我们从Python开源项目中，提取了以下12个代码示例，用于说明如何使用scipy.ndimage.binary_closing()。
def process_failure(name): name = name.replace("mask","truth") name2 = name.replace("truth","") image,_,_ = image_read_write.load_itk_image(name2) #image_cropped = image[:,120:420,60:460] image_mask = np.zeros(image.shape) center = 256 cc,rr = circle(center+20,center,160) image_mask[:,cc,rr] = 1 image[image>threshold]=0 image[image!=0]=1 image = image*image_mask #image_cropped[image_cropped>threshold]=0 #image_cropped[image_cropped!=0]=1 kernel20 = np.zeros((15,15)) cc,rr = circle(7,7,8) kernel20[cc,rr]=1 image = binary_closing(image, [kernel20],1) #image[:,:,:]=0 #image[:,120:420,60:460]=image_cropped truth,_,_ = image_read_write.load_itk_image(name) print evaluator.calculate_dice(image,truth,name) image = np.array(image,dtype=np.int8) #LoadImages.save_itk(image,name)
def find_facade_cuts(facade_sig, dilation_amount=60): edges = facade_sig > facade_sig.mean() + facade_sig.std() edges = binary_closing(edges, structure=np.ones(dilation_amount)) run, start, val = run_length_encode(edges) result = [] for s, e in zip(start[val], start[val] + run[val]): result.append(s + facade_sig[s:e].argmax()) result = [0] + result + [len(facade_sig) - 1] result = np.unique(result) return np.array(result)
def process_image(name, do_closing, closing_structure): image_train,_,_ = image_read_write.load_itk_image(name) name = name.replace("mask","truth") image_truth,_,_ = image_read_write.load_itk_image(name) truth = np.zeros(image_truth.shape, dtype=np.uint8) truth[image_truth >0]=1 if do_closing: image_train = binary_closing(image_train, closing_structure,1) image_train = binary_closing(image_train, [[[1]],[[1]],[[1]],[[1]],[[1]]],1) score = calculate_dice(image_train,truth, name) return score
def closed(self, structure, iterations=1): """ This function ... :param structure: :return: """ data = ndimage.binary_closing(self, structure, iterations) # Return the new mask #data, name=None, description=None return Mask(data, name=self.name, description=self.description) # -----------------------------------------------------------------
def localization(x, y): """Simple post-processing and get IVDs positons. Return: positons: calculated by `ndimage.measurements.center_of_mass` y: after fill holes and remove small objects. """ labels, nums = label(y, return_num=True) areas = np.array([prop.filled_area for prop in regionprops(labels)]) assert nums >= 7, 'Fail in this test, should detect at least seven regions.' # Segment a joint region which should be separate (if any). while np.max(areas) > 10000: y = ndimage.binary_opening(y, structure=np.ones((3, 3, 3))) areas = np.array([prop.filled_area for prop in regionprops(label(y))]) # Remove small objects. threshold = sorted(areas, reverse=True)[7] y = morphology.remove_small_objects(y, threshold + 1) # Fill holes. y = ndimage.binary_closing(y, structure=np.ones((3, 3, 3))) y = morphology.remove_small_holes(y, min_size=512, connectivity=3) positions = ndimage.measurements.center_of_mass(x, label(y), range(1, 8)) return np.array(positions), y
def main(args): if args.threshold is None: print("Please provide a binarization threshold") return 1 data, hdr = read(args.input, inc_header=True) mask = data >= args.threshold if args.minvol is not None: mask = binary_volume_opening(mask, args.minvol) if args.fill: mask = binary_fill_holes(mask) if args.extend is not None and args.extend > 0: if args.relion: se = binary_sphere(args.extend, False) mask = binary_dilation(mask, structure=se, iterations=1) else: dt = distance_transform_edt(~mask) mask = mask | (dt <= args.edge_width) if args.close: se = binary_sphere(args.extend, False) mask = binary_closing(mask, structure=se, iterations=1) final = mask.astype(np.single) if args.edge_width is not None: dt = distance_transform_edt(~mask) # Compute *outward* distance transform of mask. idx = (dt <= args.edge_width) & (dt > 0) # Identify edge points by distance from mask. x = np.arange(1, args.edge_width + 1) # Domain of the edge profile. if "sin" in args.edge_profile: y = np.sin(np.linspace(np.pi/2, 0, args.edge_width + 1)) # Range of the edge profile. f = interp1d(x, y[1:]) final[idx] = f(dt[idx]) # Insert edge heights interpolated at distance transform values. write(args.output, final, psz=hdr["xlen"] / hdr["nx"]) return 0
def run(self, ips, snap, img, para = None): strc = np.ones((para['h'], para['w']), dtype=np.uint8) ndimg.binary_closing(snap, strc, output=img) img *= 255
def run(self, ips, imgs, para = None): strc = np.ones((para['r'], para['r'], para['r']), dtype=np.uint8) imgs[:] = ndimg.binary_closing(imgs, strc) imgs *= 255
def gradient_threshold(in_file, in_segm, thresh=1.0, out_file=None): """ Compute a threshold from the histogram of the magnitude gradient image """ import os.path as op import numpy as np import nibabel as nb from scipy import ndimage as sim struc = sim.iterate_structure(sim.generate_binary_structure(3, 2), 2) if out_file is None: fname, ext = op.splitext(op.basename(in_file)) if ext == '.gz': fname, ext2 = op.splitext(fname) ext = ext2 + ext out_file = op.abspath('{}_gradmask{}'.format(fname, ext)) imnii = nb.load(in_file) hdr = imnii.get_header().copy() hdr.set_data_dtype(np.uint8) # pylint: disable=no-member data = imnii.get_data().astype(np.float32) mask = np.zeros_like(data, dtype=np.uint8) # pylint: disable=no-member mask[data > 15.] = 1 segdata = nb.load(in_segm).get_data().astype(np.uint8) segdata[segdata > 0] = 1 segdata = sim.binary_dilation(segdata, struc, iterations=2, border_value=1).astype(np.uint8) # pylint: disable=no-member mask[segdata > 0] = 1 mask = sim.binary_closing(mask, struc, iterations=2).astype(np.uint8) # pylint: disable=no-member # Remove small objects label_im, nb_labels = sim.label(mask) artmsk = np.zeros_like(mask) if nb_labels > 2: sizes = sim.sum(mask, label_im, list(range(nb_labels + 1))) ordered = list(reversed(sorted(zip(sizes, list(range(nb_labels + 1)))))) for _, label in ordered[2:]: mask[label_im == label] = 0 artmsk[label_im == label] = 1 mask = sim.binary_fill_holes(mask, struc).astype(np.uint8) # pylint: disable=no-member nb.Nifti1Image(mask, imnii.get_affine(), hdr).to_filename(out_file) return out_file
