OpenCV  4.2.0
Open Source Computer Vision
Kernel API

Table of Contents

G-API Kernel API

The core idea behind G-API is portability – a pipeline built with G-API must be portable (or at least able to be portable). It means that either it works out-of-the box when compiled for new platform, or G-API provides necessary tools to make it running there, with little-to-no changes in the algorithm itself.

This idea can be achieved by separating kernel interface from its implementation. Once a pipeline is built using kernel interfaces, it becomes implementation-neutral – the implementation details (i.e. which kernels to use) are passed on a separate stage (graph compilation).

Kernel-implementation hierarchy may look like:

Kernel API/implementation hierarchy example

A pipeline itself then can be expressed only in terms of A, B, and so on, and choosing which implementation to use in execution becomes an external parameter.

Defining a kernel

G-API provides a macro to define a new kernel interface – G_TYPED_KERNEL():

#include <opencv2/gapi.hpp>
static cv::GMatDesc // outMeta's return value type
outMeta(cv::GMatDesc in , // descriptor of input GMat
int ddepth , // depth parameter
cv::Mat /* coeffs */, // (unused)
cv::Point /* anchor */, // (unused)
double /* scale */, // (unused)
int /* border */, // (unused)
cv::Scalar /* bvalue */ ) // (unused)
return in.withDepth(ddepth);

This macro is a shortcut to a new type definition. It takes three arguments to register a new type, and requires type body to be present (see below). The macro arguments are:

  1. Kernel interface name – also serves as a name of new type defined with this macro;
  2. Kernel signature – an std::function<>-like signature which defines API of the kernel;
  3. Kernel's unique name – used to identify kernel when its type informattion is stripped within the system.

Kernel declaration may be seen as function declaration – in both cases a new entity must be used then according to the way it was defined.

Kernel signature defines kernel's usage syntax – which parameters it takes during graph construction. Implementations can also use this signature to derive it into backend-specific callback signatures (see next chapter).

Kernel may accept values of any type, and G-API dynamic types are handled in a special way. All other types are opaque to G-API and passed to kernel in outMeta() or in execution callbacks as-is.

Kernel's return value can only be of G-API dynamic type – cv::GMat, cv::GScalar, or cv::GArray<T>. If an operation has more than one output, it should be wrapped into an std::tuple<> (which can contain only mentioned G-API types). Arbitrary-output-number operations are not supported.

Once a kernel is defined, it can be used in pipelines with special, G-API-supplied method "::on()". This method has the same signature as defined in kernel, so this code:

cv::GMat out = GFilter2D::on(/* GMat */ in,
/* int */ -1,
/* Mat */ conv_kernel_mat,
/* Point */ cv::Point(-1,-1),
/* double */ 0.,
/* int */ cv::BORDER_DEFAULT,
/* Scalar */ cv::Scalar(0));

is a perfectly legal construction. This example has some verbosity, though, so usually a kernel declaration comes with a C++ function wrapper ("factory method") which enables optional parameters, more compact syntax, Doxygen comments, etc:

int ddepth,
cv::Point anchor = cv::Point(-1,-1),
double scale = 0.,
int border = cv::BORDER_DEFAULT,
return GFilter2D::on(in, ddepth, k, anchor, scale, border, bval);

so now it can be used like:

cv::GMat out = filter2D(in, -1, conv_kernel_mat);

Extra information

In the current version, kernel declaration body (everything within the curly braces) must contain a static function outMeta(). This function establishes a functional dependency between operation's input and output metadata.

Metadata is an information about data kernel operates on. Since non-G-API types are opaque to G-API, G-API cares only about G* data descriptors (i.e. dimensions and format of cv::GMat, etc).

outMeta() is also an example of how kernel's signature can be transformed into a derived callback – note that in this example, outMeta() signature exactly follows the kernel signature (defined within the macro) but is different – where kernel expects cv::GMat, outMeta() takes and returns cv::GMatDesc (a G-API structure metadata for cv::GMat).

