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)