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#include "guidedfilter.h" | ||
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static cv::Mat boxfilter(const cv::Mat &I, int r) | ||
{ | ||
cv::Mat result; | ||
cv::blur(I, result, cv::Size(r, r)); | ||
return result; | ||
} | ||
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static cv::Mat convertTo(const cv::Mat &mat, int depth) | ||
{ | ||
if (mat.depth() == depth) | ||
return mat; | ||
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cv::Mat result; | ||
mat.convertTo(result, depth); | ||
return result; | ||
} | ||
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class GuidedFilterImpl | ||
{ | ||
public: | ||
virtual ~GuidedFilterImpl() {} | ||
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cv::Mat filter(const cv::Mat &p, int depth); | ||
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protected: | ||
int Idepth; | ||
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private: | ||
virtual cv::Mat filterSingleChannel(const cv::Mat &p) const = 0; | ||
}; | ||
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class GuidedFilterMono : public GuidedFilterImpl | ||
{ | ||
public: | ||
GuidedFilterMono(const cv::Mat &I, int r, double eps); | ||
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private: | ||
virtual cv::Mat filterSingleChannel(const cv::Mat &p) const; | ||
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private: | ||
int r; | ||
double eps; | ||
cv::Mat I, mean_I, var_I; | ||
}; | ||
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class GuidedFilterColor : public GuidedFilterImpl | ||
{ | ||
public: | ||
GuidedFilterColor(const cv::Mat &I, int r, double eps); | ||
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private: | ||
virtual cv::Mat filterSingleChannel(const cv::Mat &p) const; | ||
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private: | ||
std::vector<cv::Mat> Ichannels; | ||
int r; | ||
double eps; | ||
cv::Mat mean_I_r, mean_I_g, mean_I_b; | ||
cv::Mat invrr, invrg, invrb, invgg, invgb, invbb; | ||
}; | ||
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cv::Mat GuidedFilterImpl::filter(const cv::Mat &p, int depth) | ||
{ | ||
cv::Mat p2 = convertTo(p, Idepth); | ||
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cv::Mat result; | ||
if (p.channels() == 1) | ||
{ | ||
result = filterSingleChannel(p2); | ||
} | ||
else | ||
{ | ||
std::vector<cv::Mat> pc; | ||
cv::split(p2, pc); | ||
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for (std::size_t i = 0; i < pc.size(); ++i) | ||
pc[i] = filterSingleChannel(pc[i]); | ||
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cv::merge(pc, result); | ||
} | ||
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return convertTo(result, depth == -1 ? p.depth() : depth); | ||
} | ||
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GuidedFilterMono::GuidedFilterMono(const cv::Mat &origI, int r, double eps) : r(r), eps(eps) | ||
{ | ||
if (origI.depth() == CV_32F || origI.depth() == CV_64F) | ||
I = origI.clone(); | ||
else | ||
I = convertTo(origI, CV_32F); | ||
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Idepth = I.depth(); | ||
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mean_I = boxfilter(I, r); | ||
cv::Mat mean_II = boxfilter(I.mul(I), r); | ||
var_I = mean_II - mean_I.mul(mean_I); | ||
} | ||
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cv::Mat GuidedFilterMono::filterSingleChannel(const cv::Mat &p) const | ||
{ | ||
cv::Mat mean_p = boxfilter(p, r); | ||
cv::Mat mean_Ip = boxfilter(I.mul(p), r); | ||
cv::Mat cov_Ip = mean_Ip - mean_I.mul(mean_p); // this is the covariance of (I, p) in each local patch. | ||
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cv::Mat a = cov_Ip / (var_I + eps); // Eqn. (5) in the paper; | ||
cv::Mat b = mean_p - a.mul(mean_I); // Eqn. (6) in the paper; | ||
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cv::Mat mean_a = boxfilter(a, r); | ||
cv::Mat mean_b = boxfilter(b, r); | ||
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return mean_a.mul(I) + mean_b; | ||
} | ||
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GuidedFilterColor::GuidedFilterColor(const cv::Mat &origI, int r, double eps) : r(r), eps(eps) | ||
{ | ||
cv::Mat I; | ||
if (origI.depth() == CV_32F || origI.depth() == CV_64F) | ||
I = origI.clone(); | ||
else | ||
I = convertTo(origI, CV_32F); | ||
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Idepth = I.depth(); | ||
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cv::split(I, Ichannels); | ||
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mean_I_r = boxfilter(Ichannels[0], r); | ||
mean_I_g = boxfilter(Ichannels[1], r); | ||
mean_I_b = boxfilter(Ichannels[2], r); | ||
