DarkHelp Namespace Reference

The DarkHelp namespace contains (almost) everything in the DarkHelp library. More...

Classes
class	Config
	All of DarkHelp's configuration is stored within an instance of this class. More...
class	DHThreads
	This class allows you to easily run multiple identical copies of a neural network to process many files at once. More...
class	NN
	Instantiate one of these objects by giving it the name of the .cfg and .weights file, then call DarkHelp::NN::predict() as often as necessary to determine what the images contain. More...
class	PositionTracker
	This class attempts to do very simple object tracking based on the position of the object. More...
struct	PredictionResult
	Structure used to store interesting information on predictions. More...

Typedefs
using	MClassProbabilities = std::map< int, float >
	Map of a class ID to a probability that this object belongs to that class. More...
using	MStr = std::map< std::string, std::string >
	Map of strings where both the key and the value are std::string. More...
using	PredictionResults = std::vector< PredictionResult >
	A vector of predictions for the image analyzed by DarkHelp::NN::predict(). More...
using	VColours = std::vector< cv::Scalar >
	Vector of colours to use by DarkHelp::NN::annotate(). More...
using	VFloat = std::vector< float >
	Vector of float used with OpenCV. More...
using	VInt = std::vector< int >
	Vector of int used with OpenCV. More...
using	VRect = std::vector< cv::Rect >
	Vector of OpenCV rectangles used with OpenCV. More...
using	VRect2d = std::vector< cv::Rect2d >
	Similar to DarkHelp::VRect, but the rectangle uses double instead of int. More...
using	VStr = std::vector< std::string >
	Vector of text strings. Typically used to store the class names. More...

Enumerations
enum	EDriver {   EDriver::kInvalid = 0,   EDriver::kMin = 1,   EDriver::kDarknet = kMin,   EDriver::kOpenCV,   EDriver::kOpenCVCPU,   EDriver::kMax = kOpenCVCPU }
	DarkHelp can utilise either libdarknet.so or OpenCV's DNN module to load the neural network and run inference. More...
enum	ESort {   ESort::kUnsorted = 0,   ESort::kAscending,   ESort::kDescending,   ESort::kPageOrder }

