void
matrix_srt(struct matrix *mm, const struct srt *srt) {
if (!srt) {
return;
}
scale_mat(mm->m, srt->scalex, srt->scaley);
rot_mat(mm->m, srt->rot);
mm->m[4] += srt->offx;
mm->m[5] += srt->offy;
}
void Tetro::draw(sf::RenderTarget& target, sf::RenderStates states) const {
sf::VertexArray vertices(sf::Quads, 4*4);
for (int i = 0; i < 4; i++) {
sf::Vertex* quad = &vertices[4*i];
sf::Transform t = scale_mat(CELL_WIDTH_HEIGHT);
_blocks[i].drawVertices(quad, t, this->getColor());
}
const float final_x = CELL_WIDTH_HEIGHT * this->_col;
const float final_y = CELL_WIDTH_HEIGHT * this->_row;
states.transform.translate(final_x, final_y);
target.draw(vertices, states);
}
void do_scene_transform(t_app *app, t_scene *scn)
{
int i;
i = 0;
app = 0;
scn->mat = scale_mat(scn->scale);
scn->mat = muli_mat4x4(rot_y_mat(scn->rot.y), scn->mat);
scn->mat = muli_mat4x4(rot_x_mat(scn->rot.x), scn->mat);
scn->mat = muli_mat4x4(rot_z_mat(scn->rot.z), scn->mat);
scn->mat = muli_mat4x4(translate_mat(scn->pos), scn->mat);
}
void Lightning::fractalize_ball(LightningNode *lightningRoot)
{
if (head == NULL || lightningRoot->depth == 0) {
return;
}
Matrix transform = Matrix();
Vector translateToTail;
double rotX;
double rotY;
double rotZ;
float scale;
LightningNode *tmp;
scale = pow(0.7, (maxFracDepth - lightningRoot->depth + 1));
for (int i = 0; i < lightningRoot->numBranches; i++) {
lightningRoot->branches[i] = getRootCopy();
tmp = lightningRoot->branches[i];
if (i == 0) {
tailPoint = topPoint;
headPoint = botPoint;
} else if (i == 1) {
tailPoint = botPoint;
headPoint = topPoint;
} else if (i == 2) {
tailPoint = lPoint;
headPoint = rPoint;
} else if (i == 3) {
tailPoint = rPoint;
headPoint = lPoint;
} else if (i == 4) {
tailPoint = fPoint;
headPoint = bPoint;
} else if (i == 5) {
tailPoint = bPoint;
headPoint = fPoint;
} else {
exit(1);
}
transform = lightningRoot->transform * scale_mat(Vector(scale, scale, scale));
translateToTail = (lightningRoot->transform * tailPoint) - (transform * (tmp->transform * headPoint));
transform = trans_mat(translateToTail) * transform;
applyToPath(tmp, transform, lightningRoot->depth - 1);
//TODO: Insert while loop for recursion
while (tmp != NULL) {
fractalize(tmp);
tmp = tmp->next;
}
}
}
void do_transform(t_app *app, t_obj *obj, t_matrix4x4 mat)
{
unsigned int i;
float proj;
i = 0;
proj = app->scene.cam.proj;
obj->mat = scale_mat(obj->scale);
obj->mat = muli_mat4x4(rot_y_mat(obj->rot.y), obj->mat);
obj->mat = muli_mat4x4(rot_x_mat(obj->rot.x), obj->mat);
obj->mat = muli_mat4x4(rot_z_mat(obj->rot.z), obj->mat);
obj->mat = muli_mat4x4(translate_mat(obj->pos), obj->mat);
obj->mat = muli_mat4x4(mat, obj->mat);
obj->mat = muli_mat4x4(cam_mat(app), obj->mat);
while (i < obj->nbr_vecs)
{
obj->vecs[i] = muli_mat4x4_vec4(obj->mat, obj->vecs_orig[i]);
i++;
}
}
void Lightning::fractalize(LightningNode *lightningRoot)
{
if (head == NULL || lightningRoot->depth == 0) {
return;
}
if (fracType == 1) {
fractalize_ball(lightningRoot);
return;
}
Matrix transform = Matrix();
Vector translateToTail;
double rotX;
double rotY;
double rotZ;
float scale;
LightningNode *tmp;
scale = pow(FRACSCALE, (maxFracDepth - lightningRoot->depth + 1));
for (int i = 0; i < lightningRoot->numBranches; i++) {
lightningRoot->branches[i] = getRootCopy();
tmp = lightningRoot->branches[i];
srand(time(NULL) * (i + 5));
rotX = ((double)(rand() % 60) - 30.0) * PI / 180.0;
srand(time(NULL) * i);
rotY = ((double)(rand() % 360)) * PI / 180.0;
srand(time(NULL) + i);
rotZ = ((double)(rand() % 60) - 30.0) * PI / 180.0;
transform = rotY_mat(rotY) * (rotZ_mat(rotZ) * rotX_mat(rotX)) * scale_mat(Vector(scale, scale, scale));
transform = lightningRoot->transform * transform;
translateToTail = (lightningRoot->transform * tailPoint) - (transform * (tmp->transform * headPoint));
transform = trans_mat(translateToTail) * transform;
