def test_cross_correlate_masked_output_range():
"""Masked normalized cross-correlation should return between 1 and -1."""
# See random number generator for reproducible results
np.random.seed(23)
# Array dimensions must match along non-transformation axes, in
# this case
# axis 0
shape1 = (15, 4, 5)
shape2 = (15, 12, 7)
# Initial array ranges between -5 and 5
arr1 = 10 * np.random.random(shape1) - 5
arr2 = 10 * np.random.random(shape2) - 5
# random masks
m1 = np.random.choice([True, False], arr1.shape)
m2 = np.random.choice([True, False], arr2.shape)
xcorr = cross_correlate_masked(arr1, arr2, m1, m2, axes=(1, 2))
# No assert array less or equal, so we add an eps
# Also could not find an `assert_array_greater`, Use (-xcorr) instead
eps = np.finfo(np.float).eps
testing.assert_array_less(xcorr, 1 + eps)
testing.assert_array_less(-xcorr, 1 + eps)
def test_ellipse_model_estimate_from_data():
data = np.array([
[264, 854], [265, 875], [268, 863], [270, 857], [275, 905], [285, 915],
[305, 925], [324, 934], [335, 764], [336, 915], [345, 925], [345, 945],
[354, 933], [355, 745], [364, 936], [365, 754], [375, 745], [375, 735],
[385, 736], [395, 735], [394, 935], [405, 727], [415, 736], [415, 727],
[425, 727], [426, 929], [435, 735], [444, 933], [445, 735], [455, 724],
[465, 934], [465, 735], [475, 908], [475, 726], [485, 753], [485, 728],
[492, 762], [495, 745], [491, 910], [493, 909], [499, 904], [505, 905],
[504, 747], [515, 743], [516, 752], [524, 855], [525, 844], [525, 885],
[533, 845], [533, 873], [535, 883], [545, 874], [543, 864], [553, 865],
[553, 845], [554, 825], [554, 835], [563, 845], [565, 826], [563, 855],
[563, 795], [565, 735], [573, 778], [572, 815], [574, 804], [575, 665],
[575, 685], [574, 705], [574, 745], [575, 875], [572, 732], [582, 795],
[579, 709], [583, 805], [583, 854], [586, 755], [584, 824], [585, 655],
[581, 718], [586, 844], [585, 915], [587, 905], [594, 824], [593, 855],
[590, 891], [594, 776], [596, 767], [593, 763], [603, 785], [604, 775],
[603, 885], [605, 753], [605, 655], [606, 935], [603, 761], [613, 802],
[613, 945], [613, 965], [615, 693], [617, 665], [623, 962], [624, 972],
[625, 995], [633, 673], [633, 965], [633, 683], [633, 692], [633, 954],
[634, 1016], [635, 664], [641, 804], [637, 999], [641, 956], [643, 946],
[643, 926], [644, 975], [643, 655], [646, 705], [651, 664], [651, 984],
[647, 665], [651, 715], [651, 725], [651, 734], [647, 809], [651, 825],
[651, 873], [647, 900], [652, 917], [651, 944], [652, 742], [648, 811],
[651, 994], [652, 783], [650, 911], [654, 879]])
# estimate parameters of real data
model = EllipseModel()
model.estimate(data)
# test whether estimated parameters are smaller then 1000, so means stable
assert_array_less(np.abs(model.params[:4]), np.array([2e3] * 4))
def test_cross_correlate_masked_output_range():
"""Masked normalized cross-correlation should return between 1 and -1."""
