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Merge pull request #1823 from emmanuelle/structure-gallery
Added sections to gallery of examples
This commit is contained in:
Steven Silvester
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Manipulating exposure and color channels
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----------------------------------------
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Edges and lines
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---------------
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Detection of features and objects
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---------------------------------
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Filtering and restoration
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-------------------------
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Operations on NumPy arrays
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--------------------------
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Segmentation of objects
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-----------------------
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Geometrical transformations and registration
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--------------------------------------------
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@@ -81,9 +81,9 @@ disk: ::
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... (nrows / 2)**2)
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>>> camera[outer_disk_mask] = 0
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.. image:: ../auto_examples/images/plot_camera_numpy_1.png
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.. image:: ../auto_examples/numpy_operations/images/plot_camera_numpy_1.png
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:width: 45%
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:target: ../auto_examples/plot_camera_numpy.html
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:target: ../auto_examples/numpy_operations/plot_camera_numpy.html
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Boolean arithmetic can be used to define more complex masks: ::
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@@ -78,8 +78,8 @@ using an array of labels to encode the regions to be represented with the
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same color.
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.. image: ../auto_examples/images/plot_join_segmentations_1.png
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:target: ../auto_examples/plot_join_segmentations.html
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.. image: ../auto_examples/segmentation/images/plot_join_segmentations_1.png
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:target: ../auto_examples/segmentation/plot_join_segmentations.html
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:align: center
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:width: 80%
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@@ -87,9 +87,9 @@ same color.
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.. topic:: Examples:
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* :ref:`example_plot_tinting_grayscale_images.py`
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* :ref:`example_plot_join_segmentations.py`
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* :ref:`example_plot_rag_mean_color.py`
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* :ref:`example_color_exposure_plot_tinting_grayscale_images.py`
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* :ref:`example_segmentation_plot_join_segmentations.py`
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* :ref:`example_segmentation_plot_rag_mean_color.py`
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Contrast and exposure
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@@ -157,16 +157,16 @@ details are enhanced in large regions with poor contrast. As a further
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refinement, histogram equalization can be performed in subregions of the
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image with :func:`equalize_adapthist`, in order to correct for exposure
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gradients across the image. See the example
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:ref:`example_plot_equalize.py`.
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:ref:`example_color_exposure_plot_equalize.py`.
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.. image:: ../auto_examples/images/plot_equalize_1.png
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:target: ../auto_examples/plot_equalize.html
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.. image:: ../auto_examples/color_exposure/images/plot_equalize_1.png
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:target: ../auto_examples/color_exposure/plot_equalize.html
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:align: center
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:width: 90%
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.. topic:: Examples:
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* :ref:`example_plot_equalize.py`
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* :ref:`example_color_exposure_plot_equalize.py`
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@@ -11,8 +11,8 @@ the coins cannot be done directly from the histogram of grey values,
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because the background shares enough grey levels with the coins that a
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thresholding segmentation is not sufficient.
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.. image:: ../auto_examples/applications/images/plot_coins_segmentation_1.png
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:target: ../auto_examples/applications/plot_coins_segmentation.html
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.. image:: ../auto_examples/xx_applications/images/plot_coins_segmentation_1.png
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:target: ../auto_examples/xx_applications/plot_coins_segmentation.html
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:align: center
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::
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@@ -26,8 +26,8 @@ Simply thresholding the image leads either to missing significant parts
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of the coins, or to merging parts of the background with the
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coins. This is due to the inhomogeneous lighting of the image.
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.. image:: ../auto_examples/applications/images/plot_coins_segmentation_2.png
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:target: ../auto_examples/applications/plot_coins_segmentation.html
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.. image:: ../auto_examples/xx_applications/images/plot_coins_segmentation_2.png
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:target: ../auto_examples/xx_applications/plot_coins_segmentation.html
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:align: center
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A first idea is to take advantage of the local contrast, that is, to
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@@ -53,8 +53,8 @@ boundary of the coins, or inside the coins.
