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export.py
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export.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
'''This module contains functions to export FITS images to other formats.
'''
#############
## LOGGING ##
#############
import logging
from fitsbits import log_sub, log_fmt, log_date_fmt
DEBUG = False
if DEBUG:
level = logging.DEBUG
else:
level = logging.INFO
LOGGER = logging.getLogger(__name__)
logging.basicConfig(
level=level,
style=log_sub,
format=log_fmt,
datefmt=log_date_fmt,
)
LOGDEBUG = LOGGER.debug
LOGINFO = LOGGER.info
LOGWARNING = LOGGER.warning
LOGERROR = LOGGER.error
LOGEXCEPTION = LOGGER.exception
#############
## IMPORTS ##
#############
import os
import os.path
import subprocess
import multiprocessing
import sys
# Ref: https://bugs.python.org/issue33725
# TLDR; Apple is trash at UNIX
if sys.platform == 'darwin':
mp = multiprocessing.get_context('forkserver')
else:
mp = multiprocessing
import glob
import numpy as np
from PIL import Image
from PIL import ImageDraw
from PIL import ImageFont
from ._extractors import clean_fname
from . import operations
from astropy import wcs
############
## CONFIG ##
############
NCPUS = mp.cpu_count()
# get the ImageFont
fontpath = os.path.join(os.path.dirname(__file__), 'DejaVuSans.ttf')
# load the font
if os.path.exists(fontpath):
fontsmall = ImageFont.truetype(fontpath, 12)
fontnormal = ImageFont.truetype(fontpath, 20)
fontlarge = ImageFont.truetype(fontpath, 28)
else:
print('could not find bundled '
'DejaVu Sans font, using ugly defaults...')
fontsmall = ImageFont.load_default()
fontnormal = ImageFont.load_default()
fontlarge = ImageFont.load_default()
##################
## IMAGE STAMPS ##
##################
def img_to_stamps(img,
stampsize=256):
'''Generate stamps for an image.
The stamps are generated for the center, the corners, and the middle of the
sides of the frame. This is useful to monitor image quality and star shapes
as a function of position on the frame.
Parameters
----------
img : np.array of 2 dimensions
The input image to process.
stampsize : int
The size in pixels of each stamp. Stamps are square so this describes
both width and height.
Returns
-------
dict
Returns a dict of the form::
{
'topleft': 2D np.array cutout,
'topcenter': 2D np.array cutout,
'topright': 2D np.array cutout,
'midleft':2D np.array cutout,
'midcenter': 2D np.array cutout,
'midright': 2D np.array cutout,
'bottomleft': 2D np.array cutout,
'bottomcenter': 2D np.array cutout,
'bottomright': 2D np.array cutout
}
'''
imgsizex, imgsizey = img.shape
xstampsize, ystampsize = stampsize, stampsize
# get the total number of possible stamps
n_possible_xstamps = imgsizex/float(xstampsize)
n_possible_ystamps = imgsizey/float(ystampsize)
# if we can actually make stamps, then go ahead
if (n_possible_xstamps >= 3) and (n_possible_ystamps >= 3):
topleft = img[:xstampsize,:ystampsize]
topcenter = img[
int(imgsizex/2-xstampsize/2):int(imgsizex/2+xstampsize/2),
:ystampsize
]
topright = img[imgsizex-xstampsize:,:ystampsize]
midleft = img[
:xstampsize,
int(imgsizey/2-ystampsize/2):int(imgsizey/2+ystampsize/2)
]
midcenter = img[
int(imgsizex/2-xstampsize/2):int(imgsizex/2+xstampsize/2),
int(imgsizey/2-ystampsize/2):int(imgsizey/2+ystampsize/2)
]
midright = img[
imgsizex-xstampsize:,
int(imgsizey/2-ystampsize/2):int(imgsizey/2+ystampsize/2)
]
bottomleft = img[:xstampsize,imgsizey-ystampsize:]
bottomcenter = img[
int(imgsizex/2-xstampsize/2):int(imgsizex/2+xstampsize/2),
imgsizey-ystampsize:
]
bottomright = img[-xstampsize:,-ystampsize:]
return {
'topleft':topleft,
'topcenter':topcenter,
'topright':topright,
'midleft':midleft,
'midcenter':midcenter,
'midright':midright,
'bottomleft':bottomleft,
'bottomcenter':bottomcenter,
'bottomright':bottomright
}
else:
LOGERROR('stampsize is too large for this image')
return None
def fits_to_stamps(fits_image,
outfile,
fits_extension=None,
trimkeys=('TRIMSEC','DATASEC','TRIMSEC0'),
scale_func=operations.clipped_linscale_img,
scale_func_params=None,
stampsize=128,
separatorwidth=1,
annotate=True,
fits_jdsrc=None,
fits_jdkey='JD',
frame_time=None):
'''This turns an FITS image into a scaled version, stamps it, and returns an
PNG/JPG file.
