我们从Python开源项目中，提取了以下8个代码示例，用于说明如何使用scipy.ndimage.labeled_comprehension()。
def remove_artifacts(self, image): """ Remove the connected components that are not within the parameters Operates in place :param image: sudoku's thresholded image w/o grid :return: None """ labeled, features = label(image, structure=CROSS) lbls = np.arange(1, features + 1) areas = extract_feature(image, labeled, lbls, np.sum, np.uint32, 0) sides = extract_feature(image, labeled, lbls, min_side, np.float32, 0, True) diags = extract_feature(image, labeled, lbls, diagonal, np.float32, 0, True) for index in lbls: area = areas[index - 1] / 255 side = sides[index - 1] diag = diags[index - 1] if side < 5 or side > 20 \ or diag < 15 or diag > 25 \ or area < 40: image[labeled == index] = 0 return None
def binary_volume_opening(vol, minvol): lb_vol, num_objs = label(vol) lbs = np.arange(1, num_objs + 1) v = labeled_comprehension(lb_vol > 0, lb_vol, lbs, np.sum, np.int, 0) ix = np.isin(lb_vol, lbs[v >= minvol]) newvol = np.zeros(vol.shape) newvol[ix] = vol[ix] return newvol
def quantile(self, q): return self._scipy_aggregate(ndimage.labeled_comprehension, func=functools.partial( numpy.percentile, q=q, ), out_dtype='float64', default=None)
def post_process_segmentation(input_scan, options, save_nifti = True): """ Post-process the probabilistic segmentation using parameters t_bin and l_min t_bin: threshold to binarize the output segmentations l_min: minimum lesion volume Inputs: - input_scan: probabilistic input image (segmentation) - options dictionary - save_nifti: save the result as nifti Output: - output_scan: final binarized segmentation """ from scipy import ndimage t_bin = options['t_bin'] l_min = options['l_min'] output_scan = np.zeros_like(input_scan) # threshold input segmentation t_segmentation = input_scan >= t_bin # filter candidates by size and store those > l_min labels, num_labels = ndimage.label(t_segmentation) label_list = np.unique(labels) num_elements_by_lesion = ndimage.labeled_comprehension(t_segmentation, labels, label_list, np.sum, float, 0) for l in range(len(num_elements_by_lesion)): if num_elements_by_lesion[l]>l_min: # assign voxels to output current_voxels = np.stack(np.where(labels == l), axis =1) output_scan[current_voxels[:,0], current_voxels[:,1], current_voxels[:,2]] = 1 #save the output segmentation as Nifti1Image if save_nifti: nifti_out = nib.Nifti1Image(output_scan, np.eye(4)) nifti_out.to_filename(os.path.join(options['test_folder'], options['test_scan'], options['experiment'], options['test_name'])) return output_scan
def segment_size_curvature(curve_Array): min_c = np.nanmin(curve_Array) max_c = np.nanmax(curve_Array) max_c = max(max_c , -min_c) max_num_label_valley = 0 max_num_label_ridge = 0 d_curve = max_c / (5 * 100) for i in range (0 , 100): k_tresh_valley = 1 * ( float(i) * d_curve) k_tresh_ridge = -1 * k_tresh_valley Val_Array = np.where(curve_Array >= k_tresh_valley, 1, 0) Rid_Array = np.where(curve_Array <= k_tresh_ridge, 1, 0) Lab_Val_Array, num_label_val = ndimage.label(Val_Array , structure = np.ones((3 , 3))) Lab_Rid_Array, num_label_rid = ndimage.label(Rid_Array , structure = np.ones((3 , 3))) labels_val = np.arange(1, num_label_val + 1) labels_rid = np.arange(1, num_label_rid + 1) try: area_label_val = ndimage.labeled_comprehension(Val_Array, Lab_Val_Array, labels_val, np.sum, int, 0) num_sig_label_valley = np.sum(area_label_val >= 1) except ValueError: num_sig_label_valley = 0 try: area_label_rid = ndimage.labeled_comprehension(Rid_Array, Lab_Rid_Array, labels_rid, np.sum, int, 0) num_sig_label_ridge = np.sum(area_label_rid >= 1) except ValueError: num_sig_label_ridge = 0 if num_sig_label_valley >= max_num_label_valley: max_num_label_valley = num_sig_label_valley valley_thresh = k_tresh_valley if num_sig_label_ridge >= max_num_label_ridge: max_num_label_ridge = num_sig_label_ridge ridge_thresh = k_tresh_ridge return valley_thresh , ridge_thresh
def neg_bond(curvature_file, valley_bond_file , ridge_bond_file , covergent_sig_file , divergent_sig_file): curve_Array = arcpy.RasterToNumPyArray(curvature_file , nodata_to_value=0) corner = arcpy.Point(arcpy.Describe(curvature_file).Extent.XMin,arcpy.Describe(curvature_file).Extent.YMin) dx = arcpy.Describe(curvature_file).meanCellWidth valley_thresh , ridge_thresh = segment_size_curvature(curve_Array) valley = np.where(curve_Array > valley_thresh , 1 , 0) ridge = np.where(curve_Array < ridge_thresh , 1 , 0) Lab_Val_Array, num_label_val = ndimage.label(valley , structure = np.ones((3 , 3))) Lab_Rid_Array, num_label_rid = ndimage.label(ridge , structure = np.ones((3 , 3))) perc90_valley = ndimage.labeled_comprehension(curve_Array, Lab_Val_Array, np.arange(1, num_label_val + 1) , percentile, float, 0) perc90_ridge = ndimage.labeled_comprehension(-1 * curve_Array, Lab_Rid_Array, np.arange(1, num_label_rid + 1) , percentile, float, 0) area_valley = ndimage.labeled_comprehension(valley, Lab_Val_Array, np.arange(1, num_label_val + 1) , np.sum, float, 0) area_ridge = ndimage.labeled_comprehension(ridge, Lab_Rid_Array, np.arange(1, num_label_rid + 1) , np.sum, float, 0) main_patch_val = perc90_valley[area_valley == np.max(area_valley)][0] main_patch_ridge = perc90_ridge[area_ridge == np.max(area_ridge)][0] sig_valley_thresh = otsu(perc90_valley[perc90_valley < main_patch_val]) sig_ridge_thresh = otsu(perc90_ridge[perc90_ridge < main_patch_ridge]) perc90_valley = perc90_valley[Lab_Val_Array - 1] perc90_valley = np.where(valley == 1 , perc90_valley , 0) perc90_ridge = perc90_ridge[Lab_Rid_Array - 1] perc90_ridge = np.where(ridge == 1 , perc90_ridge , 0) sig_valley_patch = np.where(perc90_valley > sig_valley_thresh , 1 , 0) sig_ridge_patch = np.where(perc90_ridge > sig_ridge_thresh , 1 , 0) arcpy.NumPyArrayToRaster(perc90_valley , corner,dx,dx , value_to_nodata=0).save(covergent_sig_file) arcpy.NumPyArrayToRaster(perc90_ridge , corner,dx,dx , value_to_nodata=0).save(divergent_sig_file) arcpy.NumPyArrayToRaster(sig_valley_patch , corner,dx,dx , value_to_nodata=0).save(valley_bond_file) arcpy.NumPyArrayToRaster(sig_ridge_patch , corner,dx,dx , value_to_nodata=0).save(ridge_bond_file) return valley_thresh
