def test_daisy_values():
image = Image([[1.0, 2.0, 3.0, 4.0], [2.0, 1.0, 3.0, 4.0], [1.0, 2.0, 3.0, 4.0], [2.0, 1.0, 3.0, 4.0]])
daisy_img = daisy(image, step=1, rings=2, radius=1, orientations=8, histograms=8)
assert_allclose(np.around(daisy_img.pixels[10, 0, 0], 6), 0.001355)
assert_allclose(np.around(daisy_img.pixels[20, 0, 1], 6), 0.032237)
assert_allclose(np.around(daisy_img.pixels[30, 1, 0], 6), 0.002032)
assert_allclose(np.around(daisy_img.pixels[40, 1, 1], 6), 0.000163)
def test_daisy_values():
image = Image([[1, 2, 3, 4], [2, 1, 3, 4], [1, 2, 3, 4], [2, 1, 3, 4]])
daisy_img = daisy(image, step=1, rings=2, radius=1, orientations=8,
histograms=8)
assert_allclose(np.around(daisy_img.pixels[0, 0, 10], 6), 0.000261)
assert_allclose(np.around(daisy_img.pixels[0, 1, 20], 6), 0.002853)
assert_allclose(np.around(daisy_img.pixels[1, 0, 30], 6), 0.007266)
assert_allclose(np.around(daisy_img.pixels[1, 1, 40], 6), 0.001971)
def test_daisy_values():
image = Image([[1., 2., 3., 4.], [2., 1., 3., 4.], [1., 2., 3., 4.],
[2., 1., 3., 4.]])
daisy_img = daisy(image, step=1, rings=2, radius=1, orientations=8,
histograms=8)
assert_allclose(np.around(daisy_img.pixels[10, 0, 0], 6), 0.001355)
assert_allclose(np.around(daisy_img.pixels[20, 0, 1], 6), 0.032237)
assert_allclose(np.around(daisy_img.pixels[30, 1, 0], 6), 0.002032)
assert_allclose(np.around(daisy_img.pixels[40, 1, 1], 6), 0.000163)
def test_daisy_values():
image = Image([[1, 2, 3, 4], [2, 1, 3, 4], [1, 2, 3, 4], [2, 1, 3, 4]])
daisy_img = daisy(image,
step=1,
rings=2,
radius=1,
orientations=8,
histograms=8)
assert_allclose(np.around(daisy_img.pixels[0, 0, 10], 6), 0.000261)
assert_allclose(np.around(daisy_img.pixels[0, 1, 20], 6), 0.002853)
assert_allclose(np.around(daisy_img.pixels[1, 0, 30], 6), 0.007266)
assert_allclose(np.around(daisy_img.pixels[1, 1, 40], 6), 0.001971)
def test_daisy_channels():
n_cases = 3
rings = np.random.randint(1, 3, [n_cases, 1])
orientations = np.random.randint(1, 7, [n_cases, 1])
histograms = np.random.randint(1, 6, [n_cases, 1])
channels = np.random.randint(1, 5, [n_cases, 1])
for i in range(n_cases):
image = Image(np.random.randn(channels[i, 0], 40, 40))
daisy_img = daisy(
image, step=4, rings=rings[i, 0], orientations=orientations[i, 0], histograms=histograms[i, 0]
)
assert_allclose(daisy_img.shape, (3, 3))
assert_allclose(daisy_img.n_channels, ((rings[i, 0] * histograms[i, 0] + 1) * orientations[i, 0]))
def test_daisy_channels():
n_cases = 3
rings = np.random.randint(1, 3, [n_cases, 1])
orientations = np.random.randint(1, 7, [n_cases, 1])
histograms = np.random.randint(1, 6, [n_cases, 1])
channels = np.random.randint(1, 5, [n_cases, 1])
for i in range(n_cases):
image = Image(np.random.randn(channels[i, 0], 40, 40))
daisy_img = daisy(image, step=4, rings=rings[i, 0],
orientations=orientations[i, 0],
histograms=histograms[i, 0])
assert_allclose(daisy_img.shape, (3, 3))
assert_allclose(daisy_img.n_channels,
((rings[i, 0] * histograms[i, 0] + 1) * orientations[i, 0]))