applyToPath(tmp, transform, lightningRoot->depth - 1);
//TODO: Insert while loop for recursion
while (tmp != NULL) {
fractalize(tmp);
tmp = tmp->next;
}
}
}
void
matrix_scale(struct matrix *m, int sx, int sy) {
scale_mat(m->m, sx, sy);
}
FlattenedNode *Lightning::lightning_recursive_flatten(FlattenedNode *graphList, Matrix transform, Matrix *rTrans, SceneNode *child, Point *eyePoint)
{
Matrix rec_transform = Matrix();
Matrix tmp_trans = Matrix();
FlattenedNode* tmp_flattened;
if (rTrans == NULL) {
rec_transform = transform * rec_transform;
} else {
rec_transform = *rTrans;
}
if (child == NULL) {
return graphList;
}
for (int i = 0; i < child->transformations.size(); i++) {
switch (child->transformations[i]->type)
{
case TRANSFORMATION_TRANSLATE:
{
tmp_trans = tmp_trans * trans_mat(child->transformations[i]->translate);
break;
}
case TRANSFORMATION_SCALE:
{
tmp_trans = tmp_trans * scale_mat(child->transformations[i]->scale);
break;
}
case TRANSFORMATION_ROTATE:
{
tmp_trans = tmp_trans * rot_mat(child->transformations[i]->rotate, child->transformations[i]->angle);
break;
}
case TRANSFORMATION_MATRIX:
{
tmp_trans = tmp_trans * child->transformations[i]->matrix;
break;
}
default:
break;
}
}
rec_transform = rec_transform * tmp_trans;
for (int i = 0; i < child->primitives.size(); i++) {
tmp_flattened = new FlattenedNode();
tmp_flattened->primitive = child->primitives[i];
if (fracType == 1) {
tmp_flattened->transform = scale_mat(Vector(0.35, 0.35, 0.35)) * rec_transform;
} else {
tmp_flattened->transform = scale_mat(Vector(STOCKSCALE, STOCKSCALE, STOCKSCALE)) * rec_transform;
}
tmp_flattened->invTransform = invert(tmp_flattened->transform);
tmp_flattened->objEyeP = tmp_flattened->invTransform * *eyePoint;
SceneMaterial *currentMaterial = &(tmp_flattened->primitive->material);
if (currentMaterial->textureMap != NULL && currentMaterial->textureMap->isUsed) {
currentMaterial->openedTMap = new ppm(currentMaterial->textureMap->filename);
}
tmp_flattened->next = graphList;
graphList = tmp_flattened;
}
for (int i = 0; i < child->children.size(); i++) {
graphList = lightning_recursive_flatten(graphList, transform, &rec_transform, child->children[i], eyePoint);
}
return graphList;
}
void Lightning::generate(unsigned length)
{
if (fracType == 1) {
generate_ball();
return;
}
double rotX;
double rotY;
double rotZ;
float scale;
int branchingTest;
unsigned totalBolts = 0;
Point minPoint, maxPoint;
Point tmpPoint;
Vector translate;
LightningNode *tmp;
LightningNode *prev;
if (head != NULL || length == 0) {
refresh();
}
if (length == 0) {
return;
}
head = new LightningNode;
tmp = head;
prev = head;
for (unsigned i = 0; i < length; i++) {
tmp->depth = maxFracDepth;
//Random rotation of the bolt
srand(time(NULL)*i);
rotX = ((double)(rand() % 60) - 30.0) * PI / 180.0;
srand(time(NULL)*(i+i));
rotY = ((double)(rand() % 360)) * PI / 180.0;
srand(time(NULL)*(i * i));
rotZ = ((double)(rand() % 60) - 30.0) * PI / 180.0;
tmp->transform = rotY_mat(rotY) * (rotZ_mat(rotZ) * rotX_mat(rotX));
//Random scaling of the bolt
scale = 1.0;//((double)(rand() % 3) / 10.0) + 0.8;
tmp->transform = scale_mat(Vector(scale, scale, scale)) * tmp->transform;
//Inheriting previous matrices
if (tmp == head) {
minPoint = tmp->transform * headPoint;
maxPoint = minPoint;
tmpPoint = tmp->transform * tailPoint;
if (tmpPoint[0] > maxPoint[0]) {
maxPoint[0] = tmpPoint[0];
} else if (tmpPoint[0] < minPoint[0]) {
minPoint[0] = tmpPoint[0];
}
if (tmpPoint[1] > maxPoint[1]) {
maxPoint[1] = tmpPoint[1];
} else if (tmpPoint[1] < minPoint[1]) {
minPoint[1] = tmpPoint[1];
}
if (tmpPoint[2] > maxPoint[2]) {
maxPoint[2] = tmpPoint[2];
} else if (tmpPoint[2] < minPoint[2]) {
minPoint[2] = tmpPoint[2];
}
} else {
tmp->transform = prev->transform * tmp->transform;
tmpPoint = tmp->transform * headPoint;
