Source code for tomopy.misc.corr

#!/usr/bin/env python
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"""
Module for data correction and masking functions.
"""

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import numpy as np
from scipy.ndimage import filters
import tomopy.util.mproc as mproc
import tomopy.util.dtype as dtype
import tomopy.util.extern as extern
import logging
import warnings
import numexpr as ne
import concurrent.futures as cf
from scipy.signal import medfilt2d

logger = logging.getLogger(__name__)


__author__ = "Doga Gursoy"
__credits__ = "Mark Rivers, Xianghui Xiao"
__copyright__ = "Copyright (c) 2015, UChicago Argonne, LLC."
__docformat__ = 'restructuredtext en'
__all__ = ['adjust_range',
           'circ_mask',
           'gaussian_filter',
           'median_filter',
           'median_filter_cuda',
           'sobel_filter',
           'remove_nan',
           'remove_neg',
           'remove_outlier',
           'remove_outlier1d',
           'remove_outlier_cuda',
           'remove_ring',
           'enhance_projs_aps_1id',
           ]


[docs]def adjust_range(arr, dmin=None, dmax=None): """ Change dynamic range of values in an array. Parameters ---------- arr : ndarray Input array. dmin, dmax : float, optional Mininum and maximum values to rescale data. Returns ------- ndarray Output array. """ if dmax is None: dmax = np.max(arr) if dmin is None: dmin = np.min(arr) if dmax < np.max(arr): arr[arr > dmax] = dmax if dmin > np.min(arr): arr[arr < dmin] = dmin return arr
[docs]def gaussian_filter(arr, sigma=3, order=0, axis=0, ncore=None): """ Apply Gaussian filter to 3D array along specified axis. Parameters ---------- arr : ndarray Input array. sigma : scalar or sequence of scalars Standard deviation for Gaussian kernel. The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes. order : {0, 1, 2, 3} or sequence from same set, optional Order of the filter along each axis is given as a sequence of integers, or as a single number. An order of 0 corresponds to convolution with a Gaussian kernel. An order of 1, 2, or 3 corresponds to convolution with the first, second or third derivatives of a Gaussian. Higher order derivatives are not implemented axis : int, optional Axis along which median filtering is performed. ncore : int, optional Number of cores that will be assigned to jobs. Returns ------- ndarray 3D array of same shape as input. """ arr = dtype.as_float32(arr) out = np.empty_like(arr) if ncore is None: ncore = mproc.mp.cpu_count() with cf.ThreadPoolExecutor(ncore) as e: slc = [slice(None)]*arr.ndim for i in range(arr.shape[axis]): slc[axis] = i e.submit(filters.gaussian_filter, arr[tuple(slc)], sigma, order=order, output=out[tuple(slc)]) return out
[docs]def median_filter(arr, size=3, axis=0, ncore=None): """ Apply median filter to 3D array along specified axis. Parameters ---------- arr : ndarray Input array. size : int, optional The size of the filter. axis : int, optional Axis along which median filtering is performed. ncore : int, optional Number of cores that will be assigned to jobs. Returns ------- ndarray Median filtered 3D array. """ arr = dtype.as_float32(arr) out = np.empty_like(arr) if ncore is None: ncore = mproc.mp.cpu_count() with cf.ThreadPoolExecutor(ncore) as e: slc = [slice(None)]*arr.ndim for i in range(arr.shape[axis]): slc[axis] = i e.submit(filters.median_filter, arr[tuple(slc)], size=(size, size), output=out[tuple(slc)]) return out
