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8e43c8f200
G-API: Implement concurrent executor #24845 ## Overview This PR introduces the new G-API executor called `GThreadedExecutor` which can be selected when the `GComputation` is compiled in `serial` mode (a.k.a `GComputation::compile(...)`) ### ThreadPool `cv::gapi::own::ThreadPool` has been introduced in order to abstract usage of threads in `GThreadedExecutor`. `ThreadPool` is implemented by using `own::concurrent_bounded_queue` `ThreadPool` has only as single method `schedule` that will push task into the queue for the further execution. The **important** notice is that if `Task` executed in `ThreadPool` throws exception - this is `UB`. ### GThreadedExecutor The `GThreadedExecutor` is mostly copy-paste of `GExecutor`, should we extend `GExecutor` instead? #### Implementation details 1. Build the dependency graph for `Island` nodes. 2. Store the tasks that don't have dependencies into separate `vector` in order to run them first. 3. at the `GThreadedExecutor::run()` schedule the tasks that don't have dependencies that will schedule their dependents and wait for the completion. ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [ ] I agree to contribute to the project under Apache 2 License. - [ ] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [ ] The PR is proposed to the proper branch - [ ] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [ ] The feature is well documented and sample code can be built with the project CMake
576 lines
20 KiB
C++
576 lines
20 KiB
C++
// 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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#include "test_precomp.hpp"
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#include <stdexcept>
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#include <ade/util/iota_range.hpp>
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#include "logger.hpp"
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#include <opencv2/gapi/core.hpp>
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#include "executor/thread_pool.hpp"
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namespace opencv_test
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{
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namespace
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{
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G_TYPED_KERNEL(GInvalidResize, <GMat(GMat,Size,double,double,int)>, "org.opencv.test.invalid_resize")
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{
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static GMatDesc outMeta(GMatDesc in, Size, double, double, int) { return in; }
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};
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GAPI_OCV_KERNEL(GOCVInvalidResize, GInvalidResize)
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{
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static void run(const cv::Mat& in, cv::Size sz, double fx, double fy, int interp, cv::Mat &out)
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{
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cv::resize(in, out, sz, fx, fy, interp);
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}
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};
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G_TYPED_KERNEL(GReallocatingCopy, <GMat(GMat)>, "org.opencv.test.reallocating_copy")
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{
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static GMatDesc outMeta(GMatDesc in) { return in; }
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};
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GAPI_OCV_KERNEL(GOCVReallocatingCopy, GReallocatingCopy)
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{
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static void run(const cv::Mat& in, cv::Mat &out)
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{
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out = in.clone();
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}
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};
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G_TYPED_KERNEL(GCustom, <GMat(GMat)>, "org.opencv.test.custom")
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{
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static GMatDesc outMeta(GMatDesc in) { return in; }
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};
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G_TYPED_KERNEL(GZeros, <GMat(GMat, GMatDesc)>, "org.opencv.test.zeros")
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{
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static GMatDesc outMeta(GMatDesc /*in*/, GMatDesc user_desc)
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{
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return user_desc;
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}
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};
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GAPI_OCV_KERNEL(GOCVZeros, GZeros)
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{
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static void run(const cv::Mat& /*in*/,
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const cv::GMatDesc& /*desc*/,
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cv::Mat& out)
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{
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out.setTo(0);
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}
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};
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G_TYPED_KERNEL(GBusyWait, <GMat(GMat, uint32_t)>, "org.busy_wait") {
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static GMatDesc outMeta(GMatDesc in, uint32_t)
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{
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return in;
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}
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};
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GAPI_OCV_KERNEL(GOCVBusyWait, GBusyWait)
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{
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static void run(const cv::Mat& in,
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const uint32_t time_in_ms,
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cv::Mat& out)
