def __init__(self, imagename, psfname=None, sourcefinder_name='pybdsm',
saveformat="gaul", makeplots=True, do_psf_corr=True,
do_local_var=True, psf_corr_region=5, local_var_region=10,
rel_excl_src=None, pos_smooth=2, neg_smooth=2, loglevel=0,
thresh_pix=5, thresh_isl=3, neg_thresh_isl=3,
neg_thresh_pix=5, reset_rel=None, prefix=None,
do_nearsources=False, savefits=False,
increase_beam_cluster=False, savemask_pos=False,
savemask_neg=False, no_smooth=True, **kw):
""" Takes in image and extracts sources and makes
reliability estimations..
imagename: Fits image
psfname: PSF fits image, optional.
sourcefinder_name: str, optional. Default 'pybdsm'.
Uses source finder specified.
makeplots: bool, optional. Default is True.
Make reliability plots.
do_psf_corr : bool, optional. Default True.
If True, PSF correlation will be added
as an extra parameter for density estimations.
NB: the PSF fits image must be provided.
do_local_var : bool, optional. Default is True.
If True, adds local variance as an extra parameter,
for density estimations.
do_nearsources: boolean. Default is False.
If true it adds number of nearest neighnours as an extra
parameter. It looks for sources around 5 beam sizes.
psf_corr_region : int, optional. Default value is 5.
Data size to correlate around a source, in beam sizes.
local_var_region: int, optional. Default 10.
Data size to compute the local variance in beam sizes.
rel_excl_src : floats, optional. Default is None.
Excludes sources in a specified region
e.g ra, dec, radius in degrees. For
2 regions: ra1, dec1, radius1: ra2, dec2, radius2, etc.
pos_smooth : float, optional. Default 2.
Masking threshold for the positive image.
For default value 2, data peaks < 2 * image noise
are masked.
neg_smooth : float, optional. Default 2.
Similar to pos_smooth but applied to the negative image.
thresh_isl : float, optional. Default is 3.
Threshold for forming islands in the positive image
thresh_pix : float, optional. Default is 5.
Threshold for model fitting, in positive image.
neg_thresh_isl : float, optional. Default is 3.
Simialr to thresh_isl but for negative image.
neg_thresh_pix : float, optional. Default is 5.
Similar to thresh_pix but for negative image.
savefits: boolean. Default is False.
If True a negative image is saved.
reset_rel: boolean. Default is False. If true then
sources with correlation < 0.002 and rel >0.60
have their reliabilities set to 0.
increase_beam_cluster: boolean, optional. If True, sources
groupings will be increase by 20% the beam size. If False,
the actual beam size will be used. Default is False.
savemask_pos: boolean, optional. If true the mask applied on
the positive side of an image after smoothing is saved.
savemask_neg: Similar to savemask_pos but for the negative
side of an image.
loglevel : int, optional. Default is 0.
Provides Pythonlogging options, 0, 1, 2 and 3 are for info, debug,
error and critial respectively.
kw : kward for source extractions. Should be a mapping e.g
kw['thresh_isl'] = 2.0 or kw['do_polarization'] = True
"""
#
self.smoothing = not no_smooth
self.prefix = prefix
# log level
self.loglevel = loglevel
self.log = utils.logger(self.loglevel, prefix=self.prefix)
# image, psf image
self.imagename = imagename
self.psfname = psfname
with pyfits.open(imagename) as hdu:
self.header = hdu[0].header
self.wcs = WCS(self.header, mode="pyfits")
self.pixelsize = abs(self.header["cdelt1"])
self.bmaj = numpy.deg2rad(self.header["BMAJ"])
# boolean optionals
self.makeplots = makeplots
self.do_local_var = do_local_var
self.nearsources = do_nearsources
self.do_psf_corr = do_psf_corr
self.savemaskpos = savemask_pos
self.savemaskneg = savemask_neg
self.savefits = savefits
self.derel = reset_rel
self.log.info("Catalogues will be saved as %s, where srl is source "
" and gaul is Gaussians. "%saveformat)
self.catalogue_format = "." + saveformat
if not self.psfname:
self.log.info(" No psf provided, do_psf_corr is set to False.")
