def run(self, workspace): assert isinstance(workspace, cpw.Workspace) image_name = self.image_name.value image = workspace.image_set.get_image(image_name, must_be_grayscale=True) workspace.display_data.statistics = [] img = image.pixel_data mask = image.mask objects = workspace.object_set.get_objects(self.primary_objects.value) global_threshold = None if self.method == M_DISTANCE_N: has_threshold = False else: thresholded_image = self.threshold_image(image_name, workspace) has_threshold = True # # Get the following labels: # * all edited labels # * labels touching the edge, including small removed # labels_in = objects.unedited_segmented.copy() labels_touching_edge = np.hstack( (labels_in[0, :], labels_in[-1, :], labels_in[:, 0], labels_in[:, -1])) labels_touching_edge = np.unique(labels_touching_edge) is_touching = np.zeros(np.max(labels_in) + 1, bool) is_touching[labels_touching_edge] = True is_touching = is_touching[labels_in] labels_in[(~is_touching) & (objects.segmented == 0)] = 0 # # Stretch the input labels to match the image size. If there's no # label matrix, then there's no label in that area. # if tuple(labels_in.shape) != tuple(img.shape): tmp = np.zeros(img.shape, labels_in.dtype) i_max = min(img.shape[0], labels_in.shape[0]) j_max = min(img.shape[1], labels_in.shape[1]) tmp[:i_max, :j_max] = labels_in[:i_max, :j_max] labels_in = tmp if self.method in (M_DISTANCE_B, M_DISTANCE_N): if self.method == M_DISTANCE_N: distances, (i, j) = scind.distance_transform_edt( labels_in == 0, return_indices=True) labels_out = np.zeros(labels_in.shape, int) dilate_mask = distances <= self.distance_to_dilate.value labels_out[dilate_mask] =\ labels_in[i[dilate_mask],j[dilate_mask]] else: labels_out, distances = propagate(img, labels_in, thresholded_image, 1.0) labels_out[distances > self.distance_to_dilate.value] = 0 labels_out[labels_in > 0] = labels_in[labels_in > 0] if self.fill_holes: small_removed_segmented_out = fill_labeled_holes(labels_out) else: small_removed_segmented_out = labels_out # # Create the final output labels by removing labels in the # output matrix that are missing from the segmented image # segmented_labels = objects.segmented segmented_out = self.filter_labels(small_removed_segmented_out, objects, workspace) elif self.method == M_PROPAGATION: labels_out, distance = propagate(img, labels_in, thresholded_image, self.regularization_factor.value) if self.fill_holes: small_removed_segmented_out = fill_labeled_holes(labels_out) else: small_removed_segmented_out = labels_out.copy() segmented_out = self.filter_labels(small_removed_segmented_out, objects, workspace) elif self.method == M_WATERSHED_G: # # First, apply the sobel filter to the image (both horizontal # and vertical). The filter measures gradient. # sobel_image = np.abs(scind.sobel(img)) # # Combine the image mask and threshold to mask the watershed # watershed_mask = np.logical_or(thresholded_image, labels_in > 0) watershed_mask = np.logical_and(watershed_mask, mask) # # Perform the first watershed # labels_out = watershed(sobel_image, labels_in, np.ones((3, 3), bool), mask=watershed_mask) if self.fill_holes: small_removed_segmented_out = fill_labeled_holes(labels_out) else: small_removed_segmented_out = labels_out.copy() segmented_out = self.filter_labels(small_removed_segmented_out, objects, workspace) elif self.method == M_WATERSHED_I: # # invert the image so that the maxima are filled first # and the cells compete over what's close to the threshold # inverted_img = 1 - img # # Same as above, but perform the watershed on the original image # watershed_mask = np.logical_or(thresholded_image, labels_in > 0) watershed_mask = np.logical_and(watershed_mask, mask) # # Perform the watershed # labels_out = watershed(inverted_img, labels_in, np.ones((3, 3), bool), mask=watershed_mask) if self.fill_holes: small_removed_segmented_out = fill_labeled_holes(labels_out) else: small_removed_segmented_out = labels_out segmented_out = self.filter_labels(small_removed_segmented_out, objects, workspace) if self.wants_discard_edge and self.wants_discard_primary: # # Make a new primary object # lookup = scind.maximum(segmented_out, objects.segmented, range(np.max(objects.segmented) + 1)) lookup = fix(lookup) lookup[0] = 0 lookup[lookup != 0] = np.arange(np.sum(lookup != 0)) + 1 segmented_labels = lookup[objects.segmented] segmented_out = lookup[segmented_out] new_objects = cpo.Objects() new_objects.segmented = segmented_labels if objects.has_unedited_segmented: new_objects.unedited_segmented = objects.unedited_segmented if objects.has_small_removed_segmented: new_objects.small_removed_segmented = objects.small_removed_segmented new_objects.parent_image = objects.parent_image primary_outline = outline(segmented_labels) if self.wants_primary_outlines: out_img = cpi.Image(primary_outline.astype(bool), parent_image=image) workspace.image_set.add(self.new_primary_outlines_name.value, out_img) else: primary_outline = outline(objects.segmented) secondary_outline = outline(segmented_out) # # Add the objects to the object set # objects_out = cpo.Objects() objects_out.unedited_segmented = small_removed_segmented_out objects_out.small_removed_segmented = small_removed_segmented_out objects_out.segmented = segmented_out objects_out.parent_image = image objname = self.objects_name.value workspace.object_set.add_objects(objects_out, objname) if self.use_outlines.value: out_img = cpi.Image(secondary_outline.astype(bool), parent_image=image) workspace.image_set.add(self.outlines_name.value, out_img) object_count = np.max(segmented_out) # # Add measurements # measurements = workspace.measurements cpmi.add_object_count_measurements(measurements, objname, object_count) cpmi.add_object_location_measurements(measurements, objname, segmented_out) # # Relate the secondary objects to the primary ones and record # the relationship. # children_per_parent, parents_of_children = \ objects.relate_children(objects_out) measurements.add_measurement(self.primary_objects.value, cpmi.FF_CHILDREN_COUNT % objname, children_per_parent) measurements.add_measurement( objname, cpmi.FF_PARENT % self.primary_objects.value, parents_of_children) image_numbers = np.ones(len(parents_of_children), int) *\ measurements.image_set_number mask = parents_of_children > 0 measurements.add_relate_measurement( self.module_num, R_PARENT, self.primary_objects.value, self.objects_name.value, image_numbers[mask], parents_of_children[mask], image_numbers[mask], np.arange(1, len(parents_of_children) + 1)[mask]) # # If primary objects were created, add them # if self.wants_discard_edge and self.wants_discard_primary: workspace.object_set.add_objects( new_objects, self.new_primary_objects_name.value) cpmi.add_object_count_measurements( measurements, self.new_primary_objects_name.value, np.max(new_objects.segmented)) cpmi.add_object_location_measurements( measurements, self.new_primary_objects_name.value, new_objects.segmented) for parent_objects, parent_name, child_objects, child_name in ( (objects, self.primary_objects.value, new_objects, self.new_primary_objects_name.value), (new_objects, self.new_primary_objects_name.value, objects_out, objname)): children_per_parent, parents_of_children = \ parent_objects.relate_children(child_objects) measurements.add_measurement( parent_name, cpmi.FF_CHILDREN_COUNT % child_name, children_per_parent) measurements.add_measurement(child_name, cpmi.FF_PARENT % parent_name, parents_of_children) if self.show_window: object_area = np.sum(segmented_out > 0) workspace.display_data.object_pct = \ 100 * object_area / np.product(segmented_out.shape) workspace.display_data.img = img workspace.display_data.segmented_out = segmented_out workspace.display_data.primary_labels = objects.segmented workspace.display_data.global_threshold = global_threshold workspace.display_data.object_count = object_count
def run(self, workspace): assert isinstance(workspace, cpw.Workspace) image_name = self.image_name.value image = workspace.image_set.get_image(image_name, must_be_grayscale = True) workspace.display_data.statistics = [] img = image.pixel_data mask = image.mask objects = workspace.object_set.get_objects(self.primary_objects.value) global_threshold = None if self.method == M_DISTANCE_N: has_threshold = False else: thresholded_image = self.threshold_image(image_name, workspace) has_threshold = True # # Get the following labels: # * all edited labels # * labels touching the edge, including small removed # labels_in = objects.unedited_segmented.copy() labels_touching_edge = np.hstack( (labels_in[0,:], labels_in[-1,:], labels_in[:,0], labels_in[:,-1])) labels_touching_edge = np.unique(labels_touching_edge) is_touching = np.zeros(np.max(labels_in)+1, bool) is_touching[labels_touching_edge] = True is_touching = is_touching[labels_in] labels_in[(~ is_touching) & (objects.segmented == 0)] = 0 # # Stretch the input labels to match the image size. If there's no # label matrix, then there's no label in that area. # if tuple(labels_in.shape) != tuple(img.shape): tmp = np.zeros(img.shape, labels_in.dtype) i_max = min(img.shape[0], labels_in.shape[0]) j_max = min(img.shape[1], labels_in.shape[1]) tmp[:i_max, :j_max] = labels_in[:i_max, :j_max] labels_in = tmp if self.method in (M_DISTANCE_B, M_DISTANCE_N): if self.method == M_DISTANCE_N: distances,(i,j) = scind.distance_transform_edt(labels_in == 0, return_indices = True) labels_out = np.zeros(labels_in.shape,int) dilate_mask = distances <= self.distance_to_dilate.value labels_out[dilate_mask] =\ labels_in[i[dilate_mask],j[dilate_mask]] else: labels_out, distances = propagate(img, labels_in, thresholded_image, 1.0) labels_out[distances>self.distance_to_dilate.value] = 0 labels_out[labels_in > 0] = labels_in[labels_in>0] if self.fill_holes: small_removed_segmented_out = fill_labeled_holes(labels_out) else: small_removed_segmented_out = labels_out # # Create the final output labels by removing labels in the # output matrix that are missing from the segmented image # segmented_labels = objects.segmented segmented_out = self.filter_labels(small_removed_segmented_out, objects, workspace) elif self.method == M_PROPAGATION: labels_out, distance = propagate(img, labels_in, thresholded_image, self.regularization_factor.value) if self.fill_holes: small_removed_segmented_out = fill_labeled_holes(labels_out) else: small_removed_segmented_out = labels_out.copy() segmented_out = self.filter_labels(small_removed_segmented_out, objects, workspace) elif self.method == M_WATERSHED_G: # # First, apply the sobel filter to the image (both horizontal # and vertical). The filter measures gradient. # sobel_image = np.abs(scind.sobel(img)) # # Combine the image mask and threshold to mask the watershed # watershed_mask = np.logical_or(thresholded_image, labels_in > 0) watershed_mask = np.logical_and(watershed_mask, mask) # # Perform the first watershed # labels_out = watershed(sobel_image, labels_in, np.ones((3,3),bool), mask=watershed_mask) if self.fill_holes: small_removed_segmented_out = fill_labeled_holes(labels_out) else: small_removed_segmented_out = labels_out.copy() segmented_out = self.filter_labels(small_removed_segmented_out, objects, workspace) elif self.method == M_WATERSHED_I: # # invert the image so that the maxima are filled first # and the cells compete over what's close to the threshold # inverted_img = 1-img # # Same as above, but perform the watershed on the original image # watershed_mask = np.logical_or(thresholded_image, labels_in > 0) watershed_mask = np.logical_and(watershed_mask, mask) # # Perform the watershed # labels_out = watershed(inverted_img, labels_in, np.ones((3,3),bool), mask=watershed_mask) if self.fill_holes: small_removed_segmented_out = fill_labeled_holes(labels_out) else: small_removed_segmented_out = labels_out segmented_out = self.filter_labels(small_removed_segmented_out, objects, workspace) if self.wants_discard_edge and self.wants_discard_primary: # # Make a new primary object # lookup = scind.maximum(segmented_out, objects.segmented, range(np.max(objects.segmented)+1)) lookup = fix(lookup) lookup[0] = 0 lookup[lookup != 0] = np.arange(np.sum(lookup != 0)) + 1 segmented_labels = lookup[objects.segmented] segmented_out = lookup[segmented_out] new_objects = cpo.Objects() new_objects.segmented = segmented_labels if objects.has_unedited_segmented: new_objects.unedited_segmented = objects.unedited_segmented if objects.has_small_removed_segmented: new_objects.small_removed_segmented = objects.small_removed_segmented new_objects.parent_image = objects.parent_image primary_outline = outline(segmented_labels) if self.wants_primary_outlines: out_img = cpi.Image(primary_outline.astype(bool), parent_image = image) workspace.image_set.add(self.new_primary_outlines_name.value, out_img) else: primary_outline = outline(objects.segmented) secondary_outline = outline(segmented_out) # # Add the objects to the object set # objects_out = cpo.Objects() objects_out.unedited_segmented = small_removed_segmented_out objects_out.small_removed_segmented = small_removed_segmented_out objects_out.segmented = segmented_out objects_out.parent_image = image objname = self.objects_name.value workspace.object_set.add_objects(objects_out, objname) if