/**
* Calculate error distance, between last through this to next,
* if this node is removed. This metric provides for Douglas-Peuker
* thinning.
*
* @param last Point previous in time to this node
* @param node This node
* @param next Point succeeding this node
*
* @return Distance error if this node is thinned
*/
static unsigned distance_metric(const TracePoint& last,
const TracePoint& node,
const TracePoint& next) {
const int d_this = last.flat_distance(node) + node.flat_distance(next);
const int d_rem = last.flat_distance(next);
return abs(d_this - d_rem);
}
TraceDelta(const TracePoint &p_last, const TracePoint &p,
const TracePoint &p_next)
:point(p),
elim_time(time_metric(p_last, p, p_next)),
elim_distance(distance_metric(p_last, p, p_next)),
delta_distance(p.flat_distance(p_last))
{
assert(elim_distance != null_delta);
}
TriangleSecondLeg::Result
TriangleSecondLeg::Calculate(const TracePoint &c, unsigned best) const
{
// this is a heuristic to remove invalid triangles
// we do as much of this in flat projection for speed
const unsigned df_2 = b.flat_distance(c);
const unsigned df_3 = c.flat_distance(a);
const unsigned df_total = df_1+df_2+df_3;
// require some distance!
if (df_total<20) {
return Result(0, 0);
}
// no point scanning if worst than best
if (df_total<= best) {
return Result(0, 0);
}
const unsigned shortest = min(df_1, min(df_2, df_3));
// require all legs to have distance
if (!shortest) {
return Result(0, 0);
}
if (is_fai && (shortest*4<df_total)) { // fails min < 25% worst-case rule!
return Result(0, 0);
}
const unsigned d = df_3+df_2;
// without FAI rules, allow any triangle
if (!is_fai) {
return Result(d, df_total);
}
if (shortest*25>=df_total*7) {
// passes min > 28% rule,
// this automatically means we pass max > 45% worst-case
return Result(d, df_total);
}
const unsigned longest = max(df_1, max(df_2, df_3));
if (longest*20>df_total*9) { // fails max > 45% worst-case rule!
return Result(0, 0);
}
// passed basic tests, now detailed ones
// find accurate min leg distance
fixed leg(0);
if (df_1 == shortest) {
leg = a.get_location().distance(b.get_location());
} else if (df_2 == shortest) {
leg = b.get_location().distance(c.get_location());
} else if (df_3 == shortest) {
leg = c.get_location().distance(a.get_location());
}
// estimate total distance by scaling.
// this is a slight approximation, but saves having to do
// three accurate distance calculations.
const fixed d_total((df_total*leg)/shortest);
if (d_total>=fixed(500000)) {
// long distance, ok that it failed 28% rule
return Result(d, df_total);
}
return Result(0, 0);
}
TriangleSecondLeg(bool _fai, const TracePoint &_a, const TracePoint &_b)
:is_fai(_fai), a(_a), b(_b), df_1(a.flat_distance(b)) {}
void update(const TracePoint &p_last, const TracePoint &p_next) {
elim_time = time_metric(p_last, point, p_next);
elim_distance = distance_metric(p_last, point, p_next);
delta_distance = point.flat_distance(p_last);
}
