def apply_contra_harmonic_mean(img, filter_size, q):
filter_size = util.format_filter_size(filter_size)
obtained, original = util.get_empty_image_with_same_dimensions(img)
if len(img.shape) == 2:
for i in range(len(original)):
for j in range(len(original[0])):
neighbors = ImageFilter.__get_neighbors_matrix(
filter_size, i, j, original)
obtained[i][j] = ImageFilter.get_contra_harmonic_mean(
neighbors, q)
else:
R = ImageFilter.apply_contra_harmonic_mean(img[:, :, 0],
filter_size, q)
G = ImageFilter.apply_contra_harmonic_mean(img[:, :, 1],
filter_size, q)
B = ImageFilter.apply_contra_harmonic_mean(img[:, :, 2],
filter_size, q)
output = np.zeros((R.shape[0], R.shape[1], 3), dtype=np.uint8)
output[:, :, 0] = R
output[:, :, 1] = G
output[:, :, 2] = B
obtained = output
return obtained
def apply_geometric_mean(img, filter_size):
filter_size = util.format_filter_size(filter_size)
obtained, original = util.get_empty_image_with_same_dimensions(img)
for i in range(len(original)):
for j in range(len(original[0])):
neighbors = ImageFilter.__get_neighbors_matrix(
filter_size, i, j, original)
obtained[i][j] = ImageFilter.get_geometric_mean(neighbors)
return obtained
def create_gaussian_kernel(filter_size, sigma):
"""
Creates a 2D gaussian kernel using filter_size and sigma
"""
filter_size = util.format_filter_size(filter_size)
ax = np.linspace(-(filter_size - 1) / 2., (filter_size - 1) / 2.,
filter_size)
xx, yy = np.meshgrid(ax, ax)
kernel = np.exp(-0.5 * (np.square(xx) + np.square(yy)) /
np.square(sigma))
return kernel / np.sum(kernel)
def apply_median(img, filter_size):
filter_size = util.format_filter_size(filter_size)
obtained, original = util.get_empty_image_with_same_dimensions(img)
if len(img.shape) == 2:
for i in range(len(original)):
for j in range(len(original[0])):
obtained[i][j] = ImageFilter.get_median(
filter_size, i, j, original)
else:
R = ImageFilter.apply_median(img[:, :, 0], filter_size)
G = ImageFilter.apply_median(img[:, :, 1], filter_size)
B = ImageFilter.apply_median(img[:, :, 2], filter_size)
output = np.zeros((R.shape[0], R.shape[1], 3), dtype=np.uint8)
output[:, :, 0] = R
output[:, :, 1] = G
output[:, :, 2] = B
obtained = output
return obtained
def test_format_negative_size():
original_size = -5
expected = 3
obtained = util.format_filter_size(original_size)
assert obtained == expected
def test_format_odd_size():
original_size = 7
expected = original_size
obtained = util.format_filter_size(original_size)
assert obtained == expected
def test_format_even_size():
original_size = 4
expected = original_size + 1
obtained = util.format_filter_size(original_size)
assert obtained == expected
def test_format_min_size():
original_size = 3
expected = 3
obtained = util.format_filter_size(original_size)
assert obtained == expected
def test_format_low_positive_size():
original_size = 1
expected = 3
obtained = util.format_filter_size(original_size)
assert obtained == expected
def test_format_size_zero():
original_size = 0
expected = 3
obtained = util.format_filter_size(original_size)
assert obtained == expected
