def select_objects(segimg, objimg, centroids, header=None):
"""Select objects based on given positions
Input:
- segimg <ndarray> :
Numpy array with SEGMENTATION image information
- objimg <ndarray> :
Numpy array with OBJECTS/original image information
- centroids <list:(int,int)> :
List of tuples with positions to search for objects [(x0,y0),(x1,y1),...]
"""
# Initialize object IDs list..
#
objIDs = [];
# Is there an identified object on given (xo,yo) point? If yes, store its ID..
#
for o_o in centroids:
xo, yo = o_o;
objid = segimg[yo,xo];
if ( objid != 0 ):
objIDs.append(objid);
else:
print >> sys.stdout, "No objects were identified for (x=%s,y=%s) position." % (xo,yo);
# Read out the objects recognized from images..
#
objs, hdrs = imcp.sextamp( segimg, objimg, header, increase=2, relative_increase=True, objIDs=objIDs );
# image_out, hdr = cutout( obj_img, header=hdr, xo=int(xo), yo=int(yo), x_size=int(x_size), y_size=int(y_size), mask=ind );
return ({'IDs' : objIDs, 'images' : objs, 'headers' : hdrs});
def select_objects(segimg, objimg, centroids, header=None):
"""Select objects based on given positions
Input:
- segimg <ndarray> :
Numpy array with SEGMENTATION image information
- objimg <ndarray> :
Numpy array with OBJECTS/original image information
- centroids <list:(int,int)> :
List of tuples with positions to search for objects [(x0,y0),(x1,y1),...]
"""
# Initialize object IDs list..
#
objIDs = []
# Is there an identified object on given (xo,yo) point? If yes, store its ID..
#
for o_o in centroids:
xo, yo = o_o
objid = segimg[yo, xo]
if (objid != 0):
objIDs.append(objid)
else:
print >> sys.stdout, "No objects were identified for (x=%s,y=%s) position." % (
xo, yo)
# Read out the objects recognized from images..
#
objs, hdrs = imcp.sextamp(segimg,
objimg,
header,
increase=2,
relative_increase=True,
objIDs=objIDs)
# image_out, hdr = cutout( obj_img, header=hdr, xo=int(xo), yo=int(yo), x_size=int(x_size), y_size=int(y_size), mask=ind );
return ({
'IDs': objIDs,
'images': objs,
'headers': hdrs
})
def pickout_obj(fits_image,
xo,
yo,
params=[],
args={},
use_header=True,
increase=0,
preset=''):
"""
Function to run SExtractor for objects segmentation and poststamp creation of a given image.
A reference (e.g, a object centroid) point is expected.
The function receives a FITS image and a pair of coordinates [xo,yo] matching a object.
The output is the matching object image array, header and ID from segmentation step.
SExtractor's command-line arguments can be passed through a dictionary [args]
{
'key_argument' : 'value_option',
...
}
, and features to output (See SE's default.param file) at catalog through a list [params].
To have borders around the identified object use [increase] to define the size of them.
The output image will have "increase" times the extracted object size.
(Notice that 0 < increase < 1 is possible and will return a poststamp with part of the object)
Preset SE's configuration parameters, for different telescope images, are available. The [preset]
parameters are chosen to be *good* parameters for those instruments: 'HST','DC4','DC5','CFHT'
Notice that this preset choices do not disable other parameters.
The flag [use_header] gives the option to use or not the header instance information.
Input:
- fits_image <str> : FITs image filename
- xo <int> : X object position in pixels
- yo <int> : Y object position in pixels
- params <list str> : list of parameters to output on sextractor catalogue
- args <dic> : dictionary with sextractor command-line arguments
- use_header <bool> : Whether or not to use (and update) header info
- increase <float> : multiplicative factor for poststamp sizing
- preset <str> : HST, DC4, DC5, CFHT
Output:
- <dic> : dictionary structure with the keys 'objIDs', 'images' and 'headers'.
Each of them is a list with corresponding information.
