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Skip build on 3.2

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Skip doc build on 3.2

Skip doc build on 3.2

Use block literal

Fix html make target

Revert change to threshold_isodata

Fix skimage.novice and thresholding warnings

Fix feature.__init__ __all__

Fix generic.py link

Doc reference fixes

Fix missing class member warnings

Add parallelization to the user guide toc

Remove unused automodule directives

Fix sub_dirs in gallery_index

Remove unused file and fix reference to api/api

Add missing links to user guide

Use pre-output links to images

Recover from Juan's doc 'upgrades'
This commit is contained in:
Steven Silvester
2015-02-07 16:40:37 -06:00
parent 09876408fc
commit be7154f23d
13 changed files with 45 additions and 44 deletions
Download Patch File Download Diff File Expand all files Collapse all files
doc/source/user_guide/numpy_images.txt
+17 -15
View File
@@ -81,7 +81,7 @@ disk: ::
... (nrows / 2)**2)
>>> camera[outer_disk_mask] = 0
.. image:: ../../_images/plot_camera_numpy_1.png
.. image:: ../auto_examples/images/plot_camera_numpy_1.png
:width: 45%
:target: ../auto_examples/plot_camera_numpy.html
@@ -123,6 +123,8 @@ the grayscale image above:
Using a 2D mask on a 2D color image
>>> from skimage import data
>>> cat = data.chelsea()
>>> reddish = cat[:, :, 0] > 160
>>> cat[reddish] = [0, 255, 0]
>>> plt.imshow(cat)
@@ -153,14 +155,14 @@ These conventions are summarized below:
.. table:: Dimension name and order conventions in scikit-image
======================== ========================================
Image type coordinates
======================== ========================================
2D grayscale (row, col)
2D multichannel (eg. RGB) (row, col, ch)
3D grayscale (pln, row, col)
3D multichannel (pln, row, col, ch)
======================== ========================================
========================= ========================================
Image type coordinates
========================= ========================================
2D grayscale (row, col)
2D multichannel (eg. RGB) (row, col, ch)
3D grayscale (pln, row, col)
3D multichannel (pln, row, col, ch)
========================= ========================================
Many functions in scikit-image operate on 3D images directly:
@@ -248,9 +250,9 @@ We can then supplement the above table as follows:
.. table:: Addendum to dimension names and orders in scikit-image
======================== ========================================
Image type coordinates
======================== ========================================
2D color video (t, row, col, ch)
3D multichannel video (t, pln, row, col, ch)
======================== ========================================
======================== ========================================
Image type coordinates
======================== ========================================
2D color video (t, row, col, ch)
3D multichannel video (t, pln, row, col, ch)
======================== ========================================
doc/source/user_guide/transforming_image_data.txt
+3 -3
View File
@@ -67,7 +67,7 @@ from RGB to grayscale::
array([[ 0.7154]])
Converting a grayscale image to RGB with :func:`gray2rgb``simply
Converting a grayscale image to RGB with :func:`gray2rgb` simply
duplicates the gray values over the three color channels.
Painting images with labels
@@ -78,7 +78,7 @@ using an array of labels to encode the regions to be represented with the
same color.
.. image:: ../../_images/plot_join_segmentations_1.png
.. image: ../auto_examples/images/plot_join_segmentations_1.png
:target: ../auto_examples/plot_join_segmentations.html
:align: center
:width: 80%
@@ -159,7 +159,7 @@ image with :func:`equalize_adapthist`, in order to correct for exposure
gradients across the image. See the example
:ref:`example_plot_equalize.py`.
.. image:: ../../_images/plot_equalize_1.png
.. image:: ../auto_examples/images/plot_equalize_1.png
:target: ../auto_examples/plot_equalize.html
:align: center
:width: 90%
doc/source/user_guide/tutorial_segmentation.txt
+9 -9
View File
@@ -11,7 +11,7 @@ the coins cannot be done directly from the histogram of grey values,
because the background shares enough grey levels with the coins that a
thresholding segmentation is not sufficient.
.. image:: ../../_images/plot_coins_segmentation_1.png
.. image:: ../auto_examples/applications/images/plot_coins_segmentation_1.png
:target: ../auto_examples/applications/plot_coins_segmentation.html
:align: center
@@ -26,7 +26,7 @@ Simply thresholding the image leads either to missing significant parts
of the coins, or to merging parts of the background with the
coins. This is due to the inhomogeneous lighting of the image.
.. image:: ../../_images/plot_coins_segmentation_2.png
.. image:: ../auto_examples/applications/images/plot_coins_segmentation_2.png
:target: ../auto_examples/applications/plot_coins_segmentation.html
:align: center
@@ -53,7 +53,7 @@ boundary of the coins, or inside the coins.
>>> from scipy import ndimage
>>> fill_coins = ndimage.binary_fill_holes(edges)
.. image:: ../../_images/plot_coins_segmentation_3.png
.. image:: ../auto_examples/applications/images/plot_coins_segmentation_3.png
:target: ../auto_examples/applications/plot_coins_segmentation.html
:align: center
@@ -62,7 +62,7 @@ we fill the inner part of the coins using the
``ndimage.binary_fill_holes`` function, which uses mathematical morphology
to fill the holes.
.. image:: ../../_images/plot_coins_segmentation_4.png
.. image:: ../auto_examples/applications/images/plot_coins_segmentation_4.png
:target: ../auto_examples/applications/plot_coins_segmentation.html
:align: center
@@ -83,7 +83,7 @@ has not been segmented correctly at all. The reason is that the contour
that we got from the Canny detector was not completely closed, therefore
the filling function did not fill the inner part of the coin.
.. image:: ../../_images/plot_coins_segmentation_5.png
.. image:: ../auto_examples/applications/images/plot_coins_segmentation_5.png
:target: ../auto_examples/applications/plot_coins_segmentation.html
:align: center
@@ -128,7 +128,7 @@ separate the coins from the background.
and here is the corresponding 2-D plot:
.. image:: ../../_images/plot_coins_segmentation_6.png
.. image:: ../auto_examples/applications/images/plot_coins_segmentation_6.png
:target: ../auto_examples/applications/plot_coins_segmentation.html
:align: center
@@ -139,7 +139,7 @@ extreme parts of the histogram of grey values::
>>> markers[coins < 30] = 1
>>> markers[coins > 150] = 2
.. image:: ../../_images/plot_coins_segmentation_7.png
.. image:: ../auto_examples/applications/images/plot_coins_segmentation_7.png
:target: ../auto_examples/applications/plot_coins_segmentation.html
:align: center
@@ -148,7 +148,7 @@ Let us now compute the watershed transform::
>>> from skimage.morphology import watershed
>>> segmentation = watershed(elevation_map, markers)
.. image:: ../../_images/plot_coins_segmentation_8.png
.. image:: ../auto_examples/applications/images/plot_coins_segmentation_8.png
:target: ../auto_examples/applications/plot_coins_segmentation.html
:align: center
@@ -165,7 +165,7 @@ We can now label all the coins one by one using ``ndimage.label``::
>>> labeled_coins, _ = ndimage.label(segmentation)
.. image:: ../../_images/plot_coins_segmentation_9.png
.. image:: ../auto_examples/applications/images/plot_coins_segmentation_9.png
:target: ../auto_examples/applications/plot_coins_segmentation.html
:align: center