Java圖像分析 - 計算垂直線

1

package ac.essex.ooechs.imaging.commons.edge.hough; 

import java.awt.image.BufferedImage; 
import java.awt.*; 
import java.util.Vector; 
import java.io.File; 

/** 
* <p/> 
* Java Implementation of the Hough Transform.<br /> 
* Used for finding straight lines in an image.<br /> 
* by Olly Oechsle 
* </p> 
* <p/> 
* Note: This class is based on original code from:<br /> 
* <a href="http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm">http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm</a> 
* </p> 
* <p/> 
* If you represent a line as:<br /> 
* x cos(theta) + y sin (theta) = r 
* </p> 
* <p/> 
* ... and you know values of x and y, you can calculate all the values of r by going through 
* all the possible values of theta. If you plot the values of r on a graph for every value of 
* theta you get a sinusoidal curve. This is the Hough transformation. 
* </p> 
* <p/> 
* The hough tranform works by looking at a number of such x,y coordinates, which are usually 
* found by some kind of edge detection. Each of these coordinates is transformed into 
* an r, theta curve. This curve is discretised so we actually only look at a certain discrete 
* number of theta values. "Accumulator" cells in a hough array along this curve are incremented 
* for X and Y coordinate. 
* </p> 
* <p/> 
* The accumulator space is plotted rectangularly with theta on one axis and r on the other. 
* Each point in the array represents an (r, theta) value which can be used to represent a line 
* using the formula above. 
* </p> 
* <p/> 
* Once all the points have been added should be full of curves. The algorithm then searches for 
* local peaks in the array. The higher the peak the more values of x and y crossed along that curve, 
* so high peaks give good indications of a line. 
* </p> 
* 
* @author Olly Oechsle, University of Essex 
*/ 

public class HoughTransform extends Thread { 

    public static void main(String[] args) throws Exception { 
     String filename = "/home/ooechs/Desktop/vase.png"; 

     // load the file using Java's imageIO library 
     BufferedImage image = javax.imageio.ImageIO.read(new File(filename)); 

     // create a hough transform object with the right dimensions 
     HoughTransform h = new HoughTransform(image.getWidth(), image.getHeight()); 

     // add the points from the image (or call the addPoint method separately if your points are not in an image 
     h.addPoints(image); 

     // get the lines out 
     Vector<HoughLine> lines = h.getLines(30); 

     // draw the lines back onto the image 
     for (int j = 0; j < lines.size(); j++) { 
      HoughLine line = lines.elementAt(j); 
      line.draw(image, Color.RED.getRGB()); 
     } 
    } 

    // The size of the neighbourhood in which to search for other local maxima 
    final int neighbourhoodSize = 4; 

    // How many discrete values of theta shall we check? 
    final int maxTheta = 180; 

    // Using maxTheta, work out the step 
    final double thetaStep = Math.PI/maxTheta; 

    // the width and height of the image 
    protected int width, height; 

    // the hough array 
    protected int[][] houghArray; 

    // the coordinates of the centre of the image 
    protected float centerX, centerY; 

    // the height of the hough array 
    protected int houghHeight; 

    // double the hough height (allows for negative numbers) 
    protected int doubleHeight; 

    // the number of points that have been added 
    protected int numPoints; 

    // cache of values of sin and cos for different theta values. Has a significant performance improvement. 
    private double[] sinCache; 
    private double[] cosCache; 

    /** 
    * Initialises the hough transform. The dimensions of the input image are needed 
    * in order to initialise the hough array. 
    * 
    * @param width The width of the input image 
    * @param height The height of the input image 
    */ 
    public HoughTransform(int width, int height) { 

     this.width = width; 
     this.height = height; 

     initialise(); 

    } 

    /** 
    * Initialises the hough array. Called by the constructor so you don't need to call it 
    * yourself, however you can use it to reset the transform if you want to plug in another 
    * image (although that image must have the same width and height) 
    */ 
    public void initialise() { 