"""
params.append('NUMBER')
# Deal with fits files and SExtracting the image..
out = _file_2_arrays(fits_image, use_header, params, args, preset)
if not out:
print >> sys.stderr, "Error: An error occured while running SE and extracting FITS files."
return (False)
objimg, segimg, header, cat = out[0], out[1], out[2], out[3]
# Is there an identified object on given (xo,yo) point?..
objid = segimg[yo, xo]
if (objid != 0):
pass
else:
print >> sys.stdout, "No objects were identified on given (line=%s,column=%s) position." % (
yo, xo)
return (None)
## objects_IDfy return a list of arrays; result from the objIDs objects located in seg image merged with obj image.
# So, each object in objIDs is returned (also inside a/the list) as array with their corresponding pixels..
#
objs, hdrs = imcp.sextamp(segimg,
objimg,
header,
increase=increase,
relative_increase=True,
objIDs=[objid])
#objout = objs[0];
#hdrout = hdrs[0];
return ({
'IDs': [objid],
'images': objs,
'headers': hdrs
})
def readout_objs(filename,
params=[],
args={},
use_header=True,
increase=0,
preset=''):
"""
Function to run SExtractor for objects identification and creates poststamp images.
The function receives a FITS image filename and optionally some other arguments.
The output are the identified object image arrays, headers and IDs from segmentation step.
SExtractor's command-line arguments can be passed through a dictionary [args]
{
'key_argument' : 'value_option',
...
}
, and features to output (See SE's default.param file) at catalog through a list [params].
To have borders around the identified object use [increase] to define the size of them.
The output image will have "increase" times the extracted object size.
(Notice that 0 < increase < 1 is possible and will return a poststamp with part of the object)
Preset SE's configuration parameters, for different telescope images, are available. The [preset]
parameters are chosen to be *good* parameters for those instruments: 'HST','DC4','DC5','CFHT'
Notice that this preset choices do not disable other parameters.
The flag [use_header] gives the option to use or not the header instance information.
Input:
- fits_image <str> : FITs image filename
- params <list str> : list of parameters to output on sextractor catalogue
- args <dic> : dictionary with sextractor command-line arguments
- use_header <bool> : Whether or not to use (and update) header info
- increase <float> : multiplicative factor for poststamp sizing
- preset <str> : HST, DC4, DC5, CFHT
Output:
- <dic> : dictionary structure with the keys 'objIDs', 'images' and 'headers'.
Each of them is a list with corresponding information.
"""
filename = fits_image
params.append('NUMBER')
# Deal with fits files and SExtracting the image..
out = _file_2_arrays(fits_image, use_header, params, args, preset)
if not out:
print >> sys.stderr, "Error: An error occured while running SE and extracting FITS files."
return (False)
objimg, segimg, header, cat = out[0], out[1], out[2], out[3]
# Open "segmentation" output catalog from SE. Notice that object IDs are supposed to lie on 1st column..
# -
# ASCII:
# try:
# objIDs = list( cat[:,0].astype(np.int32) );
# except:
# objIDs = list( cat.astype(np.int32) );
# -
# FITS:
objIDs = cat.data.field('NUMBER')
if (len(objIDs) == 0):
print >> sys.stdout, "No objects were identified on given image."
return (None)
## objects_IDfy return a list of arrays; result from the objIDs objects located in seg image merged with obj image.
# So, each object in objIDs is returned (also inside a/the list) as array with their corresponding pixels..
#
objs, hdrs = imcp.sextamp(segimg,
objimg,
header,
increase=increase,
relative_increase=True,
objIDs=objIDs)
return ({
'IDs': objIDs,
'images': objs,
'headers': hdrs
})
def pickout_obj(fits_image, xo, yo, params=[], args={}, use_header=True, increase=0, preset=''):
"""
Function to run SExtractor for objects segmentation and poststamp creation of a given image.
A reference (e.g, a object centroid) point is expected.
The function receives a FITS image and a pair of coordinates [xo,yo] matching a object.
The output is the matching object image array, header and ID from segmentation step.
SExtractor's command-line arguments can be passed through a dictionary [args]
{
'key_argument' : 'value_option',
...
}
, and features to output (See SE's default.param file) at catalog through a list [params].
To have borders around the identified object use [increase] to define the size of them.
The output image will have "increase" times the extracted object size.
(Notice that 0 < increase < 1 is possible and will return a poststamp with part of the object)
Preset SE's configuration parameters, for different telescope images, are available. The [preset]
parameters are chosen to be *good* parameters for those instruments: 'HST','DC4','DC5','CFHT'
Notice that this preset choices do not disable other parameters.
