def test_01_03_diagonal_lines(self):
img = np.ones((20, 30)) * .5
i, j = np.mgrid[0:20, 0:30]
img[(i == j - 3) & (i <= 15)] = 1
img[(i == j + 3)] = 0
expected = np.zeros((20, 30), bool)
expected[(i >= j - 3) & (i <= j + 3)] = True
result = F.line_integration(img, -45, 1, 0)
self.assertTrue(np.mean(result[expected]) > .5)
self.assertTrue(np.mean(result[~expected]) < .25)
def test_01_02_two_lines(self):
img = np.ones((20, 30)) * .5
img[8, 10:20] = 1
img[12, 10:20] = 0
result = F.line_integration(img, 0, 1, 0)
expected = np.zeros((20, 30))
expected[9:12, 10:20] = 1
expected[8, 10:20] = .5
expected[12, 10:20] = .5
np.testing.assert_almost_equal(result, expected)
def test_01_05_smooth(self):
img = np.ones((30, 20)) * .5
img[10, 10] = 1
img[20, 10] = 0
result = F.line_integration(img, 0, 1, .5)
part = result[15, :]
part = (part - np.min(part)) / (np.max(part) - np.min(part))
expected = np.exp(-(np.arange(20) - 10)**2 * 2)
expected = (expected - np.min(expected)) / (np.max(expected) -
np.min(expected))
np.testing.assert_almost_equal(part, expected)
def test_01_04_decay(self):
img = np.ones((25, 23)) * .5
img[10, 10] = 1
img[20, 10] = 0
result = F.line_integration(img, 0, .9, 0)
decay_part = result[11:20, 10]
expected = .9**np.arange(1, 10) + .9**np.arange(9, 0, -1)
expected = ((expected - np.min(expected)) /
(np.max(expected) - np.min(expected)))
decay_part = ((decay_part - np.min(decay_part)) /
(np.max(decay_part) - np.min(decay_part)))
np.testing.assert_almost_equal(decay_part, expected)
def test_01_01_nothing(self):
img = np.zeros((23, 17))
result = F.line_integration(img, 0, .95, 2.0)
np.testing.assert_almost_equal(result, 0)
def run(self, workspace):
image = workspace.image_set.get_image(self.image_name.value,
must_be_grayscale = True)
#
# Match against Matlab's strel('disk') operation.
#
radius = (float(self.object_size.value)-1.0) / 2.0
mask = image.mask if image.has_mask else None
pixel_data = image.pixel_data
if self.method == ENHANCE:
if self.enhance_method == E_SPECKLES:
if self.speckle_accuracy == S_SLOW:
result = white_tophat(pixel_data, radius, mask)
else:
#
# white_tophat = img - opening
# = img - dilate(erode)
# = img - median_filter(median_filter(0%) 100%)
result = pixel_data - median_filter(
median_filter(pixel_data, mask, radius, percent = 0),
mask, radius, percent = 100)
if mask is not None:
result[~mask] = pixel_data[~mask]
elif self.enhance_method == E_NEURITES:
if self.neurite_choice == N_GRADIENT:
#
# white_tophat = img - opening
# black_tophat = closing - img
# desired effect = img + white_tophat - black_tophat
# = img + img - opening - closing + img
# = 3*img - opening - closing
result = (3 * pixel_data -
opening(pixel_data, radius, mask) -
closing(pixel_data, radius, mask))
result[result > 1] = 1
result[result < 0] = 0
else:
sigma = self.smoothing.value
smoothed = gaussian_filter(pixel_data, sigma)
L = hessian(smoothed, return_hessian = False,
return_eigenvectors = False)
