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// Copyright (c) 2012 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.
#ifndef LIBMV_IMAGE_CORRELATION_H
#define LIBMV_IMAGE_CORRELATION_H
#include "libmv/logging/logging.h"
#include "libmv/image/image.h"
namespace libmv {
inline double PearsonProductMomentCorrelation(
const FloatImage &image_and_gradient1_sampled,
const FloatImage &image_and_gradient2_sampled) {
assert(image_and_gradient1_sampled.Width() ==
image_and_gradient2_sampled.Width());
assert(image_and_gradient1_sampled.Height() ==
image_and_gradient2_sampled.Height());
const int width = image_and_gradient1_sampled.Width(),
height = image_and_gradient1_sampled.Height();
double sX = 0, sY = 0, sXX = 0, sYY = 0, sXY = 0;
for (int r = 0; r < height; ++r) {
for (int c = 0; c < width; ++c) {
double x = image_and_gradient1_sampled(r, c, 0);
double y = image_and_gradient2_sampled(r, c, 0);
sX += x;
sY += y;
sXX += x * x;
sYY += y * y;
sXY += x * y;
}
}
// Normalize.
double N = width * height;
sX /= N;
sY /= N;
sXX /= N;
sYY /= N;
sXY /= N;
double var_x = sXX - sX * sX;
double var_y = sYY - sY * sY;
double covariance_xy = sXY - sX * sY;
double correlation = covariance_xy / sqrt(var_x * var_y);
LG << "Covariance xy: " << covariance_xy
<< ", var 1: " << var_x << ", var 2: " << var_y
<< ", correlation: " << correlation;
return correlation;
}
} // namespace libmv
#endif // LIBMV_IMAGE_IMAGE_CORRELATION_H
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