Added colored bands and added more pens.

Models/model_sk_convnext_v1.py

@@ -11,13 +11,24 @@ import matplotlib.pyplot as plt
 # This model was trained with 13,185 images
 # See CNNImageProcessor solution for creating the test images for training this model
 # **************************************** I M P O R T A N T ********************************************************
+# There is a shell script in the Scripts folder. setup_tf_gpu.sh  Copy the script to the CNN folder and run it.  It will
+# create the venv enviroment and install python 3.10 and tensorflow (gpu)
+# to start the environment "source tf_gpu/bin/activate"
+# then type "code ."
 # Train the model on EUPORIE laptop using the GPU card with WSL2.  (Windows Subsystem For Linux).  I am running Ubuntu1 22.04.2
 # To launch WSL open up a command prompt, run powershell and type "wsl".
 # The folder structure will be /home/pi/CNN.
 # You can access the folder structure through windows explorer.  type "\\wsl$" in explorer and navigate to the folder.
 # drop in the Data and Model and run the model
-# There is a shell script in the Scripts folder. setup_tf_gpu.sh  Copy teh script to the CNN folder and run it.  It will
+#
-# create the venv enviroment and install python 3.10 and tensorflow (gpu)
+# If you want to run the model_host.py from the WSL2 environment (recommended for backtesting speed) you first need to get the address that was assigned to the 
+# WSL environment.  On the WSL inatance type "hostname -I".  Next, from the host environment you need to forward port requests
+# on port 5000 (Flask Listener Port) to the WSL environment.  Here is how to do that.  Predictions on the WSL environment
+# run considerable faster than on the host because the WSL environment is able to utilize the GPU.
+# {To Create a Port Forward} netsh interface portproxy add v4tov4 listenport=5000 listenaddress=0.0.0.0 connectport=5000 connectaddress=172.29.110.64
+# {To List Port Forwards} netsh interface portproxy show all
+# {To Remove the Port Forward}  netsh interface portproxy delete v4tov4 listenport=5000 listenaddress=0.0.0.0
+# hostname -I 
 # ********************************************************************************************************************
 
 # Figure out if we are training in CPU or GPU