#
# The positive values are darker pixels with lighter
# neighbors. The original ImageJ code scales the result
# by sigma squared - I have a feeling this might be
# a first-order correction for e**(-2*sigma), possibly
# because the hessian is taken from one pixel away
# and the gradient is less as sigma gets larger.
#
result = -L[:, :, 0] * (L[:, :, 0] < 0) * sigma * sigma
if image.has_mask:
result[~mask] = pixel_data[~mask]
elif self.enhance_method == E_DARK_HOLES:
min_radius = max(1,int(self.hole_size.min / 2))
max_radius = int((self.hole_size.max+1)/2)
result = enhance_dark_holes(pixel_data, min_radius,
max_radius, mask)
elif self.enhance_method == E_CIRCLES:
result = circular_hough(pixel_data, radius + .5, mask=mask)
elif self.enhance_method == E_TEXTURE:
result = variance_transform(pixel_data,
self.smoothing.value,
mask = mask)
elif self.enhance_method == E_DIC:
result = line_integration(pixel_data,
self.angle.value,
self.decay.value,
self.smoothing.value)
else:
raise NotImplementedError("Unimplemented enhance method: %s"%
self.enhance_method.value)
elif self.method == SUPPRESS:
if image.has_mask:
result = opening(image.pixel_data, radius, image.mask)
else:
result = opening(image.pixel_data, radius)
else:
raise ValueError("Unknown filtering method: %s"%self.method)
result_image = cpi.Image(result, parent_image=image)
workspace.image_set.add(self.filtered_image_name.value, result_image)
if self.show_window:
workspace.display_data.image = image.pixel_data
workspace.display_data.result = result
def run(self, workspace):
image = workspace.image_set.get_image(self.image_name.value,
must_be_grayscale=True)
#
# Match against Matlab's strel('disk') operation.
#
radius = (float(self.object_size.value) - 1.0) / 2.0
mask = image.mask if image.has_mask else None
pixel_data = image.pixel_data
if self.method == ENHANCE:
if self.enhance_method == E_SPECKLES:
if self.speckle_accuracy == S_SLOW:
result = white_tophat(pixel_data, radius, mask)
else:
#
# white_tophat = img - opening
# = img - dilate(erode)
# = img - median_filter(median_filter(0%) 100%)
result = pixel_data - median_filter(median_filter(
pixel_data, mask, radius, percent=0),
mask,
radius,
percent=100)
if mask is not None:
result[~mask] = pixel_data[~mask]
elif self.enhance_method == E_NEURITES:
if self.neurite_choice == N_GRADIENT:
#
# white_tophat = img - opening
# black_tophat = closing - img
# desired effect = img + white_tophat - black_tophat
# = img + img - opening - closing + img
# = 3*img - opening - closing
result = (3 * pixel_data -
opening(pixel_data, radius, mask) -
closing(pixel_data, radius, mask))
result[result > 1] = 1
result[result < 0] = 0
else:
sigma = self.smoothing.value
smoothed = gaussian_filter(pixel_data, sigma)
L = hessian(smoothed,
return_hessian=False,
return_eigenvectors=False)
#
# The positive values are darker pixels with lighter
# neighbors. The original ImageJ code scales the result
# by sigma squared - I have a feeling this might be
# a first-order correction for e**(-2*sigma), possibly
# because the hessian is taken from one pixel away
# and the gradient is less as sigma gets larger.
#
result = -L[:, :, 0] * (L[:, :, 0] < 0) * sigma * sigma
if image.has_mask:
result[~mask] = pixel_data[~mask]
elif self.enhance_method == E_DARK_HOLES:
min_radius = max(1, int(self.hole_size.min / 2))
max_radius = int((self.hole_size.max + 1) / 2)
result = enhance_dark_holes(pixel_data, min_radius, max_radius,
mask)
elif self.enhance_method == E_CIRCLES:
result = circular_hough(pixel_data, radius + .5, mask=mask)
elif self.enhance_method == E_TEXTURE:
result = variance_transform(pixel_data,
self.smoothing.value,
mask=mask)
elif self.enhance_method == E_DIC:
result = line_integration(pixel_data, self.angle.value,
self.decay.value,
self.smoothing.value)
else:
raise NotImplementedError("Unimplemented enhance method: %s" %
self.enhance_method.value)
elif self.method == SUPPRESS:
if image.has_mask:
result = opening(image.pixel_data, radius, image.mask)
else:
result = opening(image.pixel_data, radius)
else:
raise ValueError("Unknown filtering method: %s" % self.method)
result_image = cpi.Image(result, parent_image=image)
workspace.image_set.add(self.filtered_image_name.value, result_image)
if self.show_window:
workspace.display_data.image = image.pixel_data
workspace.display_data.result = result
