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Merge pull request #274 from ahojnnes/doc-update

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DOC: Overall cleanups.
This commit is contained in:
Stefan van der Walt
2012-08-26 06:20:47 -07:00
parent c11e65bf4b 00b3e90e42
commit e7f2b0fabc
10 changed files with 62 additions and 56 deletions
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doc/examples/plot_polygon.py
+5 -5
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@@ -1,10 +1,10 @@
"""
====================
Approximate Polygons
====================
===================================
Approximatea and subdivide polygons
===================================
This example shows how to approximate polygonal chains with the Douglas-Peucker
algorithm.
This example shows how to approximate (Douglas-Peucker algorithm) and subdivide
(B-Splines) polygonal chains.
"""
import numpy as np
skimage/data/__init__.py
+4 -3
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@@ -29,8 +29,9 @@ def load(f):
def camera():
"""Gray-level "camera" image, often used for segmentation
and denoising examples.
"""Gray-level "camera" image.
Often used for segmentation and denoising examples.
"""
return load("camera.png")
@@ -49,7 +50,7 @@ def lena():
def text():
""" Gray-level "text" image used for corner detection.
"""Gray-level "text" image used for corner detection.
Notes
-----
skimage/draw/_draw.pyx
+10 -7
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@@ -65,6 +65,7 @@ def line(int y, int x, int y2, int x2):
return rr, cc
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.nonecheck(False)
@@ -74,9 +75,9 @@ def polygon(y, x, shape=None):
Parameters
----------
y : (N,) ndarray
y coordinates of vertices of polygon
Y-coordinates of vertices of polygon.
x : (N,) ndarray
x coordinates of vertices of polygon
X-coordinates of vertices of polygon.
shape : tuple, optional
image shape which is used to determine maximum extents of output pixel
coordinates. This is useful for polygons which exceed the image size.
@@ -121,6 +122,7 @@ def polygon(y, x, shape=None):
return np.array(rr), np.array(cc)
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.nonecheck(False)
@@ -131,9 +133,9 @@ def ellipse(double cy, double cx, double b, double a, shape=None):
Parameters
----------
cy, cx : double
centre coordinate of ellipse
Centre coordinate of ellipse.
b, a: double
minor and major semi-axes. (x/a)**2 + (y/b)**2 = 1
Minor and major semi-axes. ``(x/a)**2 + (y/b)**2 = 1``.
Returns
-------
@@ -166,20 +168,21 @@ def ellipse(double cy, double cx, double b, double a, shape=None):
return np.array(rr), np.array(cc)
def circle(double cy, double cx, double radius, shape=None):
"""Generate coordinates of pixels within circle.
Parameters
----------
cy, cx : double
centre coordinate of circle
Centre coordinate of circle.
radius: double
radius of circle
Radius of circle.
Returns
-------
rr, cc : ndarray of int
Pixel coordinates of ellipse.
Pixel coordinates of circle.
May be used to directly index into an array, e.g.
``img[rr, cc] = 1``.
"""
skimage/exposure/exposure.py
+6
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@@ -135,30 +135,36 @@ def rescale_intensity(image, in_range=None, out_range=None):
Examples
--------
By default, intensities are stretched to the limits allowed by the dtype:
>>> image = np.array([51, 102, 153], dtype=np.uint8)
>>> rescale_intensity(image)
array([ 0, 127, 255], dtype=uint8)
It's easy to accidentally convert an image dtype from uint8 to float:
>>> 1.0 * image
array([ 51., 102., 153.])
Use `rescale_intensity` to rescale to the proper range for float dtypes:
>>> image_float = 1.0 * image
>>> rescale_intensity(image_float)
array([ 0. , 0.5, 1. ])
To maintain the low contrast of the original, use the `in_range` parameter:
>>> rescale_intensity(image_float, in_range=(0, 255))
array([ 0.2, 0.4, 0.6])
If the min/max value of `in_range` is more/less than the min/max image
intensity, then the intensity levels are clipped:
>>> rescale_intensity(image_float, in_range=(0, 102))
array([ 0.5, 1. , 1. ])
If you have an image with signed integers but want to rescale the image to
just the positive range, use the `out_range` parameter:
>>> image = np.array([-10, 0, 10], dtype=np.int8)
>>> rescale_intensity(image, out_range=(0, 127))
array([ 0, 63, 127], dtype=int8)
skimage/feature/_texture.pyx
+2 -1
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@@ -95,7 +95,8 @@ def _local_binary_pattern(np.ndarray[double, ndim=2] image,
R : float
Radius of circle (spatial resolution of the operator).
method : {'D', 'R', 'U', 'V'}
Method to determine the pattern::
Method to determine the pattern.
* 'D': 'default'
* 'R': 'ror'
* 'U': 'uniform'
skimage/feature/peak.py
+4 -9
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@@ -15,21 +15,16 @@ def peak_local_max(image, min_distance=10, threshold='deprecated',
Parameters
----------
image: ndarray of floats
image : ndarray of floats
Input image.
min_distance: int
min_distance : int
Minimum number of pixels separating peaks and image boundary.
threshold : float
Deprecated. See `threshold_rel`.
threshold_abs: float
threshold_abs : float
Minimum intensity of peaks.
threshold_rel: float
threshold_rel : float
Minimum intensity of peaks calculated as `max(image) * threshold_rel`.
num_peaks : int
Maximum number of peaks. When the number of peaks exceeds `num_peaks`,
return `num_peaks` coordinates based on peak intensity.
skimage/feature/texture.py
+3 -3
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@@ -139,7 +139,6 @@ def greycoprops(P, prop='contrast'):
`P[i,j,d,theta]` is the number of times that grey-level j
occurs at a distance d and at an angle theta from
grey-level i.
prop : {'contrast', 'dissimilarity', 'homogeneity', 'energy', \
'correlation', 'ASM'}, optional
The property of the GLCM to compute. The default is 'contrast'.
