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https://github.com/PhotonVision/photonvision
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ColourShape benchmark and moar docs (#11)
* Added benchmark * Rebase * [Server] ColouredShapePipeline Benchmarks and Documentation * [Server] ColouredShapePipeline Benchmarks and Documentation * Added benchmark * Rebase * [Server] ColouredShapePipeline Benchmarks and Documentation * [Server] ColouredShapePipeline Benchmarks and Documentation * [Server] ColouredShapePipeline Benchmarks and Documentation * [CSP] Rebase off master * [CSP] Remove unused variables * [CSP] Make circles Mat private and final
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/*
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* Copyright (C) 2020 Photon Vision.
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*
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* This program is free software: you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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*
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* This program is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with this program. If not, see <https://www.gnu.org/licenses/>.
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*/
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package org.photonvision.common;
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/*
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* Copyright (C) 2020 Photon Vision.
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*
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* This program is free software: you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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*
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* This program is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with this program. If not, see <https://www.gnu.org/licenses/>.
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*/
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import java.util.ArrayList;
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import java.util.Collections;
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import java.util.List;
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import org.junit.jupiter.api.BeforeAll;
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import org.junit.jupiter.api.Test;
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import org.photonvision.common.util.TestUtils;
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import org.photonvision.common.util.math.MathUtils;
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import org.photonvision.common.util.numbers.NumberListUtils;
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import org.photonvision.vision.frame.FrameProvider;
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import org.photonvision.vision.frame.provider.FileFrameProvider;
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import org.photonvision.vision.opencv.CVMat;
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import org.photonvision.vision.opencv.ContourGroupingMode;
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import org.photonvision.vision.opencv.ContourIntersectionDirection;
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import org.photonvision.vision.opencv.ContourShape;
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import org.photonvision.vision.pipeline.CVPipeline;
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import org.photonvision.vision.pipeline.ColoredShapePipeline;
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import org.photonvision.vision.pipeline.result.CVPipelineResult;
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/** Various tests that check performance on long-running tasks (i.e. a pipeline) */
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public class ShapeBenchmarkTest {
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@BeforeAll
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public static void init() {
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TestUtils.loadLibraries();
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}
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@Test
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public void Shape240pBenchmark() {
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var pipeline = new ColoredShapePipeline();
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pipeline.getSettings().hsvHue.set(60, 100);
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pipeline.getSettings().hsvSaturation.set(100, 255);
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pipeline.getSettings().hsvValue.set(190, 255);
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pipeline.getSettings().outputShowThresholded = true;
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pipeline.getSettings().outputShowMultipleTargets = true;
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pipeline.getSettings().contourGroupingMode = ContourGroupingMode.Single;
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pipeline.getSettings().contourIntersection = ContourIntersectionDirection.Up;
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pipeline.getSettings().desiredShape = ContourShape.Custom;
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pipeline.getSettings().allowableThreshold = 10;
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pipeline.getSettings().accuracyPercentage = 30.0;
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var frameProvider =
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new FileFrameProvider(
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TestUtils.getWPIImagePath(TestUtils.WPI2019Image.kCargoSideStraightDark72in),
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TestUtils.WPI2019Image.FOV);
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frameProvider.setImageReloading(true);
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benchmarkPipeline(frameProvider, pipeline, 5);
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}
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@Test
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public void Shape480pBenchmark() {
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var pipeline = new ColoredShapePipeline();
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pipeline.getSettings().hsvHue.set(60, 100);
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pipeline.getSettings().hsvSaturation.set(100, 255);
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pipeline.getSettings().hsvValue.set(190, 255);
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pipeline.getSettings().outputShowThresholded = true;
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pipeline.getSettings().outputShowMultipleTargets = true;
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pipeline.getSettings().contourGroupingMode = ContourGroupingMode.Single;
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pipeline.getSettings().contourIntersection = ContourIntersectionDirection.Up;
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pipeline.getSettings().desiredShape = ContourShape.Custom;
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pipeline.getSettings().allowableThreshold = 10;
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pipeline.getSettings().accuracyPercentage = 30.0;
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var frameProvider =
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new FileFrameProvider(
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TestUtils.getWPIImagePath(TestUtils.WPI2020Image.kBlueGoal_084in_Center),
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TestUtils.WPI2020Image.FOV);
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frameProvider.setImageReloading(true);
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benchmarkPipeline(frameProvider, pipeline, 5);
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}
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@Test
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public void Shape720pBenchmark() {
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var pipeline = new ColoredShapePipeline();
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pipeline.getSettings().hsvHue.set(60, 100);
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pipeline.getSettings().hsvSaturation.set(100, 255);
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pipeline.getSettings().hsvValue.set(190, 255);
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pipeline.getSettings().outputShowThresholded = true;
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pipeline.getSettings().outputShowMultipleTargets = true;
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pipeline.getSettings().contourGroupingMode = ContourGroupingMode.Single;
