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crmask.py 31.40 KiB
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#!/usr/bin/env python3
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##############################################################
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#Author  Hu Yi (NAOC, huyi.naoc@gmail.com), Zhang Yajie (TJU)#
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##############################################################
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#Thanks to Zhang Keming who is the author of deepCR.
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#Thanks to the authors of astropy and ccdproc.
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#Software package dependencies, numpy, astropy, ccdproc and deepCR
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#Installation dependencies on Ubuntu 20.04
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#apt install python3-numpy python3-astropy python3-ccdproc python3-pip
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#python3 -m pip install pytorch deepCR
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#Version 0.2
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#changelog
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#V0.2 training method are developed and test.
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import configparser
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import numpy as np
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from astropy.io import fits as pyfits
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from astropy import units as u
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from astropy.nddata import CCDData
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from pathlib import Path
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import ccdproc
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import datetime
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import sys
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__all__ = ['CRMask']
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class CRMask:
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    def __init__(self, obj, flag = None, mask = None, clean = None, sky = None, save_flag = True, update_flag = True, save_name = None, flag_suffix = 'flg', clean_suffix = 'crclean', append_flag = False, mask_suffix = 'crmask', model = 'deepCR', fill_flag = True, fill_method = 'inpainting', gpu_flag = False, config_path = 'crmask.ini', **kwargs):
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        """
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            Instantiation of CRMask with specified model configuration.
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            Parameters
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            ----------
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            obj : string, Path, astropy.io.fits.HDUList, numpy.ndarray, astropy.nddata.CCDData or list of string
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                if model is ``deepCR``, ``lacosmic``, obj is input image to be cosmic-ray masked
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                if model is ``deepCR_train``, obj is input training and validating images
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            flag : (optional) string, Path, astropy.io.fits.HDUList, numpy.ndarray or astropy.nddata.CCDData
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                flag image(s), default is None
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            mask : (optional) string or list of string
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                mask image(s), default is None, necessary when model is ``deepCR_train`` or ``benchmark``, otherwise, let it alone
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            sky : (optional) string or list of string