'''
compressed_ext = operations.compressed_fits_ext(fits_image)
if fits_extension is None and compressed_ext:
img, hdr = operations.read_fits(fits_image,
ext=compressed_ext[0])
elif (fits_extension is not None):
img, hdr = operations.read_fits(fits_image, ext=fits_extension)
else:
img, hdr = operations.read_fits(fits_image)
trimmed_img = operations.trim_image(img, hdr, trimkeys=trimkeys)
if isinstance(scale_func_params, dict):
scaled_img = scale_func(trimmed_img, **scale_func_params)
else:
scaled_img = scale_func(trimmed_img)
image_stamps = img_to_stamps(scaled_img, stampsize=stampsize)
toprow_xsize, toprow_ysize = image_stamps['topright'].shape
toprow_separr = np.array([[255.0]*separatorwidth]*toprow_ysize)
# build stacks
topleft = image_stamps['topleft']
midleft = image_stamps['midleft']
bottomleft = image_stamps['bottomleft']
topcenter = image_stamps['topcenter']
midcenter = image_stamps['midcenter']
bottomcenter = image_stamps['bottomcenter']
topright = image_stamps['topright']
midright = image_stamps['midright']
bottomright = image_stamps['bottomright']
toprow_stamp = np.hstack((topleft,
toprow_separr,
midleft,
toprow_separr,
bottomleft))
midrow_xsize, midrow_ysize = midright.shape
midrow_separr = np.array([[255.0]*separatorwidth]*midrow_ysize)
# similarly, these should be midleft, midcenter, midright
midrow_stamp = np.hstack((topcenter,
midrow_separr,
midcenter,
midrow_separr,
bottomcenter))
bottomrow_xsize, bottomrow_ysize = bottomright.shape
bottomrow_ysize = bottomright.shape[1]
bottomrow_separr = np.array([[255.0]*separatorwidth]*bottomrow_ysize)
# similarly, these should be bottomleft, bottomcenter, bottomright
bottomrow_stamp = np.hstack((topright,
bottomrow_separr,
midright,
bottomrow_separr,
bottomright))
full_stamp = np.vstack(
(toprow_stamp,
np.array([255.0]*(toprow_xsize*3 + separatorwidth*2)),
midrow_stamp,
np.array([255.0]*(midrow_xsize*3 + separatorwidth*2)),
bottomrow_stamp)
)
full_stamp = np.flipud(full_stamp)
pillow_image = Image.fromarray(full_stamp)
pillow_image = pillow_image.convert('L')
# annotate the image if told to do so
if annotate:
draw = ImageDraw.Draw(pillow_image)
# if we're supposed to use another file for the JD source, do so
# this is useful for subtracted images
if fits_jdsrc is not None and os.path.exists(fits_jdsrc):
framejd = operations.get_header_keyword(fits_jdsrc, fits_jdkey)
elif frame_time is not None:
framejd = frame_time
else:
framejd = hdr[fits_jdkey] if fits_jdkey in hdr else None
if framejd is not None:
timeannotation = '%.5f' % framejd
draw.text((5, pillow_image.size[1] - 15),
timeannotation,
font=fontsmall,
fill=255)
# draw the image basename
basename_annotation = os.path.splitext(
clean_fname(fits_image, basename=True)
)[0]
draw.text((5, 5),
basename_annotation,
font=fontsmall,
fill=255)
del draw
pillow_image.save(outfile)
return outfile
def parallel_fits_stamp_worker(task):
'''
This is a parallel worker for the FITS to zscaled stamps process.
