no commit message

src/main/java/com/yuxue/controller/FaceController.java

@@ -4,6 +4,8 @@ import org.opencv.core.Core;
 import org.opencv.core.Mat;
 import org.opencv.core.MatOfRect;
 import org.opencv.core.Rect;
+import org.opencv.imgcodecs.Imgcodecs;
+import org.opencv.imgproc.Imgproc;
 import org.opencv.objdetect.CascadeClassifier;
 
 /**
@@ -18,25 +20,20 @@ public class FaceController {
     }
 
     public static void main(String[] args) {
-        FaceController demo = new FaceController();
+        
-        demo.run();
+     // Create a face detector from the cascade file in the resources directory.
-    }
-
-    public void run() {
-
-        // Create a face detector from the cascade file in the resources directory.
         // 创建识别器
-        CascadeClassifier faceDetector = new CascadeClassifier(getClass().getResource("/haarcascades/lbpcascade_frontalface.xml").getPath());
+        CascadeClassifier faceDetector = new CascadeClassifier("/src/main/resources/haarcascades/lbpcascade_frontalface.xml");
 
-        String imgPath = "/src/main/resources/DetectFace/huge.jpg";
+        String imgPath = "/src/main/resources/DetectFace/AverageMaleFace.jpg";
-        Mat image = null; //Highgui.imread(imgPath,3);
+        Mat image = Imgcodecs.imread(imgPath);
         
-        String filename = "D:/DetectFace/huge1.png";
+        Mat dst = new Mat();
-        // System.out.println(String.format("Writing %s", filename));
+        Imgproc.Canny(image, dst, 130, 250);
 
         // Detect faces in the image. MatOfRect is a special container class for Rect.
         MatOfRect faceDetections = new MatOfRect();
-        faceDetector.detectMultiScale(image, faceDetections);
+        faceDetector.detectMultiScale(dst, faceDetections);
 
         System.out.println(String.format("识别出 %s 张人脸", faceDetections.toArray().length));
 
@@ -48,6 +45,5 @@ public class FaceController {
         // Save the visualized detection.
         // System.out.println(String.format("Writing %s", filename));
         //Highgui.imwrite(filename, image);
-        return;
     }
 }

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

@@ -9,6 +9,9 @@ import org.bytedeco.javacpp.opencv_core.Mat;
 import org.bytedeco.javacpp.opencv_core.Rect;
 import org.bytedeco.javacpp.opencv_core.Size;
 import org.bytedeco.javacpp.opencv_ml.SVM;
+import org.opencv.core.CvType;
+import org.opencv.imgcodecs.Imgcodecs;
+import org.opencv.imgproc.Imgproc;
 
 
 /**
@@ -17,9 +20,9 @@ import org.bytedeco.javacpp.opencv_ml.SVM;
  * @date 2020-04-26 15:21
  */
 public class PlateJudge {
-    
+
     private SVM svm = SVM.create();
-    
+
     /**
      * EasyPR的getFeatures回调函数, 用于从车牌的image生成svm的训练特征features
      */
@@ -29,13 +32,14 @@ public class PlateJudge {
      * 模型存储路径
      */
     private String path = "res/model/svm.xml";
-    
+
 
     public PlateJudge() {
         svm.clear();
-        svm=SVM.loadSVM(path, "svm");
+        // svm=SVM.loadSVM(path, "svm");
+        svm=SVM.load(path);
     }
-    
+
     /**
      * 对单幅图像进行SVM判断
      * @param inMat
@@ -43,13 +47,21 @@ public class PlateJudge {
      */
     public int plateJudge(final Mat inMat) {
         Mat features = this.features.getHistogramFeatures(inMat);
-        
         // 通过直方图均衡化后的彩色图进行预测
         Mat p = features.reshape(1, 1);
         p.convertTo(p, opencv_core.CV_32FC1);
         float ret = svm.predict(features);
-       
         return (int) ret;
+
+        /*opencv_imgproc.cvtColor(inMat, inMat, Imgproc.COLOR_BGR2GRAY);
+        Mat features = new Mat();
+        opencv_imgproc.Canny(inMat, features, 130, 250);
+
+        Mat p = features.reshape(1, 1);
+        p.convertTo(p, opencv_core.CV_32FC1);
+
+        float ret = svm.predict(p);
+        return (int) ret;*/
     }
 