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{
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using namespace std::chrono;
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auto s = high_resolution_clock::now();
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in.copyTo(out);
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auto e = high_resolution_clock::now();
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const auto elapsed_in_ms =
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static_cast<int32_t>(duration_cast<milliseconds>(e-s).count());
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int32_t diff = time_in_ms - elapsed_in_ms;
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const auto need_to_wait_in_ms = static_cast<uint32_t>(std::max(0, diff));
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s = high_resolution_clock::now();
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e = s;
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while (duration_cast<milliseconds>(e-s).count() < need_to_wait_in_ms) {
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e = high_resolution_clock::now();
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}
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}
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};
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// These definitions test the correct macro work if the kernel has multiple output values
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G_TYPED_KERNEL(GRetGArrayTupleOfGMat2Kernel, <GArray<std::tuple<GMat, GMat>>(GMat, Scalar)>, "org.opencv.test.retarrayoftupleofgmat2kernel") {};
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G_TYPED_KERNEL(GRetGArraTupleyOfGMat3Kernel, <GArray<std::tuple<GMat, GMat, GMat>>(GMat)>, "org.opencv.test.retarrayoftupleofgmat3kernel") {};
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G_TYPED_KERNEL(GRetGArraTupleyOfGMat4Kernel, <GArray<std::tuple<GMat, GMat, GMat, GMat>>(GMat)>, "org.opencv.test.retarrayoftupleofgmat4kernel") {};
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G_TYPED_KERNEL(GRetGArraTupleyOfGMat5Kernel, <GArray<std::tuple<GMat, GMat, GMat, GMat, GMat>>(GMat)>, "org.opencv.test.retarrayoftupleofgmat5kernel") {};
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G_TYPED_KERNEL(GRetGArraTupleyOfGMat6Kernel, <GArray<std::tuple<GMat, GMat, GMat, GMat, GMat, GMat>>(GMat)>, "org.opencv.test.retarrayoftupleofgmat6kernel") {};
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G_TYPED_KERNEL(GRetGArraTupleyOfGMat7Kernel, <GArray<std::tuple<GMat, GMat, GMat, GMat, GMat, GMat, GMat>>(GMat)>, "org.opencv.test.retarrayoftupleofgmat7kernel") {};
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G_TYPED_KERNEL(GRetGArraTupleyOfGMat8Kernel, <GArray<std::tuple<GMat, GMat, GMat, GMat, GMat, GMat, GMat, GMat>>(GMat)>, "org.opencv.test.retarrayoftupleofgmat8kernel") {};
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G_TYPED_KERNEL(GRetGArraTupleyOfGMat9Kernel, <GArray<std::tuple<GMat, GMat, GMat, GMat, GMat, GMat, GMat, GMat, GMat>>(GMat)>, "org.opencv.test.retarrayoftupleofgmat9kernel") {};
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G_TYPED_KERNEL(GRetGArraTupleyOfGMat10Kernel, <GArray<std::tuple<GMat, GMat, GMat, GMat, GMat, GMat, GMat, GMat, GMat, GMat>>(GMat)>, "org.opencv.test.retarrayoftupleofgmat10kernel") {};
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G_TYPED_KERNEL_M(GRetGMat2Kernel, <std::tuple<GMat, GMat>(GMat, GMat, GMat)>, "org.opencv.test.retgmat2kernel") {};
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G_TYPED_KERNEL_M(GRetGMat3Kernel, <std::tuple<GMat, GMat, GMat>(GMat, GScalar)>, "org.opencv.test.retgmat3kernel") {};
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G_TYPED_KERNEL_M(GRetGMat4Kernel, <std::tuple<GMat, GMat, GMat, GMat>(GMat, GArray<int>, GScalar)>, "org.opencv.test.retgmat4kernel") {};
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G_TYPED_KERNEL_M(GRetGMat5Kernel, <std::tuple<GMat, GMat, GMat, GMat, GMat>(GMat)>, "org.opencv.test.retgmat5kernel") {};
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G_TYPED_KERNEL_M(GRetGMat6Kernel, <std::tuple<GMat, GMat, GMat, GMat, GMat, GMat>(GMat)>, "org.opencv.test.retgmat6kernel") {};
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G_TYPED_KERNEL_M(GRetGMat7Kernel, <std::tuple<GMat, GMat, GMat, GMat, GMat, GMat, GMat>(GMat)>, "org.opencv.test.retgmat7kernel") {};
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G_TYPED_KERNEL_M(GRetGMat8Kernel, <std::tuple<GMat, GMat, GMat, GMat, GMat, GMat, GMat, GMat>(GMat)>, "org.opencv.test.retgmat8kernel") {};
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G_TYPED_KERNEL_M(GRetGMat9Kernel, <std::tuple<GMat, GMat, GMat, GMat, GMat, GMat, GMat, GMat, GMat>(GMat)>, "org.opencv.test.retgmat9kernel") {};
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G_TYPED_KERNEL_M(GRetGMat10Kernel, <std::tuple<GMat, GMat, GMat, GMat, GMat, GMat, GMat, GMat, GMat, GMat>(GMat)>, "org.opencv.test.retgmat10kernel") {};
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}
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TEST(GAPI_Pipeline, OverloadUnary_MatMat)
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{
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cv::GMat in;
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cv::GComputation comp(in, cv::gapi::bitwise_not(in));
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cv::Mat in_mat = cv::Mat::eye(32, 32, CV_8UC1);
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cv::Mat ref_mat = ~in_mat;
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cv::Mat out_mat;
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comp.apply(in_mat, out_mat);
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EXPECT_EQ(0, cvtest::norm(out_mat, ref_mat, NORM_INF));
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out_mat = cv::Mat();
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auto cc = comp.compile(cv::descr_of(in_mat));
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cc(in_mat, out_mat);
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EXPECT_EQ(0, cvtest::norm(out_mat, ref_mat, NORM_INF));
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}
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TEST(GAPI_Pipeline, OverloadUnary_MatScalar)
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{
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cv::GMat in;
