void World::queryLine(point_t v1, point_t v2, QueryCallback* qc) {
if (use_partitioning) {
//spatial hash version: check every rect in bucket intersecting line
int min_x = (v1.getX()) / bucket_width;
int max_x = (v2.getX()) / bucket_width;
if (min_x > max_x)
std::swap(min_x, max_x);
int min_y = (v1.getY()) / bucket_height;
int max_y = (v2.getY()) / bucket_height;
if (min_y > max_y)
std::swap(min_y, max_y);
//todo: iterates over full box containing line; just iterate over any bucket intersecting.
//iterate over all buckets intersecting line:
for (int i = min_x; i <= max_x; i++)
for (int j = min_y; j <= max_y; j++)
if (bucket* b = getBucket(i, j))
//iterate over all rects in bucket:
for (auto iter : b->rect_v)
//check if rect intersects the line:
if (iter->queryOnLine(v1, v2))
//halt if QueryCallback returns false.
if (!qc->onMatch(iter,
iter->getContactPointOnLine(v1, v2)))
return;
} else {
//non spatial hash version: check every rect
for (auto iter : v_rect) {
if (iter->queryOnLine(v1, v2))
if (!qc->onMatch(iter, iter->getContactPointOnLine(v1, v2)))
return;
}
}
}
static int _crossTwoCircles(point_t& pt1, point_t& pt2,
const point_t& c1, double r1,
const point_t& c2, double r2)
{
double d, a, b, c, p, q, r;
double cos_value[2], sin_value[2];
if (mgEquals(c1.x, c2.x) && mgEquals(c1.y, c2.y) && mgEquals(r1, r2)) {
return -1;
}
d = c1.distanceTo(c2);
if (d > r1 + r2 || d < fabs(r1 - r2)) {
return 0;
}
a = 2.0 * r1 * (c1.x - c2.x);
b = 2.0 * r1 * (c1.y - c2.y);
c = r2 * r2 - r1 * r1 - c1.distanceSquare(c2);
p = a * a + b * b;
q = -2.0 * a * c;
if (mgEquals(d, r1 + r2) || mgEquals(d, fabs(r1 - r2))) {
cos_value[0] = -q / p / 2.0;
sin_value[0] = sqrt(1 - cos_value[0] * cos_value[0]);
pt1.x = r1 * cos_value[0] + c1.x;
pt1.y = r1 * sin_value[0] + c1.y;
if (!mgEquals(pt1.distanceSquare(c2), r2 * r2)) {
pt1.y = c1.y - r1 * sin_value[0];
}
return 1;
}
r = c * c - b * b;
cos_value[0] = (sqrt(q * q - 4.0 * p * r) - q) / p / 2.0;
cos_value[1] = (-sqrt(q * q - 4.0 * p * r) - q) / p / 2.0;
sin_value[0] = sqrt(1 - cos_value[0] * cos_value[0]);
sin_value[1] = sqrt(1 - cos_value[1] * cos_value[1]);
pt1.x = r1 * cos_value[0] + c1.x;
pt2.x = r1 * cos_value[1] + c1.x;
pt1.y = r1 * sin_value[0] + c1.y;
pt2.y = r1 * sin_value[1] + c1.y;
if (!mgEquals(pt1.distanceSquare(c2), r2 * r2)) {
pt1.y = c1.y - r1 * sin_value[0];
}
if (!mgEquals(pt2.distanceSquare(c2), r2 * r2)) {
pt2.y = c1.y - r1 * sin_value[1];
}
if (mgEquals(pt1.y, pt2.y) && mgEquals(pt1.x, pt2.x)) {
if (pt1.y > 0) {
pt2.y = -pt2.y;
} else {
pt1.y = -pt1.y;
}
}
return 2;
}
void fill_t::solid_fill(point_t p,float width,float height){
//std::cout<<"width "<<width<<" and height "<<height<<std::endl;
std::queue <point_t> fillQueue;
fillQueue.push(p);
float pointx=p.getX();
float pointy=p.getY();
color_t c=colorArray[(int)pointx][(int)pointy];
color_t pixels;
while(!fillQueue.empty()){
// std::cout<<c.getR()<<" ANSSS"<<std::endl;
// exit(0);
p=fillQueue.front();
pointx=p.getX();
pointy=p.getY();
fillQueue.pop();
//Added Canvas size check
if(pointx>=width || pointy>=height || pointx<0 || pointy<0)
continue;
pixels = colorArray[(int)pointx][(int)pointy];
if( (pixels.getR()==c.getR()) && (pixels.getG()==c.getG()) && (pixels.getB()==c.getB()) )
{
point_t p1(pointx, pointy);
p1.draw(pen_t(color1,1));
fillQueue.push(point_t(pointx+1,pointy));
fillQueue.push(point_t(pointx,pointy+1));
fillQueue.push(point_t(pointx-1,pointy));
fillQueue.push(point_t(pointx,pointy-1));
}
}
}
bool Rect::queryOnLine(point_t v1, point_t v2) {
//checks each edge of rect to see if line intersects
return (lineSegmentsIntersect({x,y},{x+w,y},v1,v2) ||
lineSegmentsIntersect({x,y},{x,y+h},v1,v2) ||
