mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-27 17:17:07 +08:00
Minor fixes and improvements: PEP8, appearance, etc.
Removed a clumsy workaround (about paths) Fixed links to image files to fix sphinx warning. Removed non-ascii character Another non ascii character And another non-ascii character... (to be squashed later on) Corrected some typos in the docstrings of sphinx-gallery files These corrections have also been submitted as a patch to the original sphinx-gallery project (#121) Corrected the appearance of two examples of the gallery Tweaked CSS for larger images Added sphinx-gallery's license and a README.txt about the origin of this directory. Edited gabor_from_astronaut example for nicer popup PEP 8 + minor fixes Removed commented lines of code
This commit is contained in:
emmanuelle
@@ -72,12 +72,12 @@ img_adapteq = exposure.equalize_adapthist(img, clip_limit=0.03)
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# Display results
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fig = plt.figure(figsize=(8, 5))
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axes = np.zeros((2,4), dtype=np.object)
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axes[0,0] = fig.add_subplot(2, 4, 1)
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for i in range(1,4):
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axes[0,i] = fig.add_subplot(2, 4, 1+i, sharex=axes[0,0], sharey=axes[0,0])
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for i in range(0,4):
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axes[1,i] = fig.add_subplot(2, 4, 5+i)
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axes = np.zeros((2, 4), dtype=np.object)
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axes[0, 0] = fig.add_subplot(2, 4, 1)
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for i in range(1, 4):
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axes[0, i] = fig.add_subplot(2, 4, 1+i, sharex=axes[0,0], sharey=axes[0,0])
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for i in range(0, 4):
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axes[1, i] = fig.add_subplot(2, 4, 5+i)
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ax_img, ax_hist, ax_cdf = plot_img_and_hist(img, axes[:, 0])
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ax_img.set_title('Low contrast image')
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@@ -60,7 +60,6 @@ h = rescale_intensity(ihc_hed[:, :, 0], out_range=(0, 1))
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d = rescale_intensity(ihc_hed[:, :, 2], out_range=(0, 1))
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zdh = np.dstack((np.zeros_like(h), d, h))
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#fig, ax = plt.subplots()
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fig = plt.figure()
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ax = plt.subplot(1, 1, 1, sharex=ax0, sharey=ax0, adjustable='box-forced')
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ax.imshow(zdh)
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@@ -3,7 +3,7 @@
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Gabors / Primary Visual Cortex "Simple Cells" from an Image
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============================================================
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How to build a (bio-plausible) "sparse" dictionary (or 'codebook', or
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How to build a (bio-plausible) *sparse* dictionary (or 'codebook', or
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'filterbank') for e.g. image classification without any fancy math and
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with just standard python scientific libraries?
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@@ -3,11 +3,10 @@
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Filling holes and finding peaks
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===============================
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In this example, we fill holes (i.e. isolated, dark spots) in an image using
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morphological reconstruction by erosion. Erosion expands the minimal values of
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the seed image until it encounters a mask image. Thus, the seed image and mask
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image represent the maximum and minimum possible values of the reconstructed
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image.
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We fill holes (i.e. isolated, dark spots) in an image using morphological
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reconstruction by erosion. Erosion expands the minimal values of the seed image
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until it encounters a mask image. Thus, the seed image and mask image represent
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the maximum and minimum possible values of the reconstructed image.
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We start with an image containing both peaks and holes:
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@@ -21,14 +20,6 @@ image = data.moon()
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# Rescale image intensity so that we can see dim features.
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image = rescale_intensity(image, in_range=(50, 200))
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fig,ax = plt.subplots(2, 2, figsize=(5, 4), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
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ax = ax.ravel()
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ax[0].imshow(image)
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ax[0].set_title('Original image')
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ax[0].axis('off')
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######################################################################
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# Now we need to create the seed image, where the minima represent the
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# starting points for erosion. To fill holes, we initialize the seed image
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@@ -46,21 +37,12 @@ mask = image
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filled = reconstruction(seed, mask, method='erosion')
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ax[1].imshow(filled)
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ax[1].set_title('after filling holes')
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ax[1].axis('off')
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######################################################################
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# As shown above, eroding inward from the edges removes holes, since (by
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# definition) holes are surrounded by pixels of brighter value. Finally, we
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# can isolate the dark regions by subtracting the reconstructed image from
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# the original image.
