def test_reference2d(self):
"""test default reference calculation in 2D
"""
random.seed(42)
im0 = random.rand(25, 25).astype('float')
im1 = random.rand(25, 25).astype('float')
im2 = random.rand(25, 25).astype('float')
imIn = ImagesLoader(self.sc).fromArrays([im0, im1, im2])
reg = Registration('crosscorr').prepare(imIn)
assert_true(allclose(reg.reference, (im0 + im1 + im2) / 3))
reg = Registration('crosscorr').prepare(imIn, startIdx=0, stopIdx=2)
assert_true(allclose(reg.reference, (im0 + im1) / 2))
reg = Registration('crosscorr').prepare(imIn, startIdx=1, stopIdx=2)
assert_true(allclose(reg.reference, im1))
reg = Registration('crosscorr').prepare(imIn, defaultNImages=1)
assert_true(allclose(reg.reference, im1))
imgs = [random.randn(25, 25).astype('float') for _ in xrange(27)]
imIn = ImagesLoader(self.sc).fromArrays(imgs)
reg = Registration('crosscorr').prepare(imIn)
expected = mean(dstack(imgs[3:23]), axis=2)
assert_true(allclose(expected, reg.reference))
def test_crosscorrImage(self):
random.seed(42)
ref = random.randn(25, 25)
reg = Registration('crosscorr')
im = shift(ref, [2, 0], mode='constant', order=0)
imIn = ImagesLoader(self.sc).fromArrays(im)
paramOut = reg.prepare(ref).fit(imIn).transformations[0].delta
imOut = reg.prepare(ref).run(imIn).first()[1]
assert_true(allclose(ref[:-2, :], imOut[:-2, :]))
assert_true(allclose(paramOut, [2, 0]))
im = shift(ref, [0, 2], mode='constant', order=0)
imIn = ImagesLoader(self.sc).fromArrays(im)
paramOut = reg.prepare(ref).fit(imIn).transformations[0].delta
imOut = reg.prepare(ref).run(imIn).first()[1]
assert_true(allclose(ref[:, :-2], imOut[:, :-2]))
assert_true(allclose(paramOut, [0, 2]))
im = shift(ref, [2, -2], mode='constant', order=0)
imIn = ImagesLoader(self.sc).fromArrays(im)
paramOut = reg.prepare(ref).fit(imIn).transformations[0].delta
imOut = reg.prepare(ref).run(imIn).first()[1]
assert_true(allclose(ref[:-2, 2:], imOut[:-2, 2:]))
assert_true(allclose(paramOut, [2, -2]))
im = shift(ref, [-2, 2], mode='constant', order=0)
imIn = ImagesLoader(self.sc).fromArrays(im)
paramOut = reg.prepare(ref).fit(imIn).transformations[0].delta
imOut = reg.prepare(ref).run(imIn).first()[1]
assert_true(allclose(ref[2:, :-2], imOut[2:, :-2]))
assert_true(allclose(paramOut, [-2, 2]))
def test_saveAndLoad(self):
# test basic saving a loading functionality
# new registration methods should add tests
# for loading and saving
random.seed(42)
ref = random.randn(25, 25)
im = shift(ref, [2, 0], mode='constant', order=0)
im2 = shift(ref, [0, 2], mode='constant', order=0)
imIn = ImagesLoader(self.sc).fromArrays([im, im2])
reg = Registration('crosscorr')
reg.prepare(ref)
model1 = reg.fit(imIn)
t = tempfile.mkdtemp()
model1.save(t + '/test.json')
# with open(t + '/test.json', 'r') as fp:
# print fp.read()
model2 = Registration.load(t + '/test.json')
# print model2
out1 = model1.transform(imIn).first()[1]
out2 = model2.transform(imIn).first()[1]
assert_true(allclose(out1, out2))
def test_crosscorrImage(self):
random.seed(42)
ref = random.randn(25, 25)
reg = Registration('crosscorr')
im = shift(ref, [2, 0], mode='constant', order=0)
imIn = ImagesLoader(self.sc).fromArrays(im)
paramOut = reg.prepare(ref).fit(imIn).transformations[0].delta
imOut = reg.prepare(ref).run(imIn).first()[1]
assert_true(allclose(ref[:-2, :], imOut[:-2, :]))
assert_true(allclose(paramOut, [2, 0]))
im = shift(ref, [0, 2], mode='constant', order=0)
imIn = ImagesLoader(self.sc).fromArrays(im)
paramOut = reg.prepare(ref).fit(imIn).transformations[0].delta
imOut = reg.prepare(ref).run(imIn).first()[1]
assert_true(allclose(ref[:, :-2], imOut[:, :-2]))
assert_true(allclose(paramOut, [0, 2]))
im = shift(ref, [2, -2], mode='constant', order=0)
imIn = ImagesLoader(self.sc).fromArrays(im)
paramOut = reg.prepare(ref).fit(imIn).transformations[0].delta
imOut = reg.prepare(ref).run(imIn).first()[1]
assert_true(allclose(ref[:-2, 2:], imOut[:-2, 2:]))
assert_true(allclose(paramOut, [2, -2]))
im = shift(ref, [-2, 2], mode='constant', order=0)
imIn = ImagesLoader(self.sc).fromArrays(im)
paramOut = reg.prepare(ref).fit(imIn).transformations[0].delta
imOut = reg.prepare(ref).run(imIn).first()[1]
assert_true(allclose(ref[2:, :-2], imOut[2:, :-2]))
assert_true(allclose(paramOut, [-2, 2]))
def test_toarray(self):
random.seed(42)
ref = random.randn(25, 25)
reg = Registration('crosscorr')
