增加绿色车牌识别

README.md

@@ -111,5 +111,5 @@ debug_char_auxRoi：
 - 入门级教程项目，本人目前也正在学习图片识别相关技术；大牛请绕路
 - 当前项目仅实现了黄牌、蓝牌车牌识别操作，接下来会继续优化代码架构，并且加上绿牌识别、车牌识别训练等操作
 - 后续会逐步加入人脸识别等功能
-
+- **车牌图片来源于网络，仅用于交流学习，不得用于商业用途；如有侵权，请联系本人删除**
 

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

@@ -29,14 +29,14 @@ public class CharsRecognise {
      * @param plate： the input plate
      * @return the result of plate recognition
      */
-    public String charsRecognise(final Mat plate) {
+    public String charsRecognise(final Mat plate, String tempPath) {
 
         // 车牌字符方块集合
         Vector<Mat> matVec = new Vector<Mat>();
         // 车牌识别结果
         String plateIdentify = "";
 
-        int result = charsSegment.charsSegment(plate, matVec);
+        int result = charsSegment.charsSegment(plate, matVec, tempPath);
         if (0 == result) {
             for (int j = 0; j < matVec.size(); j++) {
                 Mat charMat = matVec.get(j);

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

@@ -19,14 +19,15 @@ import static org.bytedeco.javacpp.opencv_imgproc.warpAffine;
 
 import java.util.Vector;
 
+import org.bytedeco.javacpp.opencv_core;
 import org.bytedeco.javacpp.opencv_core.Mat;
 import org.bytedeco.javacpp.opencv_core.MatVector;
 import org.bytedeco.javacpp.opencv_core.Rect;
 import org.bytedeco.javacpp.opencv_core.Scalar;
 import org.bytedeco.javacpp.opencv_core.Size;
-import org.bytedeco.javacpp.opencv_core;
 import org.bytedeco.javacpp.opencv_imgcodecs;
 
+import com.yuxue.enumtype.PlateColor;
 import com.yuxue.util.Convert;
 
 /**
@@ -56,7 +57,7 @@ public class CharsSegment {
     private float bluePercent = DEFAULT_BLUEPERCEMT;
     private float whitePercent = DEFAULT_WHITEPERCEMT;
 
-    private boolean isDebug = false;
+    private boolean isDebug = true;
     
 
     /**
@@ -69,7 +70,7 @@ public class CharsSegment {
      *         <li>-3: null;
      *         </ul>
      */
-    public int charsSegment(final Mat input, Vector<Mat> resultVec) {
+    public int charsSegment(final Mat input, Vector<Mat> resultVec, String tempPath) {
         if (input.data().isNull()) {
             return -3;
         }
@@ -84,7 +85,8 @@ public class CharsSegment {
         int h = input.rows();
         Mat tmpMat = new Mat(input, new Rect((int) (w * 0.1), (int) (h * 0.1), (int) (w * 0.8), (int) (h * 0.8)));
         
-        switch (getPlateType(tmpMat, true)) {
+        PlateColor color= getPlateType(tmpMat, true);
+        switch (color) {
         case BLUE:
             threshold(input_grey, img_threshold, 10, 255, CV_THRESH_OTSU + CV_THRESH_BINARY);
             break;
@@ -93,21 +95,24 @@ public class CharsSegment {
             threshold(input_grey, img_threshold, 10, 255, CV_THRESH_OTSU + CV_THRESH_BINARY_INV);
             break;
             
+        case GREEN:
+            threshold(input_grey, img_threshold, 10, 255, CV_THRESH_OTSU + CV_THRESH_BINARY_INV);
+            break;
+
         default:
             return -3;
         }
 
         if (this.isDebug) {
-            opencv_imgcodecs.imwrite("tmp/debug_char_threshold.jpg", img_threshold);
+            opencv_imgcodecs.imwrite(tempPath + "debug_char_threshold.jpg", img_threshold);
         }
 
         // 去除车牌上方的柳钉以及下方的横线等干扰  //会导致虚拟机崩溃
         // clearLiuDing(img_threshold);
-
-        if (this.isDebug) {
-            String str = "tmp/debug_char_clearLiuDing.jpg";
+        /*if (this.isDebug) {
+            String str = tempPath + "debug_char_clearLiuDing.jpg";
             opencv_imgcodecs.imwrite(str, img_threshold);
-        }
+        }*/
 
