bool Downsampler::operator() (const Streamline<>& in, Streamline<>& out) const
{
out.clear();
out.index = in.index;
out.weight = in.weight;
if (ratio <= 1 || in.empty())
return false;
if (in.size() == 1)
return true;
out.push_back (in.front());
const size_t midpoint = in.size()/2;
size_t index = (((midpoint - 1) % ratio) + 1);
while (index < in.size() - 1) {
out.push_back (in[index]);
index += ratio;
}
out.push_back (in.back());
return true;
}
bool FixedNumPoints::operator() (const Streamline<>& in, Streamline<>& out) const
{
// Perform an explicit calculation of streamline length
// From this, derive the spline position of each sample
assert (in.size() > 1);
out.clear();
out.index = in.index;
out.weight = in.weight;
value_type length = 0.0;
vector<value_type> steps;
for (size_t i = 1; i != in.size(); ++i) {
const value_type dist = (in[i] - in[i-1]).norm();
length += dist;
steps.push_back (dist);
}
steps.push_back (value_type(0));
Math::Hermite<value_type> interp (hermite_tension);
Streamline<> temp (in);
const size_t s = temp.size();
temp.insert (temp.begin(), temp[0] + (temp[0] - temp[ 1 ]));
temp.push_back ( temp[s] + (temp[s] - temp[s-1]));
value_type cumulative_length = value_type(0);
size_t input_index = 0;
for (size_t output_index = 0; output_index != num_points; ++output_index) {
const value_type target_length = length * output_index / value_type(num_points-1);
while (input_index < s && (cumulative_length + steps[input_index] < target_length))
cumulative_length += steps[input_index++];
if (input_index == s) {
out.push_back (temp[s]);
break;
}
const value_type mu = (target_length - cumulative_length) / steps[input_index];
interp.set (mu);
out.push_back (interp.value (temp[input_index], temp[input_index+1], temp[input_index+2], temp[input_index+3]));
}
return true;
}
bool Worker::operator() (Streamline<>& in, Streamline<>& out) const
{
out.clear();
out.index = in.index;
out.weight = in.weight;
if (!thresholds (in)) {
// Want to test thresholds before wasting time on resampling
if (inverse)
in.swap (out);
return true;
}
// Assign to ROIs
if (properties.include.size() || properties.exclude.size()) {
include_visited.assign (properties.include.size(), false);
if (ends_only) {
for (size_t i = 0; i != 2; ++i) {
const Eigen::Vector3f& p (i ? in.back() : in.front());
properties.include.contains (p, include_visited);
if (properties.exclude.contains (p)) {
if (inverse)
in.swap (out);
return true;
}
}
} else {
for (const auto& p : in) {
properties.include.contains (p, include_visited);
if (properties.exclude.contains (p)) {
if (inverse)
in.swap (out);
return true;
}
}
}
// Make sure all of the include regions were visited
for (const auto& i : include_visited) {
if (!i) {
if (inverse)
in.swap (out);
return true;
}
}
}
if (properties.mask.size()) {
// Split tck into separate tracks based on the mask
vector<vector<Eigen::Vector3f>> cropped_tracks;
vector<Eigen::Vector3f> temp;
for (const auto& p : in) {
const bool contains = properties.mask.contains (p);
if (contains == inverse) {
if (temp.size() >= 2)
cropped_tracks.push_back (temp);
temp.clear();
} else {
temp.push_back (p);
}
}
if (temp.size() >= 2)
cropped_tracks.push_back (temp);
if (cropped_tracks.empty())
return true;
if (cropped_tracks.size() == 1) {
cropped_tracks[0].swap (out);
return true;
}
// Stitch back together in preparation for sending down queue as a single track
out.push_back ({ NaN, NaN, NaN });
for (const auto& i : cropped_tracks) {
for (const auto& p : i)
out.push_back (p);
out.push_back ({ NaN, NaN, NaN });
}
return true;
} else {
if (!inverse)
in.swap (out);
return true;
}
}
