我们从Python开源项目中，提取了以下14个代码示例，用于说明如何使用scipy.ndimage.binary_opening()。
def opening(self, structure, iterations=1): """ This function ... :param structure: :param iterations: :return: """ data = ndimage.binary_opening(self, structure, iterations) # Return the new mask #data, name=None, description=None return Mask(data, name=self.name, description=self.description) # -----------------------------------------------------------------
def _prepare_mask(mask, label, erode=True): fgmask = mask.copy() if np.issubdtype(fgmask.dtype, np.integer): if isinstance(label, string_types): label = FSL_FAST_LABELS[label] fgmask[fgmask != label] = 0 fgmask[fgmask == label] = 1 else: fgmask[fgmask > .95] = 1. fgmask[fgmask < 1.] = 0 if erode: # Create a structural element to be used in an opening operation. struc = nd.generate_binary_structure(3, 2) # Perform an opening operation on the background data. fgmask = nd.binary_opening(fgmask, structure=struc).astype(np.uint8) return fgmask
def get_centers_of_mass_from_blobs(segmentation_layer, iterations=3): """ Determine the centers of each object in an image ::param segmentation_layer: NxM ndarray image mask of all target objects ::param iterations: threshold for removal of small non-target objects ::return centers_of_mass: a np ndarray of x,y coordinates for the center of each target object """ segmentation_layer = ndimage.binary_opening(segmentation_layer, iterations=iterations) # remove small objects labels, label_number = ndimage.measurements.label(segmentation_layer) # label remaining blobs centers_of_mass = np.zeros((label_number, 2)) for i in range(label_number): idx = np.where(labels == i+1) # calculate the center of mass for each blob centers_of_mass[i, 1] = np.mean(idx[1].astype(float)) # must be float centers_of_mass[i, 0] = np.mean(idx[0].astype(float)) # must be float return centers_of_mass
def morphological_opening_background(x): # Compute an opening, which is the combination of an erosion and a dilation (morphological operators). for i in range(x.shape[0]): background_predictions = x[i, BACKGROUND_CLASS, :, :, :] binarized_predictions = np.array(background_predictions > 0, dtype=np.int32) opened_predictions = binary_opening(binarized_predictions, np.ones((MORPHOLOGICAL_STRUCTURE_SIZE,) * 3)) x[i, BACKGROUND_CLASS, :, :, :] *= opened_predictions return x
def granulometry(data, sizes=None): s = max(data.shape) if sizes == None: sizes = range(1, s/2, 2) granulo = [ndimage.binary_opening(data, \ structure=disk_structure(n)).sum() for n in sizes] return granulo
def mask_binary(imageHDU,LowestContour,selem): map = imageHDU[0].data mask = binary_opening(map > LowestContour, selem) MaskedMap = mask*map imageHDU[0].data = MaskedMap return imageHDU, mask
def _run_interface(self, runtime): in_file = nb.load(self.inputs.in_file) data = in_file.get_data() mask = np.zeros_like(data, dtype=np.uint8) mask[data <= 0] = 1 # Pad one pixel to control behavior on borders of binary_opening mask = np.pad(mask, pad_width=(1,), mode='constant', constant_values=1) # Remove noise struc = nd.generate_binary_structure(3, 2) mask = nd.binary_opening(mask, structure=struc).astype( np.uint8) # Remove small objects label_im, nb_labels = nd.label(mask) if nb_labels > 2: sizes = nd.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 # Un-pad mask = mask[1:-1, 1:-1, 1:-1] # If mask is small, clean-up if mask.sum() < 500: mask = np.zeros_like(mask, dtype=np.uint8) out_img = in_file.__class__(mask, in_file.affine, in_file.header) out_img.header.set_data_dtype(np.uint8) out_file = fname_presuffix(self.inputs.in_file, suffix='_rotmask', newpath='.') out_img.to_filename(out_file) self._results['out_file'] = out_file return runtime
def artifact_mask(imdata, airdata, distance, zscore=10.): """Computes a mask of artifacts found in the air region""" from statsmodels.robust.scale import mad if not np.issubdtype(airdata.dtype, np.integer): airdata[airdata < .95] = 0 airdata[airdata > 0.] = 1 bg_img = imdata * airdata if np.sum((bg_img > 0).astype(np.uint8)) < 100: return np.zeros_like(airdata) # Find the background threshold (the most frequently occurring value # excluding 0) bg_location = np.median(bg_img[bg_img > 0]) bg_spread = mad(bg_img[bg_img > 0]) bg_img[bg_img > 0] -= bg_location bg_img[bg_img > 0] /= bg_spread # Apply this threshold to the background voxels to identify voxels # contributing artifacts. qi1_img = np.zeros_like(bg_img) qi1_img[bg_img > zscore] = 1 qi1_img[distance < .10] = 0 # Create a structural element to be used in an opening operation. struc = nd.generate_binary_structure(3, 1) qi1_img = nd.binary_opening(qi1_img, struc).astype(np.uint8) qi1_img[airdata <= 0] = 0 return qi1_img
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 run(self, ips, snap, img, para = None): strc = np.ones((para['h'], para['w']), dtype=np.uint8) ndimg.binary_opening(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_opening(imgs, strc) imgs *= 255
