darknet.hpp File Reference

Include this file to get access to the new Darknet V3 C++ API. More...

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Classes
struct	Darknet::Parm
	Structure returned by Darknet::parse_arguments(). More...
struct	Darknet::Prediction
	A much-simplified version of the old API structure DarknetDetection. More...

Namespaces
namespace	Darknet
	The namespace for the C++ Darknet API.

Typedefs
using	Darknet::Parms = std::vector< Parm >
	Structure returned by Darknet::parse_arguments().
using	Darknet::Predictions = std::vector< Prediction >
	Each image or video frame may contain many predictions.
using	Darknet::Probabilities = std::map< int, float >
	The likelyhood of a specific object class having been predicted.
using	Darknet::Box = DarknetBox
	Convenient simple types used in the Darknet/YOLO C++ API.
using	Darknet::Detection = DarknetDetection
	Convenient simple types used in the Darknet/YOLO C++ API.
using	Darknet::Image = DarknetImage
	Convenient simple types used in the Darknet/YOLO C++ API.
using	Darknet::MMats = std::map< int, cv::Mat >
	Convenient simple types used in the Darknet/YOLO C++ API.
using	Darknet::MStr = std::map< std::string, std::string >
	Convenient simple types used in the Darknet/YOLO C++ API.
using	Darknet::MStrInt = std::map< std::string, int >
	Convenient simple types used in the Darknet/YOLO C++ API.
using	Darknet::NetworkPtr = DarknetNetworkPtr
	Convenient simple types used in the Darknet/YOLO C++ API.
using	Darknet::SInt = std::set< int >
	Convenient simple types used in the Darknet/YOLO C++ API.
using	Darknet::SStr = std::set< std::string >
	Convenient simple types used in the Darknet/YOLO C++ API.
using	Darknet::VFloat = std::vector< float >
	Convenient simple types used in the Darknet/YOLO C++ API.
using	Darknet::VInt = std::vector< int >
	Convenient simple types used in the Darknet/YOLO C++ API.
using	Darknet::VScalars = std::vector< cv::Scalar >
	Convenient simple types used in the Darknet/YOLO C++ API.
using	Darknet::VStr = std::vector< std::string >
	Convenient simple types used in the Darknet/YOLO C++ API.

Enumerations
enum class	Darknet::EParmType {   Darknet::kUnknown ,   Darknet::kCfgFilename ,   Darknet::kNamesFilename ,   Darknet::kWeightsFilename ,   Darknet::kDirectory ,   Darknet::kFilename ,   Darknet::kOther }
	When parsing command-line parameters, each parameter is assigned a "type". More...