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                sky image(s), default is None, optional when model is ``deepCR_train``, otherwise, let it alone
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            save_flag : (optional) boolean
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                whether save CR mask (and cleaned) image, default is True
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            update_flag : (optional) boolean
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                whether update flag image, default is True
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            save_name : (optional) string
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                output mask, cleaned and flag filename. default is None. If save_name is None, use the filename as the input. And if save_flag is True, save_name is None, and obj is a numpy.ndarray or a astropy.nddata.CCDData, cr_mask will raise a ValueError exception
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            flag_suffix : (optional) string
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                suffix name of flag file, if flag is a numpy.ndarray or a astropy.nddata.CCDData, default is ``flg``
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            mask_suffix : (optional) string
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                suffix name of mask file, default is ``crmask``
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            clean_suffix : (optional) string
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                suffix name of cleaned file, default is ``crclean``
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            model : (optional) string
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                model type, can be ``deepCR``, ``lacosmic``, ``deepCR_train`` or ``benchmark`` default is ``deepCR``
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            fill_flag : (optional) boolean
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                whehter generate cleaned image, default is True
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            fill_method : (optional) string
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                fill method for CR contaminated pixel, can be ``inpainting``, ``meanmask``, ``meanmed``, default is ``inpainting``
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            gpu_flag : (optional) boolean
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                whether use GPU, default is False
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            config_path : (optional) string
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                configuration file path, default is ``./crmask.ini``
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        """
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self.model = model if model == 'deepCR_train': self.image_sets = obj self.mask_sets = mask self.ignore_sets = flag self.sky_sets = sky self.gpu_flag = gpu_flag self.clean_sets = clean if config_path != None: config = configparser.ConfigParser() config.read(config_path) if config.has_option('global', 'gpu_flag'): self.append_flag = config.getboolean('global', 'gpu_flag') self.config = config return if model == 'benchmark': self.res = obj self.ref = mask return if isinstance(obj, str) or isinstance(obj, Path): self.hdulist = pyfits.open(obj) elif isinstance(obj, pyfits.HDUList): self.hdulist = obj elif isinstance(obj, np.ndarray) or isinstance(obj, CCDData): hdulist = pyfits.HDUList() primary = pyfits.PrimaryHDU() header = primary.header header['NEXTEND'] = 1 hdulist.append(primary) image = pyfits.ImageHDU() header = image.header header['BITPIX'] = -32 header['NAXIS'] = 2 header.insert('NAXIS', ('NAXIS1', obj.shape[0]), after = True) header.insert('NAXIS1', ('NAXIS2', obj.shape[1]), after = True) header['EXTNAME'] = 'SCI' header['EXTVER'] = 1 if isinstance(obj, CCDData): image.data = np.asarray(CCDData) else: image.data = obj hdulist.append(image) self.hdulist = hdulist else: raise TypeError('For cosmic ray masking and cleaning, obj must be a string, or a pathlib.Path object, or a astropy.io.fits.HDUList object!') if flag != None: if isinstance(flag, str) or isinstance(flag, Path): flag_hdulist = pyfits.open(flag, mode = 'update') elif isinstance(flag, pyfits.HDUList): flag_hdulist = flag elif isinstance(flag, np.ndarray) or isinstance(flag, CCDData): flag_hdulist = pyfits.HDUList() flag_primary = pyfits.PrimaryHDU(header = self.hdulist[0].header) flag_hdulist.append(flag_hdulist) if isinstance(obj, CCDData): flag_data = np.asarray(CCDData) flag_image = pyfits.ImageHDU(data=flag_data, header=self.hdulist[1].header) else: flag_image = pyfits.ImageHDU(data=flag, header=self.hdulist[1].header) flag_hdulist.append(flag_image) else: raise TypeError('For cosmic ray masking and cleaning, mask must be a string, or a pathlib.Path object, or a astropy.io.fits.HDUList object!') self.flag = flag_hdulist else: self.flag = None self.update_flag = update_flag self.save_flag = save_flag
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self.save_name = save_name self.flag_suffix = flag_suffix self.clean_suffix = clean_suffix self.append_flag = append_flag self.mask_suffix = mask_suffix self.gpu_flag = gpu_flag self.fill_method = fill_method self.model = model if config_path != None: config = configparser.ConfigParser() config.read(config_path) if config.has_option('global', 'save_flag'): self.save_flag = config.getboolean('global', 'save_flag') if config.has_option('global', 'clean_suffix'): self.clean_suffix = config.get('global', 'clean_suffix') if config.has_option('global', 'append_flag'): self.append_flag = config.getboolean('global', 'append_flag') if config.has_option('global', 'flag_suffix'): self.flag_suffix = config.get('global', 'flag_suffix') if config.has_option('global', 'mask_suffix'): self.mask_suffix = config.get('global', 'mask_suffix') if config.has_option('global', 'gpu_flag'): self.append_flag = config.getboolean('global', 'gpu_flag') if config.has_option('global', 'fill_flag'): self.fill_flag = config.getboolean('global', 'fill_flag') if config.has_option('global', 'fill_method'): self.fill_method = config.get('global', 'fill_method') if config.has_option('global', 'model'): self.model = config.get('global', 'model') self.config = config def cr_mask_lacosmic(self): """ This method is called by `cr_mask`, do NOT use it directly. """ config = self.config if config.has_option('lacosmic', 'sigclip'): sigclip = config.getfloat('lacosmic', 'sigclip') else: sigclip = 4.0 if config.has_option('lacosmic', 'sigfrac'): sigfrac = config.getfloat('lacosmic', 'sigfrac') else: sigfrac = 0.3 if config.has_option('lacosmic', 'objlim'): objlim = config.getfloat('lacosmic', 'objlim') else: objlim = 5.0 if config.has_option('lacosmic', 'gain'): gain = config.getfloat('lacosmic', 'gain') else: gain = 1.0 if config.has_option('lacosmic', 'readnoise'):
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readnoise = config.getfloat('lacosmic', 'readnoise') else: readnoise = 6.5 if config.has_option('lacosmic', 'satlevel'): satlevel = config.getfloat('lacosmic', 'satlevel') else: satlevel = 65535.0 if config.has_option('lacosmic', 'pssl'): pssl = config.getfloat('lacosmic', 'pssl') else: pssl = 0.0 if config.has_option('lacosmic', 'niter'): niter = config.getint('lacosmic', 'niter') else: niter = 4 if config.has_option('lacosmic', 'sepmed'): sepmed = config.getboolean('lacosmic', 'sepmed') else: sepmed = True if config.has_option('lacosmic', 'cleantype'): cleantype = config.get('lacosmic', 'cleantype') else: cleantype = self.fill_method if config.has_option('lacosmic', 'fsmode'): fsmode = config.get('lacosmic', 'fsmode') else: fsmode = 'median' if config.has_option('lacosmic', 'psfmodel'): psfmodel = config.get('lacosmic', 'psfmodel') else: psfmodel = 'gauss' if config.has_option('lacosmic', 'psffwhm'): psffwhm = config.getfloat('lacosmic', 'psffwhm') else: psffwhm = 2.5 if config.has_option('lacosmic', 'psfsize'): psfsize = config.getint('lacosmic', 'psfsize') else: psfsize = 7 if config.has_option('lacosmic', 'psfk'): psfk = config.get('lacosmic', 'psfk') else: psfk = None if config.has_option('lacosmic', 'psfbeta'): psfbeta = config.getfloat('lacosmic', 'psfbeta') else: psfbeta = 4.765 if config.has_option('lacosmic', 'gain_apply'): gain_apply = config.getboolean('lacosmic', 'gain_apply') else: gain_apply = True data = self.hdulist[1].data start_time = datetime.datetime.now().strftime("%Y-%m-%dT%H:%M:%S.