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今天在网上看到了一篇关于JAVA图像处理的文章，博主贴出了一个处理类：特点是高品质缩小，具体代码如下： import java.awt.image.BufferedImage; import java.io.File; import java.io.FileOutputStream; import java.io.IOException; import javax.imageio.ImageIO; public class ImageScale { private int width; private int height; private int scaleWidth; double support = ( double) 3.0; double[] contrib; double[] normContrib; double[] tmpContrib; int startContrib, stopContrib; int nDots; int nHalfDots; public BufferedImage imageZoomOut(BufferedImage srcBufferImage, int w, int h, boolean lockScale) { width = srcBufferImage.getWidth(); height = srcBufferImage.getHeight(); scaleWidth = w; if (lockScale) { h = w * height / width; } if (DetermineResultSize(w, h) == 1) { return srcBufferImage; } CalContrib(); BufferedImage pbOut = HorizontalFiltering(srcBufferImage, w); BufferedImage pbFinalOut = VerticalFiltering(pbOut, h); return pbFinalOut; } /** * 决定图像尺寸 */ private int DetermineResultSize( int w, int h) { double scaleH, scaleV; scaleH = ( double) w / ( double) width; scaleV = ( double) h / ( double) height; // 需要判断一下scaleH，scaleV，不做放大操作 if (scaleH >= 1.0 && scaleV >= 1.0) { return 1; } return 0; } // end of DetermineResultSize() private double Lanczos( int i, int inWidth, int outWidth, double Support) { double x; x = ( double) i * ( double) outWidth / ( double) inWidth; return Math.sin(x * Math.PI) / (x * Math.PI) * Math.sin(x * Math.PI / Support) / (x * Math.PI / Support); } // end of Lanczos() // // Assumption: same horizontal and vertical scaling factor // private void CalContrib() { nHalfDots = ( int) (( double) width * support / ( double) scaleWidth); nDots = nHalfDots * 2 + 1; try { contrib = new double[nDots]; normContrib = new double[nDots]; tmpContrib = new double[nDots]; } catch (Exception e) { System.out.println("init contrib,normContrib,tmpContrib" + e); } int center = nHalfDots; contrib[center] = 1.0; double weight = 0.0; int i = 0; for (i = 1; i <= center; i++) { contrib[center + i] = Lanczos(i, width, scaleWidth, support); weight += contrib[center + i]; } for (i = center - 1; i >= 0; i--) { contrib[i] = contrib[center * 2 - i]; } weight = weight * 2 + 1.0; for (i = 0; i <= center; i++) { normContrib[i] = contrib[i] / weight; } for (i = center + 1; i < nDots; i++) { normContrib[i] = normContrib[center * 2 - i]; } } // end of CalContrib() // 处理边缘 private void CalTempContrib( int start, int stop) { double weight = 0; int i = 0; for (i = start; i <= stop; i++) { weight += contrib[i]; } for (i = start; i <= stop; i++) { tmpContrib[i] = contrib[i] / weight; } } // end of CalTempContrib() private int GetRedValue( int rgbValue) { int temp = rgbValue & 0x00ff0000; return temp >> 16; } private int GetGreenValue( int rgbValue) { int temp = rgbValue & 0x0000ff00; return temp >> 8; } private int GetBlueValue( int rgbValue) { return rgbValue & 0x000000ff; } private int ComRGB( int redValue, int greenValue, int blueValue) { return (redValue << 16) + (greenValue << 8) + blueValue; } // 行水平滤波 private int HorizontalFilter(BufferedImage bufImg, int startX, int stopX, int start, int stop, int y, double[] pContrib) { double valueRed = 0.0; double valueGreen = 0.0; double valueBlue = 0.0; int valueRGB = 0; int i, j; for (i = startX, j = start; i <= stopX; i++, j++) { valueRGB = bufImg.getRGB(i, y); valueRed += GetRedValue(valueRGB) * pContrib[j]; valueGreen += GetGreenValue(valueRGB) * pContrib[j]; valueBlue += GetBlueValue(valueRGB) * pContrib[j]; } valueRGB = ComRGB(Clip(( int) valueRed), Clip(( int) valueGreen), Clip(( int) valueBlue)); return valueRGB; } // end of HorizontalFilter() // 图片水平滤波 private BufferedImage HorizontalFiltering(BufferedImage bufImage, int iOutW) { int dwInW = bufImage.getWidth(); int dwInH = bufImage.getHeight(); int value = 0; BufferedImage pbOut = new BufferedImage(iOutW, dwInH, BufferedImage.TYPE_INT_RGB); for ( int x = 0; x < iOutW; x++) { int startX; int start; int X = ( int) ((( double) x) * (( double) dwInW) / (( double) iOutW) + 0.5); int y = 0; startX = X - nHalfDots; if (startX < 0) { startX = 0; start = nHalfDots - X; } else { start = 0; } int stop; int stopX = X + nHalfDots; if (stopX > (dwInW - 1)) { stopX = dwInW - 1; stop = nHalfDots + (dwInW - 1 - X); } else { stop = nHalfDots * 2; } if (start > 0 || stop < nDots - 1) { CalTempContrib(start, stop); for (y = 0; y < dwInH; y++) { value = HorizontalFilter(bufImage, startX, stopX, start, stop, y, tmpContrib); pbOut.setRGB(x, y, value); } } else { for (y = 0; y < dwInH; y++) { value = HorizontalFilter(bufImage, startX, stopX, start, stop, y, normContrib); pbOut.setRGB(x, y, value); } } } return pbOut; } // end of HorizontalFiltering() private int VerticalFilter(BufferedImage pbInImage, int startY, int stopY, int start, int stop, int x, double[] pContrib) { double valueRed = 0.0; double valueGreen = 0.0; double valueBlue = 0.0; int valueRGB = 0; int i, j; for (i = startY, j = start; i <= stopY; i++, j++) { valueRGB = pbInImage.getRGB(x, i); valueRed += GetRedValue(valueRGB) * pContrib[j]; valueGreen += GetGreenValue(valueRGB) * pContrib[j]; valueBlue += GetBlueValue(valueRGB) * pContrib[j]; } valueRGB = ComRGB(Clip(( int) valueRed), Clip(( int) valueGreen), Clip(( int) valueBlue)); // System.out.println(valueRGB); return valueRGB; } // end of VerticalFilter() private BufferedImage VerticalFiltering(BufferedImage pbImage, int iOutH) { int iW = pbImage.getWidth(); int iH = pbImage.getHeight(); int value = 0; BufferedImage pbOut = new BufferedImage(iW, iOutH, BufferedImage.TYPE_INT_RGB); for ( int y = 0; y < iOutH; y++) { int startY; int start; int Y = ( int) ((( double) y) * (( double) iH) / (( double) iOutH) + 0.5); startY = Y - nHalfDots; if (startY < 0) { startY = 0; start = nHalfDots - Y; } else { start = 0; } int stop; int stopY = Y + nHalfDots; if (stopY > ( int) (iH - 1)) { stopY = iH - 1; stop = nHalfDots + (iH - 1 - Y); } else { stop = nHalfDots * 2; } if (start > 0 || stop < nDots - 1) { CalTempContrib(start, stop); for ( int x = 0; x < iW; x++) { value = VerticalFilter(pbImage, startY, stopY, start, stop, x, tmpContrib); pbOut.setRGB(x, y, value); } } else { for ( int x = 0; x < iW; x++) { value = VerticalFilter(pbImage, startY, stopY, start, stop, x, normContrib); pbOut.setRGB(x, y, value); } } } return pbOut; } // end of VerticalFiltering() int Clip( int x) { if (x < 0) return 0; if (x > 255) return 255; return x; } public static void main(String[] args) throws IOException { ImageScale is = new ImageScale(); String path = "D:\\My Documents\\My Pictures\\pictrue\\"; BufferedImage image1 = ImageIO.read( new File(path + "test.jpg")); int w = 200, h = 400; BufferedImage image2 = is.imageZoomOut(image1, w, h, true); FileOutputStream out = new FileOutputStream(path + "test_2.jpg"); ImageIO.write(image2, "jpeg", out); } }
上面的代码中，本人做了一点小改进：imageZoomOut方法中，添加了一个lockScale参数，如果为true则表明保持纵横比。 程序运行的效果如下： test.jpg（原图）： test_2.jpg（程序生成的图片）：
转载于:https://www.cnblogs.com/cuizhf/p/3554374.html
相关资源：java 图片缩放处理