Functions
std::filesystem::path	combine (const std::string &key, const std::filesystem::path &cfg_filename, const std::filesystem::path &names_filename, const std::filesystem::path &weights_filename)
	Combine together the 3 files that make up a neural network, and obfuscate them using the given key phrase. More...
std::string	duration_string (const std::chrono::high_resolution_clock::duration duration)
	Format a duration as a text string which is typically added to images or video frames during annotation. More...
size_t	edit_cfg_file (const std::string &cfg_filename, MStr m)
	This is used to insert lines into the [net] section of the configuration file. More...
void	extract (const std::string &key, const std::filesystem::path &bundle, std::filesystem::path &cfg_filename, std::filesystem::path &names_filename, std::filesystem::path &weights_filename)
	Extract the 3 files that make up a neural network. More...
cv::Mat	fast_resize_ignore_aspect_ratio (const cv::Mat &mat, const cv::Size &desired_size)
	Resize the given image as quickly as possible to the given dimensions. More...
void	fix_out_of_bound_normalized_rect (float &cx, float &cy, float &w, float &h)
	Automatically called by DarkHelp::NN::predict_internal() when DarkHelp::Config::fix_out_of_bound_values has been set. More...
VColours	get_default_annotation_colours ()
	Obtain a vector of at least 25 different bright colours that may be used to annotate images. More...
std::ostream &	operator<< (std::ostream &os, const DarkHelp::PositionTracker &tracker)
	Convenience function to stream the entire object tracker as text. More...
std::ostream &	operator<< (std::ostream &os, const DarkHelp::PositionTracker::Obj &obj)
	Convenience function to stream a single tracked object as a line of text. More...
std::ostream &	operator<< (std::ostream &os, const PredictionResult &pred)
	Convenience function to stream a single result as a "readable" line of text. More...
std::ostream &	operator<< (std::ostream &os, const PredictionResults &results)
	Convenience function to stream an entire vector of results as readable text. More...
void	pixelate_rectangle (const cv::Mat &src, cv::Mat &dst, const cv::Rect &r, const int size=15)
	Pixelate the given rectangle. More...
void	pixelate_rectangles (const cv::Mat &src, cv::Mat &dst, const PredictionResults &prediction_results, const int size=15)
	Pixelate all of the predictions. More...
void	pixelate_rectangles (const cv::Mat &src, cv::Mat &dst, const PredictionResults &prediction_results, const std::set< int > &class_filter, const int size=15)
	Pixelate only the predictions where the class ID matches a value in the class filter. More...
void	pixelate_rectangles (const cv::Mat &src, cv::Mat &dst, const VRect &rects, const int size=15)
	Pixelate all of the rectangles. More...
cv::Mat	resize_keeping_aspect_ratio (cv::Mat mat, const cv::Size &desired_size)
	Convenience function to resize an image yet retain the exact original aspect ratio. More...
cv::Mat	slow_resize_ignore_aspect_ratio (const cv::Mat &mat, const cv::Size &desired_size)
	Similar to DarkHelp::fast_resize_ignore_aspect_ratio() but uses OpenCV algorithms that result in better quality images at a cost of slower speed. More...
void	toggle_output_redirection ()
	Toggle STDOUT and STDERR output redirection. More...
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 the .cfg, .weights, and .names are assigned where they should be. More...
std::string	version ()
	Get a version string for the DarkHelp library. E.g., could be 1.0.0-123. More...
bool	yolo_annotations_file_exists (const std::string &image_filename)
	Check to see if the given image has a corresponding .txt file for YOLO annotations. More...
std::string	yolo_annotations_filename (const std::string &image_filename)
	Given an image filename, get the corresponding filename where the YOLO annotations should be saved. More...
PredictionResults	yolo_load_annotations (const cv::Size &image_size, const std::string &filename)
	Load the YOLO annotations from file. More...
cv::Mat	yolo_load_image_and_annotations (const std::string &image_filename, PredictionResults &annotations)
	Load the given image and read in the corresponding YOLO annotations from the .txt file. More...
std::string	yolo_save_annotations (const std::string &filename, const PredictionResults &annotations)
	Save the given annotations to the .txt file. More...

Detailed Description

The DarkHelp namespace contains (almost) everything in the DarkHelp library.

Prior to version 1.4, DarkHelp was the name of a class. But in October/November 2021, a large code re-organization took place, and the previous class definition was split into multiple classes across several files. This makes things easier to manage and was needed to support other projects like DarkHelpFPS.

Appologies to everyone who has code that relied on the previous DarkHelp API. The old Darkhelp class has been renamed to DarkHelp::NN, and the settings that used to be in DarkHelp have been moved to DarkHelp::NN::config.

Since

November 2021

See also

DarkHelp::Config

DarkHelp::PredictionResult

DarkHelp::NN

Typedef Documentation

MClassProbabilities

using DarkHelp::MClassProbabilities = typedef std::map<int, float>

Map of a class ID to a probability that this object belongs to that class.

The key is the zero-based index of the class, while the value is the probability that the object belongs to that class.

See also

DarkHelp::PredictionResult::all_probabilities

MStr

using DarkHelp::MStr = typedef std::map<std::string, std::string>

Map of strings where both the key and the value are std::string.

PredictionResults

using DarkHelp::PredictionResults = typedef std::vector<PredictionResult>

A vector of predictions for the image analyzed by DarkHelp::NN::predict().

Each DarkHelp::PredictionResult entry in the vector represents a different object in the image.

See also

DarkHelp::PredictionResult

DarkHelp::NN::prediction_results

DarkHelp::Config::sort_predictions

VColours

using DarkHelp::VColours = typedef std::vector<cv::Scalar>

Vector of colours to use by DarkHelp::NN::annotate().

See also

DarkHelp::Config::annotation_colours

DarkHelp::get_default_annotation_colours()

VFloat

using DarkHelp::VFloat = typedef std::vector<float>

Vector of float used with OpenCV.

VInt

using DarkHelp::VInt = typedef std::vector<int>

Vector of int used with OpenCV.

VRect

using DarkHelp::VRect = typedef std::vector<cv::Rect>

Vector of OpenCV rectangles used with OpenCV.