%f") cleaned, masked = ccdproc.cosmicray_lacosmic(data, sigclip, sigfrac, objlim, gain, readnoise, satlevel, pssl, niter, sepmed, cleantype, fsmode, psfmodel, psffwhm, psfsize, psfk, psfbeta, False, gain_apply) end_time = datetime.datetime.now().strftime("%Y-%m-%dT%H:%M:%S.%f")
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masked_hdulist = pyfits.HDUList() masked_primary = pyfits.PrimaryHDU(header=self.hdulist[0].header) masked_hdulist.append(masked_primary) masked = masked.astype(np.uint8) masked_image = pyfits.ImageHDU(data=masked, header=self.hdulist[1].header) masked_hdulist.append(masked_image) #TODO #add history keywords here if self.flag != None: self.flag[1].data |= (mask<<4) self.flag[1].header.add_history('Use ccdproc.cosimicray_lacosmicfor cosmic ray detecting') value = 'CRMask start at {0}'.format(start_time) self.flag[1].header.add_history(value) value = 'CRMask end at {0}'.format(end_time) self.flag[1].header.add_history(value) masked_hdulist[1].header.add_history('Use ccdproc.cosimicray_lacosmic for cosmic ray detecting') value = 'CRMask start at {0}'.format(start_time) masked_hdulist[1].header.add_history(value) value = 'CRMask end at {0}'.format(end_time) masked_hdulist[1].header.add_history(value) if self.fill_flag: cleaned_hdulist = pyfits.HDUList() cleaned_primary = pyfits.PrimaryHDU(header=self.hdulist[0].header) cleaned_hdulist.append(cleaned_primary) cleaned_image = pyfits.ImageHDU(data=cleaned, header=self.hdulist[1].header) cleaned_hdulist.append(cleaned_image) cleaned_hdulist[1].header.add_history('Use ccdproc.cosimicray_lacosmic for cosmic ray detecting') value = 'CRMask start at {0}'.format(start_time) cleaned_hdulist[1].header.add_history(value) value = 'CRMask end at {0}'.format(end_time) cleaned_hdulist[1].header.add_history(value) return masked_hdulist, cleaned_hdulist else: return masked_hdulist def cr_mask_deepCR(self): """ This method is called by `cr_mask`, do NOT use it directly. """ from deepCR import deepCR config = self.config if config.has_option('deepCR', 'threshold'): threshold = config.getfloat('deepCR', 'threshold') else: threshold = 0.5 if config.has_option('deepCR', 'inpaint'): inpaint = config.getboolean('deepCR', 'inpaint') else: inpaint = True if config.has_option('deepCR', 'binary'): binary = config.getboolean('deepCR', 'binary') else: binary = True if config.has_option('deepCR', 'patch'): patch = config.getint('deepCR', 'patch') else: patch = 256 if config.has_option('deepCR', 'segment'): segment = config.getboolean('deepCR', 'segment') else:
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segment = True if config.has_option('deepCR', 'parallel'): parallel = config.getboolean('deepCR', 'parallel') else: parallel = True if config.has_option('deepCR', 'clean_model'): clean_model = config.get('deepCR', 'clean_model') else: clean_model = 'ACS-WFC-F606W-2-32' if config.has_option('deepCR', 'inpaint_model'): inpaint_model = config.get('deepCR', 'inpaint_model') else: inpaint_model = 'ACS-WFC-F606W-2-32' if config.has_option('deepCR', 'n_jobs'): n_jobs = config.getint('deepCR', 'n_jobs') else: n_jobs = -1 if config.has_option('deepCR', 'hidden'): hidden = config.getint('deepCR', 'hidden') else: hidden = 32 if self.gpu_flag: model = deepCR(clean_model, inpaint_model, device = 'GPU', hidden = hidden) else: model = deepCR(clean_model, inpaint_model, device = 'CPU', hidden = hidden) data = self.hdulist[1].data masked_hdulist = pyfits.HDUList() masked_primary = pyfits.PrimaryHDU(header=self.hdulist[0].header) masked_hdulist.append(masked_primary) if inpaint: cleaned_hdulist = pyfits.HDUList() cleaned_primary = pyfits.PrimaryHDU(header=self.hdulist[0].header) cleaned_hdulist.append(cleaned_primary) start_time = datetime.datetime.now().strftime("%Y-%m-%dT%H:%M:%S.