一、javaCV图像处理之1：实时视频添加文字水印并截取视频图像保存成图片，实现文字水印的字体、位置、大小、粗度、翻转、平滑等操作

二、javaCV图像处理之2：实时视频添加图片水印，实现不同大小图片叠加，图像透明度控制

三、opencv图像处理3：使用opencv原生方法遍历摄像头设备及调用（方便多摄像头遍历及调用，相比javacv更快的摄像头读取速度和效率，方便读取后的图像处理）

四、opencv图像处理系列：国内车辆牌照检测识别系统（万份测试准确率99.7%以上）

package cc.eguid.charsocr;import java.awt.Image;
import java.awt.image.BufferedImage;
import java.awt.image.DataBuffer;
import java.awt.image.DataBufferByte;
import java.awt.image.SampleModel;
import java.math.BigDecimal;
import java.util.Vector;import org.bytedeco.javacpp.opencv_imgcodecs;
import org.bytedeco.javacpp.Pointer;
import org.bytedeco.javacpp.opencv_core;
import org.bytedeco.javacpp.opencv_core.CvType;
import org.bytedeco.javacpp.opencv_core.CvTypeInfo;
import org.bytedeco.javacpp.opencv_core.Mat;import cc.eguid.charsocr.core.CharsRecognise;
import cc.eguid.charsocr.core.PlateDetect;/*** 车牌识别* @author eguid**/
public class PlateRecognition {static PlateDetect plateDetect =null;static CharsRecognise cr=null;static{plateDetect=new PlateDetect();plateDetect.setPDLifemode(true);cr = new CharsRecognise();}/*** 单个车牌识别* @param mat* @return*/public static String plateRecognise(Mat mat){Vector<Mat> matVector = new Vector<Mat>(1);if (0 == plateDetect.plateDetect(mat, matVector)) {if(matVector.size()>0){return cr.charsRecognise(matVector.get(0));}}return null;}/*** 多车牌识别* @param mat* @return*/public static String[] mutiPlateRecognise(Mat mat){PlateDetect plateDetect = new PlateDetect();plateDetect.setPDLifemode(true);Vector<Mat> matVector = new Vector<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;}/*** 单个车牌识别* @param mat* @return*/public static String plateRecognise(String imgPath){Mat src = opencv_imgcodecs.imread(imgPath);return plateRecognise(src);}/*** 多车牌识别* @param mat* @return*/public static String[] mutiPlateRecognise(String imgPath){Mat src = opencv_imgcodecs.imread(imgPath);return mutiPlateRecognise(src);}public static void main(String[] args){int sum=100;int errNum=0;int sumTime=0;long longTime=0;for(int i=sum;i>0;i--){String imgPath = "res/image/test_image/plate_judge.jpg";Mat src = opencv_imgcodecs.imread(imgPath);long now =System.currentTimeMillis();String ret=plateRecognise(src);System.err.println(ret);long s=System.currentTimeMillis()-now;if(s>longTime){longTime=s;}sumTime+=s;if(!"川A0CP56".equals(ret)){errNum++;}}System.err.println("总数量："+sum);System.err.println("单次最长耗时："+longTime+"ms");BigDecimal errSum=new BigDecimal(errNum);BigDecimal sumNum=new BigDecimal(sum);BigDecimal c=sumNum.subtract(errSum).divide(sumNum).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+"%");}
}