javacv识别车牌实现

使用JavaCV识别车牌可以分为以下几个步骤：

1. 加载图像：使用OpenCV加载车牌图像。

2. 车牌定位：使用图像处理技术定位车牌区域。一般的方法是使用形态学处理和轮廓检测。

3. 车牌字符分割：使用垂直投影法或者基于连通域的方法对车牌字符进行分割。

4. 车牌字符识别：使用OCR技术对分割出来的字符进行识别。常用的OCR引擎有Tesseract-OCR和AipOcr等。

以下是一个简单的代码示例：

import static org.bytedeco.javacpp.opencv_core.*;
import static org.bytedeco.javacpp.opencv_highgui.*;
import static org.bytedeco.javacpp.opencv_imgproc.*;

public class LicensePlateRecognizer {
    public static void main(String[] args) {
        // 读取车牌图像
        Mat src = imread("license_plate.jpg");

        // 车牌定位
        Mat gray = new Mat();
        cvtColor(src, gray, COLOR_BGR2GRAY);
        Mat binary = new Mat();
        threshold(gray, binary, 0, 255, THRESH_BINARY_INV | THRESH_OTSU);
        Mat kernel = getStructuringElement(MORPH_RECT, new Size(3, 3));
        morphologyEx(binary, binary, MORPH_OPEN, kernel);
        Mat contours = new Mat();
        findContours(binary, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);
        Rect roi = null;
        for (int i = 0; i < contours.size().height(); i++) {
            Rect rect = boundingRect(contours.row(i));
            if (rect.width() > rect.height() * 2 && rect.width() < src.size().width() * 0.8 && rect.height() > src.size().height() * 0.1) {
                roi = rect;
                break;
            }
        }
        if (roi == null) {
            System.out.println("未找到车牌");
            return;
        }
        Mat plate = new Mat(src, roi);

        // 车牌字符分割
        cvtColor(plate, gray, COLOR_BGR2GRAY);
        threshold(gray, binary, 0, 255, THRESH_BINARY_INV | THRESH_OTSU);
        findContours(binary, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);
        Mat result = new Mat();
        int i = 0;
        for (int j = 0; j < contours.size().height(); j++) {
            Rect rect = boundingRect(contours.row(j));
            if (rect.width() > 10 && rect.height() > 20) {
                Mat character = new Mat(gray, rect);
                resize(character, character, new Size(20, 20));
                character.convertTo(character, CV_32FC1);
                result.push_back(character.reshape(1, 1));
            }
        }

        // 车牌字符识别
        String[] labels = {"0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z"};
        Mat labelsMat = new Mat(labels.length, 1, CV_32FC1);
        for (int j = 0; j < labels.length; j++) {
            labelsMat.put(j, 0, j);
        }
        KNearest knn = KNearest.create();
        knn.train(result, ROW_SAMPLE, labelsMat);
        Mat test = new Mat();
        resize(gray, test, new Size(120, 30));
        test.convertTo(test, CV_32FC1);
        Mat response = new Mat();
        knn.findNearest(test.reshape(1, 1), 3, response);
        int label = (int) response.get(0, 0)[0];
        System.out.println("识别结果：" + labels[label]);
    }
}

这是一个简单的识别车牌的示例，实际应用中还需要考虑更多的情况，比如光照条件、车牌颜色等因素对识别的影响。