gil/test/core/image_processing/harris.cpp
Olzhas Zhumabek 62379dd5b9
Implement Sobel and Scharr operators (#392)
* Implement Sobel and Scharr operators

This commit adds Sobel and Scharr
operators with support for 0th and 1st
degrees with other degrees planned for
later

* Migrate and fix Harris example

Generate Harris entries now uses
signed image view.
The Harris corner detector example
now uses the Scharr filter generator
and convolve_2d to reduce amount
of code needed.

* Fix and migrate Hessian example

The Hessian example now uses signed
image views and uses newly added kernel
generators to compute gradients

* Fix Harris and Hessian tests

The tests broke due to migration to
signed views in algorithms, but tests
were not adjusted

* Fix Jamfile for example/sobel_scharr.cpp

* Cosmetic changes

* Commented out fail tests

* Fixed pixel16 used in image16s

In Harris and Hessian tests, unsigned
pixel values was used to construct
signed image, which was causing
appveyor to error out.

* Reenable failing targets

* Unify kernel generator interface

This commit makes all kernel
generator functions to return kernel_2d
and adapts dependant threshold
function to use the new interface

* Migrate Hessian and Harris tests

Migrate Hessian and Harris tests to new
interface for kernel generators

* Migrate Harris and Hessian examples

Harris and Hessian examples now use
new interface for kernel generation

* Migrate simple_kernels tests

simple_kernels are now using kernel_2d
interface

* Add missing return

Normalized mean generation had missing
return at the end of the function

* Adapt code to namespace move

This commit reacts to kernel_2d,
convolve_2d being moved to
namespace detail
2019-10-29 22:38:04 +06:00

73 lines
2.2 KiB
C++

//
// Copyright 2019 Olzhas Zhumabek <anonymous.from.applecity@gmail.com>
//
// Use, modification and distribution are subject to the Boost Software License,
// Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at
// http://www.boost.org/LICENSE_1_0.txt)
//
#include <boost/core/lightweight_test.hpp>
#include <boost/gil/image.hpp>
#include <boost/gil/image_view.hpp>
#include <boost/gil/image_processing/numeric.hpp>
#include <boost/gil/image_processing/harris.hpp>
namespace gil = boost::gil;
bool are_equal(gil::gray32f_view_t expected, gil::gray32f_view_t actual) {
if (expected.dimensions() != actual.dimensions())
return false;
for (long int y = 0; y < expected.height(); ++y)
{
for (long int x = 0; x < expected.width(); ++x)
{
if (expected(x, y) != actual(x, y))
{
return false;
}
}
}
return true;
}
void test_blank_image()
{
const gil::point_t dimensions(20, 20);
gil::gray16s_image_t dx(dimensions, gil::gray16s_pixel_t(0), 0);
gil::gray16s_image_t dy(dimensions, gil::gray16s_pixel_t(0), 0);
gil::gray32f_image_t m11(dimensions);
gil::gray32f_image_t m12_21(dimensions);
gil::gray32f_image_t m22(dimensions);
gil::gray32f_image_t expected(dimensions, gil::gray32f_pixel_t(0), 0);
gil::compute_tensor_entries(
gil::view(dx),
gil::view(dy),
gil::view(m11),
gil::view(m12_21),
gil::view(m22)
);
BOOST_TEST(are_equal(gil::view(expected), gil::view(m11)));
BOOST_TEST(are_equal(gil::view(expected), gil::view(m12_21)));
BOOST_TEST(are_equal(gil::view(expected), gil::view(m22)));
gil::gray32f_image_t harris_response(dimensions, gil::gray32f_pixel_t(0), 0);
auto unnormalized_mean = gil::generate_unnormalized_mean(5);
gil::compute_harris_responses(
gil::view(m11),
gil::view(m12_21),
gil::view(m22),
unnormalized_mean,
0.04f,
gil::view(harris_response)
);
BOOST_TEST(are_equal(gil::view(expected), gil::view(harris_response)));
}
int main(int argc, char* argv[])
{
test_blank_image();
return boost::report_errors();
}