[docs]def median_filter_cuda(arr, size=3, axis=0): """ Apply median filter to 3D array along 0 axis with GPU support. The winAllow is for A6000, Tian X support 3 to 8 Parameters ---------- arr : ndarray Input array. size : int, optional The size of the filter. axis : int, optional Axis along which median filtering is performed. Returns ------- ndarray Median filtered 3D array. Example ------- import tomocuda tomocuda.remove_outlier_cuda(arr, dif, 5) For more information regarding install and using tomocuda, check https://github.com/kyuepublic/tomocuda for more information """ try: import tomocuda winAllow = range(2, 16) if(axis != 0): arr = np.swapaxes(arr, 0, axis) if size in winAllow: loffset = int(size/2) roffset = int((size-1)/2) prjsize = arr.shape[0] imsizex = arr.shape[2] imsizey = arr.shape[1] filter = tomocuda.mFilter(imsizex, imsizey, prjsize, size) out = np.zeros( shape=(prjsize, imsizey, imsizex), dtype=np.float32) for step in range(prjsize): # im_noisecu = arr[:][step][:].astype(np.float32) im_noisecu = arr[step].astype(np.float32) im_noisecu = np.lib.pad(im_noisecu, ((loffset, roffset), (loffset, roffset)), 'symmetric') im_noisecu = im_noisecu.flatten() filter.setCuImage(im_noisecu) filter.run2DFilter(size) results = filter.retreive() results = results.reshape(imsizey, imsizex) out[step] = results if(axis != 0): out = np.swapaxes(out, 0, axis) else: warnings.warn("Window size not support, using cpu median filter") out = median_filter(arr, size, axis) except ImportError: warnings.warn("The tomocuda is not support, using cpu median filter") out = median_filter(arr, size, axis) return out
[docs]def sobel_filter(arr, axis=0, ncore=None): """ Apply Sobel filter to 3D array along specified axis. Parameters ---------- arr : ndarray Input array. axis : int, optional Axis along which sobel filtering is performed. ncore : int, optional Number of cores that will be assigned to jobs. Returns ------- ndarray 3D array of same shape as input. """ arr = dtype.as_float32(arr) out = np.empty_like(arr) if ncore is None: ncore = mproc.mp.cpu_count() with cf.ThreadPoolExecutor(ncore) as e: slc = [slice(None)]*arr.ndim for i in range(arr.shape[axis]): slc[axis] = i e.submit(filters.sobel, arr[slc], output=out[slc]) return out
[docs]def remove_nan(arr, val=0., ncore=None): """ Replace NaN values in array with a given value. Parameters ---------- arr : ndarray Input array. val : float, optional Values to be replaced with NaN values in array. ncore : int, optional Number of cores that will be assigned to jobs. Returns ------- ndarray Corrected array. """ arr = dtype.as_float32(arr) val = np.float32(val) with mproc.set_numexpr_threads(ncore): ne.evaluate('where(arr!=arr, val, arr)', out=arr) return arr
[docs]def remove_neg(arr, val=0., ncore=None): """ Replace negative values in array with a given value. Parameters ---------- arr : ndarray Input array. val : float, optional Values to be replaced with negative values in array. ncore : int, optional Number of cores that will be assigned to jobs. Returns ------- ndarray Corrected array. """ arr = dtype.as_float32(arr) val = np.float32(val) with mproc.set_numexpr_threads(ncore): ne.evaluate('where(arr<0, val, arr)', out=arr) return arr
[docs]def remove_outlier(arr, dif, size=3, axis=0, ncore=None, out=None): """ Remove high intensity bright spots from a N-dimensional array by chunking along the specified dimension, and performing (N-1)-dimensional median filtering along the other dimensions. Parameters ---------- arr : ndarray Input array. dif : float Expected difference value between outlier value and the median value of the array. size : int Size of the median filter. axis : int, optional Axis along which to chunk. ncore : int, optional Number of cores that will be assigned to jobs. out : ndarray, optional Output array for result. If same as arr, process will be done in-place. Returns ------- ndarray Corrected array. """ arr = dtype.as_float32(arr) dif = np.float32(dif) tmp = np.empty_like(arr) ncore, chnk_slices = mproc.get_ncore_slices(arr.shape[axis], ncore=ncore) filt_size = [size]*arr.ndim filt_size[axis] = 1 with cf.ThreadPoolExecutor(ncore) as e: slc = [slice(None)]*arr.ndim for i in range(ncore): slc[axis] = chnk_slices[i] e.submit(filters.median_filter, arr[tuple(slc)], size=filt_size, output=tmp[tuple(slc)]) with mproc.set_numexpr_threads(ncore): out = ne.evaluate('where(arr-tmp>=dif,tmp,arr)', out=out) return out