lineSegmentsIntersect({x,y+h},{x+w,y+h},v1,v2) ||
lineSegmentsIntersect({x+w,y},{x+w,y+h},v1,v2) ||
//checks if line contained within rect
queryContains(v1.getX(),v1.getY()));
}
/// the out integral channel will be resized to the required dimensions
void get_integral_channels(const integral_channels_t &in,
const point_t &modelWindowSize, const point_t &dataOffset, const int resizing_factor,
integral_channels_t &out)
{
get_integral_channels(in,
dataOffset.x(), dataOffset.y(),
modelWindowSize.x(), modelWindowSize.y(),
resizing_factor,
out);
return;
}
void fill_t::find_min_max(point_t p,int& YMin, int& YMax, int& XMin, int& XMax,float width,float height){
//std::cout<<"Min max\n";
std::queue <point_t> fmmQueue;
fmmQueue.push(p);
float pointx = p.getX();
float pointy = p.getY();
color_t c=colorArray[(int)pointx][(int)pointy];
color_t pixels;
while(!fmmQueue.empty()){
p=fmmQueue.front();
pointx=p.getX();
pointy=p.getY();
fmmQueue.pop();
//Added Canvas size check
if(pointx>=width || pointy>=height || pointx<0 || pointy<0)
continue;
pixels = colorArray[(int)pointx][(int)pointy];
//glReadPixels(pointx,pointy,1.0,1.0,GL_RGB,GL_FLOAT,pixels);
if( (pixels.getR()==c.getR()) && (pixels.getG()==c.getG()) && (pixels.getB()==c.getB()) )
{
if(pointy<YMin){
YMin=pointy;
}
if(pointy>YMax){
YMax=pointy;
}
if(pointx<XMin){
XMin=pointx;
}
if(pointx>XMax){
XMax=pointx;
}
p.draw(pen_t(color_t(0.5,0.5,0.5),1));
//std::cout<<" let me pass!!! I am the point "<<pointx<<" and "<<pointy<<std::endl;
fmmQueue.push(point_t(pointx+1,pointy));
fmmQueue.push(point_t(pointx,pointy+1));
fmmQueue.push(point_t(pointx-1,pointy));
fmmQueue.push(point_t(pointx,pointy-1));
// fmmQueue.push(point_t(pointx+1,pointy+1));
// fmmQueue.push(point_t(pointx-1,pointy-1));
// fmmQueue.push(point_t(pointx+1,pointy-1));
// fmmQueue.push(point_t(pointx-1,pointy+1));
}
}
}
void TrainingData::addPositiveSamples(const std::vector<std::string> &filenamesPositives,
const point_t &modelWindowSize, const point_t &dataOffset)
{
const size_t
initialNumberOfTrainingSamples = getNumExamples(), // yl images number have been added to the training set
finalNumberOfTrainingSamples = initialNumberOfTrainingSamples + filenamesPositives.size();
if(finalNumberOfTrainingSamples > getMaxNumExamples())
{
throw std::runtime_error("TrainingData::addPositiveSamples is trying to add more data than initially specified");
}
printf("\nCollecting %zi positive samples\n", filenamesPositives.size());
boost::progress_display progress_indicator(filenamesPositives.size());
meta_datum_t metaDatum;
integral_channels_t sampleIntegralChannels;
// integralChannelsComputer is already multithreaded, so no benefit on paralelizing this for loop
for (size_t filenameIndex = 0; filenameIndex < filenamesPositives.size(); filenameIndex +=1)
{
gil::rgb8_image_t image;
gil::rgb8c_view_t image_view = doppia::open_image(filenamesPositives[filenameIndex].c_str(), image);
_integralChannelsComputer.set_image(image_view);
_integralChannelsComputer.compute();
get_integral_channels(_integralChannelsComputer.get_integral_channels(),
modelWindowSize, dataOffset, _integralChannelsComputer.get_shrinking_factor(),// shrinking factor = 4
sampleIntegralChannels);
metaDatum.filename = filenamesPositives[filenameIndex];
metaDatum.imageClass = 1;//classes[k];
metaDatum.x = dataOffset.x();
metaDatum.y = dataOffset.y();
setDatum(initialNumberOfTrainingSamples + filenameIndex,
metaDatum, sampleIntegralChannels);
++progress_indicator;
} // end of "for each filename"
return;
}
chassis_id PhysicsRegion::queryFirstPoint(point_t point,
flag_plane p) {
struct : public rects::QueryCallback {
const PhysicsRegion* parent;
chassis_id ret=NO_CHASSIS;
bool onMatch(rects::Rect* r, point_t at) {
