mirror of
https://github.com/wassname/scikit-image.git
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Added sections to gallery of examples
Modified travis_script.sh to account for the new structure of the gallery Added README.txt files in directories of gallery examples Fixed references to gallery images in user guide pages Fixed broken links
This commit is contained in:
emmanuelle
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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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