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ax[2].imshow(image-filled)
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ax[2].set_title('holes')
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ax[2].axis('off')
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######################################################################
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#
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# Alternatively, we can find bright spots in an image using morphological
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# reconstruction by dilation. Dilation is the inverse of erosion and expands
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# the *maximal* values of the seed image until it encounters a mask image.
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@@ -72,7 +54,23 @@ seed = np.copy(image)
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seed[1:-1, 1:-1] = image.min()
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rec = reconstruction(seed, mask, method='dilation')
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ax[3].imshow(image-rec)
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fig, ax = plt.subplots(2, 2, figsize=(5, 4), sharex=True, sharey=True,
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subplot_kw={'adjustable': 'box-forced'})
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ax = ax.ravel()
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ax[0].imshow(image, cmap='gray')
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ax[0].set_title('Original image')
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ax[0].axis('off')
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ax[1].imshow(filled, cmap='gray')
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ax[1].set_title('after filling holes')
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ax[1].axis('off')
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ax[2].imshow(image-filled, cmap='gray')
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ax[2].set_title('holes')
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ax[2].axis('off')
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ax[3].imshow(image-rec, cmap='gray')
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ax[3].set_title('peaks')
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ax[3].axis('off')
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plt.show()
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@@ -12,19 +12,12 @@ neighborhood around a pixel.
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In this document we outline the following basic morphological operations:
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1. Erosion
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2. Dilation
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3. Opening
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4. Closing
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5. White Tophat
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6. Black Tophat
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7. Skeletonize
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8. Convex Hull
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@@ -46,6 +39,7 @@ ax.imshow(orig_phantom, cmap=plt.cm.gray)
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######################################################################
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# Let's also define a convenience function for plotting comparisons:
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def plot_comparison(original, filtered, filter_name):
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fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(8, 4), sharex=True,
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@@ -188,9 +182,9 @@ plot_comparison(phantom, b_tophat, 'black tophat')
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#
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# 1. Erosion <-> Dilation
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#
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# 2. Opening <-> Closing
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# 2. Opening <-> Closing
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#
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# 3. White tophat <-> Black tophat
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# 3. White tophat <-> Black tophat
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#
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#Skeletonize
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#===========
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@@ -467,7 +467,7 @@ for ax in ax.ravel():
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######################################################################
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#
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# Feature extraction
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# ===================
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# ===================
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#
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# Local histograms can be exploited to compute local entropy, which is
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# related to the local image complexity. Entropy is computed using base 2
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@@ -0,0 +1,27 @@
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Copyright (c) 2015, Óscar Nájera
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All rights reserved.
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions are met:
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* Redistributions of source code must retain the above copyright notice, this
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list of conditions and the following disclaimer.
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* Redistributions in binary form must reproduce the above copyright notice,
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this list of conditions and the following disclaimer in the documentation
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and/or other materials provided with the distribution.
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* Neither the name of sphinx-gallery nor the names of its
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contributors may be used to endorse or promote products derived from
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this software without specific prior written permission.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
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IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
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DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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||||
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
||||
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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@@ -0,0 +1,6 @@
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This directory was taken from
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https://github.com/sphinx-gallery/sphinx-gallery
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Files should not diverge from the original sphinx-gallery project, and
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any modifications should be submitted as pull requests to sphinx-gallery
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(with some exceptions such as CSS tweaking).
