import numpy as np
import cv2
import math

# Generate kernels for Sobel filter
def createSobelKernels():

    sobelX = np.array((
	[-1, 0, 1],
	[-2, 0, 2],
	[-1, 0, 1]), dtype="int")

    sobelY = np.array((
	[-1, -2, -1],
	[ 0,  0,  0],
	[ 1,  2,  1]), dtype="int")

    return sobelX, sobelY

#Gernerate averaging blurring filter
def createBlurKernel():

    blur = np.array((
	[1/9, 1/9, 1/9],
	[1/9, 1/9, 1/9],
	[1/9, 1/9, 1/9]), dtype="float")

    return blur

# Convolve the image with the given kernel
def convolve(img, kernel):
    img_h, img_w = img.shape
    ker_h, ker_w = kernel.shape

    output = np.full((img_h, img_w), 0, dtype = np.uint8)

    # Padding to avoid shrinking the  image size
    padding = (int)(((ker_w - 1) / 2) -1)
    for y in range(img_h):
        for x in range(img_w):

            if(y <= padding or x <= padding or y >= (img_h - 1 - padding)or x >= (img_w - 1 - padding)):
                continue
            else:
                roi = img[y - (1 + padding):y + (2 + padding), x - (1 + padding) :x + (2 + padding)]
                value = float((roi * kernel).sum())

                #Cap value so it stays in 0-255 range
                if(value > 255):
                    value = 255
                elif(value < 0):
                    value = 0

                output[y,x] = np.uint8(value)

    return output;

# sum Sobel derivatives to obtain the result
def sumDerivatives(y_der, x_der):

    img_h, img_w = y_der.shape
    output = np.full((img_h, img_w), 0, dtype = np.uint8)

    for y in range(img_h):
        for x in range(img_w):
            y_val = int(y_der[y,x])
            x_val = int(x_der[y,x])

            value = int(math.sqrt((y_val * y_val) + (x_val * x_val)))

            if(value > 255):
                value = 255
            elif(value < 0):
                value = 0

            output[y,x] = np.uint8(value)

    return output

#Main()

print("Image file name (must be inside 'images' folder) :")
file = input()

print("Running...")
img = cv2.imread("images/" + file, cv2.IMREAD_GRAYSCALE)
cv2.imshow("Original", img)

blur_kernel = createBlurKernel()
blurred_img = convolve(img, blur_kernel)
cv2.imshow("Blurred", blurred_img)

sobelX, sobelY = createSobelKernels()
x_derivative = convolve(blurred_img, sobelX)
y_derivative = convolve(blurred_img, sobelY)

cv2.imshow("X Derivative", x_derivative)
cv2.imshow("Y Derivative", y_derivative)

sobelImg = sumDerivatives(x_derivative, y_derivative)
cv2.imshow("Sobel", sobelImg)

print("Done!")

k = cv2.waitKey(0)

if k == 27 :
    cv2.destroyAllWindows()