HaarObjectDetector Class

public class HaarObjectDetector : IObjectDetector

Public Class HaarObjectDetector
	Implements IObjectDetector

// In order to use a HaarObjectDetector, first we have to tell it
// which type of objects we would like to detect. And in a Haar detector,
// different object classifiers are specified in terms of a HaarCascade.

// The framework comes with some built-in cascades for common body
// parts, such as Face and Nose. However, it is also possible to
// load a cascade from cascade XML definitions in OpenCV 2.0 format.

// In this example, we will be creating a cascade for a Face detector:
var cascade = new Accord.Vision.Detection.Cascades.FaceHaarCascade();

// Note: In the case we would like to load it from XML, we could use:
// var cascade = HaarCascade.FromXml("filename.xml");

// Now, create a new Haar object detector with the cascade:
var detector = new HaarObjectDetector(cascade, minSize: 50, 
    searchMode: ObjectDetectorSearchMode.NoOverlap);

// Note that we have specified that we do not want overlapping objects,
// and that the minimum object an object can have is 50 pixels. Now, we
// can use the detector to classify a new image. For instance, consider
// the famous Lena picture:

Bitmap bmp = Accord.Imaging.Image.Clone(Resources.lena_color);

// We have to call ProcessFrame to detect all rectangles containing the 
// object we are interested in (which in this case, is the face of Lena):
Rectangle[] rectangles = detector.ProcessFrame(bmp);

// The answer will be a single rectangle of dimensions
// 
//   {X = 126 Y = 112 Width = 59 Height = 59}
// 
// which indeed contains the only face in the picture.

// Let's test the detector using a sample video from 
// the collection of test videos in the framework:
TestVideos ds = new TestVideos(basePath);
string fileName = ds["crowd.mp4"];

// In this example, we will be creating a cascade for a Face detector:
var cascade = new Accord.Vision.Detection.Cascades.FaceHaarCascade();

// Now, create a new Haar object detector with the cascade:
var detector = new HaarObjectDetector(cascade, minSize: 25,
    searchMode: ObjectDetectorSearchMode.Average,
    scalingMode: ObjectDetectorScalingMode.SmallerToGreater,
    scaleFactor: 1.1f)
{
    Suppression = 5 // This should make sure we only report regions as faces if 
    // they have been detected at least 5 times within different cascade scales.
};

// Now, let's open the video using FFMPEG:
var video = new VideoFileReader();
video.Open(fileName);

// And then check the contents of one of the frames:
Bitmap frame = video.ReadVideoFrame(frameIndex: 0);

// Creating bitmaps and locking them is an expensive 
// operation. Instead, let's allocate once and reuse
BitmapData bitmapData = frame.LockBits(ImageLockMode.ReadWrite);
UnmanagedImage unmanagedImage = new UnmanagedImage(bitmapData);

// We will create a color marker to show the faces 
var objectMarker = new RectanglesMarker(Color.Red);

// This example is going to show two different ways to save results to disk. The 
// first is to save the results frame-by-frame, saving each individual frame as 
// a separate .png file. The second is to save them as a video in .mp4 format.

// To save results as a movie clip in mp4 format, you can use:
VideoFileWriter writer = new VideoFileWriter();
writer.Open(Path.Combine(basePath, "detected_faces.mp4"), frame.Width, frame.Height);

// Now, for each frame of the video
for (int frameIndex = 0; frameIndex < video.FrameCount; frameIndex++)
{
    // Read the current frame into the bitmap data
    video.ReadVideoFrame(frameIndex, bitmapData);

    // Feed the frame to the tracker
    Rectangle[] faces = detector.ProcessFrame(unmanagedImage);

    // Mark the location of the tracker object in red color
    objectMarker.Rectangles = faces;
    objectMarker.ApplyInPlace(unmanagedImage); // overwrite the frame

    // Save it to disk: first saving each frame separately:
    frame.Save(Path.Combine(basePath, "frame_{0}.png".Format(frameIndex)));

    // And then, saving as a .mp4 file:
    writer.WriteVideoFrame(bitmapData);
}

// The generated video can be seen at https://1drv.ms/v/s!AoiTwBxoR4OAoLJhPozzixD25XcbiQ
video.Close();
writer.Close();