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"""Benchmarks for `skimage.segmentation`.
See "Writing benchmarks" in the asv docs for more information.
"""
import numpy as np
from numpy.lib import NumpyVersion as Version
import skimage
from skimage import data, filters, segmentation
from . import _channel_kwarg
try:
from skimage.segmentation import watershed
except ImportError:
# older scikit-image had this function under skimage.morphology
from skimage.morphology import watershed
class SlicSegmentation:
"""Benchmark for segmentation routines in scikit-image."""
def setup(self):
self.image = np.random.random((200, 200, 100))
self.image[:100, :100, :] += 1
self.image[150:, 150:, :] += 0.5
self.msk = np.zeros((200, 200, 100))
self.msk[10:-10, 10:-10, 10:-10] = 1
self.msk_slice = self.msk[..., 50]
if Version(skimage.__version__) >= Version('0.17.0'):
self.slic_kwargs = dict(start_label=1)
else:
self.slic_kwargs = {}
def time_slic_basic(self):
segmentation.slic(
self.image,
enforce_connectivity=False,
**_channel_kwarg(False),
**self.slic_kwargs,
)
def time_slic_basic_multichannel(self):
segmentation.slic(
self.image,
enforce_connectivity=False,
**_channel_kwarg(True),
**self.slic_kwargs,
)
def peakmem_setup(self):
"""peakmem includes the memory used by setup.
Peakmem benchmarks measure the maximum amount of RAM used by a
function. However, this maximum also includes the memory used
by ``setup`` (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 slic (see
``peakmem_slic_basic``, below).
References
----------
.. [1]: https://asv.readthedocs.io/en/stable/writing_benchmarks.html#peak-memory
"""
pass
def peakmem_slic_basic(self):
segmentation.slic(
self.image,
enforce_connectivity=False,
**_channel_kwarg(False),
**self.slic_kwargs,
)
def peakmem_slic_basic_multichannel(self):
segmentation.slic(
self.image,
enforce_connectivity=False,
**_channel_kwarg(True),
**self.slic_kwargs,
)
class MaskSlicSegmentation(SlicSegmentation):
"""Benchmark for segmentation routines in scikit-image."""
def setup(self):
try:
mask = np.zeros((64, 64)) > 0
mask[10:-10, 10:-10] = 1
segmentation.slic(np.ones_like(mask), mask=mask, **_channel_kwarg(False))
except TypeError:
raise NotImplementedError("masked slic unavailable")
self.image = np.random.random((200, 200, 100))
self.image[:100, :100, :] += 1
self.image[150:, 150:, :] += 0.5
self.msk = np.zeros((200, 200, 100))
self.msk[10:-10, 10:-10, 10:-10] = 1
self.msk_slice = self.msk[..., 50]
if Version(skimage.__version__) >= Version('0.17.0'):
self.slic_kwargs = dict(start_label=1)
else:
self.slic_kwargs = {}
def time_mask_slic(self):
segmentation.slic(
self.image,
enforce_connectivity=False,
mask=self.msk,
**_channel_kwarg(False),
)
def time_mask_slic_multichannel(self):
segmentation.slic(
self.image,
enforce_connectivity=False,
mask=self.msk_slice,
**_channel_kwarg(True),
)
class Watershed:
param_names = ["seed_count", "connectivity", "compactness"]
params = [(5, 500), (1, 2), (0, 0.01)]
def setup(self, *args):
self.image = filters.sobel(data.coins())
def time_watershed(self, seed_count, connectivity, compactness):
watershed(self.image, seed_count, connectivity, compactness=compactness)
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_watershed(self, seed_count, connectivity, compactness):
watershed(self.image, seed_count, connectivity, compactness=compactness)
|