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cv::GComputation comp(in, cv::gapi::sum(in));
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cv::Mat in_mat = cv::Mat::eye(32, 32, CV_8UC1);
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cv::Scalar ref_scl = cv::sum(in_mat);
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cv::Scalar out_scl;
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comp.apply(in_mat, out_scl);
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EXPECT_EQ(out_scl, ref_scl);
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out_scl = cv::Scalar();
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auto cc = comp.compile(cv::descr_of(in_mat));
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cc(in_mat, out_scl);
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EXPECT_EQ(out_scl, ref_scl);
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}
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TEST(GAPI_Pipeline, OverloadBinary_Mat)
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{
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cv::GMat a, b;
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cv::GComputation comp(a, b, cv::gapi::add(a, b));
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cv::Mat in_mat = cv::Mat::eye(32, 32, CV_8UC1);
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cv::Mat ref_mat = (in_mat+in_mat);
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cv::Mat out_mat;
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comp.apply(in_mat, in_mat, out_mat);
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EXPECT_EQ(0, cvtest::norm(out_mat, ref_mat, NORM_INF));
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out_mat = cv::Mat();
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auto cc = comp.compile(cv::descr_of(in_mat), cv::descr_of(in_mat));
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cc(in_mat, in_mat, out_mat);
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EXPECT_EQ(0, cvtest::norm(out_mat, ref_mat, NORM_INF));
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}
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TEST(GAPI_Pipeline, OverloadBinary_Scalar)
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{
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cv::GMat a, b;
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cv::GComputation comp(a, b, cv::gapi::sum(a + b));
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cv::Mat in_mat = cv::Mat::eye(32, 32, CV_8UC1);
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cv::Scalar ref_scl = cv::sum(in_mat+in_mat);
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cv::Scalar out_scl;
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comp.apply(in_mat, in_mat, out_scl);
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EXPECT_EQ(out_scl, ref_scl);
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out_scl = cv::Scalar();
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auto cc = comp.compile(cv::descr_of(in_mat), cv::descr_of(in_mat));
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cc(in_mat, in_mat, out_scl);
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EXPECT_EQ(out_scl, ref_scl);
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}
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TEST(GAPI_Pipeline, Sharpen)
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{
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const cv::Size sz_in (1280, 720);
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const cv::Size sz_out( 640, 480);
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cv::Mat in_mat (sz_in, CV_8UC3);
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in_mat = cv::Scalar(128, 33, 53);
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cv::Mat out_mat(sz_out, CV_8UC3);
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cv::Mat out_mat_y;
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cv::Mat out_mat_ocv(sz_out, CV_8UC3);
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float sharpen_coeffs[] = {
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0.0f, -1.f, 0.0f,
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-1.0f, 5.f, -1.0f,
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0.0f, -1.f, 0.0f
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};
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cv::Mat sharpen_kernel(3, 3, CV_32F, sharpen_coeffs);
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// G-API code //////////////////////////////////////////////////////////////
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cv::GMat in;
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auto vga = cv::gapi::resize(in, sz_out);
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auto yuv = cv::gapi::RGB2YUV(vga);
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auto yuv_p = cv::gapi::split3(yuv);
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auto y_sharp = cv::gapi::filter2D(std::get<0>(yuv_p), -1, sharpen_kernel);
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auto yuv_new = cv::gapi::merge3(y_sharp, std::get<1>(yuv_p), std::get<2>(yuv_p));
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auto out = cv::gapi::YUV2RGB(yuv_new);
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cv::GComputation c(cv::GIn(in), cv::GOut(y_sharp, out));
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c.apply(cv::gin(in_mat), cv::gout(out_mat_y, out_mat));
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// OpenCV code /////////////////////////////////////////////////////////////
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{
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cv::Mat smaller;
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cv::resize(in_mat, smaller, sz_out);
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cv::Mat yuv_mat;
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cv::cvtColor(smaller, yuv_mat, cv::COLOR_RGB2YUV);
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std::vector<cv::Mat> yuv_planar(3);
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cv::split(yuv_mat, yuv_planar);
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cv::filter2D(yuv_planar[0], yuv_planar[0], -1, sharpen_kernel);
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cv::merge(yuv_planar, yuv_mat);
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cv::cvtColor(yuv_mat, out_mat_ocv, cv::COLOR_YUV2RGB);
