From 5078da0aed06e4b512a0bc880ad407745e128d35 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= <hannesschoenberger@gmail.com>
Date: Tue, 25 Dec 2012 11:30:21 +0100
Subject: [PATCH] Add test cases for chambolle tv denoising implementation

---
 skimage/filter/tests/test_denoise.py | 51 ++++++++++++++++++++++++++++
 1 file changed, 51 insertions(+)

diff --git a/skimage/filter/tests/test_denoise.py b/skimage/filter/tests/test_denoise.py
index 10745b72..c8e7bb02 100644
--- a/skimage/filter/tests/test_denoise.py
+++ b/skimage/filter/tests/test_denoise.py
@@ -8,6 +8,57 @@ lena = img_as_float(data.lena()[:256, :256])
 lena_gray = color.rgb2gray(lena)
 
 
+def test_denoise_tv_chambolle_2d():
+    # lena image
+    img = lena_gray
+    # add noise to lena
+    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_chambolle(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)
+
+
+def test_denoise_tv_chambolle_float_result_range():
+    # lena image
+    img = lena_gray
+    int_lena = np.multiply(img, 255).astype(np.uint8)
+    assert np.max(int_lena) > 1
+    denoised_int_lena = filter.denoise_tv_chambolle(int_lena, weight=60.0)
+    # test if the value range of output float data is within [0.0:1.0]
+    assert denoised_int_lena.dtype == np.float
+    assert np.max(denoised_int_lena) <= 1.0
+    assert np.min(denoised_int_lena) >= 0.0
+
+
+def test_denoise_tv_chambolle_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_chambolle(mask.astype(np.uint8), weight=100)
+    assert res.dtype == np.float
+    assert res.std() * 255 < mask.std()
+
+    # test wrong number of dimensions
+    assert_raises(ValueError, filter.denoise_tv_chambolle,
+                  np.random.random((8, 8, 8, 8)))
+
+
 def test_denoise_tv_bregman_2d():
     img = lena_gray
     # add some random noise