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Merge pull request #295 from tonysyu/doc-cleanups

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Doc cleanups
This commit is contained in:
Stefan van der Walt
2012-09-02 22:12:13 -07:00
parent dfbbb4d766 427a79fc6a
commit 2949d18247
12 changed files with 105 additions and 129 deletions
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TASKS.txt
+11 -58
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@@ -1,64 +1,6 @@
.. role:: strike
SciPy 2012 Sprint
=================
Welcome! Stefan van der Walt and Tony Yu are organizing a coding sprint for
scikits-image at SciPy 2012. Anyone who's interested can join the party on July
20th starting at 9 AM (location to be determined).
We have a list of tasks for all levels of programmers, but we'd be really
interested in new ideas as well.
Basic
-----
These tasks should just require some basic Python knowledge.
Code review
```````````
* geometric transformation PR
* morphological reconstruction PR
* Testing visualization tools
Docs
````
* Task-based examples (`Where's Waldo`_, `Flatten Sudoku Puzzle`_)
* Organize/add-topics to user guide (Add overview of packages)
.. _Flatten Sudoku Puzzle: http://stackoverflow.com/questions/10196198/how-to-remove-convexity-defects-in-sudoku-square/11366549#11366549
.. _Where's Waldo: http://stackoverflow.com/questions/8479058/how-do-i-find-waldo-with-mathematica
Features
````````
* Add text, anti-aliasing to the draw module
* Lab color space conversion
* Add slicing to ImageCollection object
* Add imread_collection to all imread backends
* Resurrect the `Image object <http://bit.ly/skimage_image_cls>`__ and add
EXIF and TIFF tags.
- Add IPython display protocol.
- Add an htmlrepr to the Image object. Should we return image objects from
all I/O routines?
* Add @greyimage decorator to check if input is a greyscale image
Intermediate
------------
These tasks may require some understanding of image processing algorithms or
scikits-image internals.
* Add binary features (BRIEF, BRISK, FREAK)
* Add `STAR features <http://pr.willowgarage.com/wiki/Star_Detector>`__
* Using the visualization tools, add an FFT-domain image editor
* `Blurring kernel estimation <http://bit.ly/Nril3u>`__
* Better video loading (move to plugin framework, add backends)
.. _howto_contribute:
How to contribute to ``skimage``
@@ -102,14 +44,19 @@ Implement Algorithms
- Convex hulls of objects in a labels matrix (simply adapt current convex hull
image code--this one's low hanging fruit). Generalise this solution to also
skeletonize objects in a labels matrix.
- Add binary features (BRIEF, BRISK, FREAK)
- Add `STAR features <http://pr.willowgarage.com/wiki/Star_Detector>`__
- `Blurring kernel estimation <http://bit.ly/Nril3u>`__
Drawing (directly on an ndarray)
````````````````````````````````
- Wu's algorithm for circles
- Text rendering
- Add anti-aliasing
Infrastructure
--------------
- Add @greyimage decorator to check if input is a greyscale image
- :strike:`Implement a new backend system so that we may start including
PyOpenCL-based algorithms`
@@ -175,6 +122,12 @@ io
* Update ``qt_plugin.py`` and other plugins to view collections.
* Rewrite GTK backend using GObject Introspection for Py3K compatibility.
* Add DICOM plugin for `GDCM <http://sourceforge.net/apps/mediawiki/gdcm>`__.
