class StepRGB(StepMIParent):
""" Stone Edge Pipeline Step RGB Object
The object is callable. It requires a valid configuration input
(file or object) when it runs.
"""
stepver = '0.1' # pipe step version
def __init__(self):
""" Constructor: Initialize data objects and variables
"""
# call superclass constructor (calls setup)
super(StepRGB, self).__init__()
# list of data
self.datalist = [] # used in run() for every new input data file
# set configuration
self.log.debug('Init: done')
def setup(self):
""" ### Names and Parameters need to be Set Here ###
Sets the internal names for the function and for saved files.
Defines the input parameters for the current pipe step.
Setup() is called at the end of __init__
The parameters are stored in a list containing the following
information:
- name: The name for the parameter. This name is used when
calling the pipe step from command line or python shell.
It is also used to identify the parameter in the pipeline
configuration file.
- default: A default value for the parameter. If nothing, set
'' for strings, 0 for integers and 0.0 for floats
- help: A short description of the parameter.
"""
### Set Names
# Name of the pipeline reduction step
self.name = 'makergb'
# Shortcut for pipeline reduction step and identifier for
# saved file names.
self.procname = 'rgb'
# Set Logger for this pipe step
self.log = logging.getLogger('stoneedge.pipe.step.%s' % self.name)
### Set Parameter list
# Clear Parameter list
self.paramlist = []
# Append parameters
self.paramlist.append([
'minpercent', 0.05,
'Specifies the percentile for the minimum scaling'
])
self.paramlist.append([
'maxpercent', 0.999,
'Specifies the percentile for the maximum scaling'
])
def run(self):
""" Runs the combining algorithm. The self.datain is run
through the code, the result is in self.dataout.
"""
''' Select 3 input dataset to use, store in datause '''
#Store number of inputs
num_inputs = len(self.datain)
# Create variable to hold input files
# Copy input to output header and filename
datause = []
self.log.debug('Number of input files = %d' % num_inputs)
# Ensure datause has 3 elements irrespective of number of input files
if num_inputs == 0: # Raise exception for no input
raise ValueError('No input')
elif num_inputs == 1:
datause = [self.datain[0], self.datain[0], self.datain[0]]
elif num_inputs == 2:
datause = [self.datain[0], self.datain[1], self.datain[1]]
else: # If inputs exceed 2 in number
# Here we know there are at least 3 files
ilist = [] # Make empty lists for each filter
rlist = []
glist = []
other = []
for element in self.datain: # Loop through the input files and add to the lists
fname = element.filename.lower()
if 'i-band' in fname or 'iband' in fname or 'iprime' in fname:
ilist.append(element)
elif 'r-band' in fname or 'rband' in fname or 'rprime' in fname:
rlist.append(element)
elif 'g-band' in fname or 'gband' in fname or 'gprime' in fname:
glist.append(element)
else:
other.append(element)
continue
self.log.debug(
'len(ilist) = %d, len(rlist) = %d, len(glist) = %d' %
(len(ilist), len(rlist), len(glist)))
# If there is at least one i-, r-, and g-band filter found in self.datain (best case)
if len(ilist) >= 1 and len(rlist) >= 1 and len(glist) >= 1:
# The first image from each filter list will be reduced in the correct order.
datause = [ilist[0], rlist[0], glist[0]]
elif len(ilist) == 0 and len(rlist) >= 1 and len(glist) >= 1:
# Cases where there is no ilist
if len(rlist) > len(glist):
datause = [rlist[0], rlist[1], glist[0]]
else:
datause = [rlist[0], glist[0], glist[1]]
elif len(glist) == 0 and len(rlist) >= 1 and len(ilist) >= 1:
# Cases where there is no glist
if len(rlist) > len(ilist):
datause = [rlist[0], rlist[1], ilist[0]]
else:
datause = [rlist[0], ilist[0], ilist[1]]
elif len(ilist) == 0 and len(rlist) >= 1 and len(glist) >= 1:
# Cases where there is no rlist
if len(ilist) > len(glist):
datause = [ilist[0], ilist[1], glist[0]]
else:
datause = [ilist[0], glist[0], glist[1]]
elif len(rlist) == 0 and len(glist) == 0:
# Case where there is only ilist
datause = [ilist[0], ilist[1], ilist[2]]
elif len(rlist) == 0 and len(ilist) == 0:
# Case where there is only glist
datause = [glist[0], glist[1], glist[2]]
elif len(ilist) == 0 and len(glist) == 0:
# Case where there is only rlist
datause = [rlist[0], rlist[1], rlist[2]]
self.log.debug(
'Files used: R = %s G = %s B = %s' %
(datause[0].filename, datause[1].filename, datause[2].filename))
self.dataout = DataFits(config=self.config)
self.dataout.header = datause[0].header
self.dataout.filename = datause[0].filename
img = datause[0].image
img1 = datause[1].image
img2 = datause[2].image
''' Finding Min/Max scaling values '''
# Create a Data Cube with floats
datacube = numpy.zeros((img.shape[0], img.shape[1], 3), dtype=float)
# Enter the image data into the cube so an absolute max can be found
datacube[:, :, 0] = img
datacube[:, :, 1] = img1
datacube[:, :, 2] = img2
# Find how many data points are in the data cube
datalength = img.shape[0] * img.shape[1] * 3
# Create a 1-dimensional array with all the data, then sort it
datacube.shape = (datalength, )
datacube.sort()
# Now use arrays for each filter to find separate min values
rarray = img.copy()
garray = img1.copy()
barray = img2.copy()
# Shape and sort the arrays
arrlength = img.shape[0] * img.shape[1]
rarray.shape = (arrlength, )
rarray.sort()
garray.shape = (arrlength, )
garray.sort()
barray.shape = (arrlength, )
barray.sort()
# Find the min/max percentile values in the data for scaling
# Values are determined by parameters in the pipe configuration file
minpercent = int(arrlength * self.getarg('minpercent'))
maxpercent = int(datalength * self.getarg('maxpercent'))
# Find the final data values to use for scaling from the image data
rminsv = rarray[minpercent] #sv stands for "scalevalue"
gminsv = garray[minpercent]
bminsv = barray[minpercent]
maxsv = datacube[maxpercent]
self.log.info(' Scale min r/g/b: %f/%f/%f' % (rminsv, gminsv, bminsv))
self.log.info(' Scale max: %f' % maxsv)
# The same min/max values will be used to scale all filters
''' Finished Finding scaling values '''
''' Combining Function '''
# Make new cube with the proper data type for color images (uint8)
# Use square root (sqrt) scaling for each filter
# log or asinh scaling is also available
#astropy.vidualizations.SqrtStretch()
imgcube = numpy.zeros((img.shape[0], img.shape[1], 3), dtype='uint8')
minsv = [rminsv, gminsv, bminsv]
for i in range(3):
# Make normalization function
norm = simple_norm(datause[i].image,
'sqrt',
min_cut=minsv[i],
max_cut=maxsv)
# Apply it
imgcube[:, :, i] = norm(datause[i].image) * 255.
self.dataout.image = imgcube
# Create variable containing all the scaled image data
imgcolor = Image.fromarray(self.dataout.image, mode='RGB')
# Save colored image as a .tif file (without the labels)
imgcolortif = imgcube.copy()
imgcolortif.astype('uint16')
### tiff.imsave('%s.tif' % self.dataout.filenamebase, imgcolortif)
''' End of combining function '''
''' Add a Label to the Image '''
draw = ImageDraw.Draw(imgcolor)
# Use a variable to make the positions and size of text relative
imgwidth = img.shape[1]
imgheight = img.shape[0]
# Open Sans-Serif Font with a size relative to the picture size
try:
# This should work on Linux
font = ImageFont.truetype(
'/usr/share/fonts/liberation/LiberationSans-Regular.ttf',
imgheight // 41)
except:
try:
# This should work on Mac
font = ImageFont.truetype('/Library/Fonts/Arial Unicode.ttf',
imgheight // 41)
except:
try:
# This should work on Windows
font = ImageFont.truetype('C:\\Windows\\Fonts\\arial.ttf',
imgheight // 41)
except:
# This should work in Colab
font = ImageFont.truetype(
'/usr/share/fonts/truetype/liberation/LiberationSans-Regular.ttf',
imgheight // 41)
# If this still doesn't work - then add more code to make it run on YOUR system
# Use the beginning of the FITS filename as the object name
filename = os.path.split(self.dataout.filename)[-1]
try:
objectname = filename.split('_')[0]
objectname = objectname[0].upper() + objectname[1:]
except Exception:
objectname = 'Unknown.'
