Resolved review comments

Author: sahehb

mevislab.github.io/content/tutorials/summary/summary8.md

@@ -210,7 +210,7 @@ Window "MainPanel" {
     minimumHeight = 300
     initCommand   = resetApplication
     Category {
-        // Vertical Layout and 4 Boxes with Horizontal Layout
+        // Vertical Layout and four Boxes with Horizontal Layout
         Vertical {
             Box Source {
                 layout = Horizontal

mevislab.github.io/content/tutorials/thirdparty/pytorch/pytorchexample2.md

@@ -121,7 +121,7 @@ def onStart():
     # Step 1: Get input image
     image = ctx.field("inputImage").image()
     imageArray = image.getTile((0, 0, 0, 0, 0, 0), image.imageExtent())
-    inputImage = imageArray[0,0,0,:,:,:].astype("float")
+    inputImage = imageArray[0, 0, 0, :, :, :].astype("float")
 
     # Step 2: Normalize input image
     values = inputImage[inputImage > inputImage.mean()]

mevislab.github.io/content/tutorials/thirdparty/pytorch/pytorchexample3.md

@@ -118,13 +118,13 @@ def grabImage():
     gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
     faces = face_cascade.detectMultiScale(gray, 1.1, 4)
     for (x, y, w, h) in faces:
-        cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)
+        cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
     # Display the output
     cv2.imshow('img', img)
 
 # Update image in interface
 def updateImage(image):
-    _interfaces[0].setImage(OpenCVUtils.convertImageToML(image), minMaxValues = [0,255])
+    _interfaces[0].setImage(OpenCVUtils.convertImageToML(image), minMaxValues = [0, 255])
 
 # Start capturing webcam
 def startCapture():
@@ -249,7 +249,7 @@ def segmentSnapshot():
     # Step 1: Get image from webcam capture
     stopCapture()
     inImage = ctx.field("PythonImage.output0").image()
-    img = inImage.getTile((0,0,0,0,0,0), inImage.imageExtent())[0,0,:,0,:,:]
+    img = inImage.getTile((0, 0, 0, 0, 0, 0), inImage.imageExtent())[0, 0, :, 0, :, :]
 
     # Step 2: Convert image into torch tensor
     img = torch.Tensor(img).type(torch.uint8)