cnts, frameDelta = motion.showMotion(gray)
#print cnts
for c in cnts:
# if the contour is too small, ignore it
# In respect to fine tuning, it looks like around 500 is optimal for testing in a room like environment.
# A change occurs if we want to notice someone walking in, or motion in a undetected scene, then we jump the gun to 4500+.
if cv2.contourArea(c) < motion.cornerDetectionThresh: # Value to fine tune.
continue
# compute the bounding box for the contour, draw it on the frame,
# and update the text
(x, y, w, h) = cv2.boundingRect(c)
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
if cascadeTime: # If we chose to perform haarcascading
cascades.eyeCascadeDetectionOfImage(frame)
'''
# if the frame could not be grabbed, then we have reached the end
# of the video
#if not grabbed:
# break
# resize the frame, convert it to grayscale, and blur it
#frame = cv2.resize(frame, (500, 500))
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # So here we convert to grayscale cause color is irelevant
gray = cv2.GaussianBlur(gray, (31, 31), 0) # We can change the region from 21 21 later for smoothing out noise.
# originally our dimensions for gaussian blur is 21 by 21
# if the first frame is None, initialize it
