ofxDarknet

ofxDarknet is a openFrameworks wrapper for darknet.

Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation. http://pjreddie.com/darknet/

Features

YOLO: Real-Time Object Detection aka Dense Captioning (http://pjreddie.com/darknet/yolo/)

Darknet comes with two pre-trained models for this task. Additionally each has a smaller (and faster) but therefore less accurate version:

MS COCO dataset (80 different classes)

yolo.cfg & yolo.weights (256 MB COCO-model)
tiny-yolo.cfg & tiny-yolo.weights (60 MB COCO-model)

	std::string datacfg = ofToDataPath( "cfg/coco.data" );
	std::string cfgfile = ofToDataPath( "cfg/tiny-yolo.cfg" );
	std::string weightfile = ofToDataPath( "tiny-yolo.weights" );
	std::string nameslist = ofToDataPath( "cfg/names.list" );
	darknet.init( cfgfile, weightfile, datacfg, nameslist );

Pascal VOC dataset (20 different classes)

yolo-voc.cfg & yolo-voc.weights (256 MB VOC-model)
tiny-yolo-voc.cfg & tiny-yolo-voc.weights (60 MB VOC-model)

	std::string datacfg = ofToDataPath( "cfg/voc.data" );
	std::string cfgfile = ofToDataPath( "cfg/tiny-yolo-voc.cfg" );
	std::string weightfile = ofToDataPath( "tiny-yolo-voc.weights" );
	std::string nameslist = ofToDataPath( "cfg/voc.names" );
	darknet.init( cfgfile, weightfile, datacfg, nameslist );

	float thresh = 0.25;
	std::vector< detected_object > detections = darknet.yolo( image.getPixelsRef(), thresh );

	for( detected_object d : detections )
	{
		ofSetColor( d.color );
		glLineWidth( ofMap( d.probability, 0, 1, 0, 8 ) );
		ofNoFill();
		ofDrawRectangle( d.rect );
		ofDrawBitmapStringHighlight( d.label + ": " + ofToString(d.probability), d.rect.x, d.rect.y + 20 );
	}

Imagenet Classification (http://pjreddie.com/darknet/imagenet/)

In order to classify an image with more classes, this is the spot. This classifies an image according to the 1000-class ImageNet Challenge.

AlexNet cfg: alexnet.cfg weights: alexnet.weights
Darknet Reference cfg: darknet.cfg weights: darknet.cfg
VGG-16 cfg: vgg-16.cfg weights: vgg-16.weights
Extraction cfg: extraction.cfg weights: extraction.weights
Darknet19 cfg: darknet16.cfg weights: darknet19.weights
Darknet19 448x448 cfg: darknet19_488.cfg weights: darknet19_488.weights

	std::string datacfg = ofToDataPath( "cfg/imagenet1k.data" );
	std::string cfgfile = ofToDataPath( "cfg/darknet.cfg" );
	std::string weightfile = ofToDataPath( "darknet.weights" );
	std::string nameslist = ofToDataPath( "cfg/imagenet.shortnames.list" );
	darknet.init( cfgfile, weightfile, datacfg, nameslist );

	classifications = darknet.classify( image.getPixelsRef() );
	int offset = 20;
	for( classification c : classifications )
	{
		std::stringstream ss;
		ss << c.label << " : " << ofToString( c.probability );
		ofDrawBitmapStringHighlight( ss.str(), 20, offset );
		offset += 20;
	}

Deep Dream (http://pjreddie.com/darknet/nightmare/)

vgg-conv.cfg & vgg-conv.weights

	std::string cfgfile = ofToDataPath( "cfg/vgg-conv.cfg" );
	std::string weightfile = ofToDataPath( "vgg-conv.weights" );
	darknet.init( cfgfile, weightfile );
	
	int max_layer = 13;
	int range = 3;
	int norm = 1;
	int rounds = 4;
	int iters = 20;
	int octaves = 4;
	float rate = 0.01;
	float thresh = 1.0;
	nightmare = darknet.nightmate( image.getPixelsRef(), max_layer, range, norm, rounds, iters, octaves, rate, thresh );

Recurrent Neural Network (http://pjreddie.com/darknet/rnns-in-darknet/)

Darknet pre-trained weights files:

ofxDarknet custom pre-trained weight files (each trained for 20h on NVidia TitanX):

Anonymous - Hypersphere Hypersphere, written by Anonymous with the help of the 4chan board /lit/ (of The Legacy of Totalitarianism in a Tundra fame) is an epic tale spanning over 700 pages. A postmodern collaborative writing effort containing Slavoj Žižek erotica, top secret Donald Trump emails, poetry, repair instructions for future cars, a history of bottles in the Ottoman empire; actually, it contains everything since it takes place in the Hypersphere, and the Hypersphere is a big place; really big in fact.
Books on art history & aesthetics
Books on digital culture

	std::string cfgfile = ofToDataPath( "cfg/rnn.cfg" );
	std::string weightfile = ofToDataPath( "shakespeare.weights" );
	darknet.init( cfgfile, weightfile );

	int character_count = 100;
	float temperature = 0.8;
	std::string seed_text = "openframeworks is ";
	std::string generated_text = darknet.rnn( character_count, seed_text, temperature );

You can train your own RNN models with darknet

	// no need to init
	darknet.train_rnn( ofToDataPath( "training_text.txt" ), "cfg/rnn.cfg" );

Setup

Windows

Install the dependencies for building darknet on Windows 10:

There are some more necessary steps that don't work with the OF project generator:

Compile as Debug or Release in x64 mode
Within VS2015 Solution Explorer, rightclick on the generated project -> Build Dependencies -> Build Customizations -> Tick CUDA 8.0
C/C++ -> All Options -> Compile As -> Default
Copy pthreadVC2.dll from ofxDarknet\libs\3rdparty\dll\x64 to your applications bin folder

OSX

An OSX version is on the way and will be updated here..

Training your own models

YOLO

tcb

Credits

Original Code: https://github.com/pjreddie/darknet
Help to compile on Windows: https://github.com/AlexeyAB/darknet/
Help to call from C++: https://github.com/prabindh/darknet

Awsomes/ofxDarknet