def test_01_02_larger_secondary(self): secondary, mask = cpo.size_similarly(np.zeros((10,20)), np.zeros((10,30))) self.assertEqual(tuple(secondary.shape), (10,20)) self.assertTrue(np.all(mask)) secondary, mask = cpo.size_similarly(np.zeros((10,20)), np.zeros((20,20))) self.assertEqual(tuple(secondary.shape), (10,20)) self.assertTrue(np.all(mask))
def test_01_02_larger_secondary(self): secondary, mask = cpo.size_similarly(np.zeros((10, 20)), np.zeros((10, 30))) self.assertEqual(tuple(secondary.shape), (10, 20)) self.assertTrue(np.all(mask)) secondary, mask = cpo.size_similarly(np.zeros((10, 20)), np.zeros((20, 20))) self.assertEqual(tuple(secondary.shape), (10, 20)) self.assertTrue(np.all(mask))
def test_01_04_size_color(self): secondary, mask = cpo.size_similarly(np.zeros((10,20), int), np.zeros((10,15,3), np.float32)) self.assertEqual(tuple(secondary.shape), (10,20,3)) self.assertTrue(np.all(mask[:10,:15])) self.assertTrue(np.all(~mask[:10,15:])) self.assertEqual(secondary.dtype, np.dtype(np.float32))
def test_01_04_size_color(self): secondary, mask = cpo.size_similarly(np.zeros((10, 20), int), np.zeros((10, 15, 3), np.float32)) self.assertEqual(tuple(secondary.shape), (10, 20, 3)) self.assertTrue(np.all(mask[:10, :15])) self.assertTrue(np.all(~mask[:10, 15:])) self.assertEqual(secondary.dtype, np.dtype(np.float32))
def run_one(self, image_name, object_name, scale, workspace): """Run, computing the area measurements for a single map of objects""" statistics = [] image = workspace.image_set.get_image(image_name, must_be_grayscale=True) objects = workspace.get_objects(object_name) pixel_data = image.pixel_data if image.has_mask: mask = image.mask else: mask = None labels = objects.segmented try: pixel_data = objects.crop_image_similarly(pixel_data) except ValueError: # # Recover by cropping the image to the labels # pixel_data, m1 = cpo.size_similarly(labels, pixel_data) if np.any(~m1): if mask is None: mask = m1 else: mask, m2 = cpo.size_similarly(labels, mask) mask[~m2] = False if np.all(labels == 0): for name in F_HARALICK: statistics += self.record_measurement(workspace, image_name, object_name, scale, name, np.zeros((0,))) else: for name, value in zip(F_HARALICK, Haralick(pixel_data, labels, scale, mask=mask).all()): statistics += self.record_measurement(workspace, image_name, object_name, scale, name, value) return statistics
def run_one_gabor(self, image_name, object_name, scale, workspace): objects = workspace.get_objects(object_name) labels = objects.segmented object_count = np.max(labels) if object_count > 0: image = workspace.image_set.get_image(image_name, must_be_grayscale=True) pixel_data = image.pixel_data labels = objects.segmented if image.has_mask: mask = image.mask else: mask = None try: pixel_data = objects.crop_image_similarly(pixel_data) if mask is not None: mask = objects.crop_image_similarly(mask) labels[~mask] = 0 except ValueError: pixel_data, m1 = cpo.size_similarly(labels, pixel_data) labels[~m1] = 0 if mask is not None: mask, m2 = cpo.size_similarly(labels, mask) labels[~m2] = 0 labels[~mask] = 0 pixel_data = normalized_per_object(pixel_data, labels) best_score = np.zeros((object_count,)) for angle in range(self.gabor_angles.value): theta = np.pi * angle / self.gabor_angles.value g = gabor(pixel_data, labels, scale, theta) score_r = fix(scind.sum(g.real, labels, np.arange(object_count, dtype=np.int32)+ 1)) score_i = fix(scind.sum(g.imag, labels, np.arange(object_count, dtype=np.int32)+ 1)) score = np.sqrt(score_r**2+score_i**2) best_score = np.maximum(best_score, score) else: best_score = np.zeros((0,)) statistics = self.record_measurement(workspace, image_name, object_name, scale, F_GABOR, best_score) return statistics
def run_object(self, image_name, object_name, bins, workspace): """Run, computing the area measurements for a single map of objects""" statistics = [] image = workspace.image_set.get_image(image_name, must_be_grayscale=True) objects = workspace.get_objects(object_name) pixel_data = image.pixel_data if image.has_mask: mask = image.mask else: mask = None labels = objects.segmented try: pixel_data = objects.crop_image_similarly(pixel_data) except ValueError: # # Recover by cropping the image to the labels # pixel_data, m1 = cpo.size_similarly(labels, pixel_data) if np.any(~m1): if mask is None: mask = m1 else: mask, m2 = cpo.size_similarly(labels, mask) mask[~m2] = False if mask is not None: labels = labels.copy() labels[~mask] = 0 hist = get_objects_histogram(pixel_data, labels, bins) if np.all(labels == 0): for b in range(0, bins): statistics += self.record_measurement( workspace, image_name, object_name, str(bins) + BINS + str(b), np.zeros((0, ))) else: for b in range(0, bins): value = hist[:, b] statistics += self.record_measurement( workspace, image_name, object_name, str(bins) + BINS + str(b), value) return statistics
def run_one_gabor(self, image_name, object_name, scale, workspace): objects = workspace.get_objects(object_name) labels = objects.segmented object_count = np.max(labels) if object_count > 0: image = workspace.image_set.get_image(image_name, must_be_grayscale=True) pixel_data = image.pixel_data labels = objects.segmented if image.has_mask: mask = image.mask else: mask = None try: pixel_data = objects.crop_image_similarly(pixel_data) if mask is not None: mask = objects.crop_image_similarly(mask) labels[~mask] = 0 except ValueError: pixel_data, m1 = cpo.size_similarly(labels, pixel_data) labels[~m1] = 0 if mask is not None: mask, m2 = cpo.size_similarly(labels, mask) labels[~m2] = 0 labels[~mask] = 0 pixel_data = normalized_per_object(pixel_data, labels) best_score = np.zeros((object_count, )) for angle in range(self.gabor_angles.value): theta = np.pi * angle / self.gabor_angles.value g = gabor(pixel_data, labels, scale, theta) score_r = fix( scind.sum(g.real, labels, np.arange(object_count, dtype=np.int32) + 1)) score_i = fix( scind.sum(g.imag, labels, np.arange(object_count, dtype=np.int32) + 1)) score = np.sqrt(score_r**2 + score_i**2) best_score = np.maximum(best_score, score) else: best_score = np.zeros((0, )) statistics = self.record_measurement(workspace, image_name, object_name, scale, F_GABOR, best_score) return statistics
def run_object(self, image_name, object_name, bins, workspace): """Run, computing the area measurements for a single map of objects""" statistics = [] image = workspace.image_set.get_image(image_name, must_be_grayscale=True) objects = workspace.get_objects(object_name) pixel_data = image.pixel_data if image.has_mask: mask = image.mask else: mask = None labels = objects.segmented try: pixel_data = objects.crop_image_similarly(pixel_data) except ValueError: # # Recover by cropping the image to the labels # pixel_data, m1 = cpo.size_similarly(labels, pixel_data) if np.any(~m1): if mask is None: mask = m1 else: mask, m2 = cpo.size_similarly(labels, mask) mask[~m2] = False if mask is not None: labels = labels.copy() labels[~mask] = 0 hist = get_objects_histogram(pixel_data, labels, bins) if np.all(labels == 0): for b in range(0,bins): statistics += self.record_measurement( workspace, image_name, object_name, str(bins) + BINS + str(b), np.zeros((0,))) else: for b in range(0,bins): value=hist[:,b] statistics += self.record_measurement( workspace, image_name, object_name, str(bins) + BINS + str(b), value) return statistics
def run_one(self, image_name, object_name, scale, angle, workspace): """Run, computing the area measurements for a single map of objects""" statistics = [] image = workspace.image_set.get_image(image_name, must_be_grayscale=True) objects = workspace.get_objects(object_name) pixel_data = image.pixel_data if image.has_mask: mask = image.mask else: mask = None labels = objects.segmented try: pixel_data = objects.crop_image_similarly(pixel_data) except ValueError: # # Recover by cropping the image to the labels # pixel_data, m1 = cpo.size_similarly(labels, pixel_data) if np.any(~m1): if mask is None: mask = m1 else: mask, m2 = cpo.size_similarly(labels, mask) mask[~m2] = False if np.all(labels == 0): for name in F_HARALICK: statistics += self.record_measurement( workspace, image_name, object_name, str(scale) + "_" + H_TO_A[angle], name, np.zeros((0, ))) else: scale_i, scale_j = self.get_angle_ij(angle, scale) for name, value in zip( F_HARALICK, Haralick(pixel_data, labels, scale_i, scale_j, mask=mask).all()): statistics += self.record_measurement( workspace, image_name, object_name, str(scale) + "_" + H_TO_A[angle], name, value) return statistics
def display(): if len(orig_axes.images) > 0: # Save zoom and scale if coming through here a second time x0, x1 = orig_axes.get_xlim() y0, y1 = orig_axes.get_ylim() set_lim = True else: set_lim = False for axes, labels, title in ((orig_axes, orig_labels, "Original: %s" % orig_objects_name), (keep_axes, orig_labels * mask, "Objects to keep"), (remove_axes, orig_labels * (~mask), "Objects to remove")): assert isinstance(axes, matplotlib.axes.Axes) labels = renumber_labels_for_display(labels) axes.clear() if np.all(labels == 0): use_cm = matplotlib.cm.gray is_blank = True else: use_cm = cm is_blank = False if wants_image_display[0]: outlines = outline(labels) image = workspace.image_set.get_image( self.image_name.value) image = image.pixel_data.astype(np.float) image, _ = cpo.size_similarly(labels, image) if image.ndim == 2: image = np.dstack((image, image, image)) if not is_blank: mappable = matplotlib.cm.ScalarMappable(cmap=use_cm) mappable.set_clim(1, labels.max()) limage = mappable.to_rgba(labels)[:, :, :3] image[outlines != 0, :] = limage[outlines != 0, :] axes.imshow(image) else: axes.imshow(labels, cmap=use_cm) axes.set_title(title, fontname=cpprefs.get_title_font_name(), fontsize=cpprefs.get_title_font_size()) if set_lim: orig_axes.set_xlim((x0, x1)) orig_axes.set_ylim((y0, y1)) figure.canvas.draw() panel.Refresh()
def run_object(self, image_name, object_name, workspace): statistics = [] input_image = workspace.image_set.get_image(image_name, must_be_grayscale = True) objects = workspace.get_objects(object_name) pixels = input_image.pixel_data if input_image.has_mask: mask = input_image.mask else: mask = None labels = objects.segmented try: pixels = objects.crop_image_similarly(pixels) except ValueError: # # Recover by cropping the image to the labels # pixels, m1 = cpo.size_similarly(labels, pixels) if np.any(~m1): if mask is None: mask = m1 else: mask, m2 = cpo.size_similarly(labels, mask) mask[~m2] = False if mask is not None: labels = labels.copy() labels[~mask] = 0 for name in self.moms.value.split(','): fn=MOM_TO_F[name] value=get_object_moment(pixels, fn) statistics+=self.record_measurement(workspace, image_name, object_name, name, value) return statistics
def filter_labels(self, labels_out, objects, workspace): """Filter labels out of the output Filter labels that are not in the segmented input labels. Optionally filter labels that are touching the edge. labels_out - the unfiltered output labels objects - the objects thing, containing both segmented and small_removed labels """ segmented_labels = objects.segmented max_out = np.max(labels_out) if max_out > 0: segmented_labels, m1 = cpo.size_similarly(labels_out, segmented_labels) segmented_labels[~m1] = 0 lookup = scind.maximum(segmented_labels, labels_out, range(max_out + 1)) lookup = np.array(lookup, int) lookup[0] = 0 segmented_labels_out = lookup[labels_out] else: segmented_labels_out = labels_out.copy() if self.wants_discard_edge: image = workspace.image_set.get_image(self.image_name.value) if image.has_mask: mask_border = image.mask & ~scind.binary_erosion(image.mask) edge_labels = segmented_labels_out[mask_border] else: edge_labels = np.hstack( ( segmented_labels_out[0, :], segmented_labels_out[-1, :], segmented_labels_out[:, 0], segmented_labels_out[:, -1], ) ) edge_labels = np.unique(edge_labels) # # Make a lookup table that translates edge labels to zero # but translates everything else to itself # lookup = np.arange(max_out + 1) lookup[edge_labels] = 0 # # Run the segmented labels through this to filter out edge # labels segmented_labels_out = lookup[segmented_labels_out] return segmented_labels_out
def display(): if len(orig_axes.images) > 0: # Save zoom and scale if coming through here a second time x0, x1 = orig_axes.get_xlim() y0, y1 = orig_axes.get_ylim() set_lim = True else: set_lim = False for axes, labels, title in ( (orig_axes, orig_labels, "Original: %s"%orig_objects_name), (keep_axes, orig_labels * mask,"Objects to keep"), (remove_axes, orig_labels * (~ mask), "Objects to remove")): assert isinstance(axes, matplotlib.axes.Axes) labels = renumber_labels_for_display(labels) axes.clear() if np.all(labels == 0): use_cm = matplotlib.cm.gray is_blank = True else: use_cm = cm is_blank = False if wants_image_display[0]: outlines = outline(labels) image = workspace.image_set.get_image(self.image_name.value) image = image.pixel_data.astype(np.float) image, _ = cpo.size_similarly(labels, image) if image.ndim == 2: image = np.dstack((image, image, image)) if not is_blank: mappable = matplotlib.cm.ScalarMappable(cmap=use_cm) mappable.set_clim(1,labels.max()) limage = mappable.to_rgba(labels)[:,:,:3] image[outlines != 0,:] = limage[outlines != 0, :] axes.imshow(image) else: axes.imshow(labels, cmap = use_cm) axes.set_title(title, fontname=cpprefs.get_title_font_name(), fontsize=cpprefs.get_title_font_size()) if set_lim: orig_axes.set_xlim((x0, x1)) orig_axes.set_ylim((y0, y1)) figure.canvas.draw() panel.Refresh()
def filter_labels(self, labels_out, objects, workspace): """Filter labels out of the output Filter labels that are not in the segmented input labels. Optionally filter labels that are touching the edge. labels_out - the unfiltered output labels objects - the objects thing, containing both segmented and small_removed labels """ segmented_labels = objects.segmented max_out = np.max(labels_out) if max_out > 0: segmented_labels, m1 = cpo.size_similarly(labels_out, segmented_labels) segmented_labels[~m1] = 0 lookup = scind.maximum(segmented_labels, labels_out, range(max_out + 1)) lookup = np.array(lookup, int) lookup[0] = 0 segmented_labels_out = lookup[labels_out] else: segmented_labels_out = labels_out.copy() if self.wants_discard_edge: image = workspace.image_set.get_image(self.image_name.value) if image.has_mask: mask_border = (image.mask & ~scind.binary_erosion(image.mask)) edge_labels = segmented_labels_out[mask_border] else: edge_labels = np.hstack( (segmented_labels_out[0, :], segmented_labels_out[-1, :], segmented_labels_out[:, 0], segmented_labels_out[:, -1])) edge_labels = np.unique(edge_labels) # # Make a lookup table that translates edge labels to zero # but translates everything else to itself # lookup = np.arange(max(max_out, np.max(segmented_labels)) + 1) lookup[edge_labels] = 0 # # Run the segmented labels through this to filter out edge # labels segmented_labels_out = lookup[segmented_labels_out] return segmented_labels_out