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@@ -1,8 +1,8 @@
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/*
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Sphinx-Gallery is has compatible CSS to fix default sphinx themes
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Sphinx-Gallery has compatible CSS to fix default sphinx themes
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Tested for Sphinx 1.3.1 for all themes: default, alabaster, sphinxdoc,
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scrolls, agogo, traditional, nature, haiku, pyramid
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Tested for Read the docs theme 0.1.7 */
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Tested for Read the Docs theme 0.1.7 */
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.sphx-glr-thumbcontainer {
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background: #fff;
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border: solid #fff 1px;
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@@ -12,7 +12,7 @@ Tested for Read the docs theme 0.1.7 */
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box-shadow: none;
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float: left;
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margin: 5px;
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min-height: 230px;
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min-height: 240px;
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padding-top: 5px;
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position: relative;
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}
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@@ -40,12 +40,12 @@ thumbnail with its default link Background color */
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}
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.sphx-glr-thumbcontainer .figure {
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margin: 10px;
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width: 160px;
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width: 200px;
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}
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.sphx-glr-thumbcontainer img {
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display: inline;
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max-height: 160px;
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width: 160px;
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max-height: 200px;
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width: 200px;
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}
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.sphx-glr-thumbcontainer[tooltip]:hover:after {
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background: rgba(0, 0, 0, 0.8);
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@@ -58,7 +58,7 @@ thumbnail with its default link Background color */
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padding: 5px 15px;
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position: absolute;
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z-index: 98;
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width: 220px;
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width: 240px;
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bottom: 52%;
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}
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.sphx-glr-thumbcontainer[tooltip]:hover:before {
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@@ -125,3 +125,13 @@ ul.sphx-glr-horizontal li {
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ul.sphx-glr-horizontal img {
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height: auto !important;
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}
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p.sphx-glr-signature a.reference.external {
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background-color: #EBECED;
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-moz-border-radius: 5px;
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-webkit-border-radius: 5px;
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border-radius: 5px;
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padding: 3px;
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position: absolute;
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right: 15px;
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}
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@@ -58,11 +58,7 @@ def get_data(url, gallery_dir):
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if sys.version_info[0] == 2 and isinstance(url, unicode):
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url = url.encode('utf-8')
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cwd = os.getcwd()
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if 'source' in cwd:
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cached_file = os.path.join(gallery_dir, 'searchindex')
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else:
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cached_file = os.path.join('source', gallery_dir, 'searchindex')
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cached_file = os.path.join(gallery_dir, 'searchindex')
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search_index = shelve.open(cached_file)
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if url in search_index:
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data = search_index[url]
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@@ -321,6 +317,8 @@ class SphinxDocLinkResolver(object):
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def _embed_code_links(app, gallery_conf, gallery_dir):
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# Add resolvers for the packages for which we want to show links
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working_dir = os.getcwd()
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os.chdir(app.builder.srcdir)
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doc_resolvers = {}
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for this_module, url in gallery_conf['reference_url'].items():
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try:
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@@ -344,8 +342,6 @@ def _embed_code_links(app, gallery_conf, gallery_dir):
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"Error:\n".format(this_module))
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print(e.args)
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working_dir = os.getcwd()
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os.chdir(app.builder.srcdir)
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html_gallery_dir = os.path.abspath(os.path.join(app.builder.outdir,
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gallery_dir))
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# patterns for replacement
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@@ -4,7 +4,7 @@ r"""
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Parser for Jupyter notebooks
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============================
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Class that holds the Ipython notebook information
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Class that holds the Jupyter notebook information
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"""
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# Author: Óscar Nájera
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@@ -48,7 +48,7 @@ def ipy_notebook_skeleton():
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def rst2md(text):
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"""Converts the RST text from the examples docstrigs and comments
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into markdown text for the IPython notebooks"""
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into markdown text for the Jupyter notebooks"""
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top_heading = re.compile(r'^=+$\s^([\w\s-]+)^=+$', flags=re.M)
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text = re.sub(top_heading, r'# \1', text)
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@@ -64,7 +64,7 @@ def rst2md(text):
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class Notebook(object):
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"""Ipython notebook object
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"""Jupyter notebook object
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Constructs the file cell-by-cell and writes it at the end"""
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|
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@@ -31,7 +31,6 @@ extensions = ['sphinx.ext.autodoc',
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'sphinx.ext.imgmath',
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'numpydoc',
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'sphinx.ext.autosummary',
|
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#'plot2rst',
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'sphinx.ext.intersphinx',
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'sphinx.ext.linkcode',
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'sphinx_gallery.gen_gallery'
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|
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@@ -81,7 +81,7 @@ disk: ::
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... (nrows / 2)**2)
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>>> camera[outer_disk_mask] = 0
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.. image:: ../../_images/sphx_glr_plot_camera_numpy_001.png
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.. image:: ../auto_examples/numpy_operations/images/sphx_glr_plot_camera_numpy_001.png
|
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:width: 45%
|
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:target: ../auto_examples/numpy_operations/plot_camera_numpy.html
|
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|
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|
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@@ -78,7 +78,7 @@ using an array of labels to encode the regions to be represented with the
|
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same color.
