def run(fits_image, preset='', use_header=True):
"""Present image for interactive choice of the object to segment
dic = run( fits_image )
Given FITS image is presented on an interacive window where the
user can click on objects for selection. A segmentation algorithm
will run on the image and selected points will be cutout if they
correspond to a segmented object. 'preset' argument can be used
to use pre-set Sextractor(SE) configuration (see sltools.Package.sextractor
for more information). If we don't want to use image's header info
'use_header' argument can be set to "False"; default is "True"
Input:
- fits_image <str> : FITS file containg the image to use
- preset <str> : SE's pre-set configuration
- use_header <bool> : whether to use (or not) image's header
Output:
- {'IDs', 'images', 'headers'}
"""
# Open given image..
#
if (use_header):
img, header = pyfits.getdata(fits_image, header=use_header)
else:
img = pyfits.getdata(fits_image, header=use_header)
header = None
# Show the image and make "clickable" for objects selection..
#
_dic = click(img, header)
x = _dic['x']
y = _dic['y']
ra = _dic['ra']
dec = _dic['dec']
del _dic
centroids = zip(x, y)
# Segment image with Sextractor..
#
if preset == 'none':
preset = ''
_dic = SE.run_segobj(fits_image, preset=preset)
if not _dic:
return False
objimg = pyfits.getdata(_dic['OBJECTS'])
segimg = pyfits.getdata(_dic['SEGMENTATION'])
cat = pyfits.open(_dic['CATALOG'])[1].data
del _dic
_dic = select_objects(segimg, objimg, centroids, header)
if not _dic:
return False
return (_dic)
def whole_image(X, Y, imagefile, PARAMS, preset):
centroids = zip(X, Y)
if len(centroids) == 0:
logging.warning("No objects in given (X,Y lists). Finishing.")
return False
# Run Sextractor over the whole image:
_Dsex = sextractor.run_segobj(imagefile, PARAMS, preset=preset)
segimg = pyfits.getdata(_Dsex['SEGMENTATION'])
objimg = pyfits.getdata(_Dsex['OBJECTS'])
objIDs = [segimg[o_o[1], o_o[0]] for o_o in centroids]
objIDs = list(set(objIDs) - set([0]))
# Take only the objects of interest...
# from image:
selobjimg = imcp.copy_objects(objimg, segimg, objIDs)
del segimg, objimg
# and catalog:
cathdu = pyfits.open(_Dsex['CATALOG'])[1]
selobjhdu = fts.select_entries(cathdu, 'NUMBER', *objIDs)
del cathdu
# Re-identify each object:
for i in range(len(objIDs)):
selobjhdu.data.field('NUMBER')[i] = i
return (selobjimg, selobjhdu)
def whole_image(X,Y,imagefile,PARAMS,preset):
centroids = zip(X,Y);
if len(centroids) == 0:
logging.warning("No objects in given (X,Y lists). Finishing.");
return False;
# Run Sextractor over the whole image:
_Dsex = sextractor.run_segobj(imagefile,PARAMS,preset=preset);
segimg = pyfits.getdata(_Dsex['SEGMENTATION']);
objimg = pyfits.getdata(_Dsex['OBJECTS']);
objIDs = [ segimg[o_o[1],o_o[0]] for o_o in centroids ];
objIDs = list(set(objIDs)-set([0]));
# Take only the objects of interest...
# from image:
selobjimg = imcp.copy_objects(objimg,segimg,objIDs);
del segimg,objimg;
# and catalog:
cathdu = pyfits.open(_Dsex['CATALOG'])[1];
selobjhdu = fts.select_entries(cathdu,'NUMBER',*objIDs);
del cathdu;
# Re-identify each object:
for i in range(len(objIDs)):
selobjhdu.data.field('NUMBER')[i] = i;
return (selobjimg,selobjhdu);
def identify_images(frame_name, params=[], args={}, preset='sims'):
"""
Identify the objects at an image. Currently run SExtractor under sltools.sltools.image.sextractor.run_segobj
"""
dict_run_segobj = run_segobj(frame_name, params, args, preset) # 'params' are the SExtractor
# parameters to output (strings, see SE's default.param). 'args' ({str:str,}) are SE command-line arguments
objimgname, segimgname, catfilename = dict_run_segobj['OBJECTS'], dict_run_segobj['SEGMENTATION'], dict_run_segobj['CATALOG'] # catfilename is the SE output catalog with the 'params' columns
frame_data = pyfits.getdata(frame_name) # to get the header add 'header=True'
nonzero_frame_data = np.nonzero(frame_data)
# make a loop over all images on the frame
return objimgname, segimgname
def _file_2_arrays(fits_image, use_header, params, args, preset):
"""SExtract the Image and read the outputs to arrays."""