translate = (prev->transform * tailPoint) - (tmpPoint);
tmp->transform = trans_mat(translate) * tmp->transform;
if (tmpPoint[0] > maxPoint[0]) {
maxPoint[0] = tmpPoint[0];
} else if (tmpPoint[0] < minPoint[0]) {
minPoint[0] = tmpPoint[0];
}
if (tmpPoint[1] > maxPoint[1]) {
maxPoint[1] = tmpPoint[1];
} else if (tmpPoint[1] < minPoint[1]) {
minPoint[1] = tmpPoint[1];
}
if (tmpPoint[2] > maxPoint[2]) {
maxPoint[2] = tmpPoint[2];
} else if (tmpPoint[2] < minPoint[2]) {
minPoint[2] = tmpPoint[2];
}
tmpPoint = tmp->transform * tailPoint;
if (tmpPoint[0] > maxPoint[0]) {
maxPoint[0] = tmpPoint[0];
} else if (tmpPoint[0] < minPoint[0]) {
minPoint[0] = tmpPoint[0];
}
if (tmpPoint[1] > maxPoint[1]) {
maxPoint[1] = tmpPoint[1];
} else if (tmpPoint[1] < minPoint[1]) {
minPoint[1] = tmpPoint[1];
}
if (tmpPoint[2] > maxPoint[2]) {
maxPoint[2] = tmpPoint[2];
} else if (tmpPoint[2] < minPoint[2]) {
minPoint[2] = tmpPoint[2];
}
}
//Give it branches
srand((time(NULL) * 24678 * i) + (i *2));
branchingTest = (rand()) % 200;
if (branchingTest >= THRESHOLD3) {
tmp->numBranches = 3;
tmp->branches = new LightningNode*[3];
tmp->branches[0] = NULL;
tmp->branches[1] = NULL;
tmp->branches[2] = NULL;
branchesPerLine += 3;
} else if (branchingTest >= THRESHOLD2) {
tmp->numBranches = 2;
tmp->branches = new LightningNode*[2];
tmp->branches[0] = NULL;
tmp->branches[1] = NULL;
branchesPerLine += 2;
} else if (branchingTest >= THRESHOLD1) {
tmp->numBranches = 1;
tmp->branches = new LightningNode*[1];
tmp->branches[0] = NULL;
branchesPerLine++;
} else {
tmp->numBranches = 0;
tmp->branches = NULL;
}
if (i < (length - 1)) {
tmp->next = new LightningNode;
prev = tmp;
tmp = tmp->next;
tmp->next = NULL;
}
}
std::cout<<"Total bolts rendered: ";
for (int i = 0; i <= maxFracDepth; i++) {
totalBolts += length * pow(branchesPerLine, i);
}
std::cout<<totalBolts<<std::endl;
centerAtOrigin(maxPoint, minPoint);
//TODO: Fractal compatibility
tmp = head;
while (tmp != NULL) {
fractalize(tmp);
tmp = tmp->next;
}
}
void rtgui_matrix_scale(struct rtgui_matrix *m, int sx, int sy)
{
scale_mat(m->m, sx, sy);
}
int main(int argc, char* argv[])
{
TEST_PARAS myparas = parse_test_paras(argc, argv, testfile, embeddingfile, trainfile);
printf("Predicting...\n");
if(!myparas.allow_self_transition)
printf("Do not allow self-transtion.\n");
if (!myparas.underflow_correction)
printf("Underflow correction disabled\n");
int new_test_song_exp = (myparas.train_test_hash_file[0] != '\0');
if(myparas.tagfile[0] == '\0' && new_test_song_exp)
{
printf("Have to support with a tag file if you want to test on unseen songs.\n");
exit(1);
}
int d;
int m;
int l;
int i;
int j;
int s;
int fr;
int to;
double* bias_terms = 0;
double** X = read_matrix_file(embeddingfile, &l, &d, &bias_terms);
double** realX;
PDATA pd = read_playlists_data(testfile);
//int k = pd.num_songs;
int k;
double llhood = 0.0;
double uniform_llhood = 0.0;
double realn = 0.0;
double not_realn= 0.0;
int* train_test_hash;
int k_train;
int k_test;
TDATA td;
if(!new_test_song_exp)
{
k = pd.num_songs;
if(myparas.tagfile[0] != '\0')
{
td = read_tag_data(myparas.tagfile);
m = td.num_tags;
myparas.num_points = l / (k + m);
realX = zerosarray(k * myparas.num_points, d);
calculate_realX(X, realX, td, k, m, d, myparas.num_points);
free_tag_data(td);
if(myparas.tag_ebd_filename[0] != '\0')
write_embedding_to_file(X + k * myparas.num_points, m * myparas.num_points, d, myparas.tag_ebd_filename, 0);
}
else
{
myparas.num_points = l / k;
realX = zerosarray(k * myparas.num_points, d);
Array2Dcopy(X, realX, l, d);
}
Array2Dfree(X, l, d);
}
else
{
printf("Prediction on unseen songs.\n");
td = read_tag_data(myparas.tagfile);
m = td.num_tags;
k = td.num_songs;
train_test_hash = read_hash(myparas.train_test_hash_file, &k_train);
k_test = k - k_train;
printf("Number of new songs %d.\n", k_test);