ret = parent->getChassisFromRect(r);
return false;
}
} qc;
qc.parent = this;
for (byte i = 0; i < getPlaneCount(); i++) {
flag_plane p_it = 1 << i;
if (p_it & p)
planes[i].queryPoint(point.getX(), point.getY(), &qc);
if (qc.ret!=NO_CHASSIS)
return qc.ret;
}
return qc.ret;
}
bool Polygon::within_n(const point_t &p, double d2) const
{
if (_in_mbr(p)) {
for (size_t i = 0; i < outer_.size(); ++i)
if (outer_[i].contains(p))
return outer_[i].within_n(p, d2);
for (size_t i = 0; i < inner_.size(); ++i)
if (inner_[i].contains(p))
return inner_[i].within_n(p, d2);
return true;
} else {
if (p.y() >= mbr_[0]) return _within_n(p, d2, 0);
if (p.x() <= mbr_[1]) return _within_n(p, d2, 1);
if (p.y() <= mbr_[2]) return _within_n(p, d2, 2);
if (p.x() >= mbr_[3]) return _within_n(p, d2, 3);
}
// not reachable
return true;
}
void ModelIO::initWrite(const std::string datasetName,
const DetectorModel::DetectorTypes type,
const std::string detectorName,
const point_t modelWindow,
const rectangle_t objectWindow)
{
doppia_protobuf::Point2d *model_window = _model.mutable_model_window_size();
model_window->set_x(modelWindow.x());
model_window->set_y(modelWindow.y());
doppia_protobuf::Box *b = _model.mutable_object_window();
b->mutable_min_corner()->set_x(objectWindow.min_corner().x());
b->mutable_min_corner()->set_y(objectWindow.min_corner().y());
b->mutable_max_corner()->set_x(objectWindow.max_corner().x());
b->mutable_max_corner()->set_y(objectWindow.max_corner().y());
_model.set_training_dataset_name(datasetName.c_str());
_model.set_detector_type(type);
_model.set_detector_name(detectorName);
return;
}
// http://mathworld.wolfram.com/Circle-LineIntersection.html
int _crossLineCircle(point_t& pt1, point_t& pt2, const point_t& a,
const point_t& b, double r)
{
point_t d(b - a);
double d2 = d.lengthSquare();
double dz = a.crossProduct(b);
double z2 = dz * dz;
double delta = r * r * d2 - z2;
if (delta < 0)
return 0;
double s = sqrt(delta) / d2;
double sx = (d.y < 0 ? -d.x : d.x) * s;
double sy = fabs(d.y) * s;
double tx = dz * d.y / d2;
double ty = -dz * d.x / d2;
pt1 = point_t(tx + sx, ty + sy);
pt2 = point_t(tx - sx, ty - sy);
return delta < 1e-8 ? 1 : 2;
}
bool Plane::is_in(const point_t &p) const
{
assert(p.size() == a.size());
double sum = 0;
for (unsigned i=0; i<a.size(); ++i)
sum += a[i] * p[i];
//std::cerr << "Sum = " << sum << std::endl;
switch(sign) {
case lt: return (sum < b);
break;
case lte: return (sum <= b);
break;
case gt: return (sum > b);
break;
case gte: return (sum >= b);
break;
}
assert(false);
return false;
}
inline bool operator==( point_t<origin_type::screen> const& lhs, point_t<origin_type::screen> const& rhs ) noexcept
{
return lhs.x() == rhs.x() && lhs.y() == rhs.y();
}
/** Returns true if intersection between the two line segments.*/
bool lineSegmentsIntersect(point_t a1, point_t a2,point_t b1,point_t b2) {
//if lines vertical:
if (a1.getX()==a2.getX())
if ((a1.getX()>b1.getX()&&a1.getX()<b2.getX())
||(a1.getX()>b2.getX()&&a1.getX()<b1.getX())) {
float m_b = (b1-b2).getSlope();
float b_b = b1.getY()-b1.getX()*m_b;
float y_intersect = b_b + m_b*a1.getX();
return ((y_intersect>a1.getY())!=(y_intersect>a2.getY()));
}
if (b1.getX()==b2.getX())
if ((b1.getX()>a1.getX()&&b1.getX()<a2.getX())
||(b1.getX()>a2.getX()&&b1.getX()<a1.getX())) {
float m_a = (a1-a2).getSlope();
float b_a = a1.getY()-a1.getX()*m_a;
float y_intersect = b_a + m_a*b1.getX();
return ((y_intersect>b1.getY())!=(y_intersect>b2.getY()));
}
//solve for intersection:
float m_a = (a1-a2).getSlope();
float m_b = (b1-b2).getSlope();
float b_a = a1.getY()-a1.getX()*m_a;
float b_b = b1.getY()-b1.getX()*m_b;
//overlapping lines do not intersect.