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}
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// Comparison //////////////////////////////////////////////////////////////
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{
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cv::Mat diff = out_mat_ocv != out_mat;
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std::vector<cv::Mat> diffBGR(3);
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cv::split(diff, diffBGR);
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EXPECT_EQ(0, cvtest::norm(diffBGR[0], NORM_INF));
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EXPECT_EQ(0, cvtest::norm(diffBGR[1], NORM_INF));
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EXPECT_EQ(0, cvtest::norm(diffBGR[2], NORM_INF));
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}
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// Metadata check /////////////////////////////////////////////////////////
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{
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auto cc = c.compile(cv::descr_of(in_mat));
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auto metas = cc.outMetas();
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ASSERT_EQ(2u, metas.size());
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auto out_y_meta = cv::util::get<cv::GMatDesc>(metas[0]);
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auto out_meta = cv::util::get<cv::GMatDesc>(metas[1]);
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// Y-output
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EXPECT_EQ(CV_8U, out_y_meta.depth);
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EXPECT_EQ(1, out_y_meta.chan);
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EXPECT_EQ(640, out_y_meta.size.width);
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EXPECT_EQ(480, out_y_meta.size.height);
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// Final output
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EXPECT_EQ(CV_8U, out_meta.depth);
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EXPECT_EQ(3, out_meta.chan);
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EXPECT_EQ(640, out_meta.size.width);
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EXPECT_EQ(480, out_meta.size.height);
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}
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}
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TEST(GAPI_Pipeline, CustomRGB2YUV)
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{
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const cv::Size sz(1280, 720);
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// BEWARE:
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//
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// std::vector<cv::Mat> out_mats_cv(3, cv::Mat(sz, CV_8U))
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//
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// creates a vector of 3 elements pointing to the same Mat!
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// FIXME: Make a G-API check for that
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const int INS = 3;
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std::vector<cv::Mat> in_mats(INS);
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for (auto i : ade::util::iota(INS))
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{
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in_mats[i].create(sz, CV_8U);
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cv::randu(in_mats[i], cv::Scalar::all(0), cv::Scalar::all(255));
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}
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const int OUTS = 3;
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std::vector<cv::Mat> out_mats_cv(OUTS);
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std::vector<cv::Mat> out_mats_gapi(OUTS);
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for (auto i : ade::util::iota(OUTS))
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{
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out_mats_cv [i].create(sz, CV_8U);
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out_mats_gapi[i].create(sz, CV_8U);
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}
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// G-API code //////////////////////////////////////////////////////////////
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{
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cv::GMat r, g, b;
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cv::GMat y = 0.299f*r + 0.587f*g + 0.114f*b;
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cv::GMat u = 0.492f*(b - y);
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cv::GMat v = 0.877f*(r - y);
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cv::GComputation customCvt({r, g, b}, {y, u, v});
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customCvt.apply(in_mats, out_mats_gapi);
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}
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// OpenCV code /////////////////////////////////////////////////////////////
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{
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cv::Mat r = in_mats[0], g = in_mats[1], b = in_mats[2];
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cv::Mat y = 0.299f*r + 0.587f*g + 0.114f*b;
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cv::Mat u = 0.492f*(b - y);
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cv::Mat v = 0.877f*(r - y);
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out_mats_cv[0] = y;
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out_mats_cv[1] = u;
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out_mats_cv[2] = v;
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}
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// Comparison //////////////////////////////////////////////////////////////
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{
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const auto diff = [](cv::Mat m1, cv::Mat m2, int t) {
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return cv::abs(m1-m2) > t;
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};
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// FIXME: Not bit-accurate even now!
