Imgproc.cvtColor
Imgproc.threshold
Imgproc.adaptiveThreshold
Imgproc.medianBlur
Core.addWeighted
工具类见 http://www.gaohaiyan.com/3229.html
本例完整代码：
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import java.awt.Color; import java.awt.image.BufferedImage; import javax.swing.ImageIcon; import javax.swing.JCheckBox; import javax.swing.JFrame; import javax.swing.JLabel; import javax.swing.JSlider; import javax.swing.event.ChangeEvent; import javax.swing.event.ChangeListener; import org.opencv.core.Core; import org.opencv.core.CvType; import org.opencv.core.Mat; import org.opencv.imgcodecs.Imgcodecs; import org.opencv.imgproc.Imgproc; /** * 二值化、锐化 */ public class Transcolor extends JFrame { private JLabel imageView; private double threshold = 0; private double maxval = 0; private int type = 0; private int adaptiveMethod = 0; private int thresholdType = 0; private int blockSize = 3; private double C = 0; private boolean isAdaptiveMethod = false; private int sharpness = 0; private boolean isSharpness = false; private Mat srcMat; public Transcolor(String path) { JLabel thresholdLabel = new JLabel("阈值:" + threshold); thresholdLabel.setBounds(15, 10, 120, 15); JSlider thresholdBar = new JSlider(1, 600); thresholdBar.setValue((int) threshold); thresholdBar.setBounds(140, 5, 220, 25); thresholdBar.addChangeListener(new ChangeListener() { @Override public void stateChanged(ChangeEvent e) { threshold = thresholdBar.getValue() * 1.0d; thresholdLabel.setText("阈值:" + threshold); setPic(); } }); JLabel maxvalLabel = new JLabel("最大值:" + maxval); maxvalLabel.setBounds(15, 40, 120, 15); JSlider maxvalBar = new JSlider(1, 600); maxvalBar.setValue((int) maxval); maxvalBar.setBounds(140, 35, 220, 25); maxvalBar.addChangeListener(new ChangeListener() { @Override public void stateChanged(ChangeEvent e) { maxval = maxvalBar.getValue() * 1.0d; maxvalLabel.setText("最大值:" + maxval); setPic(); } }); JLabel typeLabel = new JLabel("类型:CV_8UC1，" + type); typeLabel.setBounds(15, 70, 120, 15); JSlider typeBar = new JSlider(0, 6); typeBar.setValue((int) type); typeBar.setBounds(140, 65, 220, 25); typeBar.addChangeListener(new ChangeListener() { @Override public void stateChanged(ChangeEvent e) { type = typeBar.getValue(); // 数据类型：U（unsigned integer）无符号整数，S（signed integer）有符号整数，F（float）浮点数 // 通道数：1-4，(5)-(8)。 // 位数：8/16/32/64。8和16位只匹配数据类型U和S，32位只匹配S和F，64位只匹配F // 类型：CV_+位数+数据类型+C通道数 // org.opencv.core.CvType switch (type) { case 0: { typeLabel.setText("类型:CV_8UC1，" + CvType.CV_8UC1); break; } case 1: { typeLabel.setText("类型:CV_8SC1，" + CvType.CV_8SC1); break; } case 2: { typeLabel.setText("类型:CV_16UC1，" + CvType.CV_16UC1); break; } case 3: { typeLabel.setText("类型:CV_16SC1，" + CvType.CV_16SC1); break; } case 4: { typeLabel.setText("类型:CV_32SC1，" + CvType.CV_32SC1); break; } case 5: { typeLabel.setText("类型:CV_32FC1，" + CvType.CV_32FC1); break; } case 6: { typeLabel.setText("类型:CV_64FC1，" + CvType.CV_64FC1); break; } } setPic(); } }); JLabel adaptiveMethodLabel = new JLabel("临界值算法:Imgproc.ADAPTIVE_THRESH_MEAN_C，" + adaptiveMethod); adaptiveMethodLabel.setBounds(15, 125, 360, 15); JSlider adaptiveMethodBar = new JSlider(0, 1); adaptiveMethodBar.setValue(adaptiveMethod); adaptiveMethodBar.setBounds(380, 120, 120, 25); adaptiveMethodBar.addChangeListener(new ChangeListener() { @Override public void stateChanged(ChangeEvent e) { adaptiveMethod = adaptiveMethodBar.getValue(); // ADAPTIVE_THRESH_MEAN_C 局部邻域块的平均值，先求出块中的均值，再减去常数C // ADAPTIVE_THRESH_GAUSSIAN_C 该算法是在区域中(x, y)周围的像素根据高斯函数按照它们离中心点的距离进行加权计算，再减去常数C。 switch (adaptiveMethod) { case 0: { adaptiveMethodLabel.setText("临界值算法:Imgproc.ADAPTIVE_THRESH_MEAN_C，" + adaptiveMethod); break; } case 1: { adaptiveMethodLabel.setText("临界值算法:Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C，" + adaptiveMethod); break; } } if (isAdaptiveMethod) setPic(); } }); JLabel thresholdTypeLabel = new JLabel("临界值类型:Imgproc.THRESH_BINARY，" + thresholdType); thresholdTypeLabel.setBounds(15, 155, 360, 15); JSlider thresholdTypeBar = new JSlider(0, 1); thresholdTypeBar.setValue(thresholdType); thresholdTypeBar.setBounds(380, 150, 120, 25); thresholdTypeBar.addChangeListener(new ChangeListener() { @Override public void stateChanged(ChangeEvent e) { thresholdType = thresholdTypeBar.getValue(); // THRESH_BINARY 表示阈值的二值化操作，大于阈值使用maxval表示，小于阈值使用0表示 // THRESH_BINARY_INV 