def test_04_01_median(self): '''test the smooth module with median filtering''' object_size = 100.0/ 40.0 np.random.seed(0) image = np.random.uniform(size=(100,100)).astype(np.float32) mask = np.ones(image.shape,bool) mask[40:60,45:65] = False expected = median_filter(image, mask, object_size / 2 + 1) workspace, module = self.make_workspace(image, mask) module.smoothing_method.value = S.MEDIAN_FILTER module.run(workspace) result = workspace.image_set.get_image(OUTPUT_IMAGE_NAME) self.assertFalse(result is None) np.testing.assert_almost_equal(result.pixel_data, expected)
def test_04_01_median(self): '''test the smooth module with median filtering''' object_size = 100.0 / 40.0 np.random.seed(0) image = np.random.uniform(size=(100, 100)).astype(np.float32) mask = np.ones(image.shape, bool) mask[40:60, 45:65] = False expected = median_filter(image, mask, object_size / 2 + 1) workspace, module = self.make_workspace(image, mask) module.smoothing_method.value = S.MEDIAN_FILTER module.run(workspace) result = workspace.image_set.get_image(OUTPUT_IMAGE_NAME) self.assertFalse(result is None) np.testing.assert_almost_equal(result.pixel_data, expected)
def run(self, workspace): image = workspace.image_set.get_image(self.image_name.value, must_be_grayscale=True) pixel_data = image.pixel_data if self.wants_automatic_object_size.value: object_size = min(30, max(1, np.mean(pixel_data.shape) / 40)) else: object_size = float(self.object_size.value) sigma = object_size / 2.35 if self.smoothing_method.value == GAUSSIAN_FILTER: def fn(image): return scind.gaussian_filter(image, sigma, mode='constant', cval=0) output_pixels = smooth_with_function_and_mask( pixel_data, fn, image.mask) elif self.smoothing_method.value == MEDIAN_FILTER: output_pixels = median_filter(pixel_data, image.mask, object_size / 2 + 1) elif self.smoothing_method.value == SMOOTH_KEEPING_EDGES: sigma_range = float(self.sigma_range.value) output_pixels = bilateral_filter(pixel_data, image.mask, sigma, sigma_range) elif self.smoothing_method.value == FIT_POLYNOMIAL: output_pixels = fit_polynomial(pixel_data, image.mask, self.clip.value) elif self.smoothing_method.value == CIRCULAR_AVERAGE_FILTER: output_pixels = circular_average_filter(pixel_data, object_size / 2 + 1, image.mask) elif self.smoothing_method.value == SM_TO_AVERAGE: if image.has_mask: mean = np.mean(pixel_data[image.mask]) else: mean = np.mean(pixel_data) output_pixels = np.ones(pixel_data.shape, pixel_data.dtype) * mean else: raise ValueError("Unsupported smoothing method: %s" % self.smoothing_method.value) output_image = cpi.Image(output_pixels, parent_image=image) workspace.image_set.add(self.filtered_image_name.value, output_image) workspace.display_data.pixel_data = pixel_data workspace.display_data.output_pixels = output_pixels
def run(self, workspace): image = workspace.image_set.get_image(self.image_name.value, must_be_grayscale=True) pixel_data = image.pixel_data if self.wants_automatic_object_size.value: object_size = min(30,max(1,np.mean(pixel_data.shape)/40)) else: object_size = float(self.object_size.value) sigma = object_size / 2.35 if self.smoothing_method.value == GAUSSIAN_FILTER: def fn(image): return scind.gaussian_filter(image, sigma, mode='constant', cval=0) output_pixels = smooth_with_function_and_mask(pixel_data, fn, image.mask) elif self.smoothing_method.value == MEDIAN_FILTER: output_pixels = median_filter(pixel_data, image.mask, object_size/2+1) elif self.smoothing_method.value == SMOOTH_KEEPING_EDGES: sigma_range = float(self.sigma_range.value) output_pixels = bilateral_filter(pixel_data, image.mask, sigma, sigma_range) elif self.smoothing_method.value == FIT_POLYNOMIAL: output_pixels = fit_polynomial(pixel_data, image.mask, self.clip.value) elif self.smoothing_method.value == CIRCULAR_AVERAGE_FILTER: output_pixels = circular_average_filter(pixel_data, object_size/2+1, image.mask) elif self.smoothing_method.value == SM_TO_AVERAGE: if image.has_mask: mean = np.mean(pixel_data[image.mask]) else: mean = np.mean(pixel_data) output_pixels = np.ones(pixel_data.shape, pixel_data.dtype) * mean else: raise ValueError("Unsupported smoothing method: %s" % self.smoothing_method.value) output_image = cpi.Image(output_pixels, parent_image = image) workspace.image_set.add(self.filtered_image_name.value, output_image) workspace.display_data.pixel_data = pixel_data workspace.display_data.output_pixels = output_pixels
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