def run_image_pair_objects(self, workspace, first_image_name, second_image_name, object_name): '''Calculate per-object correlations between intensities in two images''' first_image = workspace.image_set.get_image(first_image_name, must_be_grayscale=True) second_image = workspace.image_set.get_image(second_image_name, must_be_grayscale=True) objects = workspace.object_set.get_objects(object_name) # # Crop both images to the size of the labels matrix # labels = objects.segmented try: first_pixels = objects.crop_image_similarly(first_image.pixel_data) first_mask = objects.crop_image_similarly(first_image.mask) except ValueError: first_pixels, m1 = cpo.size_similarly(labels, first_image.pixel_data) first_mask, m1 = cpo.size_similarly(labels, first_image.mask) first_mask[~m1] = False try: second_pixels = objects.crop_image_similarly( second_image.pixel_data) second_mask = objects.crop_image_similarly(second_image.mask) except ValueError: second_pixels, m1 = cpo.size_similarly(labels, second_image.pixel_data) second_mask, m1 = cpo.size_similarly(labels, second_image.mask) second_mask[~m1] = False mask = ((labels > 0) & first_mask & second_mask) first_pixels = first_pixels[mask] second_pixels = second_pixels[mask] labels = labels[mask] result = [] first_pixel_data = first_image.pixel_data first_mask = first_image.mask first_pixel_count = np.product(first_pixel_data.shape) second_pixel_data = second_image.pixel_data second_mask = second_image.mask second_pixel_count = np.product(second_pixel_data.shape) # # Crop the larger image similarly to the smaller one # if first_pixel_count < second_pixel_count: second_pixel_data = first_image.crop_image_similarly( second_pixel_data) second_mask = first_image.crop_image_similarly(second_mask) elif second_pixel_count < first_pixel_count: first_pixel_data = second_image.crop_image_similarly( first_pixel_data) first_mask = second_image.crop_image_similarly(first_mask) mask = (first_mask & second_mask & (~np.isnan(first_pixel_data)) & (~np.isnan(second_pixel_data))) if np.any(mask): # # Perform the correlation, which returns: # [ [ii, ij], # [ji, jj] ] # fi = first_pixel_data[mask] si = second_pixel_data[mask] n_objects = objects.count # Handle case when both images for the correlation are completely masked out if n_objects == 0: corr = np.zeros((0, )) overlap = np.zeros((0, )) K1 = np.zeros((0, )) K2 = np.zeros((0, )) M1 = np.zeros((0, )) M2 = np.zeros((0, )) RWC1 = np.zeros((0, )) RWC2 = np.zeros((0, )) C1 = np.zeros((0, )) C2 = np.zeros((0, )) elif np.where(mask)[0].__len__() == 0: corr = np.zeros((n_objects, )) corr[:] = np.NaN overlap = K1 = K2 = M1 = M2 = RWC1 = RWC2 = C1 = C2 = corr else: # # The correlation is sum((x-mean(x))(y-mean(y)) / # ((n-1) * std(x) *std(y))) # lrange = np.arange(n_objects, dtype=np.int32) + 1 area = fix(scind.sum(np.ones_like(labels), labels, lrange)) mean1 = fix(scind.mean(first_pixels, labels, lrange)) mean2 = fix(scind.mean(second_pixels, labels, lrange)) # # Calculate the standard deviation times the population. # std1 = np.sqrt( fix( scind.sum((first_pixels - mean1[labels - 1])**2, labels, lrange))) std2 = np.sqrt( fix( scind.sum((second_pixels - mean2[labels - 1])**2, labels, lrange))) x = first_pixels - mean1[labels - 1] # x - mean(x) y = second_pixels - mean2[labels - 1] # y - mean(y) corr = fix( scind.sum(x * y / (std1[labels - 1] * std2[labels - 1]), labels, lrange)) # Explicitly set the correlation to NaN for masked objects corr[scind.sum(1, labels, lrange) == 0] = np.NaN result += [[ first_image_name, second_image_name, object_name, "Mean Correlation coeff", "%.3f" % np.mean(corr) ], [ first_image_name, second_image_name, object_name, "Median Correlation coeff", "%.3f" % np.median(corr) ], [ first_image_name, second_image_name, object_name, "Min Correlation coeff", "%.3f" % np.min(corr) ], [ first_image_name, second_image_name, object_name, "Max Correlation coeff", "%.3f" % np.max(corr) ]] # Threshold as percentage of maximum intensity of objects in each channel tff = (self.thr.value / 100) * fix( scind.maximum(first_pixels, labels, lrange)) tss = (self.thr.value / 100) * fix( scind.maximum(second_pixels, labels, lrange)) combined_thresh = (first_pixels > tff[labels - 1]) & ( second_pixels > tss[labels - 1]) fi_thresh = first_pixels[combined_thresh] si_thresh = second_pixels[combined_thresh] tot_fi_thr = scind.sum( first_pixels[first_pixels > tff[labels - 1]], labels[first_pixels > tff[labels - 1]], lrange) tot_si_thr = scind.sum( second_pixels[second_pixels > tss[labels - 1]], labels[second_pixels > tss[labels - 1]], lrange) nonZero = (fi > 0) | (si > 0) xvar = np.var(fi[nonZero], axis=0, ddof=1) yvar = np.var(si[nonZero], axis=0, ddof=1) xmean = np.mean(fi[nonZero], axis=0) ymean = np.mean(si[nonZero], axis=0) z = fi[nonZero] + si[nonZero] zvar = np.var(z, axis=0, ddof=1) covar = 0.5 * (zvar - (xvar + yvar)) denom = 2 * covar num = (yvar - xvar) + np.sqrt((yvar - xvar) * (yvar - xvar) + 4 * (covar * covar)) a = (num / denom) b = (ymean - a * xmean) i = 1 while i > 0.003921568627: thr_fi_c = i thr_si_c = (a * i) + b combt = (fi < thr_fi_c) | (si < thr_si_c) costReg = scistat.pearsonr(fi[combt], si[combt]) if costReg[0] <= 0: break i = i - 0.003921568627 # Costes' thershold for entire image is applied to each object fi_above_thr = first_pixels > thr_fi_c si_above_thr = second_pixels > thr_si_c combined_thresh_c = fi_above_thr & si_above_thr fi_thresh_c = first_pixels[combined_thresh_c] si_thresh_c = second_pixels[combined_thresh_c] if np.any(fi_above_thr): tot_fi_thr_c = scind.sum(first_pixels[first_pixels > thr_fi_c], labels[first_pixels > thr_fi_c], lrange) else: tot_fi_thr_c = np.zeros(len(lrange)) if np.any(si_above_thr): tot_si_thr_c = scind.sum( second_pixels[second_pixels > thr_si_c], labels[second_pixels > thr_si_c], lrange) else: tot_si_thr_c = np.zeros(len(lrange)) # Manders Coefficient M1 = np.zeros(len(lrange)) M2 = np.zeros(len(lrange)) if np.any(combined_thresh): M1 = np.array( scind.sum(fi_thresh, labels[combined_thresh], lrange)) / np.array(tot_fi_thr) M2 = np.array( scind.sum(si_thresh, labels[combined_thresh], lrange)) / np.array(tot_si_thr) result += [[ first_image_name, second_image_name, object_name, "Mean Manders coeff", "%.3f" % np.mean(M1) ], [ first_image_name, second_image_name, object_name, "Median Manders coeff", "%.3f" % np.median(M1) ], [ first_image_name, second_image_name, object_name, "Min Manders coeff", "%.3f" % np.min(M1) ], [ first_image_name, second_image_name, object_name, "Max Manders coeff", "%.3f" % np.max(M1) ]] result += [[ second_image_name, first_image_name, object_name, "Mean Manders coeff", "%.3f" % np.mean(M2) ], [ second_image_name, first_image_name, object_name, "Median Manders coeff", "%.3f" % np.median(M2) ], [ second_image_name, first_image_name, object_name, "Min Manders coeff", "%.3f" % np.min(M2) ], [ second_image_name, first_image_name, object_name, "Max Manders coeff", "%.3f" % np.max(M2) ]] # RWC Coefficient RWC1 = np.zeros(len(lrange)) RWC2 = np.zeros(len(lrange)) [Rank1] = np.lexsort(([labels], [first_pixels])) [Rank2] = np.lexsort(([labels], [second_pixels])) Rank1_U = np.hstack( [[False], first_pixels[Rank1[:-1]] != first_pixels[Rank1[1:]]]) Rank2_U = np.hstack( [[False], second_pixels[Rank2[:-1]] != second_pixels[Rank2[1:]]]) Rank1_S = np.cumsum(Rank1_U) Rank2_S = np.cumsum(Rank2_U) Rank_im1 = np.zeros(first_pixels.shape, dtype=int) Rank_im2 = np.zeros(second_pixels.shape, dtype=int) Rank_im1[Rank1] = Rank1_S Rank_im2[Rank2] = Rank2_S R = max(Rank_im1.max(), Rank_im2.max()) + 1 Di = abs(Rank_im1 - Rank_im2) weight = (R - Di) * 1.0 / R weight_thresh = weight[combined_thresh] if np.any(combined_thresh_c): RWC1 = np.array( scind.sum(fi_thresh * weight_thresh, labels[combined_thresh], lrange)) / np.array(tot_fi_thr) RWC2 = np.array( scind.sum(si_thresh * weight_thresh, labels[combined_thresh], lrange)) / np.array(tot_si_thr) result += [[ first_image_name, second_image_name, object_name, "Mean RWC coeff", "%.3f" % np.mean(RWC1) ], [ first_image_name, second_image_name, object_name, "Median RWC coeff", "%.3f" % np.median(RWC1) ], [ first_image_name, second_image_name, object_name, "Min RWC coeff", "%.3f" % np.min(RWC1) ], [ first_image_name, second_image_name, object_name, "Max RWC coeff", "%.3f" % np.max(RWC1) ]] result += [[ second_image_name, first_image_name, object_name, "Mean RWC coeff", "%.3f" % np.mean(RWC2) ], [ second_image_name, first_image_name, object_name, "Median RWC coeff", "%.3f" % np.median(RWC2) ], [ second_image_name, first_image_name, object_name, "Min RWC coeff", "%.3f" % np.min(RWC2) ], [ second_image_name, first_image_name, object_name, "Max RWC coeff", "%.3f" % np.max(RWC2) ]] # Costes Automated Threshold C1 = np.zeros(len(lrange)) C2 = np.zeros(len(lrange)) if np.any(combined_thresh_c): C1 = np.array( scind.sum(fi_thresh_c, labels[combined_thresh_c], lrange)) / np.array(tot_fi_thr_c) C2 = np.array( scind.sum(si_thresh_c, labels[combined_thresh_c], lrange)) / np.array(tot_si_thr_c) result += [[ first_image_name, second_image_name, object_name, "Mean Manders coeff (Costes)", "%.3f" % np.mean(C1) ], [ first_image_name, second_image_name, object_name, "Median Manders coeff (Costes)", "%.3f" % np.median(C1) ], [ first_image_name, second_image_name, object_name, "Min Manders coeff (Costes)", "%.3f" % np.min(C1) ], [ first_image_name, second_image_name, object_name, "Max Manders coeff (Costes)", "%.3f" % np.max(C1) ]] result += [[ second_image_name, first_image_name, object_name, "Mean Manders coeff (Costes)", "%.3f" % np.mean(C2) ], [ second_image_name, first_image_name, object_name, "Median Manders coeff (Costes)", "%.3f" % np.median(C2) ], [ second_image_name, first_image_name, object_name, "Min Manders coeff (Costes)", "%.3f" % np.min(C2) ], [ second_image_name, first_image_name, object_name, "Max Manders coeff (Costes)", "%.3f" % np.max(C2) ]] # Overlap Coefficient fpsq = scind.sum(first_pixels[combined_thresh]**2, labels[combined_thresh], lrange) spsq = scind.sum(second_pixels[combined_thresh]**2, labels[combined_thresh], lrange) pdt = np.sqrt(np.array(fpsq) * np.array(spsq)) if np.any(combined_thresh): overlap = fix( scind.sum( first_pixels[combined_thresh] * second_pixels[combined_thresh], labels[combined_thresh], lrange) / pdt) K1 = fix((scind.sum( first_pixels[combined_thresh] * second_pixels[combined_thresh], labels[combined_thresh], lrange)) / (np.array(fpsq))) K2 = fix( scind.sum( first_pixels[combined_thresh] * second_pixels[combined_thresh], labels[combined_thresh], lrange) / np.array(spsq)) else: overlap = K1 = K2 = np.zeros(len(lrange)) result += [[ first_image_name, second_image_name, object_name, "Mean Overlap coeff", "%.3f" % np.mean(overlap) ], [ first_image_name, second_image_name, object_name, "Median Overlap coeff", "%.3f" % np.median(overlap) ], [ first_image_name, second_image_name, object_name, "Min Overlap coeff", "%.3f" % np.min(overlap) ], [ first_image_name, second_image_name, object_name, "Max Overlap coeff", "%.3f" % np.max(overlap) ]] measurement = ("Correlation_Correlation_%s_%s" % (first_image_name, second_image_name)) overlap_measurement = (F_OVERLAP_FORMAT % (first_image_name, second_image_name)) k_measurement_1 = (F_K_FORMAT % (first_image_name, second_image_name)) k_measurement_2 = (F_K_FORMAT % (second_image_name, first_image_name)) manders_measurement_1 = (F_MANDERS_FORMAT % (first_image_name, second_image_name)) manders_measurement_2 = (F_MANDERS_FORMAT % (second_image_name, first_image_name)) rwc_measurement_1 = (F_RWC_FORMAT % (first_image_name, second_image_name)) rwc_measurement_2 = (F_RWC_FORMAT % (second_image_name, first_image_name)) costes_measurement_1 = (F_COSTES_FORMAT % (first_image_name, second_image_name)) costes_measurement_2 = (F_COSTES_FORMAT % (second_image_name, first_image_name)) workspace.measurements.add_measurement(object_name, measurement, corr) workspace.measurements.add_measurement(object_name, overlap_measurement, overlap) workspace.measurements.add_measurement(object_name, k_measurement_1, K1) workspace.measurements.add_measurement(object_name, k_measurement_2, K2) workspace.measurements.add_measurement(object_name, manders_measurement_1, M1) workspace.measurements.add_measurement(object_name, manders_measurement_2, M2) workspace.measurements.add_measurement(object_name, rwc_measurement_1, RWC1) workspace.measurements.add_measurement(object_name, rwc_measurement_2, RWC2) workspace.measurements.add_measurement(object_name, costes_measurement_1, C1) workspace.measurements.add_measurement(object_name, costes_measurement_2, C2) if n_objects == 0: return [[ first_image_name, second_image_name, object_name, "Mean correlation", "-" ], [ first_image_name, second_image_name, object_name, "Median correlation", "-" ], [ first_image_name, second_image_name, object_name, "Min correlation", "-" ], [ first_image_name, second_image_name, object_name, "Max correlation", "-" ]] else: return result