def plot_dust_overlay(nh3_image_fits,h2_image_fits,region,plot_param,v_min,v_max,maskLim,obsMaskFits): text_size = 14 b18_text_size = 20 # Contour parameters (currently NH3 moment 0) cont_color='black' cont_lw = 0.6 cont_levs=2**np.arange( 0,20)*plot_param['w11_step'] # Masking of small (noisy) regions selem = np.array([[0,1,0],[1,1,1],[0,1,0]]) LowestContour = cont_levs[0]*0.5 w11_hdu = fits.open(nh3_image_fits) map = w11_hdu[0].data mask = binary_opening(map > LowestContour, selem) MaskedMap = mask*map w11_hdu[0].data = MaskedMap # Labels if region == 'B18': label_colour = 'white' text_size = b18_text_size else: label_colour = 'white' fig=aplpy.FITSFigure(h2_image_fits,figsize=(plot_param['size_x'], plot_param['size_y'])) fig.show_colorscale(cmap='hot',vmin=v_min,vmax=v_max,stretch='log',vmid=v_min-v_min*plot_param['vmid_scale']) fig.set_nan_color('0.95') # Observations mask contour fig.show_contour(obsMaskFits,colors='white',levels=1,linewidths=1.5) # NH3 moment contours fig.show_contour(w11_hdu,colors=cont_color,levels=cont_levs,linewidths=cont_lw) fig.axis_labels.set_font(family='sans_serif',size=text_size) # Ticks fig.tick_labels.set_font(family='sans_serif',size=text_size) fig.ticks.set_color('white') fig.tick_labels.set_xformat('hh:mm:ss') fig.tick_labels.set_style('colons') fig.tick_labels.set_yformat('dd:mm') # Scale bar # magic line of code to obtain scale in arcsec obtained from # http://www.astropy.org/astropy-tutorials/Quantities.html ang_sep = (plot_param['scalebar_size'].to(u.au)/plot_param['distance']).to(u.arcsec, equivalencies=u.dimensionless_angles()) fig.add_scalebar(ang_sep.to(u.degree)) fig.scalebar.set_font(family='sans_serif',size=text_size) fig.scalebar.set_corner(plot_param['scalebar_pos']) fig.scalebar.set(color='white') fig.scalebar.set_label('{0:4.2f}'.format(plot_param['scalebar_size'])) fig.add_label(plot_param['label_xpos'], plot_param['label_ypos'], '{0}\n{1}'.format(region,'500 $\mu$m'), relative=True, color=label_colour, horizontalalignment=plot_param['label_align'], family='sans_serif',size=text_size) fig.save( 'figures/{0}_continuum_image.pdf'.format(region),adjust_bbox=True,dpi=200)#, bbox_inches='tight') fig.close()
def plot_abundance(nh3_cont_fits,nh3_col_hdu,h2_col_hdu,region,plot_pars,maskLim,obsMaskFits): text_size = 14 b18_text_size = 20 if region == 'B18': text_size = b18_text_size # Get protostellar locations ra_prot, de_prot = get_prot_loc(region) # Contour parameters (currently NH3 moment 0) cont_color='0.6' cont_lw = 0.6 cont_levs=2**np.arange( 0,20)*plot_param['w11_step'] # Calculate abundance log_xnh3 = nh3_col_hdu[0].data - np.log10(h2_col_hdu.data) log_xnh3_hdu = fits.PrimaryHDU(log_xnh3,nh3_col_hdu[0].header) log_xnh3_hdu.writeto('../testing/{0}/parameterMaps/{0}_XNH3_{1}.fits'.format(region,file_extension),clobber=True) fig=aplpy.FITSFigure(log_xnh3_hdu,figsize=(plot_param['size_x'], plot_param['size_y'])) fig.show_colorscale(cmap='YlOrRd_r',vmin=plot_param['xnh3_lim'][0],vmax=plot_param['xnh3_lim'][1]) #fig.set_nan_color('0.95') # Observations mask contour fig.show_contour(obsMaskFits,colors='white',levels=1,linewidths=1.5) # NH3 moment contours # Masking of small (noisy) regions selem = np.array([[0,1,0],[1,1,1],[0,1,0]]) LowestContour = cont_levs[0]*0.5 w11_hdu = fits.open(nh3_cont_fits) map = w11_hdu[0].data mask = binary_opening(map > LowestContour, selem) MaskedMap = mask*map w11_hdu[0].data = MaskedMap fig.show_contour(w11_hdu,colors=cont_color,levels=cont_levs,linewidths=cont_lw) # Ticks fig.ticks.set_color('black') fig.tick_labels.set_font(family='sans_serif',size=text_size) fig.tick_labels.set_xformat('hh:mm:ss') fig.tick_labels.set_style('colons') fig.tick_labels.set_yformat('dd:mm') # Scale bar ang_sep = (plot_param['scalebar_size'].to(u.au)/plot_param['distance']).to(u.arcsec, equivalencies=u.dimensionless_angles()) fig.add_colorbar() fig.colorbar.show(box_orientation='horizontal', width=0.1, pad=0.0, location='top', ticks=[-10,-9.5,-9,-8.5,-8,-7.5,-7,-6.5]) fig.colorbar.set_font(family='sans_serif',size=text_size) fig.add_scalebar(ang_sep.to(u.degree)) fig.scalebar.set_font(family='sans_serif',size=text_size) fig.scalebar.set_corner(plot_param['scalebar_pos']) fig.scalebar.set(color='black') fig.scalebar.set_label('{0:4.2f}'.format(plot_param['scalebar_size'])) label_colour = 'black' fig.add_label(plot_param['label_xpos'], plot_param['label_ypos'], '{0}\n{1}'.format(region,r'$\mathrm{log} \ X(\mathrm{NH}_3)$'), relative=True, color=label_colour, horizontalalignment=plot_param['label_align'], family='sans_serif',size=text_size) fig.save( 'figures/{0}_xnh3_image.pdf'.format(region),adjust_bbox=True,dpi=200)#, bbox_inches='tight') # Add protostars fig.show_markers(ra_prot,de_prot,marker='*',s=50, c='white',edgecolors='black',linewidth=0.5,zorder=4) fig.save( 'figures/{0}_xnh3_image_prot.pdf'.format(region),adjust_bbox=True,dpi=200) fig.close()
