Add DAISY visualization + example plot.

doc/examples/plot_daisy.py

@@ -0,0 +1,28 @@
+"""
+===============================
+Dense DAISY feature description
+===============================
+
+The DAISY local image descriptor is based on gradient orientation histograms
+similar to the SIFT descriptor. It is formulated in a way that allows for fast
+dense extraction which is useful for e.g. bag-of-features image
+representations.
+
+In this example a limited number of DAISY descriptors are extracted at a large
+scale for illustrative purposes.
+"""
+
+from skimage.feature import daisy
+from skimage import data
+import matplotlib.pyplot as plt
+
+
+img = data.camera()
+descs, descs_img = daisy(img, step=180, radius=58, rings=2, histograms=6,
+                         orientations=8, visualize=True)
+
+plt.axis('off')
+plt.imshow(descs_img)
+descs_num = descs.shape[0] * descs.shape[1]
+plt.title('%i DAISY descriptors extracted:' % descs_num)
+plt.show()

doc/examples/plot_shapes.py

@@ -13,7 +13,7 @@ This example shows how to fill several different shapes:
 
 import matplotlib.pyplot as plt
 
-from skimage.draw import line, polygon, circle, ellipse
+from skimage.draw import line, polygon, circle, circle_perimeter, ellipse
 import numpy as np
 
 
@@ -42,5 +42,9 @@ img[rr,cc,:] = (255, 255, 0)
 rr, cc = ellipse(300, 300, 100, 200, img.shape)
 img[rr,cc,2] = 255
 
+# circle
+rr, cc = circle_perimeter(120, 400, 50)
+img[rr, cc, :] = (255, 0, 255)
+
 plt.imshow(img)
-plt.show()
+plt.show()