代码解释: 我想对100个车牌图片进行识别，怎么做public static void main(String[] args) { // 多张车牌图片路径 String[] imgPaths = {"res/image/test_image/plate_locate.jpg", "res/image/test_image/test.jpg", "res/image/test_image/plate_detect.jpg", "res/general_test/京A88731.jpg"}; int sum = imgPaths.length; // 总共处理的图片数量 int errNum = 0; // 识别错误的数量 int sumTime = 0; // 总耗时 long longTime = 0; // 最长处理时长 for (int i = 0; i < sum; i++) { opencv_core.Mat src = opencv_imgcodecs.imread(imgPaths[i]); String[] ret = multiPlateRecognise(src); long now = System.currentTimeMillis(); System.err.println(Arrays.toString(ret)); long s = System.currentTimeMillis() - now; if (s > longTime) { longTime = s; } sumTime += s; boolean flag =false;//是否有一个车牌号识别错误 for (String plate:ret) { if (plate != null && !"苏EUK722".equals(plate) && !"苏A0CP56".equals(plate) && !"沪BS7821".equals(plate) && !"京A88731".equals(plate)) { flag = true; break; } } if (flag) { errNum++; } } BigDecimal errSum = new BigDecimal(errNum); BigDecimal sumNum = new BigDecimal(sum); BigDecimal c = sumNum.subtract(errSum).divide(sumNum,2, RoundingMode.HALF_UP).multiply(new BigDecimal(100)); System.err.println("总耗时：" + sumTime + "ms,平均处理时长：" + sumTime/sum + "ms,错误数量：" + errNum + "，正确识别率：" + c + "%"); } }

这段代码实现了对100个车牌图片进行识别的功能。首先定义了一个包含多张车牌图片路径的字符串数组imgPaths，然后通过循环遍历数组中的每张图片，分别调用multiPlateRecognise方法进行识别，并记录处理时间、错误数量等信息。最后计算出正确识别率并打印输出。其中BigDecimal类用于处理精度问题，RoundingMode.HALF_UP表示四舍五入。

相关问题

我想对100个车牌图片进行识别，main函数怎么改 public static void main(String[] args) { // 多张车牌图片路径 String[] imgPaths = {"res/image/test_image/plate_locate.jpg", "res/image/test_image/test.jpg", "res/image/test_image/plate_detect.jpg", "res/general_test/京A88731.jpg"}; int sum = imgPaths.length; // 总共处理的图片数量 int errNum = 0; // 识别错误的数量 int sumTime = 0; // 总耗时 long longTime = 0; // 最长处理时长 for (int i = 0; i < sum; i++) { opencv_core.Mat src = opencv_imgcodecs.imread(imgPaths[i]); String[] ret = multiPlateRecognise(src); long now = System.currentTimeMillis(); System.err.println(Arrays.toString(ret)); long s = System.currentTimeMillis() - now; if (s > longTime) { longTime = s; } sumTime += s; boolean flag =false;//是否有一个车牌号识别错误 for (String plate:ret) { if (plate != null && !"苏EUK722".equals(plate) && !"苏A0CP56".equals(plate) && !"沪BS7821".equals(plate) && !"京A88731".equals(plate)) { flag = true; break; } } if (flag) { errNum++; } } BigDecimal errSum = new BigDecimal(errNum); BigDecimal sumNum = new BigDecimal(sum); BigDecimal c = sumNum.subtract(errSum).divide(sumNum,2, RoundingMode.HALF_UP).multiply(new BigDecimal(100)); System.err.println("总耗时：" + sumTime + "ms,平均处理时长：" + sumTime/sum + "ms,错误数量：" + errNum + "，正确识别率：" + c + "%"); } }

如果想对100个车牌图片进行识别，可以修改imgPaths数组中的路径，将其替换为100张车牌图片的路径，例如：

String[] imgPaths = {"res/image/test_image/plate_locate.jpg", "res/image/test_image/test.jpg", "res/image/test_image/plate_detect.jpg", "res/general_test/京A88731.jpg", "path/to/image1.jpg", "path/to/image2.jpg", ..., "path/to/image100.jpg"};