VRect2d

using DarkHelp::VRect2d = typedef std::vector<cv::Rect2d>

Similar to DarkHelp::VRect, but the rectangle uses double instead of int.

VStr

using DarkHelp::VStr = typedef std::vector<std::string>

Vector of text strings. Typically used to store the class names.

Enumeration Type Documentation

EDriver

enum DarkHelp::EDriver

strong

DarkHelp can utilise either libdarknet.so or OpenCV's DNN module to load the neural network and run inference.

OpenCV is much faster, but support for it is relatively new in DarkHelp and support for newer models like YOLOv4 requires very recent versions of OpenCV. The default is kDarknet.

See also

DarkHelp::NN::init()

If using kOpenCV or kOpenCVCPU you can customize the backend and target after DarkHelp::init() is called. For example:

DarkHelp::NN nn;
nn.init("rocks.cfg", "rocks.weights", "rocks.names", true, DarkHelp::EDriver::kOpenCV);
nn.opencv_net.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA);
nn.opencv_net.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA_FP16);

Since

October 2021

Enumerator
kInvalid
kMin
kDarknet	Use libdarknet.so.
kOpenCV	Use OpenCV's dnn module. Attempts to use CUDA, and will automatically revert to CPU if CUDA is not available.
kOpenCVCPU	Use OpenCV's dnn module, but skip CUDA and only use the CPU.
kMax

ESort

enum DarkHelp::ESort

strong

See also

DarkHelp::Config::sort_predictions

Enumerator
kUnsorted	Do not sort predictions.
kAscending	Sort predictions using DarkHelp::PredictionResult::best_probability in ascending order (low values first, high values last).
kDescending	Sort predictions using DarkHelp::PredictionResult::best_probability in descending order (high values first, low values last).
kPageOrder	Sort predictions based loosely on where they appear within the image. From top-to-bottom, and left-to-right.

Function Documentation

combine()

std::filesystem::path DarkHelp::combine	(	const std::string &	key,
		const std::filesystem::path &	cfg_filename,
		const std::filesystem::path &	names_filename,
		const std::filesystem::path &	weights_filename
	)

Combine together the 3 files that make up a neural network, and obfuscate them using the given key phrase.

Normally, this is done using the DarkHelpCombine command line tool.

Note

This is not encryption. It only performs obfuscation if the key is not empty. If the key is empty, then no obfuscation is performed but the 3 files are still bundled together.

Command-line example:

DarkHelpCombine "this is my passkey phrase" animals.cfg animals.names animals_best.weights

Once you have a .dh bundle file (the output of running DarkHelpCombine) you can load it into DarkHelp using either the DarkHelp::NN::NN() or DarkHelp::DHThreads::DHThreads() constructors that takes a bundle filename and the key phrase.

Since

2024-04-13

References verify_cfg_and_weights().

Referenced by main().

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duration_string()

std::string DarkHelp::duration_string

(

const std::chrono::high_resolution_clock::duration

duration

)

Format a duration as a text string which is typically added to images or video frames during annotation.

For example, this might return "912 microseconds" or "375 milliseconds".

See also

DarkHelp::NN::annotate()

Referenced by DarkHelp::NN::annotate(), and DarkHelp::NN::duration_string().

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edit_cfg_file()

size_t DarkHelp::edit_cfg_file	(	const std::string &	cfg_filename,
		DarkHelp::MStr	m
	)

This is used to insert lines into the [net] section of the configuration file.

Pass in a map of key-value pairs, and if the key exists it will be modified. If the key does not exist, then it will be added to the bottom of the [net] section.

For example, this is used by DarkHelp::NN::init() when DarkHelp::Config::modify_batch_and_subdivisions is enabled.

Returns

The number of lines that were modified or had to be inserted into the configuration file.

Exceptions

std::invalid_argument	if the cfg file does not exist or cannot be opened
std::runtime_error	if a valid start and end to the [net] section wasn't found in the .cfg file
std::runtime_error	if we cannot write a new .cfg file
std::runtime_error	if we cannot rename the .cfg file

Referenced by DarkHelp::DHThreads::init(), and DarkHelp::NN::init().