out = sextractor.run_segobj(fits_image, params, args, preset=preset)
if (out == False):
print >> sys.stderr, "Error: Sextractor raised and error coded during segmentation. Finishing run."
return (False)
objimg = pyfits.getdata(out['OBJECTS'])
segimg = pyfits.getdata(out['SEGMENTATION'])
tbhdu = pyfits.open(out['CATALOG'])[1]
if (use_header):
header = pyfits.getheader(fits_image)
else:
header = None
return (objimg, segimg, header, tbhdu)
def _file_2_arrays(fits_image, use_header, params, args, preset):
"""SExtract the Image and read the outputs to arrays."""
out = sextractor.run_segobj(fits_image, params, args, preset=preset);
if (out == False):
print >> sys.stderr, "Error: Sextractor raised and error coded during segmentation. Finishing run."
return (False);
objimg = pyfits.getdata( out['OBJECTS'] );
segimg = pyfits.getdata( out['SEGMENTATION'] );
tbhdu = pyfits.open(out['CATALOG'])[1];
if (use_header):
header = pyfits.getheader( fits_image );
else:
header = None;
return (objimg, segimg, header, tbhdu);
def run(regionfile,
imagefile,
args={},
preset='',
shape=(100, 100),
tablefile="objectsTb",
stampsfile="pstamp_"):
"""
Runs the pipeline for arc postage stamps creation and segmentation (in this order)
For each point inside (DS9) 'regionfile' take a window of size 'shape' around it.
Sextractor config parameters can be passed using 'args'. So far, catalog output
parameters are declared through the global variable 'PARAMS'.
Use 'preset' for optimized settings. See sltools.image.sextractor for more info.
Input:
- regionfile str : DS9 region file
- imagefile str : FITS filename
- args {'key',value} : Sextractor config params
- preset str : See sltools.image.sextractor
- shape (int,int) : Object cutouts shape (dx,dy)
- tablefile str : FITS table output filename
- stampsfile str : Output file rootnames
Output:
Write to 'tablefile' FITS table the segmented objects properties ('PARAMS')
---
"""
rootname = imagefile[:-5]
# Object's centroid will be placed over postamp's central point
x_i = shape[0] / 2
y_i = shape[1] / 2
x_size, y_size = shape
tbList = []
flList = []
# Read DS9 regionfile and input imagefile..
#
D_in = asc.read_ds9cat(regionfile)
X = asarray(D_in['x'])
Y = asarray(D_in['y'])
centroids = XY = zip(X, Y)
R = asarray(D_in['size'])
logging.debug("Centroids: %s", XY)
if len(XY) == 0:
logging.warning("No objects in given Centroids list. Finishing.")
return
img, hdr = pyfits.getdata(imagefile, header=True)
# For each "centroid":
#
for i in xrange(len(XY)):
logging.info("Process %s, point: %s", i, XY[i])
# Take a snapshot
#
obj, hdr = imcp.cutout(img,
hdr,
xo=X[i],
yo=Y[i],
x_size=x_size,
y_size=y_size)
file_i = rootname + "_pstamp_" + str(X[i]) + "_" + str(Y[i]) + ".fits"
pyfits.writeto(file_i, obj, hdr, clobber=True)
logging.info("Snapshot %s created", file_i)
flList.append(file_i)
# Run Sextractor over newly created sanpshot..
#
Dsex = SE.run_segobj(file_i, PARAMS, preset) ### SEXTRACTOR CALL
segimg = pyfits.getdata(Dsex['SEGMENTATION'])
cathdu = pyfits.open(Dsex['CATALOG'])[1]
logging.info("Segmentation done.")