'''
fits, options = task
try:
outpngpath = '%s-stamps-3x3.png' % clean_fname(fits)
donepng = fits_to_stamps(
fits,
outpngpath,
**options
)
LOGINFO('%s -> %s OK' % (fits, donepng))
return donepng
except Exception:
LOGEXCEPTION('could not convert %s to stamp PNG' % fits)
return None
def parallel_fitslist_to_stamps(fitslist,
ext=None,
trimkeys=('TRIMSEC','DATASEC','TRIMSEC0'),
stampsize=128,
separatorwidth=1,
annotate=True,
nworkers=NCPUS,
maxworkertasks=1000):
'''
This drives parallel execution of FITS to stamps.
'''
tasks = [(x, {'fits_extension':ext,
'trimkeys':trimkeys,
'stampsize':stampsize,
'annotate':annotate,
'separatorwidth':separatorwidth}) for x in
fitslist]
pool = mp.Pool(nworkers, maxtasksperchild=maxworkertasks)
results = pool.map(parallel_fits_stamp_worker, tasks)
pool.close()
pool.join()
return results
def parallel_fitsdir_to_stamps(fitsdir,
fitsglob,
ext=None,
trimkeys=('TRIMSEC','DATASEC','TRIMSEC0'),
stampsize=128,
separatorwidth=1,
annotate=True,
nworkers=NCPUS,
maxworkertasks=1000):
'''
This makes stamps for a directory of FITS files.
'''
fitslist = glob.glob(os.path.join(fitsdir, fitsglob))
return parallel_fitslist_to_stamps(
fitslist,
ext=ext,
trimkeys=trimkeys,
stampsize=stampsize,
separatorwidth=separatorwidth,
annotate=annotate,
nworkers=nworkers,
maxworkertasks=maxworkertasks
)
######################
## FULL FRAME JPEGS ##
######################
def nparray_to_full_jpeg(
array,
outfile,
scale_func=operations.clipped_linscale_img,
scale_func_params=None,
flip=True,
resize=False,
resizefrac=None,
):
'''
This writes a numpy array to a JPEG.
'''
# scale the image if requested
if scale_func is not None and isinstance(scale_func_params, dict):
scaled_img = scale_func(array,**scale_func_params)
elif scale_func is not None:
scaled_img = scale_func(array)
else:
scaled_img = array
# flip the image if requested
if flip is True:
scaled_img = np.flipud(scaled_img)
# convert to PIL.Image
pillow_image = Image.fromarray(scaled_img)
pillow_image = pillow_image.convert('L')
# resize the image if requested
if resize and resizefrac is not None and resizefrac > 0:
pillow_image = pillow_image.resize(
(int(scaled_img.shape[1]*resizefrac),
int(scaled_img.shape[0]*resizefrac)),
Image.BICUBIC
)
# save the file and return
pillow_image.save(outfile, optimize=True, quality=85)
return os.path.abspath(outfile)
def fits_to_full_jpeg(
fits_image,
out_fname=None,
ext=None,
trim=True,
trimkeys=('TRIMSEC','DATASEC','TRIMSEC0'),
scale_func=operations.clipped_linscale_img,
scale_func_params=None,
flip=True,
resize=False,
resizefrac=None,
annotate=True,
fits_jdsrc=None,
fits_jdkey='JD',
frame_time=None,
fits_imagetype_key='IMAGETYP',
fits_exptime_key='EXPTIME',
fits_filters_key='FILTERS',
fits_project_key='PROJID',
fits_object_key='OBJECT',
):
'''This converts a FITS image to a full frame JPEG.