if (m_a==m_b) return false;
float x_intersect = (b_a+b_b)/(m_a-m_b);
return ((x_intersect>a1.getX()&&x_intersect<a2.getX())||(x_intersect>a2.getX()&&x_intersect<a1.getX())) &&
((x_intersect>b1.getX()&&x_intersect<b2.getX())||(x_intersect>b2.getX()&&x_intersect<b1.getX()));
}
inline bool operator==( point_t<origin_type::client> const& lhs, point_t<origin_type::client> const& rhs ) noexcept
{
return lhs.window_handle() == rhs.window_handle() && lhs.x() == rhs.x() && lhs.y() == rhs.y();
}
/** Returns point of intersection between the two line segments.*/
point_t lineSegmentsIntersectCoord(point_t a1, point_t a2,point_t b1,point_t b2) {
point_t DEFAULT_RETURN = {-1,-1};
//solve for intersection:
float m_a;
float b_a;
if (a1.getX()!=a2.getX()) {
m_a = (a1-a2).getSlope();
b_a = a1.getY()-a1.getX()*m_a;
}
float m_b;
float b_b;
if (b1.getX()!=b2.getX()) {
m_b = (b1-b2).getSlope();
b_b = b1.getY()-b1.getX()*m_b;
}
if (a1.getX()==a2.getX()&&b1.getX()==b2.getX())
return DEFAULT_RETURN;
if (a1.getX()==a2.getX())
return {a1.getX(),b_b+m_b*a1.getX()};
if (b1.getX()==b2.getX())
return {b1.getX(),b_a+m_a*b1.getX()};
//overlapping lines do not intersect.
if (m_a==m_b) return DEFAULT_RETURN;
float x_intersect = (b_a+b_b)/(m_a-m_b);
return {x_intersect, b_a + x_intersect*m_a};
}
inline bool operator<( point_t<Origin> const& lhs, point_t<Origin> const& rhs ) noexcept
{
return lhs.x() * lhs.x() + lhs.y() * lhs.y() < rhs.x() * rhs.x() + rhs.y() * rhs.y();
}
double get_angle(point_t v1, point_t v2) {
return atan2(v1.dot(v2), v1.cross(v2));
}
void fill_t::printPointColor(point_t p){
std::cout<<"COLOR "<<colorArray[(int)p.getX()][(int)p.getY()].getR()<<std::endl;
}
void TrainingData::addNegativeSamples(const std::vector<std::string> &filenamesBackground,
const point_t &modelWindowSize, const point_t &dataOffset,
const size_t numNegativeSamplesToAdd)
{
const size_t
initialNumberOfTrainingSamples = getNumExamples(),
finalNumberOfTrainingSamples = initialNumberOfTrainingSamples + numNegativeSamplesToAdd;
if(finalNumberOfTrainingSamples > getMaxNumExamples())
{
throw std::runtime_error("TrainingData::addNegativeSamples is trying to add more data than initially specified");
}
printf("\nCollecting %zi random negative samples\n", numNegativeSamplesToAdd);
boost::progress_display progress_indicator(numNegativeSamplesToAdd);
meta_datum_t metaDatum;
integral_channels_t sampleIntegralChannels;
#if defined(DEBUG)
srand(1);
#else
srand(time(NULL));
#endif
srand(1);
const int samplesPerImage = std::max<int>(1, numNegativeSamplesToAdd / filenamesBackground.size());
// FIXME no idea what the +1 does
const int
minWidth = (modelWindowSize.x()+1 + 2*dataOffset.x()),
minHeight = (modelWindowSize.y()+1 + 2*dataOffset.y());
const float maxSkippedFraction = 0.25;
size_t numNegativesSamplesAdded = 0, numSkippedImages = 0, filenameIndex = 0;
// integralChannelsComputer is already multithreaded, so no benefit on paralelizing this for loop
while (numNegativesSamplesAdded < numNegativeSamplesToAdd)
{
if (filenameIndex >= filenamesBackground.size())
{
// force to loop until we have reached the desired number of samples
filenameIndex = 0;
}
const string &filename = filenamesBackground[filenameIndex];
filenameIndex +=1;
gil::rgb8c_view_t imageView;
gil::rgb8_image_t image;
imageView = doppia::open_image(filename.c_str(), image);
if ((imageView.width() < minWidth) or (imageView.height() < minHeight))
{
// if input image is too small, we skip it
//printf("Skipping negative sample %s, because it is too small\n", filename.c_str());
numSkippedImages += 1;
const float skippedFraction = static_cast<float>(numSkippedImages) / filenamesBackground.size();
if (skippedFraction > maxSkippedFraction)
{
printf("Skipped %i images (out of %zi, %.3f%%) because they where too small\n",
numSkippedImages, filenamesBackground.size(), skippedFraction*100);
throw std::runtime_error("Too many negatives images where skipped. Dataset needs to be fixed");
}
continue;
}
const int
maxRandomX = (imageView.width() - modelWindowSize.x()+1 - 2*dataOffset.x()),
maxRandomY = (imageView.height() - modelWindowSize.y()+1 - 2*dataOffset.y());
_integralChannelsComputer.set_image(imageView);
_integralChannelsComputer.compute();
metaDatum.filename = filename;
metaDatum.imageClass = _backgroundClassLabel;
size_t numSamplesForImage = std::min<size_t>(samplesPerImage,
(numNegativeSamplesToAdd - numNegativesSamplesAdded));
numSamplesForImage = 1;
for (size_t randomSampleIndex = 0; randomSampleIndex < numSamplesForImage; randomSampleIndex += 1)
{
//const point_t::coordinate_t
size_t
x = dataOffset.x() + rand() % maxRandomX,