logging.info("Files %s,%s,%s created", Dsex['SEGMENTATION'],
Dsex['OBJECTS'], Dsex['CATALOG'])
objID = segobjs.centroid2id(segimg, (x_i, y_i))
# The following block searchs for the nearest neighbour and check if the IDs are
# in accordance. If the given centroid is not part of an object (i.e, ID==0),
# the nearest object is taken in place; and if the IDs do not match and non-zero
# valued just an warning is printed to "debug" level.
#
centroids_sex = zip(cathdu.data.field('X_IMAGE'),
cathdu.data.field('Y_IMAGE'))
[logging.debug("SE Centroids: %s", _c) for _c in centroids_sex]
nrst_indx_dist = tbmtch.nearest_neighbour([centroids[i]],
centroids_sex)
logging.debug("Nearest point index|distance: %s (length:%s)",
nrst_indx_dist, len(nrst_indx_dist))
_indx, _dist = nrst_indx_dist[0]
nrst_obj = cathdu.data[_indx]
_id = nrst_obj.field('NUMBER')
if objID == 0:
objID = _id
logging.warning(
"Object ID is 0 for Centroid:%s. Using object (ID) %s, instead.",
centroids[i], _id)
if objID != _id:
logging.debug(
"Oops, two different objects were found matching point %s, objIDs %s and %s",
centroids[i], objID, _id)
logging.info("ObjectID: %s being readout", objID)
tbList.append(fts.select_entries(cathdu, 'NUMBER', objID))
arc_i = rootname + "_nrstArc_" + str(X[i]) + "_" + str(Y[i]) + ".fits"
arc = imcp.select(segimg, obj, objID)
pyfits.writeto(arc_i, arc, hdr, clobber=True)
# Create output table
new_tbhdu = tbList[0]
for i in xrange(1, len(tbList)):
new_tbhdu = fts.extend_tbHDU(new_tbhdu, tbList[i])
tb_flList = fts.dict_to_tbHDU({'filename': flList})
new_tbhdu = fts.merge_tbHDU(new_tbhdu, tb_flList)
tablefile = rootname + "_arcscat.fits"
new_tbhdu.writeto(tablefile, clobber=True)
return
def run(regionfile, imagefile, args={}, preset='', shape=(100,100), tablefile="objectsTb",stampsfile="pstamp_"):
"""
Runs the pipeline for arc postage stamps creation and segmentation (in this order)
For each point inside (DS9) 'regionfile' take a window of size 'shape' around it.
Sextractor config parameters can be passed using 'args'. So far, catalog output
parameters are declared through the global variable 'PARAMS'.
Use 'preset' for optimized settings. See sltools.image.sextractor for more info.
Input:
- regionfile str : DS9 region file
- imagefile str : FITS filename
- args {'key',value} : Sextractor config params
- preset str : See sltools.image.sextractor
- shape (int,int) : Object cutouts shape (dx,dy)
- tablefile str : FITS table output filename
- stampsfile str : Output file rootnames
Output:
Write to 'tablefile' FITS table the segmented objects properties ('PARAMS')
---
"""
rootname = imagefile[:-5]
# Object's centroid will be placed over postamp's central point
x_i = shape[0]/2
y_i = shape[1]/2
x_size,y_size = shape
tbList = []
flList = []
# Read DS9 regionfile and input imagefile..
#
D_in = asc.read_ds9cat(regionfile)
X = asarray(D_in['x'])
Y = asarray(D_in['y'])
centroids = XY = zip(X,Y)
R = asarray(D_in['size'])
logging.debug("Centroids: %s",XY)
if len(XY) == 0:
logging.warning("No objects in given Centroids list. Finishing.")
return
img,hdr = pyfits.getdata(imagefile,header=True)
# For each "centroid":
#
for i in xrange(len(XY)):
logging.info("Process %s, point: %s",i,XY[i])
# Take a snapshot
#
obj,hdr = imcp.cutout(img,hdr,xo=X[i],yo=Y[i],x_size=x_size,y_size=y_size)
file_i = rootname+"_pstamp_"+str(X[i])+"_"+str(Y[i])+".fits"
pyfits.writeto(file_i,obj,hdr,clobber=True)
logging.info("Snapshot %s created",file_i)
flList.append(file_i)
# Run Sextractor over newly created sanpshot..
#
Dsex = SE.run_segobj(file_i, PARAMS,preset) ### SEXTRACTOR CALL
segimg = pyfits.getdata(Dsex['SEGMENTATION'])
cathdu = pyfits.open(Dsex['CATALOG'])[1]
logging.info("Segmentation done.")
