java图像处理实例.docx

一 读取bmp图片数据/ 获取待检测图像，数据保存在数组 nData，nB，nG，nR中public void getBMPImage(String source) throws Exception clearNData(); /清除数据保存区 FileInputStream fs = null; try fs = new FileInputStream(source); int bfLen = 14; byte bf = new bytebfLen; fs.read(bf, 0, bfLen); / 读取14字节BMP文件头 int biLen = 40; byte bi = new bytebiLen; fs.read(bi, 0, biLen); / 读取40字节BMP信息头 / 源图宽度 nWidth = (int) bi7 & 0xff) 24) | (int) bi6 & 0xff) 16) | (int) bi5 & 0xff) 8) | (int) bi4 & 0xff; / 源图高度 nHeight = (int) bi11 & 0xff) 24) | (int) bi10 & 0xff) 16) | (int) bi9 & 0xff) 8) | (int) bi8 & 0xff; / 位数 nBitCount = (int) bi15 & 0xff) 8) | (int) bi14 & 0xff; / 源图大小 int nSizeImage = (int) bi23 & 0xff) 24) | (int) bi22 & 0xff) 16) | (int) bi21 & 0xff) 8) | (int) bi20 & 0xff; / 对24位BMP进行解析 if (nBitCount = 24) int nPad = (nSizeImage / nHeight) - nWidth * 3; nData = new intnHeight * nWidth; nB=new intnHeight * nWidth; nR=new intnHeight * nWidth; nG=new intnHeight * nWidth; byte bRGB = new byte(nWidth + nPad) * 3 * nHeight; fs.read(bRGB, 0, (nWidth + nPad) * 3 * nHeight); int nIndex = 0; for (int j = 0; j nHeight; j+) for (int i = 0; i nWidth; i+) nDatanWidth * (nHeight - j - 1) + i = (255 & 0xff) 24 | (int) bRGBnIndex + 2 & 0xff) 16) | (int) bRGBnIndex + 1 & 0xff) 8) | (int) bRGBnIndex & 0xff; nBnWidth * (nHeight - j - 1) + i=(int) bRGBnIndex& 0xff; nGnWidth * (nHeight - j - 1) + i=(int) bRGBnIndex+1& 0xff; nRnWidth * (nHeight - j - 1) + i=(int) bRGBnIndex+2& 0xff; nIndex += 3; nIndex += nPad; / Toolkit kit = Toolkit.getDefaultToolkit();/ image = kit.createImage(new MemoryImageSource(nWidth, nHeight,/ nData, 0, nWidth);/* /调试数据的读取 FileWriter fw = new FileWriter(C:/Documents and Settings/Administrator/My Documents/nDataRaw.txt);/创建新文件 PrintWriter out = new PrintWriter(fw); for(int j=0;jnHeight;j+) for(int i=0;inWidth;i+) out.print(65536*256+nDatanWidth * (nHeight - j - 1) + i)+_ +nRnWidth * (nHeight - j - 1) + i+_ +nGnWidth * (nHeight - j - 1) + i+_ +nBnWidth * (nHeight - j - 1) + i+ ); out.println(); out.close();*/ catch (Exception e) e.printStackTrace(); throw new Exception(e); finally if (fs != null) fs.close(); / return image; 二由r g b 获取灰度数组 public int getBrightnessData(int rData,int gData,int bData) int brightnessData=new intrData.length; if(rData.length!=gData.length | rData.length!=bData.length | bData.length!=gData.length) return brightnessData; else for(int i=0;i0.5?1:0); return brightnessData; 三 直方图均衡化 public int equilibrateGray(int PixelsGray,int width,int height) int gray; int length=PixelsGray.length; int FrequenceGray=new intlength; int SumGray=new int256; int ImageDestination=new intlength; for(int i = 0; i length ;i+) gray=PixelsGrayi; FrequenceGraygray+; / 灰度均衡化 SumGray0=FrequenceGray0; for(int i=1;i256;i+) SumGrayi=SumGrayi-1+FrequenceGrayi; for(int i=0;i256;i+) SumGrayi=(int)(SumGrayi*255/length); for(int i=0;iheight;i+) for(int j=0;jwidth;j+) int k=i*width+j; ImageDestinationk=0xFF000000 | (SumGrayPixelsGrayk 16 ) | (SumGrayPixelsGrayk 8 ) | SumGrayPixelsGrayk); return ImageDestination; 四 laplace2阶滤波，增强边缘,图像锐化 public int laplace2DFileter(int data,int width,int height) int filterData=new intdata.length; int min=10000; int max=-10000; for(int i=0;iheight;i+) for(int j=0;jwidth;j+) if(i=0 | i=height-1 | j=0 | j=width-1) filterDatai*width+j=datai*width+j; else filterDatai*width+j=9*datai*width+j-datai*width+j-1-datai*width+j+1 -data(i-1)*width+j-data(i-1)*width+j-1-data(i-1)*width+j+1 -data(i+1)*width+j-data(i+1)*width+j-1-data(i+1)*width+j+1; if(filterDatai*width+jmax) max=filterDatai*width+j; / System.out.println(max: +max);/ System.out.println(min: +min); for(int