y = dataOffset.y() + rand() % maxRandomY;
//printf("random x,y == %i, %i\n", x,y);
const point_t randomOffset(x,y);
metaDatum.x = randomOffset.x(); metaDatum.y = randomOffset.y();
get_integral_channels(_integralChannelsComputer.get_integral_channels(),
modelWindowSize, randomOffset, _integralChannelsComputer.get_shrinking_factor(),
sampleIntegralChannels);
setDatum(initialNumberOfTrainingSamples + numNegativesSamplesAdded,
metaDatum, sampleIntegralChannels);
numNegativesSamplesAdded += 1;
++progress_indicator;
}
} // end of "for each background image"
if (numSkippedImages > 0)
{
const float skippedFraction = static_cast<float>(numSkippedImages) / filenamesBackground.size();
printf("Skipped %zi images (out of %zi, %.3f%%) because they where too small\n",
numSkippedImages, filenamesBackground.size(), skippedFraction*100);
}
return;
}
void TrainingData::addNegativeSamples(const std::vector<std::string> &filenamesBackground,
const point_t &modelWindowSize, const point_t &dataOffset,
const size_t numNegativeSamplesToAdd)
{
int feature_extraction_time = 0, image_loading_time = 0, tmp_time;
const size_t
initialNumberOfTrainingSamples = get_num_examples(),
finalNumberOfTrainingSamples = initialNumberOfTrainingSamples + numNegativeSamplesToAdd;
if(finalNumberOfTrainingSamples > getMaxNumExamples())
{
throw std::runtime_error("TrainingData::addNegativeSamples is trying to add more data than initially specified");
}
printf("\nCollecting %zi random negative samples\n", numNegativeSamplesToAdd);
doppia::progress_display_with_eta progress_indicator(numNegativeSamplesToAdd);
meta_datum_t metaDatum;
integral_channels_t sampleIntegralChannels;
#if defined(DEBUG)
srand(1);
#else
srand(time(NULL));
#endif
srand(1);
const int samplesPerImage = std::max<int>(1, numNegativeSamplesToAdd / filenamesBackground.size());
// FIXME no idea what the +1 does
const int
minWidth = (modelWindowSize.x()+1 + 2*dataOffset.x()),
minHeight = (modelWindowSize.y()+1 + 2*dataOffset.y());
const float maxSkippedFraction = 0.25;
size_t numNegativesSamplesAdded = 0, numSkippedImages = 0, filenameIndex = 0;
// integralChannelsComputer is already multithreaded, so no benefit on paralelizing this for loop
while (numNegativesSamplesAdded < numNegativeSamplesToAdd)
{
if (filenameIndex >= filenamesBackground.size())
{
// force to loop until we have reached the desired number of samples
filenameIndex = 0;
}
const string &background_image_path = filenamesBackground[filenameIndex];
filenameIndex +=1;
gil::rgb8c_view_t imageView;
gil::rgb8_image_t image;
//tmp_time = (int)round(omp_get_wtime());
imageView = doppia::open_image(background_image_path.c_str(), image);
//image_loading_time += (int)round(omp_get_wtime()) - tmp_time;
if ((imageView.width() < minWidth) or (imageView.height() < minHeight))
{
// if input image is too small, we skip it
//printf("Skipping negative sample %s, because it is too small\n", filename.c_str());
numSkippedImages += 1;
const float skippedFraction = static_cast<float>(numSkippedImages) / filenamesBackground.size();
if (skippedFraction > maxSkippedFraction)
{
printf("Skipped %zi images (out of %zi, %.3f%%) because they where too small (or too big to process)\n",
numSkippedImages, filenamesBackground.size(), skippedFraction*100);
throw std::runtime_error("Too many negatives images where skipped. Dataset needs to be fixed");
}
continue;
}
const int
maxRandomX = (imageView.width() - modelWindowSize.x()+1 - 2*dataOffset.x()),
maxRandomY = (imageView.height() - modelWindowSize.y()+1 - 2*dataOffset.y());
try
{
// FIXME harcoded values
const size_t
expected_channels_size = imageView.size()*10,
max_texture_size = 134217728; // 2**27 for CUDA capability 2.x
if(expected_channels_size > max_texture_size)
{
throw std::invalid_argument("The image is monstruously big!");
}
const boost::filesystem::path file_path = background_image_path;
#if BOOST_VERSION <= 104400
const std::string filename = file_path.filename();
#else
const std::string filename = file_path.filename().string();
#endif
tmp_time = (int)round(omp_get_wtime());
_integralChannelsComputer->set_image(imageView, filename);
image_loading_time += (int)round(omp_get_wtime()) - tmp_time;
tmp_time = (int)round(omp_get_wtime());
_integralChannelsComputer->compute();
feature_extraction_time += (int)round(omp_get_wtime()) - tmp_time;
}
catch(std::exception &e)
{
printf("Computing integral channels of image %s \033[1;31mfailed\033[0m (size %zix%zi). Skipping it. Error was:\n%s\n",
background_image_path.c_str(),
imageView.width(), imageView.height(),
e.what());
numSkippedImages += 1;
continue; // we skip this image
}
catch(...)
