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_07_01_kirsch(self):
r = np.random.RandomState([ord(_) for _ in "test_07_01_kirsch"])
i, j = np.mgrid[-20:20, -20:20]
image = (np.sqrt(i * i + j * j) <= 10).astype(float) * .5
image = image + r.uniform(size=image.shape) * .1
workspace, module = self.make_workspace(image)
module.method.value = F.M_KIRSCH
module.run(workspace)
output = workspace.image_set.get_image(OUTPUT_IMAGE_NAME)
result = kirsch(image)
np.testing.assert_almost_equal(output.pixel_data, result, decimal=4)
def test_07_01_kirsch(self):
r = np.random.RandomState([ord(_) for _ in "test_07_01_kirsch"])
i, j = np.mgrid[-20:20, -20:20]
image = (np.sqrt(i * i + j * j) <= 10).astype(float) * .5
image = image + r.uniform(size=image.shape) * .1
workspace, module = self.make_workspace(image)
module.method.value = F.M_KIRSCH
module.run(workspace)
output = workspace.image_set.get_image(OUTPUT_IMAGE_NAME)
result = kirsch(image)
np.testing.assert_almost_equal(output.pixel_data, result, decimal=4)