111

src/main/java/Test.java

@@ -7,8 +7,6 @@ import org.opencv.core.Point;
 import org.opencv.core.Rect;
 import org.opencv.core.RotatedRect;
 import org.opencv.core.Scalar;
-import org.opencv.core.Size;
-import org.opencv.highgui.HighGui;
 import org.opencv.imgcodecs.Imgcodecs;
 import org.opencv.imgproc.Imgproc;
 
@@ -139,21 +137,22 @@ public class Test extends DllLoad {
             Imgproc.line(src, point[2], point[3], new Scalar(0, 0, 255));
             Imgproc.line(src, point[3], point[0], new Scalar(0, 0, 255));
 
-            Mat dst = new Mat(src,rotatedRect.boundingRect());
+            Mat dst = new Mat(src, rotatedRect.boundingRect());
             show(dst);
 
             Mat dst2 = new Mat(dst.size(), dst.type());
             //旋转中心点
-            Point cp = new Point(src.width() / 2, src.height() / 2);
+            Point cp = new Point(dst.width() / 2, dst.height() / 2);
+            System.out.println(rotatedRect.angle);
             //旋转角度
-            int angle = 45;
+            double angle = rotatedRect.angle - 90;
             //缩放系数
             int scale = 1;
             //获取变换矩阵
             Mat matrix2D = Imgproc.getRotationMatrix2D(cp, angle, scale);
-            Imgproc.warpAffine(dst,dst2,matrix2D,dst2.size(),Imgproc.INTER_NEAREST);
+            Imgproc.warpAffine(dst, dst2, matrix2D, dst2.size(), Imgproc.INTER_NEAREST);
 
-//            show(dst2);
+            show(dst2);
 
 //            Mat mat = new Mat();
 //            Imgproc.Sobel(src, mat, -1, 0, 1);
@@ -170,7 +169,8 @@ public class Test extends DllLoad {
 //            Imgproc.drawContours(src, matOfPoints, -1, new Scalar(0, 255, 0), 1, Imgproc.LINE_AA);
         }
 
-        show(src);
+//        show(src);
+        System.exit(0);
 //
 //        // 创建输出单通道图像
 //        Mat grayImage = new Mat(src.rows(), src.cols(), CvType.CV_8SC1);

src/main/java/base/ImageGray.java

@@ -0,0 +1,25 @@
+package base;
+
+import org.opencv.core.Mat;
+import org.opencv.imgcodecs.Imgcodecs;
+import org.opencv.imgproc.Imgproc;
+
+import java.io.File;
+
+/**
+ * 图片转为灰色
+ */
+public class ImageGray extends DllLoad {
+
+    public static void main(String[] args) {
+        File file = new File("imgs/3.png");
+        Mat src = Imgcodecs.imread(file.getAbsolutePath());
+
+        Mat dst = new Mat();
+        //图片灰度化 https://blog.csdn.net/ren365880/article/details/103869207
+        Imgproc.cvtColor(src, dst, Imgproc.COLOR_BGR2GRAY);
+
+        show(dst);
+        System.exit(0);
+    }
+}

src/main/java/base/ImageThreshold.java

@@ -0,0 +1,46 @@
+package base;
+
+import org.opencv.core.Mat;
+import org.opencv.imgcodecs.Imgcodecs;
+import org.opencv.imgproc.Imgproc;
+
+import java.io.File;
+
+/**
+ * 图片二值化
+ */
+public class ImageThreshold extends DllLoad {
+
+    public static void main(String[] args) {
+        File file = new File("imgs/1.png");
+        Mat src = Imgcodecs.imread(file.getAbsolutePath());
+
+        Mat gray = new Mat();
+        //图片灰度化
+        Imgproc.cvtColor(src, gray, Imgproc.COLOR_BGR2GRAY);
+
+        {// 全局二值化 - 适应OTSU算法实
+            //一般,我们不知道用什么阈值可以获得更好的二值化图像.OTSU会通过一系列计算,计算出,比较优的阈值(可以理解为,在
+            //图像直方图中,如果存在两峰,那么,我们的阈值应该尽可能的在两峰之间的峰谷).因此,对于非双峰图,用这种方法可能效果
+            //不太理想,OTSU是计算二值化的算法,计算二值化的算法有很多,一般使用OTSU
+            Mat dst = new Mat();
+            Imgproc.threshold(gray, dst, 0, 255, Imgproc.THRESH_BINARY | Imgproc.THRESH_OTSU);
+            show(dst);
+        }
+
+        {// 全局二值化 - 不适应OTSU算法实
+            // 这里指定127为阔值，127以下转为黑色，127以上转为255(白)色
+            Mat dst2 = new Mat();
+            Imgproc.threshold(gray, dst2, 127, 255, Imgproc.THRESH_BINARY);
+            show(dst2);
+        }
+
+        {// 局部二值化
+            Mat dst = new Mat();
+            Imgproc.adaptiveThreshold(gray,dst,255,Imgproc.ADAPTIVE_THRESH_MEAN_C,Imgproc.THRESH_BINARY,11,2);
+            show(dst);
+        }
+
+        System.exit(0);
+    }
+}