################################################################################
# Preprocessing stuff
# cv2.namedWindow('cannyOutput')
################################################################################
c = cv2.VideoCapture(0)
_, f = c.read()
avg2 = np.float32(f)
background = BackgroundRemoval.preprocessbackground(c, f, avg2)
_, f = c.read()
gray = cv2.cvtColor(f, cv2.COLOR_BGR2GRAY)
while True:
_, f = c.read()
gray = cv2.cvtColor(f, cv2.COLOR_BGR2GRAY)
image_nobackground = BackgroundRemoval.removebackground(gray, background)
b, g, r = cv2.split(f)
nb = np.minimum(image_nobackground, b)
ng = np.minimum(image_nobackground, g)
nr = np.minimum(image_nobackground, r)
bn, gn, rn = normalized(ng, nr, ng)
backgroundRemovedImage = cv2.merge((nb, ng, nr))
res = ColorSegmenter.getMagentaBlob(backgroundRemovedImage)
objectdetection = cv2.cvtColor(res, cv2.COLOR_BGR2GRAY)
edged = cv2.Canny(objectdetection, 100, 250)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (7, 7))
closed = cv2.morphologyEx(edged, cv2.MORPH_CLOSE, kernel)
contours, hierarchy = cv2.findContours(closed, 2, 1)
areaContours = []
r1 = clahe.apply(r)
#bgr = cv2.merge((b1,g1,r1))
return b1,g1,r1
c = cv2.VideoCapture(0)
_,f = c.read()
avg2 = np.float32(f)
# Get clean - unmoving background
background=BackgroundRemoval.preprocessbackground(c, f, avg2)
# get new frame
_,f = c.read()
gray=cv2.cvtColor(f, cv2.COLOR_BGR2GRAY)
while True:
_,f = c.read()
# Remove fixed background from image
mask=BackgroundRemoval.removebackground(f, background)
#split channels to remove backround and normalize color
b,g,r = cv2.split(f)
# remove background
nb=np.minimum(mask, b)
ng=np.minimum(mask, g)
nr=np.minimum(mask, r)
# normalize color
bn,gn,rn = normalized(ng,nr,ng)
# return to color image
backgroundRemovedImage=cv2.merge((nb, ng, nr))
# Color segmentation - Filter out non magenta
res = ColorSegmenter.getMagentaBlob(backgroundRemovedImage)
