content: package org.opencv.test.dnn;
  
  import java.io.File;
  import java.io.FileInputStream;
  import java.io.IOException;
  import java.util.ArrayList;
  import java.util.List;
  import org.opencv.core.Core;
  import org.opencv.core.Mat;
  import org.opencv.core.MatOfInt;
  import org.opencv.core.MatOfFloat;
  import org.opencv.core.MatOfByte;
  import org.opencv.core.Scalar;
  import org.opencv.core.Size;
  import org.opencv.dnn.DictValue;
  import org.opencv.dnn.Dnn;
  import org.opencv.dnn.Layer;
  import org.opencv.dnn.Net;
  import org.opencv.imgcodecs.Imgcodecs;
  import org.opencv.imgproc.Imgproc;
  import org.opencv.test.OpenCVTestCase;
  
  /*
  *  regression test for #12324,
  *    testing various java.util.List invocations,
  *    which use the LIST_GET macro
  */
  
  public class DnnListRegressionTest extends OpenCVTestCase {
  
      private final static String ENV_OPENCV_DNN_TEST_DATA_PATH = "OPENCV_DNN_TEST_DATA_PATH";
  
      private final static String ENV_OPENCV_TEST_DATA_PATH = "OPENCV_TEST_DATA_PATH";
  
      String modelFileName = "";
      String sourceImageFile = "";
  
      Net net;
  
      @Override
      protected void setUp() throws Exception {
          super.setUp();
  
          String envDnnTestDataPath = System.getenv(ENV_OPENCV_DNN_TEST_DATA_PATH);
  
          if(envDnnTestDataPath == null){
              isTestCaseEnabled = false;
              return;
          }
  
          File dnnTestDataPath = new File(envDnnTestDataPath);
          modelFileName =  new File(dnnTestDataPath, "dnn/tensorflow_inception_graph.pb").toString();
  
          String envTestDataPath = System.getenv(ENV_OPENCV_TEST_DATA_PATH);
  
          if(envTestDataPath == null) throw new Exception(ENV_OPENCV_TEST_DATA_PATH + " has to be defined!");
  
          File testDataPath = new File(envTestDataPath);
  
          File f = new File(testDataPath, "dnn/grace_hopper_227.png");
          sourceImageFile = f.toString();
          if(!f.exists()) throw new Exception("Test image is missing: " + sourceImageFile);
  
          net = Dnn.readNetFromTensorflow(modelFileName);
  
          Mat image = Imgcodecs.imread(sourceImageFile);
          assertNotNull("Loading image from file failed!", image);
  
          Mat inputBlob = Dnn.blobFromImage(image, 1.0, new Size(224, 224), new Scalar(0), true, true);
          assertNotNull("Converting image to blob failed!", inputBlob);
  
          net.setInput(inputBlob, "input");
      }
  
      public void testSetInputsNames() {
          List<String> inputs = new ArrayList();
          inputs.add("input");
          try {
              net.setInputsNames(inputs);
          } catch(Exception e) {
              fail("Net setInputsNames failed: " + e.getMessage());
          }
      }
  
      public void testForward() {
          List<Mat> outs = new ArrayList();
          List<String> outNames = new ArrayList();
          outNames.add("softmax2");
          try {
              net.forward(outs,outNames);
          } catch(Exception e) {
              fail("Net forward failed: " + e.getMessage());
          }
      }
  
      public void testGetMemoryConsumption() {
          int layerId = 1;
          List<MatOfInt> netInputShapes = new ArrayList();
          netInputShapes.add(new MatOfInt(1, 3, 224, 224));
          long[] weights=null;
          long[] blobs=null;
          try {
              net.getMemoryConsumption(layerId, netInputShapes, weights, blobs);
          } catch(Exception e) {
              fail("Net getMemoryConsumption failed: " + e.getMessage());
          }
      }
  
      public void testGetFLOPS() {
          int layerId = 1;
          List<MatOfInt> netInputShapes = new ArrayList();
          netInputShapes.add(new MatOfInt(1, 3, 224, 224));
          try {
              net.getFLOPS(layerId, netInputShapes);
          } catch(Exception e) {
              fail("Net getFLOPS failed: " + e.getMessage());
          }
      }
  }
  