增加黑牌识别（待测试）

src/main/java/com/yuxue/constant/Constant.java

@@ -13,11 +13,11 @@ public class Constant {
     public static final String UTF8 = "UTF-8";
 
     // 车牌识别， 默认车牌图片保存路径
-    // public static String DEFAULT_DIR = "./PlateDetect/"; // 使用项目的相对路径
+    //public static String DEFAULT_DIR = "./PlateDetect/"; // 使用项目的相对路径
     public static String DEFAULT_DIR = "D:/PlateDetect/"; // 使用盘符的绝对路径
 
     // 车牌识别， 默认车牌图片处理过程temp路径
-    // public static String DEFAULT_TEMP_DIR = "./PlateDetect/temp/"; // 使用项目的相对路径
+    //public static String DEFAULT_TEMP_DIR = "./PlateDetect/temp/"; // 使用项目的相对路径
     public static String DEFAULT_TEMP_DIR = "D:/PlateDetect/temp/"; // 使用盘符的绝对路径
 
     // 车牌识别，默认处理图片类型
@@ -26,7 +26,7 @@ public class Constant {
     public static String DEFAULT_ANN_PATH = "res/model/ann.xml";
     //public static String DEFAULT_ANN_PATH = "D:/PlateDetect/train/chars_recognise_ann/ann.xml";
     
-    public static String DEFAULT_SVM_PATH = "res/model/svm.xml";
+    public static String DEFAULT_SVM_PATH = "res/model/svm.xml";   //svm2.xml为测试用
     
     public static final int DEFAULT_WIDTH = 136;    // cols
     public static final int DEFAULT_HEIGHT = 36;    // rows

src/main/java/com/yuxue/easypr/core/CoreFunc.java

@@ -119,7 +119,7 @@ public class CoreFunc {
      * 
      * @param src
      *            车牌mat
-     * @param r
+     * @param color
      *            颜色模板
      * @param adaptive_minsv
      *            S和V的最小值由adaptive_minsv这个bool值判断
@@ -159,7 +159,10 @@ public class CoreFunc {
             return PlateColor.YELLOW;
         } else if (plateColorJudge(src, PlateColor.GREEN, adaptive_minsv) == true) {
             return PlateColor.GREEN;
-        } else {
+        } else if (plateColorJudge(src, PlateColor.BLACK, adaptive_minsv) == true) {
+            return PlateColor.BLACK;
+        }
+        else {
             return PlateColor.UNKNOWN;
         }
     }

src/main/java/com/yuxue/easypr/core/PlateJudge.java

@@ -1,6 +1,8 @@
 package com.yuxue.easypr.core;
 
+import com.yuxue.train.SVMTrain;
 import org.bytedeco.javacpp.opencv_core;
+
 import org.bytedeco.javacpp.opencv_imgproc;
 
 import java.util.Vector;
@@ -10,9 +12,11 @@ import org.bytedeco.javacpp.opencv_core.Rect;
 import org.bytedeco.javacpp.opencv_core.Size;
 import org.bytedeco.javacpp.opencv_ml.SVM;
 
+
 import com.yuxue.constant.Constant;
 
 
+
 /**
  * 车牌判断
  * @author yuxue
@@ -28,7 +32,7 @@ public class PlateJudge {
     
     public void loadSVM(String path) {
         svm.clear();
-        // svm=SVM.loadSVM(path, "svm");
+        //svm=SVM.loadSVM(path, "svm");
         svm=SVM.load(path);
     }
     
@@ -36,7 +40,7 @@ public class PlateJudge {
      * EasyPR的getFeatures回调函数, 用于从车牌的image生成svm的训练特征features
      */
     private SVMCallback features = new Features();
-    
+
 
     /**
      * 对单幅图像进行SVM判断
@@ -47,12 +51,13 @@ public class PlateJudge {
         int ret = 1;
         // 使用com.yuxue.train.SVMTrain 生成的训练库文件
         Mat features = this.features.getHistogramFeatures(inMat);
-        /*Mat samples = features.reshape(1, 1);
+        //Mat features = SVMTrain.getFeature(inMat);
-        samples.convertTo(samples, opencv_core.CV_32F);*/
+        Mat samples = features.reshape(1, 1);
-        
+        samples.convertTo(samples, opencv_core.CV_32F);
-        Mat p = features.reshape(1, 1);
+
-        p.convertTo(p, opencv_core.CV_32FC1);
+        /*Mat p = features.reshape(1, 1);
-        ret = (int) svm.predict(features);
+        p.convertTo(p, opencv_core.CV_32FC1);*/
+        ret = (int) svm.predict(samples);
         return ret;
         
         // 使用com.yuxue.train.PlateRecoTrain 生成的训练库文件
@@ -62,13 +67,13 @@ public class PlateJudge {
         Mat dst = new Mat();
         opencv_imgproc.Canny(grayImage, dst, 130, 250);
         Mat samples = dst.reshape(1, 1);
-        samples.convertTo(samples, opencv_core.CV_32F);*/
+        samples.convertTo(samples, opencv_core.CV_32F);
         