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extract()

void DarkHelp::extract	(	const std::string &	key,
		const std::filesystem::path &	bundle,
		std::filesystem::path &	cfg_filename,
		std::filesystem::path &	names_filename,
		std::filesystem::path &	weights_filename
	)

Extract the 3 files that make up a neural network.

Note

Usually, this does not need to be called directly. Instead, see the DarkHelp::NN::NN() constructor which takes the bundle filename and the key phrase.

See also

DarkHelp::combine()

Since

2024-04-13

Referenced by DarkHelp::DHThreads::init(), and DarkHelp::NN::init().

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fast_resize_ignore_aspect_ratio()

cv::Mat DarkHelp::fast_resize_ignore_aspect_ratio	(	const cv::Mat &	mat,
		const cv::Size &	desired_size
	)

Resize the given image as quickly as possible to the given dimensions.

This will sacrifice quality for speed. If OpenCV has been compiled with support for CUDA, then this will utilise the GPU to do the resizing.

Note

Timing tests on Jetson devices as well as full NVIDIA GPUs show this is not significantly different than the usual call to cv::resize(). Probably would be of bigger impact if the image resizing was done on a different thread, and then fed to DarkHelp for inference so the image resize and inference can happen in parallel.

See also

DarkHelp::Config::use_fast_image_resize

DarkHelp::slow_resize_ignore_aspect_ratio()

Referenced by DarkHelp::NN::predict_internal_darknet(), and DarkHelp::NN::predict_internal_opencv().

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fix_out_of_bound_normalized_rect()

void DarkHelp::fix_out_of_bound_normalized_rect	(	float &	cx,
		float &	cy,
		float &	w,
		float &	h
	)

Automatically called by DarkHelp::NN::predict_internal() when DarkHelp::Config::fix_out_of_bound_values has been set.

Referenced by DarkHelp::NN::predict_internal_darknet(), DarkHelp::NN::predict_internal_opencv(), and yolo_load_annotations().

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get_default_annotation_colours()

DarkHelp::VColours DarkHelp::get_default_annotation_colours

(

)

Obtain a vector of at least 25 different bright colours that may be used to annotate images.

OpenCV uses BGR, not RGB. For example:

• "{0, 0, 255}" is pure red
• "{255, 0, 0}" is pure blue

The colours returned by this function are intended to be used by OpenCV, and thus are in BGR format.

See also

DarkHelp::Config::annotation_colours

Default colours returned by this method are:

Index	RGB Hex	Name
0	FF355E	Radical Red
1	299617	Slimy Green
2	FFCC33	Sunglow
3	AF6E4D	Brown Sugar
4	FF00FF	Pure magenta
5	50BFE6	Blizzard Blue
6	CCFF00	Electric Lime
7	00FFFF	Pure cyan
8	8D4E85	Razzmic Berry
9	FF48CC	Purple Pizzazz
10	00FF00	Pure green
11	FFFF00	Pure yellow
12	5DADEC	Blue Jeans
13	FF6EFF	Shocking Pink
14	AAF0D1	Magic Mint
15	FFC000	Orange
16	9C51B6	Purple Plum
17	FF9933	Neon Carrot
18	66FF66	Screamin' Green
19	FF0000	Pure red
20	4B0082	Indigo
21	FF6037	Outrageous Orange
22	FFFF66	Laser Lemon
23	FD5B78	Wild Watermelon
24	0000FF	Pure blue

Referenced by DarkHelp::NN::annotate(), and DarkHelp::Config::reset().

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operator<<() [1/4]

std::ostream & DarkHelp::operator<<	(	std::ostream &	os,
		const DarkHelp::PositionTracker &	tracker
	)

Convenience function to stream the entire object tracker as text.

Mostly intended for debug or logging purposes.

Example:

auto results = nn.predict("traffic.jpg");
tracker.add(results);
std::cout << tracker << std::endl;

References DarkHelp::PositionTracker::most_recent_frame_id, DarkHelp::PositionTracker::most_recent_object_id, and DarkHelp::PositionTracker::objects.

operator<<() [2/4]

std::ostream & DarkHelp::operator<<	(	std::ostream &	os,
		const DarkHelp::PositionTracker::Obj &	obj
	)

Convenience function to stream a single tracked object as a line of text.