def run(self, workspace): '''Run the module on the image set''' seed_objects_name = self.seed_objects_name.value skeleton_name = self.image_name.value seed_objects = workspace.object_set.get_objects(seed_objects_name) labels = seed_objects.segmented labels_count = np.max(labels) label_range = np.arange(labels_count, dtype=np.int32) + 1 skeleton_image = workspace.image_set.get_image(skeleton_name, must_be_binary=True) skeleton = skeleton_image.pixel_data if skeleton_image.has_mask: skeleton = skeleton & skeleton_image.mask try: labels = skeleton_image.crop_image_similarly(labels) except: labels, m1 = cpo.size_similarly(skeleton, labels) labels[~m1] = 0 # # The following code makes a ring around the seed objects with # the skeleton trunks sticking out of it. # # Create a new skeleton with holes at the seed objects # First combine the seed objects with the skeleton so # that the skeleton trunks come out of the seed objects. # # Erode the labels once so that all of the trunk branchpoints # will be within the labels # # # Dilate the objects, then subtract them to make a ring # my_disk = morph.strel_disk(1.5).astype(int) dilated_labels = grey_dilation(labels, footprint=my_disk) seed_mask = dilated_labels > 0 combined_skel = skeleton | seed_mask closed_labels = grey_erosion(dilated_labels, footprint=my_disk) seed_center = closed_labels > 0 combined_skel = combined_skel & (~seed_center) # # Fill in single holes (but not a one-pixel hole made by # a one-pixel image) # if self.wants_to_fill_holes: def size_fn(area, is_object): return (~is_object) and (area <= self.maximum_hole_size.value) combined_skel = morph.fill_labeled_holes(combined_skel, ~seed_center, size_fn) # # Reskeletonize to make true branchpoints at the ring boundaries # combined_skel = morph.skeletonize(combined_skel) # # The skeleton outside of the labels # outside_skel = combined_skel & (dilated_labels == 0) # # Associate all skeleton points with seed objects # dlabels, distance_map = propagate.propagate(np.zeros(labels.shape), dilated_labels, combined_skel, 1) # # Get rid of any branchpoints not connected to seeds # combined_skel[dlabels == 0] = False # # Find the branchpoints # branch_points = morph.branchpoints(combined_skel) # # Odd case: when four branches meet like this, branchpoints are not # assigned because they are arbitrary. So assign them. # # . . # B. # .B # . . # odd_case = (combined_skel[:-1, :-1] & combined_skel[1:, :-1] & combined_skel[:-1, 1:] & combined_skel[1, 1]) branch_points[:-1, :-1][odd_case] = True branch_points[1:, 1:][odd_case] = True # # Find the branching counts for the trunks (# of extra branches # eminating from a point other than the line it might be on). # branching_counts = morph.branchings(combined_skel) branching_counts = np.array([0, 0, 0, 1, 2])[branching_counts] # # Only take branches within 1 of the outside skeleton # dilated_skel = scind.binary_dilation(outside_skel, morph.eight_connect) branching_counts[~dilated_skel] = 0 # # Find the endpoints # end_points = morph.endpoints(combined_skel) # # We use two ranges for classification here: # * anything within one pixel of the dilated image is a trunk # * anything outside of that range is a branch # nearby_labels = dlabels.copy() nearby_labels[distance_map > 1.5] = 0 outside_labels = dlabels.copy() outside_labels[nearby_labels > 0] = 0 # # The trunks are the branchpoints that lie within one pixel of # the dilated image. # if labels_count > 0: trunk_counts = fix( scind.sum(branching_counts, nearby_labels, label_range)).astype(int) else: trunk_counts = np.zeros((0, ), int) # # The branches are the branchpoints that lie outside the seed objects # if labels_count > 0: branch_counts = fix( scind.sum(branch_points, outside_labels, label_range)) else: branch_counts = np.zeros((0, ), int) # # Save the endpoints # if labels_count > 0: end_counts = fix(scind.sum(end_points, outside_labels, label_range)) else: end_counts = np.zeros((0, ), int) # # Save measurements # m = workspace.measurements assert isinstance(m, cpmeas.Measurements) feature = "_".join((C_NEURON, F_NUMBER_TRUNKS, skeleton_name)) m.add_measurement(seed_objects_name, feature, trunk_counts) feature = "_".join( (C_NEURON, F_NUMBER_NON_TRUNK_BRANCHES, skeleton_name)) m.add_measurement(seed_objects_name, feature, branch_counts) feature = "_".join((C_NEURON, F_NUMBER_BRANCH_ENDS, skeleton_name)) m.add_measurement(seed_objects_name, feature, end_counts) # # Collect the graph information # if self.wants_neuron_graph: trunk_mask = (branching_counts > 0) & (nearby_labels != 0) intensity_image = workspace.image_set.get_image( self.intensity_image_name.value) edge_graph, vertex_graph = self.make_neuron_graph( combined_skel, dlabels, trunk_mask, branch_points & ~trunk_mask, end_points, intensity_image.pixel_data) image_number = workspace.measurements.image_set_number edge_path, vertex_path = self.get_graph_file_paths( m, m.image_number) workspace.interaction_request(self, m.image_number, edge_path, edge_graph, vertex_path, vertex_graph, headless_ok=True) if self.show_window: workspace.display_data.edge_graph = edge_graph workspace.display_data.vertex_graph = vertex_graph workspace.display_data.intensity_image = intensity_image.pixel_data # # Make the display image # if self.show_window or self.wants_branchpoint_image: branchpoint_image = np.zeros( (skeleton.shape[0], skeleton.shape[1], 3)) trunk_mask = (branching_counts > 0) & (nearby_labels != 0) branch_mask = branch_points & (outside_labels != 0) end_mask = end_points & (outside_labels != 0) branchpoint_image[outside_skel, :] = 1 branchpoint_image[trunk_mask | branch_mask | end_mask, :] = 0 branchpoint_image[trunk_mask, 0] = 1 branchpoint_image[branch_mask, 1] = 1 branchpoint_image[end_mask, 2] = 1 branchpoint_image[dilated_labels != 0, :] *= .875 branchpoint_image[dilated_labels != 0, :] += .1 if self.show_window: workspace.display_data.branchpoint_image = branchpoint_image if self.wants_branchpoint_image: bi = cpi.Image(branchpoint_image, parent_image=skeleton_image) workspace.image_set.add(self.branchpoint_image_name.value, bi)
def run(self, workspace): '''Run the module on the image set''' seed_objects_name = self.seed_objects_name.value skeleton_name = self.image_name.value seed_objects = workspace.object_set.get_objects(seed_objects_name) labels = seed_objects.segmented labels_count = np.max(labels) label_range = np.arange(labels_count,dtype=np.int32)+1 skeleton_image = workspace.image_set.get_image( skeleton_name, must_be_binary = True) skeleton = skeleton_image.pixel_data if skeleton_image.has_mask: skeleton = skeleton & skeleton_image.mask try: labels = skeleton_image.crop_image_similarly(labels) except: labels, m1 = cpo.size_similarly(skeleton, labels) labels[~m1] = 0 # # The following code makes a ring around the seed objects with # the skeleton trunks sticking out of it. # # Create a new skeleton with holes at the seed objects # First combine the seed objects with the skeleton so # that the skeleton trunks come out of the seed objects. # # Erode the labels once so that all of the trunk branchpoints # will be within the labels # # # Dilate the objects, then subtract them to make a ring # my_disk = morph.strel_disk(1.5).astype(int) dilated_labels = grey_dilation(labels, footprint=my_disk) seed_mask = dilated_labels > 0 combined_skel = skeleton | seed_mask closed_labels = grey_erosion(dilated_labels, footprint = my_disk) seed_center = closed_labels > 0 combined_skel = combined_skel & (~seed_center) # # Fill in single holes (but not a one-pixel hole made by # a one-pixel image) # if self.wants_to_fill_holes: def size_fn(area, is_object): return (~ is_object) and (area <= self.maximum_hole_size.value) combined_skel = morph.fill_labeled_holes( combined_skel, ~seed_center, size_fn) # # Reskeletonize to make true branchpoints at the ring boundaries # combined_skel = morph.skeletonize(combined_skel) # # The skeleton outside of the labels # outside_skel = combined_skel & (dilated_labels == 0) # # Associate all skeleton points with seed objects # dlabels, distance_map = propagate.propagate(np.zeros(labels.shape), dilated_labels, combined_skel, 1) # # Get rid of any branchpoints not connected to seeds # combined_skel[dlabels == 0] = False # # Find the branchpoints # branch_points = morph.branchpoints(combined_skel) # # Odd case: when four branches meet like this, branchpoints are not # assigned because they are arbitrary. So assign them. # # . . # B. # .B # . . # odd_case = (combined_skel[:-1,:-1] & combined_skel[1:,:-1] & combined_skel[:-1,1:] & combined_skel[1,1]) branch_points[:-1,:-1][odd_case] = True branch_points[1:,1:][odd_case] = True # # Find the branching counts for the trunks (# of extra branches # eminating from a point other than the line it might be on). # branching_counts = morph.branchings(combined_skel) branching_counts = np.array([0,0,0,1,2])[branching_counts] # # Only take branches within 1 of the outside skeleton # dilated_skel = scind.binary_dilation(outside_skel, morph.eight_connect) branching_counts[~dilated_skel] = 0 # # Find the endpoints # end_points = morph.endpoints(combined_skel) # # We use two ranges for classification here: # * anything within one pixel of the dilated image is a trunk # * anything outside of that range is a branch # nearby_labels = dlabels.copy() nearby_labels[distance_map > 1.5] = 0 outside_labels = dlabels.copy() outside_labels[nearby_labels > 0] = 0 # # The trunks are the branchpoints that lie within one pixel of # the dilated image. # if labels_count > 0: trunk_counts = fix(scind.sum(branching_counts, nearby_labels, label_range)).astype(int) else: trunk_counts = np.zeros((0,),int) # # The branches are the branchpoints that lie outside the seed objects # if labels_count > 0: branch_counts = fix(scind.sum(branch_points, outside_labels, label_range)) else: branch_counts = np.zeros((0,),int) # # Save the endpoints # if labels_count > 0: end_counts = fix(scind.sum(end_points, outside_labels, label_range)) else: end_counts = np.zeros((0,), int) # # Save measurements # m = workspace.measurements assert isinstance(m, cpmeas.Measurements) feature = "_".join((C_NEURON, F_NUMBER_TRUNKS, skeleton_name)) m.add_measurement(seed_objects_name, feature, trunk_counts) feature = "_".join((C_NEURON, F_NUMBER_NON_TRUNK_BRANCHES, skeleton_name)) m.add_measurement(seed_objects_name, feature, branch_counts) feature = "_".join((C_NEURON, F_NUMBER_BRANCH_ENDS, skeleton_name)) m.add_measurement(seed_objects_name, feature, end_counts) # # Collect the graph information # if self.wants_neuron_graph: trunk_mask = (branching_counts > 0) & (nearby_labels != 0) intensity_image = workspace.image_set.get_image( self.intensity_image_name.value) edge_graph, vertex_graph = self.make_neuron_graph( combined_skel, dlabels, trunk_mask, branch_points & ~trunk_mask, end_points, intensity_image.pixel_data) image_number = workspace.measurements.image_set_number edge_path, vertex_path = self.get_graph_file_paths(m, m.image_number) workspace.interaction_request( self, m.image_number, edge_path, edge_graph, vertex_path, vertex_graph, headless_ok = True) if self.show_window: workspace.display_data.edge_graph = edge_graph workspace.display_data.vertex_graph = vertex_graph workspace.display_data.intensity_image = intensity_image.pixel_data # # Make the display image # if self.show_window or self.wants_branchpoint_image: branchpoint_image = np.zeros((skeleton.shape[0], skeleton.shape[1], 3)) trunk_mask = (branching_counts > 0) & (nearby_labels != 0) branch_mask = branch_points & (outside_labels != 0) end_mask = end_points & (outside_labels != 0) branchpoint_image[outside_skel,:] = 1 branchpoint_image[trunk_mask | branch_mask | end_mask,:] = 0 branchpoint_image[trunk_mask,0] = 1 branchpoint_image[branch_mask,1] = 1 branchpoint_image[end_mask, 2] = 1 branchpoint_image[dilated_labels != 0,:] *= .875 branchpoint_image[dilated_labels != 0,:] += .1 if self.show_window: workspace.display_data.branchpoint_image = branchpoint_image if self.wants_branchpoint_image: bi = cpi.Image(branchpoint_image, parent_image = skeleton_image) workspace.image_set.add(self.branchpoint_image_name.value, bi)
def run_image_pair_objects(self, workspace, first_image_name, second_image_name, object_name): '''Calculate per-object correlations between intensities in two images''' first_image = workspace.image_set.get_image(first_image_name, must_be_grayscale=True) second_image = workspace.image_set.get_image(second_image_name, must_be_grayscale=True) objects = workspace.object_set.get_objects(object_name) # # Crop both images to the size of the labels matrix # labels = objects.segmented try: first_pixels = objects.crop_image_similarly(first_image.pixel_data) first_mask = objects.crop_image_similarly(first_image.mask) except ValueError: first_pixels, m1 = cpo.size_similarly(labels, first_image.pixel_data) first_mask, m1 = cpo.size_similarly(labels, first_image.mask) first_mask[~m1] = False try: second_pixels = objects.crop_image_similarly(second_image.pixel_data) second_mask = objects.crop_image_similarly(second_image.mask) except ValueError: second_pixels, m1 = cpo.size_similarly(labels, second_image.pixel_data) second_mask, m1 = cpo.size_similarly(labels, second_image.mask) second_mask[~m1] = False mask = ((labels > 0) & first_mask & second_mask) first_pixels = first_pixels[mask] second_pixels = second_pixels[mask] labels = labels[mask] if len(labels)==0: n_objects = 0 else: n_objects = np.max(labels) if n_objects == 0: corr = np.zeros((0,)) else: # # The correlation is sum((x-mean(x))(y-mean(y)) / # ((n-1) * std(x) *std(y))) # lrange = np.arange(n_objects,dtype=np.int32)+1 area = fix(scind.sum(np.ones_like(labels), labels, lrange)) mean1 = fix(scind.mean(first_pixels, labels, lrange)) mean2 = fix(scind.mean(second_pixels, labels, lrange)) # # Calculate the standard deviation times the population. # std1 = np.sqrt(fix(scind.sum((first_pixels-mean1[labels-1])**2, labels, lrange))) std2 = np.sqrt(fix(scind.sum((second_pixels-mean2[labels-1])**2, labels, lrange))) x = first_pixels - mean1[labels-1] # x - mean(x) y = second_pixels - mean2[labels-1] # y - mean(y) corr = fix(scind.sum(x * y / (std1[labels-1] * std2[labels-1]), labels, lrange)) corr[~ np.isfinite(corr)] = 0 measurement = ("Correlation_Correlation_%s_%s" % (first_image_name, second_image_name)) workspace.measurements.add_measurement(object_name, measurement, corr) if n_objects == 0: return [[first_image_name, second_image_name, object_name, "Mean correlation","-"], [first_image_name, second_image_name, object_name, "Median correlation","-"], [first_image_name, second_image_name, object_name, "Min correlation","-"], [first_image_name, second_image_name, object_name, "Max correlation","-"]] else: return [[first_image_name, second_image_name, object_name, "Mean correlation","%.2f"%np.mean(corr)], [first_image_name, second_image_name, object_name, "Median correlation","%.2f"%np.median(corr)], [first_image_name, second_image_name, object_name, "Min correlation","%.2f"%np.min(corr)], [first_image_name, second_image_name, object_name, "Max correlation","%.2f"%np.max(corr)]]