{
printf("Computing integral channels of %s \033[1;31mfailed\033[0m (size %zix%zi). Skipping it. Received unknown error.\n",
background_image_path.c_str(),
imageView.width(), imageView.height());
numSkippedImages += 1;
continue; // we skip this image
}
metaDatum.filename = background_image_path;
metaDatum.imageClass = _backgroundClassLabel;
size_t numSamplesForImage = std::min<size_t>(samplesPerImage,
(numNegativeSamplesToAdd - numNegativesSamplesAdded));
numSamplesForImage = 1;
for (size_t randomSampleIndex = 0; randomSampleIndex < numSamplesForImage; randomSampleIndex += 1)
{
//const point_t::coordinate_t
size_t
x = dataOffset.x() + rand() % maxRandomX,
y = dataOffset.y() + rand() % maxRandomY;
//printf("random x,y == %i, %i\n", x,y);
const point_t randomOffset(x,y);
metaDatum.x = randomOffset.x(); metaDatum.y = randomOffset.y();
//tmp_time = (int)round(omp_get_wtime());
get_integral_channels(_integralChannelsComputer->get_integral_channels(),
modelWindowSize, randomOffset, doppia::IntegralChannelsForPedestrians::get_shrinking_factor(),
sampleIntegralChannels);
//image_loading_time += (int)round(omp_get_wtime()) - tmp_time;
setDatum(initialNumberOfTrainingSamples + numNegativesSamplesAdded,
metaDatum, sampleIntegralChannels);
numNegativesSamplesAdded += 1;
++progress_indicator;
}
} // end of "for each background image"
if (numSkippedImages > 0)
{
const float skippedFraction = static_cast<float>(numSkippedImages) / filenamesBackground.size();
printf("Skipped %zi images (out of %zi, %.3f%%) because they where too small (or too big to process)\n",
numSkippedImages, filenamesBackground.size(), skippedFraction*100);
}
printf("Time elapsed while loading negative images: %02d:%02d:%02d\n",
image_loading_time/3600, (image_loading_time%3600)/60, image_loading_time%60);
printf("Time elapsed while extracting features from negative images: %02d:%02d:%02d\n",
feature_extraction_time/3600, (feature_extraction_time%3600)/60, feature_extraction_time%60);
return;
}
inline POINT point_t<origin_type::screen>::client_to_screen( point_t< origin_type::client > const& src ) noexcept
{
POINT dst = src.data();
ClientToScreen( src.window_handle(), &dst );
return dst;
}
inline int
extract_y( const point_t& pt ) {
return pt.get_y();
}
// volume evaluation with partial derivatives
/* virtual */ void operator()(
/* pointer to first point in rowwise control point grid */
const_point_iterator points, /* order in u dir */
std::size_t order_u, /* order in v dir */
std::size_t order_v, /* order in w dir */
std::size_t order_w, /* u-parameter for point to evaluate */
value_type u, /* v-parameter for point to evaluate */
value_type v, /* w-parameter for point to evaluate */
value_type w, /* resulting point at [u,v,w] */
point_t& point, /* first partial derivative in u at [u,v,w] */
point_t& du, /* first partial derivative in v at [u,v,w] */
point_t& dv, /* first partial derivative in w at [u,v,w] */
point_t& dw) const {
pointmesh3d<point_t> mesh(points,
points + order_u * order_v * order_w,
order_u,
order_v,
order_w);
// transform control points to homogenous space
std::for_each(
mesh.begin(), mesh.end(), std::mem_fn(&point_t::project_to_homogenous));
// first decasteljau in u direction until only u-linear volume is left for u
for (std::size_t jv = 0; jv != order_v; ++jv) {
for (std::size_t jw = 0; jw != order_w; ++jw) {
for (std::size_t i = 0; i != order_u - 2; ++i) {
for (std::size_t j = 0; j != order_u - 1 - i; ++j) {
mesh(j, jv, jw) =
(value_type(1) - u) * mesh(j, jv, jw) + u * mesh(j + 1, jv, jw);
}
}
}
}
// secondly decasteljau in v direction until only uv-linear volume is left
