8 #ifndef BOOST_GIL_IMAGE_PROCESSING_NUMERIC_HPP 9 #define BOOST_GIL_IMAGE_PROCESSING_NUMERIC_HPP 11 #include <boost/gil/extension/numeric/kernel.hpp> 12 #include <boost/gil/extension/numeric/convolve.hpp> 13 #include <boost/gil/image_view.hpp> 14 #include <boost/gil/typedefs.hpp> 15 #include <boost/gil/detail/math.hpp> 20 namespace boost {
namespace gil {
33 inline double normalized_sinc(
double x)
35 return std::sin(x * boost::gil::pi) / (x * boost::gil::pi);
45 inline double lanczos(
double x, std::ptrdiff_t a)
52 return normalized_sinc(x) / normalized_sinc(x / static_cast<double>(a));
57 inline void compute_tensor_entries(
58 boost::gil::gray16s_view_t dx,
59 boost::gil::gray16s_view_t dy,
60 boost::gil::gray32f_view_t m11,
61 boost::gil::gray32f_view_t m12_21,
62 boost::gil::gray32f_view_t m22)
64 for (std::ptrdiff_t y = 0; y < dx.height(); ++y) {
65 for (std::ptrdiff_t x = 0; x < dx.width(); ++x) {
66 auto dx_value = dx(x, y);
67 auto dy_value = dy(x, y);
68 m11(x, y) = dx_value * dx_value;
69 m12_21(x, y) = dx_value * dy_value;
70 m22(x, y) = dy_value * dy_value;
81 template <
typename T =
float,
typename Allocator = std::allocator<T>>
84 if (side_length % 2 != 1)
85 throw std::invalid_argument(
"kernel dimensions should be odd and equal");
86 const float entry = 1.0f / static_cast<float>(side_length * side_length);
88 detail::kernel_2d<T, Allocator> result(side_length, side_length / 2, side_length / 2);
89 for (
auto& cell: result) {
100 template <
typename T =
float,
typename Allocator = std::allocator<T>>
103 if (side_length % 2 != 1)
104 throw std::invalid_argument(
"kernel dimensions should be odd and equal");
106 detail::kernel_2d<T, Allocator> result(side_length, side_length / 2, side_length / 2);
107 for (
auto& cell: result) {
119 template <
typename T =
float,
typename Allocator = std::allocator<T>>
122 if (side_length % 2 != 1)
123 throw std::invalid_argument(
"kernel dimensions should be odd and equal");
126 const double denominator = 2 * boost::gil::pi * sigma * sigma;
127 auto middle = side_length / 2;
128 std::vector<T, Allocator> values(side_length * side_length);
129 for (std::size_t y = 0; y < side_length; ++y)
131 for (std::size_t x = 0; x < side_length; ++x)
133 const auto delta_x = middle > x ? middle - x : x - middle;
134 const auto delta_y = middle > y ? middle - y : y - middle;
135 const double power = (delta_x * delta_x + delta_y * delta_y) / (2 * sigma * sigma);
136 const double nominator = std::exp(-power);
137 const float value = static_cast<float>(nominator / denominator);
138 values[y * side_length + x] = value;
142 return detail::kernel_2d<T, Allocator>(values.begin(), values.size(), middle, middle);
152 template <
typename T =
float,
typename Allocator = std::allocator<T>>
159 return get_identity_kernel<T, Allocator>();
163 detail::kernel_2d<T, Allocator> result(3, 1, 1);
164 std::copy(dx_sobel.begin(), dx_sobel.end(), result.begin());
168 throw std::logic_error(
"not supported yet");
172 throw std::runtime_error(
"unreachable statement");
182 template <
typename T =
float,
typename Allocator = std::allocator<T>>
189 return get_identity_kernel<T, Allocator>();
193 detail::kernel_2d<T, Allocator> result(3, 1, 1);
194 std::copy(dx_scharr.begin(), dx_scharr.end(), result.begin());
198 throw std::logic_error(
"not supported yet");
202 throw std::runtime_error(
"unreachable statement");
212 template <
typename T =
float,
typename Allocator = std::allocator<T>>
219 return get_identity_kernel<T, Allocator>();
223 detail::kernel_2d<T, Allocator> result(3, 1, 1);
224 std::copy(dy_sobel.begin(), dy_sobel.end(), result.begin());
228 throw std::logic_error(
"not supported yet");
232 throw std::runtime_error(
"unreachable statement");
242 template <
typename T =
float,
typename Allocator = std::allocator<T>>
249 return get_identity_kernel<T, Allocator>();
253 detail::kernel_2d<T, Allocator> result(3, 1, 1);
254 std::copy(dy_scharr.begin(), dy_scharr.end(), result.begin());
258 throw std::logic_error(
"not supported yet");
262 throw std::runtime_error(
"unreachable statement");
274 template <
typename GradientView,
typename OutputView>
284 detail::convolve_2d(dx, sobel_x, ddxx);
285 detail::convolve_2d(dx, sobel_y, dxdy);
286 detail::convolve_2d(dy, sobel_y, ddyy);
detail::kernel_2d< T, Allocator > generate_dy_sobel(unsigned int degree=1)
Generates Sobel operator in vertical directionGenerates a kernel which will represent Sobel operator ...
Definition: numeric.hpp:213
void compute_hessian_entries(GradientView dx, GradientView dy, OutputView ddxx, OutputView dxdy, OutputView ddyy)
Compute xy gradient, and second order x and y gradientsHessian matrix is defined as a matrix of parti...
Definition: numeric.hpp:275
double lanczos(double x, std::ptrdiff_t a)
Lanczos response at point xLanczos response is defined as: x == 0: 1 -a < x && x < a: 0 otherwise: no...
Definition: numeric.hpp:45
BOOST_FORCEINLINE auto copy(boost::gil::pixel< T, CS > *first, boost::gil::pixel< T, CS > *last, boost::gil::pixel< T, CS > *dst) -> boost::gil::pixel< T, CS > *
Copy when both src and dst are interleaved and of the same type can be just memmove.
Definition: algorithm.hpp:139
detail::kernel_2d< T, Allocator > generate_dx_scharr(unsigned int degree=1)
Generate Scharr operator in horizontal directionGenerates a kernel which will represent Scharr operat...
Definition: numeric.hpp:183
detail::kernel_2d< T, Allocator > generate_dx_sobel(unsigned int degree=1)
Generates Sobel operator in horizontal directionGenerates a kernel which will represent Sobel operato...
Definition: numeric.hpp:153
detail::kernel_2d< T, Allocator > generate_unnormalized_mean(std::size_t side_length)
Generate kernel with all 1sFills supplied view with 1s (ones)
Definition: numeric.hpp:101
detail::kernel_2d< T, Allocator > generate_dy_scharr(unsigned int degree=1)
Generate Scharr operator in vertical directionGenerates a kernel which will represent Scharr operator...
Definition: numeric.hpp:243
detail::kernel_2d< T, Allocator > generate_gaussian_kernel(std::size_t side_length, double sigma)
Generate Gaussian kernelFills supplied view with values taken from Gaussian distribution....
Definition: numeric.hpp:120
detail::kernel_2d< T, Allocator > generate_normalized_mean(std::size_t side_length)
Generate mean kernelFills supplied view with normalized mean in which all entries will be equal to.
Definition: numeric.hpp:82