def mean_filter_fast(a_img: ImageImpl, kernel_size: int) -> ImageImpl:
"""
Given an Image instance, apply the mean filter using a square kernel of size
kernel_size.
:param a_img: Image instance
:param kernel_size: kernel size int
:return: transformed image
"""
kernel_size = int(kernel_size)
a_img.convolution_fast(
np.ones((kernel_size, kernel_size)),
)
return a_img
def high_filter_fast(a_img: ImageImpl, kernel_size: int) -> ImageImpl:
"""
Given a Image instance, apply high value filter.
:param a_img:
:param kernel_size:
:return: transformed Image instance
"""
kernel_size = int(kernel_size)
kernel = np.ones((kernel_size, kernel_size)) * -1
# augment the center value
kernel[int(kernel_size / 2), int(kernel_size / 2)] = kernel_size
a_img.convolution_fast(
kernel,
)
return a_img
def gaussian_filter_fast(a_img: ImageImpl, kernel_size: int, sigma: float) -> ImageImpl:
"""
Given an Image instance, apply the weighted gaussian filter using a square kernel of size
kernel_size.
:param a_img: Image instance
:param kernel_size: kernel size int
:param sigma: sigma value
:return: transformed image
"""
kernel_size = int(kernel_size)
kernel = gaussian_kernel(kernel_size, sigma) * MAX_PIXEL_VALUE
a_img.convolution_fast(
(1 / kernel_size ** 2) * kernel,
)
return a_img