The default scaling function is operations.clipped_linscale_img, mostly
because it's faster than operations.zscale_img.
'''
# handle .fz and non-zero extension FITS reading
compressed_ext = operations.compressed_fits_ext(fits_image)
if ext is None and compressed_ext:
img, hdr = operations.read_fits(fits_image,
ext=compressed_ext[0])
elif (ext is not None):
img, hdr = operations.read_fits(fits_image, ext=ext)
else:
img, hdr = operations.read_fits(fits_image)
# trim the image if requested
if trim:
trimmed_img = operations.trim_image(img, hdr, trimkeys=trimkeys)
else:
trimmed_img = img
# scale the image if requested
if scale_func is not None and isinstance(scale_func_params, dict):
scaled_img = scale_func(trimmed_img,**scale_func_params)
elif scale_func is not None:
scaled_img = scale_func(trimmed_img)
else:
scaled_img = trimmed_img
# flip the image if requested
if flip is True:
scaled_img = np.flipud(scaled_img)
# convert to PIL.Image
pillow_image = Image.fromarray(scaled_img)
pillow_image = pillow_image.convert('L')
# resize the image if requested
if resize and resizefrac is not None and resizefrac > 0:
pillow_image = pillow_image.resize(
(int(scaled_img.shape[1]*resizefrac),
int(scaled_img.shape[0]*resizefrac)),
Image.BICUBIC
)
# annotate the image if told to do so
if annotate:
draw = ImageDraw.Draw(pillow_image)
annotation = "%s: %s - %s - %s - proj%s - %s" % (
clean_fname(fits_image, basename=True),
(hdr[fits_imagetype_key].lower()
if fits_imagetype_key in hdr else 'imgtype_unknown'),
(hdr[fits_exptime_key]
if fits_exptime_key in hdr else 'exptime_unknown'),
(hdr[fits_filters_key].replace('+','') if
fits_filters_key in hdr else 'filt_unknown'),
(hdr[fits_project_key]
if fits_project_key in hdr else '_unknown'),
hdr[fits_object_key] if fits_object_key in hdr else 'obj_unknown'
)
draw.text((10,10),
annotation,
font=fontnormal,
fill=255)
# now add the time as well
# if we're supposed to use another file for the JD source, do so
# this is useful for subtracted images
if fits_jdsrc is not None and os.path.exists(fits_jdsrc):
framejd = operations.get_header_keyword(fits_jdsrc, fits_jdkey)
elif frame_time is not None:
framejd = frame_time
else:
framejd = hdr[fits_jdkey] if fits_jdkey in hdr else None
if framejd is not None:
timeannotation = '%.5f' % framejd
draw.text((10, pillow_image.size[1] - 40),
timeannotation,
font=fontlarge,
fill=255)
del draw
# finally, generate the output file name if None is given
if not out_fname:
out_fname = '%s-%s-%s-%s-proj%s-%s.jpg' % (
fits_image.replace('.fits','').replace('.fz',''),
(hdr[fits_imagetype_key].lower()
if fits_imagetype_key in hdr else 'imgtype_unknown'),
(hdr[fits_exptime_key]
if fits_exptime_key in hdr else 'exptime_unknown'),
(hdr[fits_filters_key].replace('+','') if
fits_filters_key in hdr else 'filt_unknown'),
hdr[fits_project_key] if fits_project_key in hdr else '_unknown',
hdr[fits_object_key] if fits_object_key in hdr else 'obj_unknown'
)
# save the file and return
pillow_image.save(out_fname, optimize=True, quality=85)
return os.path.abspath(out_fname)
def parallel_jpeg_worker(task):
'''
This wraps imageutils.fits_to_full_jpeg.
