The preceding technique, which uses cv2.cornerHarris, is great for detecting corners and has a distinct advantage because corners are corners; they are detected even if the image is rotated. However, if we scale an image to a smaller or larger size, some parts of the image may lose or even gain a corner quality.
For example, take a look at the following corner detections in an image of the F1 Italian Grand Prix track:
Here is the corner detection result with a smaller version of the same image:
You will notice how the corners are a lot more condensed; however, even though we gained some corners, we lost others! In particular, let's examine the Variante Ascari chicane, which looks like a squiggle at the end of the part of the track that runs straight from northwest to southeast. In the larger version of the image, both...
