def test_01_smooth_zero(self):
"""Make sure smooth_with_function_and_mask doesn't crash on all-zeros"""
result = cpms.smooth_with_function_and_mask(np.zeros((10,10)),
self.function,
np.zeros((10,10),bool))
self.assertTrue(np.all(np.abs(result) < .00001))
def test_01_smooth_zero(self):
"""Make sure smooth_with_function_and_mask doesn't crash on all-zeros"""
result = cpms.smooth_with_function_and_mask(np.zeros((10, 10)),
self.function,
np.zeros((10, 10), bool))
self.assertTrue(np.all(np.abs(result) < .00001))
def test_03_smooth_unmasked_square(self):
np.random.seed(0)
image = np.random.uniform(size=(10, 10))
mask = np.ones((10, 10), bool)
image[2:7, 3:8] = 1
expected = self.function(image)
result = cpms.smooth_with_function_and_mask(image, self.function, mask)
self.assertTrue(np.all(np.abs(result - expected) < .00001))
def test_03_smooth_unmasked_square(self):
np.random.seed(0)
image = np.random.uniform(size=(10,10))
mask = np.ones((10,10),bool)
image[2:7,3:8] = 1
expected = self.function(image)
result = cpms.smooth_with_function_and_mask(image, self.function, mask)
self.assertTrue(np.all(np.abs(result - expected) < .00001))
def test_02_smooth_masked_square(self):
"""smooth_with_function_and_mask on a masked square does nothing"""
np.random.seed(0)
image = np.random.uniform(size=(10, 10))
mask = np.zeros((10, 10), bool)
mask[2:7, 3:8] = True
image[mask] = 1
result = cpms.smooth_with_function_and_mask(image, self.function, mask)
self.assertTrue(np.all(np.abs(result[mask] - 1) < .00001))
def test_02_smooth_masked_square(self):
"""smooth_with_function_and_mask on a masked square does nothing"""
np.random.seed(0)
image = np.random.uniform(size=(10,10))
mask = np.zeros((10,10),bool)
mask[2:7,3:8] = True
image[mask] = 1
result = cpms.smooth_with_function_and_mask(image, self.function, mask)
self.assertTrue(np.all(np.abs(result[mask] - 1) < .00001))
def test_03_02_gaussian_auto_large(self):
'''Test the smooth module with Gaussian smoothing in large automatic mode'''
sigma = 30.0 / 2.35
image = np.random.uniform(size=(3200,100)).astype(np.float32)
mask = np.ones(image.shape,bool)
mask[40:60,45:65] = False
fn = lambda x: gaussian_filter(x, sigma, mode='constant', cval = 0.0)
expected = smooth_with_function_and_mask(image, fn, mask)
workspace, module = self.make_workspace(image, mask)
module.smoothing_method.value = S.GAUSSIAN_FILTER
module.run(workspace)
result = workspace.image_set.get_image(OUTPUT_IMAGE_NAME)
self.assertFalse(result is None)
np.testing.assert_almost_equal(result.pixel_data, expected)
def test_03_02_gaussian_auto_large(self):
'''Test the smooth module with Gaussian smoothing in large automatic mode'''
sigma = 30.0 / 2.35
image = np.random.uniform(size=(3200, 100)).astype(np.float32)
mask = np.ones(image.shape, bool)
mask[40:60, 45:65] = False
fn = lambda x: gaussian_filter(x, sigma, mode='constant', cval=0.0)
expected = smooth_with_function_and_mask(image, fn, mask)
workspace, module = self.make_workspace(image, mask)
module.smoothing_method.value = S.GAUSSIAN_FILTER
module.run(workspace)
result = workspace.image_set.get_image(OUTPUT_IMAGE_NAME)
self.assertFalse(result is None)
np.testing.assert_almost_equal(result.pixel_data, expected)
def run(self, workspace):
image = workspace.image_set.get_image(self.image_name.value,
must_be_grayscale=True)
pixel_data = image.pixel_data
if self.wants_automatic_object_size.value:
object_size = min(30, max(1, np.mean(pixel_data.shape) / 40))
else:
object_size = float(self.object_size.value)
sigma = object_size / 2.35
if self.smoothing_method.value == GAUSSIAN_FILTER:
def fn(image):
return scind.gaussian_filter(image,
sigma,
mode='constant',
cval=0)
output_pixels = smooth_with_function_and_mask(
pixel_data, fn, image.mask)
elif self.smoothing_method.value == MEDIAN_FILTER:
output_pixels = median_filter(pixel_data, image.mask,
object_size / 2 + 1)
elif self.smoothing_method.value == SMOOTH_KEEPING_EDGES:
sigma_range = float(self.sigma_range.value)
output_pixels = bilateral_filter(pixel_data, image.mask, sigma,
sigma_range)
elif self.smoothing_method.value == FIT_POLYNOMIAL:
output_pixels = fit_polynomial(pixel_data, image.mask,
self.clip.value)
elif self.smoothing_method.value == CIRCULAR_AVERAGE_FILTER:
output_pixels = circular_average_filter(pixel_data,
object_size / 2 + 1,
image.mask)
elif self.smoothing_method.value == SM_TO_AVERAGE:
if image.has_mask:
mean = np.mean(pixel_data[image.mask])
else:
mean = np.mean(pixel_data)
output_pixels = np.ones(pixel_data.shape, pixel_data.dtype) * mean
else:
raise ValueError("Unsupported smoothing method: %s" %
self.smoothing_method.value)
output_image = cpi.Image(output_pixels, parent_image=image)
workspace.image_set.add(self.filtered_image_name.value, output_image)
workspace.display_data.pixel_data = pixel_data
workspace.display_data.output_pixels = output_pixels
def run(self, workspace):
image = workspace.image_set.get_image(self.image_name.value,
must_be_grayscale=True)
pixel_data = image.pixel_data
if self.wants_automatic_object_size.value:
object_size = min(30,max(1,np.mean(pixel_data.shape)/40))
else:
object_size = float(self.object_size.value)
sigma = object_size / 2.35
if self.smoothing_method.value == GAUSSIAN_FILTER:
def fn(image):
return scind.gaussian_filter(image, sigma,
mode='constant', cval=0)
output_pixels = smooth_with_function_and_mask(pixel_data, fn,
image.mask)
elif self.smoothing_method.value == MEDIAN_FILTER:
output_pixels = median_filter(pixel_data, image.mask,
object_size/2+1)
elif self.smoothing_method.value == SMOOTH_KEEPING_EDGES:
sigma_range = float(self.sigma_range.value)
output_pixels = bilateral_filter(pixel_data, image.mask,
sigma, sigma_range)
elif self.smoothing_method.value == FIT_POLYNOMIAL:
output_pixels = fit_polynomial(pixel_data, image.mask,
self.clip.value)
elif self.smoothing_method.value == CIRCULAR_AVERAGE_FILTER:
output_pixels = circular_average_filter(pixel_data, object_size/2+1, image.mask)
elif self.smoothing_method.value == SM_TO_AVERAGE:
if image.has_mask:
mean = np.mean(pixel_data[image.mask])
else:
mean = np.mean(pixel_data)
output_pixels = np.ones(pixel_data.shape, pixel_data.dtype) * mean
else:
raise ValueError("Unsupported smoothing method: %s" %
self.smoothing_method.value)
output_image = cpi.Image(output_pixels, parent_image = image)
workspace.image_set.add(self.filtered_image_name.value,
output_image)
workspace.display_data.pixel_data = pixel_data
workspace.display_data.output_pixels = output_pixels
