149 lines
4.8 KiB
Java
149 lines
4.8 KiB
Java
package org.opencv.test.dnn;
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import java.io.File;
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import java.io.FileInputStream;
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import java.io.IOException;
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import java.util.ArrayList;
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import java.util.List;
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import org.opencv.core.Core;
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import org.opencv.core.Mat;
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import org.opencv.core.MatOfFloat;
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import org.opencv.core.MatOfByte;
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import org.opencv.core.Scalar;
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import org.opencv.core.Size;
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import org.opencv.dnn.DictValue;
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import org.opencv.dnn.Dnn;
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import org.opencv.dnn.Layer;
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import org.opencv.dnn.Net;
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import org.opencv.imgcodecs.Imgcodecs;
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import org.opencv.imgproc.Imgproc;
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import org.opencv.test.OpenCVTestCase;
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public class DnnTensorFlowTest extends OpenCVTestCase {
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private final static String ENV_OPENCV_DNN_TEST_DATA_PATH = "OPENCV_DNN_TEST_DATA_PATH";
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private final static String ENV_OPENCV_TEST_DATA_PATH = "OPENCV_TEST_DATA_PATH";
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String modelFileName = "";
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String sourceImageFile = "";
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Net net;
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private static void normAssert(Mat ref, Mat test) {
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final double l1 = 1e-5;
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final double lInf = 1e-4;
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double normL1 = Core.norm(ref, test, Core.NORM_L1) / ref.total();
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double normLInf = Core.norm(ref, test, Core.NORM_INF) / ref.total();
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assertTrue(normL1 < l1);
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assertTrue(normLInf < lInf);
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}
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@Override
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protected void setUp() throws Exception {
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super.setUp();
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String envDnnTestDataPath = System.getenv(ENV_OPENCV_DNN_TEST_DATA_PATH);
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if(envDnnTestDataPath == null){
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isTestCaseEnabled = false;
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return;
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}
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File dnnTestDataPath = new File(envDnnTestDataPath);
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modelFileName = new File(dnnTestDataPath, "dnn/tensorflow_inception_graph.pb").toString();
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String envTestDataPath = System.getenv(ENV_OPENCV_TEST_DATA_PATH);
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if(envTestDataPath == null) throw new Exception(ENV_OPENCV_TEST_DATA_PATH + " has to be defined!");
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File testDataPath = new File(envTestDataPath);
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File f = new File(testDataPath, "dnn/grace_hopper_227.png");
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sourceImageFile = f.toString();
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if(!f.exists()) throw new Exception("Test image is missing: " + sourceImageFile);
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net = Dnn.readNetFromTensorflow(modelFileName);
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}
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public void testGetLayerTypes() {
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List<String> layertypes = new ArrayList();
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net.getLayerTypes(layertypes);
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assertFalse("No layer types returned!", layertypes.isEmpty());
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}
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public void testGetLayer() {
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List<String> layernames = net.getLayerNames();
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assertFalse("Test net returned no layers!", layernames.isEmpty());
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String testLayerName = layernames.get(0);
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DictValue layerId = new DictValue(testLayerName);
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assertEquals("DictValue did not return the string, which was used in constructor!", testLayerName, layerId.getStringValue());
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Layer layer = net.getLayer(layerId);
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assertEquals("Layer name does not match the expected value!", testLayerName, layer.get_name());
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}
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public void checkInceptionNet(Net net)
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{
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Mat image = Imgcodecs.imread(sourceImageFile);
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assertNotNull("Loading image from file failed!", image);
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Mat inputBlob = Dnn.blobFromImage(image, 1.0, new Size(224, 224), new Scalar(0), true, true);
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assertNotNull("Converting image to blob failed!", inputBlob);
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net.setInput(inputBlob, "input");
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Mat result = new Mat();
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try {
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net.setPreferableBackend(Dnn.DNN_BACKEND_OPENCV);
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result = net.forward("softmax2");
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}
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catch (Exception e) {
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fail("DNN forward failed: " + e.getMessage());
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}
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assertNotNull("Net returned no result!", result);
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result = result.reshape(1, 1);
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Core.MinMaxLocResult minmax = Core.minMaxLoc(result);
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assertEquals("Wrong prediction", (int)minmax.maxLoc.x, 866);
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Mat top5RefScores = new MatOfFloat(new float[] {
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0.63032645f, 0.2561979f, 0.032181446f, 0.015721032f, 0.014785315f
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}).reshape(1, 1);
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Core.sort(result, result, Core.SORT_DESCENDING);
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normAssert(result.colRange(0, 5), top5RefScores);
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}
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public void testTestNetForward() {
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checkInceptionNet(net);
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}
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public void testReadFromBuffer() {
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File modelFile = new File(modelFileName);
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byte[] modelBuffer = new byte[ (int)modelFile.length() ];
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try {
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FileInputStream fis = new FileInputStream(modelFile);
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fis.read(modelBuffer);
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fis.close();
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} catch (IOException e) {
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fail("Failed to read a model: " + e.getMessage());
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}
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net = Dnn.readNetFromTensorflow(new MatOfByte(modelBuffer));
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checkInceptionNet(net);
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}
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public void testGetAvailableTargets() {
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List<Integer> targets = Dnn.getAvailableTargets(Dnn.DNN_BACKEND_OPENCV);
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assertTrue(targets.contains(Dnn.DNN_TARGET_CPU));
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}
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}
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