thresholding-1.9.py

# import the necessary packages
import argparse
import cv2

# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True, help="Path to the image")
args = vars(ap.parse_args())

# load the image, convert it to grayscale, and blur it slightly
image = cv2.imread(args["image"])
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (7, 7), 0)
cv2.imshow("Image", image)

cv2.waitKey()

# apply basic thresholding -- the first parameter is the image
# we want to threshold, the second value is our threshold check
# if a pixel value is greater than our threshold (in this case,
# 200), we set it to be BLACK, otherwise it is WHITE.
(T, threshInv) = cv2.threshold(blurred, 200, 255, cv2.THRESH_BINARY_INV)
cv2.imshow("Threshold Binary Inverse", threshInv)

# using normal thresholding (rather than inverse thresholding),
# we can change the last argument in the function to make the coins
# black rather than white.
(T, thresh) = cv2.threshold(gray, 95, 255, cv2.THRESH_BINARY)
cv2.imshow("Threshold Binary", thresh)
cv2.waitKey()
print("Value is {}".format(T))

# finally, we can visualize only the masked regions in the image
cv2.imshow("Output", cv2.bitwise_and(image, image, mask=threshInv))


cv2.destroyAllWindows()