def convolve2d(img, kernel):
"""
Performs convolution in 2D array.
:param img: numpy 2D array: input array
:param kernel: array 3x3: convolution kernel
:return: numpy 2D array: result of convolution, image has same shape as input
"""
kernel = np.flip(kernel, 0)
kernel = np.flip(kernel, 1)
output = np.zeros(img.shape)
window_size = 3
padded_input = ImageUtilities.pad_image(img, 1)
h, w = img.shape
for x in range(0, w):
for y in range(0, h):
window = padded_input[y:y + window_size, x:x + window_size]
window = np.multiply(window, kernel)
output[y, x] = window.sum()
return output
def median_filter(img, window_size):
"""
Filters image using median filter.
:param img: numpy 2D array: array to filter
:param window_size: int: size of filter
:return: numpy 2D array: filtered image, same shape as input
"""
if Config.USE_CV2_FILTERS:
return cv2.medianBlur(img, 3)
output = np.zeros(img.shape)
padded_input = ImageUtilities.pad_image(img, 1)
half_win_size = window_size // 2
h, w = img.shape
mid = (window_size**2) // 2
for x in range(0, w):
for y in range(0, h):
window = padded_input[y:y + window_size, x:x + window_size]
window = window.flatten()
window.sort()
val = window[mid]
output[y][x] = val
return output