Color example :)

doc/examples/plot_quickshift.py

@@ -1,47 +1,31 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-from scipy import ndimage
-#from skimage.data import lena
-#from skimage.util import img_as_float
+from skimage.data import lena
 from skimage.segmentation import quickshift
+from skimage.util import img_as_float
 
 from IPython.core.debugger import Tracer
 tracer = Tracer()
 
 
-def microstructure(l=256):
-    """
-    Synthetic binary data: binary microstructure with blobs.
-
-    Parameters
-    ----------
-
-    l: int, optional
-        linear size of the returned image
-    """
-    n = 5
-    x, y = np.ogrid[0:l, 0:l]
-    mask = np.zeros((l, l))
-    generator = np.random.RandomState(1)
-    points = l * generator.rand(2, n ** 2)
-    mask[(points[0]).astype(np.int), (points[1]).astype(np.int)] = 1
-    mask = ndimage.gaussian_filter(mask, sigma=l / (4. * n))
-    return (mask > mask.mean()).astype(np.float)
-
-
-#img = img_as_float(lena()[250:300, 250:300])
-img = microstructure(l=50)
-segments = quickshift(img.reshape(50, 50, 1))
-segments = np.unique(segments, return_inverse=True)[1].reshape(50, 50)
-intensities = np.bincount(segments.ravel(), img.ravel())
-counts = np.bincount(segments.ravel())
-intensities /= counts
+img = img_as_float(lena())[::3, ::3, :].copy("C")
+segments = quickshift(img, sigma=2)
+segments = np.unique(segments, return_inverse=True)[1].reshape(img.shape[:2])
 
+plt.subplot(131, title="original")
 plt.imshow(img, interpolation='nearest')
-plt.figure()
-plt.imshow(segments, interpolation='nearest')
-plt.figure()
-plt.imshow(intensities[segments], interpolation='nearest')
+
+plt.subplot(132, title="superpixels")
+# shuffle the labels for better visualization
+permuted_labels = np.random.permutation(segments.max() + 1)
+plt.imshow(permuted_labels[segments], interpolation='nearest')
+
+plt.subplot(133, title="mean color")
+colors = [np.bincount(segments.ravel(), img[:, :, c].ravel()) for c in
+        xrange(img.shape[2])]
+counts = np.bincount(segments.ravel())
+colors = np.vstack(colors) / counts
+plt.imshow(colors.T[segments], interpolation='nearest')
 plt.show()
 print("num segments: %d" % len(np.unique(segments)))