task[0] = FITS path
task[1] = {'ext', 'resize', 'flip', 'outsizex', 'outsizey'}
'''
try:
return fits_to_full_jpeg(task[0], **task[1])
except Exception:
LOGEXCEPTION('failed to make JPEG for %s' % (task[0],))
return None
def parallel_frame_jpegs(
infits,
fitsglob='*.fits',
outf_dir=None,
outf_extension='jpg',
outf_postfix=None,
ext=None,
trim=True,
trimkeys=('TRIMSEC','DATASEC','TRIMSEC0'),
scale_func=operations.clipped_linscale_img,
scale_func_params=None,
flip=True,
resize=False,
resizefrac=None,
annotate=True,
fits_jdsrc=None,
fits_jdkey='JD',
frame_time=None,
fits_imagetype_key='IMAGETYP',
fits_exptime_key='EXPTIME',
fits_filters_key='FILTERS',
fits_project_key='PROJID',
fits_object_key='OBJECT',
nworkers=NCPUS,
maxworkertasks=1000
):
'''
This makes JPEGs out of the frames in fitsdir.
'''
# initialize the pool of workers
pool = mp.Pool(nworkers, maxtasksperchild=maxworkertasks)
if isinstance(infits,str):
fitslist = sorted(glob.glob(os.path.join(os.path.abspath(infits),
fitsglob)))
elif isinstance(infits, list):
fitslist = infits
if outf_postfix is None:
outf_postfix = ''
else:
outf_postfix = '-%s' % outf_postfix
out_flist = ['%s%s.%s' % (clean_fname(x),
outf_postfix,
outf_extension) for x in fitslist]
if outf_dir is not None:
if not os.path.exists(outf_dir):
os.makedirs(outf_dir)
out_flist = [os.path.join(outf_dir, x) for x in out_flist]
# only make JPEGs that don't yet exist
work_on_flist = []
work_on_outlist = []
for f, o in zip(fitslist, out_flist):
if not os.path.exists(o):
work_on_flist.append(f)
work_on_outlist.append(o)
tasks = [
(x,{'ext':ext,
'out_fname':y,
'trim':trim,
'scale_func':scale_func,
'scale_func_params':scale_func_params,
'trimkeys':trimkeys,
'resize':resize,
'flip':flip,
'resize':resize,
'resizefrac':resizefrac,
'annotate':annotate,
'fits_imagetype_key':fits_imagetype_key,
'fits_exptime_key':fits_exptime_key,
'fits_filters_key':fits_filters_key,
'fits_project_key':fits_project_key,
'fits_object_key':fits_object_key,
'fits_jdsrc':fits_jdsrc,
'fits_jdkey':fits_jdkey,
'frame_time':frame_time})
for x,y in zip(work_on_flist, work_on_outlist)
]
# fire up the pool of workers
pool.map(parallel_jpeg_worker, tasks)
# wait for the processes to complete work
pool.close()
pool.join()
resultdict = {x:y for x,y in zip(fitslist, out_flist) if os.path.exists(y)}
return resultdict
#################################
## FITS TO SUB-IMAGE BOX JPEGS ##
#################################
def fits_radecbox_to_jpeg(
fits_image,
box,
boxtype='center',
wcsfrom=None,
out_fname=None,
ext=None,
trim=True,
trimkeys=('TRIMSEC','DATASEC','TRIMSEC0'),
scale_func=operations.zscale_image,
scale_func_params=None,
flip=True,
annotate=True,
fits_imagetype_key='IMAGETYP',
fits_exptime_key='EXPTIME',
fits_filters_key='FILTERS',
fits_project_key='PROJID',
fits_object_key='OBJECT',
fits_jdsrc=None,
fits_jdkey='JD',
frame_time=None,
):
'''
This converts an radec box inside a FITS image to a JPEG.
boxtype is either 'center' or 'bounds'.