@@ -241,8 +240,9 @@ def local_binary_pattern(image, P, R, method='default'):
angular space).
R : float
Radius of circle (spatial resolution of the operator).
method : {'D', 'R', 'U', 'V'}
Method to determine the pattern::
method : {'default', 'ror', 'uniform', 'var'}
Method to determine the pattern.
* 'default': original local binary pattern which is gray scale but not
rotation invariant.
* 'ror': extension of default implementation which is gray scale and
skimage/filter/thresholding.py
+2 -2
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@@ -16,7 +16,7 @@ def threshold_adaptive(image, block_size, method='gaussian', offset=0,
Parameters
----------
image : NxM ndarray
image : (N, M) ndarray
Input image.
block_size : int
Uneven size of pixel neighborhood which is used to calculate the
@@ -45,7 +45,7 @@ def threshold_adaptive(image, block_size, method='gaussian', offset=0,
Returns
-------
threshold : NxM ndarray
threshold : (N, M) ndarray
Thresholded binary image
References
skimage/transform/_geometric.py
+25 -25
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@@ -7,7 +7,7 @@ from skimage.util import img_as_float
def _stackcopy(a, b):
"""Copy b into each color layer of a, such that::
a[:,:,0] = a[:,:,1] = ... = b
a[:,:,0] = a[:,:,1] = ... = b
Parameters
----------
@@ -37,12 +37,12 @@ class GeometricTransform(object):
Parameters
----------
coords : (N, 2) array
source coordinates
Source coordinates.
Returns
-------
coords : (N, 2) array
transformed coordinates
Transformed coordinates.
"""
raise NotImplementedError()
@@ -53,12 +53,12 @@ class GeometricTransform(object):
Parameters
----------
coords : (N, 2) array
source coordinates
Source coordinates.
Returns
-------
coords : (N, 2) array
transformed coordinates
Transformed coordinates.
"""
raise NotImplementedError()
@@ -182,9 +182,9 @@ class ProjectiveTransform(GeometricTransform):
Parameters
----------
src : (N, 2) array
source coordinates
Source coordinates.
dst : (N, 2) array
destination coordinates
Destination coordinates.
"""
xs = src[:, 0]
@@ -260,13 +260,13 @@ class AffineTransform(ProjectiveTransform):
matrix : (3, 3) array, optional
Homogeneous transformation matrix.
scale : (sx, sy) as array, list or tuple, optional
scale factors
Scale factors.
rotation : float, optional
rotation angle in counter-clockwise direction as radians
Rotation angle in counter-clockwise direction as radians.
shear : float, optional
shear angle in counter-clockwise direction as radians
Shear angle in counter-clockwise direction as radians.
translation : (tx, ty) as array, list or tuple, optional
translation parameters
Translation parameters.
"""
@@ -345,11 +345,11 @@ class SimilarityTransform(ProjectiveTransform):
matrix : (3, 3) array, optional
Homogeneous transformation matrix.
scale : float, optional
scale factor
Scale factor.
rotation : float, optional
rotation angle in counter-clockwise direction as radians
Rotation angle in counter-clockwise direction as radians.
translation : (tx, ty) as array, list or tuple, optional
x, y translation parameters
x, y translation parameters.
"""
@@ -420,9 +420,9 @@ class SimilarityTransform(ProjectiveTransform):
Parameters
----------
src : (N, 2) array
source coordinates
Source coordinates.
dst : (N, 2) array
destination coordinates
Destination coordinates.
"""
xs = src[:, 0]
@@ -530,11 +530,11 @@ class PolynomialTransform(GeometricTransform):
Parameters
----------
src : (N, 2) array
source coordinates
Source coordinates.
dst : (N, 2) array
destination coordinates
Destination coordinates.
order : int
polynomial order (number of coefficients is order + 1)
Polynomial order (number of coefficients is order + 1).
"""
xs = src[:, 0]
@@ -576,7 +576,7 @@ class PolynomialTransform(GeometricTransform):
Returns
-------
coords : (N, 2) array
transformed coordinates
Transformed coordinates.
"""
x = coords[:, 0]
@@ -692,7 +692,7 @@ def matrix_transform(coords, matrix):
Returns
-------
coords : (N, 2) array
transformed coordinates
Transformed coordinates.
"""
return ProjectiveTransform(matrix)(coords)
@@ -705,13 +705,13 @@ def warp_coords(orows, ocols, bands, coord_transform_fn,
Parameters
----------
orows : int
number of output rows
Number of output rows.
ocols : int
number of output columns
Number of output columns.
bands : int
number of color bands (aka channels)
Number of color bands (aka channels).
coord_transform_fn : callable like GeometricTransform.inverse
Return input coordinates for given output coordinates
Return input coordinates for given output coordinates.
dtype : np.dtype or string
dtype for return value (sane choices: float32 or float64)
skimage/transform/radon_transform.py
+1 -1
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@@ -100,7 +100,7 @@ def iradon(radon_image, theta=None, output_size=None,
the image corresponds to a projection along a different angle.
theta : array_like, dtype=float, optional
Reconstruction angles (in degrees). Default: m angles evenly spaced
between 0 and 180 (if the shape of `radon_image` is nxm)
between 0 and 180 (if the shape of `radon_image` is (N, M)).
output_size : int
Number of rows and columns in the reconstruction.
filter : str, optional (default ramp)