     // Calculate the maximum height the hough array needs to have 
     houghHeight = (int) (Math.sqrt(2) * Math.max(height, width))/2; 

     // Double the height of the hough array to cope with negative r values 
     doubleHeight = 2 * houghHeight; 

     // Create the hough array 
     houghArray = new int[maxTheta][doubleHeight]; 

     // Find edge points and vote in array 
     centerX = width/2; 
     centerY = height/2; 

     // Count how many points there are 
     numPoints = 0; 

     // cache the values of sin and cos for faster processing 
     sinCache = new double[maxTheta]; 
     cosCache = sinCache.clone(); 
     for (int t = 0; t < maxTheta; t++) { 
      double realTheta = t * thetaStep; 
      sinCache[t] = Math.sin(realTheta); 
      cosCache[t] = Math.cos(realTheta); 
     } 
    } 

    /** 
    * Adds points from an image. The image is assumed to be greyscale black and white, so all pixels that are 
    * not black are counted as edges. The image should have the same dimensions as the one passed to the constructor. 
    */ 
    public void addPoints(BufferedImage image) { 

     // Now find edge points and update the hough array 
     for (int x = 0; x < image.getWidth(); x++) { 
      for (int y = 0; y < image.getHeight(); y++) { 
       // Find non-black pixels 
       if ((image.getRGB(x, y) & 0x000000ff) != 0) { 
        addPoint(x, y); 
       } 
      } 
     } 
    } 

    /** 
    * Adds a single point to the hough transform. You can use this method directly 
    * if your data isn't represented as a buffered image. 
    */ 
    public void addPoint(int x, int y) { 

     // Go through each value of theta 
     for (int t = 0; t < maxTheta; t++) { 

      //Work out the r values for each theta step 
      int r = (int) (((x - centerX) * cosCache[t]) + ((y - centerY) * sinCache[t])); 

      // this copes with negative values of r 
      r += houghHeight; 

      if (r < 0 || r >= doubleHeight) continue; 

      // Increment the hough array 
      houghArray[t][r]++; 

     } 

     numPoints++; 
    } 

    /** 
    * Once points have been added in some way this method extracts the lines and returns them as a Vector 
    * of HoughLine objects, which can be used to draw on the 
    * 
    * @param percentageThreshold The percentage threshold above which lines are determined from the hough array 
    */ 
    public Vector<HoughLine> getLines(int threshold) { 

     // Initialise the vector of lines that we'll return 
     Vector<HoughLine> lines = new Vector<HoughLine>(20); 

     // Only proceed if the hough array is not empty 
     if (numPoints == 0) return lines; 

     // Search for local peaks above threshold to draw 
     for (int t = 0; t < maxTheta; t++) { 
      loop: 
      for (int r = neighbourhoodSize; r < doubleHeight - neighbourhoodSize; r++) { 

       // Only consider points above threshold 
       if (houghArray[t][r] > threshold) { 

        int peak = houghArray[t][r]; 

        // Check that this peak is indeed the local maxima 
        for (int dx = -neighbourhoodSize; dx <= neighbourhoodSize; dx++) { 
         for (int dy = -neighbourhoodSize; dy <= neighbourhoodSize; dy++) { 
          int dt = t + dx; 
          int dr = r + dy; 
          if (dt < 0) dt = dt + maxTheta; 
          else if (dt >= maxTheta) dt = dt - maxTheta; 
          if (houghArray[dt][dr] > peak) { 
           // found a bigger point nearby, skip 
           continue loop; 
          } 
         } 
        } 

        // calculate the true value of theta 
        double theta = t * thetaStep; 

        // add the line to the vector 
        lines.add(new HoughLine(theta, r)); 

       } 
      } 
     } 

     return lines; 
    } 

    /** 
    * Gets the highest value in the hough array 
    */ 
    public int getHighestValue() { 
     int max = 0; 
     for (int t = 0; t < maxTheta; t++) { 
      for (int r = 0; r < doubleHeight; r++) { 
       if (houghArray[t][r] > max) { 
        max = houghArray[t][r]; 
       } 
      } 
     } 
     return max; 
    } 

