When working with mathematical simulations or engineering problems, it is not unusual to handle curves that contains thousands of points. Usually, displaying all the points is not useful, a number of them will be rendered on the same pixel since the screen precision is finite. Hence, you use a lot of resource for nothing!
This article presents a fast 2D-line approximation algorithm based on the Douglas-Peucker algorithm (see ), well-known in the cartography community. It computes a hull, scaled by a tolerance factor, around the curve by choosing a minimum of key points. This algorithm has several advantages:
- It is fast!: For a n point curve, computation time of the approximation is proportional to nlog_2(n),
- You don't need a priori knowledge on the curve,
- The compression ratio can be easily tuned using the tolerance parameter.
The class has been integrated to a plotting library: Plot Graphic Library.
Douglas-Peucker (DP) Line Simplification Algorithm
The DP line simplification algorithm was originally proposed in . John Hershberger and Jack Snoeyink have implemented it in C in  as a package named
DPsimp is a Douglas-Peucker line simplification algorithm implementation by John Hershberger and Jack Snoeyink. They analyze the line simplification algorithm reported by Douglas and Peucker and show that its worst case is quadratic in n, the number of input points. The algorithm is based on path hulls, that uses the geometric structure of the problem to attain a worst-case running time proportional to nlog_2(n), which is the best case of the Douglas algorithm.
The algorithm is based on a recursive construction of path hull, as depicted in the picture below. They did all the hard work (writing the base code in C), I ported it to C++ templates.
Modifications to DPSimp
DPSimp was using a recursive call in the
DP method. This could lead to a stack overflow when the algorithm would go deep in recursion. To avoid this problem an internal stack has been added to the class to mimic the recursive function call without stack overflow.
Concepts and class design
points denote all the points of the original curve and the
keys the points from the original curve that are kept for the approximation.
The idea is that the user provides a container for the
PointContainer, and for the
KeyContainer, and the link between those containers will be the line simplification class, denoted
How do we build a class hierarchy without restricting ourselves to particular container? In fact, one user might store it's
vector< pair<T,T>> and another one in 2 separate
vector<T>. Of course, the same argument applies to the
A possible answer is templating. Passing the
KeyContainer as template arguments for
LineApproximator allows to build the approximation class without specifying the containers, since the class is built at compilation time (We could write interface for those containers but in fact, I'm too lazy for that ).
With this idea in mind, here are the specifications of the container:
Point a structure, template or class that has
y of type
T as member,
PointRef a structure, template or class that has
y of type
T& as member,
PointContainer behaves like a vector of
- has random access iterator,
const_iterator points to a structure similar to
iterator points to a structure similar to
operator const returns a
operator returns a
A simple example of valid
However, a hybrid container has been developed to handle the case where
y are in separate containers (See below).
KeyContainer behaves like a list of
A simple example of valid
Again, a hydrid container to handle the case where the
keys must be outputted in separate containers is provided.
All the classes are templated to support
double version of the algorithm.
TDPHull is the user interface to the DP algorithm. However, it relies on a series of subclasses detailed below:
TLine<T, TPointContainer, TKeyContainer>
|2D Line template
||Points and keys|
TLineApproximator<T, TPointContainer, TKeyContainer>
|2D Line approximator base class
||Default interface to approximation algorithms|
TDPHull<T, TPointContainer, TKeyContainer>
|Implementing Douglas-Peukler algorithm
||User front end|
TPathHull<T, TPointContainer, TKeyContainer>
||Internal in |
|A pair of
T: x, y
|Template for 2D point |
|A pair of
T&: x, y
|Template for 2D point |
|2D Homogenous point,
|Internal structure to |
How to use TDPHull?