logging.info("Files %s,%s,%s created",Dsex['SEGMENTATION'],Dsex['OBJECTS'],Dsex['CATALOG'])
objID = segobjs.centroid2id(segimg,(x_i,y_i))
# The following block searchs for the nearest neighbour and check if the IDs are
# in accordance. If the given centroid is not part of an object (i.e, ID==0),
# the nearest object is taken in place; and if the IDs do not match and non-zero
# valued just an warning is printed to "debug" level.
#
centroids_sex = zip(cathdu.data.field('X_IMAGE'),cathdu.data.field('Y_IMAGE'))
[ logging.debug("SE Centroids: %s",_c) for _c in centroids_sex ]
nrst_indx_dist = tbmtch.nearest_neighbour([centroids[i]],centroids_sex)
logging.debug("Nearest point index|distance: %s (length:%s)",nrst_indx_dist,len(nrst_indx_dist))
_indx,_dist = nrst_indx_dist[0]
nrst_obj = cathdu.data[_indx]
_id = nrst_obj.field('NUMBER')
if objID == 0:
objID = _id
logging.warning("Object ID is 0 for Centroid:%s. Using object (ID) %s, instead.",centroids[i],_id)
if objID != _id:
logging.debug("Oops, two different objects were found matching point %s, objIDs %s and %s",centroids[i],objID,_id)
logging.info("ObjectID: %s being readout",objID)
tbList.append(fts.select_entries(cathdu,'NUMBER',objID))
arc_i = rootname+"_nrstArc_"+str(X[i])+"_"+str(Y[i])+".fits"
arc = imcp.select(segimg,obj,objID)
pyfits.writeto(arc_i,arc,hdr,clobber=True)
# Create output table
new_tbhdu = tbList[0]
for i in xrange(1,len(tbList)):
new_tbhdu = fts.extend_tbHDU(new_tbhdu,tbList[i])
tb_flList = fts.dict_to_tbHDU({'filename':flList})
new_tbhdu = fts.merge_tbHDU(new_tbhdu,tb_flList)
tablefile = rootname+"_arcscat.fits"
new_tbhdu.writeto(tablefile,clobber=True)
return
def run(regionfile,
imagefile,
args={},
preset='HST_Arcs',
shape=(100,
100)): # , tablefile="objectsTb"):# ,stampsfile="pstamp_"):
"""
Runs the pipeline for postamps creation and segmented
run( regionfile, imagefile [,...] )
For each point listed inside DS9 'regionfile', considered object
centroids in 'imagefile', a snapshot and object segmentation are
done; shape and rootname (suffixed by "objID", fits extension)
are passed through 'shape' and 'stampsfile', resp.
Sextractor config parameters can be passed using 'args'. So far,
catalog output parameters are hardcoded.
Use 'preset' for optimized settings. See sltools.image.sextractor
for more info.
Input:
- regionfile : str
DS9 region file
- imagefile : str
FITS filename
- args : {'option':'value',}
Sextractor config params
- preset : str
See sltools.image.sextractor
- shape : (int,int)
Object cutouts shape (pixels,pixels)
- tablefile : str
FITS table output filename
- stampsfile : str
Output file rootnames
Output:
- tbhdu : pyfits.BinTableHDU
Output (fits) table with objects' computed parameters
---
"""
# Sextractor Params:
PARAMS = [
'NUMBER', 'ELONGATION', 'ELLIPTICITY', 'ISOAREA_IMAGE', 'A_IMAGE',
'B_IMAGE', 'THETA_IMAGE', 'X_IMAGE', 'Y_IMAGE', 'X2_IMAGE', 'Y2_IMAGE',
'XY_IMAGE'
]
# Object's centroid will be placed over postamp's central point
x_i = shape[0] / 2
y_i = shape[1] / 2
_tbList = []
_flList = []
_imList = []
