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import numpy as np
from scipy import ndimage as ndi
from skimage.color import rgb2gray
from skimage import data, img_as_float
# guard against import of a non-existent registration module in older skimage
try:
from skimage import registration
except ImportError:
pass
# deal with move and rename of phase_cross_correlation across versions
try:
from skimage.registration import phase_cross_correlation
except ImportError:
try:
from skimage.feature import register_translation
phase_cross_correlation = register_translation
except ImportError:
phase_cross_correlation = None
class RegistrationSuite:
"""Benchmark for registration routines in scikit-image."""
param_names = ["dtype"]
params = [(np.float32, np.float64)]
def setup(self, *args):
I0, I1, _ = data.stereo_motorcycle()
self.I0 = rgb2gray(I0)
self.I1 = rgb2gray(I1)
def time_tvl1(self, dtype):
registration.optical_flow_tvl1(self.I0, self.I1, dtype=dtype)
def time_ilk(self, dtype):
registration.optical_flow_ilk(self.I0, self.I1, dtype=dtype)
class PhaseCrossCorrelationRegistration:
"""Benchmarks for registration.phase_cross_correlation in scikit-image"""
param_names = ["ndims", "image_size", "upsample_factor", "dtype"]
params = [(2, 3), (32, 100), (1, 5, 10), (np.complex64, np.complex128)]
def setup(self, ndims, image_size, upsample_factor, dtype, *args):
if phase_cross_correlation is None:
raise NotImplementedError("phase_cross_correlation unavailable")
shifts = (-2.3, 1.7, 5.4, -3.2)[:ndims]
phantom = img_as_float(data.binary_blobs(length=image_size, n_dim=ndims))
self.reference_image = np.fft.fftn(phantom).astype(dtype, copy=False)
self.shifted_image = ndi.fourier_shift(self.reference_image, shifts)
self.shifted_image = self.shifted_image.astype(dtype, copy=False)
def time_phase_cross_correlation(self, ndims, image_size, upsample_factor, *args):
phase_cross_correlation(
self.reference_image,
self.shifted_image,
upsample_factor=upsample_factor,
space="fourier",
)
def peakmem_reference(self, *args):
"""Provide reference for memory measurement with empty benchmark.
Peakmem benchmarks measure the maximum amount of RAM used by a
function. However, this maximum also includes the memory used
during the setup routine (as of asv 0.2.1; see [1]_).
Measuring an empty peakmem function might allow us to disambiguate
between the memory used by setup and the memory used by target (see
other ``peakmem_`` functions below).
References
----------
.. [1]: https://asv.readthedocs.io/en/stable/writing_benchmarks.html#peak-memory
"""
pass
def peakmem_phase_cross_correlation(
self, ndims, image_size, upsample_factor, *args
):
phase_cross_correlation(
self.reference_image,
self.shifted_image,
upsample_factor=upsample_factor,
space="fourier",
)
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