for (std::size_t ju = 0; ju != 2; ++ju) {
for (std::size_t jw = 0; jw != order_w; ++jw) {
for (std::size_t i = 0; i != order_v - 2; ++i) {
for (std::size_t j = 0; j != order_v - 1 - i; ++j) {
mesh(ju, j, jw) =
(value_type(1) - v) * mesh(ju, j, jw) + v * mesh(ju, j + 1, jw);
}
}
}
}
// thirdly decasteljau until only trilinear volume is left
for (std::size_t ju = 0; ju != 2; ++ju) {
for (std::size_t jv = 0; jv != 2; ++jv) {
for (std::size_t i = 0; i != order_w - 2; ++i) {
for (std::size_t j = 0; j != order_w - 1 - i; ++j) {
mesh(ju, jv, j) =
(value_type(1) - w) * mesh(ju, jv, j) + w * mesh(ju, jv, j + 1);
}
}
}
}
// evaluate for u leaving a linear patch dependending on v,w
point_t vw00 = (value_type(1) - u) * mesh(0, 0, 0) + u * mesh(1, 0, 0);
point_t vw10 = (value_type(1) - u) * mesh(0, 1, 0) + u * mesh(1, 1, 0);
point_t vw01 = (value_type(1) - u) * mesh(0, 0, 1) + u * mesh(1, 0, 1);
point_t vw11 = (value_type(1) - u) * mesh(0, 1, 1) + u * mesh(1, 1, 1);
// evaluate for v leaving a linear patch dependending on u,w
point_t uw00 = (value_type(1) - v) * mesh(0, 0, 0) + v * mesh(0, 1, 0);
point_t uw10 = (value_type(1) - v) * mesh(1, 0, 0) + v * mesh(1, 1, 0);
point_t uw01 = (value_type(1) - v) * mesh(0, 0, 1) + v * mesh(0, 1, 1);
point_t uw11 = (value_type(1) - v) * mesh(1, 0, 1) + v * mesh(1, 1, 1);
// evaluating v,w plane for v resulting in last linear interpolation in w ->
// to compute first partial derivative in w
point_t w0 = (value_type(1) - v) * vw00 + v * vw10;
point_t w1 = (value_type(1) - v) * vw01 + v * vw11;
// evaluating v,w plane for w resulting in last linear interpolation in v ->
// to compute first partial derivative in v
point_t v0 = (value_type(1) - w) * vw00 + w * vw01;
point_t v1 = (value_type(1) - w) * vw10 + w * vw11;
// evaluating v,w plane for w resulting in last linear interpolation in v ->
// to compute first partial derivative in v
point_t u0 = (value_type(1) - w) * uw00 + w * uw01;
point_t u1 = (value_type(1) - w) * uw10 + w * uw11;
// last interpolation and back projection to euclidian space
point = (value_type(1) - w) * w0 + w * w1;
point.project_to_euclidian();
// M.S. Floater '91 :
//
// w[0]{n-1}(t) * w[1]{n-1}(t)
// P'(t) = n * --------------------------- * P[1]{n-1}(t) - P[0]{n-1}(t)
// w[0]{n})^2
//
// 1. recalculate overwritten helping point P[0, n-1]
// 2. project P[0, n-1] and P[1, n-1] into plane w=1
// 3. use formula above to find out the correct length of P'(t)
du = (order_u - value_type(1)) *
((u0.weight() * u1.weight()) / (point.weight() * point.weight())) *
(u1.as_euclidian() - u0.as_euclidian());
dv = (order_v - value_type(1)) *
((v0.weight() * v1.weight()) / (point.weight() * point.weight())) *
(v1.as_euclidian() - v0.as_euclidian());
dw = (order_w - value_type(1)) *
((w0.weight() * w1.weight()) / (point.weight() * point.weight())) *
(w1.as_euclidian() - w0.as_euclidian());
}
//friend point_t<T> operator+(const point_t<T>& p, const point_t<T>& q);
friend point_t<T> operator+(const point_t<T>& p, const point_t<T>& q) {
point_t<T> tmp = p;
for (int i = 0; i < T; i++)
tmp.coord(i) += q.coord(i);
return tmp;
}
bool operator ==(const point_t& a, const point_t& b)
{
return QuasiEqual(a.x(), b.x(), margin) && QuasiEqual(a.y(), b.y(), margin) && QuasiEqual(a.z(), b.z(), margin);
}
void fill_t::checkerboard_fill(point_t p,float width,float height){
int minx=p.getX();
int miny=p.getY();
int maxx=p.getX();
int maxy=p.getY();
find_min_max(p,miny,maxy,minx,maxx,width,height);
std::queue <point_t> fillQueue;
fillQueue.push(p);
float pointx=p.getX();
float pointy=p.getY();
color_t c=colorArray[(int)pointx][(int)pointy];
color_t pixels;
