31 lines
1.2 KiB
Python
31 lines
1.2 KiB
Python
import pytest
|
|
import numpy as np
|
|
from skimage.feature import multiscale_basic_features
|
|
|
|
|
|
@pytest.mark.parametrize('edges', (False, True))
|
|
@pytest.mark.parametrize('texture', (False, True))
|
|
def test_multiscale_basic_features(edges, texture):
|
|
img = np.zeros((20, 20, 3))
|
|
img[:10] = 1
|
|
img += 0.05 * np.random.randn(*img.shape)
|
|
features = multiscale_basic_features(img, edges=edges, texture=texture, multichannel=True)
|
|
n_sigmas = 6
|
|
intensity = True
|
|
assert features.shape[-1] == 3 * n_sigmas * (int(intensity) + int(edges) + 2 * int(texture))
|
|
assert features.shape[:-1] == img.shape[:-1]
|
|
|
|
|
|
def test_multiscale_basic_features_channel():
|
|
img = np.zeros((10, 10, 5))
|
|
img[:10] = 1
|
|
img += 0.05 * np.random.randn(*img.shape)
|
|
n_sigmas = 2
|
|
features = multiscale_basic_features(img, sigma_min=1, sigma_max=2, multichannel=True)
|
|
assert features.shape[-1] == 5 * n_sigmas * 4
|
|
assert features.shape[:-1] == img.shape[:-1]
|
|
# Consider last axis as spatial dimension
|
|
features = multiscale_basic_features(img, sigma_min=1, sigma_max=2)
|
|
assert features.shape[-1] == n_sigmas * 5
|
|
assert features.shape[:-1] == img.shape
|