%f") if binary: if inpaint: masked, cleaned = model.clean(data, threshold = threshold, inpaint = inpaint, segment = segment, patch = patch, parallel = parallel, n_jobs = n_jobs) else: masked = model.clean(data, threshold = threshold, inpaint = inpaint, segment = segment, patch = patch, parallel = parallel, n_jobs = n_jobs) else: if inpaint: masked, cleaned = model.clean(data, threshold = threshold, inpaint = inpaint, binary = False, segment = segment, patch = patch, parallel = parallel, n_jobs = n_jobs) else: masked = model.clean(data, threshold = threshold, inpaint = inpaint, binary = False, segment = segment, patch = patch, parallel = parallel, n_jobs = n_jobs) end_time = datetime.datetime.now().strftime("%Y-%m-%dT%H:%M:%S.%f") masked = masked.astype(np.uint8) masked_image = pyfits.ImageHDU(data=masked, header=self.hdulist[1].header) masked_hdulist.append(masked_image) #TODO #add history keywords here if self.flag != None: self.flag[1].data |= (mask<<4) self.flag[1].header.add_history('Use deepCR for cosmic ray detecting') value = 'CRMask start at {0}'.format(start_time) self.flag[1].header.add_history(value) value = 'CRMask end at {0}'.format(end_time) self.flag[1].header.add_history(value) masked_hdulist[1].header.add_history('Use deepCR for cosmic ray detecting') value = 'CRMask start at {0}'.format(start_time)
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masked_hdulist[1].header.add_history(value) value = 'CRMask end at {0}'.format(end_time) masked_hdulist[1].header.add_history(value) value = 'deepCR mask model {0}'.format(clean_model) masked_hdulist[1].header.add_history(value) if inpaint: cleaned_image = pyfits.ImageHDU(data=cleaned, header=self.hdulist[1].header) cleaned_hdulist.append(cleaned_image) cleaned_hdulist[1].header.add_history('Use deepCR for cosmic ray detecting') value = 'CRMask start at {0}'.format(start_time) cleaned_hdulist[1].header.add_history(value) value = 'CRMask end at {0}'.format(end_time) cleaned_hdulist[1].header.add_history(value) value = 'deepCR inpaint model {0}'.format(inpaint_model) cleaned_hdulist[1].header.add_history(value) return masked_hdulist,cleaned_hdulist else: return masked_hdulist def cr_mask(self): """ Cosmic ray detection and mask. Returns ------- masked : numpy.ndarray cosmic ray masked image. cleaned : numpy.ndarray, optional Only returned if `fill_flag` is True cosmic ray cleaned image. Examples ------- >>> from crmask import CRMask >>> crobj = CRMask('xxxx.fits', 'deepCR') >>> crobj.cr_mask() """ if self.model == 'lacosmic': if self.fill_flag: masked, cleaned = CRMask.cr_mask_lacosmic(self) else: masked = CRMask.cr_mask_lacosmic(self) elif self.model == 'deepCR': if self.fill_flag: masked, cleaned = CRMask.cr_mask_deepCR(self) else: masked = CRMask.cr_mask_deepCR(self) else: raise ValueError('Cosmic ray model are not supported!') #save the result to a file. output = None if self.save_flag: if self.save_name != None: output = self.save_name else: output = self.hdulist.filename() #append to the input if self.append_flag: self.hdulist.append(masked[1]) if self.fill_flag: self.hdulist.append(cleaned[1]) if output != None: self.hdulist.writeto(output) else: #TODO #raise an exception. pass
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else: if output != None: if output.split('.')[-1] == 'fits': mask_output = output[0:-4] + self.mask_suffix + '.fits' clean_output = output[0:-4] + self.clean_suffix + '.fits' else: mask_output = output + '.' + self.mask_suffix + '.fits' clean_output = output + '.' + self.clean_suffix + '.fits' masked.writeto(mask_output) if self.fill_flag: cleaned.writeto(clean_output) else: #TODO #raise an exception here pass #update flag file. if self.update_flag and self.flag != None: flag_output = self.flag.filename() if flag_output == None: if output != None: if output.split('.')