Fix test cases for TV denoising

skimage/filter/tests/test_denoise.py

@@ -9,24 +9,17 @@ lena_gray = color.rgb2gray(lena)
 
 
 def test_denoise_tv_2d():
-    # lena image
     img = lena_gray
-    # add noise to lena
+    # add some random noise
     img += 0.5 * img.std() * np.random.random(img.shape)
-    # clip noise so that it does not exceed allowed range for float images.
     img = np.clip(img, 0, 1)
-    # denoise
-    denoised_lena = filter.denoise_tv(img, weight=60.0)
-    # which dtype?
-    assert denoised_lena.dtype in [np.float, np.float32, np.float64]
-    from scipy import ndimage
-    grad = ndimage.morphological_gradient(img, size=((3, 3)))
-    grad_denoised = ndimage.morphological_gradient(
-        denoised_lena, size=((3, 3)))
-    # test if the total variation has decreased
-    assert grad_denoised.dtype == np.float
-    assert (np.sqrt((grad_denoised**2).sum())
-            < np.sqrt((grad**2).sum()) / 2)
+
+    out1 = filter.denoise_tv(img, weight=10)
+    out2 = filter.denoise_tv(img, weight=5)
+
+    # make sure noise is reduced
+    assert img.std() > out1.std()
+    assert out1.std() > out2.std()
 
 
 def test_denoise_tv_float_result_range():
@@ -42,20 +35,17 @@ def test_denoise_tv_float_result_range():
 
 
 def test_denoise_tv_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.random(mask.shape)
-    mask[mask < 0] = 0
-    mask[mask > 255] = 255
-    res = filter.denoise_tv(mask.astype(np.uint8), weight=100)
-    assert res.dtype == np.float
-    assert res.std() * 255 < mask.std()
+    img = lena
+    # add some random noise
+    img += 0.5 * img.std() * np.random.random(img.shape)
+    img = np.clip(img, 0, 1)
 
-    # test wrong number of dimensions
-    assert_raises(ValueError, filter.denoise_tv, np.random.random((8, 8, 8, 8)))
+    out1 = filter.denoise_tv(img, weight=10)
+    out2 = filter.denoise_tv(img, weight=5)
+
+    # make sure noise is reduced
+    assert img.std() > out1.std()
+    assert out1.std() > out2.std()
 
 
 def test_denoise_bilateral_2d():