Add tests for intensity_range and rename parameter

skimage/exposure/exposure.py

@@ -3,7 +3,7 @@ import numpy as np
 
 from skimage import img_as_float
 from skimage.util.dtype import dtype_range, dtype_limits
-from skimage._shared.utils import deprecated, deprecation_warning
+from skimage._shared.utils import deprecation_warning
 
 
 __all__ = ['histogram', 'cumulative_distribution', 'equalize',
@@ -136,7 +136,7 @@ def equalize_hist(image, nbins=256):
     return out.reshape(image.shape)
 
 
-def intensity_range(image, range_values='image', zero_min=False):
+def intensity_range(image, range_values='image', clip_negative=False):
     """Return image intensity range (min, max) based on desired value type.
 
     Parameters
@@ -160,9 +160,9 @@ def intensity_range(image, range_values='image', zero_min=False):
             intensity range explicitly. This option is included for functions
             that use `intensity_range` to support all desired range types.
 
-    zero_min : bool
-        If True, the image dtype's min is truncated to 0. Note that this only
-        applies to the output range if `range_values` specifies a dtype.
+    clip_negative : bool
+        If True, clip the negative range (i.e. return 0 for min intensity)
+        even if the image dtype allows negative values.
     """
     if range_values == 'dtype':
         range_values = image.dtype.type
@@ -172,7 +172,7 @@ def intensity_range(image, range_values='image', zero_min=False):
         i_max = np.max(image)
     elif range_values in DTYPE_RANGE:
         i_min, i_max = DTYPE_RANGE[range_values]
-        if zero_min:
+        if clip_negative:
             i_min = 0
     else:
         i_min, i_max = range_values
@@ -263,7 +263,7 @@ def rescale_intensity(image, in_range='image', out_range='dtype'):
         deprecation_warning(msg.format(out_range))
 
     imin, imax = intensity_range(image, in_range)
-    omin, omax = intensity_range(image, out_range, zero_min=(imin >= 0))
+    omin, omax = intensity_range(image, out_range, clip_negative=(imin >= 0))
 
     image = np.clip(image, imin, imax)
 

skimage/exposure/tests/test_exposure.py

@@ -3,9 +3,11 @@ import warnings
 import numpy as np
 from numpy.testing import assert_array_almost_equal as assert_close
 from numpy.testing import assert_array_equal, assert_raises
+
 import skimage
 from skimage import data
 from skimage import exposure
+from skimage.exposure.exposure import intensity_range
 from skimage.color import rgb2gray
 from skimage.util.dtype import dtype_range
 
@@ -41,6 +43,36 @@ def check_cdf_slope(cdf):
     assert 0.9 < slope < 1.1
 
 
+# Test intensity range
+# ====================
+
+
+def test_intensity_range_uint8():
+    image = np.array([0, 1], dtype=np.uint8)
+    input_and_expected = [('image', [0, 1]),
+                          ('dtype', [0, 255]),
+                          ((10, 20), [10, 20])]
+    for range_values, expected_values in input_and_expected:
+        out = intensity_range(image, range_values=range_values)
+        yield assert_array_equal, out, expected_values
+
+
+def test_intensity_range_float():
+    image = np.array([0.1, 0.2], dtype=np.float64)
+    input_and_expected = [('image', [0.1, 0.2]),
+                          ('dtype', [-1, 1]),
+                          ((0.3, 0.4), [0.3, 0.4])]
+    for range_values, expected_values in input_and_expected:
+        out = intensity_range(image, range_values=range_values)
+        yield assert_array_equal, out, expected_values
+
+
+def test_intensity_range_clipped_float():
+    image = np.array([0.1, 0.2], dtype=np.float64)
+    out = intensity_range(image, range_values='dtype', clip_negative=True)
+    assert_array_equal(out, (0, 1))
+
+
 # Test rescale intensity
 # ======================
 
@@ -134,7 +166,7 @@ def test_adapthist_grayscale():
     img = rgb2gray(img)
     img = np.dstack((img, img, img))
     adapted = exposure.equalize_adapthist(img, 10, 9, clip_limit=0.01,
-                        nbins=128)
+                                          nbins=128)
     assert_almost_equal = np.testing.assert_almost_equal
     assert img.shape == adapted.shape
     assert_almost_equal(peak_snr(img, adapted), 97.531, 3)
@@ -374,6 +406,6 @@ def test_adjust_inv_sigmoid_cutoff_half():
     assert_array_equal(result, expected)
 
 
-def test_neggative():
+def test_negative():
     image = np.arange(-10, 245, 4).reshape(8, 8).astype(np.double)
     assert_raises(ValueError, exposure.adjust_gamma, image)