82 lines
2.9 KiB
Python
82 lines
2.9 KiB
Python
|
import numpy as np
|
||
|
|
try:
|
||
|
|
import matplotlib.pyplot as plt
|
||
|
|
except ImportError:
|
||
|
|
plt = None
|
||
|
|
|
||
|
|
from skimage._shared.testing import assert_equal
|
||
|
|
|
||
|
|
from skimage.feature.util import (FeatureDetector, DescriptorExtractor,
|
||
|
|
_prepare_grayscale_input_2D,
|
||
|
|
_mask_border_keypoints, plot_matches)
|
||
|
|
|
||
|
|
from skimage._shared import testing
|
||
|
|
|
||
|
|
|
||
|
|
def test_feature_detector():
|
||
|
|
with testing.raises(NotImplementedError):
|
||
|
|
FeatureDetector().detect(None)
|
||
|
|
|
||
|
|
|
||
|
|
def test_descriptor_extractor():
|
||
|
|
with testing.raises(NotImplementedError):
|
||
|
|
DescriptorExtractor().extract(None, None)
|
||
|
|
|
||
|
|
|
||
|
|
def test_prepare_grayscale_input_2D():
|
||
|
|
with testing.raises(ValueError):
|
||
|
|
_prepare_grayscale_input_2D(np.zeros((3, 3, 3)))
|
||
|
|
with testing.raises(ValueError):
|
||
|
|
_prepare_grayscale_input_2D(np.zeros((3, 1)))
|
||
|
|
with testing.raises(ValueError):
|
||
|
|
_prepare_grayscale_input_2D(np.zeros((3, 1, 1)))
|
||
|
|
img = _prepare_grayscale_input_2D(np.zeros((3, 3)))
|
||
|
|
img = _prepare_grayscale_input_2D(np.zeros((3, 3, 1)))
|
||
|
|
img = _prepare_grayscale_input_2D(np.zeros((1, 3, 3)))
|
||
|
|
|
||
|
|
|
||
|
|
def test_mask_border_keypoints():
|
||
|
|
keypoints = np.array([[0, 0], [1, 1], [2, 2], [3, 3], [4, 4]])
|
||
|
|
assert_equal(_mask_border_keypoints((10, 10), keypoints, 0),
|
||
|
|
[1, 1, 1, 1, 1])
|
||
|
|
assert_equal(_mask_border_keypoints((10, 10), keypoints, 2),
|
||
|
|
[0, 0, 1, 1, 1])
|
||
|
|
assert_equal(_mask_border_keypoints((4, 4), keypoints, 2),
|
||
|
|
[0, 0, 1, 0, 0])
|
||
|
|
assert_equal(_mask_border_keypoints((10, 10), keypoints, 5),
|
||
|
|
[0, 0, 0, 0, 0])
|
||
|
|
assert_equal(_mask_border_keypoints((10, 10), keypoints, 4),
|
||
|
|
[0, 0, 0, 0, 1])
|
||
|
|
|
||
|
|
|
||
|
|
@testing.skipif(plt is None, reason="Matplotlib not installed")
|
||
|
|
def test_plot_matches():
|
||
|
|
fig, ax = plt.subplots(nrows=1, ncols=1)
|
||
|
|
|
||
|
|
shapes = (((10, 10), (10, 10)),
|
||
|
|
((10, 10), (12, 10)),
|
||
|
|
((10, 10), (10, 12)),
|
||
|
|
((10, 10), (12, 12)),
|
||
|
|
((12, 10), (10, 10)),
|
||
|
|
((10, 12), (10, 10)),
|
||
|
|
((12, 12), (10, 10)))
|
||
|
|
|
||
|
|
keypoints1 = 10 * np.random.rand(10, 2)
|
||
|
|
keypoints2 = 10 * np.random.rand(10, 2)
|
||
|
|
idxs1 = np.random.randint(10, size=10)
|
||
|
|
idxs2 = np.random.randint(10, size=10)
|
||
|
|
matches = np.column_stack((idxs1, idxs2))
|
||
|
|
|
||
|
|
for shape1, shape2 in shapes:
|
||
|
|
img1 = np.zeros(shape1)
|
||
|
|
img2 = np.zeros(shape2)
|
||
|
|
plot_matches(ax, img1, img2, keypoints1, keypoints2, matches)
|
||
|
|
plot_matches(ax, img1, img2, keypoints1, keypoints2, matches,
|
||
|
|
only_matches=True)
|
||
|
|
plot_matches(ax, img1, img2, keypoints1, keypoints2, matches,
|
||
|
|
keypoints_color='r')
|
||
|
|
plot_matches(ax, img1, img2, keypoints1, keypoints2, matches,
|
||
|
|
matches_color='r')
|
||
|
|
plot_matches(ax, img1, img2, keypoints1, keypoints2, matches,
|
||
|
|
alignment='vertical')
|