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Fixed database typo and removed unnecessary class identifier.

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Batuhan Berk Başoğlu 2020-10-14 10:10:37 -04:00
parent 00ad49a143
commit 45fb349a7d
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venv/Lib/site-packages/skimage/feature/tests/test_haar.py Normal file
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from random import shuffle
from itertools import chain
import pytest
import numpy as np
from numpy.testing import assert_allclose
from numpy.testing import assert_array_equal
from skimage.transform import integral_image
from skimage.feature import haar_like_feature
from skimage.feature import haar_like_feature_coord
from skimage.feature import draw_haar_like_feature
def test_haar_like_feature_error():
img = np.ones((5, 5), dtype=np.float32)
img_ii = integral_image(img)
feature_type = 'unknown_type'
with pytest.raises(ValueError):
haar_like_feature(img_ii, 0, 0, 5, 5, feature_type=feature_type)
haar_like_feature_coord(5, 5, feature_type=feature_type)
draw_haar_like_feature(img, 0, 0, 5, 5, feature_type=feature_type)
feat_coord, feat_type = haar_like_feature_coord(5, 5, 'type-2-x')
with pytest.raises(ValueError):
haar_like_feature(img_ii, 0, 0, 5, 5, feature_type=feat_type[:3],
feature_coord=feat_coord)
@pytest.mark.parametrize("dtype", [np.uint8, np.int8,
np.float32, np.float64])
@pytest.mark.parametrize("feature_type,shape_feature,expected_feature_value",
[('type-2-x', (84,), [0.]),
('type-2-y', (84,), [0.]),
('type-3-x', (42,), [-4., -3., -2., -1.]),
('type-3-y', (42,), [-4., -3., -2., -1.]),
('type-4', (36,), [0.])])
def test_haar_like_feature(feature_type, shape_feature,
expected_feature_value, dtype):
# test Haar-like feature on a basic one image
img = np.ones((5, 5), dtype=dtype)
img_ii = integral_image(img)
haar_feature = haar_like_feature(img_ii, 0, 0, 5, 5,
feature_type=feature_type)
assert_allclose(np.sort(np.unique(haar_feature)), expected_feature_value)
@pytest.mark.parametrize("dtype", [np.uint8, np.int8,
np.float32, np.float64])
@pytest.mark.parametrize("feature_type", ['type-2-x', 'type-2-y',
'type-3-x', 'type-3-y',
'type-4'])
def test_haar_like_feature_fused_type(dtype, feature_type):
# check that the input type is kept
img = np.ones((5, 5), dtype=dtype)
img_ii = integral_image(img)
expected_dtype = img_ii.dtype
# to avoid overflow, unsigned type are converted to signed
if 'uint' in expected_dtype.name:
expected_dtype = np.dtype(expected_dtype.name.replace('u', ''))
haar_feature = haar_like_feature(img_ii, 0, 0, 5, 5,
feature_type=feature_type)
assert haar_feature.dtype == expected_dtype
def test_haar_like_feature_list():
img = np.ones((5, 5), dtype=np.int8)
img_ii = integral_image(img)
feature_type = ['type-2-x', 'type-2-y', 'type-3-x', 'type-3-y', 'type-4']
haar_list = haar_like_feature(img_ii, 0, 0, 5, 5,
feature_type=feature_type)
haar_all = haar_like_feature(img_ii, 0, 0, 5, 5)
assert_array_equal(haar_list, haar_all)
@pytest.mark.parametrize("feature_type", ['type-2-x', 'type-2-y',
'type-3-x', 'type-3-y',
'type-4',
['type-2-y', 'type-3-x',
'type-4']])
def test_haar_like_feature_precomputed(feature_type):
img = np.ones((5, 5), dtype=np.int8)
img_ii = integral_image(img)
if isinstance(feature_type, list):
# shuffle the index of the feature to be sure that we are output
# the features in the same order
shuffle(feature_type)
feat_coord, feat_type = zip(*[haar_like_feature_coord(5, 5, feat_t)
for feat_t in feature_type])
feat_coord = np.concatenate(feat_coord)
feat_type = np.concatenate(feat_type)
else:
feat_coord, feat_type = haar_like_feature_coord(5, 5, feature_type)
haar_feature_precomputed = haar_like_feature(img_ii, 0, 0, 5, 5,
feature_type=feat_type,
feature_coord=feat_coord)
haar_feature = haar_like_feature(img_ii, 0, 0, 5, 5, feature_type)
assert_array_equal(haar_feature_precomputed, haar_feature)
@pytest.mark.parametrize("feature_type,height,width,expected_coord",
[('type-2-x', 2, 2,
[[[(0, 0), (0, 0)], [(0, 1), (0, 1)]],
[[(1, 0), (1, 0)], [(1, 1), (1, 1)]]]),
('type-2-y', 2, 2,
[[[(0, 0), (0, 0)], [(1, 0), (1, 0)]],
[[(0, 1), (0, 1)], [(1, 1), (1, 1)]]]),
('type-3-x', 3, 3,
[[[(0, 0), (0, 0)], [(0, 1), (0, 1)],
[(0, 2), (0, 2)]],
[[(0, 0), (1, 0)], [(0, 1), (1, 1)],
[(0, 2), (1, 2)]],
[[(1, 0), (1, 0)], [(1, 1), (1, 1)],
[(1, 2), (1, 2)]],
[[(1, 0), (2, 0)], [(1, 1), (2, 1)],
[(1, 2), (2, 2)]],
[[(2, 0), (2, 0)], [(2, 1), (2, 1)],
[(2, 2), (2, 2)]]]),
('type-3-y', 3, 3,
[[[(0, 0), (0, 0)], [(1, 0), (1, 0)],
[(2, 0), (2, 0)]],
[[(0, 0), (0, 1)], [(1, 0), (1, 1)],
[(2, 0), (2, 1)]],
[[(0, 1), (0, 1)], [(1, 1), (1, 1)],
[(2, 1), (2, 1)]],
[[(0, 1), (0, 2)], [(1, 1), (1, 2)],
[(2, 1), (2, 2)]],
[[(0, 2), (0, 2)], [(1, 2), (1, 2)],
[(2, 2), (2, 2)]]]),
('type-4', 2, 2,
[[[(0, 0), (0, 0)], [(0, 1), (0, 1)],
[(1, 1), (1, 1)], [(1, 0), (1, 0)]]])])
def test_haar_like_feature_coord(feature_type, height, width, expected_coord):
feat_coord, feat_type = haar_like_feature_coord(width, height,
feature_type)
# convert the output to a full numpy array just for comparison
feat_coord = np.array([hf for hf in feat_coord])
assert_array_equal(feat_coord, expected_coord)
assert np.all(feat_type == feature_type)
@pytest.mark.parametrize("max_n_features,nnz_values", [(None, 46),
(1, 8)])
def test_draw_haar_like_feature(max_n_features, nnz_values):
img = np.zeros((5, 5), dtype=np.float32)
coord, _ = haar_like_feature_coord(5, 5, 'type-4')
image = draw_haar_like_feature(img, 0, 0, 5, 5, coord,
max_n_features=max_n_features,
random_state=0)
assert image.shape == (5, 5, 3)
assert np.count_nonzero(image) == nnz_values