From 1f4dd28cb801ad16c67981d0333942d8c6125bf9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= Date: Mon, 11 Jul 2016 22:30:08 +0200 Subject: [PATCH] Improve docstrings for captions --- .../color_exposure/plot_ihc_color_separation.py | 2 ++ doc/examples/edges/plot_contours.py | 8 ++++---- doc/examples/edges/plot_line_hough_transform.py | 7 +++---- doc/examples/features_detection/plot_glcm.py | 7 +++---- doc/examples/features_detection/plot_hog.py | 10 +++++----- doc/examples/features_detection/plot_template.py | 4 ++-- 6 files changed, 19 insertions(+), 19 deletions(-) diff --git a/doc/examples/color_exposure/plot_ihc_color_separation.py b/doc/examples/color_exposure/plot_ihc_color_separation.py index 2623355c..1a21987b 100644 --- a/doc/examples/color_exposure/plot_ihc_color_separation.py +++ b/doc/examples/color_exposure/plot_ihc_color_separation.py @@ -3,6 +3,8 @@ Immunohistochemical staining colors separation ============================================== +Color deconvolution consists in the separation of features by their colors. + In this example we separate the immunohistochemical (IHC) staining from the hematoxylin counterstaining. The separation is achieved with the method described in [1]_, known as "color deconvolution". diff --git a/doc/examples/edges/plot_contours.py b/doc/examples/edges/plot_contours.py index e686b4ac..35c61804 100644 --- a/doc/examples/edges/plot_contours.py +++ b/doc/examples/edges/plot_contours.py @@ -3,10 +3,10 @@ Contour finding =============== -``skimage.measure.find_contours`` uses a marching squares method to find -constant valued contours in an image. Array values are linearly interpolated -to provide better precision of the output contours. Contours which intersect -the image edge are open; all others are closed. +A marching squares method can be used to find constant valued contours in an +image. Array values are linearly interpolated to provide better precision of +the output contours. Contours which intersect the image edge are open; all +others are closed. The `marching squares algorithm `__ is a special case of diff --git a/doc/examples/edges/plot_line_hough_transform.py b/doc/examples/edges/plot_line_hough_transform.py index 06f23203..e013efb1 100644 --- a/doc/examples/edges/plot_line_hough_transform.py +++ b/doc/examples/edges/plot_line_hough_transform.py @@ -3,12 +3,11 @@ Straight line Hough transform ============================= -The Hough transform in its simplest form is a `method to detect straight lines -`__. +The Hough transform in its simplest form is a method to detect straight lines. In the following example, we construct an image with a line intersection. We -then use the Hough transform to explore a parameter space for straight lines -that may run through the image. +then use the `Hough transform `__. +to explore a parameter space for straight lines that may run through the image. Algorithm overview ------------------ diff --git a/doc/examples/features_detection/plot_glcm.py b/doc/examples/features_detection/plot_glcm.py index a5d17c82..47b17432 100644 --- a/doc/examples/features_detection/plot_glcm.py +++ b/doc/examples/features_detection/plot_glcm.py @@ -3,10 +3,9 @@ GLCM Texture Features ===================== -This example illustrates texture classification using texture -classification using grey level co-occurrence matrices (GLCMs). -A GLCM is a histogram of co-occurring greyscale values at a given -offset over an image. +This example illustrates texture classification using grey level +co-occurrence matrices (GLCMs). A GLCM is a histogram of co-occurring +greyscale values at a given offset over an image. In this example, samples of two different textures are extracted from an image: grassy areas and sky areas. For each patch, a GLCM with diff --git a/doc/examples/features_detection/plot_hog.py b/doc/examples/features_detection/plot_hog.py index e7b524cb..e3c89b63 100644 --- a/doc/examples/features_detection/plot_hog.py +++ b/doc/examples/features_detection/plot_hog.py @@ -3,12 +3,12 @@ Histogram of Oriented Gradients =============================== -The `Histogram of Oriented Gradient -`__ (HOG) feature -descriptor [1]_ is popular for object detection. +The Histogram of Oriented Gradient (HOG) feature descriptor is popular +for object detection [1]_. -In the following example, we compute the HOG descriptor and display -a visualisation. +In the following example, we compute the `HOG descriptor +`__ +and display a visualisation. Algorithm overview ------------------ diff --git a/doc/examples/features_detection/plot_template.py b/doc/examples/features_detection/plot_template.py index c7b4f01a..abf97b4b 100644 --- a/doc/examples/features_detection/plot_template.py +++ b/doc/examples/features_detection/plot_template.py @@ -3,8 +3,8 @@ Template Matching ================= -In this example, we use template matching to identify the occurrence of an -image patch (in this case, a sub-image centered on a single coin). Here, we +We use template matching to identify the occurrence of an image patch +(in this case, a sub-image centered on a single coin). Here, we return a single match (the exact same coin), so the maximum value in the ``match_template`` result corresponds to the coin location. The other coins look similar, and thus have local maxima; if you expect multiple matches, you