ENH: Use fast homography instead of PIL's rotate. Minor cleanup.

scikits/image/transform/tests/test_radon_transform.py

@@ -2,17 +2,34 @@ import numpy as np
 from numpy.testing import *
 from scikits.image.transform import *
 
+def rescale(x):
+    x = x.astype(float, copy=True)
+    x -= x.min()
+    x /= x.max()
+    return x
 
 def test_radon_iradon():
     size = 100
     image = np.tri(size) + np.tri(size)[::-1]
     for filter_type in ["ramp", "shepp-logan", "cosine", "hamming", "hann"]:
         reconstructed = iradon(radon(image), filter=filter_type)
-        delta = np.sum(abs(image/np.max(image) - reconstructed/np.max(reconstructed)))/(size*size)
-        assert delta < 0.1
+
+        image = rescale(image)
+        reconstructed = rescale(reconstructed)
+        delta = np.mean(np.abs(image - reconstructed))
+
+        ## print delta
+        ## import matplotlib.pyplot as plt
+        ## f, (ax1, ax2) = plt.subplots(1, 2)
+        ## ax1.imshow(image, cmap=plt.cm.gray)
+        ## ax2.imshow(reconstructed, cmap=plt.cm.gray)
+        ## plt.show()
+
+        assert delta < 0.05
+
     reconstructed = iradon(radon(image), filter="ramp", interpolation="nearest")
-    delta = np.sum(abs(image/np.max(image) - reconstructed/np.max(reconstructed)))/(size*size)
-    assert delta < 0.1
+    delta = np.mean(abs(image - reconstructed))
+    assert delta < 0.05
     
         
 if __name__ == "__main__":