public ObjectDetectorTest()
{
var path = this.GetDataFile("face_detector.svm");
this._Scanner = new ScanFHogPyramid <PyramidDown, DefaultFHogFeatureExtractor>(6);
this._ObjectDetector = new ObjectDetector <ScanFHogPyramid <PyramidDown, DefaultFHogFeatureExtractor> >(this._Scanner);
this._ObjectDetector.Deserialize(path.FullName);
}
/// <summary>
/// Initializes a new instance of the <see cref="SimpleFaceDetector"/> class with the model file path that this detector uses.
/// </summary>
/// <param name="modelPath">The model file path that this detector uses.</param>
/// <exception cref="FileNotFoundException">The model file is not found.</exception>
public SimpleFaceDetector(string modelPath)
{
if (!File.Exists(modelPath))
{
throw new FileNotFoundException(modelPath);
}
this._Scanner = new ScanFHogPyramid <PyramidDown, DefaultFHogFeatureExtractor>(6);
this._ObjectDetector = new ObjectDetector <ScanFHogPyramid <PyramidDown, DefaultFHogFeatureExtractor> >(this._Scanner);
this._ObjectDetector.Deserialize(modelPath);
}
private static void Main(string[] args)
{
try
{
// In this example we are going to train a face detector based on the
// small faces dataset in the examples/faces directory. So the first
// thing we do is load that dataset. This means you need to supply the
// path to this faces folder as a command line argument so we will know
// where it is.
if (args.Length != 1)
{
Console.WriteLine("Give the path to the examples/faces directory as the argument to this");
Console.WriteLine("program. For example, if you are in the examples folder then execute ");
Console.WriteLine("this program by running: ");
Console.WriteLine(" ./fhog_object_detector_ex faces");
Console.WriteLine();
return;
}
var facesDirectory = args[0];
// The faces directory contains a training dataset and a separate
// testing dataset. The training data consists of 4 images, each
// annotated with rectangles that bound each human face. The idea is
// to use this training data to learn to identify human faces in new
// images.
//
// Once you have trained an object detector it is always important to
// test it on data it wasn't trained on. Therefore, we will also load
// a separate testing set of 5 images. Once we have a face detector
// created from the training data we will see how well it works by
// running it on the testing images.
//
// So here we create the variables that will hold our dataset.
// images_train will hold the 4 training images and face_boxes_train
// holds the locations of the faces in the training images. So for
// example, the image images_train[0] has the faces given by the
// rectangles in face_boxes_train[0].
IList <Matrix <byte> > tmpImagesTrain;
IList <Matrix <byte> > tmpImagesTest;
IList <IList <Rectangle> > tmpFaceBoxesTrain;
IList <IList <Rectangle> > tmpFaceBoxesTest;
// Now we load the data. These XML files list the images in each
// dataset and also contain the positions of the face boxes. Obviously
// you can use any kind of input format you like so long as you store
// the data into images_train and face_boxes_train. But for convenience
// dlib comes with tools for creating and loading XML image dataset
// files. Here you see how to load the data. To create the XML files
// you can use the imglab tool which can be found in the tools/imglab
// folder. It is a simple graphical tool for labeling objects in images
// with boxes. To see how to use it read the tools/imglab/README.txt
// file.
Dlib.LoadImageDataset(Path.Combine(facesDirectory, "training.xml"), out tmpImagesTrain, out tmpFaceBoxesTrain);
Dlib.LoadImageDataset(Path.Combine(facesDirectory, "testing.xml"), out tmpImagesTest, out tmpFaceBoxesTest);
// Now we do a little bit of pre-processing. This is optional but for
// this training data it improves the results. The first thing we do is
// increase the size of the images by a factor of two. We do this
// because it will allow us to detect smaller faces than otherwise would
// be practical (since the faces are all now twice as big). Note that,
// in addition to resizing the images, these functions also make the
// appropriate adjustments to the face boxes so that they still fall on
// top of the faces after the images are resized.
var imageTrain = new List <Matrix <byte> >(tmpImagesTrain);
var faceBoxesTrain = new List <IList <Rectangle> >(tmpFaceBoxesTrain);
Dlib.UpsampleImageDataset(2, imageTrain, faceBoxesTrain);
var imageTest = new List <Matrix <byte> >(tmpImagesTest);
var faceBoxesTest = new List <IList <Rectangle> >(tmpFaceBoxesTest);
Dlib.UpsampleImageDataset(2, imageTest, faceBoxesTest);
// Since human faces are generally left-right symmetric we can increase
// our training dataset by adding mirrored versions of each image back
// into images_train. So this next step doubles the size of our
// training dataset. Again, this is obviously optional but is useful in
// many object detection tasks.