表示阈值的二值化翻转操作，大于阈值的使用0表示，小于阈值的使用最大值表示 switch (thresholdType) { case 0: { thresholdTypeLabel.setText("临界值类型:Imgproc.THRESH_BINARY，" + thresholdType); break; } case 1: { thresholdTypeLabel.setText("临界值类型:Imgproc.CV_THRESH_BINARY_INV，" + thresholdType); break; } } if (isAdaptiveMethod) setPic(); } }); JLabel blockSizeLabel = new JLabel("像素临界范围:" + blockSize); blockSizeLabel.setBounds(15, 185, 120, 15); JSlider blockSizeBar = new JSlider(3, 300); blockSizeBar.setValue(blockSize); blockSizeBar.setBounds(140, 180, 220, 25); blockSizeBar.addChangeListener(new ChangeListener() { @Override public void stateChanged(ChangeEvent e) { int value = blockSizeBar.getValue(); if (value % 2 == 0) { value = value + 1; } blockSize = value; // 必须是大于1的奇数 blockSizeLabel.setText("像素临界范围:" + blockSize); if (isAdaptiveMethod) setPic(); } }); JCheckBox adaptiveMethodBtn = new JCheckBox("高级（灰度图自动临界算法）"); adaptiveMethodBtn.setBounds(40, 95, 200, 25); adaptiveMethodBtn.addChangeListener(new ChangeListener() { @Override public void stateChanged(ChangeEvent e) { isAdaptiveMethod = !isAdaptiveMethod; if (isAdaptiveMethod) { maxvalLabel.setForeground(Color.RED); adaptiveMethodLabel.setForeground(Color.RED); thresholdTypeLabel.setForeground(Color.RED); blockSizeLabel.setForeground(Color.RED); } else { maxvalLabel.setForeground(Color.BLACK); adaptiveMethodLabel.setForeground(Color.BLACK); thresholdTypeLabel.setForeground(Color.BLACK); blockSizeLabel.setForeground(Color.BLACK); } } }); JCheckBox sharpnessBtn = new JCheckBox("锐化"); sharpnessBtn.setBounds(40, 210, 200, 25); sharpnessBtn.addChangeListener(new ChangeListener() { @Override public void stateChanged(ChangeEvent e) { isSharpness = !isSharpness; } }); JLabel sharpnessLabel = new JLabel("锐化:" + sharpness); sharpnessLabel.setBounds(15, 235, 60, 15); JSlider sharpnessBar = new JSlider(1, 90); // 81 sharpnessBar.setValue(sharpness); sharpnessBar.setBounds(80, 235, 220, 25); sharpnessBar.addChangeListener(new ChangeListener() { @Override public void stateChanged(ChangeEvent e) { int value = sharpnessBar.getValue(); if (value % 2 == 0) { value = value + 1; } sharpness = value; // 必须是奇数 sharpnessLabel.setText("锐化:" + sharpness); if (isSharpness) setPic(); } }); srcMat = Imgcodecs.imread(path); int width = srcMat.width(); int height = srcMat.height(); imageView = new JLabel(""); imageView.setBounds(40, 270, width, height); this.setTitle("图片二值化、锐化"); this.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE); this.setSize(width + 100, height + 320); this.getContentPane().setLayout(null); this.getContentPane().add(thresholdLabel); this.getContentPane().add(thresholdBar); this.getContentPane().add(maxvalLabel); this.getContentPane().add(maxvalBar); this.getContentPane().add(typeLabel); this.getContentPane().add(typeBar); this.getContentPane().add(adaptiveMethodBtn); this.getContentPane().add(adaptiveMethodLabel); this.getContentPane().add(adaptiveMethodBar); this.getContentPane().add(blockSizeLabel); this.getContentPane().add(blockSizeBar); this.getContentPane().add(thresholdTypeLabel); this.getContentPane().add(thresholdTypeBar); this.getContentPane().add(sharpnessBtn); this.getContentPane().add(sharpnessLabel); this.getContentPane().add(sharpnessBar); this.getContentPane().add(imageView); setIcon(srcMat); } private void setPic() { Mat mat = new Mat(srcMat.rows(), srcMat.cols(), srcMat.type()); if (isAdaptiveMethod) { Mat tmp = new Mat(srcMat.rows(), srcMat.cols(), srcMat.type()); // 须先得到灰度图 Imgproc.cvtColor(srcMat, tmp, Imgproc.COLOR_BGR2GRAY); Imgproc.adaptiveThreshold(tmp, mat, maxval, adaptiveMethod, thresholdType, blockSize, C); setIcon(mat); return; } if (isSharpness) { Imgproc.medianBlur(srcMat, mat, sharpness); // 中间值模糊 // Mat src1, double alpha, Mat src2, double beta, double gamma, Mat dst Core.addWeighted(srcMat, 2.1, mat, -1.1, 0, mat); // 图片叠加 src1*alpha+src2*beta+gamma setIcon(mat); return; } Imgproc.threshold(srcMat, mat, threshold, maxval, type); setIcon(mat); } private void setIcon(Mat mat) { BufferedImage image = CVUtil.matToBufferedImage(mat); // CVUtil 见 http://www.gaohaiyan.com/3229.html imageView.setIcon(new ImageIcon(image)); } } |
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声明
本文由崔维友 威格灵 cuiweiyou vigiles cuiweiyou 原创，转载请注明出处：http://www.gaohaiyan.com/3239.html
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