cgal/Documentation/doc/Introduction.txt

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/*!
\example Convex_hull_2/array_convex_hull_2.cpp
\example Convex_hull_2/vector_convex_hull_2.cpp
\example Convex_hull_2/iostream_convex_hull_2.cpp
\example Convex_hull_2/convex_hull_yz.cpp
*/
/*!
\page introduction Hello World
\autotoc
\author %CGAL Editorial Board
The goal of this chapter is to get a first idea of the look and feel of a program that uses CGAL.
We will take a closer look at four \cgal example
programs, all of them computing the 2D convex hull of a set of points.
You will find the same ideas in other
# Points in a Built-in Array # {#intro_array}
In the first example we have as input an array of five points.
As the convex hull of these points is a subset of the input
it is safe to provide an array for storing the result which
has the same size.
\cgalexample{Convex_hull_2/array_convex_hull_2.cpp}
All \cgal header files are in the subdirectory `include/CGAL`. All \cgal
classes and functions are in the namespace `CGAL`. The geometric
primitives, like the point type, are defined in a kernel. \cgal comes
with several kernels, and as the convex hull algorithm only makes
comparisons of coordinates and orientation tests of input points,
we can choose a kernel that provides exact predicates, but no
exact geometric construction.
The convex hull function takes three arguments, the start
and past-the-end pointer for the input, and the start pointer of the
array for the result. The function returns the pointer
into the result array just behind the last convex hull
point written, so the pointer difference tells us how
many points are on the convex hull.
# Points in a Vector # {#intro_vector}
In the second example we replace the built-in array
by a `std::vector` of the Standard Template Library.
\cgalexample{Convex_hull_2/vector_convex_hull_2.cpp}
We put some points in the vector calling the `push_back()`
method of the `std::vector` class.
We then call the convex hull function. The first two arguments,
`points.begin()` and `points.end()` are *iterators*, which are a
generalization of pointers: they can be dereferenced and
incremented. The convex hull function is *generic* in the sense
that it takes as input whatever can be dereferenced and incremented.
The third argument is where the result gets written to. In the
previous example we provided a pointer to allocated memory. The
generalization of such a pointer is the *output iterator*, which
allows to increment and assign a value to the dereferenced iterator.
In this example we start with an empty vector which grows as needed.
Therefore, we cannot simply pass it `result.begin()`, but an output
iterator generated by the helper function
`std::back_inserter(result)`. This output iterator does nothing when
incremented, and calls `result.push_back(..)` on the assignment.
# Points in Streams # {#intro_streams}
The next example program reads a sequence of points from standard
input `std::cin` and writes the points on the convex hull to standard
output `std::cout`.
Instead of storing the points in a container such as an `std::vector`,
and passing the begin/end iterator of the vector to the convex hull
function, we use helper classes that turn file pointers into
iterators.
\cgalexample{Convex_hull_2/iostream_convex_hull_2.cpp}
In the example code you see input and output stream iterators
templated with the point type. A `std::istream_iterator<Point_2>`
hence allows to traverse a sequence of objects of type `Point_2`, which
come from standard input as we pass `std::cin` to the
constructor of the iterator. The variable `input_end` denotes
end-of-file.
A `std::ostream_iterator<Point_2>` is an output iterator, that is an
iterator to which, when dereferenced, we can assign a value. When
such an assignment to the output iterator happens somewhere inside the
convex hull function, the iterator just writes the assigned point to
standard output, because the iterator was constructed with
`std::cout`.
The call to the convex hull function takes three arguments, the input
iterator range, and the output iterator to which the result gets
written.
If you know the \stl, the Standard Template Library, the above makes
perfect sense, as this is the way the \stl decouples algorithms from
containers. If you don't know the \stl, you maybe better first
familiarize yourself with its basic ideas.
# About Traits Classes # {#intro_traits}
If you look at the manual page of the function `CGAL::convex_hull_2()`
and the other 2D convex hull algorithms, you see that they come in two
versions. In the examples we have seen so far the function that takes two
iterators for the sequnce of input points and an output iterator for
writing the result to. The second version has an additional template
parameter `Traits`, and an additional parameter of this type.
What are the geometric primitives a typical convex hull algorithm
uses? Of course, this depends on the algorithm, so let us consider
what is probably the simplest efficient algorithm, the so-called
"Graham/Andrew Scan". This algorithm first sorts the points from left
to right, and then builds the convex hull incrementally by adding one
point after another from the sorted list. To do this, it must at least
know about some point type, it should have some idea how to sort those
points, and it must be able to evaluate the orientation of a triple of
points.
And that is where the template parameter `Traits` comes in.
For `CGAL::ch_graham_andrew` it must provide the following nested types:
- `Traits::Point_2`
- `Traits::Less_xy_2`
- `Traits::Left_turn_2`
- `Traits::Equal_2`
As you can guess, `Left_turn_2` is responsible for the orientation
test, while `Less_xy_2` does the sorting. The requirements these
types have to satisfy are documented in full with the concept
`ConvexHullTraits_2`.
The types are regrouped for a simple reason. The alternative would
have been a rather lengthy function template, and an even longer
function call.
\code{.cpp}
template <class InputIterator, class OutputIterator, class Point_2, class Less_xy_2, class Lef_turn_2, class Equal_2>
OutputIterator
ch_graham_andrew( InputIterator first,
InputIterator beyond,
OutputIterator result);
\endcode
There are two obvious questions: What can be used as argument for
this template parameter? And why do we have template parameters at all?
Any %CGAL `Kernel` provides what is required by `ConvexHullTraits_2`.
As for the second question, think about an application where we want to
compute the convex hull of 3D points projected into the `yz` plane. Using
the class `CGAL::Projection_traits_yz_3` this is a small modification
of the previous example.
\cgalexample{Convex_hull_2/convex_hull_yz.cpp}
Another example would be about a user defined point type, or a point
type coming from a third party library other than %CGAL. Put the point
type together with the required predicates for this point type in the
scope of a class, and you can run `CGAL::convex_hull_2()` with these
points.
Finally, let us explain why a traits object that is passed to the
convex hull function? It would allow to use a more general projection
traits object to store state, for example if the projection plane was
given by a direction, which is hardwired in the class
`CGAL::Projection_traits_yz_3`.
# Further Reading # {#intro_further}
We also recommend the standard text books by
Josuttis \cite cgal:j-csl-99, or Austern \cite cgal:a-gps-98 for the
\stl and its notion of *concepts* and *models*.
Other resources for \cgal are the tutorials at
http://www.cgal.org/Tutorials/ and the user support page at
http://www.cgal.org/.
*/