# Untitled

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Aug 21st, 2019
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1. int main(int argc, char **argv) {
2.
3. const int terms = 8;
4. Var x("x"), y("y"), i("i");
5.
6. Param<int> width;
7. Param<int> height;
8. Param<float> mu;
9. Param<float> beta;
10. Param<float> sigma;
11. Param<float> gamma;
12. Param<float> tau;
13. Param<int> order, samples;
14. Param<double> learning_rate;
15. RDom s(0, width, 0, height);
16.
17. s.where(abs(sqrt(s.x * s.x + s.y * s.y) - mu) < 3 * sigma);
18.
19. // int n = 5; //number of features
20. ImageParam disparities(Float(64), 2);
21. ImageParam left_features(Float(64), 3);
22. ImageParam right_features(Float(64), 3);
23. ImageParam a(Float(64), 1);
24. ImageParam b(Float(64), 1);
25. ImageParam c(Float(64), 1);
26. ImageParam point_tri_map(Int(32), 2);
27.
28. Func mean;
29. mean(x, y) = a(point_tri_map(x, y)) * x + b(point_tri_map(x, y)) * y + c(point_tri_map(x, y));
30.
31. Func diff;
32. diff(x, y, i) = left_features(x, y, i) - right_features(x, y, i); // for i = 0 to n
33.
34. Func norm;
35. RDom n_feat(0, 5);
36. norm(x, y) = sum(abs(diff(x, y, n_feat)));
37.
38. ImageParam d(Int(32), 2);
39.
40. Func energy;
41. energy(x, y) = beta * norm(x - d(x, y), y) - log (gamma + exp(- pow((d(x, y) - mean(x, y)), 2)) / (2 * sigma * sigma));
42.
43. RDom img(0, width, 0, height);
44. Func err;
45. // err() = f64(0);
46. err() = sum(energy(img.x, img.y));
47.