def run_one_tamura(self, image_name, object_name, workspace): """Run, computing the area measurements for a single map of objects""" statistics = [] image = workspace.image_set.get_image(image_name, must_be_grayscale=True) objects = workspace.get_objects(object_name) pixel_data = image.pixel_data if image.has_mask: mask = image.mask else: mask = None labels = objects.segmented try: pixel_data = objects.crop_image_similarly(pixel_data) except ValueError: # # Recover by cropping the image to the labels # pixel_data, m1 = cpo.size_similarly(labels, pixel_data) if np.any(~m1): if mask is None: mask = m1 else: mask, m2 = cpo.size_similarly(labels, mask) mask[~m2] = False if np.all(labels == 0): for name in F_ALL: statistics += self.record_measurement( workspace, image_name, object_name, "", "%s_%s" % (F_TAMURA, name), np.zeros((0, ))) else: labs = np.unique(labels) values = np.zeros([np.max(labs) + 1, 2]) for l in labs: if l != 0: px = pixel_data px[np.where(labels != l)] = 0.0 values[l, 0] = self.contrast(px) values[l, 1] = self.directionality(px) statistics += self.record_measurement( workspace, image_name, object_name, "-", "%s_%s" % (F_TAMURA, F_2), values[:, 0]) statistics += self.record_measurement( workspace, image_name, object_name, "-", "%s_%s" % (F_TAMURA, F_3), values[:, 1]) coars = np.zeros([np.max(labs) + 1]) coars_hist = np.zeros([np.max(labs) + 1, HIST_COARS_BINS]) for l in labs: if l != 0: px = pixel_data px[np.where(labels != l)] = 0.0 coars[l], coars_hist[l, :] = self.coarseness(px) statistics += self.record_measurement( workspace, image_name, object_name, "-", "%s_%s" % (F_TAMURA, F_1), coars) for b in range(0, HIST_COARS_BINS): value = coars_hist[1:, b] name = "CoarsenessHist_%dBinsHist_Bin%d" % (HIST_COARS_BINS, b) statistics += self.record_measurement( workspace, image_name, object_name, "-", "%s_%s" % (F_TAMURA, name), value) return statistics
def run(self, workspace): '''Run the module on the image set''' seed_objects_name = self.seed_objects_name.value skeleton_name = self.image_name.value seed_objects = workspace.object_set.get_objects(seed_objects_name) labels = seed_objects.segmented labels_count = np.max(labels) label_range = np.arange(labels_count, dtype=np.int32) + 1 skeleton_image = workspace.image_set.get_image(skeleton_name, must_be_binary=True) skeleton = skeleton_image.pixel_data if skeleton_image.has_mask: skeleton = skeleton & skeleton_image.mask try: labels = skeleton_image.crop_image_similarly(labels) except: labels, m1 = cpo.size_similarly(skeleton, labels) labels[~m1] = 0 # # The following code makes a ring around the seed objects with # the skeleton trunks sticking out of it. # # Create a new skeleton with holes at the seed objects # First combine the seed objects with the skeleton so # that the skeleton trunks come out of the seed objects. # # Erode the labels once so that all of the trunk branchpoints # will be within the labels # # # Dilate the objects, then subtract them to make a ring # my_disk = morph.strel_disk(1.5).astype(int) dilated_labels = grey_dilation(labels, footprint=my_disk) seed_mask = dilated_labels > 0 combined_skel = skeleton | seed_mask closed_labels = grey_erosion(dilated_labels, footprint=my_disk) seed_center = closed_labels > 0 combined_skel = combined_skel & (~seed_center) # # Fill in single holes (but not a one-pixel hole made by # a one-pixel image) # if self.wants_to_fill_holes: def size_fn(area, is_object): return (~is_object) and (area <= self.maximum_hole_size.value) combined_skel = morph.fill_labeled_holes(combined_skel, ~seed_center, size_fn) # # Reskeletonize to make true branchpoints at the ring boundaries # combined_skel = morph.skeletonize(combined_skel) # # The skeleton outside of the labels # outside_skel = combined_skel & (dilated_labels == 0) # # Associate all skeleton points with seed objects # dlabels, distance_map = propagate.propagate(np.zeros(labels.shape), dilated_labels, combined_skel, 1) # # Get rid of any branchpoints not connected to seeds # combined_skel[dlabels == 0] = False # # Find the branchpoints # branch_points = morph.branchpoints(combined_skel) # # Odd case: when four branches meet like this, branchpoints are not # assigned because they are arbitrary. So assign them. # # . . # B. # .B # . . # odd_case = (combined_skel[:-1, :-1] & combined_skel[1:, :-1] & combined_skel[:-1, 1:] & combined_skel[1, 1]) branch_points[:-1, :-1][odd_case] = True branch_points[1:, 1:][odd_case] = True # # Find the branching counts for the trunks (# of extra branches # eminating from a point other than the line it might be on). # branching_counts = morph.branchings(combined_skel) branching_counts = np.array([0, 0, 0, 1, 2])[branching_counts] # # Only take branches within 1 of the outside skeleton # dilated_skel = scind.binary_dilation(outside_skel, morph.eight_connect) branching_counts[~dilated_skel] = 0 # # Find the endpoints # end_points = morph.endpoints(combined_skel) # # We use two ranges for classification here: # * anything within one pixel of the dilated image is a trunk # * anything outside of that range is a branch # nearby_labels = dlabels.copy() nearby_labels[distance_map > 1.5] = 0 outside_labels = dlabels.copy() outside_labels[nearby_labels > 0] = 0 # # The trunks are the branchpoints that lie within one pixel of # the dilated image. # if labels_count > 0: trunk_counts = fix( scind.sum(branching_counts, nearby_labels, label_range)).astype(int) else: trunk_counts = np.zeros((0, ), int) # # The branches are the branchpoints that lie outside the seed objects # if labels_count > 0: branch_counts = fix( scind.sum(branch_points, outside_labels, label_range)) else: branch_counts = np.zeros((0, ), int) # # Save the endpoints # if labels_count > 0: end_counts = fix(scind.sum(end_points, outside_labels, label_range)) else: end_counts = np.zeros((0, ), int) # # Save measurements # m = workspace.measurements assert isinstance(m, cpmeas.Measurements) feature = "_".join((C_NEURON, F_NUMBER_TRUNKS, skeleton_name)) m.add_measurement(seed_objects_name, feature, trunk_counts) feature = "_".join( (C_NEURON, F_NUMBER_NON_TRUNK_BRANCHES, skeleton_name)) m.add_measurement(seed_objects_name, feature, branch_counts) feature = "_".join((C_NEURON, F_NUMBER_BRANCH_ENDS, skeleton_name)) m.add_measurement(seed_objects_name, feature, end_counts) # # Collect the graph information # if self.wants_neuron_graph: trunk_mask = (branching_counts > 0) & (nearby_labels != 0) intensity_image = workspace.image_set.get_image( self.intensity_image_name.value) edge_graph, vertex_graph = self.make_neuron_graph( combined_skel, dlabels, trunk_mask, branch_points & ~trunk_mask, end_points, intensity_image.pixel_data) # # Add an image number column to both and change vertex index # to vertex number (one-based) # image_number = workspace.measurements.image_set_number vertex_graph = np.rec.fromarrays( (np.ones(len(vertex_graph)) * image_number, np.arange(1, len(vertex_graph) + 1), vertex_graph['i'], vertex_graph['j'], vertex_graph['labels'], vertex_graph['kind']), names=("image_number", "vertex_number", "i", "j", "labels", "kind")) edge_graph = np.rec.fromarrays( (np.ones(len(edge_graph)) * image_number, edge_graph["v1"], edge_graph["v2"], edge_graph["length"], edge_graph["total_intensity"]), names=("image_number", "v1", "v2", "length", "total_intensity")) path = self.directory.get_absolute_path(m) edge_file = m.apply_metadata(self.edge_file_name.value) edge_path = os.path.abspath(os.path.join(path, edge_file)) vertex_file = m.apply_metadata(self.vertex_file_name.value) vertex_path = os.path.abspath(os.path.join(path, vertex_file)) d = self.get_dictionary(workspace.image_set_list) for file_path, table, fmt in ((edge_path, edge_graph, "%d,%d,%d,%d,%.4f"), (vertex_path, vertex_graph, "%d,%d,%d,%d,%d,%s")): # # Delete files first time through / otherwise append # if not d.has_key(file_path): d[file_path] = True if os.path.exists(file_path): if workspace.frame is not None: import wx if wx.MessageBox( "%s already exists. Do you want to overwrite it?" % file_path, "Warning: overwriting file", style=wx.YES_NO, parent=workspace.frame) != wx.YES: raise ValueError("Can't overwrite %s" % file_path) os.remove(file_path) fd = open(file_path, 'wt') header = ','.join(table.dtype.names) fd.write(header + '\n') else: fd = open(file_path, 'at') np.savetxt(fd, table, fmt) fd.close() if workspace.frame is not None: workspace.display_data.edge_graph = edge_graph workspace.display_data.vertex_graph = vertex_graph # # Make the display image # if workspace.frame is not None or self.wants_branchpoint_image: branchpoint_image = np.zeros( (skeleton.shape[0], skeleton.shape[1], 3)) trunk_mask = (branching_counts > 0) & (nearby_labels != 0) branch_mask = branch_points & (outside_labels != 0) end_mask = end_points & (outside_labels != 0) branchpoint_image[outside_skel, :] = 1 branchpoint_image[trunk_mask | branch_mask | end_mask, :] = 0 branchpoint_image[trunk_mask, 0] = 1 branchpoint_image[branch_mask, 1] = 1 branchpoint_image[end_mask, 2] = 1 branchpoint_image[dilated_labels != 0, :] *= .875 branchpoint_image[dilated_labels != 0, :] += .1 if workspace.frame: workspace.display_data.branchpoint_image = branchpoint_image if self.wants_branchpoint_image: bi = cpi.Image(branchpoint_image, parent_image=skeleton_image) workspace.image_set.add(self.branchpoint_image_name.value, bi)
def run(self, workspace): '''Run the module on an image set''' object_name = self.object_name.value remaining_object_name = self.remaining_objects.value original_objects = workspace.object_set.get_objects(object_name) if self.mask_choice == MC_IMAGE: mask = workspace.image_set.get_image(self.masking_image.value, must_be_binary = True) mask = mask.pixel_data else: masking_objects = workspace.object_set.get_objects( self.masking_objects.value) mask = masking_objects.segmented > 0 if self.wants_inverted_mask: mask = ~mask # # Load the labels # labels = original_objects.segmented.copy() nobjects = np.max(labels) # # Resize the mask to cover the objects # mask, m1 = cpo.size_similarly(labels, mask) mask[~m1] = False # # Apply the mask according to the overlap choice. # if nobjects == 0: pass elif self.overlap_choice == P_MASK: labels = labels * mask else: pixel_counts = fix(scind.sum(mask, labels, np.arange(1, nobjects+1,dtype=np.int32))) if self.overlap_choice == P_KEEP: keep = pixel_counts > 0 else: total_pixels = fix(scind.sum(np.ones(labels.shape), labels, np.arange(1, nobjects+1,dtype=np.int32))) if self.overlap_choice == P_REMOVE: keep = pixel_counts == total_pixels elif self.overlap_choice == P_REMOVE_PERCENTAGE: fraction = self.overlap_fraction.value keep = pixel_counts / total_pixels >= fraction else: raise NotImplementedError("Unknown overlap-handling choice: %s", self.overlap_choice.value) keep = np.hstack(([False], keep)) labels[~ keep[labels]] = 0 # # Renumber the labels matrix if requested # if self.retain_or_renumber == R_RENUMBER: unique_labels = np.unique(labels[labels!=0]) indexer = np.zeros(nobjects+1, int) indexer[unique_labels] = np.arange(1, len(unique_labels)+1) labels = indexer[labels] parent_objects = unique_labels else: parent_objects = np.arange(1, nobjects+1) # # Add the objects # remaining_objects = cpo.Objects() remaining_objects.segmented = labels remaining_objects.unedited_segmented = original_objects.unedited_segmented workspace.object_set.add_objects(remaining_objects, remaining_object_name) # # Add measurements # m = workspace.measurements m.add_measurement(remaining_object_name, I.FF_PARENT % object_name, parent_objects) if np.max(original_objects.segmented) == 0: child_count = np.array([],int) else: child_count = fix(scind.sum(labels, original_objects.segmented, np.arange(1, nobjects+1,dtype=np.int32))) child_count = (child_count > 0).astype(int) m.add_measurement(object_name, I.FF_CHILDREN_COUNT % remaining_object_name, child_count) if self.retain_or_renumber == R_RETAIN: remaining_object_count = nobjects else: remaining_object_count = len(unique_labels) I.add_object_count_measurements(m, remaining_object_name, remaining_object_count) I.add_object_location_measurements(m, remaining_object_name, labels) # # Add an outline if asked to do so # if self.wants_outlines.value: outline_image = cpi.Image(outline(labels) > 0, parent_image = original_objects.parent_image) workspace.image_set.add(self.outlines_name.value, outline_image) # # Save the input, mask and output images for display # if self.show_window: workspace.display_data.original_labels = original_objects.segmented workspace.display_data.final_labels = labels workspace.display_data.mask = mask
