wassname/scikit-image
1
0
Fork 0
mirror of https://github.com/wassname/scikit-image.git synced 2026-06-29 03:37:34 +08:00
Code Issues Packages Projects Releases Wiki Activity

Yen PEP8 and docstrings

Browse Source
This commit is contained in:
radioxoma
2013-08-15 23:03:54 +03:00
committed by radioxoma
parent e41887c66d
commit 8beb2ae605
1 changed files with 13 additions and 10 deletions
Download Patch File Download Diff File Expand all files Collapse all files
skimage/filter/thresholding.py
+13 -10
View File
@@ -95,14 +95,15 @@ def threshold_otsu(image, nbins=256):
----------
image : array
Input image.
nbins : int
nbins : int, optional
Number of bins used to calculate histogram. This value is ignored for
integer arrays.
Returns
-------
threshold : float
Threshold value.
Upper threshold value. All pixels intensities that less or equal of
this value assumed as foreground.
References
----------
@@ -113,7 +114,7 @@ def threshold_otsu(image, nbins=256):
>>> from skimage.data import camera
>>> image = camera()
>>> thresh = threshold_otsu(image)
>>> binary = image > thresh
>>> binary = image <= thresh
"""
hist, bin_centers = histogram(image, nbins)
hist = hist.astype(float)
@@ -142,14 +143,15 @@ def threshold_yen(image, nbins=256):
----------
image : array
Input image.
nbins : int
nbins : int, optional
Number of bins used to calculate histogram. This value is ignored for
integer arrays.
Returns
-------
threshold : float
Threshold value.
Upper threshold value. All pixels intensities that less or equal of
this value assumed as foreground.
References
----------
@@ -167,7 +169,7 @@ def threshold_yen(image, nbins=256):
>>> from skimage.data import camera
>>> image = camera()
>>> thresh = threshold_yen(image)
>>> binary = image > thresh
>>> binary = image <= thresh
"""
hist, bin_centers = histogram(img, nbins)
norm_histo = hist.astype(float) / hist.sum() # Probability mass function
@@ -175,9 +177,10 @@ def threshold_yen(image, nbins=256):
P1_sq = np.cumsum(norm_histo ** 2)
# Get cumsum calculated from end of squared array:
P2_sq = np.cumsum(norm_histo[::-1] ** 2)[::-1]
# P2_sq indexes is shifted +1. I assume, with P1[:-1] it's help avoid '-inf' in crit.
# In ImageJ Yen implementation, all invalid values replaced by zero.
crit = -1*np.log(P1_sq[:-1]*P2_sq[1:]) + 2.0*np.log(P1[:-1]*(1.0-P1[:-1]))
# P2_sq indexes is shifted +1. I assume, with P1[:-1] it's help avoid '-inf'
# in crit. ImageJ Yen implementation replaces those values by zero.
crit = -1 * np.log(P1_sq[:-1] * P2_sq[1:]) + \
2.0 * np.log(P1[:-1] * (1.0 - P1[:-1]))
max_crit = np.argmax(crit)
threshold = bin_centers[:-1][max_crit]
return threshold
return threshold