float trans_x,trans_y;
while(!fillQueue.empty()){
// std::cout<<c.getR()<<" ANSSS"<<std::endl;
// exit(0);
p=fillQueue.front();
pointx=p.getX();
pointy=p.getY();
fillQueue.pop();
//Added Canvas size check
if(pointx>=width || pointy>=height || pointx<0 || pointy<0)
continue;
//glReadPixels(pointx,pointy,1.0,1.0,GL_RGB,GL_FLOAT,pixels);
pixels = colorArray[(int)pointx][(int)pointy];
if( (pixels.getR()==c.getR()) && (pixels.getG()==c.getG()) && (pixels.getB()==c.getB()) )
{
point_t p1(pointx, pointy);
trans_x=pointx-minx;
trans_y=pointy-miny;
if((((int)trans_x/16)%2)==0){
if((((int)trans_y/16)%2)==0){
p1.draw(pen_t(color1,1));
}
else{
p1.draw(pen_t(color2,1));
}
}
else{
if((((int)trans_y/16)%2)==0){
p1.draw(pen_t(color2,1));
}
else{
p1.draw(pen_t(color1,1));
}
}
fillQueue.push(point_t(pointx+1,pointy));
fillQueue.push(point_t(pointx,pointy+1));
fillQueue.push(point_t(pointx-1,pointy));
fillQueue.push(point_t(pointx,pointy-1));
}
}
}
void TrainingData::addHardNegativeSamples(const std::vector<std::string> &filenamesHardNegatives,
const point_t &modelWindowSize, const point_t &dataOffset)
{
int feature_extraction_time = 0, image_loading_time = 0, tmp_time;
const size_t
initialNumberOfTrainingSamples = get_num_examples(),
finalNumberOfTrainingSamples = initialNumberOfTrainingSamples + filenamesHardNegatives.size();
if(finalNumberOfTrainingSamples > getMaxNumExamples())
{
throw std::runtime_error("TrainingData::addHardNegativeSamples "
"is trying to add more data than initially specified");
}
printf("\nCollecting %zi hard negative samples\n", filenamesHardNegatives.size());
doppia::progress_display_with_eta progress_indicator(filenamesHardNegatives.size());
meta_datum_t metaDatum;
integral_channels_t sampleIntegralChannels;
// integralChannelsComputer is already multithreaded, so no benefit on paralelizing this for loop
for (size_t filenameIndex = 0; filenameIndex < filenamesHardNegatives.size(); filenameIndex +=1)
{
tmp_time = (int)round(omp_get_wtime());
gil::rgb8_image_t image;
gil::rgb8c_view_t image_view = doppia::open_image(filenamesHardNegatives[filenameIndex].c_str(), image);
const boost::filesystem::path file_path = filenamesHardNegatives[filenameIndex];
#if BOOST_VERSION <= 104400
const std::string filename = file_path.filename();
#else
const std::string filename = file_path.filename().string();
#endif
_integralChannelsComputer->set_image(image_view, filename);
image_loading_time += (int)round(omp_get_wtime()) - tmp_time;
tmp_time = (int)round(omp_get_wtime());
_integralChannelsComputer->compute();
feature_extraction_time += (int)round(omp_get_wtime()) - tmp_time;
get_integral_channels(_integralChannelsComputer->get_integral_channels(),
modelWindowSize, dataOffset, doppia::IntegralChannelsForPedestrians::get_shrinking_factor(),
sampleIntegralChannels);
metaDatum.filename = filenamesHardNegatives[filenameIndex];
metaDatum.imageClass = _backgroundClassLabel;
metaDatum.x = dataOffset.x();
metaDatum.y = dataOffset.y();
setDatum(initialNumberOfTrainingSamples + filenameIndex, metaDatum, sampleIntegralChannels);
++progress_indicator;
} // end of "for each filename"
printf("Time elapsed while loading images for hard negatives extraction: %02d:%02d:%02d\n",
image_loading_time/3600, (image_loading_time%3600)/60, image_loading_time%60);
printf("Time elapsed while extracting features from hard negatives: %02d:%02d:%02d\n",
feature_extraction_time/3600, (feature_extraction_time%3600)/60, feature_extraction_time%60);
return;
}
static POINT screen_to_client( HWND hwnd, point_t< origin_type::screen > const& src )
{
POINT dst = src.data();
ScreenToClient( hwnd, &dst );
return dst;
}
