def run_one_gabor(self, image_name, object_name, scale, workspace):
objects = workspace.get_objects(object_name)
labels = objects.segmented
object_count = np.max(labels)
if object_count > 0:
image = workspace.image_set.get_image(image_name,
must_be_grayscale=True)
pixel_data = image.pixel_data
labels = objects.segmented
if image.has_mask:
mask = image.mask
else:
mask = None
try:
pixel_data = objects.crop_image_similarly(pixel_data)
if mask is not None:
mask = objects.crop_image_similarly(mask)
labels[~mask] = 0
except ValueError:
pixel_data, m1 = cpo.size_similarly(labels, pixel_data)
labels[~m1] = 0
if mask is not None:
mask, m2 = cpo.size_similarly(labels, mask)
labels[~m2] = 0
labels[~mask] = 0
pixel_data = normalized_per_object(pixel_data, labels)
best_score = np.zeros((object_count,))
for angle in range(self.gabor_angles.value):
theta = np.pi * angle / self.gabor_angles.value
g = gabor(pixel_data, labels, scale, theta)
score_r = fix(scind.sum(g.real, labels,
np.arange(object_count, dtype=np.int32)+ 1))
score_i = fix(scind.sum(g.imag, labels,
np.arange(object_count, dtype=np.int32)+ 1))
score = np.sqrt(score_r**2+score_i**2)
best_score = np.maximum(best_score, score)
else:
best_score = np.zeros((0,))
statistics = self.record_measurement(workspace,
image_name,
object_name,
scale,
F_GABOR,
best_score)
return statistics
def run_one_gabor(self, image_name, object_name, scale, workspace):
objects = workspace.get_objects(object_name)
labels = objects.segmented
object_count = np.max(labels)
if object_count > 0:
image = workspace.image_set.get_image(image_name,
must_be_grayscale=True)
pixel_data = image.pixel_data
labels = objects.segmented
if image.has_mask:
mask = image.mask
else:
mask = None
try:
pixel_data = objects.crop_image_similarly(pixel_data)
if mask is not None:
mask = objects.crop_image_similarly(mask)
labels[~mask] = 0
except ValueError:
pixel_data, m1 = cpo.size_similarly(labels, pixel_data)
labels[~m1] = 0
if mask is not None:
mask, m2 = cpo.size_similarly(labels, mask)
labels[~m2] = 0
labels[~mask] = 0
pixel_data = normalized_per_object(pixel_data, labels)
best_score = np.zeros((object_count, ))
for angle in range(self.gabor_angles.value):
theta = np.pi * angle / self.gabor_angles.value
g = gabor(pixel_data, labels, scale, theta)
score_r = fix(
scind.sum(g.real, labels,
np.arange(object_count, dtype=np.int32) + 1))
score_i = fix(
scind.sum(g.imag, labels,
np.arange(object_count, dtype=np.int32) + 1))
score = np.sqrt(score_r**2 + score_i**2)
best_score = np.maximum(best_score, score)
else:
best_score = np.zeros((0, ))
statistics = self.record_measurement(workspace, image_name,
object_name, scale, F_GABOR,
best_score)
return statistics
def run_image_gabor(self, image_name, scale, workspace):
image = workspace.image_set.get_image(image_name,
must_be_grayscale=True)
pixel_data = image.pixel_data
labels = np.ones(pixel_data.shape, int)
if image.has_mask:
labels[~image.mask] = 0
pixel_data = stretch(pixel_data, labels > 0)
best_score = 0
for angle in range(self.gabor_angles.value):
theta = np.pi * angle / self.gabor_angles.value
g = gabor(pixel_data, labels, scale, theta)
score_r = np.sum(g.real)
score_i = np.sum(g.imag)
score = np.sqrt(score_r**2 + score_i**2)
best_score = max(best_score, score)
statistics = self.record_image_measurement(workspace, image_name,
scale, F_GABOR, best_score)
return statistics
def run_image_gabor(self, image_name, scale, workspace):
image = workspace.image_set.get_image(image_name,
must_be_grayscale=True)
pixel_data = image.pixel_data
labels = np.ones(pixel_data.shape, int)
if image.has_mask:
labels[~image.mask] = 0
pixel_data = stretch(pixel_data, labels > 0)
best_score = 0
for angle in range(self.gabor_angles.value):
theta = np.pi * angle / self.gabor_angles.value
g = gabor(pixel_data, labels, scale, theta)
score_r = np.sum(g.real)
score_i = np.sum(g.imag)
score = np.sqrt(score_r**2+score_i**2)
best_score = max(best_score, score)
statistics = self.record_image_measurement(workspace,
image_name,
scale,
F_GABOR,
best_score)
return statistics