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_per_layer(self, image, channel):
if channel >= 0:
pixel_data = image.pixel_data[:,:,channel].squeeze()
else:
pixel_data = image.pixel_data
mask = image.mask
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,
mask)
elif self.smoothing_method.value == MEDIAN_FILTER:
output_pixels = median_filter(pixel_data, 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, mask,
sigma, sigma_range)
elif self.smoothing_method.value == FIT_POLYNOMIAL:
output_pixels = fit_polynomial(pixel_data, mask,
self.clip.value)
elif self.smoothing_method.value == CIRCULAR_AVERAGE_FILTER:
output_pixels = circular_average_filter(pixel_data,
object_size / 2 + 1, mask)
elif self.smoothing_method.value == SM_TO_AVERAGE:
if image.has_mask:
mean = np.mean(pixel_data[mask])
else:
mean = np.mean(pixel_data)
output_pixels = np.ones(pixel_data.shape, pixel_data.dtype) * mean
elif self.smoothing_method.value == REMOVE_OUTLIER:
# TODO: implement how this deals with masks.
nbhood = self.outlierneighbourhood.value
output_pixels = self.remove_outlier_pixels(pixel_data,
threshold=self.treshold.value,
radius=nbhood,
mode='max')
else:
raise ValueError("Unsupported smoothing method: %s" %
self.smoothing_method.value)
return 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
def test_05_01_bilateral(self):
'''test the smooth module with bilateral filtering'''
sigma = 16.0
sigma_range = .2
np.random.seed(0)
image = np.random.uniform(size=(100,100)).astype(np.float32)
mask = np.ones(image.shape,bool)
mask[40:60,45:65] = False
expected = bilateral_filter(image, mask, sigma, sigma_range)
workspace, module = self.make_workspace(image, mask)
module.smoothing_method.value = S.SMOOTH_KEEPING_EDGES
module.sigma_range.value = sigma_range
module.wants_automatic_object_size.value = False
module.object_size.value = 16.0 * 2.35
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_05_01_bilateral(self):
'''test the smooth module with bilateral filtering'''
sigma = 16.0
sigma_range = .2
np.random.seed(0)
image = np.random.uniform(size=(100, 100)).astype(np.float32)
mask = np.ones(image.shape, bool)
mask[40:60, 45:65] = False
expected = bilateral_filter(image, mask, sigma, sigma_range)
workspace, module = self.make_workspace(image, mask)
module.smoothing_method.value = S.SMOOTH_KEEPING_EDGES
module.sigma_range.value = sigma_range
module.wants_automatic_object_size.value = False
module.object_size.value = 16.0 * 2.35
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_00_01_all_masked(self):
'''Test the bilateral filter of a completely masked array'''
np.random.seed(0)
image = np.random.uniform(size=(10, 10))
result = F.bilateral_filter(image, np.zeros((10, 10), bool), 5.0, .1)
self.assertTrue(np.all(result == image))
def test_00_00_zeros(self):
'''Test the bilateral filter of an array of all zeros'''
result = F.bilateral_filter(np.zeros((10, 10)), np.ones((10, 10),
bool), 5.0, .1)
self.assertTrue(np.all(result == 0))