- if boxtype == 'bounds', then box is: [rmin, rmax, dmin, dmax]
- if boxtype == 'center', then box is: [rcenter, dcenter, rwidth, dwidth]
wcsfrom indicates where the WCS for the image comes from:
- None, take the WCS from the image itself
- a path to a file, take the WCS from the specified file.
'''
# handle .fz and non-zero extension FITS reading
compressed_ext = operations.compressed_fits_ext(fits_image)
if ext is None and compressed_ext:
img, hdr = operations.read_fits(fits_image,
ext=compressed_ext[0])
elif (ext is not None):
img, hdr = operations.read_fits(fits_image, ext=ext)
else:
img, hdr = operations.read_fits(fits_image)
# get the WCS header
try:
if wcsfrom and os.path.exists(wcsfrom):
w = wcs.WCS(wcsfrom)
else:
w = wcs.WCS(hdr)
except Exception:
LOGEXCEPTION("no WCS found for FITS: %s, can't continue" % fits_image)
return None
# convert the radec box into a box in pixel space
if boxtype == 'bounds':
rd = np.array([[box[0], box[2]],
[box[1], box[3]]])
# we use 0 here for the origin because we'll be cutting using np.arrays
LOGINFO('Requested coords = %s' % repr(rd))
pix = w.all_world2pix(rd,0)
elif boxtype == 'center':
rd = np.array(
[
[box[0] - (box[2])/2.0,
box[1] - (box[3])/2.0],
[box[0] + (box[2])/2.0,
box[1] + (box[3])/2.0],
]
)
LOGINFO('Requested coords = %s' % repr(rd))
pix = w.all_world2pix(rd,0)
# do the cutout using a box generated by the radec -> pix bits above
x1, x2, y1, y2 = pix[0,0], pix[1,0], pix[0,1], pix[1,1]
# figure out xmin, xmax, ymin, ymax
if x1 > x2:
xmin = x2
xmax = x1
else:
xmin = x1
xmax = x2
if y1 > y2:
ymin = y2
ymax = y1
else:
ymin = y1
ymax = y2
# round the pix coords to integers
xmin, xmax = int(np.round(xmin)), int(np.round(xmax))
ymin, ymax = int(np.round(ymin)), int(np.round(ymax))
LOGINFO('Pixel box xmin = %s, xmax = %s' % (xmin, xmax))
LOGINFO('Pixel box ymin = %s, ymax = %s' % (ymin, ymax))
#
# now, read in the image
#
# trim the image if requested
if trim:
trimmed_img = operations.trim_image(img, hdr, trimkeys=trimkeys)
else:
trimmed_img = img
# scale the image if requested
if scale_func is not None and isinstance(scale_func_params, dict):
scaled_img = scale_func(trimmed_img,**scale_func_params)
elif scale_func is not None:
scaled_img = scale_func(trimmed_img)
else:
scaled_img = trimmed_img
# make sure we take care of edges
if xmin < 0:
xmin = 0
if xmax >= scaled_img.shape[1]:
xmax = scaled_img.shape[1] - 1
if ymin < 0:
ymin = 0
if ymax >= scaled_img.shape[0]:
ymax = scaled_img.shape[0] - 1
#
# apply the box
#
# numpy is y,x so make sure to reverse the order
boxed_img = scaled_img[ymin:ymax, xmin:xmax]
# flip the image if requested
if flip is True:
boxed_img = np.flipud(boxed_img)
# convert to PIL.Image
pillow_image = Image.fromarray(boxed_img)
pillow_image = pillow_image.convert('L')
# annotate the image if told to do so
if annotate:
draw = ImageDraw.Draw(pillow_image)
# if we're supposed to use another file for the JD source, do so
# this is useful for subtracted images
if fits_jdsrc is not None and os.path.exists(fits_jdsrc):
framejd = float(operations.get_header_keyword(fits_jdsrc,
fits_jdkey))
