4.3 KiB
Graph API
Introduction
OpenCV Graph API (or G-API) is a new OpenCV module targeted to make regular image processing fast and portable. These two goals are achieved by introducing a new graph-based model of execution.
G-API is a special module in OpenCV -- in contrast with the majority of other main modules, this one acts as a framework rather than some specific CV algorithm. G-API provides means to define CV operations, construct graphs (in form of expressions) using it, and finally implement and run the operations for a particular backend.
@note G-API is a new module and now is in active development. It's API is volatile at the moment and there may be minor but compatibility-breaking changes in the future.
Contents
G-API documentation is organized into the following chapters:
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@subpage gapi_purposes
The motivation behind G-API and its goals.
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@subpage gapi_hld
General overview of G-API architecture and its major internal components.
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@subpage gapi_kernel_api
Learn how to introduce new operations in G-API and implement it for various backends.
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@subpage gapi_impl
Low-level implementation details of G-API, for those who want to contribute.
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API Reference: functions and classes
API Example
A very basic example of G-API pipeline is shown below:
@include modules/gapi/samples/api_example.cpp
G-API is a separate OpenCV module so its header files have to be
included explicitly. The first four lines of main() create and
initialize OpenCV's standard video capture object, which fetches
video frames from either an attached camera or a specified file.
G-API pipelie is constructed next. In fact, it is a series of G-API
operation calls on cv::GMat data. The important aspect of G-API is
that this code block is just a declaration of actions, but not the
actions themselves. No processing happens at this point, G-API only
tracks which operations form pipeline and how it is connected. G-API
Data objects (here it is cv::GMat) are used to connect operations
each other. in is an empty cv::GMat signalling that it is a
beginning of computation.
After G-API code is written, it is captured into a call graph with
instantiation of cv::GComputation object. This object takes
input/output data references (in this example, in and out
cv::GMat objects, respectively) as parameters and reconstructs the
call graph based on all the data flow between in and out.
cv::GComputation is a thin object in sense that it just captures which
operations form up a computation. However, it can be used to execute
computations -- in the following processing loop, every captured frame (a
cv::Mat input_frame) is passed to cv::GComputation::apply().
cv::GComputation::apply() is a polimorphic method which accepts a variadic number of arguments. Since this computation is defined on one input, one output, a special overload of cv::GComputation::apply() is used to pass input data and get output data.
Internally, cv::GComputation::apply() compiles the captured graph for the given input parameters and executes the compiled graph on data immediately.
There is a number important concepts can be outlines with this examle:
- Graph declaration and graph execution are distinct steps;
- Graph is built implicitly from a sequence of G-API expressions;
- G-API supports function-like calls -- e.g. cv::gapi::resize(), and operators, e.g operator|() which is used to compute bitwise OR;
- G-API syntax aims to look pure: every operation call within a graph yields a new result, thus forming a directed acyclic graph (DAG);
- Graph declaration is not bound to any data -- real data objects (cv::Mat) come into picture after the graph is already declared.
See [tutorials and porting examples](@ref tutorial_table_of_content_gapi) to learn more on various G-API features and concepts.