本文整理汇总了Java中org.opencv.imgproc.Imgproc.boxFilter方法的典型用法代码示例。如果您正苦于以下问题：Java Imgproc.boxFilter方法的具体用法？Java Imgproc.boxFilter怎么用？Java Imgproc.boxFilter使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类org.opencv.imgproc.Imgproc的用法示例。
在下文中一共展示了Imgproc.boxFilter方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: GuidedImageFilter
import org.opencv.imgproc.Imgproc; //导入方法依赖的package包/类
/**
* Guided Image Filter for grayscale image, O(1) time implementation of guided filter
*
* @param I guidance image (should be a gray-scale/single channel image)
* @param p filtering input image (should be a gray-scale/single channel image)
* @param r local window radius
* @param eps regularization parameter
* @return filtered image
*/
public static Mat GuidedImageFilter(Mat I, Mat p, int r, double eps) {
I.convertTo(I, CvType.CV_64FC1);
p.convertTo(p, CvType.CV_64FC1);
//[hei, wid] = size(I);
int rows = I.rows();
int cols = I.cols();
// N = boxfilter(ones(hei, wid), r); % the size of each local patch; N=(2r+1)^2 except for boundary pixels.
Mat N = new Mat();
Imgproc.boxFilter(Mat.ones(rows, cols, I.type()), N, -1, new Size(r, r));
// mean_I = boxfilter(I, r) ./ N;
Mat mean_I = new Mat();
Imgproc.boxFilter(I, mean_I, -1, new Size(r, r));
// mean_p = boxfilter(p, r) ./ N
Mat mean_p = new Mat();
Imgproc.boxFilter(p, mean_p, -1, new Size(r, r));
// mean_Ip = boxfilter(I.*p, r) ./ N;
Mat mean_Ip = new Mat();
Imgproc.boxFilter(I.mul(p), mean_Ip, -1, new Size(r, r));
// cov_Ip = mean_Ip - mean_I .* mean_p; % this is the covariance of (I, p) in each local patch.
Mat cov_Ip = new Mat();
Core.subtract(mean_Ip, mean_I.mul(mean_p), cov_Ip);
// mean_II = boxfilter(I.*I, r) ./ N;
Mat mean_II = new Mat();
Imgproc.boxFilter(I.mul(I), mean_II, -1, new Size(r, r));
// var_I = mean_II - mean_I .* mean_I;
Mat var_I = new Mat();
Core.subtract(mean_II, mean_I.mul(mean_I), var_I);
// a = cov_Ip ./ (var_I + eps); % Eqn. (5) in the paper;
Mat a = new Mat();
Core.add(var_I, new Scalar(eps), a);
Core.divide(cov_Ip, a, a);
//b = mean_p - a .* mean_I; % Eqn. (6) in the paper;
Mat b = new Mat();
Core.subtract(mean_p, a.mul(mean_I), b);
// mean_a = boxfilter(a, r) ./ N;
Mat mean_a = new Mat();
Imgproc.boxFilter(a, mean_a, -1, new Size(r, r));
Core.divide(mean_a, N, mean_a);
// mean_b = boxfilter(b, r) ./ N;
Mat mean_b = new Mat();
Imgproc.boxFilter(b, mean_b, -1, new Size(r, r));
Core.divide(mean_b, N, mean_b);
// q = mean_a .* I + mean_b; % Eqn. (8) in the paper;
Mat q = new Mat();
Core.add(mean_a.mul(I), mean_b, q);
q.convertTo(q, CvType.CV_32F);
return q;
}
示例2: GuidedImageFilter
import org.opencv.imgproc.Imgproc; //导入方法依赖的package包/类
/**
* Guided Image Filter for grayscale image, O(1) time implementation of guided filter
*
* @param I
* guidance image (should be a gray-scale/single channel image)
* @param p
* filtering input image (should be a gray-scale/single channel
* image)
* @param r
* local window radius
* @param eps
* regularization parameter
* @return filtered image
*/
public static Mat GuidedImageFilter(Mat I, Mat p, int r, double eps) {
I.convertTo(I, CvType.CV_64FC1);
p.convertTo(p, CvType.CV_64FC1);
//[hei, wid] = size(I);
int rows = I.rows();
int cols = I.cols();
// N = boxfilter(ones(hei, wid), r); % the size of each local patch; N=(2r+1)^2 except for boundary pixels.
Mat N = new Mat();
Imgproc.boxFilter(Mat.ones(rows, cols, I.type()), N, -1, new Size(r, r));
// mean_I = boxfilter(I, r) ./ N;
Mat mean_I = new Mat();
Imgproc.boxFilter(I, mean_I, -1, new Size(r, r));
// mean_p = boxfilter(p, r) ./ N
Mat mean_p = new Mat();
Imgproc.boxFilter(p, mean_p, -1, new Size(r, r));
// mean_Ip = boxfilter(I.*p, r) ./ N;
Mat mean_Ip = new Mat();
Imgproc.boxFilter(I.mul(p), mean_Ip, -1, new Size(r, r));
// cov_Ip = mean_Ip - mean_I .* mean_p; % this is the covariance of (I, p) in each local patch.
Mat cov_Ip = new Mat();
Core.subtract(mean_Ip, mean_I.mul(mean_p), cov_Ip);
// mean_II = boxfilter(I.*I, r) ./ N;
Mat mean_II = new Mat();
Imgproc.boxFilter(I.mul(I), mean_II, -1, new Size(r, r));
// var_I = mean_II - mean_I .* mean_I;
Mat var_I = new Mat();
Core.subtract(mean_II, mean_I.mul(mean_I), var_I);
// a = cov_Ip ./ (var_I + eps); % Eqn. (5) in the paper;
Mat a = new Mat();
Core.add(var_I, new Scalar(eps), a);
Core.divide(cov_Ip, a, a);
//b = mean_p - a .* mean_I; % Eqn. (6) in the paper;
Mat b = new Mat();
Core.subtract(mean_p, a.mul(mean_I), b);
// mean_a = boxfilter(a, r) ./ N;
Mat mean_a = new Mat();
Imgproc.boxFilter(a, mean_a, -1, new Size(r, r));
Core.divide(mean_a, N, mean_a);
// mean_b = boxfilter(b, r) ./ N;
Mat mean_b = new Mat();
Imgproc.boxFilter(b, mean_b, -1, new Size(r, r));
Core.divide(mean_b, N, mean_b);
// q = mean_a .* I + mean_b; % Eqn. (8) in the paper;
Mat q = new Mat();
Core.add(mean_a.mul(I), mean_b, q);
//for (int i = 0; i < rows; i++) {
// for (int j = 0; j < cols; j++) {
// if (q.get(i, j)[0] <= 0)
// q.put(i, j, 1.0 / 255);
// }
//}
q.convertTo(q, CvType.CV_32F);
return q;
}