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A small C++ wrapper for pjreddie/darknet detector (yolo v2), for use in UTAT project.
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Darknet C++ Wrapper

A small C++ wrapper for detecting objects using pjreddie/darknet detector (yolo v2), for use in UTAT project.


  1. Download and make sure you can compile and run darknet yolo.

    Note: OpenCV and GPU(CUDA) must be enabled when compiling.

  2. Make a copy of the darknet source code, including the make file.
    The following steps are performed on this copy, unless stated otherwise.

  3. Remove or rename the main function in the darknet source code.
    There is a main function in the original darknet. In order to use darknet as a library, this function must be removed or renamed. For example, rename this function to _main instead of main.
    The function is located in src/darknet.c.

  4. Copy the content inside wrapper folder to somewhere near the source file.
    This location will need to be manually added to the make file in later steps.

  5. Add your own C++ file in the same location, include darknet.h, and use the Darknet class.
    See example/main.cpp for example usage.

  6. Add your source code / folder to the make file manually.
    Make sure to use g++ instead of gcc for compiling the .cpp files.

  7. Compile and run!

  8. To train the network, use the original darknet application.


Darknet class


See example.cpp for an example program that reads image from the webcam using OpenCV, detects objects using darknet, and visualizes the detection.


  • static Darknet* get_current()
    Returns the current instance of darknet.
    Returns nullptr if none are instantiated.

  • Darknet()
    Default constructor.
    The initialize method must be called before darknet is used.
    Note: Only one instance can be constructed.

  • ~Darknet()
    Default destructor.

  • void initialize(int gpu_id = 0)
    Initializes darknet with the given gpu_id.
    This method cannot be called more than once.


    • int gpu_id
      The default GPU id to be used in computation.
  • void load_command_args(int argc, char** argv)
    Sets the object properties of darknet using command line arguments in the command format of the original darknet application.

    See original darknet documentation for detail.


    • int argc
      Number of arguments.

    • char** argv
      list of arguments in the form of an array of C-style strings.

  • void load_command_args(const std::vectorstd::string& args)
    This is an overloaded method. Instead of using C-style command arguments, this method parses a std::vector list of C++ strings for convenience.

  • void run()
    Runs the darknet application using the object properties as parameters.
    Depending on the operation property, the behavior of this method may vary.
    This method cannot be called more than once.

  • void process(cv::Mat& image, process_func_ptr process_func = nullptr)
    If module is set to "detector" and operation is set to "detect", calling this function will process image by detecting objects using darknet's neural network and calling process_func with the detected data, including object names, positions and bounding boxes, and confidences (probabilities).
    If module and operation is in other configurations, this method has no effect.
    run must be called before this method.


    • cv::Mat& image
      The OpenCV image to be processed.

    • process_func_ptr process_func
      The callback function to be used in processing the data.
      Format of the callback function:
      void process_func(int num, const char** names, box* boxes, float* probs)


      • int num
        Number of objects detected.

      • const char** names
        An array of C-styled strings. Length is num.
        Contains names of the objects detected.

      • box* boxes
        An array of box. Length is num.
        Contains bounding boxes of the object detected.
        box is a struct with 4 properties:

        • x: x coordinate of the center.
        • y: y coordinate of the center.
        • w: width of the bounding box.
        • h: height of the bounding box.

        All properties are in the range of 0.0 to 1.0, where 1.0 is the full width/height of the image.

      • float probs
        * An array of floats. Length is num.
        Contains the confidence/probability of the objects detected.


  • std::string module
    The darknet module/option to use.
    Corresponds to the first argument in darknet command.
    Valid values:

    • "detector"
  • std::string operation
    The detector operation to use.
    Corresponds to the second argument in darknet command.
    Valid values:

    • "detect"
    • "test"
    • "train"
    • "valid"
    • "recall"
  • std::string datacfg
    The path to the data config file.
    Example: "cfg/"

  • std::string cfg
    The path to the neural network config file.
    Example: "cfg/yolo.cfg"

  • std::string weights
    The path to the trained weights file.
    Example: "yolo.weights"

  • std::string prefix
    If this property is not an empty string, visualization of each frame processed will be saved to hard drive, with this property being the prefix for the name of the files.

  • float thresh
    The confidence threshold for detection. Any detected objects with confidence (probably) lower than this property will be discarded.
    Default: 0.24.

  • int frame_skip

  • bool visualize
    If true, a window will be opened that shows detected objects in each frame processed.

  • bool multithread
    If true, multi-threading will be used to improve performance.
    Note: this will make each process call processes the second last image passed in.

  • std::string gpu_list
    A list of GPUs to use in computation. Separate the GPU indexes with commas.

  • bool clear

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