The porting to C++ try for the Darknet neural network repository

Original repository taken from https://github.com/AlexeyAB/darknet

Some parts of the repository taken from the C++ template project located at https://github.com/yuriysydor1991/cpp-app-template.git

Work in progress. Do not expect project to work in the current state

See more at the kytok.org.ua

In order to train the network on your own data set you'll need annotated images with the bounding box labels in the Darknet/YOLO format. It's recommended to prepare such annotations with the ImagesAnnotator tool available at https://github.com/yuriysydor1991/ImagesAnnotator.

Document is under the refinement

The very same sections are also available in Ukrainian under doc/README.uk_UA.md. The whole documentation may also be generated as a browsable HTML site (including the rendered PlantUML class diagrams) with Doxygen - see Documentation build.

1. Cloning the C++ template project
2. Forking and replacing the origin
3. Requirements
   1. Required tools for the GNU/Linux based OS
   2. Required tools for the MS Windows based OS
   3. Optional for the tests
   4. Optional for the documentation
   5. Optional for the code formatting
   6. Optional for the code analyzer (cppcheck)
   7. Optional for the code analyzer with clang-tidy
   8. Optional for the memory check with Valgrind
   9. Optional for the flatpak packager
   10. Optional for the Docker container runs
   11. Optional for the snap packager
4. Project structure
   1. Implement code straight away!
   2. Changing the project and executable name
   3. Version tracking and other project parameters
   4. Minimal possible versions
   5. Project tests
      1. Google Test
   6. Extensions
   7. Project components and architecture
      1. Application runtime components
      2. Darknet adaptor components
      3. Supporting components
5. Project build
   1. IDE build
   2. Command line build
   3. Enabling testing
      1. Enabling unit testing
      2. Disabling system GTest probe
   4. Documentation build
   5. Configuring the documentation install support
   6. Enabling and performing code formatting target
   7. Enabling the static code analyzer target with cppcheck
   8. Enabling the static code analyzer with clang-tidy
   9. Enabling the dynamic memory check target with valgrind
   10. Enabling DEB package generation with cpack
   11. Enabling the flatpak package generation support
   12. Enabling the Docker container build and run
   13. Enabling sanitizers
   14. Enabling the libcurl
   15. Enabling gprof profiler analysis
   16. Enabling vagrind's callgrind profiler analysis
   17. Enabling Jenkins pipeline inside Docker container
   18. Enabling the nlohmann json library
   19. Enabling the snap packager
   20. Enabling FreeBSD pkg package generation with cpack
   21. Enabling WIX MSI package generation with cpack
   22. Enabling RPM package generation with cpack
   23. Enabling the zlib library
   24. Enabling the libpng library
   25. Enabling the libjpeg library
   26. Enabling the libwebp library
   27. Enabling the lunasvg library (SVG)
   28. Enabling the giflib library (GIF)
   29. Enabling the libtiff library (TIFF)
   30. Enabling the OpenEXR library (EXR / HDR)
   31. Enabling the OpenJPEG library (JPEG 2000)
   32. Enabling the libavif library (AVIF)
   33. Enabling the libheif library (HEIF/HEIC)
6. Run the executable
   1. IDE run
   2. Command line run
      1. Searching for the generated executable
      2. Starting the generated executable
      3. Available command line parameters
   3. Tests run
      1. Run tests by the ctest
7. Installing