self.do_psf_corr = False
# computing negative noise
self.noise, self.mean = utils.negative_noise(self.imagename, self.prefix)
self.log.info(" The negative noise is %e Jy/beam"%self.noise)
if self.noise == 0:
self.log.debug(" The negative noise is 0, check image")
# source finder initialization
self.sourcefinder_name = sourcefinder_name
self.log.info(" Using %s source finder to extract the sources."%
self.sourcefinder_name)
self.negimage = self.prefix + "_negative.fits"
utils.invert_image(self.imagename, self.negimage)
# smoothing factors
self.pos_smooth = pos_smooth
self.neg_smooth = neg_smooth
# region to evaluate
self.corrstep = psf_corr_region
self.localstep = local_var_region
self.radiusrm = rel_excl_src
self.do_beam = increase_beam_cluster
beam_pix = int(round(numpy.rad2deg(self.bmaj)/self.pixelsize))
self.locstep = self.localstep * beam_pix
self.cfstep = self.corrstep * beam_pix
self.bmin, self.bpa = self.header["BMIN"], self.header["BPA"]
self.opts_pos = {}
if self.do_beam:
bmaj = self.header["BMAJ"]
self.opts_pos["beam"] = (1.2*bmaj, 1.2*self.bmin, self.bpa)
# Pybdsm or source finder fitting thresholds
self.thresh_isl = thresh_isl
self.thresh_pix = thresh_pix
self.opts_pos = dict(thresh_pix=self.thresh_pix,
thresh_isl=self.thresh_isl)
self.opts_pos.update(kw)
self.opts_neg = {}
self.opts_neg.update(kw)
self.neg_thresh_isl = neg_thresh_isl
self.neg_thresh_pix = neg_thresh_pix
self.opts_neg["thresh_isl"] = self.neg_thresh_isl
self.opts_neg["thresh_pix"] = self.neg_thresh_pix
def __init__(self,
imagename,
psfname=None,
sourcefinder_name='pybdsm',
saveformat="gaul",
makeplots=True,
do_psf_corr=True,
do_local_var=True,
psf_corr_region=5,
local_var_region=10,
rel_excl_src=None,
pos_smooth=2,
neg_smooth=2,
loglevel=0,
thresh_pix=5,
thresh_isl=3,
neg_thresh_isl=3,
neg_thresh_pix=5,
reset_rel=None,
prefix=None,
do_nearsources=False,
savefits=False,
increase_beam_cluster=False,
savemask_pos=False,
savemask_neg=False,
no_smooth=True,
**kw):
""" Takes in image and extracts sources and makes
reliability estimations..
imagename: Fits image
psfname: PSF fits image, optional.
sourcefinder_name: str, optional. Default 'pybdsm'.
Uses source finder specified.
makeplots: bool, optional. Default is True.
Make reliability plots.
do_psf_corr : bool, optional. Default True.
If True, PSF correlation will be added
as an extra parameter for density estimations.
NB: the PSF fits image must be provided.
do_local_var : bool, optional. Default is True.
If True, adds local variance as an extra parameter,
for density estimations.
do_nearsources: boolean. Default is False.
If true it adds number of nearest neighnours as an extra
parameter. It looks for sources around 5 beam sizes.
psf_corr_region : int, optional. Default value is 5.
Data size to correlate around a source, in beam sizes.
local_var_region: int, optional. Default 10.
Data size to compute the local variance in beam sizes.
rel_excl_src : floats, optional. Default is None.
Excludes sources in a specified region
e.g ra, dec, radius in degrees. For
2 regions: ra1, dec1, radius1: ra2, dec2, radius2, etc.
pos_smooth : float, optional. Default 2.
Masking threshold for the positive image.
For default value 2, data peaks < 2 * image noise
are masked.
neg_smooth : float, optional. Default 2.
Similar to pos_smooth but applied to the negative image.
thresh_isl : float, optional. Default is 3.
Threshold for forming islands in the positive image
thresh_pix : float, optional. Default is 5.
Threshold for model fitting, in positive image.
neg_thresh_isl : float, optional. Default is 3.
Simialr to thresh_isl but for negative image.
neg_thresh_pix : float, optional. Default is 5.
Similar to thresh_pix but for negative image.
savefits: boolean. Default is False.
If True a negative image is saved.
reset_rel: boolean. Default is False. If true then
sources with correlation < 0.002 and rel >0.60
have their reliabilities set to 0.
increase_beam_cluster: boolean, optional. If True, sources
groupings will be increase by 20% the beam size. If False,
the actual beam size will be used. Default is False.
savemask_pos: boolean, optional. If true the mask applied on
the positive side of an image after smoothing is saved.
savemask_neg: Similar to savemask_pos but for the negative
side of an image.
loglevel : int, optional. Default is 0.
