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