gradients.py

import cv2

image = cv2.imread(r"D:\computer_vision\data\hat_ex.png")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# set the kernel size, depending on whether we are using the Sobel
# operator of the Scharr operator, then compute the gradients along
# the x and y axis, respectively
ksize = -1
gX = cv2.Sobel(gray, ddepth=cv2.CV_32F, dx=1, dy=0, ksize=ksize)
gY = cv2.Sobel(gray, ddepth=cv2.CV_32F, dx=0, dy=1, ksize=ksize)
# the gradient magnitude images are now of the floating point data
# type, so we need to take care to convert them back a to unsigned
# 8-bit integer representation so other OpenCV functions can operate
# on them and visualize them
gX = cv2.convertScaleAbs(gX)
gY = cv2.convertScaleAbs(gY)
# combine the gradient representations into a single image
combined = cv2.addWeighted(gX, 0.5, gY, 0.5, 0)
# show our output images
cv2.imshow("Sobel/Scharr X", gX)
cv2.imshow("Sobel/Scharr Y", gY)
cv2.imshow("Sobel/Scharr Combined", combined)
cv2.waitKey(0)