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,
verbose=True)
search.extend(_search)
plot.searchInspector(search)
return spline_sampler.f(image, spline_model.warp(p)).reshape(image.shape)
# Form some test data (lena, lena rotated 20 degrees)
image = register.RegisterData(misc.lena())
template = register.RegisterData(warp(misc.lena()))
# Form the registrator.
spline = register.Register(model.CubicSpline, metric.Residual, sampler.Spline)
# Image pyramid registration can be executed like so:
fullSearch = []
displacement = None
for factor in [20., 10., 5.]:
downImage = image.downsample(factor)
downTemplate = template.downsample(factor)
step, search = spline.register(downImage,
downTemplate,
displacement=displacement,
verbose=True)
displacement = step.displacement
fullSearch.extend(search)
plot.searchInspector(fullSearch)
from imreg.visualize import plot
from imreg import register
# Form some test data (lena, lena rotated 20 degrees)
image = imread('data/frown.png')[:, :, 0]
template = imread('data/smile.png')[:, :, 0]
# Form the affine registration instance.
affine = register.Register(
model.CubicSpline,
metric.Residual,
sampler.Spline
)
# Coerce the image data into RegisterData.
image = register.RegisterData(image)
template = register.RegisterData(template)
# Smooth the template and image.
image.smooth(1.5)
template.smooth(1.5)
# Register.
step, search = affine.register(
image,
template,
verbose=True
)
plot.searchInspector(search)
template = register.RegisterData(warp(misc.lena()))
# Form the registrator.
spline = register.Register(
model.CubicSpline,
metric.Residual,
sampler.Spline
)
# Image pyramid registration can be executed like so:
fullSearch = []
displacement = None
for factor in [ 20., 10., 5.]:
downImage = image.downsample(factor)
downTemplate = template.downsample(factor)
step, search = spline.register(
downImage,
downTemplate,
displacement=displacement,
verbose=True
)
displacement = step.displacement
fullSearch.extend(search)
plot.searchInspector(fullSearch)