* Add ``imread_collection`` to all ``imread`` backends
* Better video loading (move to plugin framework, add backends)
viewer
``````
* Using the visualization tools, add an FFT-domain image editor
docs
````
doc/tools/apigen.py
-24
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@@ -269,10 +269,6 @@ class ApiDocWriter(object):
ad = '.. AUTO-GENERATED FILE -- DO NOT EDIT!\n\n'
chap_title = uri_short
#ad += (chap_title+'\n'+ self.rst_section_levels[1] * len(chap_title)
# + '\n\n')
# Set the chapter title to read 'module' for all modules except for the
# main packages
if '.' in uri:
@@ -281,21 +277,8 @@ class ApiDocWriter(object):
title = ':mod:`' + uri_short + '`'
ad += title + '\n' + self.rst_section_levels[1] * len(title)
if len(classes):
ad += '\nInheritance diagram for ``%s``:\n\n' % uri
ad += '.. inheritance-diagram:: %s \n' % uri
ad += ' :parts: 3\n'
ad += '\n.. automodule:: ' + uri + '\n'
ad += '\n.. currentmodule:: ' + uri + '\n'
# multi_class = len(classes) > 1
# multi_fx = len(functions) > 1
# if multi_class:
# ad += '\n' + 'Classes' + '\n' + \
# self.rst_section_levels[2] * 7 + '\n'
# elif len(classes) and multi_fx:
# ad += '\n' + 'Class' + '\n' + \
# self.rst_section_levels[2] * 5 + '\n'
for c in classes:
ad += '\n:class:`' + c + '`\n' \
+ self.rst_section_levels[2] * \
@@ -305,15 +288,8 @@ class ApiDocWriter(object):
ad += ' :members:\n' \
' :undoc-members:\n' \
' :show-inheritance:\n' \
' :inherited-members:\n' \
'\n' \
' .. automethod:: __init__\n'
# if multi_fx:
# ad += '\n' + 'Functions' + '\n' + \
# self.rst_section_levels[2] * 9 + '\n\n'
# elif len(functions) and multi_class:
# ad += '\n' + 'Function' + '\n' + \
# self.rst_section_levels[2] * 8 + '\n\n'
ad += '.. autosummary::\n\n'
for f in functions:
ad += ' ' + uri + '.' + f + '\n'
skimage/filter/thresholding.py
+7 -5
View File
@@ -24,11 +24,13 @@ def threshold_adaptive(image, block_size, method='gaussian', offset=0,
method : {'generic', 'gaussian', 'mean', 'median'}, optional
Method used to determine adaptive threshold for local neighbourhood in
weighted mean image.
* 'generic': use custom function (see `param` parameter)
* 'gaussian': apply gaussian filter (see `param` parameter for custom
sigma value)
* 'mean': apply arithmetic mean filter
* 'median' apply median rank filter
* 'generic': use custom function (see `param` parameter)
* 'gaussian': apply gaussian filter (see `param` parameter for custom
sigma value)
* 'mean': apply arithmetic mean filter
* 'median' apply median rank filter
By default the 'gaussian' method is used.
offset : float, optional
Constant subtracted from weighted mean of neighborhood to calculate
skimage/graph/_mcp.pyx
+8 -8
View File
@@ -102,7 +102,7 @@ def _offset_edge_map(shape, offsets):
"""
indices = np.indices(shape) # indices.shape = (n,)+shape
#get the distance from each index to the upper or lower edge in each dim
pos_edges = (shape - indices.T).T
neg_edges = -1 - indices
@@ -112,7 +112,7 @@ def _offset_edge_map(shape, offsets):
mins = offsets.min(axis=0)
for pos, neg, mx, mn in zip(pos_edges, neg_edges, maxes, mins):
pos[pos > mx] = 0
neg[neg < mn] = 0
neg[neg < mn] = 0
return pos_edges.astype(EDGE_D), neg_edges.astype(EDGE_D)
def make_offsets(d, fully_connected):
@@ -130,8 +130,8 @@ def make_offsets(d, fully_connected):
-------
offsets : list of tuples of length `d`
Example
-------
Examples
--------
The singly-connected 2-d neighborhood is four offsets:
@@ -216,7 +216,7 @@ cdef class MCP:
`costs` array at each point on the path. The class MCP_Geometric, on the
other hand, accounts for the fact that diagonal vs. axial moves are of
different lengths, and weights the path cost accordingly.
Array elements with infinite or negative costs will simply be ignored, as
will paths whose cumulative cost overflows to infinite.
