图像处理之错切变换

图像处理之错切变换

一：基本数学知识：

图像错切变换在图像几何形变方面非常有用，常见的错切变换分为X方向与Y方向的

错切变换。对应的数学矩阵分别如下：

  

根据上述矩阵假设P(x1, y1)为错切变换之前的像素点，则错切变换以后对应的像素

P’(x2, y2)当X方向错切变换时：

当Y方向错切变换时：

二：程序实现基本思路

实现图像错切变换时，必须考虑图像将目标像素点坐标变为源相点坐标时小数部分对

像素值的影响，这里通过临近点插值算法实现了目标像素值的计算。根据目标像素计

算源像素的公式可以根据上面的数学公式运算以后分别求的x1,y1的值。由于错切以

后图像会在宽或者高上比原图像大，多出来的这些背景像素默认填充颜色为黑色。

类ShearFilter实现了图像水平或者垂直方向的错切变换，支持角度与背景颜色参数

设置。

 三：编程关键点解析

Ø  计算错切以后图像的宽与高

        double angleValue = (angle/180.0d) * Math.PI;
        outh = vertical ? (int)(height + width * Math.tan(angleValue)) : height;
        outw = vertical ? width : (int)(width + height * Math.tan(angleValue));
        System.out.println("after shear, new width : " + outw);
        System.out.println("after shear, new height: " + outh);

Ø  根据目标像素点坐标计算源像素点坐标

	double prow = vertical ? row + Math.tan(angleValue) * (col - width) : row;
	double pcol = vertical ? col : col + Math.tan(angleValue) * (row - height);
        int[] rgb = getPixel(inPixels, width, height, prow, pcol);

Ø  临近点插值计算目标像素点像素值

private int[] getPixel(int[] input, int width, int height, 
		double prow, double pcol) {
	double row = Math.floor(prow);
	double col = Math.floor(pcol);
	if(row < 0 || row >= height) {
		return new int[]{backgroundColor.getRed(), 
				backgroundColor.getGreen(), 
				backgroundColor.getBlue()};
	}
	if(col < 0 || col >= width) {
		return new int[]{backgroundColor.getRed(), 
				backgroundColor.getGreen(), 
				backgroundColor.getBlue()};
	}
	double u = vertical ? (prow - row) : pcol - col;
	int nextCol = (int)(col + 1);
	int nextRow = (int)(row + 1);
	if((col + 1) >= width) {
		nextCol = (int)col;
	}
	if((row + 1) >= height) {
		nextRow = (int)row;
	}
	int index1 = (int)(row * width + col);
	int index2 = vertical ? (int)(nextRow * width + col) : (int)(row * width + nextCol);
	
	int tr1, tr2;
	int tg1, tg2;
	int tb1, tb2;
	
    tr1 = (input[index1] >> 16) & 0xff;
    tg1 = (input[index1] >> 8) & 0xff;
    tb1 = input[index1] & 0xff;
    
    tr2 = (input[index2] >> 16) & 0xff;
    tg2 = (input[index2] >> 8) & 0xff;
    tb2 = input[index2] & 0xff;
    
    int tr = (int)(tr1 * (1-u) + tr2 * u);
    int tg = (int)(tg1 * (1-u) + tg2 * u);
    int tb = (int)(tb1 * (1-u) + tb2 * u);
    
	return new int[]{tr, tg, tb};
}

四：运行效果

五：类ShearFilter完整代码

package com.gloomyfish.filter.study;

import java.awt.Color;
import java.awt.image.BufferedImage;
import java.awt.image.ColorModel;

public class ShearFilter extends AbstractBufferedImageOp {
	private int outw;
	private int outh;
	private double angle;
	private Color backgroundColor;
	private boolean vertical;
	
	public void setVertical(boolean vertical) {
		this.vertical = vertical;
	}

	public ShearFilter()
	{
		backgroundColor = Color.BLACK;
		vertical = false;
		this.angle = 20;
	}
	
	public int getOutw() {
		return outw;
	}
	public void setOutw(int outw) {
		this.outw = outw;
	}
	public int getOuth() {
		return outh;
	}
	public void setOuth(int outh) {
		this.outh = outh;
	}
	public double getAngle() {
		return angle;
	}
	public void setAngle(double angle) {
		this.angle = angle;
	}
	public Color getBackgroundColor() {
		return backgroundColor;
	}
	public void setBackgroundColor(Color backgroundColor) {
		this.backgroundColor = backgroundColor;
	}
	@Override
	public BufferedImage filter(BufferedImage src, BufferedImage dest) {
		int width = src.getWidth();
        int height = src.getHeight();

        double angleValue = (angle/180.0d) * Math.PI;
        outh = vertical ? (int)(height + width * Math.tan(angleValue)) : height;
        outw = vertical ? width : (int)(width + height * Math.tan(angleValue));
        System.out.println("after shear, new width : " + outw);
        System.out.println("after shear, new height: " + outh);
        
        int[] inPixels = new int[width*height];
        int[] outPixels = new int[outh*outw];
        getRGB( src, 0, 0, width, height, inPixels );
        int index = 0;
        for(int row=0; row= height) {
			return new int[]{backgroundColor.getRed(), backgroundColor.getGreen(), backgroundColor.getBlue()};
		}
		if(col < 0 || col >= width) {
			return new int[]{backgroundColor.getRed(), backgroundColor.getGreen(), backgroundColor.getBlue()};
		}
		double u = vertical ? (prow - row) : pcol - col;
		int nextCol = (int)(col + 1);
		int nextRow = (int)(row + 1);
		if((col + 1) >= width) {
			nextCol = (int)col;
		}
		if((row + 1) >= height) {
			nextRow = (int)row;
		}
		int index1 = (int)(row * width + col);
		int index2 = vertical ? (int)(nextRow * width + col) : (int)(row * width + nextCol);
		
		int tr1, tr2;
		int tg1, tg2;
		int tb1, tb2;
		
        tr1 = (input[index1] >> 16) & 0xff;
        tg1 = (input[index1] >> 8) & 0xff;
        tb1 = input[index1] & 0xff;
        
        tr2 = (input[index2] >> 16) & 0xff;
        tg2 = (input[index2] >> 8) & 0xff;
        tb2 = input[index2] & 0xff;
        
        int tr = (int)(tr1 * (1-u) + tr2 * u);
        int tg = (int)(tg1 * (1-u) + tg2 * u);
        int tb = (int)(tb1 * (1-u) + tb2 * u);
        
		return new int[]{tr, tg, tb};
	}
	
    public BufferedImage createCompatibleDestImage(BufferedImage src, ColorModel dstCM) {
        if ( dstCM == null )
            dstCM = src.getColorModel();
        return new BufferedImage(dstCM, dstCM.createCompatibleWritableRaster(outw, outh), dstCM.isAlphaPremultiplied(), null);
    }

}

下半年事情比较多，博客一直没有更新，感谢众多网友的关注与留言

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