28sobel_and_laplacian.py

import numpy as np
import argparse
import cv2

ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required = True, help = "Path to the image")
args = vars(ap.parse_args())

# when computing gradients and edges we (normally) compute them on a single channel - in this case using grayscale image
image = cv2.imread(args["image"])
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.imshow("Original", image)

# image
# data type - for the output image

# if you don't use a floating point data type when computing the gradient magnitude image, you will miss edges, specifically the white-to-black transitions

lap = cv2.Laplacian(image, cv2.CV_64F)
lap = np.uint8(np.absolute(lap))
cv2.imshow("Laplacian", lap)
# cv2.waitKey(0)

sobelX = cv2.Sobel(image, cv2.CV_64F, 1, 0)
sobelY = cv2.Sobel(image, cv2.CV_64F, 0, 1)

sobelX = np.uint8(np.absolute(sobelX))
sobelY = np.uint8(np.absolute(sobelY))

sobelCombined = cv2.bitwise_or(sobelX, sobelY)

cv2.imshow("Sobel X", sobelX)
cv2.imshow("Sobel Y", sobelY)
cv2.imshow("Sobel Combined", sobelCombined)
cv2.waitKey(0)