@@ -295,7 +295,7 @@ cdef class MCP:
pos, neg = _offset_edge_map(costs.shape, self.offsets)
self.flat_pos_edge_map = pos.reshape((self.dim, size), order='F')
self.flat_neg_edge_map = neg.reshape((self.dim, size), order='F')
# The offset lengths are the distances traveled along each offset
self.offset_lengths = np.sqrt(
@@ -449,7 +449,7 @@ cdef class MCP:
# edge along any axis
is_at_edge = 0
for d in range(dim):
if (flat_pos_edge_map[d, index] != 0 or
if (flat_pos_edge_map[d, index] != 0 or
flat_neg_edge_map[d, index] != 0):
is_at_edge = 1
break
@@ -490,7 +490,7 @@ cdef class MCP:
new_cost = flat_costs[new_index]
if new_cost < 0 or new_cost == inf:
continue
# Now we ask the heap to append or update the cost to
# this new point, but only if that point isn't already
# in the heap, or it is but the new cost is lower.
skimage/io/_io.py
+2 -2
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@@ -29,8 +29,8 @@ class Image(np.ndarray):
def __new__(cls, arr, **kwargs):
"""Set the image data and tags according to given parameters.
Input:
------
Parameters
----------
arr : ndarray
Image data.
kwargs : Image tags as keywords
skimage/io/_plugins/tifffile_plugin.ini
+1 -1
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@@ -1,3 +1,3 @@
[tifffile]
description = Open and save TIFF and TIFF-based (LSM, STK, etc.) images using tifffile.py
description = Load and save TIFF and TIFF-based images using tifffile.py
provides = imread, imsave
skimage/measure/_regionprops.py
+55 -12
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@@ -58,102 +58,145 @@ def regionprops(label_image, properties=['Area', 'Centroid'],
Parameters
----------
label_image : N x M ndarray
label_image : (N, M) ndarray
Labelled input image.
properties : {'all', list}
Shape measurements to be determined for each labelled image region.
Default is `['Area', 'Centroid']`. The following properties can be
determined:
* Area : int
Number of pixels of region.
* BoundingBox : tuple
Bounding box `(min_row, min_col, max_row, max_col)`
* CentralMoments : 3 x 3 ndarray
* CentralMoments : (3, 3) ndarray
Central moments (translation invariant) up to 3rd order.
mu_ji = sum{ array(x, y) * (x - x_c)^j * (y - y_c)^i }
where the sum is over the `x`, `y` coordinates of the region,
and `x_c` and `y_c` are the coordinates of the region's centroid.
* Centroid : array
Centroid coordinate tuple `(row, col)`.
* ConvexArea : int
Number of pixels of convex hull image.
* ConvexImage : (H, J) ndarray
Binary convex hull image which has the same size as bounding box.
* Coordinates : (N, 2) ndarray
Coordinate list `(row, col)` of the region.
* Eccentricity : float
Eccentricity of the ellipse that has the same second-moments as the
region. The eccentricity is the ratio of the distance between its
minor and major axis length. The value is between 0 and 1.
* EquivDiameter : float
The diameter of a circle with the same area as the region.
* EulerNumber : int
Euler number of region. Computed as number of objects (= 1)
subtracted by number of holes (8-connectivity).
* Extent : float
Ratio of pixels in the region to pixels in the total bounding box.
Computed as `Area / (rows*cols)`
* FilledArea : int
Number of pixels of filled region.
* FilledImage : (H, J) ndarray
Binary region image with filled holes which has the same size as
bounding box.
* HuMoments : tuple
Hu moments (translation, scale and rotation invariant).
* Image : (H, J) ndarray
Sliced binary region image which has the same size as bounding box.
* MajorAxisLength : float
The length of the major axis of the ellipse that has the same
normalized second central moments as the region.
* MaxIntensity: float
Value with the greatest intensity in the region.
* MeanIntensity: float
Value with the mean intensity in the region.