objectname = 'Object: %s' % objectname
# Read labels at their respective position (kept relative to image size)
# Left corner: object, observer, observatory
# Right corner: Filters used for red, green, and blue colors
draw.text((imgwidth / 100, imgheight / 1.114),
objectname, (255, 255, 255),
font=font)
# Read FITS keywords for the observer, observatory, and filters
if 'OBSERVER' in self.dataout.header:
observer = 'Observer: %s' % self.dataout.getheadval('OBSERVER')
draw.text((imgwidth / 100, imgheight / 1.073),
observer, (255, 255, 255),
font=font)
if 'OBSERVAT' in self.dataout.header:
observatory = 'Observatory: %s' % self.dataout.getheadval(
'OBSERVAT')
draw.text((imgwidth / 100, imgheight / 1.035),
observatory, (255, 255, 255),
font=font)
if 'FILTER' in datause[0].header:
red = 'R: %s' % datause[0].getheadval('FILTER')
draw.text((imgwidth / 1.15, imgheight / 1.114),
red, (255, 255, 255),
font=font)
if 'FILTER' in datause[1].header:
green = 'G: %s' % datause[1].getheadval('FILTER')
draw.text((imgwidth / 1.15, imgheight / 1.073),
green, (255, 255, 255),
font=font)
if 'FILTER' in datause[2].header:
blue = 'B: %s' % datause[2].getheadval('FILTER')
draw.text((imgwidth / 1.15, imgheight / 1.035),
blue, (255, 255, 255),
font=font)
# Make image name
imgname = self.dataout.filenamebegin
if imgname[-1] in '_-,.': imgname = imgname[:-1]
imgname += '.jpg'
# Save the completed image
imgcolor.save(imgname)
self.log.info('Saving file %sjpg' % self.dataout.filenamebegin)
''' End of Label Code '''
# Set complete flag
self.dataout.setheadval('COMPLETE', 1,
'Data Reduction Pipe: Complete Data Flag')
def reset(self):
""" Resets the step to the same condition as it was when it was
created. Internal variables are reset, any stored data is
erased.
"""
self.log.debug('Reset: done')
def test(self):
""" Test Pipe Step Parent Object:
Runs a set of basic tests on the object
"""
# log message
self.log.info('Testing pipe step rgb')
# log message
self.log.info('Testing pipe step rgb - Done')
def run(self):
""" Runs the combining algorithm. The self.datain is run
through the code, the result is in jpeg_dataout.