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cv::Mat
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diff_y = diff(out_mats_cv[0], out_mats_gapi[0], 2),
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diff_u = diff(out_mats_cv[1], out_mats_gapi[1], 2),
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diff_v = diff(out_mats_cv[2], out_mats_gapi[2], 2);
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EXPECT_EQ(0, cvtest::norm(diff_y, NORM_INF));
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EXPECT_EQ(0, cvtest::norm(diff_u, NORM_INF));
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EXPECT_EQ(0, cvtest::norm(diff_v, NORM_INF));
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}
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}
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TEST(GAPI_Pipeline, PipelineWithInvalidKernel)
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{
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cv::GMat in, out;
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cv::Mat in_mat(500, 500, CV_8UC1), out_mat;
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out = GInvalidResize::on(in, cv::Size(300, 300), 0.0, 0.0, cv::INTER_LINEAR);
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const auto pkg = cv::gapi::kernels<GOCVInvalidResize>();
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cv::GComputation comp(cv::GIn(in), cv::GOut(out));
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EXPECT_THROW(comp.apply(in_mat, out_mat, cv::compile_args(pkg)), std::logic_error);
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}
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TEST(GAPI_Pipeline, InvalidOutputComputation)
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{
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cv::GMat in1, out1, out2, out3;
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std::tie(out1, out2, out2) = cv::gapi::split3(in1);
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cv::GComputation c({in1}, {out1, out2, out3});
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cv::Mat in_mat;
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cv::Mat out_mat1, out_mat2, out_mat3, out_mat4;
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std::vector<cv::Mat> u_outs = {out_mat1, out_mat2, out_mat3, out_mat4};
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std::vector<cv::Mat> u_ins = {in_mat};
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EXPECT_THROW(c.apply(u_ins, u_outs), std::logic_error);
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}
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TEST(GAPI_Pipeline, PipelineAllocatingKernel)
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{
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cv::GMat in, out;
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cv::Mat in_mat(500, 500, CV_8UC1), out_mat;
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out = GReallocatingCopy::on(in);
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|
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const auto pkg = cv::gapi::kernels<GOCVReallocatingCopy>();
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cv::GComputation comp(cv::GIn(in), cv::GOut(out));
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|
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EXPECT_THROW(comp.apply(in_mat, out_mat, cv::compile_args(pkg)), std::logic_error);
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}
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|
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TEST(GAPI_Pipeline, CreateKernelImplFromLambda)
|
|
{
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cv::Size size(300, 300);
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int type = CV_8UC3;
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cv::Mat in_mat(size, type);