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pipeline.getSettings().contourIntersection = ContourIntersectionDirection.Up;
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pipeline.getSettings().desiredShape = ContourShape.Custom;
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pipeline.getSettings().allowableThreshold = 10;
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pipeline.getSettings().accuracyPercentage = 30.0;
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var frameProvider =
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new FileFrameProvider(
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TestUtils.getWPIImagePath(TestUtils.WPI2020Image.kBlueGoal_084in_Center_720p),
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TestUtils.WPI2020Image.FOV);
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frameProvider.setImageReloading(true);
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benchmarkPipeline(frameProvider, pipeline, 5);
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}
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@Test
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public void Shape1920x1440Benchmark() {
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var pipeline = new ColoredShapePipeline();
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pipeline.getSettings().hsvHue.set(60, 100);
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pipeline.getSettings().hsvSaturation.set(100, 255);
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pipeline.getSettings().hsvValue.set(190, 255);
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pipeline.getSettings().outputShowThresholded = true;
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pipeline.getSettings().outputShowMultipleTargets = true;
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pipeline.getSettings().contourGroupingMode = ContourGroupingMode.Single;
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pipeline.getSettings().contourIntersection = ContourIntersectionDirection.Up;
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pipeline.getSettings().desiredShape = ContourShape.Custom;
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pipeline.getSettings().allowableThreshold = 10;
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pipeline.getSettings().accuracyPercentage = 30.0;
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var frameProvider =
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new FileFrameProvider(
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TestUtils.getWPIImagePath(TestUtils.WPI2019Image.kCargoStraightDark72in_HighRes),
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TestUtils.WPI2019Image.FOV);
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frameProvider.setImageReloading(true);
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benchmarkPipeline(frameProvider, pipeline, 5);
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}
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private static <P extends CVPipeline> void benchmarkPipeline(
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FrameProvider frameProvider, P pipeline, int secondsToRun) {
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CVMat.enablePrint(false);
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// warmup for 5 loops.
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System.out.println("Warming up for 5 loops...");
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for (int i = 0; i < 5; i++) {
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pipeline.run(frameProvider.get());
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}
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final List<Double> processingTimes = new ArrayList<>();
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final List<Double> latencyTimes = new ArrayList<>();
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var frameProps = frameProvider.get().frameStaticProperties;
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// begin benchmark
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System.out.println(
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"Beginning "
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+ secondsToRun
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+ " second benchmark at resolution "
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+ frameProps.imageWidth
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+ "x"
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+ frameProps.imageHeight);
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var benchmarkStartMillis = System.currentTimeMillis();
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do {
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CVPipelineResult pipelineResult = pipeline.run(frameProvider.get());
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pipelineResult.release();
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processingTimes.add(pipelineResult.processingMillis);
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latencyTimes.add(pipelineResult.getLatencyMillis());
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} while (System.currentTimeMillis() - benchmarkStartMillis < secondsToRun * 1000);
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System.out.println("Benchmark complete.");
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var processingMin = Collections.min(processingTimes);
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var processingMean = NumberListUtils.mean(processingTimes);
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var processingMax = Collections.max(processingTimes);
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var latencyMin = Collections.min(latencyTimes);
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var latencyMean = NumberListUtils.mean(latencyTimes);
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var latencyMax = Collections.max(latencyTimes);
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String processingResult =
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"Processing times - "
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+ "Min: "
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+ MathUtils.roundTo(processingMin, 3)
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+ "ms ("
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+ MathUtils.roundTo(1000 / processingMin, 3)
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+ " FPS), "
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+ "Mean: "
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+ MathUtils.roundTo(processingMean, 3)
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+ "ms ("
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+ MathUtils.roundTo(1000 / processingMean, 3)
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+ " FPS), "
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+ "Max: "
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+ MathUtils.roundTo(processingMax, 3)
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+ "ms ("
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+ MathUtils.roundTo(1000 / processingMax, 3)
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+ " FPS)";
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System.out.println(processingResult);
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String latencyResult =
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"Latency times - "
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+ "Min: "
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+ MathUtils.roundTo(latencyMin, 3)
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+ "ms ("
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+ MathUtils.roundTo(1000 / latencyMin, 3)
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+ " FPS), "
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+ "Mean: "
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+ MathUtils.roundTo(latencyMean, 3)
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+ "ms ("
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+ MathUtils.roundTo(1000 / latencyMean, 3)
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+ " FPS), "
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+ "Max: "
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+ MathUtils.roundTo(latencyMax, 3)
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+ "ms ("
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+ MathUtils.roundTo(1000 / latencyMax, 3)
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+ " FPS)";
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System.out.println(latencyResult);
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}
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}
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