    /** 
    * Gets the hough array as an image, in case you want to have a look at it. 
    */ 
    public BufferedImage getHoughArrayImage() { 
     int max = getHighestValue(); 
     BufferedImage image = new BufferedImage(maxTheta, doubleHeight, BufferedImage.TYPE_INT_ARGB); 
     for (int t = 0; t < maxTheta; t++) { 
      for (int r = 0; r < doubleHeight; r++) { 
       double value = 255 * ((double) houghArray[t][r])/max; 
       int v = 255 - (int) value; 
       int c = new Color(v, v, v).getRGB(); 
       image.setRGB(t, r, c); 
      } 
     } 
     return image; 
    } 

}

來源：http://vase.essex.ac.uk/software/HoughTransform/HoughTransform.java.html

來源

2010-10-14 12:12:14 Symaptax

2

一個簡單的分割算法可以幫助你。該算法繼承人如何工作的：

掃描像素從左至右記錄第一黑色（無論您線的顏色）像素的位置。
進行這個過程除非你找到一個完整的掃描時你找不到黑色像素。也要記錄這個位置。
我們是只是感興趣的Y位置在這裏。現在使用這個Y位置水平分割圖像。
現在我們將做同樣的過程但這次我們是在我們剛剛創建的段（在一次一列）要掃描從上到下。
這次我們感興趣的是X 職位。
所以最後我們得到每一行行的範圍，或者你可以說每個行都有一個包圍盒。
這些邊界框的總數是行數。

您可以根據您的需要在算法中做很多優化。

來源

2010-10-14 12:14:55

+0

+1很好的解釋。這用於OCR以及字符分割。 – 2010-10-14 12:16:09

+0

是否有Java API來實現這一目標？ – Sid 2010-10-14 12:36:47

0

這取決於它們看起來像多少。

把圖像以1位（黑色和白色）以保留線和帶來的背景的純白色
或許做簡單的清理像斑點去除（去除任何小的黑色成分）的方法。

然後，

查找黑像素
使用洪水填充算法，以發現其程度
查看該形狀滿足標準，作爲一個線路（lineCount ++若有）
刪除它
重複此操作，直到沒有黑色像素

在很大程度上取決於你做的有多好＃3，只是在這一節的一些想法

使用霍夫檢查你有一條線，而且它是垂直的（ISH）
（後＃ 1）將其旋轉到垂直和檢查它的寬/高比

來源

2010-10-14 12:25:19

1

我實現使用Marvin Framework簡單的解決方案（必須得到改善），該發現的垂直線的開始和結束點，並打印線的總數找到。

方法：

二值化利用給定的閾值的圖像。
對於每個像素，如果是黑色（固體），試圖找到一條垂直線
保存的X，Y的起點和終點
線的最小lenght的？這是一條可以接受的路線！
用紅色打印起點，用綠色打印終點。

輸出圖像顯示如下：

enter image description here

的程序輸出：

Vertical line fount at: (74,9,70,33) 
Vertical line fount at: (113,9,109,31) 
Vertical line fount at: (80,10,76,32) 
Vertical line fount at: (137,11,133,33) 
Vertical line fount at: (163,11,159,33) 
Vertical line fount at: (184,11,180,33) 
Vertical line fount at: (203,11,199,33) 
Vertical line fount at: (228,11,224,33) 
Vertical line fount at: (248,11,244,33) 
Vertical line fount at: (52,12,50,33) 
Vertical line fount at: (145,13,141,35) 
Vertical line fount at: (173,13,169,35) 
Vertical line fount at: (211,13,207,35) 
Vertical line fount at: (94,14,90,36) 
Vertical line fount at: (238,14,236,35) 
Vertical line fount at: (130,16,128,37) 
Vertical line fount at: (195,16,193,37) 
Vertical lines total: 17