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cv::randu(in_mat, cv::Scalar::all(0), cv::Scalar::all(255));
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int value = 5;
|
|
|
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cv::GMat in;
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cv::GMat out = GCustom::on(in);
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cv::GComputation comp(in, out);
|
|
|
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// OpenCV //////////////////////////////////////////////////////////////////////////
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auto ref_mat = in_mat + value;
|
|
|
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// G-API //////////////////////////////////////////////////////////////////////////
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auto impl = cv::gapi::cpu::ocv_kernel<GCustom>([&value](const cv::Mat& src, cv::Mat& dst)
|
|
{
|
|
dst = src + value;
|
|
});
|
|
|
|
cv::Mat out_mat;
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|
auto pkg = cv::gapi::kernels(impl);
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|
comp.apply(in_mat, out_mat, cv::compile_args(pkg));
|
|
|
|
EXPECT_EQ(0, cv::norm(out_mat, ref_mat));
|
|
}
|
|
|
|
TEST(GAPI_Pipeline, ReplaceDefaultByLambda)
|
|
{
|
|
cv::Size size(300, 300);
|
|
int type = CV_8UC3;
|
|
cv::Mat in_mat1(size, type);
|
|
cv::Mat in_mat2(size, type);
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|
cv::randu(in_mat2, cv::Scalar::all(0), cv::Scalar::all(255));
|
|
cv::randu(in_mat1, cv::Scalar::all(0), cv::Scalar::all(255));
|
|
|
|
cv::GMat in1, in2;
|
|
cv::GMat out = cv::gapi::add(in1, in2);
|
|
cv::GComputation comp(cv::GIn(in1, in2), cv::GOut(out));
|
|
|
|
// OpenCV //////////////////////////////////////////////////////////////////////////
|
|
cv::Mat ref_mat = in_mat1 + in_mat2;
|
|
|
|
|
|
// G-API //////////////////////////////////////////////////////////////////////////
|
|
bool is_called = false;
|
|
auto impl = cv::gapi::cpu::ocv_kernel<cv::gapi::core::GAdd>([&is_called]
|
|
(const cv::Mat& src1, const cv::Mat& src2, int, cv::Mat& dst)
|
|
{
|
|
is_called = true;
|
|
dst = src1 + src2;
|
|
});
|
|
|
|
cv::Mat out_mat;
|
|
auto pkg = cv::gapi::kernels(impl);
|
|
comp.apply(cv::gin(in_mat1, in_mat2), cv::gout(out_mat), cv::compile_args(pkg));
|
|
|
|
EXPECT_EQ(0, cv::norm(out_mat, ref_mat));
|
|
EXPECT_TRUE(is_called);
|
|
}
|
|
|
|
struct AddImpl
|
|
{
|
|
void operator()(const cv::Mat& in1, const cv::Mat& in2, int, cv::Mat& out)
|
|
{
|
|
out = in1 + in2;
|
|
is_called = true;
|
|
}
|
|
|
|
bool is_called = false;
|
|
};
|
|
|
|
TEST(GAPI_Pipeline, ReplaceDefaultByFunctor)
|
|
{
|
|
cv::Size size(300, 300);
|
|
int type = CV_8UC3;
|
|
cv::Mat in_mat1(size, type);
|
|
cv::Mat in_mat2(size, type);
|
|
cv::randu(in_mat2, cv::Scalar::all(0), cv::Scalar::all(255));
|
|
cv::randu(in_mat1, cv::Scalar::all(0), cv::Scalar::all(255));
|
|
|
|
cv::GMat in1, in2;
|
|
cv::GMat out = cv::gapi::add(in1, in2);
|
|
cv::GComputation comp(cv::GIn(in1, in2), cv::GOut(out));
|
|
|
|
// OpenCV //////////////////////////////////////////////////////////////////////////
|
|
cv::Mat ref_mat = in_mat1 + in_mat2;
|
|
|
|
|
|
// G-API ///////////////////////////////////////////////////////////////////////////
|
|
AddImpl f;
|
|
EXPECT_FALSE(f.is_called);
|
|
auto impl = cv::gapi::cpu::ocv_kernel<cv::gapi::core::GAdd>(f);
|
|
|
|
cv::Mat out_mat;
|
|
auto pkg = cv::gapi::kernels(impl);
|
|
comp.apply(cv::gin(in_mat1, in_mat2), cv::gout(out_mat), cv::compile_args(pkg));
|
|
|
|
EXPECT_EQ(0, cv::norm(out_mat, ref_mat));
|
|
EXPECT_TRUE(f.is_called);
|
|
}
|
|
|
|
TEST(GAPI_Pipeline, GraphOutputIs1DMat)
|
|
{
|
|
int dim = 100;
|
|
cv::Mat in_mat(1, 1, CV_8UC3);
|
|
cv::Mat out_mat;
|
|
|
|
cv::GMat in;
|
|
auto cc = cv::GComputation(in, GZeros::on(in, cv::GMatDesc(CV_8U, {dim})))
|
|
.compile(cv::descr_of(in_mat), cv::compile_args(cv::gapi::kernels<GOCVZeros>()));
|
|
|
|
// NB: Computation is able to write 1D output cv::Mat to empty out_mat.
