def AddImages(img1, img2, result_image):
    for i in range(0, img1.size):
        result_image[i] = img1[i] + img2[i]

def GetFilteredValue(kernel, img, x, y):
    final_value = -1
    for i in range(-1, 2):
        for j in range(-1, 2):
        #Check if the number is inside the bounderies
            if ((x + i >= 0 and x + i <= img.shape[1]) and (y + j >= 0 and y + j <= img.shape[0])):
                   final_value += img[x + i][y + j] * kernel[i][j]
            else:
                continue

    return final_value;

def ApplyKernel(kernel, orig_img, result_img):
    for i in range(0, img.shape[1]):
        for j in range(0, img.shape[0]):
            result_img[i][j] = GetFilteredValue(kernel, orig_img, i, j)

def NormalizeValue(value, maxim, minim):
    return ((value - minim) / (maxim - minim))*255

def GetMaxValueFromMatrix(array):
    return_value = -1
    for i in range(0, array.shape[1]):
        for j in range(0, array.shape[0]):
            if array[i][j] > return_value:
                return_value = array[i][j]

def GetMinValueFromMatrix(array):
    return_value = 10000
    for i in range(0, array.shape[1]):
        for j in range(0, array.shape[0]):
            if array[i][j] < return_value:
                return_value = array[i][j]

def NormalizeImage(img):
    max_value = GetMaxValueFromMatrix(img)
    min_value = GetMinValueFromMatrix(img)

    for i in range(0, img.shape[1]):
        for j in range(0, img.shape[0]):
            NormalizeValue(img[i][j], max_value, min_value)