#Using Sobel and SCHAAR kernels to calculate image gradient magnatude.
#
#
# Jay Summet
# CS 4475 2024
#
#Python 3.10, OpenCV 4.5.x



import cv2
import numpy as np


#Used for final output display
def scaleTo8Bit(image, displayMin = None, displayMax=None ):

   if displayMin == None:
       displayMin = np.min(image)

   if displayMax == None:
      displayMax = np.max(image)

   np.clip(image, displayMin, displayMax, out=image)

   image = image - displayMin
   cf = 255. / (displayMax - displayMin)
   imageOut = ( cf * image).astype(np.uint8)

   return imageOut


A = cv2.imread("A.jpg", cv2.IMREAD_GRAYSCALE)
cv2.imshow("A", A)

Aflt = A.astype(float)

#Calculate the X and Y first derivatives separately.


#Same bit depth as original, first derivative X, 3x3 kernel.
#Can also use  (-1) instead of size 3 sobel kernel to use a SCHARR kernel.
GradX = cv2.Sobel(Aflt, -1, 1,0,3)

#Same bit depth as original, first derivative Y, 3x3 kernel.
GradY = cv2.Sobel(Aflt, -1, 0,1,3)

#Square them.
GradX = cv2.multiply(GradX, GradX)
GradY = cv2.multiply(GradY, GradY)

#Add the result and take the square root.
GM = cv2.add(GradY,GradX)
GM = cv2.pow(GM, 0.5)



gm8bit = scaleTo8Bit(GM)
cv2.imshow("GM", gm8bit)

cv2.imwrite("A-GM.png", gm8bit)

cv2.waitKey(0)
cv2.startWindowThread()