'''Adaptive thresholding assumes that smaller regions of the image
are most likely to have approximately constant level of light.
The adaptive threshold can be performed by cv2.adaptiveThreshold()
as follows in the example:'''
img = cv2.imread("myimage.png")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #Convert to grayscale
lowpass = cv2.GaussianBlur(gray, (7, 7), 0) #Filter high frequency noise.
''' all pixel values above adaptive threshold T will be set to 255
else zero, this is accomplished by cv2.THRESH_BINARY method which create
a binary image in the neighborhood region. ADAPTIVE_THRESH_MEAN_C
specifies that adptive threshod T is calculated by neighbourhood mean.
20 is the size of the filter 19X19 (must be odd). 8 is
the fine tuning value used to improve adptive threshold (is arbitrary).
'''
thresh = cv2.adaptiveThreshold(lowpass, 255,cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY,19, 8)
#cv2.ADAPTIVE_THRESH_MEAN_C can be subtituded by
#cv2.ADAPTIVE_THRESH_GAUSSIAN_C which is a weighted mean.
cv2.imshow("Mean Adaptive Thresholding", thresh)
cv2.waitKey(0)