[docs]def remove_outlier1d(arr, dif, size=3, axis=0, ncore=None, out=None): """ Remove high intensity bright spots from an array, using a one-dimensional median filter along the specified axis. Parameters ---------- arr : ndarray Input array. dif : float Expected difference value between outlier value and the median value of the array. size : int Size of the median filter. axis : int, optional Axis along which median filtering is performed. ncore : int, optional Number of cores that will be assigned to jobs. out : ndarray, optional Output array for result. If same as arr, process will be done in-place. Returns ------- ndarray Corrected array. """ arr = dtype.as_float32(arr) dif = np.float32(dif) tmp = np.empty_like(arr) other_axes = [i for i in range(arr.ndim) if i != axis] largest = np.argmax([arr.shape[i] for i in other_axes]) lar_axis = other_axes[largest] ncore, chnk_slices = mproc.get_ncore_slices( arr.shape[lar_axis], ncore=ncore) filt_size = [1]*arr.ndim filt_size[axis] = size with cf.ThreadPoolExecutor(ncore) as e: slc = [slice(None)]*arr.ndim for i in range(ncore): slc[lar_axis] = chnk_slices[i] e.submit(filters.median_filter, arr[slc], size=filt_size, output=tmp[slc], mode='mirror') with mproc.set_numexpr_threads(ncore): out = ne.evaluate('where(arr-tmp>=dif,tmp,arr)', out=out) return out
[docs]def remove_outlier_cuda(arr, dif, size=3, axis=0): """ Remove high intensity bright spots from a 3D array along axis 0 dimension using GPU. Parameters ---------- arr : ndarray Input array. dif : float Expected difference value between outlier value and the median value of the array. size : int Size of the median filter. axis : int, optional Axis along which outlier removal is performed. Returns ------- ndarray Corrected array. Example ------- >>> import tomocuda >>> tomocuda.remove_outlier_cuda(arr, dif, 5) For more information regarding install and using tomocuda, check https://github.com/kyuepublic/tomocuda for more information """ arr = dtype.as_float32(arr) dif = np.float32(dif) try: import tomocuda winAllow = range(2, 16) if(axis != 0): arr = np.swapaxes(arr, 0, axis) if size in winAllow: prjsize = arr.shape[0] loffset = int(size/2) roffset = int((size-1)/2) imsizex = arr.shape[2] imsizey = arr.shape[1] filter = tomocuda.mFilter(imsizex, imsizey, prjsize, size) out = np.zeros(shape=(prjsize, imsizey, imsizex), dtype=np.float32) for step in range(prjsize): im_noisecu = arr[step].astype(np.float32) im_noisecu = np.lib.pad(im_noisecu, ((loffset, roffset), (loffset, roffset)), 'symmetric') im_noisecu = im_noisecu.flatten() filter.setCuImage(im_noisecu) filter.run2DRemoveOutliner(size, dif) results = filter.retreive() results = results.reshape(imsizey, imsizex) out[step] = results if(axis != 0): out = np.swapaxes(out, 0, axis) else: warnings.warn("Window size not support, using cpu outlier removal") out = remove_outlier(arr, dif, size) except ImportError: warnings.warn("The tomocuda is not support, using cpu outlier removal") out = remove_outlier(arr, dif, size) return out
[docs]def remove_ring(rec, center_x=None, center_y=None, thresh=300.0, thresh_max=300.0, thresh_min=-100.0, theta_min=30, rwidth=30, int_mode='WRAP', ncore=None, nchunk=None, out=None): """ Remove ring artifacts from images in the reconstructed domain. Descriptions of parameters need to be more clear for sure. Parameters ---------- arr : ndarray Array of reconstruction data center_x : float, optional abscissa location of center of rotation center_y : float, optional ordinate location of center of rotation thresh : float, optional maximum value of an offset due to a ring artifact thresh_max : float, optional max value for portion of image to filter thresh_min : float, optional min value for portion of image to filer theta_min : int, optional minimum angle in degrees (int) to be considered ring artifact rwidth : int, optional Maximum width of the rings to be filtered in pixels int_mode : str, optional 'WRAP' for wrapping at 0 and 360 degrees, 'REFLECT' for reflective boundaries at 0 and 180 degrees. ncore : int, optional Number