         // 正样本为0 负样本为1
-        /*if(svm.predict(samples) <= 0) {
+        if(svm.predict(samples) <= 0) {
             ret = 1;
-        }*/
+        }
-        /*ret = (int)svm.predict(samples);
+        ret = (int)svm.predict(samples);
         System.err.println(ret);
         return ret ;*/
         

src/main/java/com/yuxue/easypr/core/PlateLocate.java

@@ -6,6 +6,7 @@ import static org.bytedeco.javacpp.opencv_core.*;
 import static org.bytedeco.javacpp.opencv_imgproc.*;
 
 import com.yuxue.constant.Constant;
+import org.bytedeco.javacpp.opencv_highgui;
 import org.bytedeco.javacpp.opencv_imgcodecs;
 
 import org.bytedeco.javacpp.opencv_core.CvPoint2D32f;
@@ -17,7 +18,6 @@ import org.bytedeco.javacpp.opencv_core.RotatedRect;
 import org.bytedeco.javacpp.opencv_core.Scalar;
 import org.bytedeco.javacpp.opencv_core.Size;
 
-
 /**
  * 车牌定位
  * @author yuxue
@@ -117,12 +117,16 @@ public class PlateLocate {
         // 高斯模糊。Size中的数字影响车牌定位的效果。
         GaussianBlur(src, src_blur, new Size(gaussianBlurSize, gaussianBlurSize), 0, 0, BORDER_DEFAULT);
         if (debug) {
+            //opencv_highgui.imshow("Plate Detected", src_blur);
+            //opencv_highgui.waitKey(0);
             opencv_imgcodecs.imwrite(tempPath + "debug_GaussianBlur.jpg", src_blur);
         }
 
         // Convert it to gray 将图像进行灰度化
         cvtColor(src_blur, src_gray, CV_RGB2GRAY);
         if (debug) {
+            //opencv_highgui.imshow("Plate Detected", src_gray);
+            //opencv_highgui.waitKey(0);
             opencv_imgcodecs.imwrite(tempPath + "debug_gray.jpg", src_gray);
         }
 
@@ -144,6 +148,8 @@ public class PlateLocate {
         addWeighted(abs_grad_x, SOBEL_X_WEIGHT, abs_grad_y, SOBEL_Y_WEIGHT, 0, grad);
 
         if (debug) {
+            //opencv_highgui.imshow("Plate Detected", grad);
+            //opencv_highgui.waitKey(0);
             opencv_imgcodecs.imwrite(tempPath + "debug_Sobel.jpg", grad);
         }
 
@@ -153,6 +159,8 @@ public class PlateLocate {
         threshold(grad, img_threshold, 0, 255, CV_THRESH_OTSU + CV_THRESH_BINARY);
 
         if (debug) {
+            //opencv_highgui.imshow("Plate Detected", img_threshold);
+            //opencv_highgui.waitKey(0);
             opencv_imgcodecs.imwrite(tempPath + "debug_threshold.jpg", img_threshold);
         }
 
@@ -162,6 +170,8 @@ public class PlateLocate {
         morphologyEx(img_threshold, img_threshold, MORPH_CLOSE, element);
 
         if (debug) {
+            //opencv_highgui.imshow("Plate Detected", img_threshold);
+            //opencv_highgui.waitKey(0);
             opencv_imgcodecs.imwrite(tempPath + "debug_morphology.jpg", img_threshold);
         }
 
@@ -178,6 +188,8 @@ public class PlateLocate {
             // 将轮廓描绘到图上输出
             drawContours(result, contours, -1, new Scalar(0, 0, 255, 255));
             opencv_imgcodecs.imwrite(tempPath + "debug_Contours.jpg", result);
+            //opencv_highgui.imshow("Plate Detected", result);
+            //opencv_highgui.waitKey(0);
         }
 
         // Start to iterate to each contour founded
@@ -275,6 +287,7 @@ public class PlateLocate {
 
         Mat resultResized = new Mat();
         resultResized.create(HEIGHT, WIDTH, TYPE);
+        //大小调整，训练之前要保证图像大小一致
         resize(img_crop, resultResized, resultResized.size(), 0, 0, INTER_CUBIC);
         if (debug) {
             opencv_imgcodecs.imwrite(tempPath + "debug_resize_" + index + ".jpg", resultResized);

src/main/java/com/yuxue/enumtype/PlateColor.java

@@ -11,12 +11,14 @@ public enum PlateColor {
     BLUE("BLUE","蓝牌", 100, 130), 
     GREEN("GREEN","绿牌", 38, 100), 
     YELLOW("YELLOW","黄牌", 15, 40),
+    BLACK("BLACK", "黑牌", 0, 180),
     UNKNOWN("UNKNOWN","未知", 0, 0);
 
     public final String code;
     public final String desc;
 