def run(self, workspace): """Run the module on the current data set workspace - has the current image set, object set, measurements and the parent frame for the application if the module is allowed to display. If the module should not display, workspace.frame is None. """ # # The object set holds "objects". Each of these is a container # for holding up to three kinds of image labels. # object_set = workspace.object_set # # Get the primary objects (the centers to be removed). # Get the string value out of primary_object_name. # primary_objects = object_set.get_objects(self.primary_objects_name.value) # # Get the cleaned-up labels image # primary_labels = primary_objects.segmented # # Do the same with the secondary object secondary_objects = object_set.get_objects(self.secondary_objects_name.value) secondary_labels = secondary_objects.segmented # # If one of the two label images is smaller than the other, we # try to find the cropping mask and we apply that mask to the larger # try: if any([p_size < s_size for p_size,s_size in zip(primary_labels.shape, secondary_labels.shape)]): # # Look for a cropping mask associated with the primary_labels # and apply that mask to resize the secondary labels # secondary_labels = primary_objects.crop_image_similarly(secondary_labels) tertiary_image = primary_objects.parent_image elif any([p_size > s_size for p_size,s_size in zip(primary_labels.shape, secondary_labels.shape)]): primary_labels = secondary_objects.crop_image_similarly(primary_labels) tertiary_image = secondary_objects.parent_image elif secondary_objects.parent_image is not None: tertiary_image = secondary_objects.parent_image else: tertiary_image = primary_objects.parent_image except ValueError: # No suitable cropping - resize all to fit the secondary # labels which are the most critical. # primary_labels, _ = cpo.size_similarly(secondary_labels, primary_labels) if secondary_objects.parent_image is not None: tertiary_image = secondary_objects.parent_image else: tertiary_image = primary_objects.parent_image if tertiary_image is not None: tertiary_image, _ = cpo.size_similarly(secondary_labels, tertiary_image) # # Find the outlines of the primary image and use this to shrink the # primary image by one. This guarantees that there is something left # of the secondary image after subtraction # primary_outline = outline(primary_labels) tertiary_labels = secondary_labels.copy() if self.shrink_primary: primary_mask = np.logical_or(primary_labels == 0, primary_outline) else: primary_mask = primary_labels == 0 tertiary_labels[primary_mask == False] = 0 # # Get the outlines of the tertiary image # tertiary_outlines = outline(tertiary_labels)!=0 # # Make the tertiary objects container # tertiary_objects = cpo.Objects() tertiary_objects.segmented = tertiary_labels tertiary_objects.parent_image = tertiary_image # # Relate tertiary objects to their parents & record # child_count_of_secondary, secondary_parents = \ secondary_objects.relate_children(tertiary_objects) if self.shrink_primary: child_count_of_primary, primary_parents = \ primary_objects.relate_children(tertiary_objects) else: # Primary and tertiary don't overlap. # Establish overlap between primary and secondary and commute _, secondary_of_primary = \ secondary_objects.relate_children(primary_objects) mask = secondary_of_primary != 0 child_count_of_primary = np.zeros(mask.shape, int) child_count_of_primary[mask] = child_count_of_secondary[ secondary_of_primary[mask] - 1] primary_parents = np.zeros(secondary_parents.shape, secondary_parents.dtype) primary_of_secondary = np.zeros(secondary_objects.count+1, int) primary_of_secondary[secondary_of_primary] = \ np.arange(1, len(secondary_of_primary)+1) primary_of_secondary[0] = 0 primary_parents = primary_of_secondary[secondary_parents] # # Write out the objects # workspace.object_set.add_objects(tertiary_objects, self.subregion_objects_name.value) # # Write out the measurements # m = workspace.measurements # # The parent/child associations # for parent_objects_name, parents_of, child_count, relationship in ( (self.primary_objects_name, primary_parents, child_count_of_primary, R_REMOVED), (self.secondary_objects_name, secondary_parents, child_count_of_secondary, R_PARENT)): m.add_measurement(self.subregion_objects_name.value, cpmi.FF_PARENT%(parent_objects_name.value), parents_of) m.add_measurement(parent_objects_name.value, cpmi.FF_CHILDREN_COUNT%(self.subregion_objects_name.value), child_count) mask = parents_of != 0 image_number = np.ones(np.sum(mask), int) * m.image_set_number child_object_number = np.argwhere(mask).flatten() + 1 parent_object_number = parents_of[mask] m.add_relate_measurement( self.module_num, relationship, parent_objects_name.value, self.subregion_objects_name.value, image_number, parent_object_number, image_number, child_object_number) object_count = tertiary_objects.count # # The object count # cpmi.add_object_count_measurements(workspace.measurements, self.subregion_objects_name.value, object_count) # # The object locations # cpmi.add_object_location_measurements(workspace.measurements, self.subregion_objects_name.value, tertiary_labels) # # The outlines # if self.use_outlines.value: out_img = cpi.Image(tertiary_outlines.astype(bool), parent_image = tertiary_image) workspace.image_set.add(self.outlines_name.value, out_img) if self.show_window: workspace.display_data.primary_labels = primary_labels workspace.display_data.secondary_labels = secondary_labels workspace.display_data.tertiary_labels = tertiary_labels workspace.display_data.tertiary_outlines = tertiary_outlines
def run(self, workspace): """Run the module on the current data set workspace - has the current image set, object set, measurements and the parent frame for the application if the module is allowed to display. If the module should not display, workspace.frame is None. """ # # The object set holds "objects". Each of these is a container # for holding up to three kinds of image labels. # object_set = workspace.object_set # # Get the primary objects (the centers to be removed). # Get the string value out of primary_object_name. # primary_objects = object_set.get_objects( self.primary_objects_name.value) # # Get the cleaned-up labels image # primary_labels = primary_objects.segmented # # Do the same with the secondary object secondary_objects = object_set.get_objects( self.secondary_objects_name.value) secondary_labels = secondary_objects.segmented # # If one of the two label images is smaller than the other, we # try to find the cropping mask and we apply that mask to the larger # try: if any([ p_size < s_size for p_size, s_size in zip( primary_labels.shape, secondary_labels.shape) ]): # # Look for a cropping mask associated with the primary_labels # and apply that mask to resize the secondary labels # secondary_labels = primary_objects.crop_image_similarly( secondary_labels) tertiary_image = primary_objects.parent_image elif any([ p_size > s_size for p_size, s_size in zip( primary_labels.shape, secondary_labels.shape) ]): primary_labels = secondary_objects.crop_image_similarly( primary_labels) tertiary_image = secondary_objects.parent_image elif secondary_objects.parent_image != None: tertiary_image = secondary_objects.parent_image else: tertiary_image = primary_objects.parent_image except ValueError: # No suitable cropping - resize all to fit the secondary # labels which are the most critical. # primary_labels, _ = cpo.size_similarly(secondary_labels, primary_labels) if secondary_objects.parent_image != None: tertiary_image = secondary_objects.parent_image else: tertiary_image = primary_objects.parent_image if tertiary_image is not None: tertiary_image, _ = cpo.size_similarly( secondary_labels, tertiary_image) # # Find the outlines of the primary image and use this to shrink the # primary image by one. This guarantees that there is something left # of the secondary image after subtraction # primary_outline = outline(primary_labels) tertiary_labels = secondary_labels.copy() primary_mask = np.logical_or(primary_labels == 0, primary_outline) tertiary_labels[primary_mask == False] = 0 # # Get the outlines of the tertiary image # tertiary_outlines = outline(tertiary_labels) != 0 # # Make the tertiary objects container # tertiary_objects = cpo.Objects() tertiary_objects.segmented = tertiary_labels tertiary_objects.parent_image = tertiary_image # # Relate tertiary objects to their parents & record # child_count_of_secondary, secondary_parents = \ secondary_objects.relate_children(tertiary_objects) child_count_of_primary, primary_parents = \ primary_objects.relate_children(tertiary_objects) if workspace.frame != None: import cellprofiler.gui.cpfigure as cpf # # Draw the primary, secondary and tertiary labels # and the outlines # window_name = "CellProfiler:%s:%d" % (self.module_name, self.module_num) my_frame = cpf.create_or_find( workspace.frame, title="IdentifyTertiaryObjects, image cycle #%d" % (workspace.measurements.image_set_number), name=window_name, subplots=(2, 2)) title = "%s, cycle # %d" % (self.primary_objects_name.value, workspace.image_set.number + 1) my_frame.subplot_imshow_labels(0, 0, primary_labels, title) my_frame.subplot_imshow_labels(1, 0, secondary_labels, self.secondary_objects_name.value, sharex=my_frame.subplot(0, 0), sharey=my_frame.subplot(0, 0)) my_frame.subplot_imshow_labels(0, 1, tertiary_labels, self.subregion_objects_name.value, sharex=my_frame.subplot(0, 0), sharey=my_frame.subplot(0, 0)) my_frame.subplot_imshow_bw(1, 1, tertiary_outlines, "Outlines", sharex=my_frame.subplot(0, 0), sharey=my_frame.subplot(0, 0)) my_frame.Refresh() # # Write out the objects # workspace.object_set.add_objects(tertiary_objects, self.subregion_objects_name.value) # # Write out the measurements # m = workspace.measurements # # The parent/child associations # for parent_objects_name, parents_of, child_count\ in ((self.primary_objects_name, primary_parents,child_count_of_primary), (self.secondary_objects_name, secondary_parents, child_count_of_secondary)): m.add_measurement(self.subregion_objects_name.value, cpmi.FF_PARENT % (parent_objects_name.value), parents_of) m.add_measurement( parent_objects_name.value, cpmi.FF_CHILDREN_COUNT % (self.subregion_objects_name.value), child_count) object_count = np.max(tertiary_labels) # # The object count # cpmi.add_object_count_measurements(workspace.measurements, self.subregion_objects_name.value, object_count) # # The object locations # cpmi.add_object_location_measurements( workspace.measurements, self.subregion_objects_name.value, tertiary_labels) # # The outlines # if self.use_outlines.value: out_img = cpi.Image(tertiary_outlines.astype(bool), parent_image=tertiary_image) workspace.image_set.add(self.outlines_name.value, out_img)
def run(self, workspace): """Run the module on the current data set workspace - has the current image set, object set, measurements and the parent frame for the application if the module is allowed to display. If the module should not display, workspace.frame is None. """ # # The object set holds "objects". Each of these is a container # for holding up to three kinds of image labels. # object_set = workspace.object_set # # Get the primary objects (the centers to be removed). # Get the string value out of primary_object_name. # primary_objects = object_set.get_objects(self.primary_objects_name.value) # # Get the cleaned-up labels image # primary_labels = primary_objects.segmented # # Do the same with the secondary object secondary_objects = object_set.get_objects(self.secondary_objects_name.value) secondary_labels = secondary_objects.segmented # # If one of the two label images is smaller than the other, we # try to find the cropping mask and we apply that mask to the larger # try: if any([p_size < s_size for p_size,s_size in zip(primary_labels.shape, secondary_labels.shape)]): # # Look for a cropping mask associated with the primary_labels # and apply that mask to resize the secondary labels # secondary_labels = primary_objects.crop_image_similarly(secondary_labels) tertiary_image = primary_objects.parent_image elif any([p_size > s_size for p_size,s_size in zip(primary_labels.shape, secondary_labels.shape)]): primary_labels = secondary_objects.crop_image_similarly(primary_labels) tertiary_image = secondary_objects.parent_image elif secondary_objects.parent_image != None: tertiary_image = secondary_objects.parent_image else: tertiary_image = primary_objects.parent_image except ValueError: # No suitable cropping - resize all to fit the secondary # labels which are the most critical. # primary_labels, _ = cpo.size_similarly(secondary_labels, primary_labels) if secondary_objects.parent_image != None: tertiary_image = secondary_objects.parent_image else: tertiary_image = primary_objects.parent_image if tertiary_image is not None: tertiary_image, _ = cpo.size_similarly(secondary_labels, tertiary_image) # # Find the outlines of the primary image and use this to shrink the # primary image by one. This guarantees that there is something left # of the secondary image after subtraction # primary_outline = outline(primary_labels) tertiary_labels = secondary_labels.copy() primary_mask = np.logical_or(primary_labels == 0, primary_outline) tertiary_labels[primary_mask == False] = 0 # # Get the outlines of the tertiary image # tertiary_outlines = outline(tertiary_labels)!=0 # # Make the tertiary objects container # tertiary_objects = cpo.Objects() tertiary_objects.segmented = tertiary_labels tertiary_objects.parent_image = tertiary_image # # Relate tertiary objects to their parents & record # child_count_of_secondary, secondary_parents = \ secondary_objects.relate_children(tertiary_objects) child_count_of_primary, primary_parents = \ primary_objects.relate_children(tertiary_objects) if workspace.frame != None: import cellprofiler.gui.cpfigure as cpf # # Draw the primary, secondary and tertiary labels # and the outlines # window_name = "CellProfiler:%s:%d"%(self.module_name,self.module_num) my_frame=cpf.create_or_find(workspace.frame, title="IdentifyTertiaryObjects, image cycle #%d"%( workspace.measurements.image_set_number), name=window_name, subplots=(2,2)) title = "%s, cycle # %d"%(self.primary_objects_name.value, workspace.image_set.number+1) my_frame.subplot_imshow_labels(0,0,primary_labels,title) my_frame.subplot_imshow_labels(1,0,secondary_labels, self.secondary_objects_name.value, sharex = my_frame.subplot(0,0), sharey = my_frame.subplot(0,0)) my_frame.subplot_imshow_labels(0, 1,tertiary_labels, self.subregion_objects_name.value, sharex = my_frame.subplot(0,0), sharey = my_frame.subplot(0,0)) my_frame.subplot_imshow_bw(1,1,tertiary_outlines, "Outlines", sharex = my_frame.subplot(0,0), sharey = my_frame.subplot(0,0)) my_frame.Refresh() # # Write out the objects # workspace.object_set.add_objects(tertiary_objects, self.subregion_objects_name.value) # # Write out the measurements # m = workspace.measurements # # The parent/child associations # for parent_objects_name, parents_of, child_count\ in ((self.primary_objects_name, primary_parents,child_count_of_primary), (self.secondary_objects_name, secondary_parents, child_count_of_secondary)): m.add_measurement(self.subregion_objects_name.value, cpmi.FF_PARENT%(parent_objects_name.value), parents_of) m.add_measurement(parent_objects_name.value, cpmi.FF_CHILDREN_COUNT%(self.subregion_objects_name.value), child_count) object_count = np.max(tertiary_labels) # # The object count # cpmi.add_object_count_measurements(workspace.measurements, self.subregion_objects_name.value, object_count) # # The object locations # cpmi.add_object_location_measurements(workspace.measurements, self.subregion_objects_name.value, tertiary_labels) # # The outlines # if self.use_outlines.value: out_img = cpi.Image(tertiary_outlines.astype(bool), parent_image = tertiary_image) workspace.image_set.add(self.outlines_name.value, out_img)
