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src/main/java/com/yuxue/util/ImageUtil.java

@@ -0,0 +1,131 @@
+package com.yuxue.util;
+
+import java.util.Arrays;
+import java.util.Map;
+import java.util.Set;
+
+import org.bytedeco.javacpp.BytePointer;
+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.Size;
+import org.bytedeco.javacpp.opencv_imgcodecs;
+import org.bytedeco.javacpp.opencv_imgproc;
+
+import com.google.common.collect.Maps;
+
+
+/**
+ * 车牌图片处理工具类
+ * @author yuxue
+ * @date 2020-05-18 12:07
+ */
+public class ImageUtil {
+
+    private static String DEFAULT_BASE_TEST_PATH = "D:/PlateDetect/temp/";
+
+    public static final int DEFAULT_GAUSSIANBLUR_SIZE = 5;
+
+    public static void main(String[] args) {
+
+        String tempPath = DEFAULT_BASE_TEST_PATH ;
+        FileUtil.createDir(tempPath); // 创建文件夹
+
+        // String filename = DEFAULT_BASE_TEST_PATH + "test01.jpg";
+        String filename = DEFAULT_BASE_TEST_PATH + "test.png";
+        Mat inMat = opencv_imgcodecs.imread(filename);
+
+        ImageUtil.rgb2Hsv(inMat, true, tempPath);
+        // ImageUtil.gaussianBlur(inMat, true, tempPath);
+    }
+
+
+    /**
+     * 获取HSV中各个颜色所对应的H的范围
+     * HSV是一种比较直观的颜色模型，所以在许多图像编辑工具中应用比较广泛，这个模型中颜色的参数分别是：色调（H, Hue），饱和度（S,Saturation），明度（V, Value）
+     * 1.PS软件时，H取值范围是0-360，S取值范围是（0%-100%），V取值范围是（0%-100%）。         
+     * 2.利用openCV中cvSplit函数的在选择图像IPL_DEPTH_32F类型时，H取值范围是0-360，S取值范围是0-1（0%-100%），V取值范围是0-1（0%-100%）。
+     * 3.利用openCV中cvSplit函数的在选择图像IPL_DEPTH_8UC类型时，H取值范围是0-180，S取值范围是0-255，V取值范围是0-255
+     * @param inMat
+     * @param debug
+     */
+    public static void getHSVValue(Mat inMat, Boolean debug, String tempPath) {
+        
+        int channels = inMat.channels();
+        int nRows = inMat.rows();
+        // 图像数据列需要考虑通道数的影响；
+        int nCols = inMat.cols() * channels;
+
+        // 连续存储的数据，按一行处理
+        if (inMat.isContinuous()) {
+            nCols *= nRows;
+            nRows = 1;
+        }
+        Map<Integer, Integer> map = Maps.newHashMap();
+        for (int i = 0; i < nRows; ++i) {
+            BytePointer p = inMat.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);
+                }
+
+            }
+        }
+
+        Set set = map.keySet();
+        Object[] arr = set.toArray();
+        Arrays.sort(arr);
+        for (Object key : arr) {
+            System.out.println(key + ": " + map.get(key));
+        }
+
+        return;
+    }
+
+    
+    /**
+     * rgb图像转换为hsv图像
+     * @param inMat
+     * @param debug
+     * @param tempPath
+     * @return
+     */
+    public static Mat rgb2Hsv(Mat inMat, Boolean debug, String tempPath) {
+        // 转到HSV空间进行处理
+        Mat hsvMat = new Mat(inMat.rows(), inMat.cols(), opencv_imgproc.CV_BGR2HSV);
+        opencv_imgproc.cvtColor(inMat, hsvMat, opencv_imgproc.CV_BGR2HSV);
+        MatVector hsvSplit = new MatVector();
+        opencv_core.split(hsvMat, hsvSplit);
+        // opencv_imgproc.equalizeHist(hsvSplit.get(2), hsvSplit.get(2));
+        // opencv_core.merge(hsvSplit, hsvMat);
+        
+        if (debug) {
+            opencv_imgcodecs.imwrite(tempPath + "hsvMat_"+System.currentTimeMillis()+".jpg", hsvMat);
+        }
+        return hsvMat;
+    }
+
+    
+    /**
+     * 高斯模糊
+     * @param inMat
+     * @param debug
+     * @return
+     */
+    public static Mat gaussianBlur(Mat inMat, Boolean debug, String tempPath) {
+        Mat gsMat = new Mat();
+        opencv_imgproc.GaussianBlur(inMat, gsMat, new Size(DEFAULT_GAUSSIANBLUR_SIZE, DEFAULT_GAUSSIANBLUR_SIZE), 0, 0, opencv_core.BORDER_DEFAULT);
+        if (debug) {
+            opencv_imgcodecs.imwrite(tempPath + "gaussianBlur.jpg", gsMat);
+        }
+        return gsMat;
+    }
+
+}

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

@@ -188,43 +188,6 @@ public class PlateRecoTest {
         String imgPath = "res/image/test_image/debug_resize_2.jpg";
         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();
         opencv_imgproc.cvtColor(src, src_hsv, opencv_imgproc.CV_BGR2HSV);