Added example and pep8 changes.

doc/examples/plot_blob.py

@@ -0,0 +1,90 @@
+"""
+==============
+Blob Detection
+==============
+
+Blobs are bright on dark or dark on bright regions in an image. In
+this example, blobs are detected using 3 algorithms. The image used
+in this case is the Hubble eXtreme Deep Field. Each bright dot in the
+image is a star or a galaxy, so we are literally counting stars.
+
+Laplacian of Gaussian (LoG)
+-----------------------------
+This is the most accurate and slowest approach. It computes the Laplacian
+of Gaussian images with successively increasing standard devation and
+stacks them up in a cube. Blobs are local maximas in this cube. Detecting
+larger blobs is especially slower because of larger kernel sizes during
+convolution. Only bright blobs on dark backgrounds are detected.
+
+Difference of Gaussian (LoG)
+----------------------------
+This is a faster approximant of LoG approach. In this case the image is
+blurred with increasing standard deviations and the difference between
+two successively blurred images are stacked up in a cube. This method
+suffers from the same disadvantage as LoG approach for detecting larger
+blobs. Blobs are again assumed to be bright on dark.
+
+Determinant of Hessian (DoH)
+----------------------------
+This is the fastest approach. It detects blobs by finding maximas in the
+matrix of the Determinant of Hessian of the image. The detection speed is
+independent of the size of blobs as internally the implementation uses
+box filters instead of convolutions. Bright on dark as well as dark on
+bright blobs are detected.The downside is that small blobs (<3px) are not
+detected accurately.
+
+"""
+
+from matplotlib import pyplot as plt
+from skimage import data
+from skimage.feature import blob_dog, blob_log, blob_doh
+from math import sqrt
+from skimage.color import rgb2gray
+
+image = data.hubble_deep_field()[0:500, 0:500]
+image_gray = rgb2gray(image)
+
+blobs = blob_log(image_gray, max_sigma=30, num_sigma=10, threshold=.1)
+
+fig = plt.figure()
+ax = fig.add_subplot(1, 1, 1)
+ax.set_title('Laplacian of Gaussian')
+plt.imshow(image)
+
+for blob in blobs:
+    y, x = blob[0], blob[1]
+    r = blob[2] * sqrt(2)
+    c = plt.Circle((x, y), r, color='#00ff00', lw=2, fill=False)
+    ax.add_patch(c)
+
+
+blobs = blob_dog(image_gray, max_sigma=30, threshold=.1)
+
+fig = plt.figure()
+ax = fig.add_subplot(1, 1, 1)
+ax.set_title('Difference of Gaussian')
+plt.imshow(image)
+
+for blob in blobs:
+    y, x = blob[0], blob[1]
+    r = blob[2] * sqrt(2)
+    c = plt.Circle((x, y), r, color='#ffff00', lw=2, fill=False)
+    ax.add_patch(c)
+
+
+blobs = blob_doh(image_gray, max_sigma=30, threshold=.01)
+
+fig = plt.figure()
+ax = fig.add_subplot(1, 1, 1)
+ax.set_title('Determinant of Hessian')
+plt.imshow(image)
+
+
+for blob in blobs:
+    y, x = blob[0], blob[1]
+    r = blob[2]
+    c = plt.Circle((x, y), r, color='#ff0000', lw=2, fill=False)
+    ax.add_patch(c)
+
+
+plt.show()

skimage/data/__init__.py

@@ -202,6 +202,7 @@ def coffee():
     """
     return load("coffee.png")
 
+
 def hubble_deep_field():
     """Hubble eXtreme Deep Field
 
@@ -217,7 +218,6 @@ def hubble_deep_field():
 
     The image was captured by NASA and may be freely used in the public domain
     according to NASA's contract (NAS5-26555).
-    
+
     """
     return load("hubble_deep_field.jpg")
-