def test_frame_similarity_ssim(self):
module = FrameSimilarityModule(name_in='simi3',
image_tag='read',
method='SSIM',
mask_radius=(0., 1.),
temporal_median='constant')
self.pipeline.add_module(module)
self.pipeline.run_module('simi3')
similarity = self.pipeline.get_attribute('read', 'SSIM', static=False)
assert len(similarity) == self.pipeline.get_shape('read')[0]
assert np.min(similarity) > 0
assert np.max(similarity) < 1
assert similarity[4] != similarity[8]
assert np.allclose(np.sum(similarity),
43.999059977871184,
rtol=limit,
atol=0.)
assert np.allclose(similarity[0],
0.9999793908738922,
rtol=limit,
atol=0.)
def test_frame_similarity_pcc(self) -> None:
module = FrameSimilarityModule(name_in='simi2',
image_tag='read',
method='PCC',
mask_radius=(0., 0.2))
self.pipeline.add_module(module)
self.pipeline.run_module('simi2')
attr = self.pipeline.get_attribute('read', 'PCC', static=False)
assert np.min(attr) > 0.
assert np.sum(attr) == pytest.approx(9.134820985662829,
rel=self.limit,
abs=0.)
assert attr.shape == (10, )
def test_frame_similarity_mse(self) -> None:
module = FrameSimilarityModule(name_in='simi1',
image_tag='read',
method='MSE',
mask_radius=(0., 0.2))
self.pipeline.add_module(module)
self.pipeline.run_module('simi1')
attr = self.pipeline.get_attribute('read', 'MSE', static=False)
assert np.min(attr) > 0.
assert np.sum(attr) == pytest.approx(0.11739141370277852,
rel=self.limit,
abs=0.)
assert attr.shape == (10, )
def test_frame_similarity_ssim(self) -> None:
module = FrameSimilarityModule(name_in='simi3',
image_tag='read',
method='SSIM',
mask_radius=(0., 0.2),
temporal_median='constant')
self.pipeline.add_module(module)
self.pipeline.run_module('simi3')
attr = self.pipeline.get_attribute('read', 'SSIM', static=False)
assert np.min(attr) > 0.
assert np.sum(attr) == pytest.approx(9.096830542868524,
rel=self.limit,
abs=0.)
assert attr.shape == (10, )
def test_frame_similarity_mse(self):
module = FrameSimilarityModule(name_in='simi1',
image_tag='read',
method='MSE')
self.pipeline.add_module(module)
self.pipeline.run_module('simi1')
similarity = self.pipeline.get_attribute('read', 'MSE', static=False)
assert len(similarity) == self.pipeline.get_shape('read')[0]
assert np.min(similarity) > 0
assert similarity[4] != similarity[8]
assert np.allclose(np.sum(similarity),
1.4345237709250252e-06,
rtol=limit,
atol=0.)
assert np.allclose(similarity[0],
3.292787144877646e-08,
rtol=limit,
atol=0.)
def test_frame_similarity_mse(self):
module = FrameSimilarityModule(name_in='simi1',
image_tag='read',
method='MSE',
mask_radius=(0., 1.))
self.pipeline.add_module(module)
self.pipeline.run_module('simi1')
similarity = self.pipeline.get_attribute('read', 'MSE', static=False)
assert len(similarity) == self.pipeline.get_shape('read')[0]
assert np.min(similarity) > 0
assert similarity[4] != similarity[8]
assert np.allclose(np.sum(similarity),
1.7938335695664495e-06,
rtol=limit,
atol=0.)
assert np.allclose(similarity[0],
4.103008589430469e-08,
rtol=limit,
atol=0.)
def test_frame_similarity_pcc(self):
module = FrameSimilarityModule(name_in='simi2',
image_tag='read',
method='PCC')
self.pipeline.add_module(module)
self.pipeline.run_module('simi2')
similarity = self.pipeline.get_attribute('read', 'PCC', static=False)
assert len(similarity) == self.pipeline.get_shape('read')[0]
assert np.min(similarity) > 0
assert np.max(similarity) < 1
assert similarity[4] != similarity[8]
assert np.allclose(np.sum(similarity),
43.854202044574045,
rtol=limit,
atol=0.)
assert np.allclose(similarity[0],
0.9966447074865488,
rtol=limit,
atol=0.)
def test_frame_similarity_pcc(self):
module = FrameSimilarityModule(name_in='simi2',
image_tag='read',
method='PCC',
mask_radius=(0., 1.))
self.pipeline.add_module(module)
self.pipeline.run_module('simi2')
similarity = self.pipeline.get_attribute('read', 'PCC', static=False)
assert len(similarity) == self.pipeline.get_shape('read')[0]
assert np.min(similarity) > 0
assert np.max(similarity) < 1
assert similarity[4] != similarity[8]
assert np.allclose(np.sum(similarity),
43.974652830856314,
rtol=limit,
atol=0.)
assert np.allclose(similarity[0],
0.9994193494590345,
rtol=limit,
atol=0.)