/
ccd_back_n_filter.py
executable file
·38 lines (33 loc) · 1.6 KB
/
ccd_back_n_filter.py
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#!/usr/bin/env python2.7
import numpy as np
import scipy
from scipy import ndimage
from skimage.restoration import denoise_tv_chambolle, denoise_bilateral
def BF_proc(newimg, backimg, FilterSts, FilterOrder, FilterSelect, back_sbs):
if back_sbs == 2 and FilterSts == 2:
if FilterSelect == 'Median':
ImageFinal = ndimage.median_filter(np.subtract(newimg, backimg), FilterOrder)
elif FilterSelect == 'Gaussian':
ImageFinal = ndimage.gaussian_filter(np.subtract(newimg, backimg), FilterOrder)
elif FilterSelect == 'Uniform':
ImageFinal = ndimage.uniform_filter(np.subtract(newimg, backimg), FilterOrder)
elif FilterSelect == 'Denoise TV':
ImageFinal = denoise_tv_chambolle(np.subtract(newimg, backimg), weight=FilterOrder, multichannel=True)
elif FilterSelect == 'Bilateral':
ImageFinal = denoise_bilateral(np.subtract(newimg, backimg), sigma_range=FilterOrder, sigma_spatial=15)
elif back_sbs == 2 and FilterSts == 0:
ImageFinal = np.subtract(newimg, backimg)
elif back_sbs == 0 and FilterSts == 2:
if FilterSelect == 'Median':
ImageFinal = ndimage.median_filter(newimg, FilterOrder)
elif FilterSelect == 'Gaussian':
ImageFinal = ndimage.gaussian_filter(newimg, FilterOrder)
elif FilterSelect == 'Uniform':
ImageFinal = ndimage.uniform_filter(newimg, FilterOrder)
elif FilterSelect == 'Denoise TV':
ImageFinal = denoise_tv_chambolle(newimg, weight=FilterOrder, multichannel=True)
elif FilterSelect == 'Bilateral':
ImageFinal = denoise_bilateral(newimg, sigma_range=FilterOrder, sigma_spatial=15)
elif back_sbs == 0 and FilterSts == 0:
ImageFinal = newimg
return ImageFinal