myparas.num_points = l / (k_train + m);
realX = zerosarray(k * myparas.num_points, d);
calculate_realX_with_hash(X, realX, td, k, m, d, myparas.num_points, k_train, train_test_hash);
free_tag_data(td);
Array2Dfree(X, l, d);
}
if(myparas.song_ebd_filename[0] != '\0')
write_embedding_to_file(realX, k * myparas.num_points, d, myparas.song_ebd_filename, 0);
if(myparas.bias_ebd_filename[0] != '\0')
{
FILE* fp = fopen(myparas.bias_ebd_filename, "w");
for( i = 0; i < k ;i++)
{
fprintf(fp, "%f", bias_terms[i]);
if ( i != k - 1)
fputc('\n', fp);
}
fclose(fp);
}
double** square_dist;
if(myparas.square_dist_filename[0] != '\0')
square_dist = zerosarray(k, k);
int n = 0;
for(i = 0; i < pd.num_playlists; i ++)
if(pd.playlists_length[i] > 0)
n += pd.playlists_length[i] - 1;
printf("Altogether %d transitions.\n", n);fflush(stdout);
PHASH* tcount;
PHASH* tcount_train;
double** tcount_full;
double** tcount_full_train;
if(myparas.use_hash_TTable)
tcount = create_empty_hash(2 * n);
else
tcount_full = zerosarray(k, k);
HELEM temp_elem;
TPAIR temp_pair;
int idx;
double temp_val;
for(i = 0; i < pd.num_playlists; i ++)
{
if(pd.playlists_length[i] > myparas.range)
{
for(j = 0; j < pd.playlists_length[i] - 1; j++)
{
temp_pair.fr = pd.playlists[i][j];
temp_pair.to = pd.playlists[i][j + myparas.range];
//printf("(%d, %d)\n", temp_pair.fr, temp_pair.to);
if(temp_pair.fr >= 0 && temp_pair.to >= 0)
{
if(myparas.use_hash_TTable)
{
idx = exist_in_hash(tcount, temp_pair);
if(idx < 0)
{
temp_elem.key = temp_pair;
temp_elem.val = 1.0;
add_entry(tcount, temp_elem);
}
else
update_with(tcount, idx, 1.0);
}
else
tcount_full[temp_pair.fr][temp_pair.to] += 1.0;
}
}
}
}
TRANSITIONTABLE ttable;
TRANSITIONTABLE BFStable;
//Need to use the training file
if(myparas.output_distr)
{
PDATA pd_train = read_playlists_data(trainfile);
if(myparas.use_hash_TTable)
tcount_train = create_empty_hash(2 * n);
else
tcount_full_train = zerosarray(k, k);
for(i = 0; i < pd_train.num_playlists; i ++)
{
if(pd_train.playlists_length[i] > 1)
{
for(j = 0; j < pd_train.playlists_length[i] - 1; j++)
{
temp_pair.fr = pd_train.playlists[i][j];
temp_pair.to = pd_train.playlists[i][j + 1];
if(myparas.use_hash_TTable)
{
idx = exist_in_hash(tcount_train, temp_pair);
if(idx < 0)
{
temp_elem.key = temp_pair;
temp_elem.val = 1.0;
add_entry(tcount_train, temp_elem);
}
else
update_with(tcount_train, idx, 1.0);
}
else
tcount_full_train[temp_pair.fr][temp_pair.to] += 1.0;
}
}
}
}
FILE* song_distr_file;
FILE* trans_distr_file;
double* song_sep_ll;
if(myparas.output_distr)
{
printf("Output likelihood distribution file turned on.\n");
if(myparas.output_distr)
{
song_distr_file = fopen(songdistrfile, "w");
trans_distr_file = fopen(transdistrfile, "w");
song_sep_ll = (double*)calloc(k, sizeof(double));
}
}
int* test_ids_for_new_songs;
if(new_test_song_exp)
test_ids_for_new_songs = get_test_ids(k, k_train, train_test_hash);
for(fr = 0; fr < k; fr++)
{
int collection_size;
int* collection_idx;
if(myparas.fast_collection)
{
collection_size = (BFStable.parray)[fr].length;
if (collection_size == 0)
continue;
collection_idx = (int*)malloc(collection_size * sizeof(int));
LINKEDELEM* tempp = (BFStable.parray)[fr].head;
for(i = 0; i < collection_size; i++)
{
collection_idx[i] = tempp -> idx;
tempp = tempp -> pnext;
}
}
else if(new_test_song_exp)
{
collection_size = k_test;
collection_idx = (int*)malloc(collection_size * sizeof(int));
int_list_copy(test_ids_for_new_songs, collection_idx, k_test);
}
else
collection_size = k;
double** delta = zerosarray(collection_size, d);
double* p = (double*)calloc(collection_size, sizeof(double));
double** tempkd = zerosarray(collection_size, d);
double* tempk = (double*)calloc(collection_size, sizeof(double));
double** mid_delta = 0;
double* mid_p = 0;
double** mid_tempkd = 0;
// I get a seg fault when these get freed. Don't understand.