Provides Pythonlogging options, 0, 1, 2 and 3 are for info, debug,
error and critial respectively.
kw : kward for source extractions. Should be a mapping e.g
kw['thresh_isl'] = 2.0 or kw['do_polarization'] = True
"""
#
self.smoothing = not no_smooth
self.prefix = prefix
# log level
self.loglevel = loglevel
self.log = utils.logger(self.loglevel, prefix=self.prefix)
# image, psf image
self.imagename = imagename
self.psfname = psfname
with pyfits.open(imagename) as hdu:
self.header = hdu[0].header
self.wcs = WCS(self.header, mode="pyfits")
self.pixelsize = abs(self.header["cdelt1"])
self.bmaj = numpy.deg2rad(self.header["BMAJ"])
# boolean optionals
self.makeplots = makeplots
self.do_local_var = do_local_var
self.nearsources = do_nearsources
self.do_psf_corr = do_psf_corr
self.savemaskpos = savemask_pos
self.savemaskneg = savemask_neg
self.savefits = savefits
self.derel = reset_rel
self.log.info("Catalogues will be saved as %s, where srl is source "
" and gaul is Gaussians. " % saveformat)
self.catalogue_format = "." + saveformat
if not self.psfname:
self.log.info(" No psf provided, do_psf_corr is set to False.")
self.do_psf_corr = False
# computing negative noise
self.noise, self.mean = utils.negative_noise(self.imagename,
self.prefix)
self.log.info(" The negative noise is %e Jy/beam" % self.noise)
if self.noise == 0:
self.log.debug(" The negative noise is 0, check image")
# source finder initialization
self.sourcefinder_name = sourcefinder_name
self.log.info(" Using %s source finder to extract the sources." %
self.sourcefinder_name)
self.negimage = self.prefix + "_negative.fits"
utils.invert_image(self.imagename, self.negimage)
# smoothing factors
self.pos_smooth = pos_smooth
self.neg_smooth = neg_smooth
# region to evaluate
self.corrstep = psf_corr_region
self.localstep = local_var_region
self.radiusrm = rel_excl_src
self.do_beam = increase_beam_cluster
beam_pix = int(round(numpy.rad2deg(self.bmaj) / self.pixelsize))
self.locstep = self.localstep * beam_pix
self.cfstep = self.corrstep * beam_pix
self.bmin, self.bpa = self.header["BMIN"], self.header["BPA"]
self.opts_pos = {}
if self.do_beam:
bmaj = self.header["BMAJ"]
self.opts_pos["beam"] = (1.2 * bmaj, 1.2 * self.bmin, self.bpa)
# Pybdsm or source finder fitting thresholds
self.thresh_isl = thresh_isl
self.thresh_pix = thresh_pix
self.opts_pos = dict(thresh_pix=self.thresh_pix,
thresh_isl=self.thresh_isl)
self.opts_pos.update(kw)
self.opts_neg = {}
self.opts_neg.update(kw)
self.neg_thresh_isl = neg_thresh_isl
self.neg_thresh_pix = neg_thresh_pix
self.opts_neg["thresh_isl"] = self.neg_thresh_isl
self.opts_neg["thresh_pix"] = self.neg_thresh_pix
def __init__(self, imagename, psfname=None, sourcefinder_name='pybdsm',
makeplots=True, do_psf_corr=True, do_local_var=True,
psf_corr_region=2, local_var_region=10, rel_excl_src=None,
pos_smooth=1.6, neg_smooth=1.6, loglevel=0, thresh_pix=5,
thresh_isl=3, neg_thresh_isl=3, neg_thresh_pix=5,
prefix=None, do_nearsources=False, **kw):
""" Takes in image and extracts sources and makes
reliability estimations..
imagename: Fits image
psfname: PSF fits image, optional.
sourcefinder_name: str, optional. Default 'pybdsm'.
Uses source finder specified by the users.
makeplots: bool, optional. Default is True.
Make reliability plots.
do_psf_corr : bool, optional. Default True.
If True, correlation of sources with PSF will be added
as an extra source parameter in reliability estimation.
But the PSF fits image must be provided.
do_local_var : bool, optional. Default is True.
Adds local variance as an extra source parameter,
similar to do_psf_corr but independent of the PSF image.
psf_corr_region : int, optional. Default value is 2.
Data size to correlate around a source in beam sizes.
local_var_region: int, optional. Default 10.
Data size to compute the local variance in beam sizes.
rel_excl_src : float numbers, optional. Default is None.