im = shift(ref, [2, 0], mode='constant', order=0)
imIn = ImagesLoader(self.sc).fromArrays(im)
paramOut = reg.prepare(ref).fit(imIn).toArray()
assert_true(isinstance(paramOut, ndarray))
assert_true(allclose(paramOut, [[2, 0]]))
def test_toarray(self):
random.seed(42)
ref = random.randn(25, 25)
reg = Registration('crosscorr')
im = shift(ref, [2, 0], mode='constant', order=0)
imIn = ImagesLoader(self.sc).fromArrays(im)
paramOut = reg.prepare(ref).fit(imIn).toArray()
assert_true(isinstance(paramOut, ndarray))
assert_true(allclose(paramOut, [[2, 0]]))
def test_run(self):
# tests the run method which combines fit and transform
random.seed(42)
ref = random.randn(25, 25)
im = shift(ref, [2, 0], mode='constant', order=0)
imIn = ImagesLoader(self.sc).fromArrays(im)
reg = Registration('crosscorr')
reg.prepare(ref)
model = reg.fit(imIn)
out1 = model.transform(imIn).first()[1]
out2 = reg.run(imIn).first()[1]
assert_true(allclose(out1, out2))
def test_reference_3d(self):
""" test default reference calculation in 3D
"""
random.seed(42)
im0 = random.randn(25, 25, 3).astype('float')
im1 = random.randn(25, 25, 3).astype('float')
imIn = ImagesLoader(self.sc).fromArrays([im0, im1])
reg = Registration('crosscorr').prepare(imIn)
assert_true(allclose(reg.reference, (im0 + im1) / 2))
def test_crosscorrVolume(self):
random.seed(42)
ref = random.randn(25, 25, 3)
im = shift(ref, [2, -2, 0], mode='constant', order=0)
imIn = ImagesLoader(self.sc).fromArrays(im)
# use 3D cross correlation
paramOut = Registration('crosscorr').prepare(ref).fit(imIn).transformations[0].delta
imOut = Registration('crosscorr').prepare(ref).run(imIn).first()[1]
assert_true(allclose(paramOut, [2, -2, 0]))
assert_true(allclose(ref[:-2, 2:, :], imOut[:-2, 2:, :]))
# use 2D cross correlation on each plane
paramOut = Registration('planarcrosscorr').prepare(ref).fit(imIn).transformations[0].delta
imOut = Registration('planarcrosscorr').prepare(ref).run(imIn).first()[1]
assert_true(allclose(paramOut, [[2, -2], [2, -2], [2, -2]]))
assert_true(allclose(ref[:-2, 2:, :], imOut[:-2, 2:, :]))
def test_reference_2d(self):
# test default reference calculation in 2D
random.seed(42)
im0 = random.rand(25, 25).astype('float')
im1 = random.rand(25, 25).astype('float')
im2 = random.rand(25, 25).astype('float')
imin = ImagesLoader(self.sc).fromArrays([im0, im1, im2])
reg = Registration('crosscorr').prepare(imin)
assert (allclose(reg.reference, (im0 + im1 + im2) / 3))
reg = Registration('crosscorr').prepare(imin, startidx=0, stopidx=2)
assert (allclose(reg.reference, (im0 + im1) / 2))
reg = Registration('crosscorr').prepare(imin, startidx=1, stopidx=2)
assert (allclose(reg.reference, im1))
def test_saveAndLoad(self):
# test basic saving a loading functionality
# new registration methods should add tests
# for loading and saving
random.seed(42)
ref = random.randn(25, 25)
im = shift(ref, [2, 0], mode='constant', order=0)
im2 = shift(ref, [0, 2], mode='constant', order=0)
imIn = ImagesLoader(self.sc).fromArrays([im, im2])
reg = Registration('crosscorr')
reg.prepare(ref)
model1 = reg.fit(imIn)
t = tempfile.mkdtemp()
model1.save(t + '/test.json')
# with open(t + '/test.json', 'r') as fp:
# print fp.read()
model2 = Registration.load(t + '/test.json')
# print model2
out1 = model1.transform(imIn).first()[1]
out2 = model2.transform(imIn).first()[1]
assert_true(allclose(out1, out2))
def test_run(self):
# tests the run method which combines fit and transform
random.seed(42)
ref = random.randn(25, 25)
im = shift(ref, [2, 0], mode='constant', order=0)
imIn = ImagesLoader(self.sc).fromArrays(im)
reg = Registration('crosscorr')
reg.prepare(ref)
model = reg.fit(imIn)
out1 = model.transform(imIn).first()[1]
out2 = reg.run(imIn).first()[1]
assert_true(allclose(out1, out2))
def test_save_load(self):
# test basic saving a loading functionality
# new registration methods should add tests
# for loading and saving
random.seed(42)
ref = random.randn(25, 25)
im = shift(ref, [2, 0], mode='constant', order=0)
imin = ImagesLoader(self.sc).fromArrays(im)
reg = Registration('crosscorr')
reg.prepare(ref)
model1 = reg.fit(imin)
t = tempfile.mkdtemp()
model1.save(t + '/test.json')
model2 = Registration.load(t + '/test.json')
out1 = model1.transform(imin).first()[1]
out2 = model2.transform(imin).first()[1]
assert (allclose(out1, out2))