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>>> from scipy import ndimage as ndi
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>>> fill_coins = ndi.binary_fill_holes(edges)
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.. image:: ../auto_examples/applications/images/plot_coins_segmentation_3.png
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:target: ../auto_examples/applications/plot_coins_segmentation.html
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.. image:: ../auto_examples/xx_applications/images/plot_coins_segmentation_3.png
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:target: ../auto_examples/xx_applications/plot_coins_segmentation.html
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:align: center
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Now that we have contours that delineate the outer boundary of the coins,
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@@ -62,8 +62,8 @@ we fill the inner part of the coins using the
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``ndi.binary_fill_holes`` function, which uses mathematical morphology
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to fill the holes.
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.. image:: ../auto_examples/applications/images/plot_coins_segmentation_4.png
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:target: ../auto_examples/applications/plot_coins_segmentation.html
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.. image:: ../auto_examples/xx_applications/images/plot_coins_segmentation_4.png
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:target: ../auto_examples/xx_applications/plot_coins_segmentation.html
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:align: center
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Most coins are well segmented out of the background. Small objects from
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@@ -83,8 +83,8 @@ has not been segmented correctly at all. The reason is that the contour
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that we got from the Canny detector was not completely closed, therefore
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the filling function did not fill the inner part of the coin.
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.. image:: ../auto_examples/applications/images/plot_coins_segmentation_5.png
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:target: ../auto_examples/applications/plot_coins_segmentation.html
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.. image:: ../auto_examples/xx_applications/images/plot_coins_segmentation_5.png
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:target: ../auto_examples/xx_applications/plot_coins_segmentation.html
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:align: center
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Therefore, this segmentation method is not very robust: if we miss a
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@@ -128,8 +128,8 @@ separate the coins from the background.
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and here is the corresponding 2-D plot:
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.. image:: ../auto_examples/applications/images/plot_coins_segmentation_6.png
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:target: ../auto_examples/applications/plot_coins_segmentation.html
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.. image:: ../auto_examples/xx_applications/images/plot_coins_segmentation_6.png
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:target: ../auto_examples/xx_applications/plot_coins_segmentation.html
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:align: center
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The next step is to find markers of the background and the coins based on the
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@@ -139,8 +139,8 @@ extreme parts of the histogram of grey values::
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>>> markers[coins < 30] = 1
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>>> markers[coins > 150] = 2
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.. image:: ../auto_examples/applications/images/plot_coins_segmentation_7.png
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:target: ../auto_examples/applications/plot_coins_segmentation.html
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.. image:: ../auto_examples/xx_applications/images/plot_coins_segmentation_7.png
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:target: ../auto_examples/xx_applications/plot_coins_segmentation.html
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:align: center
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Let us now compute the watershed transform::
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@@ -148,8 +148,8 @@ Let us now compute the watershed transform::
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>>> from skimage.morphology import watershed
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>>> segmentation = watershed(elevation_map, markers)
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.. image:: ../auto_examples/applications/images/plot_coins_segmentation_8.png
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:target: ../auto_examples/applications/plot_coins_segmentation.html
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.. image:: ../auto_examples/xx_applications/images/plot_coins_segmentation_8.png
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:target: ../auto_examples/xx_applications/plot_coins_segmentation.html
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:align: center
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With this method, the result is satisfying for all coins. Even if the
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@@ -165,7 +165,7 @@ We can now label all the coins one by one using ``ndi.label``::
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>>> labeled_coins, _ = ndi.label(segmentation)
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.. image:: ../auto_examples/applications/images/plot_coins_segmentation_9.png
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:target: ../auto_examples/applications/plot_coins_segmentation.html
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.. image:: ../auto_examples/xx_applications/images/plot_coins_segmentation_9.png
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:target: ../auto_examples/xx_applications/plot_coins_segmentation.html
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:align: center
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@@ -69,7 +69,7 @@ touch $MPL_DIR/matplotlibrc
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echo 'backend : Template' > $MPL_DIR/matplotlibrc
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for f in doc/examples/*.py; do
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for f in doc/examples/*/*.py; do
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python "$f"
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if [ $? -ne 0 ]; then
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exit 1
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@@ -81,7 +81,7 @@ section_end "Run.doc.examples"
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section "Run.doc.applications"
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for f in doc/examples/applications/*.py; do
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for f in doc/examples/xx_applications/*.py; do
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python "$f"
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if [ $? -ne 0 ]; then
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exit 1
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