Moved the tests of tv_denoise to filter/tests

scikits/image/filter/tests/test_tv_denoise.py

@@ -0,0 +1,58 @@
+import numpy as np
+from numpy.testing import run_module_suite
+import scikits.image.filter as F
+
+class TestTvDenoise():
+
+    def test_tv_denoise_2d(self):
+        """
+        Apply the TV denoising algorithm on the lena image provided
+        by scipy
+        """
+        import scipy
+        # lena image
+        lena = scipy.lena().astype(np.float)
+        # add noise to lena
+        lena += 0.5 * lena.std()*np.random.randn(*lena.shape)
+        # denoise
+        denoised_lena = F.tv_denoise(lena, weight=60.0)
+        # which dtype?
+        assert denoised_lena.dtype in [np.float, np.float32, np.float64]
+        from scipy import ndimage
+        grad = ndimage.morphological_gradient(lena, size=((3,3)))
+        grad_denoised = ndimage.morphological_gradient(denoised_lena, size=((3,3)))
+        # test if the total variation has decreased
+        assert np.sqrt((grad_denoised**2).sum()) < np.sqrt((grad**2).sum())
+        denoised_lena_int = F.tv_denoise(lena.astype(np.int32), \
+                weight=60.0, keep_type=True)
+        assert denoised_lena_int.dtype is np.dtype('int32')
+
+
+    def test_tv_denoise_3d(self):
+        """
+        Apply the TV denoising algorithm on a 3D image representing
+        a sphere.
+        """
+        x, y, z = np.ogrid[0:40, 0:40, 0:40]
+        mask = (x -22)**2 + (y - 20)**2 + (z - 17)**2 < 8**2
+        mask = 100 * mask.astype(np.float)
+        mask += 60
+        mask += 20*np.random.randn(*mask.shape)
+        mask[mask < 0] = 0
+        mask[mask > 255] = 255
+        res = F.tv_denoise(mask.astype(np.uint8), weight=100, keep_type=True)
+        assert res.std() < mask.std()
+        assert res.dtype is np.dtype('uint8')
+        res = F.tv_denoise(mask.astype(np.uint8), weight=100)
+        assert res.std() < mask.std()
+        assert res.dtype is not np.dtype('uint8')
+        # test wrong number of dimensions
+        a = np.random.random((8, 8, 8, 8))
+        try:
+            res = F.tv_denoise(a)
+        except ValueError:
+            pass
+
+
+if __name__ == "__main__":
+    run_module_suite()

scikits/image/filter/tv_denoise.py

@@ -269,45 +269,3 @@ def tv_denoise(im, eps=2.e-4, weight=50, keep_type=False, n_iter_max=200):
     else:
         raise ValueError('only 2-d and 3-d images may be denoised with this function')
 
-def test_tv_denoise():
-    """
-    Apply the TV denoising algorithm on the lena image provided
-    by scipy
-    """
-    import scipy
-    lena = scipy.lena().astype(np.float)
-    lena += 0.5 * lena.std()*np.random.randn(*lena.shape)
-    denoised_lena = tv_denoise(lena, weight=60.0)
-    assert denoised_lena.dtype in [np.float, np.float32, np.float64]
-    from scipy import ndimage
-    grad = ndimage.morphological_gradient(lena, size=((3,3)))
-    grad_denoised = ndimage.morphological_gradient(denoised_lena, size=((3,3)))
-    assert np.sqrt((grad_denoised**2).sum()) < np.sqrt((grad**2).sum())
-    denoised_lena_int = tv_denoise(lena.astype(np.int32), \
-            weight=60.0, keep_type=True)
-    assert denoised_lena_int.dtype is np.dtype('int32')
-
-def test_tv_denoise_3d():
-    """
-    Apply the TV denoising algorithm on a 3D image representing
-    a sphere.
-    """
-    x, y, z = np.ogrid[0:40, 0:40, 0:40]
-    mask = (x -22)**2 + (y - 20)**2 + (z - 17)**2 < 8**2
-    mask = 100 * mask.astype(np.float)
-    mask += 60
-    mask += 20*np.random.randn(*mask.shape)
-    mask[mask < 0] = 0
-    mask[mask > 255] = 255
-    res = tv_denoise(mask.astype(np.uint8), weight=100, keep_type=True)
-    assert res.std() < mask.std()
-    assert res.dtype is np.dtype('uint8')
-    res = tv_denoise(mask.astype(np.uint8), weight=100)
-    assert res.std() < mask.std()
-    assert res.dtype is not np.dtype('uint8')
-    # test wrong number of dimensions
-    a = np.random.random((8, 8, 8, 8))
-    try:
-        res = tv_denoise(a)
-    except ValueError:
-        pass