Dlib.AddImageLeftRightFlips(imageTrain, faceBoxesTrain);
Console.WriteLine($"num training images: {imageTrain.Count()}");
Console.WriteLine($"num testing images: {imageTest.Count()}");
// Finally we get to the training code. dlib contains a number of
// object detectors. This typedef tells it that you want to use the one
// based on Felzenszwalb's version of the Histogram of Oriented
// Gradients (commonly called HOG) detector. The 6 means that you want
// it to use an image pyramid that downsamples the image at a ratio of
// 5/6. Recall that HOG detectors work by creating an image pyramid and
// then running the detector over each pyramid level in a sliding window
// fashion.
using (var scanner = new ScanFHogPyramid <PyramidDown, DefaultFHogFeatureExtractor>(6))
{
// The sliding window detector will be 80 pixels wide and 80 pixels tall.
scanner.SetDetectionWindowSize(80, 80);
using (var trainer = new StructuralObjectDetectionTrainer <ScanFHogPyramid <PyramidDown, DefaultFHogFeatureExtractor> >(scanner))
{
// Set this to the number of processing cores on your machine.
trainer.SetNumThreads(4);
// The trainer is a kind of support vector machine and therefore has the usual SVM
// C parameter. In general, a bigger C encourages it to fit the training data
// better but might lead to overfitting. You must find the best C value
// empirically by checking how well the trained detector works on a test set of
// images you haven't trained on. Don't just leave the value set at 1. Try a few
// different C values and see what works best for your data.
trainer.SetC(1);
// We can tell the trainer to print it's progress to the console if we want.
trainer.BeVerbose();
// The trainer will run until the "risk gap" is less than 0.01. Smaller values
// make the trainer solve the SVM optimization problem more accurately but will
// take longer to train. For most problems a value in the range of 0.1 to 0.01 is
// plenty accurate. Also, when in verbose mode the risk gap is printed on each
// iteration so you can see how close it is to finishing the training.
trainer.SetEpsilon(0.01);
// Now we run the trainer. For this example, it should take on the order of 10
// seconds to train.
var detector = trainer.Train(imageTrain, faceBoxesTrain);
// Now that we have a face detector we can test it. The first statement tests it
// on the training data. It will print the precision, recall, and then average precision.
using (var matrix = Dlib.TestObjectDetectionFunction(detector, imageTrain, faceBoxesTrain))
Console.WriteLine($"training results: {matrix}");
// However, to get an idea if it really worked without overfitting we need to run
// it on images it wasn't trained on. The next line does this. Happily, we see
// that the object detector works perfectly on the testing images.
using (var matrix = Dlib.TestObjectDetectionFunction(detector, imageTest, faceBoxesTest))
Console.WriteLine($"testing results: {matrix}");
// If you have read any papers that use HOG you have probably seen the nice looking
// "sticks" visualization of a learned HOG detector. This next line creates a
// window with such a visualization of our detector. It should look somewhat like
// a face.
using (var fhog = Dlib.DrawFHog(detector))
using (var hogwin = new ImageWindow(fhog, "Learned fHOG detector"))
{
// Now for the really fun part. Let's display the testing images on the screen and
// show the output of the face detector overlaid on each image. You will see that
// it finds all the faces without false alarming on any non-faces.
using (var win = new ImageWindow())
for (var i = 0; i < imageTest.Count; ++i)
{
// Run the detector and get the face detections.
var dets = detector.Operator(imageTest[i]);
win.ClearOverlay();
win.SetImage(imageTest[i]);
win.AddOverlay(dets, new RgbPixel(255, 0, 0));
Console.WriteLine("Hit enter to process the next image...");
Console.ReadKey();
Console.WriteLine("");
}
}
// Like everything in dlib, you can save your detector to disk using the
// serialize() function.