     // opencv颜色识别的HSV中各个颜色所对应的H的范围： Orange 0-22 Yellow 22- 38 Green 38-75 Blue 75-130
+    //新增黑牌H范围0-180，S范围0-255，V范围0-46
     public final int minH;
     public final int maxH;
 

src/main/java/com/yuxue/train/SVMTrain.java

@@ -1,5 +1,8 @@
 package com.yuxue.train;
 
+import java.awt.image.BufferedImage;
+import java.awt.image.DataBufferByte;
+import java.awt.image.WritableRaster;
 import java.io.File;
 import java.util.List;
 
@@ -140,6 +143,9 @@ public class SVMTrain {
         doPridect(svm, DEFAULT_PATH + "test/debug_resize_3.jpg");
         doPridect(svm, DEFAULT_PATH + "test/S22_KG2187_3.jpg");
         doPridect(svm, DEFAULT_PATH + "test/S22_KG2187_5.jpg");
+        doPridect(svm, DEFAULT_PATH + "test/BE_16888.jpg");
+        doPridect(svm, DEFAULT_PATH + "test/BS_36359.jpg");
+        doPridect(svm, DEFAULT_PATH + "test/1.png");
     }
 
     public static void doPridect(SVM svm, String imgPath) {
@@ -170,6 +176,26 @@ public class SVMTrain {
     }
 
 
+    /**
+     * org.opencv.core.Mat转java.awt.image.BufferedImage
+     * @param matrix
+     * @return
+     */
+    public static BufferedImage toBufferedImage(Mat matrix) {
+        int type = BufferedImage.TYPE_BYTE_GRAY;
+        if (matrix.channels() > 1) {
+            type = BufferedImage.TYPE_3BYTE_BGR;
+        }
+        int bufferSize = matrix.channels() * matrix.cols() * matrix.rows();
+        byte[] buffer = new byte[bufferSize];
+        matrix.get(0, 0, buffer); // get all pixel from martix
+        BufferedImage image = new BufferedImage(matrix.cols(), matrix.rows(), type);
+        final byte[] targetPixels = ((DataBufferByte) image.getRaster().getDataBuffer()).getData();
+        System.arraycopy(buffer, 0, targetPixels, 0, buffer.length);
+        return image;
+    }
+
+
     public static Mat getFeature(Mat inMat) {
         
         Mat histogram = getHistogramFeatures(inMat);

src/test/java/com/yuxue/test/PlateRecoTest.java

@@ -18,6 +18,11 @@ import com.yuxue.easypr.core.PlateLocate;
 import com.yuxue.enumtype.Direction;
 import com.yuxue.enumtype.PlateColor;
 
+import static org.bytedeco.javacpp.opencv_core.CV_32F;
+import static org.bytedeco.javacpp.opencv_core.CV_8UC3;
+import static org.bytedeco.javacpp.opencv_imgproc.INTER_CUBIC;
+import static org.bytedeco.javacpp.opencv_imgproc.resize;
+
 /**
  * EasyPr车牌识别测试类 
  * - https://gitee.com/easypr/EasyPR 很老的项目了 
@@ -89,9 +94,10 @@ public class PlateRecoTest {
                 Mat img = matVector.get(i);
                 // 弹窗显示
                 opencv_highgui.imshow("Plate Detected", img);
-
+                opencv_highgui.waitKey(0);
                 String str = "d:/test/" + i + ".png";
                 opencv_imgcodecs.imwrite(str, img);
+
             }
         }
     }
@@ -104,7 +110,7 @@ public class PlateRecoTest {
      */
     @Test
     public void testPlateLocate() {
-        String imgPath = "res/image/test_image/test.jpg";
+        String imgPath = "res/image/test_image/test3.jpg";
 
         Mat src = opencv_imgcodecs.imread(imgPath);
 
@@ -177,7 +183,27 @@ public class PlateRecoTest {
         System.out.println(result);
     }
 
+    /**
+     * 切图
+     */
+    @Test
+    public void testCut() {
+        String path = "D:/PlateDetect/train/plate_detect_svm/test/";
+        Mat inMat = opencv_imgcodecs.imread(path + "BS_36359.jpg");
+        Mat grayImage = new Mat();
+        opencv_imgproc.cvtColor(inMat, grayImage, opencv_imgproc.CV_RGB2GRAY);
+        Mat dst = new Mat();
+        opencv_imgproc.Canny(grayImage, dst, 130, 250);
+        Mat samples = dst.reshape(1, 1);
+        samples.convertTo(samples, CV_32F);
+
+        //Mat resultResized = new Mat();
+        //resultResized.create(36,136, CV_8UC3);
+        //大小调整，训练之前要保证图像大小一致
+        //resize(img_crop, resultResized, resultResized.size(), 0, 0, INTER_CUBIC);
+        opencv_imgcodecs.imwrite("D:/" + "debug_resize" + ".jpg", samples);
 
+    }
 
 
     /**