array2D.h: cleanup
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@@ -82,8 +82,8 @@ int calculate_subsampling(int w, int h, int r)
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void guidedFilter(const array2D<float> &guide, const array2D<float> &src, array2D<float> &dst, int r, float epsilon, bool multithread, int subsampling)
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{
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const int W = src.width();
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const int H = src.height();
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const int W = src.getWidth();
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const int H = src.getHeight();
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if (subsampling <= 0) {
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subsampling = calculate_subsampling(W, H, r);
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@@ -94,8 +94,8 @@ void guidedFilter(const array2D<float> &guide, const array2D<float> &src, array2
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const auto apply =
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[=](Op op, array2D<float> &res, const array2D<float> &a, const array2D<float> &b, const array2D<float> &c=array2D<float>()) -> void
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{
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const int w = res.width();
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const int h = res.height();
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const int w = res.getWidth();
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const int h = res.getHeight();
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#ifdef _OPENMP
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#pragma omp parallel for if (multithread)
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@@ -142,12 +142,12 @@ void guidedFilter(const array2D<float> &guide, const array2D<float> &src, array2
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const auto f_subsample =
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[=](array2D<float> &d, const array2D<float> &s) -> void
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{
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if (d.width() == s.width() && d.height() == s.height()) {
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if (d.getWidth() == s.getWidth() && d.getHeight() == s.getHeight()) {
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#ifdef _OPENMP
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# pragma omp parallel for if (multithread)
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#endif
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for (int y = 0; y < s.height(); ++y) {
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for (int x = 0; x < s.width(); ++x) {
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for (int y = 0; y < s.getHeight(); ++y) {
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for (int x = 0; x < s.getWidth(); ++x) {
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d[y][x] = s[y][x];
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}
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}
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@@ -164,9 +164,9 @@ void guidedFilter(const array2D<float> &guide, const array2D<float> &src, array2
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const auto f_mean =
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[multithread](array2D<float> &d, array2D<float> &s, int rad) -> void
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{
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rad = LIM(rad, 0, (min(s.width(), s.height()) - 1) / 2 - 1);
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// boxblur(s, d, rad, s.width(), s.height(), multithread);
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boxblur(static_cast<float**>(s), static_cast<float**>(d), rad, s.width(), s.height(), multithread);
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rad = LIM(rad, 0, (min(s.getWidth(), s.getHeight()) - 1) / 2 - 1);
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// boxblur(s, d, rad, s.getWidth(), s.getHeight(), multithread);
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boxblur(static_cast<float**>(s), static_cast<float**>(d), rad, s.getWidth(), s.getHeight(), multithread);
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};
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array2D<float> I1(w, h);
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@@ -225,10 +225,10 @@ void guidedFilter(const array2D<float> &guide, const array2D<float> &src, array2
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DEBUG_DUMP(meanb);
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// speedup by heckflosse67
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const int Ws = meana.width();
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const int Hs = meana.height();
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const int Wd = q.width();
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const int Hd = q.height();
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const int Ws = meana.getWidth();
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const int Hs = meana.getHeight();
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const int Wd = q.getWidth();
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const int Hd = q.getHeight();
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const float col_scale = float(Ws) / float(Wd);
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const float row_scale = float(Hs) / float(Hd);
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@@ -249,8 +249,8 @@ void guidedFilterLog(const array2D<float> &guide, float base, array2D<float> &ch
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#ifdef _OPENMP
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# pragma omp parallel for if (multithread)
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#endif
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for (int y = 0; y < chan.height(); ++y) {
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for (int x = 0; x < chan.width(); ++x) {
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for (int y = 0; y < chan.getHeight(); ++y) {
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for (int x = 0; x < chan.getWidth(); ++x) {
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chan[y][x] = xlin2log(max(chan[y][x], 0.f), base);
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}
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}
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@@ -260,8 +260,8 @@ void guidedFilterLog(const array2D<float> &guide, float base, array2D<float> &ch
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#ifdef _OPENMP
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# pragma omp parallel for if (multithread)
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#endif
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for (int y = 0; y < chan.height(); ++y) {
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for (int x = 0; x < chan.width(); ++x) {
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for (int y = 0; y < chan.getHeight(); ++y) {
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for (int x = 0; x < chan.getWidth(); ++x) {
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chan[y][x] = xlog2lin(max(chan[y][x], 0.f), base);
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}
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}
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