* MinIntensity: float
Value with the least intensity in the region.
* MinorAxisLength : float
The length of the minor axis of the ellipse that has the same
normalized second central moments as the region.
* Moments : 3 x 3 ndarray
* Moments : (3, 3) ndarray
Spatial moments up to 3rd order.
m_ji = sum{ array(x, y) * x^j * y^i }
where the sum is over the `x`, `y` coordinates of the region.
* NormalizedMoments : 3 x 3 ndarray
* NormalizedMoments : (3, 3) ndarray
Normalized moments (translation and scale invariant) up to 3rd
order.
nu_ji = mu_ji / m_00^[(i+j)/2 + 1]
where `m_00` is the zeroth spatial moment.
* Orientation : float
Angle between the X-axis and the major axis of the ellipse that has
the same second-moments as the region. Ranging from `-pi/2` to
`pi/2` in counter-clockwise direction.
* Perimeter : float
Perimeter of object which approximates the contour as a line through
the centers of border pixels using a 4-connectivity.
* Solidity : float
Ratio of pixels in the region to pixels of the convex hull image.
* WeightedCentralMoments : 3 x 3 ndarray
* WeightedCentralMoments : (3, 3) ndarray
Central moments (translation invariant) of intensity image up to 3rd
order.
wmu_ji = sum{ array(x, y) * (x - x_c)^j * (y - y_c)^i }
where the sum is over the `x`, `y` coordinates of the region,
and `x_c` and `y_c` are the coordinates of the region's centroid.
* WeightedCentroid : array
Centroid coordinate tuple `(row, col)` weighted with intensity
image.
* WeightedHuMoments : tuple
Hu moments (translation, scale and rotation invariant) of intensity
image.
* WeightedMoments : (3, 3) ndarray
Spatial moments of intensity image up to 3rd order.
wm_ji = sum{ array(x, y) * x^j * y^i }
where the sum is over the `x`, `y` coordinates of the region.
* WeightedNormalizedMoments : 3 x 3 ndarray
* WeightedNormalizedMoments : (3, 3) ndarray
Normalized moments (translation and scale invariant) of intensity
image up to 3rd order.
wnu_ji = wmu_ji / wm_00^[(i+j)/2 + 1]
where `wm_00` is the zeroth spatial moment (intensity-weighted
area).
intensity_image : (N, M) ndarray, optional
Intensity image with same size as labelled image. Default is None.
@@ -214,11 +257,11 @@ def regionprops(label_image, properties=['Area', 'Centroid'],
cc = m[1, 0] / m[0, 0]
mu = _moments.central_moments(array, cr, cc, 3)
#: elements of the inertia tensor [a b; b c]
# elements of the inertia tensor [a b; b c]
a = mu[2, 0] / mu[0, 0]
b = mu[1, 1] / mu[0, 0]
c = mu[0, 2] / mu[0, 0]
#: eigen values of inertia tensor
# eigen values of inertia tensor
l1 = (a + c) / 2 + sqrt(4 * b ** 2 + (a - c) ** 2) / 2
l2 = (a + c) / 2 - sqrt(4 * b ** 2 + (a - c) ** 2) / 2
@@ -378,7 +421,7 @@ def perimeter(image, neighbourhood=4):
----------
image : array
binary image
neighbourhood: 4 or 8, optional
neighbourhood : 4 or 8, optional
neighbourhood connectivity for border pixel determination, default 4
Returns
@@ -388,9 +431,9 @@ def perimeter(image, neighbourhood=4):
References
----------
K. Benkrid, D. Crookes. Design and FPGA Implementation of a Perimeter
Estimator. The Queen's University of Belfast.
http://www.cs.qub.ac.uk/~d.crookes/webpubs/papers/perimeter.doc
.. [1] K. Benkrid, D. Crookes. Design and FPGA Implementation of
a Perimeter Estimator. The Queen's University of Belfast.
http://www.cs.qub.ac.uk/~d.crookes/webpubs/papers/perimeter.doc
"""
if neighbourhood == 4:
strel = STREL_4
skimage/morphology/greyreconstruct.py
+11 -10
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@@ -101,17 +101,18 @@ def reconstruction(seed, mask, method='dilation', selem=None, offset=None):
Notes
-----
The algorithm is taken from:
[1] Robinson, "Efficient morphological reconstruction: a downhill filter",
Pattern Recognition Letters 25 (2004) 1759-1767.