     /**
@@ -81,7 +93,7 @@ public class PlateJudge {
         return 0;
     }
 
-    
+
     public void setModelPath(String path) {
         this.path = path;
     }
@@ -89,5 +101,5 @@ public class PlateJudge {
     public final String getModelPath() {
         return path;
     }
-    
+
 }

src/test/java/com/yuxue/test/PlateDetectTrainTest.java

@@ -38,20 +38,22 @@ public class PlateDetectTrainTest {
     }
 
     public static void main(String[] arg) {
-        // 用于存放所有样本矩阵
+
-        Mat trainingDataMat = null;
+
-        
         // 正样本  // 136 × 36 像素  训练的源图像文件要相同大小
         List<File> imgList1 = FileUtil.listFile(new File(DEFAULT_PATH + "/learn/HasPlate"), Constant.DEFAULT_TYPE, false);
-        
+
         // 负样本   // 136 × 36 像素 训练的源图像文件要相同大小
         List<File> imgList2 = FileUtil.listFile(new File(DEFAULT_PATH + "/learn/NoPlate"), Constant.DEFAULT_TYPE, false);
- 
+
         // 标记：正样本用 0 表示，负样本用 1 表示。
         int labels[] = createLabelArray(imgList1.size(), imgList2.size());
-        
+
         // 图片数量
         int sample_num = labels.length;
+
+        // 用于存放所有样本矩阵
+        Mat trainingDataMat = null;
         
         // 存放标记的Mat,每个图片都要给一个标记
         Mat labelsMat = new Mat(sample_num, 1, CvType.CV_32SC1);
@@ -66,14 +68,13 @@ public class PlateDetectTrainTest {
             } else {
                 path = imgList2.get(i - imgList1.size()).getAbsolutePath(); 
             }
-            
-            Mat src = Imgcodecs.imread(path);
 
-            // 创建一个行数为18(正负样本总数量为18),列数为 rows*cols 的矩阵
+            Mat src = Imgcodecs.imread(path);
+            
+            // 创建一个行数为sample_num, 列数为 rows*cols 的矩阵
             if (trainingDataMat == null) {
                 trainingDataMat = new Mat(sample_num, src.rows() * src.cols(), CvType.CV_32FC1);// CV_32FC1 是规定的训练用的图片格式。
             }
-
             // 转成灰度图并检测边缘 // 这里是为了过滤不需要的特征，减少训练时间。实际处理按情况论。
             Imgproc.cvtColor(src, src, Imgproc.COLOR_BGR2GRAY); // 转成灰度图
             Mat dst = new Mat(src.rows(), src.cols(), src.type());// 此时的 dst 是8u1c。
@@ -104,7 +105,7 @@ public class PlateDetectTrainTest {
      * @param labelsMat 存放标识的矩阵
      */
     public static void MySvm(Mat trainingDataMat, Mat labelsMat, String savePath) {
-        
+
         // 配置SVM训练器参数
         TermCriteria criteria = new TermCriteria(TermCriteria.EPS + TermCriteria.MAX_ITER, 20000, 0.0001);
         SVM svm = SVM.create();
@@ -121,14 +122,14 @@ public class PlateDetectTrainTest {
         TrainData td = TrainData.create(trainingDataMat, Ml.ROW_SAMPLE, labelsMat);// 类封装的训练数据
         boolean success = svm.train(td.getSamples(), Ml.ROW_SAMPLE, td.getResponses());// 训练统计模型
         System.err.println("Svm training result: " + success);
-        
+
         svm.save(savePath);// 保存模型
     }
-    
+
-    
+
     public static int[] createLabelArray(Integer i1, Integer i2) {
         int labels[] = new int[i1 + i2];
-        
+
         for (int i = 0; i < labels.length; i++) {
             if(i < i1) {
                 labels[i] = 0;