         // 找轮廓
         Mat img_contours = new Mat();
@@ -126,10 +131,14 @@ public class CharsSegment {
         Vector<Rect> vecRect = new Vector<Rect>();
         for (int i = 0; i < contours.size(); ++i) {
             Rect mr = boundingRect(contours.get(i));
-            if (verifySizes(new Mat(img_threshold, mr)))
+            /*Mat temp = new Mat(img_threshold, mr);
+            String str = tempPath + "temp_"+i+".jpg";
+            opencv_imgcodecs.imwrite(str, temp);*/
+            
+            if (verifySizes(new Mat(img_threshold, mr))) {
                 vecRect.add(mr);
             }
-
+        }
         if (vecRect.size() == 0) {
             return -3;
         }
@@ -139,19 +148,20 @@ public class CharsSegment {
         SortRect(vecRect, sortedRect);
 
         // 获得指示城市的特定Rect,如苏A的"A"
-        int specIndex = GetSpecificRect(sortedRect);
+        int specIndex = GetSpecificRect(sortedRect, color);
+        System.err.println(specIndex);
 
         if (this.isDebug) {
             if (specIndex < sortedRect.size()) {
                 Mat specMat = new Mat(img_threshold, sortedRect.get(specIndex));
-                String str = "tmp/debug_specMat.jpg";
+                String str = tempPath + "debug_specMat.jpg";
                 opencv_imgcodecs.imwrite(str, specMat);
             }
         }
 
         // 根据特定Rect向左反推出中文字符
         // 这样做的主要原因是根据findContours方法很难捕捉到中文字符的准确Rect，因此仅能
-        // 退过特定算法来指定
+        // 通过特定算法来指定
         Rect chineseRect = new Rect();
         if (specIndex < sortedRect.size()) {
             chineseRect = GetChineseRect(sortedRect.get(specIndex));
@@ -161,7 +171,7 @@ public class CharsSegment {
 
         if (this.isDebug) {
             Mat chineseMat = new Mat(img_threshold, chineseRect);
-            String str = "tmp/debug_chineseMat.jpg";
+            String str = tempPath + "debug_chineseMat.jpg";
             opencv_imgcodecs.imwrite(str, chineseMat);
         }
 
@@ -170,7 +180,7 @@ public class CharsSegment {
         // 其余的Rect只按照顺序去6个，车牌只可能是7个字符！这样可以避免阴影导致的“1”字符
         Vector<Rect> newSortedRect = new Vector<Rect>();
         newSortedRect.add(chineseRect);
-        RebuildRect(sortedRect, newSortedRect, specIndex);
+        RebuildRect(sortedRect, newSortedRect, specIndex, color);
 
         if (newSortedRect.size() == 0) {
             return -3;
@@ -182,7 +192,7 @@ public class CharsSegment {
 
             auxRoi = preprocessChar(auxRoi);
             if (this.isDebug) {
-                String str = "tmp/debug_char_auxRoi_" + Integer.valueOf(i).toString() + ".jpg";
+                String str = tempPath + "debug_char_auxRoi_" + Integer.valueOf(i).toString() + ".jpg";
                 opencv_imgcodecs.imwrite(str, auxRoi);
             }
             resultVec.add(auxRoi);
@@ -191,12 +201,11 @@ public class CharsSegment {
     }
 
     /**
-     * 字符尺寸验证
-     * 
+     * 字符尺寸验证；去掉尺寸不符合的图块
      * @param r
      * @return
      */
-    private Boolean verifySizes(Mat r) {
+    public static Boolean verifySizes(Mat r) {
         float aspect = 45.0f / 90.0f;
         float charAspect = (float) r.cols() / (float) r.rows();
         float error = 0.7f;
@@ -212,8 +221,7 @@ public class CharsSegment {
         // % of pixel in area
         float percPixels = area / bbArea;
 
-        return percPixels <= 1 && charAspect > minAspect && charAspect < maxAspect && r.rows() >= minHeight
-                && r.rows() < maxHeight;
+        return percPixels <= 1 && charAspect > minAspect && charAspect < maxAspect && r.rows() >= minHeight && r.rows() < maxHeight;
     }
 
     /**
@@ -290,11 +298,10 @@ public class CharsSegment {
 
     /**
      * 找出指示城市的字符的Rect，例如苏A7003X，就是A的位置
-     * 
      * @param vecRect
      * @return
      */
-    private int GetSpecificRect(final Vector<Rect> vecRect) {
+    private int GetSpecificRect(final Vector<Rect> vecRect, PlateColor color) {
         Vector<Integer> xpositions = new Vector<Integer>();
         int maxHeight = 0;
         int maxWidth = 0;
@@ -314,12 +321,19 @@ public class CharsSegment {
             Rect mr = vecRect.get(i);
             int midx = mr.x() + mr.width() / 2;
 