The algorithm is taken from [1]_. Applications for greyscale reconstruction
are discussed in [2]_ and [3]_.
Applications for greyscale reconstruction are discussed in:
[2] Vincent, L., "Morphological Grayscale Reconstruction in Image Analysis:
Applications and Efficient Algorithms", IEEE Transactions on Image
Processing (1993)
[3] Soille, P., "Morphological Image Analysis: Principles and Applications",
Chapter 6, 2nd edition (2003), ISBN 3540429883.
References
----------
.. [1] Robinson, "Efficient morphological reconstruction: a downhill
filter", Pattern Recognition Letters 25 (2004) 1759-1767.
.. [2] Vincent, L., "Morphological Grayscale Reconstruction in Image
Analysis: Applications and Efficient Algorithms", IEEE Transactions
on Image Processing (1993)
.. [3] Soille, P., "Morphological Image Analysis: Principles and
Applications", Chapter 6, 2nd edition (2003), ISBN 3540429883.
"""
assert tuple(seed.shape) == tuple(mask.shape)
if method == 'dilation' and np.any(seed > mask):
skimage/transform/_geometric.py
+1 -1
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@@ -787,7 +787,7 @@ def warp(image, inverse_map=None, map_args={}, output_shape=None, order=1,
----------
image : 2-D array
Input image.
inverse_map : transformation object, callable xy = f(xy, **kwargs)
inverse_map : transformation object, callable ``xy = f(xy, **kwargs)``
Inverse coordinate map. A function that transforms a (N, 2) array of
``(x, y)`` coordinates in the *output image* into their corresponding
coordinates in the *source image* (e.g. a transformation object or its
skimage/transform/hough_transform.py
+6 -5
View File
@@ -61,8 +61,9 @@ except ImportError:
pass
def probabilistic_hough(img, threshold=10, line_length=50, line_gap=10, theta=None):
"""Performs a progressive probabilistic line Hough transform and returns the detected lines.
def probabilistic_hough(img, threshold=10, line_length=50, line_gap=10,
theta=None):
"""Return lines from a progressive probabilistic line Hough transform.
Parameters
----------
@@ -87,9 +88,9 @@ def probabilistic_hough(img, threshold=10, line_length=50, line_gap=10, theta=No
References
----------
.. [1] C. Galamhos, J. Matas and J. Kittler,"Progressive probabilistic Hough
transform for line detection", in IEEE Computer Society Conference on
Computer Vision and Pattern Recognition, 1999.
.. [1] C. Galamhos, J. Matas and J. Kittler, "Progressive probabilistic
Hough transform for line detection", in IEEE Computer Society
Conference on Computer Vision and Pattern Recognition, 1999.
"""
return _probabilistic_hough(img, threshold, line_length, line_gap, theta)
skimage/util/dtype.py
+1 -1
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@@ -344,7 +344,7 @@ def img_as_bool(image, force_copy=False):
Returns
-------
out : ndarray of bool (bool_)
out : ndarray of bool (`bool_`)
Output image.
Notes
skimage/util/montage.py
+2 -2
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@@ -45,8 +45,8 @@ def montage2d(arr_in, fill='mean', rescale_intensity=False):
Output array where 'alpha' has been determined automatically to
fit (at least) the `n_images` in `arr_in`.
Example
-------
Examples
--------
>>> import numpy as np
>>> from skimage.util.montage import montage2d
>>> arr_in = np.arange(3 * 2 * 2).reshape(3, 2, 2)