FFTW is used to compute FFT and IFFT of images. API Wrappers have been created in Sirius to make FFTW object lifetime management easier. On top of that, thread safety was taken into account to allow computation in a multi-threaded context.
GDAL is used to load image into memory and to save computed image. Just as FFTW, API wrappers have been created for GDAL object lifetime management.
Sirius relies heavily on modern C++ features (C++11/14) such as smart pointers (std::unique_ptr, std::shared_ptr), concurrency API (std::async) or lambdas.
Smart pointers are used to manage object lifetime (RAII idiom). When a smart pointer goes out of scope, the default behavior is to delete the managed resource using delete or delete[] (when the smart pointer resource is declared as an array).
When the resource type has its own deleter, it is possible to customize the smart pointer deleter. This feature is used in Sirius for example on FFTW types (fftw_complex arrays, double arrays, fftw_plan) and GDAL type (GDALDataset).
namespace gdal {
namespace detail {
struct DatasetDeleter {
void operator()(::GDALDataset* dataset) { ::GDALClose(dataset); }
};
} // namespace detail
using DatasetUPtr = std::unique_ptr<::GDALDataset, detail::DatasetDeleter>;
} // namespace gdalSirius multi-threaded streaming is based on lambdas and task mechanism:
- One task will read an image block from the input image and feed an input queue
- N worker tasks will consume the input queue, compute the resampling and feed an output queue
- One task will consume the output queue and write the resampled block into the output image.
Tasks are created using std::async with the policy std::launch::async (force the creation of a new thread to execute the given task).
auto task = []() { ... };
auto task_future = std::async(std::launch::async, task);
// wait end of task or exception
task_future.get();Sirius is using a singleton to address the thread safety issue of FFTW plan creation.
This singleton is responsible of creating and deleting plan and is interacting with the fftw_plan smart pointer deleter.
Meyers singleton implementation is used.
class Dummy {
public:
// Get Dummy singleton instance
static Dummy& Instance() {
static Dummy instance;
return instance;
}
private:
Dummy() = default;
~Dummy() = default;
// not copyable
Dummy(const Dummy&) = delete;
Dummy& operator=(const Dummy&) = delete;
// not moveable
Dummy(Dummy&&) = delete;
Dummy& operator=(Dummy&&) = delete;
};C++11 standard guarantees that a unique instance will be initialized and that this initialization is thread safe.
The FrequencyResampler is the combination of two algorithms:
- an image decomposition algorithm
- a zoom algorithm
Sirius provides an IFrequencyResampler factory to compose the resampler requested by a client.
API clients should only deal with IFrequencyResampler interface and should not be concerned by IFrequencyResampler implementations.
FrequencyResampler is the composition of an image decomposition algorithm and a zoom algorithm.
The policy/strategy pattern is used to adapt the internal behavior of this class.
An algorithm implementation must comply with an an algorithm type implicit interface.
E.g, zoom algorithms should comply with:
class ZoomStrategy {
public:
Image Zoom(int zoom, const Image& padded_image, const Filter& filter) const;
};Image decomposition algorithms should comply with:
class ImageDecompositionPolicy {
public:
Image DecomposeAndZoom(int zoom, const Image& padded_image,
const Filter& filter) const;
};Sirius is using spdlog as its logger library.
This library makes it easy to generate and format logs in an application. Sinks are powerful components to generate (colored) logs in stdout or stderr but you can also send your logs in (rotating) files or to syslog without overhead.
Sirius provides LOG macro to create logs easily. CMake ENABLE_LOGS option is also available to lighten the generated binaries by removing all log strings.
Sirius is using a basic Last Recently Used (LRU) cache implementation to optimize some computation at the cost of memory overhead. CMake ENABLE_CACHE_OPTIMIZATION option is available to control this behavior.
FFTW plans are cached so that a plan with a given size is reused if it has already been created. Filter FFTs are also cached to be reused on a specific image FFTs.
Sirius multi-threaded streaming is using ConcurrentQueue instances to feed resampler workers and to feed output stream.
This queue implementation is based on std::condition_variable.
ConcurrentQueue objects have a maximum size and an active status.
When maximum size is reached, Push operations will block until an element is popped from the queue. Likewise, Pop operations will block until there is an element to pop from the queue.
To unblock Push or Pop operations (e.g. after an error occurred), you can deactivate the queue. Push new elements in a inactive queue will fail and pop element will be possible as long as there is an element inside the queue.