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Merge 4a79238 into daa991a
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tkumpumaki authored Sep 19, 2022
2 parents daa991a + 4a79238 commit d3e5df6
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49 changes: 48 additions & 1 deletion benchmarks/benchmark_filters.py
Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
# https://asv.readthedocs.io/en/latest/writing_benchmarks.html
import numpy as np

from skimage import data, filters
from skimage import data, filters, color
from skimage.filters.thresholding import threshold_li


Expand Down Expand Up @@ -103,3 +103,50 @@ def time_integer_image(self):

def time_float32_image(self):
result1 = threshold_li(self.image_float32)


class RidgeFilters:
"""Benchmark ridge filters in scikit-image."""

def setup(self):
# Ensure memory footprint of lazy import is included in reference
self._ = filters.meijering, filters.sato, filters.frangi, filters.hessian
self.image = color.rgb2gray(data.retina())

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 time_meijering(self):
filters.meijering(self.image)

def peakmem_meijering(self):
filters.meijering(self.image)

def time_sato(self):
filters.sato(self.image)

def peakmem_sato(self):
filters.sato(self.image)

def time_frangi(self):
filters.frangi(self.image)

def peakmem_frangi(self):
filters.frangi(self.image)

def time_hessian(self):
filters.hessian(self.image)

def peakmem_hessian(self):
filters.hessian(self.image)
25 changes: 16 additions & 9 deletions skimage/filters/ridges.py
Original file line number Diff line number Diff line change
Expand Up @@ -149,7 +149,9 @@ def meijering(image, sigmas=range(1, 10, 2), alpha=None,
mtx = linalg.circulant(
[1, *[alpha] * (image.ndim - 1)]).astype(image.dtype)

filtered = []
# Generate empty array for storing maximum value
# from different (sigma) scales
filtered_max = np.zeros(image.shape, image.dtype)
for sigma in sigmas: # Filter for all sigmas.
eigvals = hessian_matrix_eigvals(hessian_matrix(
image, sigma, mode=mode, cval=cval, use_gaussian_derivatives=True))
Expand All @@ -164,8 +166,9 @@ def meijering(image, sigmas=range(1, 10, 2), alpha=None,
max_val = vals.max()
if max_val > 0:
vals /= max_val
filtered.append(vals)
return np.max(filtered, axis=0) # Return pixel-wise max over all sigmas.
filtered_max = np.maximum(filtered_max, vals)

return filtered_max # Return pixel-wise max over all sigmas.


def sato(image, sigmas=range(1, 10, 2), black_ridges=True,
Expand Down Expand Up @@ -221,7 +224,9 @@ def sato(image, sigmas=range(1, 10, 2), black_ridges=True,
if not black_ridges: # Normalize to black ridges.
image = -image

filtered = []
# Generate empty array for storing maximum value
# from different (sigma) scales
filtered_max = np.zeros(image.shape, image.dtype)
for sigma in sigmas: # Filter for all sigmas.
eigvals = hessian_matrix_eigvals(hessian_matrix(
image, sigma, mode=mode, cval=cval, use_gaussian_derivatives=True))
Expand All @@ -232,8 +237,8 @@ def sato(image, sigmas=range(1, 10, 2), black_ridges=True,
eigvals = eigvals[:-1]
vals = (sigma ** 2
* np.prod(np.maximum(eigvals, 0), 0) ** (1 / len(eigvals)))
filtered.append(vals)
return np.max(filtered, axis=0) # Return pixel-wise max over all sigmas.
filtered_max = np.maximum(filtered_max, vals)
return filtered_max # Return pixel-wise max over all sigmas.


def frangi(image, sigmas=range(1, 10, 2), scale_range=None,
Expand Down Expand Up @@ -318,7 +323,9 @@ def frangi(image, sigmas=range(1, 10, 2), scale_range=None,
if not black_ridges: # Normalize to black ridges.
image = -image

filtered = []
# Generate empty array for storing maximum value
# from different (sigma) scales
filtered_max = np.zeros(image.shape, image.dtype)
for sigma in sigmas: # Filter for all sigmas.
eigvals = hessian_matrix_eigvals(hessian_matrix(
image, sigma, mode=mode, cval=cval, use_gaussian_derivatives=True))
Expand All @@ -345,8 +352,8 @@ def frangi(image, sigmas=range(1, 10, 2), scale_range=None,
vals = 1.0 - np.exp(-r_a**2 / (2 * alpha**2)) # plate sensitivity
vals *= np.exp(-r_b**2 / (2 * beta**2)) # blobness
vals *= 1.0 - np.exp(-s**2 / (2 * gamma**2)) # structuredness
filtered.append(vals)
return np.max(filtered, axis=0) # Return pixel-wise max over all sigmas.
filtered_max = np.maximum(filtered_max, vals)
return filtered_max # Return pixel-wise max over all sigmas.


def hessian(image, sigmas=range(1, 10, 2), scale_range=None, scale_step=None,
Expand Down

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