Enhancements.

MarketDataLib/CNNProcessing/CNNClient.cs

@@ -11,7 +11,7 @@ namespace MarketData.CNNProcessing
 {
   public class CNNClient
 	{
-	  public enum Model{resnet50,inception,vgg16,lenet5,ping};
+	  public enum Model{resnet50,resnet50B,inception,vgg16,lenet5,ping};
 		private static readonly string Alive="Alive";
     private readonly HttpClient client = new HttpClient();
 		private string baseUrl;
@@ -42,6 +42,15 @@ namespace MarketData.CNNProcessing
 			}
     }
 
+// This method is used to process an image through PIL.  All images that are being used in training should ultimately be processed through
+// PIL (Python Image Library) so that images can be normalized by python prior to training.  We do this because we use PIL during the 
+// prediction process and we want all images used in training/validation and all images used for prediction to be touched by and processed
+// by PIL
+    public Stream ProcessImage(Stream stream)
+    {
+      return UploadImage(baseUrl+"/process_image", stream);
+    }
+
     public bool Ping()
     {
 		  try
@@ -56,6 +65,7 @@ namespace MarketData.CNNProcessing
 			  return false;
 			}
     }
+
     private async Task<String> Upload(String url,Stream stream)
     {
       try
@@ -78,6 +88,31 @@ namespace MarketData.CNNProcessing
         return null;
       }
     }
+    /// <summary>
+    /// This method uploads an image stream to the specified url and receives the processed image back.
+    /// The processed image simply processes the image through PIL (Python Image Library)
+    /// </summary>
+    private Stream UploadImage(String url,Stream stream)
+    {
+      try
+      {
+        using (HttpRequestMessage request = new HttpRequestMessage(HttpMethod.Post, url))
+        {
+				  int streamEnd = Convert.ToInt32(stream.Length);
+				  byte[] byteArray = new byte[streamEnd];
+				  stream.Read(byteArray, 0, streamEnd);
+          request.Content=new ByteArrayContent(byteArray);
+          HttpResponseMessage response = client.SendAsync(request).GetAwaiter().GetResult();
+          return response.Content.ReadAsStreamAsync().GetAwaiter().GetResult();
+        }
+      }
+      catch(Exception exception)
+      {
+        MDTrace.WriteLine(LogLevel.DEBUG,String.Format("Exception encountered: {0}",exception.ToString()));
+        return null;
+      }
+    }
+
     private async Task<String> Upload(String url)
     {
       try

MarketDataLib/CNNProcessing/ImageHelper.cs

@@ -27,6 +27,11 @@ namespace MarketData.CNNProcessing
     }
 
     public void Dispose()
+    {
+      DisposeAll();
+    }
+
+    private void DisposeAll()
     {
       if(null!=bitmap)
       {
@@ -55,20 +60,38 @@ namespace MarketData.CNNProcessing
       return copy;
     }
 
+    public bool LoadImage(Stream stream)
+    {
+      try
+      {
+        DisposeAll();
+        Image image=Image.FromStream(stream);
+        bitmap = new Bitmap(image);
+        height = bitmap.Height;
+        width = bitmap.Width;
+        return true;
+      }
+      catch(Exception exception)
+      {
+        MDTrace.WriteLine(LogLevel.DEBUG,exception.ToString());
+        return false;
+      }
+    }
+
     public bool LoadImage(string pathFileName) 
     {
       Stream bitmapStream = null;
       try
       {
-        bitmapStream = File.Open(pathFileName,FileMode.Open);
-        Image image = Image.FromStream(bitmapStream);
-        bitmap=new Bitmap(image);
+        DisposeAll();
+        bitmap = new Bitmap(pathFileName);
         width=bitmap.Width;
         height=bitmap.Height;
         return true;
       }
-      catch(Exception)
+      catch(Exception exception)
       {
+        MDTrace.WriteLine(LogLevel.DEBUG,exception.ToString());
         return false;
       }
       finally
@@ -125,8 +148,9 @@ namespace MarketData.CNNProcessing
         graphics.Dispose();
         return true;
       }
-      catch(Exception)
+      catch(Exception exception)
       {
+        MDTrace.WriteLine(LogLevel.DEBUG,exception.ToString());
         return false;
       }
       finally