In the following examples, we adopt the following notations
using namespace hull; using namespace std;
typedef vector<TPoint<float>> VectorPointContainer;
typedef vector<MyPointContainer::const_iterator> ListKeyContainer;
typedef TDPHull<float, VectorPointContainer, ListKeyContainer> CDPHullF;
The approximation methods throw exception, so you should always enclose them in a
The data points are, by default, normalized before approximation. This is in order to reduce numerical errors in the gradients computations. This happens when the data is badly scaled: using big numbers close together will lead to disastrous loss of significant numbers.
However, if you feel confident about your data, you can disable it by using
Handling points and keys
Get a reference to the point container and modify it:
TDPHull<float>::PointContainer& pc = dp.GetPoints();
for (UINT i=0;i<pc.size();i++)
If you are using normalization (default behavior), do not forget to re-compute the data bounding box after your changes:
You can control the compression ratio by different ways:
- Setting the tolerance
- Setting a compression ratio, an acceptable compression threshold:
The method uses dichotomy to accelerate convergence.
- Setting the desired number of points, an acceptable number of points threshold:
The easiest part of the job:
or by using
ShrinkNorm, Shrink methods.
Accessing the approximated curve
The keys are stored as
PointContainer::const_iterator. You can access the key container by using
const TDPHull<float>::KeyContainer& kc = dp.GetKeys();
TDPHull<float>::KeyContainer::const_iterator it; for (it = kc.begin(); it != kc.end(); it++)
Implementing your own algorithm
All you have to do is inherit a class from
TLineApproximator and override the function
You can implement your own containers for
keys as long as they meet the requirements.
Separate containers for x,y
It is not unusual to have
y stored in separate containers and moreover these containers are not of the same type. To tackle this problem, two wrapper templates have been written:
TKeyDoubleContainer which serve as an interface between the approximation algorithms and the containers:
CVectorX vX; CVectorY vY;
typedef TPointDoubleContainer<float, CVectorX,
typedef TKeyDoubleContainer<float, CListX, CVectorY> HybridKeyContainer;
TDPHull< float, HybridPointContainer, HydridKeyContainer> dp;
dp.GetPoints().SetContainers( &vX, &vY);
dp.GetKeys().SetContainers( &lKeyX, &vKeyY);
Using the demo
The demo shows a real time approximation of a curve by different algorithms.
- You can stop/start the animation using the toolbar buttons,
- You can modify the shrinking ration with the scroll bar,
- You can load your own data with the menu, File->Load Data Set. The file must be formatted with a pair
y per line.
Using it in your project
Insert the following files in your project and you're done.
- The original curve must not self-intersect. This means also that the start and end points must be different, no closed curve !
- Sick dataset and stack overflow: solved. The problem was due to recursion leading to stack overflow. It is solved now.
- Got rid of DP recursion by adding an internal function call stack. Hence, the stack overflow problem is solved!!!
- Applied stuff I learned in Effective STL to the classes: using algorithms, functors, etc...
class T to
- Better floating point comparison using
- More and more templating,
- Detecting when curve is closed
- Hybrid containers
- Fixed bug in compute limits
- Added LOTS of
ASSERT, so Debug version is significantly slower than release build
- Fixed a bug in the
SLimits structure (
TPathHull working with iterators rather that
- Added exception to handle errors
- Fixed a bug in
TDPHull::ComputeKeys. Was using
pc.end() rather that
- Added base class
- Added proposed algorithm by S.Rog: see
- Updated demo
- Added data normalization for better numerical behavior. Avoids the algorithm to crash when using badly conditioned data. Problem submitted by Corey W.
- Templated version
- Got rid of macro and rewrote in more OOP style
- D. H. Douglas and T. K. Peucker. Algorithms for the reduction of the number of points required to represent a line or its caricature. The Canadian Cartographer, 10(2):112--122, 1973.
- J. Hershberger and J. Snoeyink. Speeding up the Douglas-Peucker line simplification algorithm. In Proc. 5th Intl. Symp. Spatial Data Handling. IGU Commission on GIS, pages 134--143, 1992. (home page).