This preview of .NET 7 includes improvements to type converters, JSON contract customization, System.Formats.Tar API updates, constraints to .NET template authoring, and performance enhancements in the CodeGen area. 

• .NET 5 provides two options of runtimes for users: the high-performance CoreCLR (for server and desktop applications), and the lightweight Mono (for mobile and WebAssembly). 

These are the customer-reported issues addressed in 15.9.8:

• ModelBus-enabled text transformation fails on 15.8.
• SSDT: Fix to improve performance of loading solutions with multiple projects. 

Use HTTP Keep Alive - It is another hidden gem which resides in response header and helps in improving the performance of our web applications significantly.

 This type of CollectionView does not support changes to its SourceCollection
from a thread different from the Dispatcher thread

 System.InvalidOperationException: This exception was thrown because the generator for control
'System.Windows.Controls.ListView Items.Count:62' with name '(unnamed)' has received sequence of
CollectionChanged events that do not agree with the current state of the Items collection.
The following differences were detected:  Accumulated count 61 is different from actual count 62.

 private static object _lock = new object();
//...
BindingOperations.EnableCollectionSynchronization(persons, _lock);

public static void EnableCollectionSynchronization(IEnumerable collection, object lockObject)
{
      MethodInfo method = typeof(BindingOperations).GetMethod("EnableCollectionSynchronization",
             new Type[] { typeof(IEnumerable), typeof(object) });
      if (method != null)
      {
         method.Invoke(null, new object[] { collection, lockObject });
      }
}

using System;
using System.Collections;
using System.Collections.ObjectModel;
using System.Collections.Specialized;
using System.Linq;
using System.Windows.Data;
using System.Windows.Threading;

namespace WpfAsyncCollection
{
    public class AsyncObservableCollection<T> : ObservableCollection<T>
    {
        public override event NotifyCollectionChangedEventHandler CollectionChanged;
        private static object _syncLock = new object();

        public AsyncObservableCollection()
        {
            enableCollectionSynchronization(this, _syncLock);
        }

        protected override void OnCollectionChanged(NotifyCollectionChangedEventArgs e)
        {
            using (BlockReentrancy())
            {
                var eh = CollectionChanged;
                if (eh == null) return;

                var dispatcher = (from NotifyCollectionChangedEventHandler nh in eh.GetInvocationList()
                                  let dpo = nh.Target as DispatcherObject
                                  where dpo != null
                                  select dpo.Dispatcher).FirstOrDefault();

                if (dispatcher != null && dispatcher.CheckAccess() == false)
                {
                    dispatcher.Invoke(DispatcherPriority.DataBind, (Action)(() => OnCollectionChanged(e)));
                }
                else
                {
                    foreach (NotifyCollectionChangedEventHandler nh in eh.GetInvocationList())
                        nh.Invoke(this, e);
                }
            }
        }

        private static void enableCollectionSynchronization(IEnumerable collection, object lockObject)
        {
            var method = typeof(BindingOperations).GetMethod("EnableCollectionSynchronization", 
                                    new Type[] { typeof(IEnumerable), typeof(object) });
            if (method != null)
            {
                // It's .NET 4.5
                method.Invoke(null, new object[] { collection, lockObject });
            }
        }
    }
}

The main new feature of VisualSVN Server 3.3 is the full support of Subversion’s locking for distributed VDFS repositories. Other significant changes include improved performance of commit to slave VDFS repositories and further optimization of VDFS replication over the WAN.  

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();
}