|
|
ASSERT_NO_THROW(cc(cv::gin(in_mat), cv::gout(out_mat)));
|
|
ASSERT_EQ(1, out_mat.size.dims());
|
|
ASSERT_EQ(dim, out_mat.size[0]);
|
|
|
|
// NB: Computation is able to write 1D output cv::Mat
|
|
// to pre-allocated with the same meta out_mat.
|
|
ASSERT_NO_THROW(cc(cv::gin(in_mat), cv::gout(out_mat)));
|
|
ASSERT_EQ(1, out_mat.size.dims());
|
|
ASSERT_EQ(dim, out_mat.size[0]);
|
|
}
|
|
|
|
TEST(GAPI_Pipeline, 1DMatBetweenIslands)
|
|
{
|
|
int dim = 100;
|
|
cv::Mat in_mat(1, 1, CV_8UC3);
|
|
cv::Mat out_mat;
|
|
|
|
cv::Mat ref_mat({dim}, CV_8U);
|
|
ref_mat.dims = 1;
|
|
ref_mat.setTo(0);
|
|
|
|
cv::GMat in;
|
|
auto out = cv::gapi::copy(GZeros::on(cv::gapi::copy(in), cv::GMatDesc(CV_8U, {dim})));
|
|
auto cc = cv::GComputation(in, out)
|
|
.compile(cv::descr_of(in_mat), cv::compile_args(cv::gapi::kernels<GOCVZeros>()));
|
|
|
|
cc(cv::gin(in_mat), cv::gout(out_mat));
|
|
|
|
EXPECT_EQ(0, cv::norm(out_mat, ref_mat));
|
|
}
|
|
|
|
TEST(GAPI_Pipeline, 1DMatWithinSingleIsland)
|
|
{
|
|
int dim = 100;
|
|
cv::Size blur_sz(3, 3);
|
|
cv::Mat in_mat(10, 10, CV_8UC3);
|
|
cv::randu(in_mat, 0, 255);
|
|
cv::Mat out_mat;
|
|
|
|
cv::Mat ref_mat({dim}, CV_8U);
|
|
ref_mat.dims = 1;
|
|
ref_mat.setTo(0);
|
|
|
|
cv::GMat in;
|
|
auto out = cv::gapi::blur(
|
|
GZeros::on(cv::gapi::blur(in, blur_sz), cv::GMatDesc(CV_8U, {dim})), blur_sz);
|
|
auto cc = cv::GComputation(in, out)
|
|
.compile(cv::descr_of(in_mat), cv::compile_args(cv::gapi::kernels<GOCVZeros>()));
|
|
|
|
cc(cv::gin(in_mat), cv::gout(out_mat));
|
|
|
|
EXPECT_EQ(0, cv::norm(out_mat, ref_mat));
|
|
}
|
|
|
|
TEST(GAPI_Pipeline, BranchesExecutedInParallel)
|
|
{
|
|
cv::GMat in;
|
|
// NB: cv::gapi::copy used to prevent fusing OCV backend operations
|
|
// into the single island where they will be executed in turn
|
|
auto out0 = GBusyWait::on(cv::gapi::copy(in), 1000u /*1sec*/);
|
|
auto out1 = GBusyWait::on(cv::gapi::copy(in), 1000u /*1sec*/);
|
|
auto out2 = GBusyWait::on(cv::gapi::copy(in), 1000u /*1sec*/);
|
|
auto out3 = GBusyWait::on(cv::gapi::copy(in), 1000u /*1sec*/);
|
|
|
|
cv::GComputation comp(cv::GIn(in), cv::GOut(out0,out1,out2,out3));
|
|
cv::Mat in_mat = cv::Mat::eye(32, 32, CV_8UC1);
|
|
cv::Mat out_mat0, out_mat1, out_mat2, out_mat3;
|
|
|
|
using namespace std::chrono;
|
|
auto s = high_resolution_clock::now();
|
|
comp.apply(cv::gin(in_mat), cv::gout(out_mat0, out_mat1, out_mat2, out_mat3),
|
|
cv::compile_args(cv::use_threaded_executor(4u),
|
|
cv::gapi::kernels<GOCVBusyWait>()));
|
|
auto e = high_resolution_clock::now();
|
|
const auto elapsed_in_ms = duration_cast<milliseconds>(e-s).count();;
|
|
|
|
EXPECT_GE(1200u, elapsed_in_ms);
|
|
}
|
|
|
|
} // namespace opencv_test
|