OpenCVSharp #18

public class ImageInfo
{    public Mat Image { set; get; }    public int ImageGroupId { set; get; }    public int ImageId { set; get; }
}

public IList<ImageInfo> ReadTrainingImages(string path, string ext)
{    var images = new List<ImageInfo>();    var imageId = 1;    foreach (var dir in new DirectoryInfo(path).GetDirectories())    {        var groupId = int.Parse(dir.Name);        foreach (var imageFile in dir.GetFiles(ext))        {            var image = processTrainingImage(new Mat(imageFile.FullName, LoadMode.GrayScale));            if (image == null)            {                continue;            }            images.Add(new ImageInfo            {                Image = image,                ImageId = imageId++,                ImageGroupId = groupId            });        }    }    return images;
}

private static Mat processTrainingImage(Mat gray)
{    var threshImage = new Mat();    Cv2.Threshold(gray, threshImage, Thresh, ThresholdMaxVal, ThresholdType.BinaryInv); // Threshold to find contour    Point[][] contours;    HiearchyIndex[] hierarchyIndexes;    Cv2.FindContours(        threshImage,        out contours,        out hierarchyIndexes,        mode: ContourRetrieval.CComp,        method: ContourChain.ApproxSimple);    if (contours.Length == 0)    {        return null;    }    Mat result = null;    var contourIndex = 0;    while ((contourIndex >= 0))    {        var contour = contours[contourIndex];        var boundingRect = Cv2.BoundingRect(contour); //Find bounding rect for each contour        var roi = new Mat(threshImage, boundingRect); //Crop the image        //Cv2.ImShow("src", gray);        //Cv2.ImShow("roi", roi);        //Cv.WaitKey(0);        var resizedImage = new Mat();        var resizedImageFloat = new Mat();        Cv2.Resize(roi, resizedImage, new Size(10, 10)); //resize to 10X10        resizedImage.ConvertTo(resizedImageFloat, MatType.CV_32FC1); //convert to float        result = resizedImageFloat.Reshape(1, 1);        contourIndex = hierarchyIndexes[contourIndex].Next;    }    return result;
}

public CvKNearest TrainData(IList<ImageInfo> trainingImages)
{    var samples = new Mat();    foreach (var trainingImage in trainingImages)    {        samples.PushBack(trainingImage.Image);    }    var labels = trainingImages.Select(x => x.ImageGroupId).ToArray();    var responses = new Mat(labels.Length, 1, MatType.CV_32SC1, labels);    var tmp = responses.Reshape(1, 1); //make continuous    var responseFloat = new Mat();    tmp.ConvertTo(responseFloat, MatType.CV_32FC1); // Convert  to float    var kNearest = new CvKNearest();    kNearest.Train(samples, responseFloat); // Train with sample and responses    return kNearest;
}

public void DoOCR(CvKNearest kNearest, string path)
{    var src = Cv2.ImRead(path);    Cv2.ImShow("Source", src);    var gray = new Mat();    Cv2.CvtColor(src, gray, ColorConversion.BgrToGray);    var threshImage = new Mat();    Cv2.Threshold(gray, threshImage, Thresh, ThresholdMaxVal, ThresholdType.BinaryInv); // Threshold to find contour    Point[][] contours;    HiearchyIndex[] hierarchyIndexes;    Cv2.FindContours(        threshImage,        out contours,        out hierarchyIndexes,        mode: ContourRetrieval.CComp,        method: ContourChain.ApproxSimple);    if (contours.Length == 0)    {        throw new NotSupportedException("Couldn't find any object in the image.");    }    //Create input sample by contour finding and cropping    var dst = new Mat(src.Rows, src.Cols, MatType.CV_8UC3, Scalar.All(0));    var contourIndex = 0;    while ((contourIndex >= 0))    {        var contour = contours[contourIndex];        var boundingRect = Cv2.BoundingRect(contour); //Find bounding rect for each contour        Cv2.Rectangle(src,            new Point(boundingRect.X, boundingRect.Y),            new Point(boundingRect.X + boundingRect.Width, boundingRect.Y + boundingRect.Height),            new Scalar(0, 0, 255),            2);        var roi = new Mat(threshImage, boundingRect); //Crop the image        var resizedImage = new Mat();        var resizedImageFloat = new Mat();        Cv2.Resize(roi, resizedImage, new Size(10, 10)); //resize to 10X10        resizedImage.ConvertTo(resizedImageFloat, MatType.CV_32FC1); //convert to float        var result = resizedImageFloat.Reshape(1, 1);        var results = new Mat();        var neighborResponses = new Mat();        var dists = new Mat();        var detectedClass = (int)kNearest.FindNearest(result, 1, results, neighborResponses, dists);        //Console.WriteLine("DetectedClass: {0}", detectedClass);        //Cv2.ImShow("roi", roi);        //Cv.WaitKey(0);        //Cv2.ImWrite(string.Format("det_{0}_{1}.png",detectedClass, contourIndex), roi);        Cv2.PutText(            dst,            detectedClass.ToString(CultureInfo.InvariantCulture),            new Point(boundingRect.X, boundingRect.Y + boundingRect.Height),            0,            1,            new Scalar(0, 255, 0),            2);        contourIndex = hierarchyIndexes[contourIndex].Next;    }    Cv2.ImShow("Segmented Source", src);    Cv2.ImShow("Detected", dst);    Cv2.ImWrite("dest.jpg", dst);    Cv2.WaitKey();
}