From 7967d5fb49cd1bb0b1ed8d2417c3ace36f47600d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= <hannesschoenberger@gmail.com>
Date: Sat, 6 Oct 2012 22:18:41 +0200
Subject: [PATCH] Refactor denoise tests and add tests for bilateral filter

---
 skimage/filter/tests/test_denoise.py | 135 ++++++++++++++++-----------
 1 file changed, 78 insertions(+), 57 deletions(-)

diff --git a/skimage/filter/tests/test_denoise.py b/skimage/filter/tests/test_denoise.py
index cc4fae7e..2efbba84 100644
--- a/skimage/filter/tests/test_denoise.py
+++ b/skimage/filter/tests/test_denoise.py
@@ -1,68 +1,89 @@
 import numpy as np
-from numpy.testing import run_module_suite
+from numpy.testing import run_module_suite, assert_raises
 
-from skimage import filter, data, color
+from skimage import filter, data, color, img_as_float
 
 
-class TestTvDenoise():
+lena = img_as_float(data.lena()[:256, :256])
+lena_gray = color.rgb2gray(lena)
 
-    def test_tv_denoise_2d(self):
-        """
-        Apply the TV denoising algorithm on the lena image provided
-        by scipy
-        """
-        # lena image
-        lena = color.rgb2gray(data.lena())[:256, :256]
-        # add noise to lena
-        lena += 0.5 * lena.std() * np.random.randn(*lena.shape)
-        # clip noise so that it does not exceed allowed range for float images.
-        lena = np.clip(lena, 0, 1)
-        # denoise
-        denoised_lena = filter.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 grad_denoised.dtype == np.float
-        assert (np.sqrt((grad_denoised**2).sum())
-                < np.sqrt((grad**2).sum()) / 2)
 
-    def test_tv_denoise_float_result_range(self):
-        # lena image
-        lena = color.rgb2gray(data.lena())[:256, :256]
-        int_lena = np.multiply(lena, 255).astype(np.uint8)
-        assert np.max(int_lena) > 1
-        denoised_int_lena = filter.tv_denoise(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_tv_denoise_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.tv_denoise(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_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 = filter.tv_denoise(mask.astype(np.uint8), weight=100)
-        assert res.dtype == np.float
-        assert res.std() * 255 < mask.std()
 
-        # test wrong number of dimensions
-        a = np.random.random((8, 8, 8, 8))
-        try:
-            res = filter.tv_denoise(a)
-        except ValueError:
-            pass
+def test_tv_denoise_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.tv_denoise(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_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.random(mask.shape)
+    mask[mask < 0] = 0
+    mask[mask > 255] = 255
+    res = filter.tv_denoise(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.tv_denoise, np.random.random((8, 8, 8, 8)))
+
+
+def test_denoise_bilateral_2d():
+    img = lena_gray
+    # add some random noise
+    img += 0.5 * img.std() * np.random.random(img.shape)
+    img = np.clip(img, 0, 1)
+
+    out1 = filter.denoise_bilateral(img, sigma_color=0.1, sigma_range=20)
+    out2 = filter.denoise_bilateral(img, sigma_color=0.2, sigma_range=30)
+
+    # make sure noise is reduced
+    assert img.std() > out1.std()
+    assert out1.std() > out2.std()
+
+
+def test_denoise_bilateral_3d():
+    img = lena
+    # add some random noise
+    img += 0.5 * img.std() * np.random.random(img.shape)
+    img = np.clip(img, 0, 1)
+
+    out1 = filter.denoise_bilateral(img, sigma_color=0.1, sigma_range=20)
+    out2 = filter.denoise_bilateral(img, sigma_color=0.2, sigma_range=30)
+
+    # make sure noise is reduced
+    assert img.std() > out1.std()
+    assert out1.std() > out2.std()
 
 
 if __name__ == "__main__":