拉普拉斯算子
Scharr算子与Sobel算子是使用一阶导数进行处理图像的边缘部分。使用任意两者之一的方法进行计算图形梯度的时候在图形的边缘处会有跃迁,即原图像的梯度函数会在边缘处存在极值。那么对其使用二阶导数情况又该如何?请看下面的示例图:
此图为一张典型的灰度跃迁图。我们可以很轻易地看到,其左侧向右部分至图形的中点在逐渐变黑,至中点后,其向右逐渐变白。那么其一阶导数图则如下所示:
Sobel算子的原理基于此,我们再对其一阶导数进行求导,其二阶导数图如下:
由图可知,在一阶导数的跃迁处,二阶导数过零点。而且最重要的是其周围存在一个或两个极值。常见的Laplacian算子如下:
Java代码(JavaFX Controller层)
public class Controller{
@FXML private Text fxText;
@FXML private ImageView imageView;
@FXML public void handleButtonEvent(ActionEvent actionEvent) throws IOException {
Node source = (Node) actionEvent.getSource();
Window theStage = source.getScene().getWindow();
FileChooser fileChooser = new FileChooser();
FileChooser.ExtensionFilter extFilter = new FileChooser.ExtensionFilter("PNG files (*.png)", "*.png");
fileChooser.getExtensionFilters().add(extFilter);
fileChooser.getExtensionFilters().add(new FileChooser.ExtensionFilter("JPG Files(*.jpg)", "*.jpg"));
File file = fileChooser.showOpenDialog(theStage);
runInSubThread(file.getPath());
}
private void runInSubThread(String filePath){
new Thread(new Runnable() {
@Override
public void run() {
try {
WritableImage writableImage = gradOfLaplacian(filePath);
Platform.runLater(new Runnable() {
@Override
public void run() {
imageView.setImage(writableImage);
}
});
} catch (IOException e) {
e.printStackTrace();
}
}
}).start();
}
private WritableImage gradOfLaplacian(String filePath) throws IOException {
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
Mat src = Imgcodecs.imread(filePath);
Mat dst = new Mat();
Imgproc.Laplacian(src, dst, CvType.CV_32F);
Core.convertScaleAbs(dst, dst);
MatOfByte matOfByte = new MatOfByte();
Imgcodecs.imencode(".jpg", dst, matOfByte);
byte[] bytes = matOfByte.toArray();
InputStream in = new ByteArrayInputStream(bytes);
BufferedImage bufImage = ImageIO.read(in);
WritableImage writableImage = SwingFXUtils.toFXImage(bufImage, null);
return writableImage;
}
}
运行图