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Merge pull request #93 from e-fominov/fhog-float
Using float instead of double for FHOG computations
This commit is contained in:
Davis E. King
commit
cfe718ea09
@ -21,16 +21,16 @@ namespace dlib
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namespace impl_fhog
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{
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template <typename image_type>
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template <typename image_type, typename T>
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inline typename dlib::enable_if_c<pixel_traits<typename image_type::pixel_type>::rgb>::type get_gradient (
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const int r,
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const int c,
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const image_type& img,
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matrix<double,2,1>& grad,
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double& len
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matrix<T,2,1>& grad,
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T& len
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)
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{
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matrix<double,2,1> grad2, grad3;
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matrix<T, 2, 1> grad2, grad3;
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// get the red gradient
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grad(0) = (int)img[r][c+1].red-(int)img[r][c-1].red;
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grad(1) = (int)img[r+1][c].red-(int)img[r-1][c].red;
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@ -39,12 +39,12 @@ namespace dlib
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// get the green gradient
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grad2(0) = (int)img[r][c+1].green-(int)img[r][c-1].green;
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grad2(1) = (int)img[r+1][c].green-(int)img[r-1][c].green;
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double v2 = length_squared(grad2);
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T v2 = length_squared(grad2);
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// get the blue gradient
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grad3(0) = (int)img[r][c+1].blue-(int)img[r][c-1].blue;
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grad3(1) = (int)img[r+1][c].blue-(int)img[r-1][c].blue;
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double v3 = length_squared(grad3);
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T v3 = length_squared(grad3);
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// pick color with strongest gradient
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if (v2 > len)
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@ -144,13 +144,13 @@ namespace dlib
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// ------------------------------------------------------------------------------------
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template <typename image_type>
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template <typename image_type, typename T>
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inline typename dlib::disable_if_c<pixel_traits<typename image_type::pixel_type>::rgb>::type get_gradient (
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const int r,
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const int c,
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const image_type& img,
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matrix<double,2,1>& grad,
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double& len
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matrix<T, 2, 1>& grad,
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T& len
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)
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{
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grad(0) = (int)get_pixel_intensity(img[r][c+1])-(int)get_pixel_intensity(img[r][c-1]);
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@ -200,7 +200,7 @@ namespace dlib
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int o,
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int x,
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int y,
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const double& value
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const T& value
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)
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{
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hog[o][y][x] = value;
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@ -255,7 +255,7 @@ namespace dlib
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int o,
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int x,
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int y,
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const double& value
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const T& value
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)
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{
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hog[y][x](o) = value;
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@ -336,7 +336,7 @@ namespace dlib
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// unit vectors used to compute gradient orientation
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matrix<double,2,1> directions[9];
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matrix<float,2,1> directions[9];
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directions[0] = 1.0000, 0.0000;
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directions[1] = 0.9397, 0.3420;
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directions[2] = 0.7660, 0.6428;
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@ -417,16 +417,16 @@ namespace dlib
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// Now process the right columns that don't fit into simd registers.
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for (; x < visible_nc; x++)
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{
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matrix<double,2,1> grad;
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double v;
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matrix<float,2,1> grad;
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float v;
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get_gradient(y,x,img,grad,v);
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// snap to one of 18 orientations
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double best_dot = 0;
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float best_dot = 0;
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int best_o = 0;
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for (int o = 0; o < 9; o++)
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{
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const double dot = dlib::dot(directions[o], grad);
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const float dot = dlib::dot(directions[o], grad);
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if (dot > best_dot)
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{
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best_dot = dot;
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@ -444,7 +444,7 @@ namespace dlib
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}
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}
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const double eps = 0.0001;
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const float eps = 0.0001;
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// compute features
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for (int y = 0; y < hog_nr; y++)
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{
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@ -572,7 +572,7 @@ namespace dlib
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}
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// unit vectors used to compute gradient orientation
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matrix<double,2,1> directions[9];
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matrix<float,2,1> directions[9];
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directions[0] = 1.0000, 0.0000;
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directions[1] = 0.9397, 0.3420;
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directions[2] = 0.7660, 0.6428;
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@ -586,8 +586,8 @@ namespace dlib
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// First we allocate memory for caching orientation histograms & their norms.
