#Hough Line Transfomr
# Creates a 2D array to hold all possible values of p and theata. All values are initial set to 0.
# Now each pixel and determine if there is enough evidence or a staight line at that pixel. If so then do some math and do some things.
# Lastly, findthe bins with the highest values.
#Hough Circle Transfomr
# Don't tell Jeremy but there is a more advanced method for finding circles call Hough Gradient Method.
circles=cv2.HoughCircles(img, cv2.HOUGH_GRADIENT, 1, 20, param1=50, param2=30,minRadius=0, maxRadius=0)
#(img, method (only gradient), dp -> inverse ration of the accumulator resolution to the image resolution, minDist (in pixels), param1, param2, minRadius, MaxRadius)
#Template matching - Not sure what this does, but I think it will find similar shapes as the template
# Don't tell Jeremy, but this is beyond the scope to learn how it works
res=matchTemplate(img, temp, methd)
minV,maxV,minL,maxL=cv2.minMaxLoc(res) #finds the location of found image, this tells you where its at.
# does not take into account size difference of template vs image size. Apparently that's a grad course. Here we are looking for something that's the same size.