elif frame_time is not None:
framejd = frame_time
else:
framejd = float(hdr[fits_jdkey]) if fits_jdkey in hdr else None
if framejd is not None:
timeannotation = 'JD %.4f' % framejd
draw.text((4, 2),
timeannotation,
font=fontsmall,
fill=255)
del draw
# finally, generate the output file name if None is given
if not out_fname:
if boxtype == 'center':
box_infostr = 'RC%.3fDC%.3f-RW%.3fDW%.3f' % (
box[0], box[1], box[2], box[3]
)
elif boxtype == 'bounds':
box_infostr = 'RL%.3fRH%.3f-DL%.3fDH%.3f' % (
box[0], box[1], box[2], box[3]
)
out_fname = '%s-%s-%s-%s-proj%s-%s-%s.jpg' % (
fits_image.replace('.fits','').replace('.fz',''),
(hdr[fits_imagetype_key].lower()
if fits_imagetype_key in hdr else 'imgtype_unknown'),
(hdr[fits_exptime_key]
if fits_exptime_key in hdr else 'exptime_unknown'),
(hdr[fits_filters_key].replace('+','') if
fits_filters_key in hdr else 'filt_unknown'),
hdr[fits_project_key] if fits_project_key in hdr else '_unknown',
hdr[fits_object_key] if fits_object_key in hdr else 'obj_unknown',
box_infostr,
)
# save the file and return
pillow_image.save(out_fname)
return os.path.abspath(out_fname)
def fits_xybox_to_jpeg(
fits_image,
box,
boxtype='center',
out_fname=None,
ext=None,
trim=True,
trimkeys=('TRIMSEC','DATASEC','TRIMSEC0'),
scale_func=operations.zscale_image,
scale_func_params=None,
flip=True,
annotate=True,
fits_imagetype_key='IMAGETYP',
fits_exptime_key='EXPTIME',
fits_filters_key='FILTERS',
fits_project_key='PROJID',
fits_object_key='OBJECT',
fits_jdsrc=None,
fits_jdkey='JD',
frame_time=None,
):
'''
This converts an x-y coords box inside a FITS image to a JPEG.
boxtype is either 'center' or 'bounds'.
- if boxtype == 'bounds', then box is: [xmin, xmax, ymin, max]
- if boxtype == 'center', then box is: [xcenter, ycenter, xwidth, ywidth]
'''
# handle .fz and non-zero extension FITS reading
compressed_ext = operations.compressed_fits_ext(fits_image)
if ext is None and compressed_ext:
img, hdr = operations.read_fits(fits_image,
ext=compressed_ext[0])
elif (ext is not None):
img, hdr = operations.read_fits(fits_image, ext=ext)
else:
img, hdr = operations.read_fits(fits_image)
#
# trim the image if requested
#
if trim:
trimmed_img = operations.trim_image(img, hdr, trimkeys=trimkeys)
else:
trimmed_img = img
# scale the image if requested
if scale_func is not None and isinstance(scale_func_params, dict):
scaled_img = scale_func(trimmed_img,**scale_func_params)
elif scale_func is not None:
scaled_img = scale_func(trimmed_img)
else:
scaled_img = trimmed_img
if boxtype == 'bounds':
x1, x2 = box[0], box[1]
y1, y2 = box[2], box[3]
# figure out xmin, xmax, ymin, ymax
if x1 > x2:
xmin = x2
xmax = x1
else:
xmin = x1
xmax = x2
if y1 > y2:
ymin = y2
ymax = y1
else:
ymin = y1
ymax = y2
# round the pix coords to integers
xmin, xmax = int(np.round(xmin)), int(np.round(xmax))
ymin, ymax = int(np.round(ymin)), int(np.round(ymax))
# make sure we take care of edges
if xmin < 0:
xmin = 0
if xmax >= img.shape[1]:
xmax = img.shape[1] - 1
if ymin < 0:
ymin = 0
if ymax >= img.shape[0]:
ymax = img.shape[0] - 1
boxed_img = scaled_img[ymin:ymax, xmin:xmax]
elif boxtype == 'center':