if (myparas.num_points == 3) {
mid_delta = zerosarray(collection_size, d);
mid_p = (double*)calloc(collection_size, sizeof(double));
mid_tempkd = zerosarray(collection_size, d);
}
for(j = 0; j < collection_size; j++)
{
for(i = 0; i < d; i++)
{
if(myparas.fast_collection || new_test_song_exp)
delta[j][i] = realX[fr][i] - realX[(myparas.num_points - 1) * k + collection_idx[j]][i];
else
delta[j][i] = realX[fr][i] - realX[(myparas.num_points - 1) * k + j][i];
}
if(myparas.num_points == 3) {
if(myparas.fast_collection || new_test_song_exp)
mid_delta[j][i] =
realX[k + fr][i] - realX[k + collection_idx[j]][i];
else
mid_delta[j][i] = realX[k + fr][i] - realX[k + j][i];
}
}
mat_mult(delta, delta, tempkd, collection_size, d);
scale_mat(tempkd, collection_size, d, -1.0);
sum_along_direct(tempkd, p, collection_size, d, 1);
if(myparas.square_dist_filename[0] != '\0')
for(i = 0; i < k; i++)
square_dist[fr][i] = -p[i];
if (bias_terms != 0)
add_vec(p, bias_terms, collection_size, 1.0);
if (myparas.num_points == 3) {
// Just use the mid_deltas (midpoint differences): square them,
// then sum and add to the p vector directly, then the midpoint
// probability is incorporated
mat_mult(mid_delta, mid_delta, mid_tempkd, collection_size, d);
scale_mat(mid_tempkd, collection_size, d, -1.0);
sum_along_direct(mid_tempkd, mid_p, collection_size, d, 1);
add_vec(p, mid_p, collection_size, 1.0);
}
if (myparas.underflow_correction == 1) {
double max_val = p[0];
for(i = 0; i < collection_size; i++)
max_val = p[i] > max_val? p[i] : max_val;
vec_scalar_sum(p, -max_val, collection_size);
}
Veccopy(p, tempk, collection_size);
exp_on_vec(tempk, collection_size);
//exp_on_vec(p, collection_size);
// underflow checking:
// for (i = 0; i < collection_size; i++)
// if (p[i] < 0.000001)
// p[i] = 0.000001;
double temp_sum;
if(myparas.allow_self_transition)
temp_sum = sum_vec(tempk, collection_size);
else
{
temp_sum = 0.0;
for(i = 0; i < collection_size; i++)
if(!myparas.fast_collection || new_test_song_exp)
temp_sum += (i != fr)? tempk[i] : 0.0;
else
temp_sum += (collection_idx[i] != fr)? tempk[i] : 0.0;
}
vec_scalar_sum(p, -log(temp_sum), collection_size);
//scale_vec(p, collection_size, 1.0 / temp_sum);
//printf("done...\n");
for(to = 0; to < k; to++)
{
if(myparas.allow_self_transition || (!myparas.allow_self_transition && fr != to))
{
temp_pair.fr = fr;
temp_pair.to = to;
//printf("(%d, %d)\n", fr, to);
if(myparas.use_hash_TTable)
idx = exist_in_hash(tcount, temp_pair);
else
idx = tcount_full[fr][to] > 0.0? 1 : -1;
//printf("%d\n", idx);fflush(stdout);
int idx_train;
//printf("done...\n");fflush(stdout);
if(myparas.output_distr)
{
if(myparas.use_hash_TTable)
idx_train = exist_in_hash(tcount_train, temp_pair);
else
idx_train = tcount_full_train[fr][to] > 0.0? 1 : -1;
}
if(idx >= 0)
{
if(myparas.fast_collection || new_test_song_exp)
{
s = -1;
for(i = 0; i < collection_size; i++)
{
if(collection_idx[i] == to)
{
s = i;
break;
}
}
}
else
s = to;
//printf("%d\n", idx);fflush(stdout);
if(myparas.use_hash_TTable)
temp_val = retrieve_value_with_idx(tcount, idx);
else
temp_val = tcount_full[fr][to];
if(s < 0)
not_realn += temp_val;
else
{
//printf("s = %d\n", s);
llhood += temp_val * p[s];
if(new_test_song_exp)
uniform_llhood += temp_val * log(1.0 / (double) k_test);
realn += temp_val;
if(myparas.output_distr)
{
//double temp_val_train = idx_train >= 0? retrieve_value_with_idx(tcount_train, idx_train): 0.0;
double temp_val_train;
if(idx_train < 0)
temp_val_train = 0.0;
else
temp_val_train = myparas.use_hash_TTable ? retrieve_value_with_idx(tcount_train, idx_train) : tcount_full_train[fr][to];
song_sep_ll[fr] += temp_val * p[s];
song_sep_ll[to] += temp_val * p[s];
fprintf(trans_distr_file, "%d %d %f\n", (int)temp_val_train, (int)temp_val, temp_val * p[s]);
}
}
}
}
}
Array2Dfree(delta, collection_size, d);
free(p);
Array2Dfree(tempkd, collection_size, d);
free(tempk);
if (myparas.num_points == 3) {