Functions
SInt	Darknet::add_skipped_class (Darknet::NetworkPtr ptr, const int class_to_skip)
	Add the given class index to the set of classes that Darknet must ignore.
cv::Mat	Darknet::annotate (const Darknet::NetworkPtr ptr, const Predictions &predictions, cv::Mat mat)
	Annotate the given image using the predictions from Darknet::predict().
SInt	Darknet::clear_skipped_classes (Darknet::NetworkPtr ptr)
	Clear the set of classes which Darknet must ignore.
MMats	Darknet::create_yolo_heatmaps (Darknet::NetworkPtr ptr, const float threshold=0.1f)
	Create several CV_32FC1 (array of 32-bit floats, single channel) cv::Mat objects representing heatmaps obtained from the internals of the YOLO layers in the network.
SInt	Darknet::del_skipped_class (Darknet::NetworkPtr ptr, const int class_to_include)
	Remove the given class index from the set of classes that Darknet must ignore.
bool	Darknet::find_neural_network_files (Darknet::Parms &parms)
	Given some hints in the parsed parameters, this function attempts to find the neural network files and converts the necessary parameter(s) to filenames.
void	Darknet::fix_out_of_bound_values (Darknet::NetworkPtr ptr, const bool toggle)
	Fix out-of-bound values returned by Darknet::predict() for objects near the edges of images.
std::string	Darknet::format_duration_string (std::chrono::high_resolution_clock::duration duration, const int decimals=3)
	Format the C++ duration as an easy-to-read text string, such as "3.432 milliseconds" or "4.250 hours".
void	Darknet::free_neural_network (Darknet::NetworkPtr &ptr)
	Free the neural network pointer allocated in Darknet::load_neural_network().
const Darknet::VScalars &	Darknet::get_class_colours (const Darknet::NetworkPtr ptr)
	Get access to the vector of colours assigned to each class when the .names file was loaded.
const Darknet::VStr &	Darknet::get_class_names (const Darknet::NetworkPtr ptr)
	Get access to the vector of names read from the .names file when the configuration was loaded.
std::filesystem::path	Darknet::get_config_filename (const Darknet::NetworkPtr ptr)
	Get the filename of the configuration file that was used to load this neural network.
std::filesystem::path	Darknet::get_config_filename (const Parms &parms)
	Get the filename of the configuration file from the provided parameters.
std::filesystem::path	Darknet::get_names_filename (const Darknet::NetworkPtr ptr)
	Get the filename of the names file that was used to load this neural network.
std::filesystem::path	Darknet::get_names_filename (const Parms &parms)
	Get the filename of the names file from the provided parameters.
std::filesystem::path	Darknet::get_weights_filename (const Darknet::NetworkPtr ptr)
	Get the filename of the weights file that was used to load this neural network.
std::filesystem::path	Darknet::get_weights_filename (const Parms &parms)
	Get the filename of the weights file from the provided parameters.
float	Darknet::iou (const cv::Rect &lhs, const cv::Rect &rhs)
	Calculate intersection-over-union given 2 OpenCV rectangles.
Darknet::NetworkPtr	Darknet::load_neural_network (const std::filesystem::path &cfg_filename, const std::filesystem::path &names_filename, const std::filesystem::path &weights_filename)
	Load a neural network (.cfg) and the corresponding weights file.
Darknet::NetworkPtr	Darknet::load_neural_network (Darknet::Parms &parms)
	Load a neural network.
void	Darknet::network_dimensions (Darknet::NetworkPtr &ptr, int &w, int &h, int &c)
	Get the network dimensions (width, height, channels).
std::ostream &	Darknet::operator<< (std::ostream &os, const Darknet::Prediction &pred)
	Display some information about this specific prediction.
std::ostream &	Darknet::operator<< (std::ostream &os, const Darknet::Predictions &preds)
	Display some information about all the predictions.
Darknet::Parms	Darknet::parse_arguments (const Darknet::VStr &v)
	Similar to the other Darknet::parse_arguments(), but uses a vector of strings as input.
Darknet::Parms	Darknet::parse_arguments (int argc, char *argv[])
	Parse common Darknet command-line parameters with the values from argc and argv in main().
Predictions	Darknet::predict (const Darknet::NetworkPtr ptr, const cv::Mat &mat)
	Get Darknet to look at the given image or video frame and return all predictions.
Predictions	Darknet::predict (const Darknet::NetworkPtr ptr, const std::filesystem::path &image_filename)
	Get Darknet to look at the given image and return all predictions.
Predictions	Darknet::predict (const Darknet::NetworkPtr ptr, Darknet::Image &img, cv::Size original_image_size=cv::Size(0, 0))
	Get Darknet to look at the given image or video frame and return all predictions.
Predictions	Darknet::predict_and_annotate (const Darknet::NetworkPtr ptr, cv::Mat mat)
	Combination of Darknet::predict() and Darknet::annotate().
cv::Mat	Darknet::resize_keeping_aspect_ratio (cv::Mat &mat, cv::Size desired_size, const cv::InterpolationFlags method=cv::InterpolationFlags::INTER_NEAREST)
	Resize the image as close as we can to the given size, but keep the aspect ratio the same as the original image.
void	Darknet::set_annotation_draw_bb (Darknet::NetworkPtr ptr, const bool toggle)
	Determines if bounding boxes are drawn when calling either Darknet::annotate() or Darknet::predict_and_annotate().
void	Darknet::set_annotation_draw_label (Darknet::NetworkPtr ptr, const bool toggle)
	Determines if text labels are drawn above the bounding boxes when calling either Darknet::annotate() or Darknet::predict_and_annotate().
void	Darknet::set_annotation_font (Darknet::NetworkPtr ptr, const cv::LineTypes line_type, const cv::HersheyFonts font_face, const int font_thickness, const double font_scale)
	Set the font characteristics to use when drawing the bounding boxes and labels in either Darknet::annotate() or Darknet::predict_and_annotate().
void	Darknet::set_annotation_line_type (Darknet::NetworkPtr ptr, const cv::LineTypes line_type)
	The OpenCV line type can impact performance.
const Darknet::VScalars &	Darknet::set_class_colours (Darknet::NetworkPtr ptr, const Darknet::VScalars &colours)
	Set the colours to use when drawing annotations.
Darknet::Parms &	Darknet::set_default_neural_network (Darknet::Parms &parms, const std::string &hint1, const std::string &hint2="", const std::string &hint3="")
	If no neural network has been set in the parameters, this can be used to provide hints as to which files need to be loaded.
void	Darknet::set_detection_threshold (Darknet::NetworkPtr ptr, float threshold)
	Detection threshold to use when Darknet::predict() is called.
void	Darknet::set_gpu_index (int idx)
	Set the GPU index to use.
void	Darknet::set_non_maximal_suppression_threshold (Darknet::NetworkPtr ptr, float threshold)
	Non-maximal suppression threshold to use when Darknet::predict() is called.
void	Darknet::set_rounded_corner_bounding_boxes (Darknet::NetworkPtr ptr, const bool toggle, const float roundness)
	This determines if annotations are drawn as circles or rounded rectangles in either Darknet::annotate() or Darknet::predict_and_annotate().
void	Darknet::set_trace (const bool flag)
	Set the Darknet::CfgAndState::is_trace flag.
void	Darknet::set_verbose (const bool flag)
	Set the Darknet::CfgAndState::is_verbose flag.
void	Darknet::show_version_info ()
	Display a few lines of text with some version information.
SInt	Darknet::skipped_classes (const Darknet::NetworkPtr ptr)
	Return the set of classes which Darknet must ignore.
SInt	Darknet::skipped_classes (Darknet::NetworkPtr ptr, const SInt &classes_to_skip)
	Set the classes which Darknet must ignore, completely over-writing all previous values.
cv::Mat	Darknet::visualize_heatmap (const cv::Mat &heatmap, const cv::ColormapTypes colourmap=cv::ColormapTypes::COLORMAP_JET)
	Convert a heatmap created with Darknet::create_yolo_heatmaps() to an easy-to-view image.

Detailed Description

Include this file to get access to the new Darknet V3 C++ API.

https://darknetcv.ai/api/api.html