def pixel_func(img, kernel):
mid = get_middle(kernel)
weight = GeneratePixelGauss(img, mid)
# need to normalize and apply cross correlation
weight_norm = custom_utils.norml_mtx(weight)
apply_weights = custom_utils.sum_cross_mtx(img, weight_norm)
return apply_weights
def DistanceFilter(img):
win = np.array(BLUR33).astype(np.float)
# normalize the window and apply convolution with zero padding
win = custom_utils.norml_mtx(win)
blurred = filters.convolve(img, win, mode='constant', cval=0)
# clip to ensure grayscale values
blurred = np.clip(blurred, 0, 1)
return blurred
def combo_func(img, kernel):
mid = get_middle(kernel)
# generate pixel gaussian
pixel = GeneratePixelGauss(img, mid)
# combine with distance gaussian
dist = np.array(BLUR33).astype(np.float)
weight = pixel * dist
# apply the new window
weight_norm = custom_utils.norml_mtx(weight)
apply_weights = custom_utils.sum_cross_mtx(img, weight_norm)
return apply_weights