用java写一个opencv4.7识别车辆的神经网络模型代码

以下是一个简单的Java代码示例，使用OpenCV 4.7和Java深度学习框架DL4J来构建和训练车辆识别神经网络模型：

import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.conf.layers.PoolingLayer;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.core.Size;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;

import java.io.IOException;

public class VehicleRecognition {
    public static void main(String[] args) throws IOException {
        // 加载OpenCV库
        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);

        // 加载车辆数据集
        DataSetIterator dataset = new MnistDataSetIterator(10, true, 123);

        // 构建神经网络
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                .seed(123)
                .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                .updater(new Nesterovs(0.006, 0.9))
                .list()
                .layer(new ConvolutionLayer.Builder(5, 5)
                        .nIn(1)
                        .stride(1, 1)
                        .nOut(20)
                        .activation(Activation.IDENTITY)
                        .weightInit(WeightInit.XAVIER)
                        .build())
                .layer(new SubsamplingLayer.Builder(PoolingType.MAX)
                        .kernelSize(2, 2)
                        .stride(2, 2)
                        .build())
                .layer(new ConvolutionLayer.Builder(5, 5)
                        .stride(1, 1)
                        .nOut(50)
                        .activation(Activation.IDENTITY)
                        .weightInit(WeightInit.XAVIER)
                        .build())
                .layer(new SubsamplingLayer.Builder(PoolingType.MAX)
                        .kernelSize(2, 2)
                        .stride(2, 2)
                        .build())
                .layer(new DenseLayer.Builder().activation(Activation.RELU)
                        .nOut(500).build())
                .layer(new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
                        .nOut(10)
                        .activation(Activation.SOFTMAX)
                        .build())
                .setInputType(InputType.convolutionalFlat(28, 28, 1))
                .backprop(true).pretrain(false).build();

        // 训练神经网络
        MultiLayerNetwork net = new MultiLayerNetwork(conf);
        net.init();

        net.setListeners(new ScoreIterationListener(10));

        while(dataset.hasNext()) {
            DataSet next = dataset.next();
            net.fit(next);
        }

        // 加载测试图片
        Mat testImage = Imgcodecs.imread("test.jpg", Imgcodecs.IMREAD_GRAYSCALE);

        // 对图片进行预处理
        Imgproc.resize(testImage, testImage, new Size(28, 28));
        testImage.convertTo(testImage, CvType.CV_32FC1);
        testImage = Core.divide(testImage, new Scalar(255.0));

        // 进行预测
        INDArray input = Nd4j.create(testImage.reshape(1, 784).data().asFloat());
        INDArray output = net.output(input);

        // 输出结果
        System.out.println("Prediction: " + output);
    }
}

需要注意的是，这段代码只是一个简单的示例，实际的车辆识别系统需要更加复杂的模型和数据集来实现准确的识别。