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(D_in,
objimg,
segimg=None,
shape=(100, 100),
objsfile='pstamp_',
hdr=None):
"""
Function to take snapshots from objects listed in a DS9's regions file
Sextractor is run over given 'imagename' and take the snapshots from objects listed
in DS9's 'regionfile'.
Input:
- regionfile : str
DS9' regions file
- imagename : str
FITS image filename
- outname_stamps : str
String to add to output name of the poststamp images.
- outname_cat : str
Output:
-> <bool>
"""
from numpy import asarray
imagename_ds9 = D_in['filename']
# Detect objects corresponding to given centroids..
#
XY = zip(asarray(D_in['x']), asarray(D_in['y']))
logging.debug("Input ['x'],['y'] points: %s" % (XY))
if (segimg != None):
logging.info(
"Segmentation image given, looking for objects for each (x,y) on it."
)
objIDs = [segobjs.centroid2ID(segimg, _xy) for _xy in XY]
else:
logging.info("No segmentation image given.")
objIDs = [0] * len(XY)
logging.debug("ObjIDs: %s " % (objIDs))
D_out = D_in
D_out['id'] = objIDs
D_out['segmented'] = [_id != 0 for _id in objIDs]
D_out['objfile'] = []
del D_out['size']
# Now lets take the detected objects snapshots..
#
for i in range(len(objIDs)):
outname = objsfile + str(i) + "_id_" + str(objIDs[i]) + ".fits"
D_out['objfile'].append(outname)
logging.info("Output objects/files (#,ID,x,y,filename): %s",
(i, objIDs[i], XY[i], outname))
_otmp, _htmp = imcp.snapshot(objimg,
hdr,
centroid=(XY[i][0], XY[i][1]),
shape=shape)
try:
pyfits.writeto(outname, _otmp, _htmp)
except IOError:
os.remove(outname)
pyfits.writeto(outname, _otmp, _htmp)
return D_out
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;