"""
''' Select 3 input dataset to use, store in datause '''
#Store number of inputs
num_inputs = len(self.datain)
# Create variable to hold input files
# Copy input to output header and filename
datause = [None, None, None]
self.log.debug('Number of input files = %d' % num_inputs)
if num_inputs == 0: # Raise exception for no input
raise ValueError('No input')
elif num_inputs == 1:
datause = [self.datain[0], self.datain[0], self.datain[0]]
elif num_inputs == 2:
datause = [self.datain[0], self.datain[0], self.datain[1]]
else:
filterorder_list = self.getarg('filterorder').split('|')
filterprefs_list = self.getarg('filterprefs').split('|')
datain_filter_list = [
element.getheadval('filter') for element in self.datain
]
used_filter_flags = [False] * len(self.datain)
if len(filterprefs_list) != 3:
self.log.error(
'Invalid number of preferred filters provided (should be 3): '
+ self.getarg('filterprefs'))
else:
# Locate data matching the filters specified in filterprefs
for i, preferred_filter in enumerate(filterprefs_list):
for j, element in enumerate(self.datain):
if element.getheadval('filter') == preferred_filter:
datause[i] = element
used_filter_flags[j] = True
break
filterorder_walker = 0
for i, channel in enumerate(datause):
if channel == None:
for ordered_filter in filterorder_list[
filterorder_walker:]:
filterorder_walker = filterorder_walker + 1
if ordered_filter in datain_filter_list:
datain_index = datain_filter_list.index(
ordered_filter)
if not used_filter_flags[datain_index]:
datause[i] = self.datain[datain_index]
used_filter_flags[datain_index] = True
break
elif channel.getheadval('filter') in filterorder_list:
filterorder_walker = filterorder_list.index(
channel.getheadval('filter'))
for i, channel in enumerate(datause):
if channel == None:
for j, datain_filter in enumerate(datain_filter_list):
if not used_filter_flags[j]:
datause[i] = self.datain[j]
used_filter_flags[j] = True
break
self.log.debug(
'Files used: R = %s G = %s B = %s' %
(datause[0].filename, datause[1].filename, datause[2].filename))
jpeg_dataout = DataFits(config=self.config)
jpeg_dataout.header = datause[0].header
jpeg_dataout.filename = datause[0].filename
img = datause[0].image
img1 = datause[1].image
img2 = datause[2].image
''' Finding Min/Max scaling values '''
# Create a Data Cube with floats
datacube = numpy.zeros((img.shape[0], img.shape[1], 3), dtype=float)
# Enter the image data into the cube so an absolute max can be found
datacube[:, :, 0] = img
datacube[:, :, 1] = img1
datacube[:, :, 2] = img2
# Find how many data points are in the data cube
datalength = img.shape[0] * img.shape[1] * 3
# Create a 1-dimensional array with all the data, then sort it
datacube.shape = (datalength, )
datacube.sort()
# Now use arrays for each filter to find separate min values
rarray = img.copy()
garray = img1.copy()
barray = img2.copy()
# Shape and sort the arrays
arrlength = img.shape[0] * img.shape[1]
rarray.shape = (arrlength, )
rarray.sort()
garray.shape = (arrlength, )
garray.sort()
barray.shape = (arrlength, )
barray.sort()
# Find the min/max percentile values in the data for scaling
# Values are determined by parameters in the pipe configuration file
minpercent = int(arrlength * self.getarg('minpercent'))
maxpercent = int(datalength * self.getarg('maxpercent'))
# Find the final data values to use for scaling from the image data
rminsv = rarray[minpercent] #sv stands for "scalevalue"
gminsv = garray[minpercent]
bminsv = barray[minpercent]
maxsv = datacube[maxpercent]
self.log.info(' Scale min r/g/b: %f/%f/%f' % (rminsv, gminsv, bminsv))
self.log.info(' Scale max: %f' % maxsv)
# The same min/max values will be used to scale all filters
''' Finished Finding scaling values '''
''' Combining Function '''
# Make new cube with the proper data type for color images (uint8)
# Use square root (sqrt) scaling for each filter
# log or asinh scaling is also available
#astropy.vidualizations.SqrtStretch()
imgcube = numpy.zeros((img.shape[0], img.shape[1], 3), dtype='uint8')
minsv = [rminsv, gminsv, bminsv]
for i in range(3):
# Make normalization function
norm = simple_norm(datause[i].image,
'sqrt',
min_cut=minsv[i],
max_cut=maxsv)
# Apply it
imgcube[:, :, i] = norm(datause[i].image) * 255.
