DarkHelp  v1.0.0-3005
C++ API for the neural network framework Darknet
Looking for a C++ dev who knows OpenCV?
I'm looking for work. Hire me!
This document assumes you already have Darknet installed, and you have a functioning neural network.
If you're not already at that step, you'll want to look up some tutorials like these ones:  

Once you have successfully trained a neural network, the next question becomes: how do you embed it into your C++ application?! Perhaps you've already looked into using Darknet's legacy C API, functions like load_network_custom(), do_nms_sort(), and get_network_boxes(). That API is not easy to work with, and there isn't much documentation nor example code.

(In case it helps, I did put together a blog post with a few details in late August 2019: https://www.ccoderun.ca/programming/2019-08-25_Darknet_C_CPP/.)

DarkHelp lets you skip those C function calls, and simplifies things with an extremely simple-to-use C++ API!

You load the neural network and the weight files, then call DarkHelp::predict() once per image you'd like analyzed. Each time you get back a std::vector of predictions.

Since annotating pictures is something that many applications want – especially during debugging – DarkHelp::annotate() is provided to easily mark up images with the detection results. To ease integrating this into larger projects, DarkHelp uses OpenCV's standard cv::Mat images, not Darknet's internal image structure. This is an example of what DarkHelp::annotate() can do with an image and a neural network that detects barcodes:

If you're looking for some sample code to get started, this example loads a network and then loops through several image files:

DarkHelp darkhelp("mynetwork.cfg", "mynetwork.weights", "mynetwork.names");
const auto image_filenames = {"image_0.jpg", "image_1.jpg", "image_2.jpg"};
for (const auto & filename : image_filenames)
// these next two lines is where DarkHelp calls into Darknet to do all the hard work
cv::Mat mat = darkhelp.annotate(); // annotates the most recent image seen by predict()
// use standard OpenCV calls to show the image results in a window
cv::imshow("prediction", mat);

The predictions are stored in a std::vector of structures. (See DarkHelp::PredictionResults.) You can get this vector and iterate through the results like this:

DarkHelp darkhelp("mynetwork.cfg", "mynetwork.weights", "mynetwork.names");
const auto results = darkhelp.predict("test_image_01.jpg");
for (const auto & det : results)
std::cout << det.name << " (" << 100.0 * det.best_probability << "% chance that this is class #" << det.best_class << ")" << std::endl;

If you have multiple classes defined in your network, then you may want to look at DarkHelp::PredictionResult::all_probabilities, not only DarkHelp::PredictionResult::best_class and DarkHelp::PredictionResult::best_probability.

The following is the shortest/simplest self-contained example showing how to load a network, run it against a set of images provided on the command-line, and then output the results as a series of coordinates, names, etc:

#include <iostream>
#include <DarkHelp.hpp>
int main(int argc, char *argv[])
DarkHelp darkhelp("driving.cfg", "driving_best.weights", "driving.names");
// Loop through all the images specified on the command-line:
for (int idx = 1; idx < argc; idx ++)
// get the predictions
const auto results = darkhelp.predict(argv[idx]);
// display the results on the console (meaning coordinates, not displaying the images themselves)
std::cout << results << std::endl; // see the output in the next block below
// to annotate the images, you'd use this instead:
// cv::Mat output = darkhelp.annotate();
// do_something_with_the_image(output);
return 0;
The order in which you specify the .cfg, .weights, and .names files in the constructor or in DarkHelp::init() is not important due to how the parameters are swapped around by DarkHelp::verify_cfg_and_weights().

Example output from sending the "results" to std::cout like the code in the previous block:

#1/74: loading image "surveillance_frame_000443.jpg"
-> prediction took 4 milliseconds
-> prediction results: 12
-> 1/12: "vehicle 84%" #0 prob=0.838765 x=573 y=223 w=24 h=19 entries=1
-> 2/12: "vehicle 85%" #0 prob=0.845121 x=1034 y=236 w=26 h=19 entries=1
-> 3/12: "motorcycle 93%" #1 prob=0.932856 x=473 y=308 w=24 h=54 entries=1
-> 4/12: "vehicle 98%" #0 prob=0.98197 x=1027 y=242 w=38 h=20 entries=1

If you call DarkHelp::annotate() to get back a OpenCV cv::Mat object, you can then display the image with all the annotations, or easily save it as a jpg or png. For example:

cv::Mat mat = darkhelp.annotate();
cv::imwrite("output.png", mat, {CV_IMWRITE_PNG_COMPRESSION, 9});

The example call to cv::imwrite() in the previous example might give something similar to this image:

Note that DarkHelp uses OpenCV internally, regardless of whether or not the client code calls DarkHelp::annotate(). This means when you link against libdarkhelp.so (dynamic) or libdarkhelp.a (static) you'll also need to link against a modern version of OpenCV.