self.use_outlines.value: out_img = cpi.Image(secondary_outline.astype(bool), parent_image = image) workspace.image_set.add(self.outlines_name.value, out_img) object_count = np.max(segmented_out) # # Add measurements # measurements = workspace.measurements cpmi.add_object_count_measurements(measurements, objname, object_count) cpmi.add_object_location_measurements(measurements, objname, segmented_out) # # Relate the secondary objects to the primary ones and record # the relationship. # children_per_parent, parents_of_children = \ objects.relate_children(objects_out) measurements.add_measurement(self.primary_objects.value, cpmi.FF_CHILDREN_COUNT%objname, children_per_parent) measurements.add_measurement(objname, cpmi.FF_PARENT%self.primary_objects.value, parents_of_children) image_numbers = np.ones(len(parents_of_children), int) *\ measurements.image_set_number mask = parents_of_children > 0 measurements.add_relate_measurement( self.module_num, R_PARENT, self.primary_objects.value, self.objects_name.value, image_numbers[mask], parents_of_children[mask], image_numbers[mask], np.arange(1, len(parents_of_children) + 1)[mask]) # # If primary objects were created, add them # if self.wants_discard_edge and self.wants_discard_primary: workspace.object_set.add_objects(new_objects, self.new_primary_objects_name.value) cpmi.add_object_count_measurements(measurements, self.new_primary_objects_name.value, np.max(new_objects.segmented)) cpmi.add_object_location_measurements(measurements, self.new_primary_objects_name.value, new_objects.segmented) for parent_objects, parent_name, child_objects, child_name in ( (objects, self.primary_objects.value, new_objects, self.new_primary_objects_name.value), (new_objects, self.new_primary_objects_name.value, objects_out, objname)): children_per_parent, parents_of_children = \ parent_objects.relate_children(child_objects) measurements.add_measurement(parent_name, cpmi.FF_CHILDREN_COUNT%child_name, children_per_parent) measurements.add_measurement(child_name, cpmi.FF_PARENT%parent_name, parents_of_children) if self.show_window: object_area = np.sum(segmented_out > 0) workspace.display_data.object_pct = \ 100 * object_area / np.product(segmented_out.shape) workspace.display_data.img = img workspace.display_data.segmented_out = segmented_out workspace.display_data.primary_outline = primary_outline workspace.display_data.secondary_outline = secondary_outline workspace.display_data.global_threshold = global_threshold workspace.display_data.object_count = object_count
def run(self, workspace): assert isinstance(workspace, cpw.Workspace) image = workspace.image_set.get_image(self.image_name.value, must_be_grayscale = True) img = image.pixel_data mask = image.mask objects = workspace.object_set.get_objects(self.primary_objects.value) global_threshold = None if self.method == M_DISTANCE_N: has_threshold = False elif self.threshold_method == cpthresh.TM_BINARY_IMAGE: binary_image = workspace.image_set.get_image(self.binary_image.value, must_be_binary = True) local_threshold = np.ones(img.shape) * np.max(img) + np.finfo(float).eps local_threshold[binary_image.pixel_data] = np.min(img) - np.finfo(float).eps global_threshold = cellprofiler.cpmath.otsu.otsu(img[mask], self.threshold_range.min, self.threshold_range.max) has_threshold = True else: local_threshold,global_threshold = self.get_threshold(img, mask, None, workspace) has_threshold = True if has_threshold: thresholded_image = img > local_threshold # # Get the following labels: # * all edited labels # * labels touching the edge, including small removed # labels_in = objects.unedited_segmented.copy() labels_touching_edge = np.hstack( (labels_in[0,:], labels_in[-1,:], labels_in[:,0], labels_in[:,-1])) labels_touching_edge = np.unique(labels_touching_edge) is_touching = np.zeros(np.max(labels_in)+1, bool) is_touching[labels_touching_edge] = True is_touching = is_touching[labels_in] labels_in[(~ is_touching) & (objects.segmented == 0)] = 0 # # Stretch the input labels to match the image size. If there's no # label matrix, then there's no label in that area. # if tuple(labels_in.shape) != tuple(img.shape): tmp = np.zeros(img.shape, labels_in.dtype) i_max = min(img.shape[0], labels_in.shape[0]) j_max = min(img.shape[1], labels_in.shape[1]) tmp[:i_max, :j_max] = labels_in[:i_max, :j_max] labels_in = tmp if self.method in (M_DISTANCE_B, M_DISTANCE_N): if self.method == M_DISTANCE_N: distances,(i,j) = scind.distance_transform_edt(labels_in == 0, return_indices = True) labels_out = np.zeros(labels_in.shape,int) dilate_mask = distances <= self.distance_to_dilate.value labels_out[dilate_mask] =\ labels_in[i[dilate_mask],j[dilate_mask]] else: labels_out, distances = propagate(img, labels_in, thresholded_image, 1.0) labels_out[distances>self.distance_to_dilate.value] = 0 labels_out[labels_in > 0] = labels_in[labels_in>0] if self.fill_holes: small_removed_segmented_out = fill_labeled_holes(labels_out) else: small_removed_segmented_out = labels_out # # Create the final output labels by removing labels in the # output matrix that are missing from the segmented image # segmented_labels = objects.segmented segmented_out = self.filter_labels(small_removed_segmented_out, objects, workspace) elif self.method == M_PROPAGATION: labels_out, distance = propagate(img, labels_in, thresholded_image, self.regularization_factor.value) if self.fill_holes: small_removed_segmented_out = fill_labeled_holes(labels_out) else: small_removed_segmented_out = labels_out.copy() segmented_out = self.filter_labels(small_removed_segmented_out, objects, workspace) elif self.method == M_WATERSHED_G: # # First, apply the sobel filter to the image (both horizontal # and vertical). The filter measures gradient. # sobel_image = np.abs(scind.sobel(img)) # # Combine the image mask and threshold to mask the watershed # watershed_mask = np.logical_or(thresholded_image, labels_in > 0) watershed_mask = np.logical_and(watershed_mask, mask) # # Perform the first watershed # labels_out = watershed(sobel_image, labels_in, np.ones((3,3),bool), mask=watershed_mask) if self.fill_holes: small_removed_segmented_out = fill_labeled_holes(labels_out) else: small_removed_segmented_out = labels_out.copy() segmented_out = self.filter_labels(small_removed_segmented_out, objects, workspace) elif self.method == M_WATERSHED_I: # # invert the image so that the maxima are filled first # and the cells compete over what's close to the threshold # inverted_img = 1-img # # Same as above, but perform the watershed on the original image # watershed_mask = np.logical_or(thresholded_image, labels_in > 0) watershed_mask = np.logical_and(watershed_mask, mask) # # Perform the watershed # labels_out = watershed(inverted_img, labels_in, np.ones((3,3),bool), mask=watershed_mask) if self.fill_holes: small_removed_segmented_out = fill_labeled_holes(labels_out) else: small_removed_segmented_out = labels_out segmented_out = self.filter_labels(small_removed_segmented_out, objects, workspace) if self.wants_discard_edge and self.wants_discard_primary: # # Make a new primary object # lookup = scind.maximum(segmented_out, objects.segmented, range(np.max(objects.segmented)+1)) lookup = fix(lookup) lookup[0] = 0 lookup[lookup != 0] = np.arange(np.sum(lookup != 0)) + 1 segmented_labels = lookup[objects.segmented] segmented_out = lookup[segmented_out] new_objects = cpo.Objects() new_objects.segmented = segmented_labels if objects.has_unedited_segmented: new_objects.unedited_segmented = objects.unedited_segmented if objects.has_small_removed_segmented: new_objects.small_removed_segmented = objects.small_removed_segmented new_objects.parent_image = objects.parent_image primary_outline = outline(segmented_labels) if self.wants_primary_outlines: out_img = cpi.Image(primary_outline.astype(bool), parent_image = image) workspace.image_set.add(self.new_primary_outlines_name.value, out_img) else: primary_outline = outline(objects.segmented) secondary_outline = outline(segmented_out) if workspace.frame != None: object_area = np.sum(segmented_out > 0) object_pct = 100 * object_area / np.product(segmented_out.shape) my_frame=workspace.create_or_find_figure(title="IdentifySecondaryObjects, image cycle #%d"%( workspace.measurements.image_set_number),subplots=(2,2)) title = "Input image, cycle #%d"%(workspace.image_set.number+1) my_frame.subplot_imshow_grayscale(0, 0, img, title) my_frame.subplot_imshow_labels(1, 0, segmented_out, "Labeled image", sharex = my_frame.subplot(0,0), sharey = my_frame.subplot(0,0)) outline_img = np.dstack((img, img, img)) cpmi.draw_outline(outline_img, secondary_outline > 0, cpprefs.get_secondary_outline_color()) my_frame.subplot_imshow(0, 1, outline_img, "Outlined image", normalize=False, sharex = my_frame.subplot(0,0), sharey = my_frame.subplot(0,0)) primary_img = np.dstack((img, img, img)) cpmi.draw_outline(primary_img, primary_outline > 0, cpprefs.get_primary_outline_color()) cpmi.draw_outline(primary_img, secondary_outline > 0, cpprefs.get_secondary_outline_color()) my_frame.subplot_imshow(1, 1, primary_img, "Primary and output outlines", normalize=False, sharex = my_frame.subplot(0,0), sharey = my_frame.subplot(0,0)) if global_threshold is not None: my_frame.status_bar.SetFields( ["Threshold: %.3f" % global_threshold, "Area covered by objects: %.1f %%" % object_pct]) else: my_frame.status_bar.SetFields( ["Area covered by objects: %.1f %%" % object_pct]) # # Add the objects to the object set # objects_out = cpo.Objects() objects_out.unedited_segmented = small_removed_segmented_out objects_out.small_removed_segmented = small_removed_segmented_out objects_out.segmented = segmented_out objects_out.parent_image = image objname = self.objects_name.value workspace.object_set.add_objects(objects_out, objname) if self.use_outlines.value: out_img = cpi.Image(secondary_outline.astype(bool), parent_image = image) workspace.image_set.add(self.outlines_name.value, out_img) object_count = np.max(segmented_out) # # Add the background measurements if made # measurements = workspace.measurements if has_threshold: if isinstance(local_threshold,np.ndarray): ave_threshold = np.mean(local_threshold) else: ave_threshold = local_threshold measurements.add_measurement(cpmeas.IMAGE, cpmi.FF_FINAL_THRESHOLD%(objname), np.array([ave_threshold], dtype=float)) measurements.add_measurement(cpmeas.IMAGE, cpmi.FF_ORIG_THRESHOLD%(objname), np.array([global_threshold], dtype=float)) wv = cpthresh.weighted_variance(img, mask, local_threshold) measurements.add_measurement(cpmeas.IMAGE, cpmi.FF_WEIGHTED_VARIANCE%(objname), np.array([wv],dtype=float)) entropies = cpthresh.sum_of_entropies(img, mask, local_threshold) measurements.add_measurement(cpmeas.IMAGE, cpmi.FF_SUM_OF_ENTROPIES%(objname), np.array([entropies],dtype=float)) cpmi.add_object_count_measurements(measurements, objname, object_count) cpmi.add_object_location_measurements(measurements, objname, segmented_out) # # Relate the secondary objects to the primary ones and record # the relationship. # children_per_parent, parents_of_children = \ objects.relate_children(objects_out) measurements.add_measurement(self.primary_objects.value, cpmi.FF_CHILDREN_COUNT%objname, children_per_parent) measurements.add_measurement(objname, cpmi.FF_PARENT%self.primary_objects.value, parents_of_children) # # If primary objects were created, add them # if self.wants_discard_edge and self.wants_discard_primary: workspace.object_set.add_objects(new_objects, self.new_primary_objects_name.value) cpmi.add_object_count_measurements(measurements, self.new_primary_objects_name.value, np.max(new_objects.segmented)) cpmi.add_object_location_measurements(measurements, self.new_primary_objects_name.value, new_objects.segmented) for parent_objects, parent_name, child_objects, child_name in ( (objects, self.primary_objects.value, new_objects, self.new_primary_objects_name.value), (new_objects, self.new_primary_objects_name.value, objects_out, objname)): children_per_parent, parents_of_children = \ parent_objects.relate_children(child_objects) measurements.add_measurement(parent_name, cpmi.FF_CHILDREN_COUNT%child_name, children_per_parent) measurements.add_measurement(child_name, cpmi.FF_PARENT%parent_name, parents_of_children)
def run(self, workspace): assert isinstance(workspace, cpw.Workspace) image = workspace.image_set.get_image(self.image_name.value, must_be_grayscale=True) img = image.pixel_data mask = image.mask objects = workspace.object_set.get_objects(self.primary_objects.value) global_threshold = None if self.method == M_DISTANCE_N: has_threshold = False elif self.threshold_method == cpthresh.TM_BINARY_IMAGE: binary_image = workspace.image_set.get_image( self.binary_image.value, must_be_binary=True) local_threshold = np.ones( img.shape) * np.max(img) + np.finfo(float).eps local_threshold[ binary_image.pixel_data] = np.min(img) - np.finfo(float).eps global_threshold = cellprofiler.cpmath.otsu.otsu( img[mask], self.threshold_range.min, self.threshold_range.max) has_threshold = True else: local_threshold, global_threshold = self.get_threshold( img, mask, None, workspace) has_threshold = True if has_threshold: thresholded_image = img > local_threshold # # Get the following labels: # * all edited labels # * labels touching the edge, including small removed # labels_in = objects.unedited_segmented.copy() labels_touching_edge = np.hstack( (labels_in[0, :], labels_in[-1, :], labels_in[:, 0], labels_in[:, -1])) labels_touching_edge = np.unique(labels_touching_edge) is_touching = np.zeros(np.max(labels_in) + 1, bool) is_touching[labels_touching_edge] = True is_touching = is_touching[labels_in] labels_in[(~is_touching) & (objects.segmented == 0)] = 0 # # Stretch the input labels to match the image size. If there's no # label matrix, then there's no label in that area. # if tuple(labels_in.shape) != tuple(img.shape): tmp = np.zeros(img.shape, labels_in.dtype) i_max = min(img.shape[0], labels_in.shape[0]) j_max = min(img.shape[1], labels_in.shape[1]) tmp[:i_max, :j_max] = labels_in[:i_max, :j_max] labels_in = tmp if self.method in (M_DISTANCE_B, M_DISTANCE_N): if self.method == M_DISTANCE_N: distances, (i, j) = scind.distance_transform_edt( labels_in == 0, return_indices=True) labels_out = np.zeros(labels_in.shape, int) dilate_mask = distances <= self.distance_to_dilate.value labels_out[dilate_mask] =\ labels_in[i[dilate_mask],j[dilate_mask]] else: labels_out, distances = propagate(img, labels_in, thresholded_image, 1.0) labels_out[distances > self.distance_to_dilate.value] = 0 labels_out[labels_in > 0] = labels_in[labels_in > 0] if self.fill_holes: small_removed_segmented_out = fill_labeled_holes(labels_out) else: small_removed_segmented_out = labels_out # # Create the final output labels by removing labels in the # output matrix that are missing from the segmented image # segmented_labels = objects.segmented segmented_out = self.filter_labels(small_removed_segmented_out, objects, workspace) elif self.method == M_PROPAGATION: labels_out, distance = propagate(img, labels_in, thresholded_image, self.regularization_factor.value) if self.fill_holes: small_removed_segmented_out = fill_labeled_holes(labels_out) else: small_removed_segmented_out = labels_out.copy() segmented_out = self.filter_labels(small_removed_segmented_out, objects, workspace) elif self.method == M_WATERSHED_G: # # First, apply the sobel filter to the image (both horizontal # and vertical). The filter measures gradient. # sobel_image = np.abs(scind.sobel(img)) # # Combine the image mask and threshold to mask the watershed # watershed_mask = np.logical_or(thresholded_image, labels_in > 0) watershed_mask = np.logical_and(watershed_mask, mask) # # Perform the first watershed # labels_out = watershed(sobel_image, labels_in, np.ones((3, 3), bool), mask=watershed_mask) if self.fill_holes: small_removed_segmented_out = fill_labeled_holes(labels_out) else: small_removed_segmented_out = labels_out.copy() segmented_out = self.filter_labels(small_removed_segmented_out, objects, workspace) elif self.method == M_WATERSHED_I: # # invert the image so that the maxima are filled first # and the cells compete over what's close to the threshold # inverted_img = 1 - img # # Same as above, but perform the watershed on the original image # watershed_mask = np.logical_or(thresholded_image, labels_in > 0) watershed_mask = np.logical_and(watershed_mask, mask) # # Perform the watershed # labels_out = watershed(inverted_img, labels_in, np.ones((3, 3), bool), mask=watershed_mask) if self.fill_holes: small_removed_segmented_out = fill_labeled_holes(labels_out) else: small_removed_segmented_out = labels_out segmented_out = self.filter_labels(small_removed_segmented_out, objects, workspace) if self.wants_discard_edge and self.wants_discard_primary: # # Make a new primary object # lookup = scind.maximum(segmented_out, objects.segmented, range(np.max(objects.segmented) + 1)) lookup = fix(lookup) lookup[0] = 0 lookup[lookup != 0] = np.arange(np.sum(lookup != 0)) + 1 segmented_labels = lookup[objects.segmented] segmented_out = lookup[segmented_out] new_objects = cpo.Objects() new_objects.segmented = segmented_labels if objects.has_unedited_segmented: new_objects.unedited_segmented = objects.unedited_segmented if objects.has_small_removed_segmented: new_objects.small_removed_segmented = objects.small_removed_segmented new_objects.parent_image = objects.parent_image primary_outline = outline(segmented_labels) if self.wants_primary_outlines: out_img = cpi.Image(primary_outline.astype(bool), parent_image=image) workspace.image_set.add(self.new_primary_outlines_name.value, out_img) else: primary_outline = outline(objects.segmented) secondary_outline = outline(segmented_out) if workspace.frame != None: object_area = np.sum(segmented_out > 0) object_pct = 100 * object_area / np.product(segmented_out.shape) my_frame = workspace.create_or_find_figure( title="IdentifySecondaryObjects, image cycle #%d" % (workspace.measurements.image_set_number), subplots=(2, 2)) title = "Input image, cycle #%d" % (workspace.image_set.number + 1) my_frame.subplot_imshow_grayscale(0, 0, img, title) my_frame.subplot_imshow_labels(1, 0, segmented_out, "Labeled image", sharex=my_frame.subplot(0, 0), sharey=my_frame.subplot(0, 0)) outline_img = np.dstack((img, img, img)) cpmi.draw_outline(outline_img, secondary_outline > 0, cpprefs.get_secondary_outline_color()) my_frame.subplot_imshow(0, 1, outline_img, "Outlined image", normalize=False, sharex=my_frame.subplot(0, 0), sharey=my_frame.subplot(0, 0)) primary_img = np.dstack((img, img, img)) cpmi.draw_outline(primary_img, primary_outline > 0, cpprefs.get_primary_outline_color()) cpmi.draw_outline(primary_img, secondary_outline > 0, cpprefs.get_secondary_outline_color()) my_frame.subplot_imshow(1, 1, primary_img, "Primary and output outlines", normalize=False, sharex=my_frame.subplot(0, 0), sharey=my_frame.subplot(0, 0)) if global_threshold is not None: my_frame.status_bar.SetFields([ "Threshold: %.3f" % global_threshold, "Area covered by objects: %.1f %%" % object_pct ]) else: my_frame.status_bar.SetFields( ["Area covered by objects: %.1f %%" % object_pct]) # # Add the objects to the object set # objects_out = cpo.Objects() objects_out.unedited_segmented = small_removed_segmented_out objects_out.small_removed_segmented = small_removed_segmented_out objects_out.segmented = segmented_out objects_out.parent_image = image objname = self.objects_name.value workspace.object_set.add_objects(objects_out, objname) if self.use_outlines.value: out_img = cpi.Image(secondary_outline.astype(bool), parent_image=image) workspace.image_set.add(self.outlines_name.value, out_img) object_count = np.max(segmented_out) # # Add the background measurements if made # measurements = workspace.measurements if has_threshold: if isinstance(local_threshold, np.ndarray): ave_threshold = np.mean(local_threshold) else: ave_threshold = local_threshold measurements.add_measurement( cpmeas.IMAGE, cpmi.FF_FINAL_THRESHOLD % (objname), np.array([ave_threshold], dtype=float)) measurements.add_measurement( cpmeas.IMAGE, cpmi.FF_ORIG_THRESHOLD % (objname), np.array([global_threshold], dtype=float)) wv = cpthresh.weighted_variance(img, mask, local_threshold) measurements.add_measurement(cpmeas.IMAGE, cpmi.FF_WEIGHTED_VARIANCE % (objname), np.array([wv], dtype=float)) entropies = cpthresh.sum_of_entropies(img, mask, local_threshold) measurements.add_measurement(cpmeas.IMAGE, cpmi.FF_SUM_OF_ENTROPIES % (objname), np.array([entropies], dtype=float)) cpmi.add_object_count_measurements(measurements, objname, object_count) cpmi.add_object_location_measurements(measurements, objname, segmented_out) # # Relate the secondary objects to the primary ones and record # the relationship. # children_per_parent, parents_of_children = \ objects.relate_children(objects_out) measurements.add_measurement(self.primary_objects.value, cpmi.FF_CHILDREN_COUNT % objname, children_per_parent) measurements.add_measurement( objname, cpmi.FF_PARENT % self.primary_objects.value, parents_of_children) # # If primary objects were created, add them # if self.wants_discard_edge and self.wants_discard_primary: workspace.object_set.add_objects( new_objects, self.new_primary_objects_name.value) cpmi.add_object_count_measurements( measurements, self.new_primary_objects_name.value, np.max(new_objects.segmented)) cpmi.add_object_location_measurements( measurements, self.new_primary_objects_name.value, new_objects.segmented) for parent_objects, parent_name, child_objects, child_name in ( (objects, self.primary_objects.value, new_objects, self.new_primary_objects_name.value), (new_objects, self.new_primary_objects_name.value, objects_out, objname)): children_per_parent, parents_of_children = \ parent_objects.relate_children(child_objects) measurements.add_measurement( parent_name, cpmi.FF_CHILDREN_COUNT % child_name, children_per_parent) measurements.add_measurement(child_name, cpmi.FF_PARENT % parent_name, parents_of_children)