[-1] == 'fits': flag_output = output[0:-4] + self.flag_suffix + '.fits' else: flag_output = output + '.' + self.flag_suffix + '.fits' else: #raise an exception here. pass self.flag.writeto(flag_output) if self.fill_flag: return (masked, cleaned) else: return masked def cr_train_deepCR_image_to_ndarray(self, image_sets, patch): """ This method is called by `cr_train`, do NOT use it directly. """ if isinstance(image_sets, str): if image_sets[-4:] == '.npy': input_image = np.load(image_sets) elif image_sets[-5:] == '.fits': image = pyfits.open(image_sets) data = image[1].data x, y = data.shape m = x // patch n = y // patch start_x = np.mod(x, patch) // 2 start_y = np.mod(y, patch) // 2 input_image = np.zeros((m * n, patch, patch), dtype = data.dtype) for i in range(m): for j in range(n): input_image[i * m + j] = data[start_x + i * patch:start_x + (i + 1) * patch, start_y + j * patch:start_y + (j + 1) * patch] #input_image = data[start_x:start_x + m * patch, start_y:start_y + m * patch].reshape(m*n,patch,patch) else: #TODO #raise an exception pass if isinstance(image_sets, list): input_list = [] for image_file in image_sets: if isinstance(image_file, str): if image_file[-4:] == '.npy': input_image = np.load(image_file) elif image_file[-5:] == '.fits': image = pyfits.open(image_file) data = image[1].data
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x, y = data.shape m = x // patch n = y // patch start_x = np.mod(x, patch) // 2 start_y = np.mod(y, patch) // 2 input_image = np.zeros((m * n, patch, patch), dtype = data.dtype) for i in range(m): for j in range(n): input_image[i * m + j] = data[start_x + i * patch:start_x + (i + 1) * patch, start_y + j * patch:start_y + (j + 1) * patch] #input_image = data[start_x:start_x + m * patch, start_y:start_y + m * patch].reshape(m*n,patch,patch) input_list.append(input_image) else: #TODO #raise an exception pass else: #TODO #raise an exception pass input_image = np.concatenate(input_list) return input_image def cr_train_deepCR_prepare_data(self, patch): """ This method is called by `cr_train`, do NOT use it directly. """ if self.image_sets != None: self.training_image = CRMask.cr_train_deepCR_image_to_ndarray(self, self.image_sets, patch) np.save('image.npy', self.training_image) else: raise ValueError('training image should not be None') if self.mask_sets != None: self.training_mask = CRMask.cr_train_deepCR_image_to_ndarray(self, self.mask_sets, patch).astype(np.int16) np.save('mask.npy', self.training_mask) else: raise ValueError('mask image should not be None') if self.ignore_sets != None: self.training_ignore = CRMask.cr_train_deepCR_image_to_ndarray(self, self.flag, patch) else: self.training_ignore = None if self.sky_sets != None: self.training_sky = CRMask.cr_train_deepCR_image_to_ndarray(self, self.sky, patch) else: self.training_sky = None def cr_train_deepCR(self): """ This method is called by `cr_train`, do NOT use it directly. """ from deepCR import train config = self.config if config.has_option('deepCR', 'aug_sky'): aug_sky = np.array(config.get('deepCR', 'aug_sky').split()).astype(np.float32) aug_sky0 = aug_sky[0] aug_sky1 = aug_sky[1] else: aug_sky0 = 0. aug_sky1 = 0. if config.has_option('deepCR', 'model_name'): model_name = config.get('deepCR', 'model_name') else: model_name = 'model'
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if config.has_option('deepCR', 'hidden'): hidden = config.getint('deepCR', 'hidden') else: hidden = 50 if config.has_option('deepCR', 'epoch'): epoch = config.getint('deepCR', 'epoch') else: epoch = 50 if config.has_option('deepCR', 'patch'): patch = config.getint('deepCR', 'patch') else: patch = 256 if config.has_option('deepCR', 'batch'): batch = config.getint('deepCR', 'batch') else: batch = 16 if config.has_option('deepCR', 'lr'): lr = config.getfloat('deepCR', 'lr') else: lr = 0.005 if config.has_option('deepCR', 'auto_lr_decay'): auto_lr_decay = config.getboolean('deepCR', 'auto_lr_decay') else: auto_lr_decay = True if config.has_option('deepCR', 'lr_decay_patience'): lr_decay_patience = config.getint('deepCR', 'lr_decay_patience') else: lr_decay_patience = 4 if config.has_option('deepCR', 'lr_decay_factor'): lr_decay_factor = config.getfloat('deepCR', 'lr_decay_factor') else: lr_decay_factor = 0.1 if config.has_option('deepCR', 'save_after'): save_after = config.getfloat('deepCR', 'save_after') else: save_after = 100000 if config.has_option('deepCR', 'plot_every'): plot_every = config.getint('deepCR', 'plot_every') else: plot_every = 10 if config.has_option('deepCR', 'verbose'): verbose = config.getboolean('deepCR', 'verbose') else: verbose = True if config.has_option('deepCR', 'use_tqdm'): use_tqdm = config.getboolean('deepCR', 'use_tqdm') else: use_tqdm = False if config.has_option('deepCR', 'use_tqdm_notebook'): use_tqdm_notebook = config.getboolean('deepCR', 'use_tqdm_notebook') else: use_tqdm_notebook = False if config.has_option('deepCR', 'directory'): directory = config.get('deepCR', 'directory') else: directory = './'
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CRMask.cr_train_deepCR_prepare_data(self, patch) print('Loading data completes!') trainer = train(self.training_image, self.training_mask, self.training_ignore, self.training_sky, [aug_sky0, aug_sky1], model_name, hidden, self.gpu_flag, epoch, batch, lr, auto_lr_decay, lr_decay_patience, lr_decay_factor, save_after, plot_every, verbose, use_tqdm, use_tqdm_notebook, directory) print('Start training!') trainer.train() trainer.save() print('Training completes!') def cr_train(self): """ Training models, only support ``deepCR_train``. It will generate pytorch's *.pth file. The train is very painful and time consuming, do NOT use it in pipelines. Returns ------- No returns Examples ------- >>> from crmask import CRMask >>> imglist = ['MSC_MS_210525170000_100000010_23_sci.fits', 'MSC_MS_210525171000_100000011_23_sci.fits', 'MSC_MS_210525172000_100000012_23_sci.fits', 'MSC_MS_210525173000_100000013_23_sci.fits', 'MSC_MS_210525174000_100000014_23_sci.fits', 'MSC_MS_210525175000_100000015_23_sci.fits', 'MSC_MS_210525180000_100000016_23_sci.fits', 'MSC_MS_210525181000_100000017_23_sci.fits', 'MSC_MS_210525182000_100000018_23_sci.fits', 'MSC_MS_210525183000_100000019_23_sci.fits'] >>> masklist = ['MSC_CRM_210525170000_100000010_23_raw.fits', 'MSC_CRM_210525171000_100000011_23_raw.fits', 'MSC_CRM_210525172000_100000012_23_raw.fits', 'MSC_CRM_210525173000_100000013_23_raw.fits', 'MSC_CRM_210525174000_100000014_23_raw.fits', 'MSC_CRM_210525175000_100000015_23_raw.fits', 'MSC_CRM_210525180000_100000016_23_raw.fits', 'MSC_CRM_210525181000_100000017_23_raw.fits', 'MSC_CRM_210525182000_100000018_23_raw.fits', 'MSC_CRM_210525183000_100000019_23_raw.fits'] >>> trainobj = CRMask(imglist, mask = masklist, model = 'deepCR_train') >>> trainobj.cr_train() """ if self.model == 'deepCR_train': CRMask.cr_train_deepCR(self) else: raise ValueError('Unsupported training model') def cr_benchmark(self): """ Do NOT use this method, just for internal test. """ if isinstance(self.res, str) or isinstance(self.res, Path): hdulist = pyfits.open(self.res) res = hdulist[1].data elif isinstance(self.res, pyfits.HDUList): res = hdulist[1].data elif isinstance(self.res, np.ndarray): res = self.res elif isinstance(self.res, CCDData): res = np.asarray(self.res) else: raise TypeError('Input result image type error') if isinstance(self.ref, str) or isinstance(self.ref, Path): hdulist = pyfits.open(self.ref) ref = hdulist[1].data elif isinstance(self.ref, pyfits.HDUList): ref = hdulist[1].data elif isinstance(self.ref, np.ndarray): ref = self.ref elif isinstance(self.ref, CCDData): ref = np.asarray(self.ref) else: raise TypeError('Input reference image type error') if res.shape != ref.shape: raise TypeError('size of input result and reference image are not the same size') ref_crpix = np.count_nonzero(ref) res_crpix = np.count_nonzero(res) true_positive = np.count_nonzero(res&ref) false_negtive = np.count_nonzero((res^ref)&ref) return true_positive / ref_crpix, false_negtive / res_crpix
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#to run the code #./MSC_crmask xxxx.fits deepCR for deepCR #./MSC_crmask xxxx.fits lacosmic for lacosmic if __name__ == '__main__': crobj = CRMask(sys.argv[1], model = sys.argv[2]) crobj.cr_mask()