jpeg_dataout.image = imgcube
# Create variable containing all the scaled image data
imgcolor = Image.fromarray(jpeg_dataout.image, mode='RGB')
# Save colored image as a .tif file (without the labels)
imgcolortif = imgcube.copy()
imgcolortif.astype('uint16')
### tiff.imsave('%s.tif' % jpeg_dataout.filenamebase, imgcolortif)
''' End of combining function '''
''' Add a Label to the Image '''
draw = ImageDraw.Draw(imgcolor)
# Use a variable to make the positions and size of text relative
imgwidth = img.shape[1]
imgheight = img.shape[0]
# Open Sans-Serif Font with a size relative to the picture size
try:
# This should work on Linux
font = ImageFont.truetype(
'/usr/share/fonts/liberation/LiberationSans-Regular.ttf',
imgheight // 41)
except:
try:
# This should work on Mac
font = ImageFont.truetype('/Library/Fonts/Arial Unicode.ttf',
imgheight // 41)
except:
try:
# This should work on Windows
font = ImageFont.truetype('C:\\Windows\\Fonts\\arial.ttf',
imgheight // 41)
except:
# This should work in Colab
font = ImageFont.truetype(
'/usr/share/fonts/truetype/liberation/LiberationSans-Regular.ttf',
imgheight // 41)
# If this still doesn't work - then add more code to make it run on YOUR system
# Use the beginning of the FITS filename as the object name
filename = os.path.split(jpeg_dataout.filename)[-1]
try:
objectname = filename.split('_')[0]
objectname = objectname[0].upper() + objectname[1:]
except Exception:
objectname = 'Unknown.'
objectname = 'Object: %s' % objectname
# Read labels at their respective position (kept relative to image size)
# Left corner: object, observer, observatory
# Right corner: Filters used for red, green, and blue colors
draw.text((imgwidth / 100, imgheight / 1.114),
objectname, (255, 255, 255),
font=font)
# Read FITS keywords for the observer, observatory, and filters
if 'OBSERVER' in jpeg_dataout.header:
observer = 'Observer: %s' % jpeg_dataout.getheadval('OBSERVER')
draw.text((imgwidth / 100, imgheight / 1.073),
observer, (255, 255, 255),
font=font)
if 'OBSERVAT' in jpeg_dataout.header:
observatory = 'Observatory: %s' % jpeg_dataout.getheadval(
'OBSERVAT')
draw.text((imgwidth / 100, imgheight / 1.035),
observatory, (255, 255, 255),
font=font)
if 'FILTER' in datause[0].header:
red = 'R: %s' % datause[0].getheadval('FILTER')
draw.text((imgwidth / 1.15, imgheight / 1.114),
red, (255, 255, 255),
font=font)
if 'FILTER' in datause[1].header:
green = 'G: %s' % datause[1].getheadval('FILTER')
draw.text((imgwidth / 1.15, imgheight / 1.073),
green, (255, 255, 255),
font=font)
if 'FILTER' in datause[2].header:
blue = 'B: %s' % datause[2].getheadval('FILTER')
draw.text((imgwidth / 1.15, imgheight / 1.035),
blue, (255, 255, 255),
font=font)
# Make image name
imgname = jpeg_dataout.filenamebegin
if imgname[-1] in '_-,.': imgname = imgname[:-1]
imgname += '.jpg'
# Save the completed image
imgcolor.save(imgname)
self.log.info('Saving file %sjpg' % jpeg_dataout.filenamebegin)
# Optional folder output setup
baseimgname = os.path.basename(imgname)
folderpaths_list = self.getarg('folderpaths').split(':')
for path in folderpaths_list:
path = time.strftime(path, time.localtime())
if not os.path.exists(path):
if self.getarg('createfolders'):
os.makedirs(path)
self.log.info('Creating directory %s' % path)
else:
self.log.info('Invalid folder path %s' % path)
try:
imgcolor.save(os.path.join(path, baseimgname))
except:
self.log.exception('Could not save image to directory %s' %
path)
''' End of Label Code '''
# Set complete flag
jpeg_dataout.setheadval('COMPLETE', 1,
'Data Reduction Pipe: Complete Data Flag')
### Make output data
self.dataout = self.datain.copy()
self.dataout.append(jpeg_dataout)