Mostly intended for debug or logging purposes.

References DarkHelp::PositionTracker::Obj::center(), DarkHelp::PositionTracker::Obj::fids_and_rects, DarkHelp::PositionTracker::Obj::first_seen_frame_id(), DarkHelp::PositionTracker::Obj::last_seen_frame_id(), DarkHelp::PositionTracker::Obj::oid, and DarkHelp::PositionTracker::Obj::size().

Referenced by operator<<().

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operator<<() [3/4]

std::ostream & DarkHelp::operator<<	(	std::ostream &	os,
		const PredictionResult &	pred
	)

Convenience function to stream a single result as a "readable" line of text.

Mostly intended for debug or logging purposes.

References DarkHelp::PredictionResult::all_probabilities, DarkHelp::PredictionResult::best_class, DarkHelp::PredictionResult::best_probability, DarkHelp::PredictionResult::name, DarkHelp::PredictionResult::object_id, DarkHelp::PredictionResult::rect, and DarkHelp::PredictionResult::tile.

operator<<() [4/4]

std::ostream & DarkHelp::operator<<	(	std::ostream &	os,
		const PredictionResults &	results
	)

Convenience function to stream an entire vector of results as readable text.

Mostly intended for debug or logging purposes.

For example:

DarkHelp darkhelp("mynetwork.cfg", "mynetwork.weights", "mynetwork.names");
const auto results = darkhelp.predict("test_image_01.jpg");
std::cout << results << std::endl;

This would generate text similar to this:

prediction results: 12
-> 1/12: "Barcode 94%" #43 prob=0.939646 x=430 y=646 w=173 h=17 entries=1
-> 2/12: "Tag 100%" #40 prob=0.999954 x=366 y=320 w=281 h=375 entries=1
-> 3/12: "G 85%, 2 12%" #19 prob=0.846418 x=509 y=600 w=28 h=37 entries=2 [ 2=0.122151 19=0.846418 ]
...

Where:

• "1/12" is the number of predictions found.
• "Barcode 94%" is the class name and the probability if DarkHelp::Config::names_include_percentage is enabled.
• "#43" is the zero-based class index.
• "prob=0.939646" is the probabilty that it is class #43. (Multiply by 100 to get percentage.)
• "x=..." are the X, Y, width, and height of the rectangle that was identified.
• "entries=1" means that only 1 class was matched. If there is more than 1 possible class, then the class index and probability for each class will be shown.

References operator<<().

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pixelate_rectangle()

void DarkHelp::pixelate_rectangle	(	const cv::Mat &	src,
		cv::Mat &	dst,
		const cv::Rect &	r,
		const int	size = 15
	)

Pixelate the given rectangle.

This will copy the src image to dst prior to pixelating if the two images are not the same size.

The size determines the width and height of the cells that will be used to pixelate the rectangle. If size is less than 5, no pixelation will take place.

Setting	Image
annotation_pixelate_enabled=false
annotation_pixelate_enabled=true annotation_pixelate_size=5
annotation_pixelate_enabled=true annotation_pixelate_size=15
annotation_pixelate_enabled=true annotation_pixelate_size=25

See also

DarkHelp::Config::annotation_pixelate_enabled

DarkHelp::Config::annotation_pixelate_size

Since

2022-07-04

Referenced by pixelate_rectangles().

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pixelate_rectangles() [1/3]

void DarkHelp::pixelate_rectangles	(	const cv::Mat &	src,
		cv::Mat &	dst,
		const PredictionResults &	prediction_results,
		const int	size = 15
	)

Pixelate all of the predictions.

See also

DarkHelp::pixelate_rectangle()

DarkHelp::Config::annotation_pixelate_enabled

DarkHelp::Config::annotation_pixelate_size

Since

2022-07-04

References pixelate_rectangle().

Referenced by DarkHelp::NN::annotate().

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pixelate_rectangles() [2/3]

void DarkHelp::pixelate_rectangles	(	const cv::Mat &	src,
		cv::Mat &	dst,
		const PredictionResults &	prediction_results,
		const std::set< int > &	class_filter,
		const int	size = 15
	)

Pixelate only the predictions where the class ID matches a value in the class filter.