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// variance of I in each local patch: the matrix Sigma in Eqn (14). | ||
// Note the variance in each local patch is a 3x3 symmetric matrix: | ||
// rr, rg, rb | ||
// Sigma = rg, gg, gb | ||
// rb, gb, bb | ||
cv::Mat var_I_rr = boxfilter(Ichannels[0].mul(Ichannels[0]), r) - mean_I_r.mul(mean_I_r) + eps; | ||
cv::Mat var_I_rg = boxfilter(Ichannels[0].mul(Ichannels[1]), r) - mean_I_r.mul(mean_I_g); | ||
cv::Mat var_I_rb = boxfilter(Ichannels[0].mul(Ichannels[2]), r) - mean_I_r.mul(mean_I_b); | ||
cv::Mat var_I_gg = boxfilter(Ichannels[1].mul(Ichannels[1]), r) - mean_I_g.mul(mean_I_g) + eps; | ||
cv::Mat var_I_gb = boxfilter(Ichannels[1].mul(Ichannels[2]), r) - mean_I_g.mul(mean_I_b); | ||
cv::Mat var_I_bb = boxfilter(Ichannels[2].mul(Ichannels[2]), r) - mean_I_b.mul(mean_I_b) + eps; | ||
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// Inverse of Sigma + eps * I | ||
invrr = var_I_gg.mul(var_I_bb) - var_I_gb.mul(var_I_gb); | ||
invrg = var_I_gb.mul(var_I_rb) - var_I_rg.mul(var_I_bb); | ||
invrb = var_I_rg.mul(var_I_gb) - var_I_gg.mul(var_I_rb); | ||
invgg = var_I_rr.mul(var_I_bb) - var_I_rb.mul(var_I_rb); | ||
invgb = var_I_rb.mul(var_I_rg) - var_I_rr.mul(var_I_gb); | ||
invbb = var_I_rr.mul(var_I_gg) - var_I_rg.mul(var_I_rg); | ||
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cv::Mat covDet = invrr.mul(var_I_rr) + invrg.mul(var_I_rg) + invrb.mul(var_I_rb); | ||
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invrr /= covDet; | ||
invrg /= covDet; | ||
invrb /= covDet; | ||
invgg /= covDet; | ||
invgb /= covDet; | ||
invbb /= covDet; | ||
} | ||
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cv::Mat GuidedFilterColor::filterSingleChannel(const cv::Mat &p) const | ||
{ | ||
cv::Mat mean_p = boxfilter(p, r); | ||
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cv::Mat mean_Ip_r = boxfilter(Ichannels[0].mul(p), r); | ||
cv::Mat mean_Ip_g = boxfilter(Ichannels[1].mul(p), r); | ||
cv::Mat mean_Ip_b = boxfilter(Ichannels[2].mul(p), r); | ||
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// covariance of (I, p) in each local patch. | ||
cv::Mat cov_Ip_r = mean_Ip_r - mean_I_r.mul(mean_p); | ||
cv::Mat cov_Ip_g = mean_Ip_g - mean_I_g.mul(mean_p); | ||
cv::Mat cov_Ip_b = mean_Ip_b - mean_I_b.mul(mean_p); | ||
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cv::Mat a_r = invrr.mul(cov_Ip_r) + invrg.mul(cov_Ip_g) + invrb.mul(cov_Ip_b); | ||
cv::Mat a_g = invrg.mul(cov_Ip_r) + invgg.mul(cov_Ip_g) + invgb.mul(cov_Ip_b); | ||
cv::Mat a_b = invrb.mul(cov_Ip_r) + invgb.mul(cov_Ip_g) + invbb.mul(cov_Ip_b); | ||
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cv::Mat b = mean_p - a_r.mul(mean_I_r) - a_g.mul(mean_I_g) - a_b.mul(mean_I_b); // Eqn. (15) in the paper; | ||
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return (boxfilter(a_r, r).mul(Ichannels[0]) | ||
+ boxfilter(a_g, r).mul(Ichannels[1]) | ||
+ boxfilter(a_b, r).mul(Ichannels[2]) | ||
+ boxfilter(b, r)); // Eqn. (16) in the paper; | ||
} | ||
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GuidedFilter::GuidedFilter(const cv::Mat &I, int r, double eps) | ||
{ | ||
CV_Assert(I.channels() == 1 || I.channels() == 3); | ||
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if (I.channels() == 1) | ||
impl_ = new GuidedFilterMono(I, 2 * r + 1, eps); | ||
else | ||
impl_ = new GuidedFilterColor(I, 2 * r + 1, eps); | ||
} | ||
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GuidedFilter::~GuidedFilter() | ||
{ | ||
delete impl_; | ||
} | ||
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cv::Mat GuidedFilter::filter(const cv::Mat &p, int depth) const | ||
{ | ||
return impl_->filter(p, depth); | ||
} | ||
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cv::Mat guidedFilter(const cv::Mat &I, const cv::Mat &p, int r, double eps, int depth) | ||
{ | ||
return GuidedFilter(I, r, eps).filter(p, depth); | ||
} |
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#ifndef GUIDED_FILTER_H | ||
#define GUIDED_FILTER_H | ||
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#include <opencv2/opencv.hpp> | ||
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class GuidedFilterImpl; | ||
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class GuidedFilter | ||
{ | ||
public: | ||
GuidedFilter(const cv::Mat &I, int r, double eps); | ||
~GuidedFilter(); | ||
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cv::Mat filter(const cv::Mat &p, int depth = -1) const; | ||
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private: | ||
GuidedFilterImpl *impl_; | ||
}; | ||
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cv::Mat guidedFilter(const cv::Mat &I, const cv::Mat &p, int r, double eps, int depth = -1); | ||
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#endif |