void lookat_rotate(Matrix T, double x, double y, double z, Matrix Matrix0)
{
static Vector N, V, U;
static Vector ep, lp;
lp.x = x;
lp.y = y;
lp.z = z;
ep.x = T[12];
ep.y = T[13];
ep.z = T[14];
points_to_array(&N, &lp, &ep);
normalize_array(&N);
U.x = T[0];
U.y = T[4];
U.z = T[8];
cross_product(&V, &N, &U);
normalize_array(&V);
cross_product(&U, &V, &N);
normalize_array(&U);
M[0] = U.x;
M[1] = V.x;
M[2] = N.x;
M[3] = 0.;
M[4] = U.y;
M[5] = V.y;
M[6] = N.y;
M[7] = 0.;
M[8] = U.z;
M[9] = V.z;
M[10] = N.z;
M[11] = 0.;
#if 0
M[12] = ep.x;
M[13] = ep.y;
M[14] = ep.z;
M[15] = 1.;
#endif
#if 0
M[12] = -U.x * ep.x - U.y * ep.y - U.z * ep.z;
M[13] = -V.x * ep.x - V.y * ep.y - V.z * ep.z;
M[14] = -N.x * ep.x - N.y * ep.y - N.z * ep.z;
#endif
M[12] = ((U.x * ep.x) + (U.y * ep.y) + (U.z * ep.z));
M[13] = ((V.x * ep.x) + (V.y * ep.y) + (V.z * ep.z));
M[14] = ((N.x * ep.x) + (N.y * ep.y) + (N.z * ep.z));
M[15] = 1.;
#ifdef DEBUG
print_array(&lp, "look point");
print_array(&ep, "eye point");
print_array(&N, "normal array");
print_array(&V, "V = N x U");
print_array(&U, "U = V x N");
print_matrix(M, "final matrix");
#endif /* DEBUG */
}
void lookat_rotate2(double, ex, double ey, double ez, double lx, double ly, double double, ex, double0,double, ex, double1 double, ex, double2,double, ex, double3 double, ex, double4)
{
static Vector N, V, U;
static Vector ep, lp;
ep.x = ex;
ep.y = ey;
ep.z = ez;
lp.x = lx;
lp.y = ly;
lp.z = lz;
points_to_array(&N, &lp, &ep);
normalize_array(&N);
U.x = 0.;
U.y = 1.;
U.z = 0.;
cross_product(&V, &N, &U);
normalize_array(&V);
cross_product(&U, &V, &N);
normalize_array(&U);
M[0] = U.x;
M[1] = V.x;
M[2] = N.x;
M[3] = 0.;
M[4] = U.y;
M[5] = V.y;
M[6] = N.y;
M[7] = 0.;
M[8] = U.z;
M[9] = V.z;
M[10] = N.z;
M[11] = 0.;
#if 0
M[12] = ep.x;
M[13] = ep.y;
M[14] = ep.z;
M[15] = 1.;
#endif
#if 0
M[12] = -U.x * ep.x - U.y * ep.y - U.z * ep.z;
M[13] = -V.x * ep.x - V.y * ep.y - V.z * ep.z;
M[14] = -N.x * ep.x - N.y * ep.y - N.z * ep.z;
#endif
M[12] = ((U.x * ep.x) + (U.y * ep.y) + (U.z * ep.z));
M[13] = ((V.x * ep.x) + (V.y * ep.y) + (V.z * ep.z));
M[14] = ((N.x * ep.x) + (N.y * ep.y) + (N.z * ep.z));
M[15] = 1.;
#ifdef DEBUG
print_array(&lp, "look point");
print_array(&ep, "eye point");
print_array(&N, "normal array");
print_array(&V, "V = N x U");
print_array(&U, "U = V x N");
print_matrix(M, "final matrix");
#endif /* DEBUG */
}
void optimize_picture(network *net, image orig, int max_layer, float scale, float rate, float thresh, int norm)
{
//scale_image(orig, 2);
//translate_image(orig, -1);
net->n = max_layer + 1;
int dx = rand()%16 - 8;
int dy = rand()%16 - 8;
int flip = rand()%2;
image crop = crop_image(orig, dx, dy, orig.w, orig.h);
image im = resize_image(crop, (int)(orig.w * scale), (int)(orig.h * scale));
if(flip) flip_image(im);