Constructor. When using this constructor, the neural network remains uninitialized until init() is ca...
Definition: DarkHelp.cpp:57
int annotation_line_thickness
Thickness of the lines to draw in annotate(). Defaults to 2.
Definition: DarkHelp.hpp:508
float hierarchy_threshold
Used during prediction.
Definition: DarkHelp.hpp:421
virtual void reset()
The opposite of init(). This is automatically called by the destructor.
Definition: DarkHelp.cpp:159
ESort sort_predictions
Determines if the predictions will be sorted the next time predict() is called.
Definition: DarkHelp.hpp:563
cv::Mat original_image
The most recent image handled by predict().
Definition: DarkHelp.hpp:536
cv::Mat annotated_image
The most recent output produced by annotate().
Definition: DarkHelp.hpp:539
float non_maximal_suppression_threshold
Non-Maximal Suppression (NMS) threshold suppresses overlapping bounding boxes and only retains the bo...
Definition: DarkHelp.hpp:435
float annotation_shade_predictions
Determines the amount of "shade" used when drawing the prediction rectangles.
Definition: DarkHelp.hpp:483
int best_class
The class that obtained the highest probability.
Definition: DarkHelp.hpp:177
static VColours get_default_annotation_colours()
Obtain a vector of at least 25 different bright colours that may be used to annotate images.
Definition: DarkHelp.cpp:428
virtual cv::Mat annotate(const float new_threshold=-1.0f)
Takes the most recent prediction_results, and applies them to the most recent original_image.
Definition: DarkHelp.cpp:241
bool annotation_auto_hide_labels
Hide the label if the size of the text exceeds the size of the prediction.
Definition: DarkHelp.hpp:466
virtual ~DarkHelp()
Destructor. This automatically calls reset() to release memory allocated by the neural network.
Definition: DarkHelp.cpp:49
PredictionResults prediction_results
A copy of the most recent results after applying the neural network to an image. This is set by predi...
Definition: DarkHelp.hpp:438
bool fix_out_of_bound_values
Darknet sometimes will return values that are out-of-bound, especially when working with low threshol...
Definition: DarkHelp.hpp:533
std::string name
A name to use for the object.
Definition: DarkHelp.hpp:190
bool names_include_percentage
Determines if the name given to each prediction includes the percentage.
Definition: DarkHelp.hpp:452
bool annotation_include_timestamp
If set to true then annotate() will display a timestamp on the bottom-left corner of the image.
Definition: DarkHelp.hpp:524
cv::HersheyFonts annotation_font_face
Font face to use in annotate(). Defaults to cv::HersheyFonts::FONT_HERSHEY_SIMPLEX.
Definition: DarkHelp.hpp:499
std::vector< std::string > VStr
Vector of text strings. Typically used to store the class names.
Definition: DarkHelp.hpp:81
cv::Size2f original_size
The original normalized width and height returned by darknet.
Definition: DarkHelp.hpp:135
double annotation_font_scale
Scaling factor used for the font in annotate(). Defaults to 0.5.
Definition: DarkHelp.hpp:502
std::map< int, float > MClassProbabilities
Map of a class ID to a probability that this object belongs to that class.
Definition: DarkHelp.hpp:90
bool include_all_names
Determine if multiple class names are included when labelling an item.
Definition: DarkHelp.hpp:490
cv::Rect rect
OpenCV rectangle which describes where the object is located in the original image.
Definition: DarkHelp.hpp:109
float threshold
Image prediction threshold.
Definition: DarkHelp.hpp:407
virtual PredictionResults predict(const std::string &image_filename, const float new_threshold=-1.0f)
Use the neural network to predict what is contained in this image.
Definition: DarkHelp.cpp:196
int annotation_font_thickness
Thickness of the font in annotate(). Defaults to 1.
Definition: DarkHelp.hpp:505
Definition: DarkHelp.hpp:542
std::chrono::high_resolution_clock::duration duration
The length of time it took to initially load the network and weights (after the DarkHelp object has b...
Definition: DarkHelp.hpp:397
@ kDescending
Sort predictions using PredictionResult::best_probability in descending order (high values first,...
void * net
The Darknet network, but stored as a void* pointer so we don't have to include darknet....
Definition: DarkHelp.hpp:385
cv::Point2f original_point
The original normalized X and Y coordinate returned by darknet.
Definition: DarkHelp.hpp:122
std::map< std::string, std::string > MStr
Map of strings where both the key and the value are std::string.
Definition: DarkHelp.hpp:78
float best_probability
The probability of the class that obtained the highest value.
Definition: DarkHelp.hpp:184
virtual DarkHelp & init(const std::string &cfg_filename, const std::string &weights_filename, const std::string &names_filename="", const bool verify_files_first=true)
Initialize ("load") the darknet neural network.
Definition: DarkHelp.cpp:108
std::vector< cv::Scalar > VColours
Vector of colours to use by annotate().
Definition: DarkHelp.hpp:84
bool annotation_include_duration
If set to true then annotate() will call duration_string() and display on the top-left of the image t...
Definition: DarkHelp.hpp:516
virtual std::string version() const
Get a version string for the DarkHelp library. E.g., could be 1.0.0-123.
Definition: DarkHelp.cpp:102
Instantiate one of these objects by giving it the name of the .cfg and .weights file,...
Definition: DarkHelp.hpp:73
@ kAscending
Sort predictions using PredictionResult::best_probability in ascending order (low values first,...
std::vector< PredictionResult > PredictionResults
A vector of predictions for the image analyzed by predict().
Definition: DarkHelp.hpp:199
virtual std::string duration_string()
Return the duration as a text string which can then be added to the image during annotation.
Definition: DarkHelp.cpp:416
VColours annotation_colours
The colours to use in annotate().
Definition: DarkHelp.hpp:496
MClassProbabilities all_probabilities
This is only useful if you have multiple classes, and an object may be one of several possible classe...
Definition: DarkHelp.hpp:170
VStr names
A vector of names corresponding to the identified classes.
Definition: DarkHelp.hpp:391
@ kUnsorted
Do not sort predictions.
Structure used to store interesting information on predictions.
Definition: DarkHelp.hpp:95
static MStr verify_cfg_and_weights(std::string &cfg_filename, std::string &weights_filename, std::string &names_filename)
Look at the names and/or the contents of all 3 files and swap the filenames around if necessary so th...
Definition: DarkHelp.cpp:464