def run(self, workspace): """Run the module on the current data set workspace - has the current image set, object set, measurements and the parent frame for the application if the module is allowed to display. If the module should not display, workspace.frame is None. """ # # The object set holds "objects". Each of these is a container # for holding up to three kinds of image labels. # object_set = workspace.object_set # # Get the primary objects (the centers to be removed). # Get the string value out of primary_object_name. # primary_objects = object_set.get_objects( self.primary_objects_name.value) # # Get the cleaned-up labels image # primary_labels = primary_objects.segmented # # Do the same with the secondary object secondary_objects = object_set.get_objects( self.secondary_objects_name.value) secondary_labels = secondary_objects.segmented # # If one of the two label images is smaller than the other, we # try to find the cropping mask and we apply that mask to the larger # try: if any([ p_size < s_size for p_size, s_size in zip( primary_labels.shape, secondary_labels.shape) ]): # # Look for a cropping mask associated with the primary_labels # and apply that mask to resize the secondary labels # secondary_labels = primary_objects.crop_image_similarly( secondary_labels) tertiary_image = primary_objects.parent_image elif any([ p_size > s_size for p_size, s_size in zip( primary_labels.shape, secondary_labels.shape) ]): primary_labels = secondary_objects.crop_image_similarly( primary_labels) tertiary_image = secondary_objects.parent_image elif secondary_objects.parent_image != None: tertiary_image = secondary_objects.parent_image else: tertiary_image = primary_objects.parent_image except ValueError: # No suitable cropping - resize all to fit the secondary # labels which are the most critical. # primary_labels, _ = cpo.size_similarly(secondary_labels, primary_labels) if secondary_objects.parent_image != None: tertiary_image = secondary_objects.parent_image else: tertiary_image = primary_objects.parent_image if tertiary_image is not None: tertiary_image, _ = cpo.size_similarly( secondary_labels, tertiary_image) # # Find the outlines of the primary image and use this to shrink the # primary image by one. This guarantees that there is something left # of the secondary image after subtraction # primary_outline = outline(primary_labels) tertiary_labels = secondary_labels.copy() if self.shrink_primary: primary_mask = np.logical_or(primary_labels == 0, primary_outline) else: primary_mask = primary_labels == 0 tertiary_labels[primary_mask == False] = 0 # # Get the outlines of the tertiary image # tertiary_outlines = outline(tertiary_labels) != 0 # # Make the tertiary objects container # tertiary_objects = cpo.Objects() tertiary_objects.segmented = tertiary_labels tertiary_objects.parent_image = tertiary_image # # Relate tertiary objects to their parents & record # child_count_of_secondary, secondary_parents = \ secondary_objects.relate_children(tertiary_objects) if self.shrink_primary: child_count_of_primary, primary_parents = \ primary_objects.relate_children(tertiary_objects) else: # Primary and tertiary don't overlap. If tertiary object # disappeared, have primary disavow knowledge of it. child_count_of_primary = np.zeros(primary_objects.count) child_count_of_primary[tertiary_objects.areas > 0] = 1 primary_parents = np.arange(1, tertiary_objects.count + 1) # # Write out the objects # workspace.object_set.add_objects(tertiary_objects, self.subregion_objects_name.value) # # Write out the measurements # m = workspace.measurements # # The parent/child associations # for parent_objects_name, parents_of, child_count\ in ((self.primary_objects_name, primary_parents,child_count_of_primary), (self.secondary_objects_name, secondary_parents, child_count_of_secondary)): m.add_measurement(self.subregion_objects_name.value, cpmi.FF_PARENT % (parent_objects_name.value), parents_of) m.add_measurement( parent_objects_name.value, cpmi.FF_CHILDREN_COUNT % (self.subregion_objects_name.value), child_count) object_count = tertiary_objects.count # # The object count # cpmi.add_object_count_measurements(workspace.measurements, self.subregion_objects_name.value, object_count) # # The object locations # cpmi.add_object_location_measurements( workspace.measurements, self.subregion_objects_name.value, tertiary_labels) # # The outlines # if self.use_outlines.value: out_img = cpi.Image(tertiary_outlines.astype(bool), parent_image=tertiary_image) workspace.image_set.add(self.outlines_name.value, out_img) if self.show_window: workspace.display_data.primary_labels = primary_labels workspace.display_data.secondary_labels = secondary_labels workspace.display_data.tertiary_labels = tertiary_labels workspace.display_data.tertiary_outlines = tertiary_outlines
def run(self, workspace): '''Run the module on the image set''' seed_objects_name = self.seed_objects_name.value skeleton_name = self.image_name.value seed_objects = workspace.object_set.get_objects(seed_objects_name) labels = seed_objects.segmented labels_count = np.max(labels) label_range = np.arange(labels_count,dtype=np.int32)+1 skeleton_image = workspace.image_set.get_image( skeleton_name, must_be_binary = True) skeleton = skeleton_image.pixel_data if skeleton_image.has_mask: skeleton = skeleton & skeleton_image.mask try: labels = skeleton_image.crop_image_similarly(labels) except: labels, m1 = cpo.size_similarly(skeleton, labels) labels[~m1] = 0 # # The following code makes a ring around the seed objects with # the skeleton trunks sticking out of it. # # Create a new skeleton with holes at the seed objects # First combine the seed objects with the skeleton so # that the skeleton trunks come out of the seed objects. # # Erode the labels once so that all of the trunk branchpoints # will be within the labels # # # Dilate the objects, then subtract them to make a ring # my_disk = morph.strel_disk(1.5).astype(int) dilated_labels = grey_dilation(labels, footprint=my_disk) seed_mask = dilated_labels > 0 combined_skel = skeleton | seed_mask closed_labels = grey_erosion(dilated_labels, footprint = my_disk) seed_center = closed_labels > 0 combined_skel = combined_skel & (~seed_center) # # Fill in single holes (but not a one-pixel hole made by # a one-pixel image) # if self.wants_to_fill_holes: def size_fn(area, is_object): return (~ is_object) and (area <= self.maximum_hole_size.value) combined_skel = morph.fill_labeled_holes( combined_skel, ~seed_center, size_fn) # # Reskeletonize to make true branchpoints at the ring boundaries # combined_skel = morph.skeletonize(combined_skel) # # The skeleton outside of the labels # outside_skel = combined_skel & (dilated_labels == 0) # # Associate all skeleton points with seed objects # dlabels, distance_map = propagate.propagate(np.zeros(labels.shape), dilated_labels, combined_skel, 1) # # Get rid of any branchpoints not connected to seeds # combined_skel[dlabels == 0] = False # # Find the branchpoints # branch_points = morph.branchpoints(combined_skel) # # Odd case: when four branches meet like this, branchpoints are not # assigned because they are arbitrary. So assign them. # # . . # B. # .B # . . # odd_case = (combined_skel[:-1,:-1] & combined_skel[1:,:-1] & combined_skel[:-1,1:] & combined_skel[1,1]) branch_points[:-1,:-1][odd_case] = True branch_points[1:,1:][odd_case] = True # # Find the branching counts for the trunks (# of extra branches # eminating from a point other than the line it might be on). # branching_counts = morph.branchings(combined_skel) branching_counts = np.array([0,0,0,1,2])[branching_counts] # # Only take branches within 1 of the outside skeleton # dilated_skel = scind.binary_dilation(outside_skel, morph.eight_connect) branching_counts[~dilated_skel] = 0 # # Find the endpoints # end_points = morph.endpoints(combined_skel) # # We use two ranges for classification here: # * anything within one pixel of the dilated image is a trunk # * anything outside of that range is a branch # nearby_labels = dlabels.copy() nearby_labels[distance_map > 1.5] = 0 outside_labels = dlabels.copy() outside_labels[nearby_labels > 0] = 0 # # The trunks are the branchpoints that lie within one pixel of # the dilated image. # if labels_count > 0: trunk_counts = fix(scind.sum(branching_counts, nearby_labels, label_range)).astype(int) else: trunk_counts = np.zeros((0,),int) # # The branches are the branchpoints that lie outside the seed objects # if labels_count > 0: branch_counts = fix(scind.sum(branch_points, outside_labels, label_range)) else: branch_counts = np.zeros((0,),int) # # Save the endpoints # if labels_count > 0: end_counts = fix(scind.sum(end_points, outside_labels, label_range)) else: end_counts = np.zeros((0,), int) # # Save measurements # m = workspace.measurements assert isinstance(m, cpmeas.Measurements) feature = "_".join((C_NEURON, F_NUMBER_TRUNKS, skeleton_name)) m.add_measurement(seed_objects_name, feature, trunk_counts) feature = "_".join((C_NEURON, F_NUMBER_NON_TRUNK_BRANCHES, skeleton_name)) m.add_measurement(seed_objects_name, feature, branch_counts) feature = "_".join((C_NEURON, F_NUMBER_BRANCH_ENDS, skeleton_name)) m.add_measurement(seed_objects_name, feature, end_counts) # # Collect the graph information # if self.wants_neuron_graph: trunk_mask = (branching_counts > 0) & (nearby_labels != 0) intensity_image = workspace.image_set.get_image( self.intensity_image_name.value) edge_graph, vertex_graph = self.make_neuron_graph( combined_skel, dlabels, trunk_mask, branch_points & ~trunk_mask, end_points, intensity_image.pixel_data) # # Add an image number column to both and change vertex index # to vertex number (one-based) # image_number = workspace.measurements.image_set_number vertex_graph = np.rec.fromarrays( (np.ones(len(vertex_graph)) * image_number, np.arange(1, len(vertex_graph) + 1), vertex_graph['i'], vertex_graph['j'], vertex_graph['labels'], vertex_graph['kind']), names = ("image_number", "vertex_number", "i", "j", "labels", "kind")) edge_graph = np.rec.fromarrays( (np.ones(len(edge_graph)) * image_number, edge_graph["v1"], edge_graph["v2"], edge_graph["length"], edge_graph["total_intensity"]), names = ("image_number", "v1", "v2", "length", "total_intensity")) path = self.directory.get_absolute_path(m) edge_file = m.apply_metadata(self.edge_file_name.value) edge_path = os.path.abspath(os.path.join(path, edge_file)) vertex_file = m.apply_metadata(self.vertex_file_name.value) vertex_path = os.path.abspath(os.path.join(path, vertex_file)) d = self.get_dictionary(workspace.image_set_list) for file_path, table, fmt in ( (edge_path, edge_graph, "%d,%d,%d,%d,%.4f"), (vertex_path, vertex_graph, "%d,%d,%d,%d,%d,%s")): # # Delete files first time through / otherwise append # if not d.has_key(file_path): d[file_path] = True if os.path.exists(file_path): if workspace.frame is not None: import wx if wx.MessageBox( "%s already exists. Do you want to overwrite it?" % file_path, "Warning: overwriting file", style = wx.YES_NO, parent = workspace.frame) != wx.YES: raise ValueError("Can't overwrite %s" % file_path) os.remove(file_path) fd = open(file_path, 'wt') header = ','.join(table.dtype.names) fd.write(header + '\n') else: fd = open(file_path, 'at') np.savetxt(fd, table, fmt) fd.close() if workspace.frame is not None: workspace.display_data.edge_graph = edge_graph workspace.display_data.vertex_graph = vertex_graph # # Make the display image # if workspace.frame is not None or self.wants_branchpoint_image: branchpoint_image = np.zeros((skeleton.shape[0], skeleton.shape[1], 3)) trunk_mask = (branching_counts > 0) & (nearby_labels != 0) branch_mask = branch_points & (outside_labels != 0) end_mask = end_points & (outside_labels != 0) branchpoint_image[outside_skel,:] = 1 branchpoint_image[trunk_mask | branch_mask | end_mask,:] = 0 branchpoint_image[trunk_mask,0] = 1 branchpoint_image[branch_mask,1] = 1 branchpoint_image[end_mask, 2] = 1 branchpoint_image[dilated_labels != 0,:] *= .875 branchpoint_image[dilated_labels != 0,:] += .1 if workspace.frame: workspace.display_data.branchpoint_image = branchpoint_image if self.wants_branchpoint_image: bi = cpi.Image(branchpoint_image, parent_image = skeleton_image) workspace.image_set.add(self.branchpoint_image_name.value, bi)
def run_image_pair_objects(self, workspace, first_image_name, second_image_name, object_name): '''Calculate per-object correlations between intensities in two images''' first_image = workspace.image_set.get_image(first_image_name, must_be_grayscale=True) second_image = workspace.image_set.get_image(second_image_name, must_be_grayscale=True) objects = workspace.object_set.get_objects(object_name) # # Crop both images to the size of the labels matrix # labels = objects.segmented try: first_pixels = objects.crop_image_similarly(first_image.pixel_data) first_mask = objects.crop_image_similarly(first_image.mask) except ValueError: first_pixels, m1 = cpo.size_similarly(labels, first_image.pixel_data) first_mask, m1 = cpo.size_similarly(labels, first_image.mask) first_mask[~m1] = False try: second_pixels = objects.crop_image_similarly( second_image.pixel_data) second_mask = objects.crop_image_similarly(second_image.mask) except ValueError: second_pixels, m1 = cpo.size_similarly(labels, second_image.pixel_data) second_mask, m1 = cpo.size_similarly(labels, second_image.mask) second_mask[~m1] = False mask = ((labels > 0) & first_mask & second_mask) first_pixels = first_pixels[mask] second_pixels = second_pixels[mask] labels = labels[mask] if len(labels) == 0: n_objects = 0 else: n_objects = np.max(labels) if n_objects == 0: rwc1 = np.zeros((0, )) rwc2 = np.zeros((0, )) else: object_labels = np.unique(labels) rwc1 = np.zeros(np.shape(object_labels)) rwc2 = np.zeros(np.shape(object_labels)) for oindex in object_labels: fi = first_pixels[labels == oindex] si = second_pixels[labels == oindex] #corr = np.corrcoef((fi,si))[1,0] # Do the ranking au, arank = np.unique(fi, return_inverse=True) bu, brank = np.unique(si, return_inverse=True) # Reverse ranking amax = np.max(arank) + 1 bmax = np.max(brank) + 1 arank = -(arank.astype(float) - amax) brank = -(brank.astype(float) - bmax) # Measure absolute difference in ranks d = np.absolute(arank - brank) # Get the maximal ranking rn = np.max(np.hstack((arank, brank))) # Calculate weights matrix w = (rn - d) / rn # Thresholding and RWC calculations t = self.manual_threshold.value #t=0.15 ta = t * np.max(fi) tb = t * np.max(si) a1 = np.array(fi, copy=True) b1 = np.array(si, copy=True) a1[fi <= ta] = 0 asum = np.sum(a1) a1[si <= tb] = 0 rwc1_temp = np.sum(a1.flatten() * w) / asum b1[si <= tb] = 0 bsum = np.sum(b1) b1[fi <= ta] = 0 rwc2_temp = np.sum(b1.flatten() * w) / bsum # And RWC values are... rwc1[oindex - 1] = rwc1_temp rwc2[oindex - 1] = rwc2_temp rwc1_measurement = ("Correlation_RWC_%s_%s" % (first_image_name, second_image_name)) rwc2_measurement = ("Correlation_RWC_%s_%s" % (second_image_name, first_image_name)) workspace.measurements.add_measurement(object_name, rwc1_measurement, rwc1) workspace.measurements.add_measurement(object_name, rwc2_measurement, rwc2) if n_objects == 0: return [[ first_image_name, second_image_name, object_name, "Mean correlation", "-" ], [ first_image_name, second_image_name, object_name, "Median correlation", "-" ], [ first_image_name, second_image_name, object_name, "Min correlation", "-" ], [ first_image_name, second_image_name, object_name, "Max correlation", "-" ]] else: return [[ first_image_name, second_image_name, object_name, "Mean correlation", "%.2f" % np.mean(rwc1) ], [ first_image_name, second_image_name, object_name, "Median correlation", "%.2f" % np.median(rwc1) ], [ first_image_name, second_image_name, object_name, "Min correlation", "%.2f" % np.min(rwc1) ], [ first_image_name, second_image_name, object_name, "Max correlation", "%.2f" % np.max(rwc1) ]]