src/test/java/com/yuxue/test/PlatePridectTest.java

@@ -28,7 +28,6 @@ public class PlatePridectTest {
     static {
         System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
     }
-
     
     public static void main(String[] args) {
         

src/test/java/com/yuxue/test/trainsvm.java

@@ -0,0 +1,98 @@
+package com.yuxue.test;
+ 
+import java.io.File;
+import java.util.ArrayList;
+ 
+import org.junit.Test;
+import org.opencv.core.Core;
+import org.opencv.core.CvType;
+import org.opencv.core.Mat;
+import org.opencv.core.TermCriteria;
+import org.opencv.imgcodecs.Imgcodecs;
+import org.opencv.ml.Ml;
+import org.opencv.ml.SVM;
+import org.opencv.ml.TrainData;
+ 
+
+/**
+ * 网上找来的训练方案
+ * @author yuxue
+ * @date 2020-05-13 16:53
+ */
+public class trainsvm {
+	static {
+		System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
+	}
+	
+	// 默认的训练操作的根目录
+    private static final String DEFAULT_PATH = "D:/PlateDetect/train/plate_detect_svm/";
+    
+	//存储训练集
+	static ArrayList<Mat> trainingImages = new ArrayList<Mat>();
+	//存储标签
+	static ArrayList<Integer> trainingLabels = new ArrayList<Integer>();
+ 
+	public static void main(String[] args) {
+ 
+		openFile(1, DEFAULT_PATH + "/learn/HasPlate");
+		openFile(0, DEFAULT_PATH + "/learn/NoPlate");
+		Mat srcImgs = new Mat();
+		Mat flags = new Mat(trainingLabels.size(), 1, CvType.CV_32SC1);
+		
+		Core.vconcat(trainingImages, srcImgs);    // 样本数量不能太大，trainingImages.size有限制
+		
+		for (int i = 0; i < trainingLabels.size(); i++) {
+			int[] val = { trainingLabels.get(i) };
+			flags.put(i, 0, val);
+		}
+		SVM svm = SVM.create();
+		svm.setKernel(SVM.LINEAR);
+		svm.setType(SVM.C_SVC);
+		svm.setGamma(1);
+		svm.setC(1);
+		svm.setCoef0(0);
+		svm.setNu(0);
+		svm.setP(0);
+		svm.setTermCriteria(new TermCriteria(TermCriteria.MAX_ITER, 20000, 0.0001));
+		TrainData trainData = TrainData.create(srcImgs, Ml.ROW_SAMPLE, flags);
+		boolean success = svm.train(trainData);
+		System.out.println(success);
+		svm.save( DEFAULT_PATH + "svm.xml");
+ 
+	}
+ 
+	//读取本地文件
+	public static void openFile(int flag, String path) {
+		File file = new File(path);
+		File[] files = file.listFiles();
+		int i = 0;
+		for (File file2 : files) {
+			Mat input = Imgcodecs.imread(file2.getPath(), 0);
+			input.convertTo(input, CvType.CV_32FC1);
+			Mat reshape = input.reshape(0, 1);
+			// System.out.println(input.dump());
+			trainingImages.add(reshape);
+			trainingLabels.add(flag);
+			i++;
+			if(i >100) {
+			    break;
+			}
+		}
+	}
+ 
+	@Test
+	public void test1() {
+ 
+		// 测试训练的效果
+		SVM svm = SVM.load("E:\\svmTrain\\bp.xml");
+        Mat responseMat = new Mat(); 
+        Mat imread = Imgcodecs.imread("C:\\Users\\98432\\Desktop\\platetest\\NoPlate\\S22_KG2187_3.jpg", 0);
+        imread.convertTo(imread, CvType.CV_32FC1);
+        Mat reshape = imread.reshape(0, 1);
+        svm.predict(reshape, responseMat, 0);  
+		System.out.println(responseMat.dump());
+        
+        
+	}
+ 
+}