of cores that will be assigned to jobs. nchunk : int, optional Chunk size for each core. out : ndarray, optional Output array for result. If same as arr, process will be done in-place. Returns ------- ndarray Corrected reconstruction data """ rec = dtype.as_float32(rec) if out is None: out = rec.copy() else: out = dtype.as_float32(out) dz, dy, dx = rec.shape if center_x is None: center_x = (dx - 1.0)/2.0 if center_y is None: center_y = (dy - 1.0)/2.0 if int_mode.lower() == 'wrap': int_mode = 0 elif int_mode.lower() == 'reflect': int_mode = 1 else: raise ValueError("int_mode should be WRAP or REFLECT") args = (center_x, center_y, dx, dy, dz, thresh_max, thresh_min, thresh, theta_min, rwidth, int_mode) axis_size = rec.shape[0] ncore, nchunk = mproc.get_ncore_nchunk(axis_size, ncore, nchunk) with cf.ThreadPoolExecutor(ncore) as e: for offset in range(0, axis_size, nchunk): slc = np.s_[offset:offset+nchunk] e.submit(extern.c_remove_ring, out[slc], *args) return out
[docs]def circ_mask(arr, axis, ratio=1, val=0., ncore=None): """ Apply circular mask to a 3D array. Parameters ---------- arr : ndarray Arbitrary 3D array. axis : int Axis along which mask will be performed. ratio : int, optional Ratio of the mask's diameter in pixels to the smallest edge size along given axis. val : int, optional Value for the masked region. Returns ------- ndarray Masked array. """ arr = dtype.as_float32(arr) val = np.float32(val) _arr = arr.swapaxes(0, axis) dx, dy, dz = _arr.shape mask = _get_mask(dy, dz, ratio) with mproc.set_numexpr_threads(ncore): ne.evaluate('where(mask, _arr, val)', out=_arr) return _arr.swapaxes(0, axis)
def _get_mask(dx, dy, ratio): """ Calculate 2D boolean circular mask. Parameters ---------- dx, dy : int Dimensions of the 2D mask. ratio : int Ratio of the circle's diameter in pixels to the smallest mask dimension. Returns ------- ndarray 2D boolean array. """ rad1 = dx / 2. rad2 = dy / 2. if dx < dy: r2 = rad1 * rad1 else: r2 = rad2 * rad2 y, x = np.ogrid[0.5 - rad1:0.5 + rad1, 0.5 - rad2:0.5 + rad2] return x * x + y * y < ratio * ratio * r2
[docs]def enhance_projs_aps_1id(imgstack, median_ks=5, ncore=None): """ Enhance the projection images with weak contrast collected at APS 1ID This filter uses a median fileter (will be switched to enhanced recursive median fileter, ERMF, in the future) for denoising, and a histogram equalization for dynamic range adjustment to bring out the details. Parameters ---------- imgstack : np.ndarray tomopy images stacks (axis_0 is the oemga direction) median_ks : int, optional 2D median filter kernel size for local noise suppresion ncore : int, optional number of cores used for speed up Returns ------- ndarray 3D enhanced image stacks. """ ncore = mproc.mp.cpu_count()-1 if ncore is None else ncore # need to use multiprocessing to speed up the process tmp = [] with cf.ProcessPoolExecutor(ncore) as e: for n_img in range(imgstack.shape[0]): tmp.append(e.submit(_enhance_img, imgstack[n_img, :, :], median_ks, ) ) return np.stack([me.result() for me in tmp], axis=0)
def _enhance_img(img, median_ks, normalized=True): """ Enhance the projection image from aps 1ID to counter its weak contrast nature Parameters ---------- img : ndarray original projection image collected at APS 1ID median_ks: int kernel size of the 2D median filter, must be odd normalized: bool, optional specify whether the enhanced image is normalized between 0 and 1, default is True Returns ------- ndarray enhanced projection image """ wgt = _calc_histequal_wgt(img) img = medfilt2d(img, kernel_size=median_ks).astype(np.float64) img = ne.evaluate('(img**2)*wgt', out=img) return img/img.max() if normalized else img def _calc_histequal_wgt(img): """ Calculate the histogram equalization weight for a given image Parameters ---------- img : ndarray 2D images Returns ------- ndarray histogram euqalization weights (0-1) in the same shape as original image """ return (np.sort(img.flatten()).searchsorted(img) + 1)/np.prod(img.shape)