Array2Dfree(mid_delta, collection_size, d);
free(mid_p);
Array2Dfree(mid_tempkd, collection_size, d);
}
if(myparas.fast_collection || new_test_song_exp)
free(collection_idx);
}
if(myparas.output_distr)
{
printf("Writing song distr.\n");
for(i = 0; i < k; i++)
fprintf(song_distr_file, "%d %f\n", (int)(pd.id_counts[i]), song_sep_ll[i]);
fclose(song_distr_file);
fclose(trans_distr_file);
free(song_sep_ll);
}
llhood /= realn;
printf("Avg log-likelihood on test: %f\n", llhood);
if(myparas.fast_collection)
printf("Ratio of transitions that do not appear in the training set: %f\n", not_realn / (realn + not_realn));
if(new_test_song_exp)
{
uniform_llhood /= realn;
printf("Avg log-likelihood for uniform baseline: %f\n", uniform_llhood);
}
if(myparas.use_hash_TTable)
free_hash(tcount);
else
Array2Dfree(tcount_full, k, k);
free_playlists_data(pd);
if(myparas.output_distr)
{
if(myparas.use_hash_TTable)
free_hash(tcount_train);
else
Array2Dfree(tcount_full_train, k, k);
}
Array2Dfree(realX, k * myparas.num_points, d);
if(new_test_song_exp)
{
free(train_test_hash);
free(test_ids_for_new_songs);
}
if(myparas.square_dist_filename[0] != '\0')
{
write_embedding_to_file(square_dist, k, k, myparas.square_dist_filename, 0);
Array2Dfree(square_dist, k, k);
}
}
void
pcl::rec_3d_framework::GlobalNNCVFHRecognizer<Distance, PointInT, FeatureT>::recognize ()
{
models_.reset (new std::vector<ModelT>);
transforms_.reset (new std::vector<Eigen::Matrix4d, Eigen::aligned_allocator<Eigen::Matrix4d> >);
PointInTPtr processed (new pcl::PointCloud<PointInT>);
PointInTPtr in (new pcl::PointCloud<PointInT>);
std::vector<pcl::PointCloud<FeatureT>, Eigen::aligned_allocator<pcl::PointCloud<FeatureT> > > signatures;
std::vector < Eigen::Vector3d > centroids;
if (indices_.size ())
pcl::copyPointCloud (*input_, indices_, *in);
else
in = input_;
{
pcl::ScopeTime t ("Estimate feature");
micvfh_estimator_->estimate (in, processed, signatures, centroids);
}
std::vector<index_score> indices_scores;
descriptor_distances_.clear ();
if (signatures.size () > 0)
{
{
pcl::ScopeTime t_matching ("Matching and roll...");
if (use_single_categories_ && (categories_to_be_searched_.size () > 0))
{
//perform search of the different signatures in the categories_to_be_searched_
for (size_t c = 0; c < categories_to_be_searched_.size (); c++)
{
std::cout << "Using category:" << categories_to_be_searched_[c] << std::endl;
for (size_t idx = 0; idx < signatures.size (); idx++)
{
/*double* hist = signatures[idx].points[0].histogram;
std::vector<double> std_hist (hist, hist + getHistogramLength (dummy));
flann_model histogram ("", std_hist);
flann::Matrix<int> indices;
flann::Matrix<double> distances;
std::map<std::string, int>::iterator it;
it = category_to_vectors_indices_.find (categories_to_be_searched_[c]);
assert (it != category_to_vectors_indices_.end ());
nearestKSearch (single_categories_index_[it->second], histogram, nmodels_, indices, distances);*/
double* hist = signatures[idx].points[0].histogram;
int size_feat = sizeof(signatures[idx].points[0].histogram) / sizeof(double);
std::vector<double> std_hist (hist, hist + size_feat);
//ModelT empty;
flann_model histogram;
histogram.descr = std_hist;
flann::Matrix<int> indices;
flann::Matrix<double> distances;
std::map<std::string, int>::iterator it;
it = category_to_vectors_indices_.find (categories_to_be_searched_[c]);
assert (it != category_to_vectors_indices_.end ());
nearestKSearch (single_categories_index_[it->second], histogram, NN_, indices, distances);
//gather NN-search results
double score = 0;
for (size_t i = 0; i < NN_; ++i)
{
score = distances[0][i];
index_score is;
is.idx_models_ = single_categories_pointers_to_models_[it->second]->at (indices[0][i]);
is.idx_input_ = static_cast<int> (idx);
is.score_ = score;
indices_scores.push_back (is);
}
}
//we cannot add more than nmodels per category, so sort here and remove offending ones...
