创建javacv识别车牌实现项目

要创建一个基于javacv的车牌识别项目，需要以下步骤：

1. 下载和安装OpenCV和javacv库
2. 导入javacv库到项目中，并添加到项目类路径下
3. 从视频或摄像头中获取图像帧
4. 对图像进行车牌检测和识别的预处理，包括图像灰度化、二值化、滤波等操作
5. 利用形态学运算处理车牌区域，去除不必要的细节和噪点
6. 对车牌字符进行分割，提取每个字符图像，并对每个字符进行识别
7. 将识别出的车牌字符组合成完整的车牌号码

以下是一个简单的代码示例，用于从摄像头获取图像并进行车牌识别：

import org.bytedeco.javacpp.opencv_core;
import org.bytedeco.javacpp.opencv_imgcodecs;
import org.bytedeco.javacpp.opencv_imgproc;
import org.bytedeco.javacpp.opencv_videoio;
import org.bytedeco.javacpp.indexer.UByteIndexer;

public class LicensePlateRecognition {
    public static void main(String[] args) {
        // 初始化摄像头
        opencv_videoio.VideoCapture camera = new opencv_videoio.VideoCapture(0);

        // 读取模板图像
        opencv_core.Mat templateImg = opencv_imgcodecs.imread("template.jpg");

        while (true) {
            // 从摄像头获取图像帧
            opencv_core.Mat frame = new opencv_core.Mat();
            camera.read(frame);

            // 转换为灰度图像
            opencv_core.Mat gray = new opencv_core.Mat();
            opencv_imgproc.cvtColor(frame, gray, opencv_imgproc.COLOR_BGR2GRAY);

            // 二值化
            opencv_core.Mat binary = new opencv_core.Mat();
            opencv_imgproc.threshold(gray, binary, 0, 255, opencv_imgproc.THRESH_BINARY_INV | opencv_imgproc.THRESH_OTSU);

            // 形态学运算处理车牌区域
            opencv_core.Mat kernel = opencv_imgproc.getStructuringElement(opencv_imgproc.MORPH_RECT, new opencv_core.Size(3, 3));
            opencv_core.Mat morph = new opencv_core.Mat();
            opencv_imgproc.morphologyEx(binary, morph, opencv_imgproc.MORPH_CLOSE, kernel);

            // 查找轮廓
            opencv_core.Mat contoursImg = frame.clone();
            opencv_core.Mat contours = new opencv_core.Mat();
            opencv_imgproc.findContours(morph, contours, new opencv_core.Mat(), opencv_imgproc.RETR_EXTERNAL, opencv_imgproc.CHAIN_APPROX_SIMPLE);

            // 遍历轮廓，找到车牌区域
            opencv_core.Rect plateRect = null;
            for (int i = 0; i < contours.size(); i++) {
                opencv_core.Mat contour = contours.get(i);
                double area = opencv_imgproc.contourArea(contour);
                if (area > 1000 && area < 10000) {
                    opencv_core.Rect rect = opencv_imgproc.boundingRect(contour);
                    double ratio = (double) rect.width() / rect.height();
                    if (ratio > 2 && ratio < 5) {
                        plateRect = rect;
                        opencv_imgproc.rectangle(contoursImg, plateRect, new opencv_core.Scalar(0, 255, 0), 2);
                    }
                }
            }

            // 如果找到车牌区域
            if (plateRect != null) {
                // 提取车牌图像
                opencv_core.Mat plateImg = gray.apply(plateRect);

                // 调整车牌图像大小
                opencv_core.Mat resizedPlateImg = new opencv_core.Mat();
                opencv_imgproc.resize(plateImg, resizedPlateImg, new opencv_core.Size(136, 36));

                // 与模板图像进行比对，找到最佳匹配的字符
                int[] matchResults = new int[10];
                for (int i = 0; i < 10; i++) {
                    opencv_core.Mat templateChar = templateImg.apply(new opencv_core.Rect(i * 14, 0, 14, 36));
                    opencv_core.Mat charImg = resizedPlateImg.apply(new opencv_core.Rect(i * 14, 0, 14, 36));
                    opencv_core.Mat diff = new opencv_core.Mat();
                    opencv_core.absdiff(templateChar, charImg, diff);
                    UByteIndexer diffIndexer = diff.createIndexer();
                    int matchValue = 0;
                    for (int y = 0; y < diff.rows(); y++) {
                        for (int x = 0; x < diff.cols(); x++) {
                            matchValue += diffIndexer.get(y, x);
                        }
                    }
                    matchResults[i] = matchValue;
                }

                // 组合车牌号码
                StringBuilder plateNumberBuilder = new StringBuilder();
                for (int i = 0; i < 10; i++) {
                    int minMatchValue = Integer.MAX_VALUE;
                    int minMatchIndex = -1;
                    for (int j = 0; j < 10; j++) {
                        if (matchResults[j] < minMatchValue) {
                            minMatchValue = matchResults[j];
                            minMatchIndex = j;
                        }
                    }
                    if (minMatchValue < 5000) {
                        plateNumberBuilder.append(minMatchIndex);
                    } else {
                        plateNumberBuilder.append("*");
                    }
                    matchResults[minMatchIndex] = Integer.MAX_VALUE;
                }
                String plateNumber = plateNumberBuilder.toString();

                // 在图像上显示车牌号码
                opencv_core.Point textPosition = new opencv_core.Point(plateRect.x(), plateRect.y() - 10);
                opencv_imgproc.putText(contoursImg, plateNumber, textPosition, opencv_imgproc.FONT_HERSHEY_SIMPLEX, 1.0, new opencv_core.Scalar(0, 0, 255), 2);
            }

            // 显示图像
            opencv_imgcodecs.imshow("Camera", contoursImg);
            opencv_imgcodecs.waitKey(1);
        }
    }
}

代码中使用了一个名为template.jpg的模板图像，其中包含了所有数字字符的样本图像。程序从摄像头获取图像帧，并对图像进行车牌检测和识别的预处理，然后进行形态学运算处理车牌区域，最后提取车牌字符并进行识别，将识别出的车牌字符组合成完整的车牌号码，并在图像上显示车牌号码。