an image from YUV color space to RGB.
|
The function converts an input image from YUV color space to RGB.
|
The conventional ranges for Y, U, and V channel values are 0 to 255.
|
|
Output image must be 8-bit unsigned 3-channel image @ref CV_8UC3.
|
|
@note Function textual ID is "org.opencv.imgproc.colorconvert.yuv2rgb"
|
|
@param src input image: 8-bit unsigned 3-channel image @ref CV_8UC3.
|
|
@sa RGB2Lab, RGB2YUV
|
*/
|
GAPI_EXPORTS GMat YUV2RGB(const GMat& src);
|
|
/** @brief Converts an image from NV12 (YUV420p) color space to RGB.
|
The function converts an input image from NV12 color space to RGB.
|
The conventional ranges for Y, U, and V channel values are 0 to 255.
|
|
Output image must be 8-bit unsigned 3-channel image @ref CV_8UC3.
|
|
@note Function textual ID is "org.opencv.imgproc.colorconvert.nv12torgb"
|
|
@param src_y input image: 8-bit unsigned 1-channel image @ref CV_8UC1.
|
@param src_uv input image: 8-bit unsigned 2-channel image @ref CV_8UC2.
|
|
@sa YUV2RGB, NV12toBGR
|
*/
|
GAPI_EXPORTS GMat NV12toRGB(const GMat& src_y, const GMat& src_uv);
|
|
/** @brief Converts an image from NV12 (YUV420p) color space to BGR.
|
The function converts an input image from NV12 color space to RGB.
|
The conventional ranges for Y, U, and V channel values are 0 to 255.
|
|
Output image must be 8-bit unsigned 3-channel image @ref CV_8UC3.
|
|
@note Function textual ID is "org.opencv.imgproc.colorconvert.nv12tobgr"
|
|
@param src_y input image: 8-bit unsigned 1-channel image @ref CV_8UC1.
|
@param src_uv input image: 8-bit unsigned 2-channel image @ref CV_8UC2.
|
|
@sa YUV2BGR, NV12toRGB
|
*/
|
GAPI_EXPORTS GMat NV12toBGR(const GMat& src_y, const GMat& src_uv);
|
|
/** @brief Converts an image from BayerGR color space to RGB.
|
The function converts an input image from BayerGR color space to RGB.
|
The conventional ranges for G, R, and B channel values are 0 to 255.
|
|
Output image must be 8-bit unsigned 3-channel image @ref CV_8UC3.
|
|
@note Function textual ID is "org.opencv.imgproc.colorconvert.bayergr2rgb"
|
|
@param src_gr input image: 8-bit unsigned 1-channel image @ref CV_8UC1.
|
|
@sa YUV2BGR, NV12toRGB
|
*/
|
GAPI_EXPORTS GMat BayerGR2RGB(const GMat& src_gr);
|
|
/** @brief Converts an image from RGB color space to HSV.
|
The function converts an input image from RGB color space to HSV.
|
The conventional ranges for R, G, and B channel values are 0 to 255.
|
|
Output image must be 8-bit unsigned 3-channel image @ref CV_8UC3.
|
|
@note Function textual ID is "org.opencv.imgproc.colorconvert.rgb2hsv"
|
|
@param src input image: 8-bit unsigned 3-channel image @ref CV_8UC3.
|
|
@sa YUV2BGR, NV12toRGB
|
*/
|
GAPI_EXPORTS GMat RGB2HSV(const GMat& src);
|
|
/** @brief Converts an image from RGB color space to YUV422.
|
The function converts an input image from RGB color space to YUV422.
|
The conventional ranges for R, G, and B channel values are 0 to 255.
|
|
Output image must be 8-bit unsigned 2-channel image @ref CV_8UC2.
|
|
@note Function textual ID is "org.opencv.imgproc.colorconvert.rgb2yuv422"
|
|
@param src input image: 8-bit unsigned 3-channel image @ref CV_8UC3.
|
|
@sa YUV2BGR, NV12toRGB
|
*/
|
GAPI_EXPORTS GMat RGB2YUV422(const GMat& src);
|
|
/** @brief Converts an image from NV12 (YUV420p) color space to RGB.
|
The function converts an input image from NV12 color space to RGB.
|
The conventional ranges for Y, U, and V channel values are 0 to 255.
|
|
Output image must be 8-bit unsigned planar 3-channel image @ref CV_8UC1.
|
Planar image memory layout is three planes laying in the memory contiguously,
|
so the image height should be plane_height*plane_number,
|
image type is @ref CV_8UC1.
|
|
@note Function textual ID is "org.opencv.imgproc.colorconvert.nv12torgbp"
|
|
@param src_y input image: 8-bit unsigned 1-channel image @ref CV_8UC1.
|
@param src_uv input image: 8-bit unsigned 2-channel image @ref CV_8UC2.
|
|
@sa YUV2RGB, NV12toBGRp, NV12toRGB
|
*/
|
GAPI_EXPORTS GMatP NV12toRGBp(const GMat &src_y, const GMat &src_uv);
|
|
/** @brief Converts an image from NV12 (YUV420p) color space to BGR.
|
The function converts an input image from NV12 color space to BGR.
|
The conventional ranges for Y, U, and V channel values are 0 to 255.
|
|
Output image must be 8-bit unsigned planar 3-channel image @ref CV_8UC1.
|
Planar image memory layout is three planes laying in the memory contiguously,
|
so the image height should be plane_height*plane_number,
|
image type is @ref CV_8UC1.
|
|
@note Function textual ID is "org.opencv.imgproc.colorconvert.nv12torgbp"
|
|
@param src_y input image: 8-bit unsigned 1-channel image @ref CV_8UC1.
|
@param src_uv input image: 8-bit unsigned 2-channel image @ref CV_8UC2.
|
|
@sa YUV2RGB, NV12toRGBp, NV12toBGR
|
*/
|
GAPI_EXPORTS GMatP NV12toBGRp(const GMat &src_y, const GMat &src_uv);
|
|
//! @} gapi_colorconvert
|
} //namespace gapi
|
} //namespace cv
|
|
#endif // OPENCV_GAPI_IMGPROC_HPP
|
