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// Copyright (c) 2007, 2008 libmv authors.
//
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to
// deal in the Software without restriction, including without limitation the
// rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
// sell copies of the Software, and to permit persons to whom the Software is
// furnished to do so, subject to the following conditions:
//
// The above copyright notice and this permission notice shall be included in
// all copies or substantial portions of the Software.
//
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
// IN THE SOFTWARE.
#include <iostream>
#include "libmv/image/convolve.h"
#include "libmv/image/image.h"
#include "libmv/numeric/numeric.h"
#include "testing/testing.h"
using namespace libmv;
namespace {
TEST(Convolve, ComputeGaussianKernel) {
Vec kernel, derivative;
ComputeGaussianKernel(1, &kernel, &derivative);
EXPECT_EQ(7, kernel.size());
// TODO(keir): Put in a more thorough test here!
}
TEST(Convolve, ConvolveGaussian) {
FloatImage im(10, 10);
im.Fill(1);
FloatImage blured;
ConvolveGaussian(im, 3, &blured);
EXPECT_NEAR(im(5, 5), 1, 1e-7);
}
TEST(Convolve, BoxFilterHorizontal) {
FloatImage im(10, 10), convolved, filtered;
im.Fill(1);
BoxFilterHorizontal(im, 3, &filtered);
Vec kernel(3);
kernel.setConstant(1.);
ConvolveHorizontal(im, kernel, &convolved);
EXPECT_EQ(filtered(5, 5), 3);
EXPECT_TRUE(filtered == convolved);
}
TEST(Convolve, BoxFilter) {
FloatImage image(5, 5), filtered;
// A single 1.0 inside a 5x5 image should expand to a 3x3 square.
image.Fill(0);
image(2, 2) = 1.0;
BoxFilter(image, 3, &filtered);
for (int j = 0; j < 5; j++) {
for (int i = 0; i < 5; i++) {
if (i == 0 || i == 4 || j == 0 || j == 4) {
EXPECT_EQ(0.0, filtered(j, i));
} else {
EXPECT_EQ(1.0, filtered(j, i));
}
}
}
}
TEST(Convolve, BlurredImageAndDerivativesChannelsFlat) {
FloatImage im(10, 10), blurred_and_derivatives;
im.Fill(1);
BlurredImageAndDerivativesChannels(im, 1.0, &blurred_and_derivatives);
EXPECT_NEAR(blurred_and_derivatives(5, 5, 0), 1.0, 1e-7);
EXPECT_NEAR(blurred_and_derivatives(5, 5, 1), 0.0, 1e-7);
EXPECT_NEAR(blurred_and_derivatives(5, 5, 2), 0.0, 1e-7);
}
TEST(Convolve, BlurredImageAndDerivativesChannelsHorizontalSlope) {
FloatImage image(10, 10), blurred_and_derivatives;
for (int j = 0; j < 10; ++j) {
for (int i = 0; i < 10; ++i) {
image(j, i) = 2*i;
}
}
BlurredImageAndDerivativesChannels(image, 0.9, &blurred_and_derivatives);
EXPECT_NEAR(blurred_and_derivatives(5, 5, 0), 10.0, 1e-7);
EXPECT_NEAR(blurred_and_derivatives(5, 5, 1), 2.0, 1e-7);
EXPECT_NEAR(blurred_and_derivatives(5, 5, 2), 0.0, 1e-7);
}
TEST(Convolve, BlurredImageAndDerivativesChannelsVerticalSlope) {
FloatImage image(10, 10), blurred_and_derivatives;
for (int j = 0; j < 10; ++j) {
for (int i = 0; i < 10; ++i) {
image(j, i) = 2*j;
}
}
BlurredImageAndDerivativesChannels(image, 0.9, &blurred_and_derivatives);
EXPECT_NEAR(blurred_and_derivatives(5, 5, 0), 10.0, 1e-7);
EXPECT_NEAR(blurred_and_derivatives(5, 5, 1), 0.0, 1e-7);
EXPECT_NEAR(blurred_and_derivatives(5, 5, 2), 2.0, 1e-7);
}
} // namespace
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