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# See "Writing benchmarks" in the asv docs for more information.
# https://asv.readthedocs.io/en/latest/writing_benchmarks.html
import math
import numpy as np
from skimage import data, img_as_float
from skimage.transform import rescale
from skimage import exposure
class ExposureSuite:
"""Benchmark for exposure routines in scikit-image."""
def setup(self):
self.image_u8 = data.moon()
self.image = img_as_float(self.image_u8)
self.image = rescale(self.image, 2.0, anti_aliasing=False)
# for Contrast stretching
self.p2, self.p98 = np.percentile(self.image, (2, 98))
def time_equalize_hist(self):
# Run 10x to average out performance
# note that this is not needed as asv does this kind of averaging by
# default, but this loop remains here to maintain benchmark continuity
for i in range(10):
exposure.equalize_hist(self.image)
def time_equalize_adapthist(self):
exposure.equalize_adapthist(self.image, clip_limit=0.03)
def time_rescale_intensity(self):
exposure.rescale_intensity(self.image, in_range=(self.p2, self.p98))
def time_histogram(self):
# Running it 10 times to achieve significant performance time.
for i in range(10):
exposure.histogram(self.image)
def time_gamma_adjust_u8(self):
for i in range(10):
_ = exposure.adjust_gamma(self.image_u8)
class MatchHistogramsSuite:
param_names = ["shape", "dtype", "multichannel"]
params = [
((64, 64), (256, 256), (1024, 1024)),
(np.uint8, np.uint32, np.float32, np.float64),
(False, True),
]
def _tile_to_shape(self, image, shape, multichannel):
n_tile = tuple(math.ceil(s / n) for s, n in zip(shape, image.shape))
if multichannel:
image = image[..., np.newaxis]
n_tile = n_tile + (3,)
image = np.tile(image, n_tile)
sl = tuple(slice(s) for s in shape)
return image[sl]
"""Benchmark for exposure routines in scikit-image."""
def setup(self, shape, dtype, multichannel):
self.image = data.moon().astype(dtype, copy=False)
self.reference = data.camera().astype(dtype, copy=False)
self.image = self._tile_to_shape(self.image, shape, multichannel)
self.reference = self._tile_to_shape(self.reference, shape, multichannel)
channel_axis = -1 if multichannel else None
self.kwargs = {'channel_axis': channel_axis}
def time_match_histogram(self, *args):
exposure.match_histograms(self.image, self.reference, **self.kwargs)
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_match_histogram(self, *args):
exposure.match_histograms(self.image, self.reference, **self.kwargs)
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