std::sort (indices_scores.begin (), indices_scores.end (), sortIndexScoresOp);
indices_scores.resize ((c + 1) * NN_);
}
}
else
{
for (size_t idx = 0; idx < signatures.size (); idx++)
{
double* hist = signatures[idx].points[0].histogram;
int size_feat = sizeof(signatures[idx].points[0].histogram) / sizeof(double);
std::vector<double> std_hist (hist, hist + size_feat);
//ModelT empty;
flann_model histogram;
histogram.descr = std_hist;
flann::Matrix<int> indices;
flann::Matrix<double> distances;
nearestKSearch (flann_index_, histogram, NN_, indices, distances);
//gather NN-search results
double score = 0;
for (int i = 0; i < NN_; ++i)
{
score = distances[0][i];
index_score is;
is.idx_models_ = indices[0][i];
is.idx_input_ = static_cast<int> (idx);
is.score_ = score;
indices_scores.push_back (is);
//ModelT m = flann_models_[indices[0][i]].model;
}
}
}
std::sort (indices_scores.begin (), indices_scores.end (), sortIndexScoresOp);
/*
* There might be duplicated candidates, in those cases it makes sense to take
* the closer one in descriptor space
*/
typename std::map<flann_model, bool> found;
typename std::map<flann_model, bool>::iterator it_map;
for (size_t i = 0; i < indices_scores.size (); i++)
{
flann_model m = flann_models_[indices_scores[i].idx_models_];
it_map = found.find (m);
if (it_map == found.end ())
{
indices_scores[found.size ()] = indices_scores[i];
found[m] = true;
}
}
indices_scores.resize (found.size ());
int num_n = std::min (NN_, static_cast<int> (indices_scores.size ()));
/*
* Filter some hypothesis regarding to their distance to the first neighbour
*/
/*std::vector<index_score> indices_scores_filtered;
indices_scores_filtered.resize (num_n);
indices_scores_filtered[0] = indices_scores[0];
double best_score = indices_scores[0].score_;
int kept = 1;
for (int i = 1; i < num_n; ++i)
{
//std::cout << best_score << indices_scores[i].score_ << (best_score / indices_scores[i].score_) << std::endl;
if ((best_score / indices_scores[i].score_) > 0.65)
{
indices_scores_filtered[i] = indices_scores[i];
kept++;
}
//best_score = indices_scores[i].score_;
}
indices_scores_filtered.resize (kept);
//std::cout << indices_scores_filtered.size () << " § " << num_n << std::endl;
indices_scores = indices_scores_filtered;
num_n = indices_scores.size ();*/
std::cout << "Number of object hypotheses... " << num_n << std::endl;
std::vector<bool> valid_trans;
std::vector < Eigen::Matrix4d, Eigen::aligned_allocator<Eigen::Matrix4d> > transformations;
micvfh_estimator_->getValidTransformsVec (valid_trans);
micvfh_estimator_->getTransformsVec (transformations);
for (int i = 0; i < num_n; ++i)
{
ModelT m = flann_models_[indices_scores[i].idx_models_].model;
int view_id = flann_models_[indices_scores[i].idx_models_].view_id;
int desc_id = flann_models_[indices_scores[i].idx_models_].descriptor_id;
int idx_input = indices_scores[i].idx_input_;
std::cout << m.class_ << "/" << m.id_ << " ==> " << indices_scores[i].score_ << std::endl;
Eigen::Matrix4d roll_view_pose;
bool roll_pose_found = getRollPose (m, view_id, desc_id, roll_view_pose);
if (roll_pose_found && valid_trans[idx_input])
{
Eigen::Matrix4d transposed = roll_view_pose.transpose ();
//std::cout << transposed << std::endl;
PointInTPtr view;
getView (m, view_id, view);
Eigen::Matrix4d model_view_pose;
getPose (m, view_id, model_view_pose);
Eigen::Matrix4d scale_mat;
scale_mat.setIdentity (4, 4);
if (compute_scale_)
{
//compute scale using the whole view
PointInTPtr view_transformed (new pcl::PointCloud<PointInT>);
Eigen::Matrix4d hom_from_OVC_to_CC;
hom_from_OVC_to_CC = transformations[idx_input].inverse () * transposed;
pcl::transformPointCloud (*view, *view_transformed, hom_from_OVC_to_CC);
Eigen::Vector3d input_centroid = centroids[indices_scores[i].idx_input_];
Eigen::Vector3d view_centroid;
getCentroid (m, view_id, desc_id, view_centroid);
Eigen::Vector4d cmatch4f (view_centroid[0], view_centroid[1], view_centroid[2], 0);
Eigen::Vector4d cinput4f (input_centroid[0], input_centroid[1], input_centroid[2], 0);
Eigen::Vector4d max_pt_input;
pcl::getMaxDistance (*processed, cinput4f, max_pt_input);
max_pt_input[3] = 0;
double max_dist_input = (cinput4f - max_pt_input).norm ();
//compute max dist for transformed model_view
pcl::getMaxDistance (*view, cmatch4f, max_pt_input);
max_pt_input[3] = 0;
double max_dist_view = (cmatch4f - max_pt_input).norm ();
cmatch4f = hom_from_OVC_to_CC * cmatch4f;
std::cout << max_dist_view << " " << max_dist_input << std::endl;
double scale_factor_view = max_dist_input / max_dist_view;
std::cout << "Scale factor:" << scale_factor_view << std::endl;
