def test_adjust_sigmoid_cutoff_zero():
"""Verifying the output with expected results for sigmoid correction
with cutoff equal to zero and gain of 10"""
image = cp.arange(0, 255, 4, np.uint8).reshape((8, 8))
# fmt: off
expected = cp.asarray([[127, 137, 147, 156, 166, 175, 183, 191],
[198, 205, 211, 216, 221, 225, 229, 232],
[235, 238, 240, 242, 244, 245, 247, 248],
[249, 250, 250, 251, 251, 252, 252, 253],
[253, 253, 253, 253, 254, 254, 254, 254],
[254, 254, 254, 254, 254, 254, 254, 254],
[254, 254, 254, 254, 254, 254, 254, 254],
[254, 254, 254, 254, 254, 254, 254, 254]],
dtype=np.uint8)
# fmt: on
result = exposure.adjust_sigmoid(image, 0, 10)
assert_array_equal(result, expected)
def test_adjust_sigmoid_cutoff_half():
"""Verifying the output with expected results for sigmoid correction
with cutoff equal to half and gain of 10"""
image = cp.arange(0, 255, 4, np.uint8).reshape((8, 8))
# fmt: off
expected = cp.asarray(
[
[1, 1, 2, 2, 3, 3, 4, 5], # noqa
[5, 6, 7, 9, 10, 12, 14, 16], # noqa
[19, 22, 25, 29, 34, 39, 44, 50], # noqa
[57, 64, 72, 80, 89, 99, 108, 118], # noqa
[128, 138, 148, 158, 167, 176, 184, 192],
[199, 205, 211, 217, 221, 226, 229, 233],
[236, 238, 240, 242, 244, 246, 247, 248],
[249, 250, 250, 251, 251, 252, 252, 253]
],
dtype=np.uint8)
# fmt: on
result = exposure.adjust_sigmoid(image, 0.5, 10)
assert_array_equal(result, expected)
def test_adjust_inv_sigmoid_cutoff_half():
"""Verifying the output with expected results for inverse sigmoid
correction with cutoff equal to half and gain of 10"""
image = cp.arange(0, 255, 4, np.uint8).reshape((8, 8))
# fmt: off
expected = cp.asarray(
[
[253, 253, 252, 252, 251, 251, 250, 249],
[249, 248, 247, 245, 244, 242, 240, 238],
[235, 232, 229, 225, 220, 215, 210, 204],
[197, 190, 182, 174, 165, 155, 146, 136],
[126, 116, 106, 96, 87, 78, 70, 62], # noqa
[55, 49, 43, 37, 33, 28, 25, 21], # noqa
[18, 16, 14, 12, 10, 8, 7, 6], # noqa
[5, 4, 4, 3, 3, 2, 2, 1]
],
dtype=np.uint8) # noqa
# fmt: on
result = exposure.adjust_sigmoid(image, 0.5, 10, True)
assert_array_equal(result, expected)
def test_adjust_sigmoid_cutoff_one():
"""Verifying the output with expected results for sigmoid correction
with cutoff equal to one and gain of 5"""
image = cp.arange(0, 255, 4, np.uint8).reshape((8, 8))
# fmt: off
expected = cp.asarray(
[
[1, 1, 1, 2, 2, 2, 2, 2], # noqa
[3, 3, 3, 4, 4, 4, 5, 5], # noqa
[5, 6, 6, 7, 7, 8, 9, 10], # noqa
[10, 11, 12, 13, 14, 15, 16, 18], # noqa
[19, 20, 22, 24, 25, 27, 29, 32], # noqa
[34, 36, 39, 41, 44, 47, 50, 54], # noqa
[57, 61, 64, 68, 72, 76, 80, 85], # noqa
[89, 94, 99, 104, 108, 113, 118, 123]
],
dtype=np.uint8) # noqa
# fmt: on
result = exposure.adjust_sigmoid(image, 1, 5)
assert_array_equal(result, expected)
def test_adjust_sigmoid_1x1_shape():
"""Check that the shape is maintained"""
img = cp.ones([1, 1])
result = exposure.adjust_sigmoid(img, 1, 5)
assert img.shape == result.shape