DGtal 1.3.0

DGtal follows the paradigm of generic programming, which aims at expressing data structures and algorithms in a broadly adaptable form so that they are directly usable for higherlevel tools and software.
Instead of coding data structures and algorithms with a set of predefined types, the code relies on abstract entities gathering syntactic, semantic and complexity constraints called concepts. Such data structures and algorithms are implemented in C++ with templated (or parametrized) classes of functions. A concept describes a set of types by listing their common interface (associated types and methods). The types that satisfy a concept are called models.
Since the types are known at compiletime, syntactical errors can be detected early, generated code can be optimized and efficiency is preserved. In DGtal, the name of the concepts always begin by C.
The core of DGtal is built on a small set of concepts:
concepts::CSpace: The concept of digital space
describes digital spaces of arbitrary dimension, with userchosen integer types. See Number and Integer Concepts and Digital Spaces, Points, Vectors and Domains of Kernel package for more infomation. The only available model of this concept is SpaceND, which defines fundamental types associated to a digital space like the type of point or vector lying in the space, etc. The point is a key geometrical element since digital geometry algorithms often deal with explicit or implicit sets of points.
concepts::CDomain, concepts::CDigitalSet: The concepts of domain and of digital set both describe sets of points that can be enumerated from a first point to a last one.
The main difference is that the former is notmutable, basically used to bound the space part where the computations have to be performed, while the latter is mutable. A key model of domain is HyperRectDomain, which is used to bound usual images. DigitalSetBySTLVector and DigitalSetBySTLSet are two models of digital set. See Sets of points in digital spaces of Kernel package for more infomation.
With a digital topology, ie. a couple of adjacency relations, describing how points are close to each others, various digital objects can be constructed from digital sets.
The Topology package also contains cartesian cellular topology to define, track and extract digital surfaces.
Note that a digital set can be converted into a domain (DigitalSetDomain), in order to construct an ad hoc domain or to bound some local computations within a subdomain.
concepts::CPointFunctor, concepts::CPointPredicate: The concept of point functor describe a mapping between the points of a digital space and a set of values. If the set of values is {0,1}, this mapping is called a point predicate.
The main method of the models of these concept is the operator()
, which must take a point as argument and must return a value. Some models of point predicate are adapters:
concepts::CConstImage, concepts::CImage: The concept of constant image is a point functor bounded by a domain. It is thus a refinement of the concept of point functor, describing a mapping between points and values, but having in addition a domain, returned by the method domain(), and a range of values, returned by the method range().
The returned range has begin(), end(), rbegin() and rend() methods returning constant iterators as every models of concepts::CConstBidirectionalRange, but has also overloaded versions of begin() and rbegin() methods taking a point as input argument in order to iterate on the values from a given location.
The concept of image provides extra services to update values.
Any model of concepts::CImage must have a method setValue() taking a point and a value as input arguments. In addition, their inner range must have outputIterator() and routputIterator() methods returning output iterators, which provides a way of incrementaly filling an image between two points of its domain. Different models of image are available: ImageContainerBySTLVector, ImageContainerBySTLMap, experimental::ImageContainerByHashTree and ImageContainerByITKImage, a wrapper for ITK images. More details are provided in the Image package documentation page.
DGtal is divided into several packages, containing concepts, models and algorithms.
The Kernel package contains the main concepts of DGtal described above and used in other higher level packages. For instance, it defines number types and fundamental structures like digital space, domains, digital sets and basic mappings between digital points and values, ie. basic point functors and point predicates.
The Topology package contains topology related concepts, models and algorithms, used in other higher level packages. It contains both the classical digital topology (à la Rosenfeld) and the cartesian cellular topology (à la Herman). Generic tools and methods are provided for defining adjacency relations, (signed) cells and incidence operations, extracting connected components, identifying simple points, tracking the boundary of a given shape, scanning digital surfaces, etc. This package is notably used by the geometry package, especially to define curves and surfaces.
The Geometry package contains geometry related concepts, models and algorithms. It provides a generic framework for the segmentation of onedimensional discrete structures, like strings, contours of 2d digital objects or nd digital curves. It also provides a generic framework for the estimation of geometrical quantities, either global, like length, or local, like normal or curvature. Several estimators are built from some well chosen segmentations. On the other hand, this package contains tools for the analysis of volumes of arbitrary dimension, by the means of separable and incremental distance transforms.
The Image package contains the concept of image and its models: either containers like ImageContainerBySTLVector, ImageContainerBySTLMap, ImageContainerByHashTree ImageContainerByITKImage or adapters like ConstImageAdapter, which provides a virtual view of the true values contained in the adapted image, or Image, which is a light proxy on another image. Moreover, it provides many functions for converting digital sets into images, conversely, images into digital sets by value comparators, and filling images from point functors or other images.
In IO package, we present DGtal tools and utilities to import/export images and visualize digital data using interactive (viewers) and noninteractive (boards) mechanisms.