def test_02_01_fit_polynomial(self):
'''Test the smooth module with polynomial fitting'''
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 = fit_polynomial(image, mask)
workspace, module = self.make_workspace(image, mask)
module.smoothing_method.value = S.FIT_POLYNOMIAL
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_02_01_fit_polynomial(self):
'''Test the smooth module with polynomial fitting'''
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 = fit_polynomial(image, mask)
workspace, module = self.make_workspace(image, mask)
module.smoothing_method.value = S.FIT_POLYNOMIAL
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
def test_02_01_fit_polynomial(self):
'''Test the smooth module with polynomial fitting'''
np.random.seed(0)
#
# Make an image that has a single sinusoidal cycle with different
# phase in i and j. Make it a little out-of-bounds to start to test
# clipping
#
i, j = np.mgrid[0:100, 0:100].astype(float) * np.pi / 50
image = (np.sin(i) + np.cos(j)) / 1.8 + .9
image += np.random.uniform(size=(100, 100)) * .1
mask = np.ones(image.shape, bool)
mask[40:60, 45:65] = False
for clip in (False, True):
expected = fit_polynomial(image, mask, clip)
self.assertEqual(np.all((expected >= 0) & (expected <= 1)), clip)
workspace, module = self.make_workspace(image, mask)
module.smoothing_method.value = S.FIT_POLYNOMIAL
module.clip.value = clip
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_02_01_fit_polynomial(self):
'''Test the smooth module with polynomial fitting'''
np.random.seed(0)
#
# Make an image that has a single sinusoidal cycle with different
# phase in i and j. Make it a little out-of-bounds to start to test
# clipping
#
i, j = np.mgrid[0:100, 0:100].astype(float) * np.pi / 50
image = (np.sin(i) + np.cos(j)) / 1.8 + .9
image += np.random.uniform(size=(100, 100)) * .1
mask = np.ones(image.shape,bool)
mask[40:60,45:65] = False
for clip in (False, True):
expected = fit_polynomial(image, mask, clip)
self.assertEqual(np.all((expected >= 0) & (expected <= 1)), clip)
workspace, module = self.make_workspace(image, mask)
module.smoothing_method.value = S.FIT_POLYNOMIAL
module.clip.value = clip
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)