最後，源代碼：

import java.awt.Color; 
import java.awt.Point; 
import marvin.image.MarvinImage; 
import marvin.io.MarvinImageIO; 
import marvin.plugin.MarvinImagePlugin; 
import marvin.util.MarvinPluginLoader; 

public class VerticalLineCounter { 

    private MarvinImagePlugin threshold = MarvinPluginLoader.loadImagePlugin("org.marvinproject.image.color.thresholding"); 

    public VerticalLineCounter(){ 
     // Binarize 
     MarvinImage image = MarvinImageIO.loadImage("./res/lines.jpg"); 
     MarvinImage binImage = image.clone(); 
     threshold.setAttribute("threshold", 127); 
     threshold.process(image, binImage); 

     // Find lines and save an output image 
     MarvinImage imageOut = findVerticalLines(binImage, image); 
     MarvinImageIO.saveImage(imageOut, "./res/lines_out.png"); 
    } 

    private MarvinImage findVerticalLines(MarvinImage binImage, MarvinImage originalImage){ 
     MarvinImage imageOut = originalImage.clone(); 
     boolean[][] processedPixels = new boolean[binImage.getWidth()][binImage.getHeight()]; 
     int color; 
     Point endPoint; 
     int totalLines=0; 
     for(int y=0; y<binImage.getHeight(); y++){ 
      for(int x=0; x<binImage.getWidth(); x++){ 
       if(!processedPixels[x][y]){ 
        color = binImage.getIntColor(x, y); 

        // Black? 
        if(color == 0xFF000000){ 
         endPoint = getEndOfLine(x,y,binImage,processedPixels); 

         // Line lenght threshold 
         if(endPoint.x - x > 5 || endPoint.y - y > 5){ 
          imageOut.fillRect(x-2, y-2, 5, 5, Color.red); 
          imageOut.fillRect(endPoint.x-2, endPoint.y-2, 5, 5, Color.green); 
          totalLines++; 
          System.out.println("Vertical line fount at: ("+x+","+y+","+endPoint.x+","+endPoint.y+")"); 
         } 
        } 
       } 
       processedPixels[x][y] = true; 
      } 
     } 
     System.out.println("Vertical lines total: "+totalLines); 
     return imageOut; 
    } 

    private Point getEndOfLine(int x, int y, MarvinImage image, boolean[][] processedPixels){ 
     int xC=x; 
     int cY=y; 
     while(true){ 
      processedPixels[xC][cY] = true; 
      processedPixels[xC-1][cY] = true; 
      processedPixels[xC-2][cY] = true; 
      processedPixels[xC-3][cY] = true; 
      processedPixels[xC+1][cY] = true; 
      processedPixels[xC+2][cY] = true; 
      processedPixels[xC+3][cY] = true; 

      if(getSafeIntColor(xC,cY,image) < 0xFF000000){ 
       // nothing 
      } 
      else if(getSafeIntColor(xC-1,cY,image) == 0xFF000000){ 
       xC = xC-2; 
      } 
      else if(getSafeIntColor(xC-2,cY,image) == 0xFF000000){ 
       xC = xC-3; 
      } 
      else if(getSafeIntColor(xC+1,cY,image) == 0xFF000000){ 
       xC = xC+2; 
      } 
      else if(getSafeIntColor(xC+2,cY,image) == 0xFF000000){ 
       xC = xC+3; 
      } 
      else{ 
       return new Point(xC, cY); 
      } 
      cY++; 
     } 
    } 
    private int getSafeIntColor(int x, int y, MarvinImage image){ 
     if(x >= 0 && x < image.getWidth() && y >= 0 && y < image.getHeight()){ 
      return image.getIntColor(x, y); 
     } 
     return -1; 
    } 
    public static void main(String args[]){ 
     new VerticalLineCounter(); 
     System.exit(0); 
    } 
}

來源

2013-07-24 15:51:22

Java圖像分析 - 計算垂直線

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