Java 기술을 사용하여 계약서에 있는 공식 인감의 진위 여부를 효과적으로 식별하는 방법

import java.awt.Image;
import java.awt.image.BufferedImage;
import javax.imageio.ImageIO;
import java.io.File;
import java.io.IOException;

public class ContractSealVerification {
    public static void main(String[] args) {
        try {
            File imageFile = new File("seal.jpg"); // 公章图像文件的路径
            BufferedImage image = ImageIO.read(imageFile); // 加载图像文件
            // 根据需要，我们可以对图像进行预处理，例如灰度化、二值化等操作
            // ...
        } catch (IOException e) {
            e.printStackTrace();
        }
    }
}

import java.awt.Image;
import java.awt.image.BufferedImage;
import javax.imageio.ImageIO;
import java.io.File;
import java.io.IOException;

public class ContractSealVerification {
    public static void main(String[] args) {
        try {
            File imageFile = new File("seal.jpg"); // 公章图像文件的路径
            BufferedImage image = ImageIO.read(imageFile); // 加载图像文件
            int[][] grayMatrix = extractGrayMatrix(image); // 提取灰度共生矩阵特征
            // ...
        } catch (IOException e) {
            e.printStackTrace();
        }
    }

    public static int[][] extractGrayMatrix(BufferedImage image) {
        // 根据需要，我们可以调整图像的大小
        int width = image.getWidth();
        int height = image.getHeight();
        int[][] grayMatrix = new int[width][height];
        
        for (int i = 0; i < width; i++) {
            for (int j = 0; j < height; j++) {
                // 将RGB颜色转换为灰度值
                int rgb = image.getRGB(i, j);
                int gray = (rgb >> 16) & 0xff; // 获取红色分量
                grayMatrix[i][j] = gray;
            }
        }
        
        return grayMatrix;
    }
}

import java.awt.Image;
import java.awt.image.BufferedImage;
import javax.imageio.ImageIO;
import java.io.File;
import java.io.IOException;
import java.util.List;
import java.util.ArrayList;
import weka.classifiers.Classifier;
import weka.classifiers.functions.LibSVM;
import weka.core.Attribute;
import weka.core.DenseInstance;
import weka.core.Instance;
import weka.core.Instances;

public class ContractSealVerification {
    public static void main(String[] args) {
        try {
            // 获取训练数据
            Instances instances = getTrainingData();
            // 建立支持向量机(SVM)分类器
            Classifier classifier = new LibSVM();
            classifier.buildClassifier(instances);
            
            // 获取待鉴别的公章图像特征
            int[][] grayMatrix = extractGrayMatrix(image);
            double[] features = extractFeatures(grayMatrix);
            Instance instance = new DenseInstance(1.0, features);
            instance.setDataset(instances);
            
            // 进行真伪预测
            double prediction = classifier.classifyInstance(instance);
            if (prediction == 0) {
                System.out.println("公章是真实的");
            } else {
                System.out.println("公章是伪造的");
            }
        } catch (Exception e) {
            e.printStackTrace();
        }
    }

    public static Instances getTrainingData() {
        // 创建属性列表
        List<Attribute> attributes = new ArrayList<>();
        // 添加特征属性
        for (int i = 0; i < numFeatures; i++) {
            Attribute attribute = new Attribute("feature" + i);
            attributes.add(attribute);
        }
        // 添加类别属性
        List<String> labels = new ArrayList<>();
        labels.add("真实");
        labels.add("伪造");
        Attribute labelAttribute = new Attribute("label", labels);
        attributes.add(labelAttribute);
        
        // 创建数据集
        Instances instances = new Instances("seal_verification", attributes, 0);
        instances.setClassIndex(instances.numAttributes() - 1);
        
        // 添加训练样本
        Instance instance1 = new DenseInstance(numFeatures + 1);
        // 设置特征值
        for (int i = 0; i < numFeatures; i++) {
            instance1.setValue(i, featureValue);
        }
        // 设置类别
        instance1.setValue(numFeatures, "真实");
        instances.add(instance1);
        // ...
        
        return instances;
    }
    
    public static double[] extractFeatures(int[][] grayMatrix) {
        // 提取图像特征
        double[] features = new double[numFeatures];
        // ...
        return features;
    }
}