def test_affine(image, template, p):
"""
Tests image registration using a affine deformation model.
"""
affine = register.Register(model.Affine, metric.Residual, sampler.Spline)
# Coerce the image data into RegisterData.
image = register.RegisterData(image)
template = register.RegisterData(template)
step, _search = affine.register(image, template)
assert np.allclose(p, step.p, atol=0.5), \
"Estimated p: {} not equal to p: {}".format(
step.p,
p
)
def test_shift(image, template, method, p):
"""
Tests image registration using a shift deformation model.
"""
shift = register.Register()
# Coerce the image data into RegisterData.
image = register.RegisterData(image)
template = register.RegisterData(template)
step, _search = shift.register(image,
template,
model.Shift(),
method=method)
assert np.allclose(p, step.p, atol=0.5), \
"Estimated p: {} not equal to p: {}".format(
step.p,
p
)
p = spline_model.identity
#TODO: Understand the effect of parameter magnitude:
p += np.random.rand(p.shape[0]) * 100 - 50
return spline_sampler.f(image, spline_model.warp(p)).reshape(image.shape)
image = misc.lena()
template = warp(image)
# Coerce the image data into RegisterData.
image = register.RegisterData(image).downsample(5.0)
template = register.RegisterData(template).downsample(5.0)
# Form the affine registration instance.
affine = register.Register(model.Affine, metric.Residual, sampler.Spline)
# Form the spline registration instance.
spline = register.Register(model.CubicSpline, metric.Residual, sampler.Spline)
# Compute an affine registration between the template and image.
step, search = affine.register(
image,
template,
)
# Compute a nonlinear (spline) registration, initialized with the warp field
# found using the affine registration.
step, _search = spline.register(image,
template,
displacement=step.displacement,
http://en.wikipedia.org/wiki/Lenna
"""
import scipy.ndimage as nd
import scipy.misc as misc
from imreg import register, model, metric
from imreg.samplers import sampler
# Form some test data (lena, lena rotated 20 degrees)
image = misc.lena()
template = nd.rotate(image, 20, reshape=False)
# Form the affine registration instance.
affine = register.Register(
model.Affine,
metric.Residual,
sampler.CubicConvolution
)
# Coerce the image data into RegisterData.
image = register.RegisterData(image).downsample(2)
template = register.RegisterData(template).downsample(2)
# Register.
step, search = affine.register(
image,
template,
verbose=True,
)
p = mymodel.identity
#TODO: Understand the effect of parameter magnitude:
p += np.random.rand(p.shape[0]) * 0.1 - 0.05
return mysampler.f(image, mymodel.warp(p)).reshape(image.shape)
image = misc.lena().astype(np.double)
image = nd.zoom(image, 0.30)
template = warp(image)
# Coerce the image data into RegisterData.
image = register.RegisterData(image)
template = register.RegisterData(template)
# Smooth the template and image.
image.smooth(0.5)
template.smooth(0.5)
# Form the affine registration instance.
reg = register.Register(model.Projective, metric.Residual,
sampler.CubicConvolution)
# Compute an affine registration between the template and image.
step, search = reg.register(
image,
template,
alpha=0.00002,
verbose=True,
)
