def run(self, workspace):
image = workspace.image_set.get_image(self.image_name.value,
must_be_grayscale=True)
orig_pixels = image.pixel_data
if image.has_mask:
mask = image.mask
else:
mask = np.ones(orig_pixels.shape, bool)
if self.method == M_SOBEL:
if self.direction == E_ALL:
output_pixels = sobel(orig_pixels, mask)
elif self.direction == E_HORIZONTAL:
output_pixels = hsobel(orig_pixels, mask)
elif self.direction == E_VERTICAL:
output_pixels = vsobel(orig_pixels, mask)
else:
raise NotImplementedError(
"Unimplemented direction for Sobel: %s",
self.direction.value)
elif self.method == M_LOG:
sigma = self.get_sigma()
size = int(sigma * 4) + 1
output_pixels = laplacian_of_gaussian(orig_pixels, mask, size,
sigma)
elif self.method == M_PREWITT:
if self.direction == E_ALL:
output_pixels = prewitt(orig_pixels)
elif self.direction == E_HORIZONTAL:
output_pixels = hprewitt(orig_pixels, mask)
elif self.direction == E_VERTICAL:
output_pixels = vprewitt(orig_pixels, mask)
else:
raise NotImplementedError(
"Unimplemented direction for Prewitt: %s",
self.direction.value)
elif self.method == M_CANNY:
high_threshold = self.manual_threshold.value
low_threshold = self.low_threshold.value
if (self.wants_automatic_low_threshold.value
or self.wants_automatic_threshold.value):
sobel_image = sobel(orig_pixels, mask)
low, high = otsu3(sobel_image[mask])
if self.wants_automatic_low_threshold.value:
low_threshold = low * self.threshold_adjustment_factor.value
if self.wants_automatic_threshold.value:
high_threshold = high * self.threshold_adjustment_factor.value
output_pixels = canny(orig_pixels, mask, self.get_sigma(),
low_threshold, high_threshold)
elif self.method == M_ROBERTS:
output_pixels = roberts(orig_pixels, mask)
else:
raise NotImplementedError(
"Unimplemented edge detection method: %s" % self.method.value)
output_image = cpi.Image(output_pixels, parent_image=image)
workspace.image_set.add(self.output_image_name.value, output_image)
if self.show_window:
workspace.display_data.orig_pixels = orig_pixels
workspace.display_data.output_pixels = output_pixels
def test_01_01_ring(self):
'''The LoG should have its lowest value in the center of the ring'''
i, j = np.mgrid[-20:21, -20:21].astype(float)
# A ring of radius 3, more or less
image = (np.abs(i**2 + j**2 - 3) < 2).astype(float)
result = F.laplacian_of_gaussian(image, None, 9, 3)
self.assertTrue((np.argmin(result) % 41,
int(np.argmin(result) / 41)) == (20, 20))
def run(self, workspace):
image = workspace.image_set.get_image(self.image_name.value,
must_be_grayscale=True)
orig_pixels = image.pixel_data
if image.has_mask:
mask = image.mask
else:
mask = np.ones(orig_pixels.shape, bool)
if self.method == M_SOBEL:
if self.direction == E_ALL:
output_pixels = sobel(orig_pixels, mask)
elif self.direction == E_HORIZONTAL:
output_pixels = hsobel(orig_pixels, mask)
elif self.direction == E_VERTICAL:
output_pixels = vsobel(orig_pixels, mask)
else:
raise NotImplementedError("Unimplemented direction for Sobel: %s", self.direction.value)
elif self.method == M_LOG:
sigma = self.get_sigma()
size = int(sigma * 4) + 1
output_pixels = laplacian_of_gaussian(orig_pixels, mask, size, sigma)
elif self.method == M_PREWITT:
if self.direction == E_ALL:
output_pixels = prewitt(orig_pixels)
elif self.direction == E_HORIZONTAL:
output_pixels = hprewitt(orig_pixels, mask)
elif self.direction == E_VERTICAL:
output_pixels = vprewitt(orig_pixels, mask)
else:
raise NotImplementedError("Unimplemented direction for Prewitt: %s", self.direction.value)
elif self.method == M_CANNY:
high_threshold = self.manual_threshold.value
low_threshold = self.low_threshold.value
if (self.wants_automatic_low_threshold.value or
self.wants_automatic_threshold.value):
sobel_image = sobel(orig_pixels, mask)
low, high = otsu3(sobel_image[mask])
if self.wants_automatic_low_threshold.value:
low_threshold = low * self.threshold_adjustment_factor.value
if self.wants_automatic_threshold.value:
high_threshold = high * self.threshold_adjustment_factor.value
output_pixels = canny(orig_pixels, mask, self.get_sigma(),
low_threshold,
high_threshold)
elif self.method == M_ROBERTS:
output_pixels = roberts(orig_pixels, mask)
elif self.method == M_KIRSCH:
output_pixels = kirsch(orig_pixels)
else:
raise NotImplementedError("Unimplemented edge detection method: %s" %
self.method.value)
output_image = cpi.Image(output_pixels, parent_image=image)
workspace.image_set.add(self.output_image_name.value, output_image)
if self.show_window:
workspace.display_data.orig_pixels = orig_pixels
workspace.display_data.output_pixels = output_pixels
def test_05_02_log_manual(self):
'''Test the laplacian of gaussian with manual sigma'''
np.random.seed(0)
image = np.random.uniform(size=(20, 20)).astype(np.float32)
workspace, module = self.make_workspace(image)
module.method.value = F.M_LOG
module.sigma.value = 4
module.wants_automatic_sigma.value = False
module.run(workspace)
output = workspace.image_set.get_image(OUTPUT_IMAGE_NAME)
sigma = 4.0
expected = FIL.laplacian_of_gaussian(image, np.ones(image.shape, bool),
int(sigma * 4) + 1,
sigma).astype(np.float32)
self.assertTrue(np.all(output.pixel_data == expected))
def test_05_02_log_manual(self):
'''Test the laplacian of gaussian with manual sigma'''
np.random.seed(0)
image = np.random.uniform(size=(20, 20)).astype(np.float32)
workspace, module = self.make_workspace(image)
module.method.value = F.M_LOG
module.sigma.value = 4
module.wants_automatic_sigma.value = False
module.run(workspace)
output = workspace.image_set.get_image(OUTPUT_IMAGE_NAME)
sigma = 4.0
expected = FIL.laplacian_of_gaussian(image,
np.ones(image.shape, bool),
int(sigma * 4) + 1,
sigma).astype(np.float32)
self.assertTrue(np.all(output.pixel_data == expected))
def test_00_01_zeros_mask(self):
result = F.laplacian_of_gaussian(np.zeros((10, 10)),
np.zeros((10, 10), bool), 9, 3)
self.assertTrue(np.all(result == 0))
def test_00_00_zeros(self):
result = F.laplacian_of_gaussian(np.zeros((10, 10)), None, 9, 3)
self.assertTrue(np.all(result == 0))
