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Open Source Computer Vision Library
https://opencv.org/
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132 lines
4.1 KiB
132 lines
4.1 KiB
#include <opencv2/videoio.hpp> |
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#include <opencv2/highgui.hpp> |
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#include <opencv2/gapi.hpp> |
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#include <opencv2/gapi/core.hpp> |
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#include <opencv2/gapi/imgproc.hpp> |
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#include <opencv2/gapi/cpu/gcpukernel.hpp> |
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#include <opencv2/gapi/fluid/core.hpp> |
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#include <opencv2/gapi/fluid/imgproc.hpp> |
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static void gscalar_example() |
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{ |
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//! [gscalar_implicit] |
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cv::GMat a; |
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cv::GMat b = a + 1; |
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//! [gscalar_implicit] |
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} |
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static void typed_example() |
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{ |
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const cv::Size sz(32, 32); |
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cv::Mat |
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in_mat1 (sz, CV_8UC1), |
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in_mat2 (sz, CV_8UC1), |
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out_mat_untyped(sz, CV_8UC1), |
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out_mat_typed1 (sz, CV_8UC1), |
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out_mat_typed2 (sz, CV_8UC1); |
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cv::randu(in_mat1, cv::Scalar::all(0), cv::Scalar::all(255)); |
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cv::randu(in_mat2, cv::Scalar::all(0), cv::Scalar::all(255)); |
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//! [Untyped_Example] |
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// Untyped G-API /////////////////////////////////////////////////////////// |
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cv::GComputation cvtU([]() |
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{ |
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cv::GMat in1, in2; |
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cv::GMat out = cv::gapi::add(in1, in2); |
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return cv::GComputation({in1, in2}, {out}); |
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}); |
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std::vector<cv::Mat> u_ins = {in_mat1, in_mat2}; |
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std::vector<cv::Mat> u_outs = {out_mat_untyped}; |
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cvtU.apply(u_ins, u_outs); |
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//! [Untyped_Example] |
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//! [Typed_Example] |
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// Typed G-API ///////////////////////////////////////////////////////////// |
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cv::GComputationT<cv::GMat (cv::GMat, cv::GMat)> cvtT([](cv::GMat m1, cv::GMat m2) |
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{ |
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return m1+m2; |
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}); |
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cvtT.apply(in_mat1, in_mat2, out_mat_typed1); |
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auto cvtTC = cvtT.compile(cv::descr_of(in_mat1), cv::descr_of(in_mat2)); |
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cvtTC(in_mat1, in_mat2, out_mat_typed2); |
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//! [Typed_Example] |
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} |
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G_TYPED_KERNEL(IAdd, <cv::GMat(cv::GMat)>, "test.custom.add") { |
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static cv::GMatDesc outMeta(const cv::GMatDesc &in) { return in; } |
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}; |
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G_TYPED_KERNEL(IFilter2D, <cv::GMat(cv::GMat)>, "test.custom.filter2d") { |
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static cv::GMatDesc outMeta(const cv::GMatDesc &in) { return in; } |
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}; |
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G_TYPED_KERNEL(IRGB2YUV, <cv::GMat(cv::GMat)>, "test.custom.add") { |
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static cv::GMatDesc outMeta(const cv::GMatDesc &in) { return in; } |
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}; |
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GAPI_OCV_KERNEL(CustomAdd, IAdd) { static void run(cv::Mat, cv::Mat &) {} }; |
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GAPI_OCV_KERNEL(CustomFilter2D, IFilter2D) { static void run(cv::Mat, cv::Mat &) {} }; |
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GAPI_OCV_KERNEL(CustomRGB2YUV, IRGB2YUV) { static void run(cv::Mat, cv::Mat &) {} }; |
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int main(int argc, char *argv[]) |
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{ |
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if (argc < 3) |
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return -1; |
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cv::Mat input = cv::imread(argv[1]); |
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cv::Mat output; |
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{ |
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//! [graph_def] |
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cv::GMat in; |
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cv::GMat gx = cv::gapi::Sobel(in, CV_32F, 1, 0); |
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cv::GMat gy = cv::gapi::Sobel(in, CV_32F, 0, 1); |
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cv::GMat g = cv::gapi::sqrt(cv::gapi::mul(gx, gx) + cv::gapi::mul(gy, gy)); |
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cv::GMat out = cv::gapi::convertTo(g, CV_8U); |
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//! [graph_def] |
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//! [graph_decl_apply] |
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//! [graph_cap_full] |
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cv::GComputation sobelEdge(cv::GIn(in), cv::GOut(out)); |
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//! [graph_cap_full] |
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sobelEdge.apply(input, output); |
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//! [graph_decl_apply] |
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//! [apply_with_param] |
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cv::gapi::GKernelPackage kernels = cv::gapi::combine |
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(cv::gapi::core::fluid::kernels(), |
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cv::gapi::imgproc::fluid::kernels()); |
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sobelEdge.apply(input, output, cv::compile_args(kernels)); |
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//! [apply_with_param] |
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//! [graph_cap_sub] |
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cv::GComputation sobelEdgeSub(cv::GIn(gx, gy), cv::GOut(out)); |
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//! [graph_cap_sub] |
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} |
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//! [graph_gen] |
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cv::GComputation sobelEdgeGen([](){ |
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cv::GMat in; |
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cv::GMat gx = cv::gapi::Sobel(in, CV_32F, 1, 0); |
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cv::GMat gy = cv::gapi::Sobel(in, CV_32F, 0, 1); |
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cv::GMat g = cv::gapi::sqrt(cv::gapi::mul(gx, gx) + cv::gapi::mul(gy, gy)); |
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cv::GMat out = cv::gapi::convertTo(g, CV_8U); |
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return cv::GComputation(in, out); |
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}); |
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//! [graph_gen] |
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cv::imwrite(argv[2], output); |
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//! [kernels_snippet] |
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cv::gapi::GKernelPackage pkg = cv::gapi::kernels |
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< CustomAdd |
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, CustomFilter2D |
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, CustomRGB2YUV |
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>(); |
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//! [kernels_snippet] |
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// Just call typed example with no input/output - avoid warnings about |
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// unused functions |
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typed_example(); |
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gscalar_example(); |
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return 0; |
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}
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