1 // Copyright (c) 2012 libmv authors.
2 //
3 // Permission is hereby granted, free of charge, to any person obtaining a copy
4 // of this software and associated documentation files (the "Software"), to
5 // deal in the Software without restriction, including without limitation the
6 // rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
7 // sell copies of the Software, and to permit persons to whom the Software is
8 // furnished to do so, subject to the following conditions:
9 //
10 // The above copyright notice and this permission notice shall be included in
11 // all copies or substantial portions of the Software.
12 //
13 // THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
14 // IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
15 // FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
16 // AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
17 // LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
18 // FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
19 // IN THE SOFTWARE.
20
21 #ifndef LIBMV_IMAGE_CORRELATION_H
22 #define LIBMV_IMAGE_CORRELATION_H
23
24 #include "libmv/logging/logging.h"
25 #include "libmv/image/image.h"
26
27 namespace libmv {
28
PearsonProductMomentCorrelation(const FloatImage & image_and_gradient1_sampled,const FloatImage & image_and_gradient2_sampled)29 inline double PearsonProductMomentCorrelation(
30 const FloatImage &image_and_gradient1_sampled,
31 const FloatImage &image_and_gradient2_sampled) {
32 assert(image_and_gradient1_sampled.Width() ==
33 image_and_gradient2_sampled.Width());
34 assert(image_and_gradient1_sampled.Height() ==
35 image_and_gradient2_sampled.Height());
36
37 const int width = image_and_gradient1_sampled.Width(),
38 height = image_and_gradient1_sampled.Height();
39 double sX = 0, sY = 0, sXX = 0, sYY = 0, sXY = 0;
40
41 for (int r = 0; r < height; ++r) {
42 for (int c = 0; c < width; ++c) {
43 double x = image_and_gradient1_sampled(r, c, 0);
44 double y = image_and_gradient2_sampled(r, c, 0);
45 sX += x;
46 sY += y;
47 sXX += x * x;
48 sYY += y * y;
49 sXY += x * y;
50 }
51 }
52
53 // Normalize.
54 double N = width * height;
55 sX /= N;
56 sY /= N;
57 sXX /= N;
58 sYY /= N;
59 sXY /= N;
60
61 double var_x = sXX - sX * sX;
62 double var_y = sYY - sY * sY;
63 double covariance_xy = sXY - sX * sY;
64
65 double correlation = covariance_xy / sqrt(var_x * var_y);
66 LG << "Covariance xy: " << covariance_xy
67 << ", var 1: " << var_x << ", var 2: " << var_y
68 << ", correlation: " << correlation;
69 return correlation;
70 }
71
72 } // namespace libmv
73
74 #endif // LIBMV_IMAGE_IMAGE_CORRELATION_H
75