The point of outMeta() is to propagate metadata information within computation from inputs to outputs and infer metadata of internal (intermediate, temporary) data objects. This information is required for further pipeline optimizations, memory allocation, and other operations done by G-API framework during graph compilation.

Implementing a kernel

Once a kernel is declared, its interface can be used to implement versions of this kernel in different backends. This concept is naturally projected from object-oriented programming "Interface/Implementation" idiom: an interface can be implemented multiple times, and different implementations of a kernel should be substitutable with each other without breaking the algorithm (pipeline) logic (Liskov Substitution Principle).

Every backend defines its own way to implement a kernel interface. This way is regular, though – whatever plugin is, its kernel implementation must be "derived" from a kernel interface type.

Kernel implementation are then organized into kernel packages. Kernel packages are passed to cv::GComputation::compile() as compile arguments, with some hints to G-API on how to select proper kernels (see more on this in "Heterogeneity"[TBD]).

For example, the aforementioned Filter2D is implemented in "reference" CPU (OpenCV) plugin this way (NOTE – this is a simplified form with improper border handling):

#include <opencv2/gapi/cpu/gcpukernel.hpp> // GAPI_OCV_KERNEL()
#include <opencv2/imgproc.hpp> // cv::filter2D()
static void
run(const cv::Mat &in, // in - derived from GMat
const int ddepth, // opaque (passed as-is)
const cv::Mat &k, // opaque (passed as-is)
const cv::Point &anchor, // opaque (passed as-is)
const double delta, // opaque (passed as-is)
const int border, // opaque (passed as-is)
const cv::Scalar &, // opaque (passed as-is)
cv::Mat &out) // out - derived from GMat (retval)
cv::filter2D(in, out, ddepth, k, anchor, delta, border);

Note how CPU (OpenCV) plugin has transformed the original kernel signature:

The basic intuition for kernel developer here is not to care where that cv::Mat objects come from instead of the original cv::GMat – and just follow the signature conventions defined by the plugin. G-API will call this method during execution and supply all the necessary information (and forward the original opaque data as-is).

Compound kernels

Sometimes kernel is a single thing only on API level. It is convenient for users, but on a particular implementation side it would be better to have multiple kernels (a subgraph) doing the thing instead. An example is goodFeaturesToTrack() – while in OpenCV backend it may remain a single kernel, with Fluid it becomes compound – Fluid can handle Harris response calculation but can't do sparse non-maxima suppression and point extraction to an STL vector:

A compound kernel implementation can be defined using a generic macro GAPI_COMPOUND_KERNEL():

#include <opencv2/gapi/gcompoundkernel.hpp> // GAPI_COMPOUND_KERNEL()
using PointArray2f = cv::GArray<cv::Point2f>;
static cv::GArrayDesc outMeta(const cv::GMatDesc &,
// No special metadata for arrays in G-API (yet)
// Define Fluid-backend-local kernels which form GoodFeatures
static cv::GMatDesc outMeta(const cv::GMatDesc &in,
return in.withType(CV_32F, 1);
static cv::GArrayDesc outMeta(const cv::GMatDesc &,
GAPI_COMPOUND_KERNEL(GFluidHarrisCorners, HarrisCorners)
static PointArray2f
expand(cv::GMat in,
int maxCorners,
double quality,
double minDist,
int blockSize,
double k)
cv::GMat response = HarrisResponse::on(in, quality, blockSize, k);
return ArrayNMS::on(response, maxCorners, minDist);
// Then implement HarrisResponse as Fluid kernel and NMSresponse
// as a generic (OpenCV) kernel

It is important to distinguish a compound kernel from G-API high-order function, i.e. a C++ function which looks like a kernel but in fact generates a subgraph. The core difference is that a compound kernel is an implementation detail and a kernel implementation may be either compound or not (depending on backend capabilities), while a high-order function is a "macro" in terms of G-API and so cannot act as an interface which then needs to be implemented by a backend.