From 879c2c7f36d1819b580e4977617ec40bb9e44cc4 Mon Sep 17 00:00:00 2001 From: Tony S Yu Date: Fri, 11 Jul 2014 16:30:18 -0500 Subject: [PATCH] Add tests for intensity_range and rename parameter --- skimage/exposure/exposure.py | 14 +++++----- skimage/exposure/tests/test_exposure.py | 36 +++++++++++++++++++++++-- 2 files changed, 41 insertions(+), 9 deletions(-) diff --git a/skimage/exposure/exposure.py b/skimage/exposure/exposure.py index 12161c4e..ad21fa9b 100644 --- a/skimage/exposure/exposure.py +++ b/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) diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py index 6471ff59..dcaf6837 100644 --- a/skimage/exposure/tests/test_exposure.py +++ b/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)