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const int cells_nr = (int)((double)img.nr()/(double)cell_size + 0.5);
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const int cells_nc = (int)((double)img.nc()/(double)cell_size + 0.5);
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const int cells_nr = (int)((float)img.nr()/(float)cell_size + 0.5);
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const int cells_nc = (int)((float)img.nc()/(float)cell_size + 0.5);
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if (cells_nr == 0 || cells_nc == 0)
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{
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@ -629,10 +629,10 @@ namespace dlib
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// First populate the gradient histograms
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for (int y = 1; y < visible_nr; y++)
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{
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const double yp = ((double)y+0.5)/(double)cell_size - 0.5;
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const float yp = ((float)y+0.5)/(float)cell_size - 0.5;
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const int iyp = (int)std::floor(yp);
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const double vy0 = yp-iyp;
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const double vy1 = 1.0-vy0;
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const float vy0 = yp - iyp;
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const float vy1 = 1.0 - vy0;
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int x;
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for (x = 1; x < visible_nc-3; x+=4)
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{
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@ -708,16 +708,16 @@ namespace dlib
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// Now process the right columns that don't fit into simd registers.
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for (; x < visible_nc; x++)
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{
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matrix<double,2,1> grad;
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double v;
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matrix<float, 2, 1> grad;
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float v;
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get_gradient(y,x,img,grad,v);
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// snap to one of 18 orientations
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double best_dot = 0;
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float best_dot = 0;
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int best_o = 0;
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for (int o = 0; o < 9; o++)
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{
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const double dot = dlib::dot(directions[o], grad);
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const float dot = dlib::dot(directions[o], grad);
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if (dot > best_dot)
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{
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best_dot = dot;
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@ -732,10 +732,10 @@ namespace dlib
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v = std::sqrt(v);
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// add to 4 histograms around pixel using bilinear interpolation
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const double xp = ((double)x+0.5)/(double)cell_size - 0.5;
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const float xp = ((double)x + 0.5) / (double)cell_size - 0.5;
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const int ixp = (int)std::floor(xp);
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const double vx0 = xp-ixp;
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const double vx1 = 1.0-vx0;
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const float vx0 = xp - ixp;
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const float vx1 = 1.0 - vx0;
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hist[iyp+1][ixp+1](best_o) += vy1*vx1*v;
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hist[iyp+1+1][ixp+1](best_o) += vy0*vx1*v;
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@ -756,7 +756,7 @@ namespace dlib
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}
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}
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const double eps = 0.0001;
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const float eps = 0.0001;
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// compute features
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for (int y = 0; y < hog_nr; y++)
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{
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@ -930,14 +930,14 @@ namespace dlib
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template <
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typename image_type
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>
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matrix<double,0,1> extract_fhog_features(
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matrix<float,0,1> extract_fhog_features(
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const image_type& img,
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int cell_size = 8,
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int filter_rows_padding = 1,
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int filter_cols_padding = 1
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)
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{
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matrix<double,0,1> feats;
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matrix<float, 0, 1> feats;
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extract_fhog_features(img, feats, cell_size, filter_rows_padding, filter_cols_padding);
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return feats;
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}
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@ -1093,7 +1093,7 @@ namespace dlib
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}
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}
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const double thresh = mean(himg) + 4*stddev(himg);
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const float thresh = mean(himg) + 4 * stddev(himg);
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if (thresh != 0)
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return matrix_cast<unsigned char>(upperbound(round(himg*255/thresh),255));
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else
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@ -1178,7 +1178,7 @@ namespace dlib
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}
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}
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const double thresh = mean(himg) + 4*stddev(himg);
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const float thresh = mean(himg) + 4 * stddev(himg);
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if (thresh != 0)
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return matrix_cast<unsigned char>(upperbound(round(himg*255/thresh),255));
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else
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