// ///////////////////////////////////////////////////////////////
// 最临近插值算法----不考虑坐标的小数部分
// 参数：img：要缩放的Image对象
// dstW:目标图像宽
// dstH:目标图像高
// comp：组件参数，比如Applet
// ///////////////////////////////////////////////////////////////
public static Image simpleScale(Image img, int dstW, int dstH) {
OperateImage OI = new OperateImage();
int[] scaled, src;
double widthFactor, heightFactor;
int srcX = 0, srcY = 0, srcW, srcH;
src = OI.takeImg(img, img.getWidth(null), img.getHeight(null));
scaled = new int[dstW * dstH]; // 存放缩放后的图片
srcW = img.getWidth(null);
srcH = img.getHeight(null);
widthFactor = srcW / (dstW + 0.0);
// System.out.println("widthFactor:"+widthFactor);
heightFactor = srcH / (dstH + 0.0);
// System.out.println("heightFactor:"+heightFactor);
for (int a = 0; a < dstH; a++)
for (int b = 0; b < dstW; b++) {
if ((b * widthFactor) % 1 >= 0.5) srcX = (int) (b * widthFactor) + 1;
else srcX = (int) (b * widthFactor);
if ((a * heightFactor) % 1 >= 0.5) srcY = (int) (a * heightFactor) + 1;
else srcY = (int) (a * heightFactor);
if (srcX > srcW - 1) srcX = srcW - 1;
if (srcY > srcH - 1) srcY = srcH - 1;
scaled[a * dstW + b] = src[srcY * srcW + srcX];
}
// System.out.println("最临近插值算法完成！");
return OI.madeImg(scaled, dstW, dstH);
}
// ///////////////////////////////////////////////////////////////
// 双线性内插值算法
// 参数：img：要缩放的Image对象
// dstW:目标图像宽
// dstH:目标图像高
// comp：组件参数，比如Applet
//
// 公式：f(i+u,j+v) = (1-u)(1-v)f(i,j) + (1-u)vf(i,j+1) + u(1-v)f(i+1,j) +
// uvf(i+1,j+1)
//
// ///////////////////////////////////////////////////////////////
public static Image doubleLinearScale(Image img, int dstW, int dstH) {
OperateImage OI = new OperateImage();
Image imgTemp;
int[] scaled, src;
int srcW, srcH;
int R, G, B;
double widthFactor, heightFactor, tempX, tempY;
// double srcX_float = 0.0, srcY_float = 0.0;// 坐标的小数部分
// int srcX_int = 0, srcY_int = 0;// 坐标的整数部分
src = OI.takeImg(img, img.getWidth(null), img.getHeight(null));
ColorModel cm = ColorModel.getRGBdefault();
for (int j = 0; j < src.length; j++) {
R = cm.getRed(src[j]);
G = cm.getGreen(src[j]);
B = cm.getBlue(src[j]);
if (R >= 200 && G >= 200 && B >= 200) src[j] = 0xffffffff;
else src[j] = 0xff000000;
}
scaled = new int[dstW * dstH]; // 存放缩放后的图片
srcW = img.getWidth(null);
srcH = img.getHeight(null);
widthFactor = srcW / (dstW + 0.0);
// System.out.println("widthFactor:"+widthFactor);
heightFactor = srcH / (dstH + 0.0);
// System.out.println("heightFactor:"+heightFactor);
for (int a = 0; a < dstH; a++)
for (int b = 0; b < dstW; b++) {
tempX = b * widthFactor;
tempY = a * heightFactor;
scaled[a * dstW + b] = getDestPixle(src, srcW, srcH, tempX, tempY);
}
// System.out.println("双线性内插值算法完成！");
imgTemp = OI.madeImg(scaled, dstW, dstH);
ImageFilter filter = new BWFilter();
return Toolkit.getDefaultToolkit()
.createImage(new FilteredImageSource(imgTemp.getSource(), filter));
// return imgTemp;
}
// ///////////////////////////////////////////////////////////////
// 最临近插值算法--考虑坐标的小数部分
// 参数：img：要缩放的Image对象
// dstW:目标图像宽
// dstH:目标图像高
// comp：组件参数，比如Applet
// ///////////////////////////////////////////////////////////////
public static Image simpleScaleSpcial(Image img, int dstW, int dstH) {
OperateImage OI = new OperateImage();
int[] scaled, src;
double widthFactor, heightFactor, tempX, tempY, distance, tempDis;
double srcX_float = 0.0, srcY_float = 0.0; // 坐标的小数部分
int srcX_int = 0, srcY_int = 0; // 坐标的整数部分
int srcX = 0, srcY = 0, srcW, srcH;
src = OI.takeImg(img, img.getWidth(null), img.getHeight(null));
scaled = new int[dstW * dstH]; // 存放缩放后的图片
srcW = img.getWidth(null);
srcH = img.getHeight(null);
widthFactor = srcW / (dstW + 0.0);
// System.out.println("widthFactor:"+widthFactor);
heightFactor = srcH / (dstH + 0.0);
// System.out.println("heightFactor:"+heightFactor);
for (int a = 0; a < dstH; a++)
for (int b = 0; b < dstW; b++) {
distance = Double.MAX_VALUE;
tempX = b * widthFactor;
tempY = a * heightFactor;
srcX_int = (int) tempX;
srcX_float = tempX % 1;
srcY_int = (int) tempY;
srcY_float = tempY % 1;
tempDis = (srcX_float * srcX_float) + (srcY_float * srcY_float);
if (tempDis < distance)
// 左上角 (0,0)
{
srcX = srcX_int;
srcY = srcY_int;
distance = tempDis;
}
tempDis = ((srcX_float - 1) * (srcX_float - 1)) + (srcY_float * srcY_float);
if (tempDis < distance)
// 右上角 (1,0)
{
srcX = srcX_int + 1;
srcY = srcY_int;
distance = tempDis;
}
tempDis = (srcX_float * srcX_float) + ((srcY_float - 1) * (srcY_float - 1));
if (tempDis < distance)
// 左下角 (0,1)
{
srcX = srcX_int;
srcY = srcY_int + 1;
distance = tempDis;
}
tempDis = ((srcX_float - 1) * (srcX_float - 1)) + ((srcY_float - 1) * (srcY_float - 1));
if (tempDis < distance)
// 右下角 (1,1)
{
srcX = srcX_int + 1;
srcY = srcY_int + 1;
}
if (srcX > srcW - 1) srcX = srcW - 1;
if (srcY > srcH - 1) srcY = srcH - 1;
scaled[a * dstW + b] = src[srcY * srcW + srcX];
}
// System.out.println("最临近插值算法(特殊)完成！");
return OI.madeImg(scaled, dstW, dstH);
}