# Read DS9 regionfile and input imagefile..
#
D_in = asc.read_ds9cat(regionfile)
X = asarray(D_in['x'])
Y = asarray(D_in['y'])
centroids = zip(X, Y)
####################################################################
# Segment the whole image at once
# Needs:
# X , Y , image(FITS) filename and Sextractor arguments/inputs
selobjimg_W, selobjhdu_W = whole_image(X, Y, imagefile, PARAMS, preset)
selobjhdu_W.writeto('Whole_image.fit', clobber=True)
pyfits.writeto('Whole_image.fits', selobjimg_W, clobber=True)
####################################################################
img, hdr = pyfits.getdata(imagefile, header=True)
logging.debug("Centroids: %s", centroids)
rootname = re.sub(".fits", "", imagefile)
# For each centroid, do:
#
i = -1
xy = centroids[:]
for o_o in centroids:
i += 1
logging.info("Process %s, point: %s", i, o_o)
# Take a snapshot
#
_obj, _hdr = imcp.snapshot(img, hdr, centroid=o_o, shape=shape)
file_i = rootname + "_ps" + str(i) + ".fits"
pyfits.writeto(file_i, _obj, _hdr, clobber=True)
del _obj, _hdr
logging.info("Poststamp %s created", file_i)
# Run Sextractor over newly created sanpshot..
#
_Dsex = sextractor.run_segobj(file_i, PARAMS, preset=preset)
### SEXTRACTOR CALL
segimg = pyfits.getdata(_Dsex['SEGMENTATION'])
objimg = pyfits.getdata(_Dsex['OBJECTS'])
cathdu = pyfits.open(_Dsex['CATALOG'])[1]
objID = segimg[y_i, x_i]
if not objID:
lixo = xy.remove(o_o)
continue
logging.info("ObjectID: %s being readout", objID)
_tbList.append(fts.select_entries(cathdu, 'NUMBER', objID))
_flList.append(file_i)
_imList.append(imcp.copy_objects(objimg, segimg, [objID]))
# Initialize output table and image..
#
selobjhdu_S = _tbList[0]
selobjimg_S = np.zeros(img.shape, dtype=img.dtype)
selobjimg_S = combine.add_images(selobjimg_S,
_imList[0],
x=xy[0][0],
y=xy[0][1])
#
# and do the same for each object
#
for i in xrange(1, len(_tbList)):
selobjhdu_S = fts.extend_tbHDU(selobjhdu_S, _tbList[i])
selobjimg_S = combine.add_images(selobjimg_S,
_imList[i],
x=xy[i][0],
y=xy[i][1])
# Write down the FITS catalog..
#
tb_flList = fts.dict_to_tbHDU({'filename': _flList})
selobjhdu_S = fts.merge_tbHDU(selobjhdu_S, tb_flList)
# And the FITS image, composed by the well segmented objects..
#
selobjhdu_S.writeto('Stamps_compose.fit', clobber=True)
pyfits.writeto('Stamps_compose.fits', selobjimg_S, clobber=True)
return
def run(regionfile, imagefile, args={}, preset='HST_Arcs', shape=(100,100)):# , tablefile="objectsTb"):# ,stampsfile="pstamp_"):
"""
Runs the pipeline for postamps creation and segmented
run( regionfile, imagefile [,...] )
For each point listed inside DS9 'regionfile', considered object
centroids in 'imagefile', a snapshot and object segmentation are
done; shape and rootname (suffixed by "objID", fits extension)
are passed through 'shape' and 'stampsfile', resp.
Sextractor config parameters can be passed using 'args'. So far,
catalog output parameters are hardcoded.
Use 'preset' for optimized settings. See sltools.image.sextractor
for more info.
Input:
- regionfile : str
DS9 region file
- imagefile : str
FITS filename
- args : {'option':'value',}
Sextractor config params
- preset : str
See sltools.image.sextractor
- shape : (int,int)
Object cutouts shape (pixels,pixels)
- tablefile : str
FITS table output filename
- stampsfile : str
Output file rootnames
Output:
- tbhdu : pyfits.BinTableHDU
Output (fits) table with objects' computed parameters
---
"""
# Sextractor Params:
PARAMS=['NUMBER','ELONGATION','ELLIPTICITY','ISOAREA_IMAGE','A_IMAGE','B_IMAGE','THETA_IMAGE','X_IMAGE','Y_IMAGE','X2_IMAGE','Y2_IMAGE','XY_IMAGE']
# Object's centroid will be placed over postamp's central point
x_i = shape[0]/2;
y_i = shape[1]/2;
_tbList = [];
_flList = [];
_imList = [];
