def test_Homogeneous(self):
"""Checks the return value for a homogeneous region."""
# Create label image with only one region
label_image = scipy.zeros(2 * 2 * 2, dtype=scipy.int8).reshape(2, 2, 2)
# Create original image with only one intensity value
original_image = scipy.zeros(2 * 2 * 2,
dtype=scipy.int8).reshape(2, 2, 2)
# Initialize object
statistics = LabelImageStatistics(label_image, original_image)
# Check created intensity distribution
intensity_distributions = statistics.get_intensity_distributions()
self.assertEqual(len(intensity_distributions), 1)
self.assertEqual(intensity_distributions[0], 0)
intensity_distribution_histogram = statistics.get_intensity_distribution_histogram(
)
self.assertEqual(
len(intensity_distribution_histogram[0]),
statistics.get_intensity_distribution_histogram_width())
self.assertEqual(
len(intensity_distribution_histogram[1]),
statistics.get_intensity_distribution_histogram_width() + 1)
self.assertEqual(
intensity_distribution_histogram[0]
[statistics.get_intensity_distribution_histogram_width() / 2], 1)
self.assertEqual(intensity_distribution_histogram[0].max(), 1)
self.assertEqual(intensity_distribution_histogram[0].min(), 0)
def test_Basic(self):
"""Test the case of a region with only one intensity value and some basic return values."""
# Create label image with only one region
label_image = scipy.zeros(2 * 2 * 2, dtype=scipy.int8).reshape(2, 2, 2)
# Create original image with only one intensity value
original_image = scipy.zeros(2 * 2 * 2,
dtype=scipy.int8).reshape(2, 2, 2)
# Initialize object
statistics = LabelImageStatistics(label_image, original_image)
# Check created intensity distribution
intensity_distributions = statistics.get_intensity_distributions()
self.assertEqual(len(intensity_distributions), 1)
self.assertEqual(intensity_distributions[0], 0)
intensity_distribution_histogram = statistics.get_intensity_distribution_histogram(
)
self.assertEqual(
len(intensity_distribution_histogram[0]),
statistics.get_intensity_distribution_histogram_width())
self.assertEqual(
len(intensity_distribution_histogram[1]),
statistics.get_intensity_distribution_histogram_width() + 1)
self.assertEqual(
intensity_distribution_histogram[0]
[statistics.get_intensity_distribution_histogram_width() / 2], 1)
self.assertEqual(intensity_distribution_histogram[0].max(), 1)
self.assertEqual(intensity_distribution_histogram[0].min(), 0)
# Check created size distribution
sizes = statistics.get_sizes()
self.assertEqual(len(sizes), 1)
self.assertEqual(sizes[0], 2 * 2 * 2)
sizes_histogram = statistics.get_size_histogram()
self.assertEqual(len(sizes_histogram[0]),
statistics.get_size_histogram_width())
self.assertEqual(len(sizes_histogram[1]),
statistics.get_size_histogram_width() + 1)
self.assertEqual(
sizes_histogram[0][statistics.get_size_histogram_width() / 2], 1)
self.assertEqual(sizes_histogram[0].max(), 1)
self.assertEqual(sizes_histogram[0].min(), 0)
# Check other statistics values
self.assertTrue(statistics.labels_are_consecutive())
self.assertEqual(statistics.get_min_intensity(), 0)
self.assertEqual(statistics.get_max_intensity(), 0)
self.assertEqual(statistics.get_min_label(), 0)
self.assertEqual(statistics.get_max_label(), 0)
self.assertEqual(statistics.get_label_count(), 1)
def test_Intensity_1(self):
"""Test a case of distributed intensity values."""
# Create label image with only one region
label_image = scipy.zeros(2*2*2, dtype=scipy.int8).reshape(2,2,2)
# Create original image with two equally distibuted intensity value
original_image = scipy.zeros(2*2*2, dtype=scipy.int8)
original_image[:4] = -1
original_image[4:] = 1
original_image = original_image.reshape(2,2,2)
# Initialize object
statistics = LabelImageStatistics(label_image, original_image)
# Computed expected result
i = scipy.array([-1,-1,-1,-1,1,1,1,1])
h = scipy.histogram(i, statistics._intensity_distribution_local_histogram_width)
hr = scipy.array(h[0]) / float(h[0].sum())
g = stats.norm(*stats.norm.fit(i))
r = abs(hr - g.pdf(h[1][:-1]))
r *= h[1][-2] - h[1][0]
r = r.sum()
# Check created intensity distribution
intensity_distributions = statistics.get_intensity_distributions()
self.assertEqual(len(intensity_distributions), 1)
self.assertEqual(intensity_distributions[0], i.std())
intensity_distribution_histogram = statistics.get_intensity_distribution_histogram()
self.assertEqual(intensity_distribution_histogram[0][statistics.get_intensity_distribution_histogram_width()/2], 1)
self.assertEqual(intensity_distribution_histogram[0].max(), 1)
self.assertEqual(intensity_distribution_histogram[0].min(), 0)
self.assertEqual(intensity_distribution_histogram[1].mean(), i.std())
def test_Basic(self):
"""Test the case of a region with only one intensity value and some basic return values."""