The flag [use_header] gives the option to use or not the header instance information.
Input:
- fits_image <str> : FITs image filename
- xo <int> : X object position in pixels
- yo <int> : Y object position in pixels
- params <list str> : list of parameters to output on sextractor catalogue
- args <dic> : dictionary with sextractor command-line arguments
- use_header <bool> : Whether or not to use (and update) header info
- increase <float> : multiplicative factor for poststamp sizing
- preset <str> : HST, DC4, DC5, CFHT
Output:
- <dic> : dictionary structure with the keys 'objIDs', 'images' and 'headers'.
Each of them is a list with corresponding information.
"""
params.append('NUMBER');
# Deal with fits files and SExtracting the image..
out = _file_2_arrays(fits_image, use_header, params, args, preset);
if not out:
print >> sys.stderr, "Error: An error occured while running SE and extracting FITS files.";
return (False);
objimg, segimg, header, cat = out[0], out[1], out[2], out[3];
# Is there an identified object on given (xo,yo) point?..
objid = segimg[yo,xo];
if ( objid != 0 ):
pass;
else:
print >> sys.stdout, "No objects were identified on given (line=%s,column=%s) position." % (yo,xo);
return (None);
## objects_IDfy return a list of arrays; result from the objIDs objects located in seg image merged with obj image.
# So, each object in objIDs is returned (also inside a/the list) as array with their corresponding pixels..
#
objs, hdrs = imcp.sextamp( segimg, objimg, header, increase=increase, relative_increase=True, objIDs=[objid] );
#objout = objs[0];
#hdrout = hdrs[0];
return ({'IDs' : [objid], 'images' : objs, 'headers' : hdrs});
def readout_objs(filename, params=[], args={}, use_header=True, increase=0, preset=''):
"""
Function to run SExtractor for objects identification and creates poststamp images.
The function receives a FITS image filename and optionally some other arguments.
The output are the identified object image arrays, headers and IDs from segmentation step.
SExtractor's command-line arguments can be passed through a dictionary [args]
{
'key_argument' : 'value_option',
...
}
, and features to output (See SE's default.param file) at catalog through a list [params].
To have borders around the identified object use [increase] to define the size of them.
The output image will have "increase" times the extracted object size.
(Notice that 0 < increase < 1 is possible and will return a poststamp with part of the object)
Preset SE's configuration parameters, for different telescope images, are available. The [preset]
parameters are chosen to be *good* parameters for those instruments: 'HST','DC4','DC5','CFHT'
Notice that this preset choices do not disable other parameters.
The flag [use_header] gives the option to use or not the header instance information.
Input:
- fits_image <str> : FITs image filename
- params <list str> : list of parameters to output on sextractor catalogue
- args <dic> : dictionary with sextractor command-line arguments
- use_header <bool> : Whether or not to use (and update) header info
- increase <float> : multiplicative factor for poststamp sizing
- preset <str> : HST, DC4, DC5, CFHT
Output:
- <dic> : dictionary structure with the keys 'objIDs', 'images' and 'headers'.
Each of them is a list with corresponding information.
"""
filename = fits_image;
params.append('NUMBER');
# Deal with fits files and SExtracting the image..
out = _file_2_arrays(fits_image, use_header, params, args, preset);
if not out:
print >> sys.stderr, "Error: An error occured while running SE and extracting FITS files.";
return (False);
objimg, segimg, header, cat = out[0], out[1], out[2], out[3];
# Open "segmentation" output catalog from SE. Notice that object IDs are supposed to lie on 1st column..
# -
# ASCII:
# try:
# objIDs = list( cat[:,0].astype(np.int32) );
# except:
# objIDs = list( cat.astype(np.int32) );
# -
# FITS:
objIDs = cat.data.field('NUMBER');
if ( len(objIDs) == 0 ):
print >> sys.stdout, "No objects were identified on given image.";
return (None);
## objects_IDfy return a list of arrays; result from the objIDs objects located in seg image merged with obj image.
# So, each object in objIDs is returned (also inside a/the list) as array with their corresponding pixels..
#
objs, hdrs = imcp.sextamp( segimg, objimg, header, increase=increase, relative_increase=True, objIDs=objIDs );
return ({'IDs' : objIDs, 'images' : objs, 'headers' : hdrs});