# Read DS9 regionfile and input imagefile..
#
D_in = asc.read_ds9cat(regionfile);
X = asarray(D_in['x']);
Y = asarray(D_in['y']);
centroids = zip(X,Y);
####################################################################
# Segment the whole image at once
# Needs:
# X , Y , image(FITS) filename and Sextractor arguments/inputs
selobjimg_W,selobjhdu_W = whole_image(X,Y,imagefile,PARAMS,preset);
selobjhdu_W.writeto('Whole_image.fit',clobber=True);
pyfits.writeto('Whole_image.fits',selobjimg_W,clobber=True);
####################################################################
img,hdr = pyfits.getdata(imagefile,header=True);
logging.debug("Centroids: %s",centroids);
rootname = re.sub(".fits","",imagefile);
# For each centroid, do:
#
i=-1;
xy = centroids[:];
for o_o in centroids:
i+=1;
logging.info("Process %s, point: %s",i,o_o);
# Take a snapshot
#
_obj,_hdr = imcp.snapshot( img, hdr, centroid=o_o, shape=shape );
file_i = rootname+"_ps"+str(i)+".fits";
pyfits.writeto(file_i,_obj,_hdr,clobber=True);
del _obj,_hdr;
logging.info("Poststamp %s created",file_i);
# Run Sextractor over newly created sanpshot..
#
_Dsex = sextractor.run_segobj(file_i, PARAMS,preset=preset); ### SEXTRACTOR CALL
segimg = pyfits.getdata(_Dsex['SEGMENTATION']);
objimg = pyfits.getdata(_Dsex['OBJECTS']);
cathdu = pyfits.open(_Dsex['CATALOG'])[1];
objID = segimg[y_i,x_i];
if not objID:
lixo = xy.remove(o_o);
continue;
logging.info("ObjectID: %s being readout",objID);
_tbList.append(fts.select_entries(cathdu,'NUMBER',objID));
_flList.append(file_i);
_imList.append(imcp.copy_objects(objimg,segimg,[objID]));
# Initialize output table and image..
#
selobjhdu_S = _tbList[0];
selobjimg_S = np.zeros(img.shape,dtype=img.dtype);
selobjimg_S = combine.add_images(selobjimg_S,_imList[0],x=xy[0][0],y=xy[0][1]);
#
# and do the same for each object
#
for i in xrange(1,len(_tbList)):
selobjhdu_S = fts.extend_tbHDU(selobjhdu_S,_tbList[i]);
selobjimg_S = combine.add_images(selobjimg_S,_imList[i],x=xy[i][0],y=xy[i][1]);
# Write down the FITS catalog..
#
tb_flList = fts.dict_to_tbHDU({'filename':_flList});
selobjhdu_S = fts.merge_tbHDU(selobjhdu_S,tb_flList);
# And the FITS image, composed by the well segmented objects..
#
selobjhdu_S.writeto('Stamps_compose.fit',clobber=True);
pyfits.writeto('Stamps_compose.fits',selobjimg_S,clobber=True)
return;
def run(fits_image, preset='', use_header=True):
"""Present image for interactive choice of the object to segment
dic = run( fits_image )
Given FITS image is presented on an interacive window where the
user can click on objects for selection. A segmentation algorithm
will run on the image and selected points will be cutout if they
correspond to a segmented object. 'preset' argument can be used
to use pre-set Sextractor(SE) configuration (see sltools.Package.sextractor
for more information). If we don't want to use image's header info
'use_header' argument can be set to "False"; default is "True"
Input:
- fits_image <str> : FITS file containg the image to use
- preset <str> : SE's pre-set configuration
- use_header <bool> : whether to use (or not) image's header
Output:
- {'IDs', 'images', 'headers'}
"""
# Open given image..
#
if (use_header):
img, header = pyfits.getdata(fits_image, header=use_header);
else:
img = pyfits.getdata(fits_image, header=use_header);
header = None;
# Show the image and make "clickable" for objects selection..
#
_dic = click(img, header);
x = _dic['x'];
y = _dic['y'];
ra = _dic['ra'];
dec = _dic['dec'];
del _dic;
centroids = zip(x,y);
# Segment image with Sextractor..
#
if preset=='none':
preset='';
_dic = SE.run_segobj(fits_image, preset=preset);
if not _dic:
return False;
objimg = pyfits.getdata( _dic['OBJECTS'] );
segimg = pyfits.getdata( _dic['SEGMENTATION'] );
cat = pyfits.open( _dic['CATALOG'] )[1].data;
del _dic;
_dic = select_objects(segimg, objimg, centroids, header);
if not _dic:
return False;
return (_dic);