然后在循环中，将sum的值修改为100即可：

int sum = 100; // 总共处理的图片数量

运行程序即可对100张车牌图片进行识别并输出结果。

我想对100个车牌图片进行识别，并计算出正确识别率，代码应该怎么修改public static String[] multiPlateRecognise(opencv_core.Mat mat) { PlateDetect plateDetect = new PlateDetect(); plateDetect.setPDLifemode(true); Vector<opencv_core.Mat> matVector = new Vector<opencv_core.Mat>(10); if (0 == plateDetect.plateDetect(mat, matVector)) { CharsRecognise cr = new CharsRecognise(); String[] results = new String[matVector.size()]; for (int i = 0; i < matVector.size(); ++i) { String result = cr.charsRecognise(matVector.get(i)); results[i] = result; } return results; } return null; } public static void main(String[] args) { // 多张车牌图片路径 String[] imgPaths = {"res/image/test_image/plate_locate.jpg", "res/image/test_image/test.jpg", "res/image/test_image/plate_detect.jpg", "res/general_test/京A88731.jpg"}; int sum = imgPaths.length; // 总共处理的图片数量 int errNum = 0; // 识别错误的数量 int sumTime = 0; // 总耗时 long longTime = 0; // 最长处理时长 for (int i = 0; i < sum; i++) { opencv_core.Mat src = opencv_imgcodecs.imread(imgPaths[i]); String[] ret = multiPlateRecognise(src); long now = System.currentTimeMillis(); System.err.println(Arrays.toString(ret)); long s = System.currentTimeMillis() - now; if (s > longTime) { longTime = s; } sumTime += s; boolean flag =false;//是否有一个车牌号识别错误 for (String plate:ret) { if (plate != null && !"苏EUK722".equals(plate) && !"苏A0CP56".equals(plate) && !"沪BS7821".equals(plate) && !"京A88731".equals(plate)) { flag = true; break; } } if (flag) { errNum++; } } BigDecimal errSum = new BigDecimal(errNum); BigDecimal sumNum = new BigDecimal(sum); BigDecimal c = sumNum.subtract(errSum).divide(sumNum,2, RoundingMode.HALF_UP).multiply(new BigDecimal(100)); System.err.println("总耗时：" + sumTime + "ms,平均处理时长：" + sumTime/sum + "ms,错误数量：" + errNum + "，正确识别率：" + c + "%"); } }

您可以将代码修改为以下形式：

public static void main(String[] args) {
    String[] imgPaths = {"res/image/test_image/plate_locate.jpg", "res/image/test_image/test.jpg", "res/image/test_image/plate_detect.jpg", "res/general_test/京A88731.jpg"};
    int sum = imgPaths.length;
    int errNum = 0;
    int sumTime = 0;
    long longTime = 0;
    for (int i = 0; i < sum; i++) {
        opencv_core.Mat src = opencv_imgcodecs.imread(imgPaths[i]);
        String[] ret = multiPlateRecognise(src);
        long now = System.currentTimeMillis();
        System.err.println(Arrays.toString(ret));
        long s = System.currentTimeMillis() - now;
        if (s > longTime) {
            longTime = s;
        }
        sumTime += s;
        boolean flag = false;
        for (String plate : ret) {
            if (plate == null) {
                continue;
            }
            String targetPlate = getTargetPlate(imgPaths[i]);
            if (!plate.equals(targetPlate)) {
                flag = true;
                break;
            }
        }
        if (flag) {
            errNum++;
        }
    }
    BigDecimal errSum = new BigDecimal(errNum);
    BigDecimal sumNum = new BigDecimal(sum);
    BigDecimal c = sumNum.subtract(errSum).divide(sumNum, 2, RoundingMode.HALF_UP).multiply(new BigDecimal(100));
    System.err.println("总耗时：" + sumTime + "ms,平均处理时长：" + sumTime / sum + "ms,错误数量：" + errNum + "，正确识别率：" + c + "%");
}

private static String getTargetPlate(String imgPath) {
    if (imgPath.contains("苏EUK722")) {
        return "苏EUK722";
    } else if (imgPath.contains("苏A0CP56")) {
        return "苏A0CP56";
    } else if (imgPath.contains("沪BS7821")) {
        return "沪BS7821";
    } else if (imgPath.contains("京A88731")) {
        return "京A88731";
    } else {
        return null;
    }
}

public static String[] multiPlateRecognise(opencv_core.Mat mat) {
    PlateDetect plateDetect = new PlateDetect();
    plateDetect.setPDLifemode(true);
    Vector<opencv_core.Mat> matVector = new Vector<opencv_core.Mat>(10);
    if (0 == plateDetect.plateDetect(mat, matVector)) {
        CharsRecognise cr = new CharsRecognise();
        String[] results = new String[matVector.size()];
        for (int i = 0; i < matVector.size(); ++i) {
            String result = cr.charsRecognise(matVector.get(i));
            results[i] = result;
        }
        return results;
    }
    return null;
}

其中，`getTargetPlate` 方法根据图片路径返回对应的正确车牌号。在 `main` 方法中，对每张图片的识别结果进行判断，如果与正确车牌号不符，则将错误数量加一。最后计算正确识别率并输出。

注意，您需要根据实际情况修改 `getTargetPlate` 方法中的车牌号。