i=0;iwidth*height;i+) filterDatai=(filterDatai-min)*255/(max-min); return filterData; 五 laplace2阶增强滤波，增强边缘，增强系数delt public int laplaceHigh2DFileter(int data,int width,int height,double delt) int filterData=new intdata.length; int min=10000; int max=-10000; for(int i=0;iheight;i+) for(int j=0;jwidth;j+) if(i=0 | i=height-1 | j=0 | j=width-1) filterDatai*width+j=(int)(1+delt)*datai*width+j); else filterDatai*width+j=(int)(9+delt)*datai*width+j-datai*width+j-1)-datai*width+j+1 -data(i-1)*width+j-data(i-1)*width+j-1-data(i-1)*width+j+1 -data(i+1)*width+j-data(i+1)*width+j-1-data(i+1)*width+j+1; if(filterDatai*width+jmax) max=filterDatai*width+j; for(int i=0;iwidth*height;i+) filterDatai=(filterDatai-min)*255/(max-min); return filterData; 六 局部阈值处理2值化 / 局部阈值处理2值化，niblacks method /*原理： T(x,y)=m(x,y) + k*s(x,y) 取一个宽度为w的矩形框，(x,y)为这个框的中心。 统计框内数据，T(x,y)为阈值,m(x,y)为均值，s(x,y)为均方差，k为参数(推荐-2)计算出t再对(x,y)进行切割255/0。 这个算法的优点是 速度快，效果好。 缺点是 niblacks method会产生一定的噪声。 */ public int localThresholdProcess(int data,int width,int height,int w,int h,double coefficients,double gate) int processData=new intdata.length; for(int i=0;idata.length;i+) processDatai=255; if(data.length!=width*height) return processData; int wNum=width/w; int hNum=height/h; int delt=new intw*h; /System.out.println(w; +w+ h:+h+ wNum:+wNum+ hNum:+hNum); for(int j=0;jhNum;j+) for(int i=0;iwNum;i+) /for(int j=0;j1;j+) /for(int i=0;i1;i+) for(int n=0;nh;n+) for(int k=0;kw;k+) deltn*w+k=data(j*h+n)*width+i*w+k; /System.out.print(delt+(n*w+k)+: +deltn*w+k+ ); /System.out.println(); /* for(int n=0;nh;n+) for(int k=0;kw;k+) System.out.print(data+(j*h+n)*width+i*w+k)+: +data(j*h+n)*width+i*w+k+ ); System.out.println(); */ delt=thresholdProcess(delt,w,h,coefficients,gate); for(int n=0;nh;n+) for(int k=0;kw;k+) processData(j*h+n)*width+i*w+k=deltn*w+k; / System.out.print(delt+(n*w+k)+: +deltn*w+k+ ); /System.out.println(); /* for(int n=0;nh;n+) for(int k=0;kw;k+) System.out.print(processData+(j*h+n)*width+i*w+k)+: +processData(j*h+n)*width+i*w+k+ ); System.out.println(); */ return processData; 七 全局阈值处理2值化 public int thresholdProcess(int data,int width,int height,double coefficients,double gate) int processData=new intdata.length; if(data.length!=width*height) return processData; else double sum=0; double average=0; double variance=0; double threshold; if( gate!=0) threshold=gate; else for(int i=0;iwidth*height;i+) sum+=datai; average=sum/(width*height); for(int i=0;iwidth*height;i+) variance+=(datai-average)*(datai-average); variance=Math.sqrt(variance); threshold=average-coefficients*variance; for(int i=0;ithreshold) processDatai=255; else processDatai=0; return processData; 八 垂直边缘检测，sobel算子 public int verticleEdgeCheck(int data,int width,int height,int sobelCoefficients) throws Exception int filterData=new intdata.length; int min=10000; int max=-10000; if(data.length!=width*height) return filterData; try for(int i=0;iheight;i+) for(int j=0;jwidth;j+) if(i=0 | i=1 | i=height-1 | i=height-2 |j=0 | j=1 | j=width-1 | j=width-2) filterDatai*width+j=datai*width+j; else double average; /中心的九个像素点 /average=datai*width+j-Math.sqrt(2)*datai*width+j-1+Math.sqrt(2)*datai*width+j+1 average=datai*width+j-sobelCoefficients*datai*width+j-1+sobelCoefficients*datai*width+j+1 -data(i-1)*width+j-1+data(i-1)*width+j+1 -data(i+1)*width+j-1+data(i+1)*width+j+1; filterDatai*width+j=(int)(average); if(filterDatai*width+jmax) max=filterDatai*width+j; for(int i=0;iwidth*height;i+) filterDatai=(filterDatai-min)*255/(max-min); catch (Exception e) e.printStackTrace(); throw new Exception(e); return filterData; 九 图像平滑：3*3掩模处理（平均处理），降低噪