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|
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.. image:: ../../_images/sphx_glr_plot_join_segmentations_001.png
|
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.. image:: ../auto_examples/segmentation/images/sphx_glr_plot_join_segmentations_001.png
|
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:target: ../auto_examples/segmentation/plot_join_segmentations.html
|
||||
:align: center
|
||||
:width: 80%
|
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@@ -159,7 +159,7 @@ image with :func:`equalize_adapthist`, in order to correct for exposure
|
||||
gradients across the image. See the example
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||||
:ref:`sphx_glr_auto_examples_color_exposure_plot_equalize.py`.
|
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|
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.. image:: ../../_images/sphx_glr_plot_equalize_001.png
|
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.. image:: ../auto_examples/color_exposure/images/sphx_glr_plot_equalize_001.png
|
||||
:target: ../auto_examples/color_exposure/plot_equalize.html
|
||||
:align: center
|
||||
:width: 90%
|
||||
|
||||
@@ -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/sphx_glr_plot_coins_segmentation_001.png
|
||||
.. image:: ../auto_examples/xx_applications/images/sphx_glr_plot_coins_segmentation_001.png
|
||||
:target: ../auto_examples/xx_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/sphx_glr_plot_coins_segmentation_002.png
|
||||
.. image:: ../auto_examples/xx_applications/images/sphx_glr_plot_coins_segmentation_002.png
|
||||
:target: ../auto_examples/xx_applications/plot_coins_segmentation.html
|
||||
:align: center
|
||||
|
||||
@@ -53,7 +53,7 @@ boundary of the coins, or inside the coins.
|
||||
>>> from scipy import ndimage as ndi
|
||||
>>> fill_coins = ndi.binary_fill_holes(edges)
|
||||
|
||||
.. image:: ../../_images/sphx_glr_plot_coins_segmentation_003.png
|
||||
.. image:: ../auto_examples/xx_applications/images/sphx_glr_plot_coins_segmentation_003.png
|
||||
:target: ../auto_examples/xx_applications/plot_coins_segmentation.html
|
||||
:align: center
|
||||
|
||||
@@ -62,7 +62,7 @@ we fill the inner part of the coins using the
|
||||
``ndi.binary_fill_holes`` function, which uses mathematical morphology
|
||||
to fill the holes.
|
||||
|
||||
.. image:: ../../_images/sphx_glr_plot_coins_segmentation_004.png
|
||||
.. image:: ../auto_examples/xx_applications/images/sphx_glr_plot_coins_segmentation_004.png
|
||||
:target: ../auto_examples/xx_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/sphx_glr_plot_coins_segmentation_005.png
|
||||
.. image:: ../auto_examples/xx_applications/images/sphx_glr_plot_coins_segmentation_005.png
|
||||
:target: ../auto_examples/xx_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/sphx_glr_plot_coins_segmentation_006.png
|
||||
.. image:: ../auto_examples/xx_applications/images/sphx_glr_plot_coins_segmentation_006.png
|
||||
:target: ../auto_examples/xx_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/sphx_glr_plot_coins_segmentation_007.png
|
||||
.. image:: ../auto_examples/xx_applications/images/sphx_glr_plot_coins_segmentation_007.png
|
||||
:target: ../auto_examples/xx_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/sphx_glr_plot_coins_segmentation_008.png
|
||||
.. image:: ../auto_examples/xx_applications/images/sphx_glr_plot_coins_segmentation_008.png
|
||||
:target: ../auto_examples/xx_applications/plot_coins_segmentation.html
|
||||
:align: center
|
||||
|
||||
@@ -165,7 +165,7 @@ We can now label all the coins one by one using ``ndi.label``::
|
||||
|
||||
>>> labeled_coins, _ = ndi.label(segmentation)
|
||||
|
||||
.. image:: ../../_images/sphx_glr_plot_coins_segmentation_009.png
|
||||
.. image:: ../auto_examples/xx_applications/images/sphx_glr_plot_coins_segmentation_009.png
|
||||
:target: ../auto_examples/xx_applications/plot_coins_segmentation.html
|
||||
:align: center
|
||||
|
||||
|
||||
Reference in New Issue
Block a user