detector.Serialize("face_detector.svm");
// Then you can recall it using the deserialize() function.
using (var tmp = new ScanFHogPyramid <PyramidDown, DefaultFHogFeatureExtractor>(6))
using (var detector2 = new ObjectDetector <ScanFHogPyramid <PyramidDown, DefaultFHogFeatureExtractor> >(tmp))
detector2.Deserialize("face_detector.svm");
// Now let's talk about some optional features of this training tool as well as some
// important points you should understand.
//
// The first thing that should be pointed out is that, since this is a sliding
// window classifier, it can't output an arbitrary rectangle as a detection. In
// this example our sliding window is 80 by 80 pixels and is run over an image
// pyramid. This means that it can only output detections that are at least 80 by
// 80 pixels in size (recall that this is why we upsampled the images after loading
// them). It also means that the aspect ratio of the outputs is 1. So if,
// for example, you had a box in your training data that was 200 pixels by 10
// pixels then it would simply be impossible for the detector to learn to detect
// it. Similarly, if you had a really small box it would be unable to learn to
// detect it.
//
// So the training code performs an input validation check on the training data and
// will throw an exception if it detects any boxes that are impossible to detect
// given your setting of scanning window size and image pyramid resolution. You
// can use a statement like:
// remove_unobtainable_rectangles(trainer, images_train, face_boxes_train)
// to automatically discard these impossible boxes from your training dataset
// before running the trainer. This will avoid getting the "impossible box"
// exception. However, I would recommend you be careful that you are not throwing
// away truth boxes you really care about. The remove_unobtainable_rectangles()
// will return the set of removed rectangles so you can visually inspect them and
// make sure you are OK that they are being removed.
//
// Next, note that any location in the images not marked with a truth box is
// implicitly treated as a negative example. This means that when creating
// training data it is critical that you label all the objects you want to detect.
// So for example, if you are making a face detector then you must mark all the
// faces in each image. However, sometimes there are objects in images you are
// unsure about or simply don't care if the detector identifies or not. For these
// objects you can pass in a set of "ignore boxes" as a third argument to the
// trainer.train() function. The trainer will simply disregard any detections that
// happen to hit these boxes.
//
// Another useful thing you can do is evaluate multiple HOG detectors together. The
// benefit of this is increased testing speed since it avoids recomputing the HOG
// features for each run of the detector. You do this by storing your detectors
// into a std::vector and then invoking evaluate_detectors() like so:
var myDetectors = new List <ObjectDetector <ScanFHogPyramid <PyramidDown, DefaultFHogFeatureExtractor> > >();
myDetectors.Add(detector);
var dect2 = Dlib.EvaluateDetectors(myDetectors, imageTrain[0]);
//
//
// Finally, you can add a nuclear norm regularizer to the SVM trainer. Doing has
// two benefits. First, it can cause the learned HOG detector to be composed of
// separable filters and therefore makes it execute faster when detecting objects.
// It can also help with generalization since it tends to make the learned HOG
// filters smoother. To enable this option you call the following function before
// you create the trainer object:
// scanner.set_nuclear_norm_regularization_strength(1.0);
// The argument determines how important it is to have a small nuclear norm. A
// bigger regularization strength means it is more important. The smaller the
// nuclear norm the smoother and faster the learned HOG filters will be, but if the
// regularization strength value is too large then the SVM will not fit the data
// well. This is analogous to giving a C value that is too small.
//
// You can see how many separable filters are inside your detector like so:
Console.WriteLine($"num filters: {Dlib.NumSeparableFilters(detector)}");
// You can also control how many filters there are by explicitly thresholding the
// singular values of the filters like this:
using (var newDetector = Dlib.ThresholdFilterSingularValues(detector, 0.1))
{
}
// That removes filter components with singular values less than 0.1. The bigger
// this number the fewer separable filters you will have and the faster the
// detector will run. However, a large enough threshold will hurt detection
// accuracy.
}
}
}
catch (Exception e)
{
Console.WriteLine(e);
}
}