resize_network(net, im.w, im.h);
layer_t last = net->layers[net->n-1];
//net->layers[net->n - 1].activation = LINEAR;
image delta = make_image(im.w, im.h, im.c);
NETWORK_STATE(state);
#ifdef GPU
state.input = cuda_make_array(im.data, im.w*im.h*im.c);
state.delta = cuda_make_array(im.data, im.w*im.h*im.c);
forward_network_gpu(*net, state);
copy_ongpu(last.outputs, last.output_gpu, 1, last.delta_gpu, 1);
cuda_pull_array(last.delta_gpu, last.delta, last.outputs);
calculate_loss(last.delta, last.delta, last.outputs, thresh);
cuda_push_array(last.delta_gpu, last.delta, last.outputs);
backward_network_gpu(*net, state);
cuda_pull_array(state.delta, delta.data, im.w*im.h*im.c);
cuda_free(state.input);
cuda_free(state.delta);
#else
state.input = im.data;
state.delta = delta.data;
forward_network(*net, state);
fltcpy(last.delta, last.output, last.outputs);
calculate_loss(last.output, last.delta, last.outputs, thresh);
backward_network(*net, state);
#endif
if(flip) flip_image(delta);
//normalize_array(delta.data, delta.w*delta.h*delta.c);
image resized = resize_image(delta, orig.w, orig.h);
image out = crop_image(resized, -dx, -dy, orig.w, orig.h);
/*
image g = grayscale_image(out);
free_image(out);
out = g;
*/
//rate = rate / abs_mean(out.data, out.w*out.h*out.c);
if(norm) normalize_array(out.data, out.w*out.h*out.c);
fltaddmul(orig.data, out.data, orig.w * orig.h * orig.c, rate);
/*
normalize_array(orig.data, orig.w*orig.h*orig.c);
scale_image(orig, sqrt(var));
translate_image(orig, mean);
*/
//translate_image(orig, 1);
//scale_image(orig, .5);
//normalize_image(orig);
constrain_image(orig);
free_image(crop);
free_image(im);
free_image(delta);
free_image(resized);
free_image(out);
}
int main() {
int i;
for (i=0; i<20; i++) {
a[i].var = i;
a[i].exp = 1;
}
normalize_array(20);
for (i=0; i<20; i++) {
a[i].var = 20 - i;
a[i].exp = 2;
}
normalize_array(20);
for (i=0; i<10; i++) {
a[i].var = 20 - i;
a[i].exp = -i;
}
for (i=10; i<20; i++) {
a[i].var = i + 1;
a[i].exp = 19 - i;
}
normalize_array(20);
normalize_array(0);
normalize_array(1);
a[0].var = 0;
a[1].var = 10;
normalize_array(2);
a[0].var = 10;
a[1].var = 0;
normalize_array(2);
for (i=0; i<40; i++) {
a[i].var = random() % 100;
a[i].exp = i;
}
normalize_array(40);
for (i=0; i<100; i++) {
a[i].var = random() % 100;
a[i].exp = i;
}
normalize_array(100);
for (i=0; i<100; i++) {
a[i].var = random() % 100;
a[i].exp = i;
}
normalize_array(100);
for (i=0; i<100; i++) {
a[i].var = random() % 100;
a[i].exp = i;
}
normalize_array(100);
for (i=0; i<100; i++) {
a[i].var = random() % 100;
a[i].exp = i;
}
normalize_array(100);
for (i=0; i<100; i++) {
a[i].var = random() % 100;
a[i].exp = i;
}
normalize_array(100);
for (i=0; i<100; i++) {
a[i].var = random() % 100;
a[i].exp = i;
}
normalize_array(100);
return 0;
}