If the class filter is empty then this will pixelate all predictions.

See also

DarkHelp::pixelate_rectangle()

DarkHelp::Config::annotation_pixelate_enabled

DarkHelp::Config::annotation_pixelate_size

DarkHelp::Config::annotation_pixelate_classes

Since

2022-07-04

References pixelate_rectangle().

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pixelate_rectangles() [3/3]

void DarkHelp::pixelate_rectangles	(	const cv::Mat &	src,
		cv::Mat &	dst,
		const VRect &	rects,
		const int	size = 15
	)

Pixelate all of the rectangles.

See also

DarkHelp::pixelate_rectangle()

DarkHelp::Config::annotation_pixelate_enabled

DarkHelp::Config::annotation_pixelate_size

Since

2022-07-04

References pixelate_rectangle().

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resize_keeping_aspect_ratio()

cv::Mat DarkHelp::resize_keeping_aspect_ratio	(	cv::Mat	mat,
		const cv::Size &	desired_size
	)

Convenience function to resize an image yet retain the exact original aspect ratio.

Performs no resizing if the image is already the desired size. Depending on the size of the original image and the desired size, a "best" size will be chosen that does not exceed the specified size. No letterboxing will be performed.

For example, if the image is 640x480, and the specified size is 400x400, the image returned will be 400x300 which maintains the original 1.333 aspect ratio.

References slow_resize_ignore_aspect_ratio().

Referenced by process_image(), and process_video().

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slow_resize_ignore_aspect_ratio()

cv::Mat DarkHelp::slow_resize_ignore_aspect_ratio	(	const cv::Mat &	mat,
		const cv::Size &	desired_size
	)

Similar to DarkHelp::fast_resize_ignore_aspect_ratio() but uses OpenCV algorithms that result in better quality images at a cost of slower speed.

See also

DarkHelp::Config::use_fast_image_resize

DarkHelp::resize_keeping_aspect_ratio()

DarkHelp::fast_resize_ignore_aspect_ratio()

Since

2023-07-08

Referenced by DarkHelp::NN::predict_internal_darknet(), DarkHelp::NN::predict_internal_opencv(), and resize_keeping_aspect_ratio().

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toggle_output_redirection()

void DarkHelp::toggle_output_redirection

(

)

Toggle STDOUT and STDERR output redirection.

The first time this is called, both STDOUT and STDERR will be redirected to /dev/null (on Linux) or NUL: (on Windows). Then when called again, both STDOUT and STDERR should be restored to their original location. This is used to temporarily redirect the flood of output from Darknet while it loads the neural network. This may be called multiple times as necessary to toggle the state of redirection.

See also

DarkHelp::Config::redirect_darknet_output

Since

2022-08-30

Referenced by DarkHelp::NN::init(), and ToggleOutputRedirection().

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verify_cfg_and_weights()

DarkHelp::MStr DarkHelp::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 the .cfg, .weights, and .names are assigned where they should be.

This is necessary because darknet tends to segfault if it is given the wrong filename. (For example, if it mistakenly tries to parse the .weights file as a .cfg file.) This function does a bit of sanity checking, determines which file is which, and also returns a map of debug information related to each file.

On input, it doesn't matter which file goes into which parameter. Simply pass in the filenames in any order.

On output, the .cfg, .weights, and .names will be set correctly. If needed for display purposes, some additional information is also passed back using the MStr string map, but most callers should ignore this output.

See also

DarkHelp::NN::init()

Exceptions

std::invalid_argument	if at least 2 unique filenames have not been provided
std::runtime_error	if the size of the files cannot be determined (one or more file does not exist?)
std::invalid_argument	if the cfg file doesn't exist
std::invalid_argument	if the cfg file doesn't contain "[net]" near the top of the file
std::invalid_argument	if the configuration file does not have a line that says "classes=..."
std::invalid_argument	if the weights file doesn't exist
std::invalid_argument	if weights file has an invalid version number (or weights file is from an extremely old version of darknet?)
std::invalid_argument	if there is a blank line in the .names file.
std::runtime_error	if the number of lines in the names file doesn't match the number of classes in the configuration file

Referenced by combine(), DarkHelp::Config::Config(), DarkHelp::DHThreads::init(), DarkHelp::NN::init(), and init().