Eigen::Matrix4d center, center_inv;
center.setIdentity (4, 4);
center (0, 3) = -cinput4f[0];
center (1, 3) = -cinput4f[1];
center (2, 3) = -cinput4f[2];
center_inv.setIdentity (4, 4);
center_inv (0, 3) = cinput4f[0];
center_inv (1, 3) = cinput4f[1];
center_inv (2, 3) = cinput4f[2];
scale_mat (0, 0) = scale_factor_view;
scale_mat (1, 1) = scale_factor_view;
scale_mat (2, 2) = scale_factor_view;
scale_mat = center_inv * scale_mat * center;
}
Eigen::Matrix4d hom_from_OC_to_CC;
hom_from_OC_to_CC = scale_mat * transformations[idx_input].inverse () * transposed * model_view_pose;
models_->push_back (m);
transforms_->push_back (hom_from_OC_to_CC);
descriptor_distances_.push_back (static_cast<double> (indices_scores[i].score_));
}
else
{
PCL_WARN("The roll pose was not found, should use CRH here... \n");
}
}
}
std::cout << "Number of object hypotheses:" << models_->size () << std::endl;
/**
* POSE REFINEMENT
**/
if (ICP_iterations_ > 0)
{
pcl::ScopeTime t ("Pose refinement");
//Prepare scene and model clouds for the pose refinement step
double VOXEL_SIZE_ICP_ = 0.005;
PointInTPtr cloud_voxelized_icp (new pcl::PointCloud<PointInT> ());
{
pcl::ScopeTime t ("Voxelize stuff...");
pcl::VoxelGrid<PointInT> voxel_grid_icp;
voxel_grid_icp.setInputCloud (processed);
voxel_grid_icp.setLeafSize (VOXEL_SIZE_ICP_, VOXEL_SIZE_ICP_, VOXEL_SIZE_ICP_);
voxel_grid_icp.filter (*cloud_voxelized_icp);
source_->voxelizeAllModels (VOXEL_SIZE_ICP_);
}
#pragma omp parallel for num_threads(omp_get_num_procs())
for (int i = 0; i < static_cast<int> (models_->size ()); i++)
{
ConstPointInTPtr model_cloud;
PointInTPtr model_aligned (new pcl::PointCloud<PointInT>);
if (compute_scale_)
{
model_cloud = models_->at (i).getAssembled (-1);
PointInTPtr model_aligned_m (new pcl::PointCloud<PointInT>);
pcl::transformPointCloud (*model_cloud, *model_aligned_m, transforms_->at (i));
pcl::VoxelGrid<PointInT> voxel_grid_icp;
voxel_grid_icp.setInputCloud (model_aligned_m);
voxel_grid_icp.setLeafSize (VOXEL_SIZE_ICP_, VOXEL_SIZE_ICP_, VOXEL_SIZE_ICP_);
voxel_grid_icp.filter (*model_aligned);
}
else
{
model_cloud = models_->at (i).getAssembled (VOXEL_SIZE_ICP_);
pcl::transformPointCloud (*model_cloud, *model_aligned, transforms_->at (i));
}
pcl::IterativeClosestPoint<PointInT, PointInT> reg;
reg.setInputCloud (model_aligned); //model
reg.setInputTarget (cloud_voxelized_icp); //scene
reg.setMaximumIterations (ICP_iterations_);
reg.setMaxCorrespondenceDistance (VOXEL_SIZE_ICP_ * 3.);
reg.setTransformationEpsilon (1e-6);
typename pcl::PointCloud<PointInT>::Ptr output_ (new pcl::PointCloud<PointInT> ());
reg.align (*output_);
Eigen::Matrix4d icp_trans = reg.getFinalTransformation ();
transforms_->at (i) = icp_trans * transforms_->at (i);
}
}
/**
* HYPOTHESES VERIFICATION
**/
if (hv_algorithm_)
{
pcl::ScopeTime t ("HYPOTHESES VERIFICATION");
std::vector<typename pcl::PointCloud<PointInT>::ConstPtr> aligned_models;
aligned_models.resize (models_->size ());
for (size_t i = 0; i < models_->size (); i++)
{
ConstPointInTPtr model_cloud;
PointInTPtr model_aligned (new pcl::PointCloud<PointInT>);
if (compute_scale_)
{
model_cloud = models_->at (i).getAssembled (-1);
PointInTPtr model_aligned_m (new pcl::PointCloud<PointInT>);
pcl::transformPointCloud (*model_cloud, *model_aligned_m, transforms_->at (i));
pcl::VoxelGrid<PointInT> voxel_grid_icp;
voxel_grid_icp.setInputCloud (model_aligned_m);
voxel_grid_icp.setLeafSize (0.005, 0.005, 0.005);
voxel_grid_icp.filter (*model_aligned);
}
else
{
model_cloud = models_->at (i).getAssembled (0.005);
pcl::transformPointCloud (*model_cloud, *model_aligned, transforms_->at (i));
}
//ConstPointInTPtr model_cloud = models_->at (i).getAssembled (0.005);
//PointInTPtr model_aligned (new pcl::PointCloud<PointInT>);
//pcl::transformPointCloud (*model_cloud, *model_aligned, transforms_->at (i));
aligned_models[i] = model_aligned;
}
std::vector<bool> mask_hv;
hv_algorithm_->setSceneCloud (input_);
hv_algorithm_->addModels (aligned_models, true);
hv_algorithm_->verify ();
hv_algorithm_->getMask (mask_hv);
boost::shared_ptr < std::vector<ModelT> > models_temp;
boost::shared_ptr < std::vector<Eigen::Matrix4d, Eigen::aligned_allocator<Eigen::Matrix4d> > > transforms_temp;
models_temp.reset (new std::vector<ModelT>);
transforms_temp.reset (new std::vector<Eigen::Matrix4d, Eigen::aligned_allocator<Eigen::Matrix4d> >);
for (size_t i = 0; i < models_->size (); i++)
{
if (!mask_hv[i])
continue;
models_temp->push_back (models_->at (i));
transforms_temp->push_back (transforms_->at (i));
}
models_ = models_temp;
transforms_ = transforms_temp;
}
}
}
