我有很多用鱼眼镜头拍摄的照片。当我想对照片进行一些图像处理(例如边缘检测)时,我想消除会严重影响结果的镜筒失真。
经过研究和大量阅读文章,我发现了这个page:他们描述了一种解决该问题的算法(和一些公式)。
我使用了这些公式,并试图在Java应用程序中实现它。不幸的是,它不起作用,我无法使其起作用。 “校正的”图像看起来与原始照片完全不同,而在中间却显示了一些神秘的圆圈。看这里:
http://imageshack.us/f/844/barreldistortioncorrect.jpg/
(这曾经是蓝色墙上的一头白牛的照片)
这是我的代码:
protected int[] correction(int[] pixels) {
//
int[] pixelsCopy = pixels.clone();
// parameters for correction
double paramA = 0.0; // affects only the outermost pixels of the image
double paramB = -0.02; // most cases only require b optimization
double paramC = 0.0; // most uniform correction
double paramD = 1.0 - paramA - paramB - paramC; // describes the linear scaling of the image
//
for(int x = 0; x < dstView.getImgWidth(); x++) {
for(int y = 0; y < dstView.getImgHeight(); y++) {
int dstX = x;
int dstY = y;
// center of dst image
double centerX = (dstView.getImgWidth() - 1) / 2.0;
double centerY = (dstView.getImgHeight() - 1) / 2.0;
// difference between center and point
double diffX = centerX - dstX;
double diffY = centerY - dstY;
// distance or radius of dst image
double dstR = Math.sqrt(diffX * diffX + diffY * diffY);
// distance or radius of src image (with formula)
double srcR = (paramA * dstR * dstR * dstR + paramB * dstR * dstR + paramC * dstR + paramD) * dstR;
// comparing old and new distance to get factor
double factor = Math.abs(dstR / srcR);
// coordinates in source image
double srcXd = centerX + (diffX * factor);
double srcYd = centerY + (diffX * factor);
// no interpolation yet (just nearest point)
int srcX = (int)srcXd;
int srcY = (int)srcYd;
if(srcX >= 0 && srcY >= 0 && srcX < dstView.getImgWidth() && srcY < dstView.getImgHeight()) {
int dstPos = dstY * dstView.getImgWidth() + dstX;
pixels[dstPos] = pixelsCopy[srcY * dstView.getImgWidth() + srcX];
}
}
}
return pixels;
}
我的问题是:
1)这个公式正确吗?
2)在将该公式转换为软件时是否犯了错误?
3)还有其他算法(例如How to simulate fisheye lens effect by openCV?或wiki/Distortion_(optics)),它们更好吗?
谢谢你的帮助!
最佳答案
您遇到的主要错误是该算法指定r_corr和r_src的单位为min((xDim-1)/2,(yDim-1)/2)。需要执行此操作以标准化计算,以使参数值不依赖于源图像的大小。照原样编写代码,您将需要为paramB使用更小的值,例如对于paramB = 0.00000002(对于尺寸为2272 x 1704的图像),它对我来说正常工作。
在计算与中心的差异时,还存在一个错误,该错误会导致生成的图像与源图像相比旋转180度。
修复这两个错误应会为您提供以下信息:
protected static int[] correction2(int[] pixels, int width, int height) {
int[] pixelsCopy = pixels.clone();
// parameters for correction
double paramA = -0.007715; // affects only the outermost pixels of the image
double paramB = 0.026731; // most cases only require b optimization
double paramC = 0.0; // most uniform correction
double paramD = 1.0 - paramA - paramB - paramC; // describes the linear scaling of the image
for (int x = 0; x < width; x++) {
for (int y = 0; y < height; y++) {
int d = Math.min(width, height) / 2; // radius of the circle
// center of dst image
double centerX = (width - 1) / 2.0;
double centerY = (height - 1) / 2.0;
// cartesian coordinates of the destination point (relative to the centre of the image)
double deltaX = (x - centerX) / d;
double deltaY = (y - centerY) / d;
// distance or radius of dst image
double dstR = Math.sqrt(deltaX * deltaX + deltaY * deltaY);
// distance or radius of src image (with formula)
double srcR = (paramA * dstR * dstR * dstR + paramB * dstR * dstR + paramC * dstR + paramD) * dstR;
// comparing old and new distance to get factor
double factor = Math.abs(dstR / srcR);
// coordinates in source image
double srcXd = centerX + (deltaX * factor * d);
double srcYd = centerY + (deltaY * factor * d);
// no interpolation yet (just nearest point)
int srcX = (int) srcXd;
int srcY = (int) srcYd;
if (srcX >= 0 && srcY >= 0 && srcX < width && srcY < height) {
int dstPos = y * width + x;
pixels[dstPos] = pixelsCopy[srcY * width + srcX];
}
}
}
return pixels;
}
使用此版本,您可以使用现有镜头数据库(例如LensFun)中的参数值(尽管您需要翻转每个参数的符号)。现在可以在http://mipav.cit.nih.gov/pubwiki/index.php/Barrel_Distortion_Correction上找到描述算法的页面