+            if(PlateColor.GREEN.equals(color)) {
+                if ((mr.width() > maxWidth * 0.8 || mr.height() > maxHeight * 0.8)
+                        && (midx < this.theMatWidth * 2 / 8 && midx > this.theMatWidth / 8)) {
+                    specIndex = i;
+                }
+            } else {
                 // 如果一个字符有一定的大小，并且在整个车牌的1/7到2/7之间，则是我们要找的特殊车牌
                 if ((mr.width() > maxWidth * 0.8 || mr.height() > maxHeight * 0.8)
                         && (midx < this.theMatWidth * 2 / 7 && midx > this.theMatWidth / 7)) {
                     specIndex = i;
                 } 
             }
+        }
 
         return specIndex;
     }
@@ -336,9 +350,12 @@ public class CharsSegment {
      * @param specIndex
      * @return
      */
-    private int RebuildRect(final Vector<Rect> vecRect, Vector<Rect> outRect, int specIndex) {
+    private int RebuildRect(final Vector<Rect> vecRect, Vector<Rect> outRect, int specIndex, PlateColor color) {
         // 最大只能有7个Rect,减去中文的就只有6个Rect
         int count = 6;
+        if(PlateColor.GREEN.equals(color)) {
+            count = 7; // 绿牌要多一个
+        }
         for (int i = 0; i < vecRect.size(); i++) {
             // 将特殊字符左边的Rect去掉，这个可能会去掉中文Rect，不过没关系，我们后面会重建。
             if (i < specIndex)
@@ -359,7 +376,7 @@ public class CharsSegment {
      * @param out
      * @return
      */
-    private void SortRect(final Vector<Rect> vecRect, Vector<Rect> out) {
+    public static void SortRect(final Vector<Rect> vecRect, Vector<Rect> out) {
         Vector<Integer> orderIndex = new Vector<Integer>();
         Vector<Integer> xpositions = new Vector<Integer>();
         for (int i = 0; i < vecRect.size(); ++i) {
@@ -432,6 +449,4 @@ public class CharsSegment {
     }
 
 
-
-
 }

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

@@ -59,8 +59,8 @@ public class CoreFunc {
         final int max_yellow = 40;
         
         // green的H范围
-        final int min_green = 38;
-        final int max_green = 75;
+        final int min_green = 8;
+        final int max_green = 150;
         
         // 转到HSV空间进行处理，颜色搜索主要使用的是H分量进行蓝色与黄色的匹配工作
         Mat src_hsv = new Mat();

src/main/java/com/yuxue/service/impl/PlateServiceImpl.java

@@ -51,12 +51,11 @@ public class PlateServiceImpl implements PlateService {
         debugMap.put("debug_result", 6); // 原图处理结果
         debugMap.put("debug_crop", 7); // 切图
         debugMap.put("debug_resize", 8); // 切图resize
-        /*debugMap.put("debug_char_threshold", 9); // 
-        debugMap.put("debug_char_clearLiuDing", 10); // 去除柳钉
+        debugMap.put("debug_char_threshold", 9); // 
+        // debugMap.put("debug_char_clearLiuDing", 10); // 去除柳钉
         debugMap.put("debug_specMat", 11); // 
         debugMap.put("debug_chineseMat", 12); // 
         debugMap.put("debug_char_auxRoi", 13); // 
-        */    
     }
     
 
@@ -83,7 +82,7 @@ public class PlateServiceImpl implements PlateService {
             if(FileUtil.checkFile(f)) {
                 e = new PlateFileEntity();
                 e.setFileName(f.getName());
-                e.setFilePath(f.getAbsolutePath());
+                e.setFilePath(f.getAbsolutePath().replaceAll("\\\\", "/"));
                 e.setFileType(f.getName().substring(f.getName().lastIndexOf(".") + 1));
                 plateFileMapper.insertSelective(e);
             }
@@ -201,10 +200,11 @@ public class PlateServiceImpl implements PlateService {
         if (0 == plateDetect.plateDetect(src, matVector)) { // 定位及判断，获取到车牌图块Mat
 
             CharsRecognise cr = new CharsRecognise();
+            cr.setCRDebug(true);
             for (int i = 0; i < matVector.size(); ++i) { // 遍历车牌图块Mat，进行识别
                 Mat img = matVector.get(i);
 
-                String palte = cr.charsRecognise(img); // 字符识别
+                String palte = cr.charsRecognise(img, tempPath); // 字符识别
                 PlateColor color = CoreFunc.getPlateType(img, true);
                 String fileName = "result_" +  i + ".png";
 

src/main/resources/static/js/model/plate/plateContentMod.js

@@ -11,14 +11,14 @@ define(['api', 'utils'], function(api, utils){
         bindBtnEvent();
 
         // 监听按钮事件
-        $('body').keydown(function (e) {
+        /*$('body').keydown(function (e) {
             if (event.keyCode==116){ //回车键 //F5按键
                 e.preventDefault();
                 setTimeout(function () {
                     $("#refreshPlate").trigger('click');
                 }, 200);
             }
-        });
+        });*/
 