# Create label image with only one region
label_image = scipy.zeros(2*2*2, dtype=scipy.int8).reshape(2,2,2)
# Create original image with only one intensity value
original_image = scipy.zeros(2*2*2, dtype=scipy.int8).reshape(2,2,2)
# Initialize object
statistics = LabelImageStatistics(label_image, original_image)
# Check created intensity distribution
intensity_distributions = statistics.get_intensity_distributions()
self.assertEqual(len(intensity_distributions), 1)
self.assertEqual(intensity_distributions[0], 0)
intensity_distribution_histogram = statistics.get_intensity_distribution_histogram()
self.assertEqual(len(intensity_distribution_histogram[0]), statistics.get_intensity_distribution_histogram_width())
self.assertEqual(len(intensity_distribution_histogram[1]), statistics.get_intensity_distribution_histogram_width() + 1)
self.assertEqual(intensity_distribution_histogram[0][statistics.get_intensity_distribution_histogram_width()/2], 1)
self.assertEqual(intensity_distribution_histogram[0].max(), 1)
self.assertEqual(intensity_distribution_histogram[0].min(), 0)
# Check created size distribution
sizes = statistics.get_sizes()
self.assertEqual(len(sizes), 1)
self.assertEqual(sizes[0], 2*2*2)
sizes_histogram = statistics.get_size_histogram()
self.assertEqual(len(sizes_histogram[0]), statistics.get_size_histogram_width())
self.assertEqual(len(sizes_histogram[1]), statistics.get_size_histogram_width() + 1)
self.assertEqual(sizes_histogram[0][statistics.get_size_histogram_width()/2], 1)
self.assertEqual(sizes_histogram[0].max(), 1)
self.assertEqual(sizes_histogram[0].min(), 0)
# Check other statistics values
self.assertTrue(statistics.labels_are_consecutive())
self.assertEqual(statistics.get_min_intensity(), 0)
self.assertEqual(statistics.get_max_intensity(), 0)
self.assertEqual(statistics.get_min_label(), 0)
self.assertEqual(statistics.get_max_label(), 0)
self.assertEqual(statistics.get_label_count(), 1)
def test_Homogeneous(self):
"""Checks the return value for a homogeneous region."""
# Create label image with only one region
label_image = scipy.zeros(2*2*2, dtype=scipy.int8).reshape(2,2,2)
# Create original image with only one intensity value
original_image = scipy.zeros(2*2*2, dtype=scipy.int8).reshape(2,2,2)
# Initialize object
statistics = LabelImageStatistics(label_image, original_image)
# Check created intensity distribution
intensity_distributions = statistics.get_intensity_distributions()
self.assertEqual(len(intensity_distributions), 1)
self.assertEqual(intensity_distributions[0], 0)
intensity_distribution_histogram = statistics.get_intensity_distribution_histogram()
self.assertEqual(len(intensity_distribution_histogram[0]), statistics.get_intensity_distribution_histogram_width())
self.assertEqual(len(intensity_distribution_histogram[1]), statistics.get_intensity_distribution_histogram_width() + 1)
self.assertEqual(intensity_distribution_histogram[0][statistics.get_intensity_distribution_histogram_width()/2], 1)
self.assertEqual(intensity_distribution_histogram[0].max(), 1)
self.assertEqual(intensity_distribution_histogram[0].min(), 0)
def test_Intensity_1(self):
"""Test a case of distributed intensity values."""
# Create label image with only one region
label_image = scipy.zeros(2 * 2 * 2, dtype=scipy.int8).reshape(2, 2, 2)
# Create original image with two equally distibuted intensity value
original_image = scipy.zeros(2 * 2 * 2, dtype=scipy.int8)
original_image[:4] = -1
original_image[4:] = 1
original_image = original_image.reshape(2, 2, 2)
# Initialize object
statistics = LabelImageStatistics(label_image, original_image)
# Computed expected result
i = scipy.array([-1, -1, -1, -1, 1, 1, 1, 1])
h = scipy.histogram(
i, statistics._intensity_distribution_local_histogram_width)
hr = scipy.array(h[0]) / float(h[0].sum())
g = stats.norm(*stats.norm.fit(i))
r = abs(hr - g.pdf(h[1][:-1]))
r *= h[1][-2] - h[1][0]
r = r.sum()
# Check created intensity distribution
intensity_distributions = statistics.get_intensity_distributions()
self.assertEqual(len(intensity_distributions), 1)
self.assertEqual(intensity_distributions[0], i.std())
intensity_distribution_histogram = statistics.get_intensity_distribution_histogram(
)
self.assertEqual(
intensity_distribution_histogram[0]
[statistics.get_intensity_distribution_histogram_width() / 2], 1)
self.assertEqual(intensity_distribution_histogram[0].max(), 1)
self.assertEqual(intensity_distribution_histogram[0].min(), 0)
self.assertEqual(intensity_distribution_histogram[1].mean(), i.std())
