compare bilateral

doc/examples/plot_compare_bilateral.py

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+"""
+====================================================
+Bilateral comparison
+====================================================
+
+In this example, we compare both bilateral implementation
+
+* filter.denoise_bilateral
+* filter.rank.bilateral_mean
+
+The first filter implements a spatial-gaussian and spectral-gaussian kernel bilateral filter whereas the latter implements
+a cylindrical kernel bilateral filter i.e. spatial-flat and spectral-flat kernel.
+
+The timing comparison is just for information since the kernel are not the same.
+
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+from skimage import data
+from skimage.filter._denoise import denoise_bilateral
+from skimage.filter.rank import bilateral_mean
+from skimage.morphology import disk
+from skimage.filter import denoise_bilateral
+import time
+
+def exec_and_timeit(func):
+    """ Decorator that returns both function results and execution time
+    (result, ms)
+    """
+    def wrapper(*arg):
+        t1 = time.time()
+        res = func(*arg)
+        t2 = time.time()
+        ms = (t2-t1)*1000.0
+        return (res,ms)
+    return wrapper
+
+
+@exec_and_timeit
+def den_bil(image):
+    return denoise_bilateral(a8,win_size=20,sigma_range=255,sigma_spatial=1)[:,:,0]*255
+
+@exec_and_timeit
+def rank_bil(image):
+    return bilateral_mean(a8.astype(np.uint16),disk(20),s0=10,s1=10)
+
+a8 = data.camera()
+selem = disk(10)
+
+f1,t1 = den_bil(a8)
+f2,t2 = rank_bil(a8)
+
+# display results
+fig, axes = plt.subplots(nrows=2, figsize=(15,10))
+ax0, ax1= axes
+
+ax0.imshow(np.hstack((f1,a8-f1)))
+ax0.set_title('denoise bilateral (%f ms)'%t1)
+ax1.imshow(np.hstack((f2,a8-f1)))
+ax1.set_title('bilateral mean (%f ms)'%t2)
+plt.show()