     }
 

src/test/java/com/yuxue/test/EasyPrTest.java

@@ -1,14 +1,22 @@
 package com.yuxue.test;
 
+import static org.bytedeco.javacpp.opencv_imgproc.CV_CHAIN_APPROX_NONE;
+import static org.bytedeco.javacpp.opencv_imgproc.CV_RETR_EXTERNAL;
+import static org.bytedeco.javacpp.opencv_imgproc.boundingRect;
+import static org.bytedeco.javacpp.opencv_imgproc.findContours;
+
 import java.io.File;
 import java.util.Vector;
 
 import org.bytedeco.javacpp.opencv_core.Mat;
+import org.bytedeco.javacpp.opencv_core.MatVector;
+import org.bytedeco.javacpp.opencv_core.Rect;
 import org.bytedeco.javacpp.opencv_imgcodecs;
 import org.junit.Test;
 
 import com.yuxue.easypr.core.CharsIdentify;
 import com.yuxue.easypr.core.CharsRecognise;
+import com.yuxue.easypr.core.CharsSegment;
 import com.yuxue.easypr.core.CoreFunc;
 import com.yuxue.easypr.core.PlateDetect;
 import com.yuxue.easypr.core.PlateLocate;
@@ -49,7 +57,7 @@ public class EasyPrTest {
             for (int i = 0; i < matVector.size(); ++i) { // 遍历检测返回的Mat集合，进行识别
                 Mat img = matVector.get(i);
 
-                String palte = cr.charsRecognise(img); // 字符识别
+                String palte = cr.charsRecognise(img, "tem/"); // 字符识别
                 PlateColor color = CoreFunc.getPlateType(img, true);
 
                 System.err.println("识别到的车牌: " + palte + "_" + color);
@@ -86,7 +94,7 @@ public class EasyPrTest {
             for (int i = 0; i < matVector.size(); ++i) {
                 Mat img = matVector.get(i);
                 // 弹窗显示
-                //opencv_imgcodecs.showImage("Plate Detected", img);
+                //opencv_highgui.imshow("Plate Detected", img);
 
                 String str = "d:/test/" + i + ".png";
                 opencv_imgcodecs.imwrite(str, img);
@@ -134,7 +142,7 @@ public class EasyPrTest {
         Mat src = opencv_imgcodecs.imread(imgPath);
         CharsRecognise cr = new CharsRecognise();
         cr.setCRDebug(true);
-        String result = cr.charsRecognise(src);
+        String result = cr.charsRecognise(src, "tem/");
         System.out.println("Chars Recognised: " + result);
     }
 
@@ -175,6 +183,58 @@ public class EasyPrTest {
         System.out.println(result);
     }
 
+    @Test
+    public void testGreenPlate() {
+        /*String imgPath = "res/image/test_image/result_0.png";
+        Mat src = opencv_imgcodecs.imread(imgPath);*/
+        
+        // 获取绿牌的H值范围
+        /*MatVector hsvSplit = new MatVector();
+        split(src_hsv, hsvSplit);
+        equalizeHist(hsvSplit.get(2), hsvSplit.get(2));
+        merge(hsvSplit, src_hsv);
+
+        int channels = src_hsv.channels();
+        int nRows = src_hsv.rows();
+        // 图像数据列需要考虑通道数的影响；
+        int nCols = src_hsv.cols() * channels;
+
+        // 连续存储的数据，按一行处理
+        if (src_hsv.isContinuous()) {
+            nCols *= nRows;
+            nRows = 1;
+        }
+        Map<Integer, Integer> map = Maps.newHashMap();
+        for (int i = 0; i < nRows; ++i) {
+            BytePointer p = src_hsv.ptr(i);
+            for (int j = 0; j < nCols; j += 3) {
+                int H = p.get(j) & 0xFF;
+                int S = p.get(j + 1) & 0xFF;
+                int V = p.get(j + 2) & 0xFF;
+
+                if(map.containsKey(H)) {
+                    int count = map.get(H);
+                    map.put(H, count+1);
+                } else {
+                    map.put(H, 1);
+                }
+                
+            }
+        }
+        map.entrySet().forEach(n->{
+            System.err.println(n.getKey() + "\t" + n.getValue());
+        });*/
+        
+        // 判断绿色车牌
+        /*Mat src_hsv = new Mat();
+        cvtColor(src, src_hsv, CV_BGR2HSV);
+        src_hsv = CoreFunc.colorMatch(src, PlateColor.GREEN, true);
+        System.err.println(CoreFunc.plateColorJudge(src, PlateColor.GREEN, true));
+        String str = "d:/PlateDetect/src_hsv.png";
+        opencv_imgcodecs.imwrite(str, src_hsv);*/
+        
+    }
+