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DOC: Fix Sphinx warnings for regionprops

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In particular:
- Indented equations in docstring for the `properties` parameter *must* be surrounded by whitespace to prevent Sphinx warnings.
- Fix reference rendering
This commit is contained in:
Tony S Yu
2012-09-02 15:46:39 -04:00
parent 0643b8108f
commit ec22dba257
1 changed files with 49 additions and 6 deletions
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skimage/measure/_regionprops.py
+49 -6
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@@ -64,96 +64,139 @@ def regionprops(label_image, properties=['Area', 'Centroid'],
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
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
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
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
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
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