Excludes sources in this region from the reliability
estimations, e.g ra, dec, radius in degrees. For
many regions: ra1, dec1, radius1: ra2, dec2, radius2.
pos_smooth : float, optional. Default 1.6
Data smoothing threshold in the positive side of an image.
For default value 1.6, data peaks < 1.6 * image noise
will be averaged out.
neg_smooth : float, optional. Default 1.6.
Similar to pos_smooth but applied to the negative side of
an image.
loglevel : int, optional. Default is 0.
Provides Pythonlogging options, 0, 1, 2 and 3 for info, debug,
error and critial respectively.
thresh_isl : float, optional. Default is 3.
Threshold for the island boundary in number of sigma above
the mean. Determines extent of island used for fitting
[pybdsm]. For positive pixels.
thresh_pix : float, optional. Default is 5.
Source detection threshold: threshold for the island
peak in number of sigma above the mean. For positive pixels.
neg_thresh_isl : float, optional. Default is 3.
Simialr to thresh_isl but applied to negative side
of the image.
neg_thresh_pix : float, optional. Default is 5.
Similar to thresh_pix but applied to the negative
side of an image.
do_nearsources: boolean. Default is False.
If true it adds number of nearest neighnours as an extra
parameter. It looks for sources around 5 beam sizes.
kw : kward for source extractions. Should be a mapping e.g
kw['thresh_isl'] = 2.0 or kw['do_polarization'] = True
"""
# image, psf image
self.imagename = imagename
self.psfname = psfname
# setting output file names
self.prefix = prefix
self.poslsm = self.prefix + "_positive.lsm.html"
self.neglsm = self.prefix + "_negative.lsm.html"
# log level
self.loglevel = loglevel
self.log = utils.logger(self.loglevel, prefix=self.prefix)
self.log.info("Loading Image data")
# reading imagename data
self.imagedata, self.wcs, self.header, self.pixelsize =\
utils.reshape_data(self.imagename, prefix=self.prefix)
self.bmaj = numpy.deg2rad(self.header["BMAJ"])
self.do_psf_corr = do_psf_corr
if not self.psfname:
self.log.info("No psf provided, do_psf_corr = False.")
self.do_psf_corr = False
# computing negative noise
self.noise = utils.negative_noise(self.imagedata)
self.log.info("The negative noise is %e"%self.noise)
if self.noise == 0:
self.log.debug("The negative noise is 0, check image")
# source finder initialization
self.sourcefinder_name = sourcefinder_name
self.log.info("Using %s source finder to extract sources."%
self.sourcefinder_name)
# making negative image
self.negativeimage = utils.invert_image(
self.imagename, self.imagedata,
self.header, self.prefix)
# boolean optionals
self.makeplots = makeplots
self.do_local_var = do_local_var
self.nearsources = do_nearsources
# smoothing factors
self.pos_smooth = pos_smooth
self.neg_smooth = neg_smooth
# region to evaluate
self.psf_corr_region = psf_corr_region
self.local_var_region = local_var_region
self.rel_excl_src = rel_excl_src
# Pybdsm or source finder fitting thresholds
self.thresh_isl = thresh_isl
self.thresh_pix = thresh_pix
self.opts_pos = dict(thresh_pix=self.thresh_pix,
thresh_isl=self.thresh_isl)
self.opts_pos.update(kw)
self.opts_neg = {}
self.neg_thresh_isl = neg_thresh_isl
self.neg_thresh_pix = neg_thresh_pix
self.opts_neg["thresh_isl"] = self.neg_thresh_isl
self.opts_neg["thresh_pix"] = self.neg_thresh_pix
# Load the trained model
#model = get_model('test_model')
model = load_model('test_model.model')
# Define classes - {0: 'zero', 1: 'one', 2: 'two', ...}
classes = dict(
enumerate([
"zero", "one", "two", "three", "four", "five", "six", "seven", "eight",
"nine"
]))
# Predict output based on input image
if (IMG_PATH):
image = cv2.imread(IMG_PATH)
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Invert image: black becomes white and white becomes black
image = utils.invert_image(image)
image = np.resize(image, (28, 28, 1))
predicted_label = predict_input(model, image, classes)
print('Predicted output = ' + predicted_label)
else:
print('Please give path of image: IMG_PATH=<path> python3 predict.py')
# Testing purposes
#mnist = tf.keras.datasets.mnist
#(X_train, y_train), (X_test, y_test) = mnist.load_data()
#X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)
#predicted_label = predict_input(model, X_test[1], classes)