blackberry 图片缩放 整理代码

最好使得时 双线性插值算法。

 

代码原型基本上是网上淘的，另外自己整理的。若侵权请提示，哥们我就删贴～

 

import javax.microedition.lcdui.Image;import net.rim.device.api.math.Fixed32;import net.rim.device.api.system.Bitmap;import net.rim.device.api.system.EncodedImage;import net.rim.device.api.system.PNGEncodedImage;public class ConstantUtil {public static Bitmap resizeBitmap(Bitmap image, int width, int height) {return rizeImageByBiliNearInterpolation(image, width, height);}//public static Bitmap resizeBitmap(String src, int width, int height) {//Bitmap image = Bitmap.getBitmapResource(src);//return rizeImageByBiliNearInterpolation(image, width, height);//}/** * 最邻近插值算法（貌似是这个算法） * @param image 需要缩放的图片 * @param width 缩放后的宽度 * @param height 缩放后的高度 * @return */private static Bitmap rizeImageByNearestNeighborInterpolation (Bitmap image, int width, int height){if (image == null ) return null ;int imageWidth = image.getWidth();int imageHeight = image.getHeight();// Need an array (for RGB, with the size of original image)int rgb[] = new int[imageWidth * imageHeight];// Get the RGB array of image into "rgb"image.getARGB(rgb, 0, imageWidth, 0, 0, imageWidth, imageHeight);// Call to rescaleArray() function and obtain rgb2int rgb2[] = rescaleArray(rgb, imageWidth, imageHeight, width, height);// Create an image with that RGB arrayBitmap temp2 = new Bitmap(width, height);temp2.setARGB(rgb2, 0, width, 0, 0, width, height);return temp2;}private static int[] rescaleArray(int[] ini, int x, int y, int x2, int y2) {int out[] = new int[x2 * y2];for (int yy = 0; yy < y2; yy++) {int dy = yy * y / y2;for (int xx = 0; xx < x2; xx++) {int dx = xx * x / x2;out[(x2 * yy) + xx] = ini[(x * dy) + dx];}}return out;}/** * Blackberry api 提供方法 * @param image * @param newWidth * @param newHeight * @return */public static EncodedImage resizeImage(EncodedImage image, int newWidth, int newHeight) { int scaleFactorX = Fixed32.div(Fixed32.toFP(image.getWidth()), Fixed32.toFP(newWidth)); int scaleFactorY = Fixed32.div(Fixed32.toFP(image.getHeight()), Fixed32.toFP(newHeight)); return image.scaleImage32(scaleFactorX, scaleFactorY);}/** * Blackberry api 提供方法 (加强版) * @param image * @param targetWidth * @param targetHeight * @return */public static EncodedImage scaleImageToSize(EncodedImage image, int targetWidth, int targetHeight) { try { if (targetWidth > 0 && targetHeight > 0) { int imageWidth = image.getWidth(); int imageHeight = image.getHeight(); float scaleX = ((float) imageWidth / (float) targetWidth) * 10000; float scaleY = ((float) imageHeight / (float) targetHeight) * 10000; int fixedX = Fixed32.tenThouToFP( (int) scaleX); int fixedY = Fixed32.tenThouToFP( (int) scaleY); EncodedImage retImage = image.scaleImage32(fixedX,fixedY); return retImage; } } catch(Exception e) { System.out.println("Exception in Utility : ScaleImageToSize() "+e.toString()); } return image; }private static EncodedImage changeBitmap2EncodedImage(Bitmap image, int width, int height){return PNGEncodedImage.encode(image);}// 使用midp2.0的getRGB()函数private static Bitmap ZoomImage(Image src, int desW, int desH) {Image desImg = null;int srcW = src.getWidth(); // 原始图像宽int srcH = src.getHeight(); // 原始图像高int[] srcBuf = new int[srcW * srcH]; // 原始图片像素信息缓存src.getRGB(srcBuf, 0, srcW, 0, 0, srcW, srcH);// 计算插值表int[] tabY = new int[desH];int[] tabX = new int[desW];int sb = 0;int db = 0;int tems = 0;int temd = 0;int distance = srcH > desH ? srcH : desH;for (int i = 0; i <= distance; i++) { /* 垂直方向 */tabY[db] = sb;tems += srcH;temd += desH;if (tems > distance) {tems -= distance;sb++;}if (temd > distance) {temd -= distance;db++;}}sb = 0;db = 0;tems = 0;temd = 0;distance = srcW > desW ? srcW : desW;for (int i = 0; i <= distance; i++) { /* 水平方向 */tabX[db] = (short) sb;tems += srcW;temd += desW;if (tems > distance) {tems -= distance;sb++;}if (temd > distance) {temd -= distance;db++;}}// 生成放大缩小后图形像素bufint[] desBuf = new int[desW * desH];int dx = 0;int dy = 0;int sy = 0;int oldy = -1;for (int i = 0; i < desH; i++) {if (oldy == tabY[i]) {System.arraycopy(desBuf, dy - desW, desBuf, dy, desW);} else {dx = 0;for (int j = 0; j < desW; j++) {desBuf[dy + dx] = srcBuf[sy + tabX[j]];dx++;}sy += (tabY[i] - oldy) * srcW;}oldy = tabY[i];dy += desW;}// 生成图片// desImg = Image.createRGBImage(desBuf, desW, desH, false);// return desImg;Bitmap bitmap = new Bitmap(desW, desH);bitmap.setARGB(desBuf, 0, desW, 0, 0, desW, desH);return bitmap;}/** * 双线性插值算法 * @param src 需要缩放的图片 * @param width_dest 缩放后的宽度 * @param height_dest 缩放后的高度 * @return */private static Bitmap rizeImageByBiliNearInterpolation(Bitmap src, int width_dest, int height_dest) {int destW = width_dest; // 缩放后图片的宽度int destH = height_dest; // 缩放图片的高度int srcW = src.getWidth();int srcH = src.getHeight();int[] destPixels = new int[destW * destH]; // array to hold destination// Image src = Image.createImage("/image.png");// create pixel arrays// int[] destPixels = new int[destW * destH]; // array to hold// destinationint[] destRed = new int[destW * destH];int[] destGreen = new int[destW * destH];int[] destBlue = new int[destW * destH];// pixelsint[] srcPixels = new int[srcW * srcH]; // array with source's pixelsint[] srcRed = new int[srcW * srcH];int[] srcGreen = new int[srcW * srcH];int[] srcBlue = new int[srcW * srcH];src.getARGB(srcPixels, 0, srcW, 0, 0, srcW, srcH);try {for (int i = 0; i < srcPixels.length; i++) {srcBlue[i] = srcPixels[i] & 0x000000ff;srcGreen[i] = (srcPixels[i] >> 8) & 0x000000ff;srcRed[i] = (srcPixels[i] >> 16) & 0x000000ff;}// simple point smapled resizing// loop through the destination pixels, find the matching pixel on// the source and use thatfor (int destY = 0; destY < destH; ++destY) {for (int destX = 0; destX < destW; ++destX) {float srcX = ((float) destX * (float) srcW) / (float) destW;float srcY = ((float) destY * (float) srcH) / (float) destH;try {destRed[destX + destY * destW] = biliNearInterpolation(srcRed,srcW, srcH, srcX, srcY);destGreen[destX + destY * destW] = biliNearInterpolation(srcGreen, srcW, srcH, srcX, srcY);destBlue[destX + destY * destW] = biliNearInterpolation(srcBlue, srcW, srcH, srcX, srcY);int value = 0xff000000;value |= destBlue[destX + destY * destW];value |= (destGreen[destX + destY * destW] << 8);value |= (destRed[destX + destY * destW] << 16);destPixels[destX + destY * destW] = value;} catch (Exception e) {e.printStackTrace();}}}} catch (Exception e) {e.printStackTrace();}Bitmap tmp = new Bitmap(destW, destH);tmp.setARGB(destPixels, 0, destW, 0, 0, destW, destH);return tmp;}/** * 双线性插值算法 * @param imageData * @param width * @param heigth * @param x * @param y * @return */private static int biliNearInterpolation(int[] imageData, int width, int heigth,float x, float y) {// 四个最临近象素的坐标(i1, j1), (i2, j1), (i1, j2), (i2, j2)int i1, i2;int j1, j2;int f1, f2, f3, f4; // 四个最临近象素值int f12, f34; // 二个插值中间值f1 = f2 = f3 = f4 = 0;// 定义一个值，当象素坐标相差小于改值时认为坐标相同int EXP = 0;// 计算四个最临近象素的坐标i1 = (int) x;i2 = i1 + 1;j1 = (int) y;j2 = j1 + 1;// 根据不同情况分别处理if ((x < 0) || (x > width - 1) || (y < 0) || (y > heigth - 1)) {return 0x00ffffff; // 要计算的点不在源图范围内，直接返回255。} else {if (Math.abs(x - width + 1) <= EXP) {// 要计算的点在图像右边缘上if (Math.abs(y - heigth + 1) <= EXP) {// 要计算的点正好是图像最右下角那一个象素，直接返回该点象素值f1 = (int) imageData[width * j1 + i1];return (int) f1;} else {// 在图像右边缘上且不是最后一点，直接一次插值即可f1 = (int) imageData[width * j1 + i1];f3 = (int) imageData[width * j1 + i2];// 返回插值结果return (int) (f1 + (y - j1) * (f3 - f1));}} else if (Math.abs(y - heigth + 1) <= EXP) {// 要计算的点在图像下边缘上且不是最后一点，直接一次插值即可f1 = (int) imageData[width * j1 + i1];f2 = (int) imageData[width * j2 + i1];// 返回插值结果return (int) (f1 + (x - i1) * (f2 - f1));} else {// 计算四个最临近象素值f1 = imageData[width * j1 + i1];f2 = imageData[width * j1 + i2];f3 = imageData[width * j2 + i1];f4 = imageData[width * j2 + i2];// 插值1f12 = (int) (f1 + (x - i1) * (f2 - f1));// 插值2f34 = (int) ((f3 + (x - i1) * (f4 - f3)));// 插值3return (int) (f12 + (y - j1) * (f34 - f12));}}}}