From 3fe03259d09ee8bcf4ffd31de066bde285017df1 Mon Sep 17 00:00:00 2001
From: Tony S Yu <tsyu80@gmail.com>
Date: Sun, 19 Aug 2012 16:24:12 -0400
Subject: [PATCH] DOC: Combine examples for finding spots and filling holes.

---
 doc/examples/plot_find_spots.py               | 69 -------------------
 ..._fill_holes.py => plot_holes_and_peaks.py} | 24 +++++--
 2 files changed, 20 insertions(+), 73 deletions(-)
 delete mode 100644 doc/examples/plot_find_spots.py
 rename doc/examples/{plot_fill_holes.py => plot_holes_and_peaks.py} (73%)

diff --git a/doc/examples/plot_find_spots.py b/doc/examples/plot_find_spots.py
deleted file mode 100644
index ff6e2196..00000000
--- a/doc/examples/plot_find_spots.py
+++ /dev/null
@@ -1,69 +0,0 @@
-"""
-==========
-Find spots
-==========
-
-In this example, we find bright spots in an image using morphological
-reconstruction by dilation. Dilation expands the maximal values of the seed
-image until it encounters a mask image. Thus, the seed image and mask image
-represent the minimum and maximum possible values of the reconstructed image.
-
-We start with an image containing both peaks and holes:
-"""
-import matplotlib.pyplot as plt
-
-from skimage import data
-from skimage.exposure import rescale_intensity
-
-image = data.moon()
-# Rescale image intensity so that we can see dim features.
-image = rescale_intensity(image, in_range=(50, 200))
-
-# convenience function for plotting images
-def imshow(image, **kwargs):
-    plt.figure(figsize=(5, 4))
-    plt.imshow(image, **kwargs)
-    plt.axis('off')
-
-imshow(image)
-plt.title('original image')
-
-"""
-.. image:: PLOT2RST.current_figure
-
-Now we need to create the seed image, where the maxima represent the starting
-points for dilation. To find spots, we initialize the seed image to the minimum
-value of the original image. Along the borders, however, we use the original
-values of the image. These border pixels will be the starting points for the
-dilation process. We then limit the dilation by setting the mask to the values
-of the original image.
-"""
-
-import numpy as np
-from skimage.morphology import reconstruction
-
-seed = np.copy(image)
-seed[1:-1, 1:-1] = image.min()
-mask = image
-
-rec = reconstruction(seed, mask, method='dilation')
-
-imshow(rec, vmin=image.min(), vmax=image.max())
-plt.title('')
-
-"""
-.. image:: PLOT2RST.current_figure
-
-As shown above, dilating inward from the edges removes peaks, since (by
-definition) peaks are surrounded by pixels of darker value. Finally, we can
-isolate the bright spots by subtracting the reconstructed image from the
-original image.
-"""
-
-imshow(image - rec)
-plt.title('"holes"')
-plt.show()
-
-"""
-.. image:: PLOT2RST.current_figure
-"""
diff --git a/doc/examples/plot_fill_holes.py b/doc/examples/plot_holes_and_peaks.py
similarity index 73%
rename from doc/examples/plot_fill_holes.py
rename to doc/examples/plot_holes_and_peaks.py
index 7257d2bb..a9c3a7fe 100644
--- a/doc/examples/plot_fill_holes.py
+++ b/doc/examples/plot_holes_and_peaks.py
@@ -1,7 +1,7 @@
 """
-==========
-Fill holes
-==========
+===============================
+Filling holes and finding peaks
+===============================
 
 In this example, we fill holes (i.e. isolated, dark spots) in an image using
 morphological reconstruction by erosion. Erosion expands the minimal values of
@@ -62,7 +62,23 @@ original image.
 """
 
 imshow(image - filled)
-plt.title('dark holes')
+plt.title('holes')
+
+"""
+.. image:: PLOT2RST.current_figure
+
+Alternatively, we can find bright spots in an image using morphological
+reconstruction by dilation. Dilation is the inverse of erosion and expands the
+*maximal* values of the seed image until it encounters a mask image. Since this
+is an inverse operation, we initialize the seed image to the minimum image
+intensity instead of the maximum. The remainder of the process is the same.
+"""
+
+seed = np.copy(image)
+seed[1:-1, 1:-1] = image.min()
+rec = reconstruction(seed, mask, method='dilation')
+imshow(image - rec)
+plt.title('peaks')
 plt.show()
 
 """