图像分析之强度直方图分析
直方图介绍
强度直方图图形化显示不同的像素值在不同的强度值上的出现频率，对于灰度图像来说强度
范围为[0~255]之间，对于RGB的彩色图像可以独立显示三种颜色的强度直方图。强度直方
图是用来寻找灰度图像二值化阈值常用而且是有效的手段之一，如果一幅灰度图像的直方图
显示为两个波峰，则二值化阈值应该是这两个波峰之间的某个灰度值。同时强度直方图是调
整图像对比度的重要依据
直方图实现方法：
对一幅灰度图像从上到下，从左到右扫描每个像素值，在每个灰度值上计算像素数目，以这
些数据为基础完成图像直方图的绘制。
运行效果如下：
程序实现：
1. 首先对一幅RGB图像完成灰度转换，转换代码如下：
2. 初始化直方图数据数组int[256] 因为灰度值的范围为0～255
3. 扫描灰度图像，完成强度数据计算。
4. 使用Java 2D绘制直方图
直方图实现源代码：
package com.gloomyfish.image.analysis; import java.awt.Color; import java.awt.Graphics2D; import java.awt.image.BufferedImage; public class HistogramAnalysisAlg { private BufferedImage srcImage; private BufferedImage histogramImage; private int size = 280; public HistogramAnalysisAlg(BufferedImage srcImage){ histogramImage = new BufferedImage(size,size, BufferedImage.TYPE_4BYTE_ABGR); this.srcImage = srcImage; } public BufferedImage getHistogram() { int[] inPixels = new int[srcImage.getWidth()*srcImage.getHeight()]; int[] intensity = new int[256]; for(int i=0; i<intensity.length; i++) { intensity[i] = 0; } getRGB( srcImage, 0, 0, srcImage.getWidth(), srcImage.getHeight(), inPixels ); int index = 0; for(int row=0; row<srcImage.getHeight(); row++) { int ta = 0, tr = 0, tg = 0, tb = 0; for(int col=0; col<srcImage.getWidth(); col++) { index = row * srcImage.getWidth() + col; ta = (inPixels[index] >> 24) & 0xff; tr = (inPixels[index] >> 16) & 0xff; tg = (inPixels[index] >> 8) & 0xff; tb = inPixels[index] & 0xff; int gray = (int)(0.299 * (double)tr + 0.587 * (double)tg + 0.114 * (double)tb); intensity[gray]++; } } // draw XY Axis lines Graphics2D g2d = histogramImage.createGraphics(); g2d.setPaint(Color.BLACK); g2d.fillRect(0, 0, size, size); g2d.setPaint(Color.WHITE); g2d.drawLine(5, 250, 265, 250); g2d.drawLine(5, 250, 5, 5); // scale to 200 g2d.setPaint(Color.GREEN); int max = findMaxValue(intensity); float rate = 200.0f/((float)max); int offset = 2; for(int i=0; i<intensity.length; i++) { int frequency = (int)(intensity[i] * rate); g2d.drawLine(5 + offset + i, 250, 5 + offset + i, 250-frequency); } // X Axis Gray intensity g2d.setPaint(Color.RED); g2d.drawString("Gray Intensity", 100, 270); return histogramImage; } private int findMaxValue(int[] intensity) { int max = -1; for(int i=0; i<intensity.length; i++) { if(max < intensity[i]) { max = intensity[i]; } } return max; } /** * A convenience method for getting ARGB pixels from an image. This tries to avoid the performance * penalty of BufferedImage.getRGB unmanaging the image. */ public int[] getRGB( BufferedImage image, int x, int y, int width, int height, int[] pixels ) { int type = image.getType(); if ( type == BufferedImage.TYPE_INT_ARGB || type == BufferedImage.TYPE_INT_RGB ) return (int [])image.getRaster().getDataElements( x, y, width, height, pixels ); return image.getRGB( x, y, width, height, pixels, 0, width ); } /** * A convenience method for setting ARGB pixels in an image. This tries to avoid the performance * penalty of BufferedImage.setRGB unmanaging the image. */ public void setRGB( BufferedImage image, int x, int y, int width, int height, int[] pixels ) { int type = image.getType(); if ( type == BufferedImage.TYPE_INT_ARGB || type == BufferedImage.TYPE_INT_RGB ) image.getRaster().setDataElements( x, y, width, height, pixels ); else image.setRGB( x, y, width, height, pixels, 0, width ); } }
测试代码如下：
package com.gloomyfish.image.analysis; import java.awt.Dimension; import java.awt.Graphics; import java.awt.Graphics2D; import java.awt.MediaTracker; import java.awt.image.BufferedImage; import java.io.File; import java.io.IOException; import javax.imageio.ImageIO; import javax.swing.JComponent; import javax.swing.JFileChooser; import javax.swing.JFrame; public class ImageAnalysisUI extends JComponent { /** * */ private static final long serialVersionUID = 1518574788794973574L; private BufferedImage rawImg; private BufferedImage modImg; private MediaTracker tracker; private Dimension mySize; public ImageAnalysisUI(File f) { try { rawImg = ImageIO.read(f); HistogramAnalysisAlg filter = new HistogramAnalysisAlg(rawImg); modImg = filter.getHistogram(); } catch (IOException e1) { e1.printStackTrace(); } tracker = new MediaTracker(this); tracker.addImage(rawImg, 1); // blocked 10 seconds to load the image data try { if (!tracker.waitForID(1, 10000)) { System.out.println("Load error."); System.exit(1); }// end if } catch (InterruptedException e) { e.printStackTrace(); System.exit(1); }// end catch mySize = new Dimension(2*rawImg.getWidth() + 20, rawImg.getHeight()*2); JFrame imageFrame = new JFrame("Gloomyfish - Image Analysis"); imageFrame.getContentPane().add(this); imageFrame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE); imageFrame.pack(); imageFrame.setVisible(true); } public void paint(Graphics g) { Graphics2D g2 = (Graphics2D) g; g2.drawImage(rawImg, 0, 0, rawImg.getWidth(), rawImg.getHeight(), null); g2.drawImage(modImg, rawImg.getWidth()+10, 0, modImg.getWidth(), modImg.getHeight(), null); g2.drawString("source image", 10, rawImg.getHeight() +10); g2.drawString("connected component labeled area", 10 + modImg.getWidth(), rawImg.getHeight() +10); } public Dimension getPreferredSize() { return mySize; } public Dimension getMinimumSize() { return mySize; } public Dimension getMaximumSize() { return mySize; } public static void main(String[] args) { JFileChooser chooser = new JFileChooser(); chooser.showOpenDialog(null); File f = chooser.getSelectedFile(); new ImageAnalysisUI(f); } }
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