void VilGMM::probabilty(const vil_image_view<vxl_byte> & image, const vil_image_view<vxl_byte> & maskImage, int mask,
vil_image_view<double> & outProb, int patchSize)
{
assert(image.ni() == maskImage.ni());
assert(image.nj() == maskImage.nj());
assert(image.nplanes() == 3);
assert(gmm_);
assert(patchSize%2 == 1);
vil_image_view<double> pixelProb(image.ni(), image.nj(), 1);
pixelProb.fill(0.0);
// probability in each pixel position
int w = image.ni();
int h = image.nj();
for (int j = 0; j<h; j++) {
for (int i = 0; i<w; i++) {
if (maskImage(i, j) == mask) {
vnl_vector<double> color(3);
for (int k = 0; k<3; k++) {
color[k] = image(i, j, k);
}
pixelProb(i, j) = (*gmm_)(color);
}
}
}
outProb = vil_image_view<double>(w, h, 1);
outProb.fill(0.0);
// average probility
for (int j = patchSize/2; j<h-patchSize/2; j++) {
for (int i = patchSize/2; i<w-patchSize/w; i++) {
if (maskImage(i, j) == mask) {
// average the value inside the patch
double val = 0;
int num = 0;
for (int k = -patchSize/2; k <= patchSize/2; k++) {
for (int m = -patchSize/2; m<= patchSize/2; m++) {
if (maskImage(i + m, j+ k) == mask) {
val += pixelProb(i+m, j+k);
num++;
}
}
}
assert(num != 0);
val /= num;
outProb(i, j) = val;
}
}
}
}
bool VilGMM::train(const vil_image_view<vxl_byte> & image, const vil_image_view<vxl_byte> & maskImage, int mask)
{
assert(image.ni() == maskImage.ni());
assert(image.nj() == maskImage.nj());
assert(image.nplanes() == 3);
vpdfl_gaussian_builder g_builder;
vpdfl_mixture_builder builder;
builder.init(g_builder,comp_n_);
builder.set_weights_fixed(false);
vcl_vector<vnl_vector<double> > data;
for (int j = 0; j<image.nj(); j++) {
for (int i = 0; i<image.ni(); i++) {
if (maskImage(i, j) == mask) {
vnl_vector<double> color(3);
for (int k = 0; k<3; k++) {
color[k] = image(i, j, k);
}
data.push_back(color);
}
}
}
if (data.size() <= comp_n_ * 20) {
return false;
}
if (gmm_) {
delete gmm_;
gmm_ = NULL;
}
gmm_ = builder.new_model();
mbl_data_array_wrapper<vnl_vector<double> > data_array(data);
builder.build(*gmm_, data_array);
if (verbose_) {
vcl_cout<<"training sample number is "<<data.size()<<vcl_endl;
vcl_cout<<"Probability distribution function is "<<gmm_<<vcl_endl;
vcl_cout<<"Mean: "<<gmm_->mean()<<vcl_endl;
vcl_cout<<"Var: "<<gmm_->variance()<<vcl_endl;
}
return true;
}
bool VilGMM::addOneImage(const vil_image_view<vxl_byte> & image, const vil_image_view<vxl_byte> & maskImage, int mask)
{
assert(image.ni() == maskImage.ni());
assert(image.nj() == maskImage.nj());
assert(image.nplanes() == 3);
for (int j = 0; j<image.nj(); j++) {
for (int i = 0; i<image.ni(); i++) {
if (maskImage(i, j) == mask) {
vnl_vector<double> color(3);
for (int k = 0; k<3; k++) {
color[k] = image(i, j, k);
}
data_.push_back(color);
}
}
}
return true;
}
void VilDraw::draw_match_vertical(const vil_image_view<vxl_byte> &image1, const vil_image_view<vxl_byte> &image2,
const vcl_vector< vgl_point_2d<double> > & pts1,
const vcl_vector< vgl_point_2d<double> > & pts2,
vil_image_view<vxl_byte> &matches, const int sample_num)
{
assert(image1.nplanes() == 3 || image1.nplanes() == 1);
assert(image2.nplanes() == 3 || image2.nplanes() == 1);
assert(pts1.size() == pts2.size());
vil_image_view<vxl_byte> rgb_image1 = image1;
vil_image_view<vxl_byte> rgb_image2 = image2;
if (image1.nplanes() == 1) {
rgb_image1 = VilUtil::gray_2_rgb(image1);
}
if (image2.nplanes() == 1) {
rgb_image2 = VilUtil::gray_2_rgb(image2);
}
int gap_width = 10;
int y_shift = gap_width + image1.nj();
int width = vcl_max(image1.ni(), image2.ni());
int height = image1.nj() + gap_width + image2.nj();
matches = vil_image_view<vxl_byte>(width, height, 3);
matches.fill(0);
//copy image 1 to left
vil_copy_to_window(rgb_image1, matches, 0, 0);
//copy image 2 to right
vil_copy_to_window(rgb_image2, matches, 0, y_shift);
for (int i = 0; i<pts1.size(); i += sample_num) {
int r = rand()%255;
int g = rand()%255;
int b = rand()%255;
//int r = 0;
//int g = 255;
//int b = 0;
vcl_vector< vxl_byte> colour;
colour.push_back(r);
colour.push_back(g);
colour.push_back(b);
vgl_point_2d<double> p1 = pts1[i];
vgl_point_2d<double> p2(vgl_point_2d<double>(pts2[i].x(), pts2[i].y() + y_shift));
VilAlgoPlus::fill_line(matches, p1, p2, colour);
// cross in left
vgl_point_2d<double> q1, q2, q3, q4;
double h_l = 5;
q1.set(p1.x() - h_l, p1.y());
q2.set(p1.x() + h_l, p1.y());
q3.set(p1.x(), p1.y() - h_l);
q4.set(p1.x(), p1.y() + h_l);
VilAlgoPlus::fill_line(matches, q1, q2, colour);
VilAlgoPlus::fill_line(matches, q3, q4, colour);
// cross in right
q1.set(p2.x() - h_l, p2.y());
q2.set(p2.x() + h_l, p2.y());
q3.set(p2.x(), p2.y() - h_l);
q4.set(p2.x(), p2.y() + h_l);
VilAlgoPlus::fill_line(matches, q1, q2, colour);
VilAlgoPlus::fill_line(matches, q3, q4, colour);
}
}
