圖像處理之基于閾值模糊

算法思想：

實(shí)現(xiàn)一個(gè)高斯卷積模糊但是只運(yùn)用與周?chē)南袼刂蹬c中心像素值差值小于閾值。兩個(gè)

像素值之間的距離計(jì)算可以選用向量距離即曼哈頓距離或者歐幾里德距離。高斯模糊

采用先XY方向一維高斯模糊完成目的是為了減小計(jì)算量。

程序效果：

關(guān)鍵代碼解釋：

分別完成XY方向的一維高斯模糊

thresholdBlur( kernel, inPixels, outPixels, width, height, true ); thresholdBlur( kernel, outPixels, inPixels, height, width, true );計(jì)算像素距離，完成像素高斯卷積代碼如下：

int d; if(euclid) { d = (int)Math.sqrt(a1*a1-a2*a2); } else { d = a1-a2; } if ( d >= -threshold && d <= threshold ) { a += f * a2; af += f; } if(euclid) { d = (int)Math.sqrt(r1*r1-r2*r2); } else { d = r1-r2; } if ( d >= -threshold && d <= threshold ) { r += f * r2; rf += f; } if(euclid) { d = (int)Math.sqrt(g1*g1-g2*g2); } else { d = g1-g2; } if ( d >= -threshold && d <= threshold ) { g += f * g2; gf += f; } if(euclid) { d = (int)Math.sqrt(b1*b1-b2*b2); } else { d = b1-b2; } if ( d >= -threshold && d <= threshold ) { b += f * b2; bf += f; }濾鏡完整代碼如下：

package com.gloomyfish.filter.study; import java.awt.image.BufferedImage; public class SmartBlurFilter extends AbstractBufferedImageOp { private int hRadius = 5; private int threshold = 50; private boolean euclid = false; public BufferedImage filter( BufferedImage src, BufferedImage dest ) { int width = src.getWidth(); int height = src.getHeight(); if ( dest == null ) dest = createCompatibleDestImage( src, null ); int[] inPixels = new int[width*height]; int[] outPixels = new int[width*height]; getRGB( src, 0, 0, width, height, inPixels ); // generate the Gaussian kernel data float[] kernel = makeKernel(hRadius); // do Gaussian X and Y direction with kernel data. // this way will proceed quickly thresholdBlur( kernel, inPixels, outPixels, width, height, true ); thresholdBlur( kernel, outPixels, inPixels, height, width, true ); // set back result data to destination image setRGB( dest, 0, 0, width, height, inPixels ); return dest; } /** * Convolve with a Gaussian matrix consisting of one row float data */ public void thresholdBlur(float[] matrix, int[] inPixels, int[] outPixels, int width, int height, boolean alpha) { int cols = matrix.length; int cols2 = cols/2; for (int y = 0; y < height; y++) { int ioffset = y*width; // index to correct row here!! int outIndex = y; for (int x = 0; x < width; x++) { float r = 0, g = 0, b = 0, a = 0; int moffset = cols2; int rgb1 = inPixels[ioffset+x]; int a1 = (rgb1 >> 24) & 0xff; int r1 = (rgb1 >> 16) & 0xff; int g1 = (rgb1 >> 8) & 0xff; int b1 = rgb1 & 0xff; float af = 0, rf = 0, gf = 0, bf = 0; for (int col = -cols2; col <= cols2; col++) { float f = matrix[moffset+col]; if (f != 0) { int ix = x+col; if (!(0 <= ix && ix < width)) ix = x; int rgb2 = inPixels[ioffset+ix]; int a2 = (rgb2 >> 24) & 0xff; int r2 = (rgb2 >> 16) & 0xff; int g2 = (rgb2 >> 8) & 0xff; int b2 = rgb2 & 0xff; int d; if(euclid) { d = (int)Math.sqrt(a1*a1-a2*a2); } else { d = a1-a2; } if ( d >= -threshold && d <= threshold ) { a += f * a2; af += f; } if(euclid) { d = (int)Math.sqrt(r1*r1-r2*r2); } else { d = r1-r2; } if ( d >= -threshold && d <= threshold ) { r += f * r2; rf += f; } if(euclid) { d = (int)Math.sqrt(g1*g1-g2*g2); } else { d = g1-g2; } if ( d >= -threshold && d <= threshold ) { g += f * g2; gf += f; } if(euclid) { d = (int)Math.sqrt(b1*b1-b2*b2); } else { d = b1-b2; } if ( d >= -threshold && d <= threshold ) { b += f * b2; bf += f; } } } // normalization process here a = af == 0 ? a1 : a/af; r = rf == 0 ? r1 : r/rf; g = gf == 0 ? g1 : g/gf; b = bf == 0 ? b1 : b/bf; // return result pixel data int ia = alpha ? PixelUtils.clamp((int)(a+0.5)) : 0xff; int ir = PixelUtils.clamp((int)(r+0.5)); int ig = PixelUtils.clamp((int)(g+0.5)); int ib = PixelUtils.clamp((int)(b+0.5)); outPixels[outIndex] = (ia << 24) | (ir << 16) | (ig << 8) | ib; outIndex += height; } } } public void setHRadius(int hRadius) { this.hRadius = hRadius; } public void setThreshold(int th) { this.threshold = th; } public void setEuclid(boolean apply) { this.euclid = apply; } }

轉(zhuǎn)載于:https://blog.51cto.com/gloomyfish/1400330

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