def do_measurements(self, workspace, image_name, object_name, center_object_name, center_choice, bin_count_settings, dd): '''Perform the radial measurements on the image set workspace - workspace that holds images / objects image_name - make measurements on this image object_name - make measurements on these objects center_object_name - use the centers of these related objects as the centers for radial measurements. None to use the objects themselves. center_choice - the user's center choice for this object: C_SELF, C_CENTERS_OF_OBJECTS or C_EDGES_OF_OBJECTS. bin_count_settings - the bin count settings group d - a dictionary for saving reusable partial results returns one statistics tuple per ring. ''' assert isinstance(workspace, cpw.Workspace) assert isinstance(workspace.object_set, cpo.ObjectSet) bin_count = bin_count_settings.bin_count.value wants_scaled = bin_count_settings.wants_scaled.value maximum_radius = bin_count_settings.maximum_radius.value image = workspace.image_set.get_image(image_name, must_be_grayscale=True) objects = workspace.object_set.get_objects(object_name) labels, pixel_data = cpo.crop_labels_and_image(objects.segmented, image.pixel_data) nobjects = np.max(objects.segmented) measurements = workspace.measurements assert isinstance(measurements, cpmeas.Measurements) if nobjects == 0: for bin in range(1, bin_count+1): for feature in (F_FRAC_AT_D, F_MEAN_FRAC, F_RADIAL_CV): feature_name = ( (feature + FF_GENERIC) % (image_name, bin, bin_count)) measurements.add_measurement( object_name, "_".join([M_CATEGORY, feature_name]), np.zeros(0)) if not wants_scaled: measurement_name = "_".join([M_CATEGORY, feature, image_name, FF_OVERFLOW]) measurements.add_measurement( object_name, measurement_name, np.zeros(0)) return [(image_name, object_name, "no objects","-","-","-","-")] name = (object_name if center_object_name is None else "%s_%s"%(object_name, center_object_name)) if dd.has_key(name): normalized_distance, i_center, j_center, good_mask = dd[name] else: d_to_edge = distance_to_edge(labels) if center_object_name is not None: # # Use the center of the centering objects to assign a center # to each labeled pixel using propagation # center_objects=workspace.object_set.get_objects(center_object_name) center_labels, cmask = cpo.size_similarly( labels, center_objects.segmented) pixel_counts = fix(scind.sum( np.ones(center_labels.shape), center_labels, np.arange(1, np.max(center_labels)+1,dtype=np.int32))) good = pixel_counts > 0 i,j = (centers_of_labels(center_labels) + .5).astype(int) if center_choice == C_CENTERS_OF_OTHER: # # Reduce the propagation labels to the centers of # the centering objects # ig = i[good] jg = j[good] lg = np.arange(1, len(i)+1)[good] center_labels = np.zeros(center_labels.shape, int) center_labels[ig,jg] = lg cl,d_from_center = propagate(np.zeros(center_labels.shape), center_labels, labels != 0, 1) # # Erase the centers that fall outside of labels # cl[labels == 0] = 0 # # If objects are hollow or crescent-shaped, there may be # objects without center labels. As a backup, find the # center that is the closest to the center of mass. # missing_mask = (labels != 0) & (cl == 0) missing_labels = np.unique(labels[missing_mask]) if len(missing_labels): all_centers = centers_of_labels(labels) missing_i_centers, missing_j_centers = \ all_centers[:, missing_labels-1] di = missing_i_centers[:, np.newaxis] - ig[np.newaxis, :] dj = missing_j_centers[:, np.newaxis] - jg[np.newaxis, :] missing_best = lg[np.lexsort((di*di + dj*dj, ))[:, 0]] best = np.zeros(np.max(labels) + 1, int) best[missing_labels] = missing_best cl[missing_mask] = best[labels[missing_mask]] # # Now compute the crow-flies distance to the centers # of these pixels from whatever center was assigned to # the object. # iii, jjj = np.mgrid[0:labels.shape[0], 0:labels.shape[1]] di = iii[missing_mask] - i[cl[missing_mask] - 1] dj = jjj[missing_mask] - j[cl[missing_mask] - 1] d_from_center[missing_mask] = np.sqrt(di*di + dj*dj) else: # Find the point in each object farthest away from the edge. # This does better than the centroid: # * The center is within the object # * The center tends to be an interesting point, like the # center of the nucleus or the center of one or the other # of two touching cells. # i,j = maximum_position_of_labels(d_to_edge, labels, objects.indices) center_labels = np.zeros(labels.shape, int) center_labels[i,j] = labels[i,j] # # Use the coloring trick here to process touching objects # in separate operations # colors = color_labels(labels) ncolors = np.max(colors) d_from_center = np.zeros(labels.shape) cl = np.zeros(labels.shape, int) for color in range(1,ncolors+1): mask = colors == color l,d = propagate(np.zeros(center_labels.shape), center_labels, mask, 1) d_from_center[mask] = d[mask] cl[mask] = l[mask] good_mask = cl > 0 if center_choice == C_EDGES_OF_OTHER: # Exclude pixels within the centering objects # when performing calculations from the centers good_mask = good_mask & (center_labels == 0) i_center = np.zeros(cl.shape) i_center[good_mask] = i[cl[good_mask]-1] j_center = np.zeros(cl.shape) j_center[good_mask] = j[cl[good_mask]-1] normalized_distance = np.zeros(labels.shape) if wants_scaled: total_distance = d_from_center + d_to_edge normalized_distance[good_mask] = (d_from_center[good_mask] / (total_distance[good_mask] + .001)) else: normalized_distance[good_mask] = \ d_from_center[good_mask] / maximum_radius dd[name] = [normalized_distance, i_center, j_center, good_mask] ngood_pixels = np.sum(good_mask) good_labels = labels[good_mask] bin_indexes = (normalized_distance * bin_count).astype(int) bin_indexes[bin_indexes > bin_count] = bin_count labels_and_bins = (good_labels-1,bin_indexes[good_mask]) histogram = coo_matrix((pixel_data[good_mask], labels_and_bins), (nobjects, bin_count+1)).toarray() sum_by_object = np.sum(histogram, 1) sum_by_object_per_bin = np.dstack([sum_by_object]*(bin_count + 1))[0] fraction_at_distance = histogram / sum_by_object_per_bin number_at_distance = coo_matrix((np.ones(ngood_pixels),labels_and_bins), (nobjects, bin_count+1)).toarray() object_mask = number_at_distance > 0 sum_by_object = np.sum(number_at_distance, 1) sum_by_object_per_bin = np.dstack([sum_by_object]*(bin_count+1))[0] fraction_at_bin = number_at_distance / sum_by_object_per_bin mean_pixel_fraction = fraction_at_distance / (fraction_at_bin + np.finfo(float).eps) masked_fraction_at_distance = masked_array(fraction_at_distance, ~object_mask) masked_mean_pixel_fraction = masked_array(mean_pixel_fraction, ~object_mask) # Anisotropy calculation. Split each cell into eight wedges, then # compute coefficient of variation of the wedges' mean intensities # in each ring. # # Compute each pixel's delta from the center object's centroid i,j = np.mgrid[0:labels.shape[0], 0:labels.shape[1]] imask = i[good_mask] > i_center[good_mask] jmask = j[good_mask] > j_center[good_mask] absmask = (abs(i[good_mask] - i_center[good_mask]) > abs(j[good_mask] - j_center[good_mask])) radial_index = (imask.astype(int) + jmask.astype(int)*2 + absmask.astype(int)*4) statistics = [] for bin in range(bin_count + (0 if wants_scaled else 1)): bin_mask = (good_mask & (bin_indexes == bin)) bin_pixels = np.sum(bin_mask) bin_labels = labels[bin_mask] bin_radial_index = radial_index[bin_indexes[good_mask] == bin] labels_and_radii = (bin_labels-1, bin_radial_index) radial_values = coo_matrix((pixel_data[bin_mask], labels_and_radii), (nobjects, 8)).toarray() pixel_count = coo_matrix((np.ones(bin_pixels), labels_and_radii), (nobjects, 8)).toarray() mask = pixel_count==0 radial_means = masked_array(radial_values / pixel_count, mask) radial_cv = np.std(radial_means,1) / np.mean(radial_means, 1) radial_cv[np.sum(~mask,1)==0] = 0 for measurement, feature, overflow_feature in ( (fraction_at_distance[:,bin], MF_FRAC_AT_D, OF_FRAC_AT_D), (mean_pixel_fraction[:,bin], MF_MEAN_FRAC, OF_MEAN_FRAC), (np.array(radial_cv), MF_RADIAL_CV, OF_RADIAL_CV)): if bin == bin_count: measurement_name = overflow_feature % image_name else: measurement_name = feature % (image_name, bin+1, bin_count) measurements.add_measurement(object_name, measurement_name, measurement) radial_cv.mask = np.sum(~mask,1)==0 bin_name = str(bin+1) if bin < bin_count else "Overflow" statistics += [(image_name, object_name, bin_name, str(bin_count), round(np.mean(masked_fraction_at_distance[:,bin]),4), round(np.mean(masked_mean_pixel_fraction[:, bin]),4), round(np.mean(radial_cv),4))] return statistics
def run_image_pair_objects(self, workspace, first_image_name, second_image_name, object_name): '''Calculate per-object correlations between intensities in two images''' first_image = workspace.image_set.get_image(first_image_name, must_be_grayscale=True) second_image = workspace.image_set.get_image(second_image_name, must_be_grayscale=True) objects = workspace.object_set.get_objects(object_name) # # Crop both images to the size of the labels matrix # labels = objects.segmented try: first_pixels = objects.crop_image_similarly(first_image.pixel_data) first_mask = objects.crop_image_similarly(first_image.mask) except ValueError: first_pixels, m1 = cpo.size_similarly(labels, first_image.pixel_data) first_mask, m1 = cpo.size_similarly(labels, first_image.mask) first_mask[~m1] = False try: second_pixels = objects.crop_image_similarly(second_image.pixel_data) second_mask = objects.crop_image_similarly(second_image.mask) except ValueError: second_pixels, m1 = cpo.size_similarly(labels, second_image.pixel_data) second_mask, m1 = cpo.size_similarly(labels, second_image.mask) second_mask[~m1] = False mask = ((labels > 0) & first_mask & second_mask) first_pixels = first_pixels[mask] second_pixels = second_pixels[mask] labels = labels[mask] result = [] first_pixel_data = first_image.pixel_data first_mask = first_image.mask first_pixel_count = np.product(first_pixel_data.shape) second_pixel_data = second_image.pixel_data second_mask = second_image.mask second_pixel_count = np.product(second_pixel_data.shape) # # Crop the larger image similarly to the smaller one # if first_pixel_count < second_pixel_count: second_pixel_data = first_image.crop_image_similarly(second_pixel_data) second_mask = first_image.crop_image_similarly(second_mask) elif second_pixel_count < first_pixel_count: first_pixel_data = second_image.crop_image_similarly(first_pixel_data) first_mask = second_image.crop_image_similarly(first_mask) mask = (first_mask & second_mask & (~ np.isnan(first_pixel_data)) & (~ np.isnan(second_pixel_data))) if np.any(mask): # # Perform the correlation, which returns: # [ [ii, ij], # [ji, jj] ] # fi = first_pixel_data[mask] si = second_pixel_data[mask] n_objects = objects.count if n_objects == 0: corr = np.zeros((0,)) overlap = np.zeros((0,)) K1 = np.zeros((0,)) K2 = np.zeros((0,)) M1 = np.zeros((0,)) M2 = np.zeros((0,)) RWC1 = np.zeros((0,)) RWC2 = np.zeros((0,)) C1 = np.zeros((0,)) C2 = np.zeros((0,)) else: # # The correlation is sum((x-mean(x))(y-mean(y)) / # ((n-1) * std(x) *std(y))) # lrange = np.arange(n_objects,dtype=np.int32)+1 area = fix(scind.sum(np.ones_like(labels), labels, lrange)) mean1 = fix(scind.mean(first_pixels, labels, lrange)) mean2 = fix(scind.mean(second_pixels, labels, lrange)) # # Calculate the standard deviation times the population. # std1 = np.sqrt(fix(scind.sum((first_pixels-mean1[labels-1])**2, labels, lrange))) std2 = np.sqrt(fix(scind.sum((second_pixels-mean2[labels-1])**2, labels, lrange))) x = first_pixels - mean1[labels-1] # x - mean(x) y = second_pixels - mean2[labels-1] # y - mean(y) corr = fix(scind.sum(x * y / (std1[labels-1] * std2[labels-1]), labels, lrange)) # Explicitly set the correlation to NaN for masked objects corr[scind.sum(1, labels, lrange) == 0] = np.NaN result += [[first_image_name, second_image_name, object_name,"Mean Correlation coeff","%.3f"%np.mean(corr)], [first_image_name, second_image_name, object_name,"Median Correlation coeff","%.3f"%np.median(corr)], [first_image_name, second_image_name, object_name,"Min Correlation coeff","%.3f"%np.min(corr)], [first_image_name, second_image_name, object_name,"Max Correlation coeff","%.3f"%np.max(corr)]] # Threshold as percentage of maximum intensity of objects in each channel tff = (self.thr.value/100) * fix(scind.maximum(first_pixels,labels, lrange)) tss = (self.thr.value/100) * fix(scind.maximum(second_pixels,labels, lrange)) combined_thresh = (first_pixels> tff[labels-1]) & (second_pixels > tss[labels-1]) fi_thresh = first_pixels[combined_thresh] si_thresh = second_pixels[combined_thresh] tot_fi_thr = scind.sum(first_pixels[first_pixels > tff[labels-1]], labels[first_pixels > tff[labels-1]],lrange) tot_si_thr = scind.sum(second_pixels[second_pixels > tss[labels-1]], labels[second_pixels > tss[labels-1]],lrange) nonZero = (fi > 0) | (si > 0) xvar = np.var(fi[nonZero],axis=0,ddof=1) yvar = np.var(si[nonZero],axis=0,ddof=1) xmean = np.mean(fi[nonZero], axis=0) ymean = np.mean(si[nonZero], axis=0) z = fi[nonZero] + si[nonZero] zvar = np.var(z,axis=0,ddof=1) covar = 0.5 * (zvar - (xvar+yvar)) denom = 2 * covar num = (yvar-xvar) + np.sqrt((yvar-xvar)*(yvar-xvar)+4*(covar*covar)) a = (num/denom) b = (ymean - a*xmean) i = 1 while(i > 0.003921568627): thr_fi_c = i thr_si_c = (a*i)+b combt = (fi < thr_fi_c) | (si < thr_si_c) costReg = scistat.pearsonr(fi[combt], si[combt]) if(costReg[0] <= 0): break i= i-0.003921568627 # Costes' thershold for entire image is applied to each object fi_above_thr = first_pixels > thr_fi_c si_above_thr = second_pixels > thr_si_c combined_thresh_c = fi_above_thr & si_above_thr fi_thresh_c = first_pixels[combined_thresh_c] si_thresh_c = second_pixels[combined_thresh_c] if np.any(fi_above_thr): tot_fi_thr_c = scind.sum(first_pixels[first_pixels > thr_fi_c],labels[first_pixels > thr_fi_c],lrange) else: tot_fi_thr_c = np.zeros(len(lrange)) if np.any(si_above_thr): tot_si_thr_c = scind.sum(second_pixels[second_pixels > thr_si_c],labels[second_pixels > thr_si_c],lrange) else: tot_si_thr_c = np.zeros(len(lrange)) # Manders Coefficient M1 = np.zeros(len(lrange)) M2 = np.zeros(len(lrange)) if np.any(combined_thresh): M1 = np.array(scind.sum(fi_thresh,labels[combined_thresh],lrange)) / np.array(tot_fi_thr) M2 = np.array(scind.sum(si_thresh,labels[combined_thresh],lrange)) / np.array(tot_si_thr) result += [[first_image_name, second_image_name, object_name,"Mean Manders coeff","%.3f"%np.mean(M1)], [first_image_name, second_image_name, object_name,"Median Manders coeff","%.3f"%np.median(M1)], [first_image_name, second_image_name, object_name,"Min Manders coeff","%.3f"%np.min(M1)], [first_image_name, second_image_name, object_name,"Max Manders coeff","%.3f"%np.max(M1)]] result += [[second_image_name, first_image_name, object_name,"Mean Manders coeff","%.3f"%np.mean(M2)], [second_image_name, first_image_name, object_name,"Median Manders coeff","%.3f"%np.median(M2)], [second_image_name, first_image_name, object_name,"Min Manders coeff","%.3f"%np.min(M2)], [second_image_name, first_image_name, object_name,"Max Manders coeff","%.3f"%np.max(M2)]] # RWC Coefficient RWC1 = np.zeros(len(lrange)) RWC2 = np.zeros(len(lrange)) [Rank1] = np.lexsort(([labels], [first_pixels])) [Rank2] = np.lexsort(([labels], [second_pixels])) Rank1_U = np.hstack([[False],first_pixels[Rank1[:-1]] != first_pixels[Rank1[1:]]]) Rank2_U = np.hstack([[False],second_pixels[Rank2[:-1]] != second_pixels[Rank2[1:]]]) Rank1_S = np.cumsum(Rank1_U) Rank2_S = np.cumsum(Rank2_U) Rank_im1 = np.zeros(first_pixels.shape, dtype=int) Rank_im2 = np.zeros(second_pixels.shape, dtype=int) Rank_im1[Rank1] = Rank1_S Rank_im2[Rank2] = Rank2_S R = max(Rank_im1.max(), Rank_im2.max()) + 1 Di = abs(Rank_im1 - Rank_im2) weight = (R-Di) * 1.0 / R weight_thresh = weight[combined_thresh] if np.any(combined_thresh_c): RWC1 = np.array(scind.sum(fi_thresh * weight_thresh,labels[combined_thresh],lrange)) / np.array(tot_fi_thr) RWC2 = np.array(scind.sum(si_thresh * weight_thresh,labels[combined_thresh],lrange)) / np.array(tot_si_thr) result += [[first_image_name, second_image_name, object_name,"Mean RWC coeff","%.3f"%np.mean(RWC1)], [first_image_name, second_image_name, object_name,"Median RWC coeff","%.3f"%np.median(RWC1)], [first_image_name, second_image_name, object_name,"Min RWC coeff","%.3f"%np.min(RWC1)], [first_image_name, second_image_name, object_name,"Max RWC coeff","%.3f"%np.max(RWC1)]] result += [[second_image_name, first_image_name, object_name,"Mean RWC coeff","%.3f"%np.mean(RWC2)], [second_image_name, first_image_name, object_name,"Median RWC coeff","%.3f"%np.median(RWC2)], [second_image_name, first_image_name, object_name,"Min RWC coeff","%.3f"%np.min(RWC2)], [second_image_name, first_image_name, object_name,"Max RWC coeff","%.3f"%np.max(RWC2)]] #Costes Automated Threshold C1 = np.zeros(len(lrange)) C2 = np.zeros(len(lrange)) if np.any(combined_thresh_c): C1 = np.array(scind.sum(fi_thresh_c,labels[combined_thresh_c],lrange)) / np.array(tot_fi_thr_c) C2 = np.array(scind.sum(si_thresh_c,labels[combined_thresh_c],lrange)) / np.array(tot_si_thr_c) result += [[first_image_name, second_image_name, object_name,"Mean Manders coeff (Costes)","%.3f"%np.mean(C1)], [first_image_name, second_image_name, object_name,"Median Manders coeff (Costes)","%.3f"%np.median(C1)], [first_image_name, second_image_name, object_name,"Min Manders coeff (Costes)","%.3f"%np.min(C1)], [first_image_name, second_image_name, object_name,"Max Manders coeff (Costes)","%.3f"%np.max(C1)] ] result += [[second_image_name, first_image_name, object_name,"Mean Manders coeff (Costes)","%.3f"%np.mean(C2)], [second_image_name, first_image_name, object_name,"Median Manders coeff (Costes)","%.3f"%np.median(C2)], [second_image_name, first_image_name, object_name,"Min Manders coeff (Costes)","%.3f"%np.min(C2)], [second_image_name, first_image_name, object_name,"Max Manders coeff (Costes)","%.3f"%np.max(C2)] ] # Overlap Coefficient fpsq = scind.sum(first_pixels[combined_thresh]**2,labels[combined_thresh],lrange) spsq = scind.sum(second_pixels[combined_thresh]**2,labels[combined_thresh],lrange) pdt = np.sqrt(np.array(fpsq) * np.array(spsq)) if np.any(combined_thresh): overlap = fix(scind.sum(first_pixels[combined_thresh] * second_pixels[combined_thresh], labels[combined_thresh], lrange) / pdt) K1 = fix((scind.sum(first_pixels[combined_thresh] * second_pixels[combined_thresh], labels[combined_thresh], lrange)) / (np.array(fpsq))) K2 = fix(scind.sum(first_pixels[combined_thresh] * second_pixels[combined_thresh], labels[combined_thresh], lrange) / np.array(spsq)) else: overlap = K1 = K2 = np.zeros(len(lrange)) result += [[first_image_name, second_image_name, object_name,"Mean Overlap coeff","%.3f"%np.mean(overlap)], [first_image_name, second_image_name, object_name,"Median Overlap coeff","%.3f"%np.median(overlap)], [first_image_name, second_image_name, object_name,"Min Overlap coeff","%.3f"%np.min(overlap)], [first_image_name, second_image_name, object_name,"Max Overlap coeff","%.3f"%np.max(overlap)]] measurement = ("Correlation_Correlation_%s_%s" % (first_image_name, second_image_name)) overlap_measurement = (F_OVERLAP_FORMAT%(first_image_name, second_image_name)) k_measurement_1 = (F_K_FORMAT%(first_image_name, second_image_name)) k_measurement_2 = (F_K_FORMAT%(second_image_name, first_image_name)) manders_measurement_1 = (F_MANDERS_FORMAT%(first_image_name, second_image_name)) manders_measurement_2 = (F_MANDERS_FORMAT%(second_image_name, first_image_name)) rwc_measurement_1 = (F_RWC_FORMAT%(first_image_name, second_image_name)) rwc_measurement_2 = (F_RWC_FORMAT%(second_image_name, first_image_name)) costes_measurement_1 = (F_COSTES_FORMAT%(first_image_name, second_image_name)) costes_measurement_2 = (F_COSTES_FORMAT%(second_image_name, first_image_name)) workspace.measurements.add_measurement(object_name, measurement, corr) workspace.measurements.add_measurement(object_name,overlap_measurement, overlap) workspace.measurements.add_measurement(object_name,k_measurement_1, K1) workspace.measurements.add_measurement(object_name,k_measurement_2, K2) workspace.measurements.add_measurement(object_name,manders_measurement_1, M1) workspace.measurements.add_measurement(object_name,manders_measurement_2, M2) workspace.measurements.add_measurement(object_name,rwc_measurement_1, RWC1) workspace.measurements.add_measurement(object_name,rwc_measurement_2, RWC2) workspace.measurements.add_measurement(object_name,costes_measurement_1, C1) workspace.measurements.add_measurement(object_name,costes_measurement_2, C2) if n_objects == 0: return [[first_image_name, second_image_name, object_name, "Mean correlation","-"], [first_image_name, second_image_name, object_name, "Median correlation","-"], [first_image_name, second_image_name, object_name, "Min correlation","-"], [first_image_name, second_image_name, object_name, "Max correlation","-"]] else: return result
def do_measurements(self, workspace, image_name, object_name, center_object_name, bin_count, dd): '''Perform the radial measurements on the image set workspace - workspace that holds images / objects image_name - make measurements on this image object_name - make measurements on these objects center_object_name - use the centers of these related objects as the centers for radial measurements. None to use the objects themselves. bin_count - bin the object into this many concentric rings d - a dictionary for saving reusable partial results returns one statistics tuple per ring. ''' assert isinstance(workspace, cpw.Workspace) assert isinstance(workspace.object_set, cpo.ObjectSet) image = workspace.image_set.get_image(image_name, must_be_grayscale=True) objects = workspace.object_set.get_objects(object_name) labels, pixel_data = cpo.crop_labels_and_image(objects.segmented, image.pixel_data) nobjects = np.max(objects.segmented) measurements = workspace.measurements assert isinstance(measurements, cpmeas.Measurements) if nobjects == 0: for bin in range(1, bin_count+1): for feature in (FF_FRAC_AT_D, FF_MEAN_FRAC, FF_RADIAL_CV): measurements.add_measurement(object_name, M_CATEGORY + "_" + feature % (image_name, bin, bin_count), np.zeros(0)) return [(image_name, object_name, "no objects","-","-","-","-")] name = (object_name if center_object_name is None else "%s_%s"%(object_name, center_object_name)) if dd.has_key(name): normalized_distance, i_center, j_center, good_mask = dd[name] else: d_to_edge = distance_to_edge(labels) if center_object_name is not None: center_objects=workspace.object_set.get_objects(center_object_name) center_labels, cmask = cpo.size_similarly( labels, center_objects.segmented) pixel_counts = fix(scind.sum(np.ones(center_labels.shape), center_labels, np.arange(1, np.max(center_labels)+1,dtype=np.int32))) good = pixel_counts > 0 i,j = (centers_of_labels(center_labels) + .5).astype(int) ig = i[good] jg = j[good] center_labels = np.zeros(center_labels.shape, int) center_labels[ig,jg] = labels[ig,jg] ## TODO: This is incorrect when objects are annular. Retrieves label# = 0 cl,d_from_center = propagate(np.zeros(center_labels.shape), center_labels, labels != 0, 1) else: # Find the point in each object farthest away from the edge. # This does better than the centroid: # * The center is within the object # * The center tends to be an interesting point, like the # center of the nucleus or the center of one or the other # of two touching cells. # i,j = maximum_position_of_labels(d_to_edge, labels, objects.indices) center_labels = np.zeros(labels.shape, int) center_labels[i,j] = labels[i,j] # # Use the coloring trick here to process touching objects # in separate operations # colors = color_labels(labels) ncolors = np.max(colors) d_from_center = np.zeros(labels.shape) cl = np.zeros(labels.shape, int) for color in range(1,ncolors+1): mask = colors == color l,d = propagate(np.zeros(center_labels.shape), center_labels, mask, 1) d_from_center[mask] = d[mask] cl[mask] = l[mask] good_mask = cl > 0 i_center = np.zeros(cl.shape) i_center[good_mask] = i[cl[good_mask]-1] j_center = np.zeros(cl.shape) j_center[good_mask] = j[cl[good_mask]-1] normalized_distance = np.zeros(labels.shape) total_distance = d_from_center + d_to_edge normalized_distance[good_mask] = (d_from_center[good_mask] / (total_distance[good_mask] + .001)) dd[name] = [normalized_distance, i_center, j_center, good_mask] ngood_pixels = np.sum(good_mask) good_labels = objects.segmented[good_mask] bin_indexes = (normalized_distance * bin_count).astype(int) labels_and_bins = (good_labels-1,bin_indexes[good_mask]) histogram = coo_matrix((image.pixel_data[good_mask], labels_and_bins), (nobjects, bin_count)).toarray() sum_by_object = np.sum(histogram, 1) sum_by_object_per_bin = np.dstack([sum_by_object]*bin_count)[0] fraction_at_distance = histogram / sum_by_object_per_bin number_at_distance = coo_matrix((np.ones(ngood_pixels),labels_and_bins), (nobjects, bin_count)).toarray() object_mask = number_at_distance > 0 sum_by_object = np.sum(number_at_distance, 1) sum_by_object_per_bin = np.dstack([sum_by_object]*bin_count)[0] fraction_at_bin = number_at_distance / sum_by_object_per_bin mean_pixel_fraction = fraction_at_distance / (fraction_at_bin + np.finfo(float).eps) masked_fraction_at_distance = masked_array(fraction_at_distance, ~object_mask) masked_mean_pixel_fraction = masked_array(mean_pixel_fraction, ~object_mask) # Anisotropy calculation. Split each cell into eight wedges, then # compute coefficient of variation of the wedges' mean intensities # in each ring. # # Compute each pixel's delta from the center object's centroid i,j = np.mgrid[0:labels.shape[0], 0:labels.shape[1]] imask = i[good_mask] > i_center[good_mask] jmask = j[good_mask] > j_center[good_mask] absmask = (abs(i[good_mask] - i_center[good_mask]) > abs(j[good_mask] - j_center[good_mask])) radial_index = (imask.astype(int) + jmask.astype(int)*2 + absmask.astype(int)*4) statistics = [] for bin in range(bin_count): bin_mask = (good_mask & (bin_indexes == bin)) bin_pixels = np.sum(bin_mask) bin_labels = labels[bin_mask] bin_radial_index = radial_index[bin_indexes[good_mask] == bin] labels_and_radii = (bin_labels-1, bin_radial_index) radial_values = coo_matrix((pixel_data[bin_mask], labels_and_radii), (nobjects, 8)).toarray() pixel_count = coo_matrix((np.ones(bin_pixels), labels_and_radii), (nobjects, 8)).toarray() mask = pixel_count==0 radial_means = masked_array(radial_values / pixel_count, mask) radial_cv = np.std(radial_means,1) / np.mean(radial_means, 1) radial_cv[np.sum(~mask,1)==0] = 0 for measurement, feature in ((fraction_at_distance[:,bin], MF_FRAC_AT_D), (mean_pixel_fraction[:,bin], MF_MEAN_FRAC), (np.array(radial_cv), MF_RADIAL_CV)): measurements.add_measurement(object_name, feature % (image_name, bin+1, bin_count), measurement) radial_cv.mask = np.sum(~mask,1)==0 statistics += [(image_name, object_name, str(bin+1), str(bin_count), round(np.mean(masked_fraction_at_distance[:,bin]),4), round(np.mean(masked_mean_pixel_fraction[:, bin]),4), round(np.mean(radial_cv),4))] return statistics