# See random number generator for reproducible results
np.random.seed(23)
# Array dimensions must match along non-transformation axes, in
# this case
# axis 0
shape1 = (15, 4, 5)
shape2 = (15, 12, 7)
# Initial array ranges between -5 and 5
arr1 = 10 * np.random.random(shape1) - 5
arr2 = 10 * np.random.random(shape2) - 5
# random masks
m1 = np.random.choice([True, False], arr1.shape)
m2 = np.random.choice([True, False], arr2.shape)
xcorr = cross_correlate_masked(arr1, arr2, m1, m2, axes=(1, 2))
# No assert array less or equal, so we add an eps
# Also could not find an `assert_array_greater`, Use (-xcorr) instead
eps = np.finfo(np.float).eps
testing.assert_array_less(xcorr, 1 + eps)
testing.assert_array_less(-xcorr, 1 + eps)
def test_ellipse_model_estimate():
for angle in range(0, 180, 15):
rad = np.deg2rad(angle)
# generate original data without noise
model0 = EllipseModel()
model0.params = (10, 20, 15, 25, rad)
t = np.linspace(0, 2 * np.pi, 100)
data0 = model0.predict_xy(t)
# add gaussian noise to data
random_state = np.random.RandomState(1234)
data = data0 + random_state.normal(size=data0.shape)
# estimate parameters of noisy data
model_est = EllipseModel()
model_est.estimate(data)
# test whether estimated parameters almost equal original parameters
assert_almost_equal(model0.params[:2], model_est.params[:2], 0)
res = model_est.residuals(data0)
assert_array_less(res, np.ones(res.shape))
def test_ellipse_model_estimate():
for angle in range(0, 180, 15):
rad = np.deg2rad(angle)
# generate original data without noise
model0 = EllipseModel()
model0.params = (10, 20, 15, 25, rad)
t = np.linspace(0, 2 * np.pi, 100)
data0 = model0.predict_xy(t)
# add gaussian noise to data
random_state = np.random.RandomState(1234)
data = data0 + random_state.normal(size=data0.shape)
# estimate parameters of noisy data
model_est = EllipseModel()
model_est.estimate(data)
# test whether estimated parameters almost equal original parameters
assert_almost_equal(model0.params[:2], model_est.params[:2], 0)
res = model_est.residuals(data0)
assert_array_less(res, np.ones(res.shape))
def test_ellipse_model_estimate_from_data():
data = np.array([
[264, 854], [265, 875], [268, 863], [270, 857], [275, 905], [285, 915],
[305, 925], [324, 934], [335, 764], [336, 915], [345, 925], [345, 945],
[354, 933], [355, 745], [364, 936], [365, 754], [375, 745], [375, 735],
[385, 736], [395, 735], [394, 935], [405, 727], [415, 736], [415, 727],
[425, 727], [426, 929], [435, 735], [444, 933], [445, 735], [455, 724],
[465, 934], [465, 735], [475, 908], [475, 726], [485, 753], [485, 728],
[492, 762], [495, 745], [491, 910], [493, 909], [499, 904], [505, 905],
[504, 747], [515, 743], [516, 752], [524, 855], [525, 844], [525, 885],
[533, 845], [533, 873], [535, 883], [545, 874], [543, 864], [553, 865],
[553, 845], [554, 825], [554, 835], [563, 845], [565, 826], [563, 855],
[563, 795], [565, 735], [573, 778], [572, 815], [574, 804], [575, 665],
[575, 685], [574, 705], [574, 745], [575, 875], [572, 732], [582, 795],
[579, 709], [583, 805], [583, 854], [586, 755], [584, 824], [585, 655],
[581, 718], [586, 844], [585, 915], [587, 905], [594, 824], [593, 855],
[590, 891], [594, 776], [596, 767], [593, 763], [603, 785], [604, 775],
[603, 885], [605, 753], [605, 655], [606, 935], [603, 761], [613, 802],
[613, 945], [613, 965], [615, 693], [617, 665], [623, 962], [624, 972],
[625, 995], [633, 673], [633, 965], [633, 683], [633, 692], [633, 954],
[634, 1016], [635, 664], [641, 804], [637, 999], [641, 956],
[643, 946], [643, 926], [644, 975], [643, 655], [646, 705], [651, 664],
[651, 984], [647, 665], [651, 715], [651, 725], [651, 734], [647, 809],
[651, 825], [651, 873], [647, 900], [652, 917], [651, 944], [652, 742],
[648, 811], [651, 994], [652, 783], [650, 911], [654, 879]
],
dtype=np.int32)
# estimate parameters of real data
model = EllipseModel()
model.estimate(data)
# test whether estimated parameters are smaller then 1000, so means stable
assert_array_less(model.params[:4], np.full(4, 1000))
# test whether all parameters are more than 0. Negative values were the
# result of an integer overflow
assert_array_less(np.zeros(4), np.abs(model.params[:4]))