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version()

std::string DarkHelp::version

(

)

Get a version string for the DarkHelp library. E.g., could be 1.0.0-123.

Referenced by configure(), and DarkHelpVersion().

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yolo_annotations_file_exists()

bool DarkHelp::yolo_annotations_file_exists

(

const std::string &

image_filename

)

Check to see if the given image has a corresponding .txt file for YOLO annotations.

This does not check the contents of the file, it only checks to see if the file exists. The annotation file is determined by calling yolo_annotations_filename().

References yolo_annotations_filename().

Referenced by yolo_load_annotations().

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yolo_annotations_filename()

std::string DarkHelp::yolo_annotations_filename

(

const std::string &

image_filename

)

Given an image filename, get the corresponding filename where the YOLO annotations should be saved.

This will be the same as the image filename but with a .txt file extension. If the filename provided already ends in .txt, then the original filename will be returned.

Referenced by yolo_annotations_file_exists(), yolo_load_annotations(), and yolo_save_annotations().

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yolo_load_annotations()

DarkHelp::PredictionResults DarkHelp::yolo_load_annotations	(	const cv::Size &	image_size,
		const std::string &	filename
	)

Load the YOLO annotations from file.

Parameters

[in]	image_size	Since YOLO annotations are normalized, the image dimensions must be provided for the cv::Rect object to be populated with the correct coordinates.
[out]	filename	Can be either the image filename, or the annotations filename. This is then used in a call to yolo_annotations_filename() to find the actual annotations filename.

Note

Some simple input validation is automatically performed on the annotations by using fix_out_of_bound_normalized_rect().

Each line of a YOLO-format annotation is composed of 5 space-delimited fields:

• the zero-based class id
• the normalized center X coordinate
• the normalized center Y coordinate
• the normalized width
• the normalized height

See also

https://www.ccoderun.ca/programming/darknet_faq/#darknet_annotations

Exceptions

std::invalid_argument	if the annotation file does not exist
std::invalid_argument	if the image dimensions appear to be invalid (both width and height should be greater than zero)

References DarkHelp::PredictionResult::all_probabilities, DarkHelp::PredictionResult::best_class, DarkHelp::PredictionResult::best_probability, fix_out_of_bound_normalized_rect(), DarkHelp::PredictionResult::name, DarkHelp::PredictionResult::original_point, DarkHelp::PredictionResult::original_size, DarkHelp::PredictionResult::rect, DarkHelp::PredictionResult::tile, yolo_annotations_file_exists(), and yolo_annotations_filename().

Referenced by yolo_load_image_and_annotations().

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yolo_load_image_and_annotations()

cv::Mat DarkHelp::yolo_load_image_and_annotations	(	const std::string &	image_filename,
		DarkHelp::PredictionResults &	annotations
	)

Load the given image and read in the corresponding YOLO annotations from the .txt file.

Both the image and the .txt file must exist.

Each line of a YOLO-format annotation is composed of 5 space-delimited fields:

• the zero-based class id
• the normalized center X coordinate
• the normalized center Y coordinate
• the normalized width
• the normalized height

See also

https://www.ccoderun.ca/programming/darknet_faq/#darknet_annotations

Exceptions

std::invalid_argument

if the image cannot be read (not an image file, or invalid filename?)

References yolo_load_annotations().

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yolo_save_annotations()

std::string DarkHelp::yolo_save_annotations	(	const std::string &	filename,
		const PredictionResults &	annotations
	)

Save the given annotations to the .txt file.

The filename can be either the image or the .txt file, and will be used to call yolo_annotations_filename().

Each line of a YOLO-format annotation is composed of 5 space-delimited fields, and is intended to be used by Darknet or Darknet-compatible software.

See also

https://www.ccoderun.ca/programming/darknet_faq/#darknet_annotations

Exceptions

std::invalid_argument

if the annotation file fails to open

References yolo_annotations_filename().

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