def run_copy_header(sourceheader, targetheader, keys):
"""
Copy information from sourceheader to targetheader
for given keys.
"""
# Read header info to dictionaries
source_header_dict = envi_header.read_hdr_file(args.sourceheader[0])
target_header_dict = envi_header.read_hdr_file(args.targetheader[0])
copy_header_dict_items(source_header_dict, target_header_dict, args.keys)
# Make a backup copy of the origional header
try:
if os.path.isfile(args.targetheader[0] + ".bak"):
print(
"Backup file {}.bak already exists. Please remove " "and retry".format(args.targetheader[0]),
file=sys.stderr,
)
sys.exit(1)
shutil.copy2(args.targetheader[0], args.targetheader[0] + ".bak")
except IOError:
print(
"Could create backup copy of header in same destination as source "
"- possibly due to folder permissions. Aborting",
file=sys.stderr,
)
except Exception:
raise
envi_header.write_envi_header(args.targetheader[0], target_header_dict)
print("Copied items to {0}, origional header " "saved as {0}.bak".format(args.targetheader[0]))
def run_copy_header(sourceheader, targetheader, keys):
"""
Copy information from sourceheader to targetheader
for given keys.
"""
# Read header info to dictionaries
source_header_dict = envi_header.read_hdr_file(args.sourceheader[0],
keep_case=True)
target_header_dict = envi_header.read_hdr_file(args.targetheader[0],
keep_case=True)
copy_header_dict_items(source_header_dict, target_header_dict, args.keys)
# Make a backup copy of the origional header
try:
if os.path.isfile(args.targetheader[0] + '.bak'):
print("Backup file {}.bak already exists. Please remove "
"and retry".format(args.targetheader[0]),
file=sys.stderr)
sys.exit(1)
shutil.copy2(args.targetheader[0], args.targetheader[0] + '.bak')
except IOError:
print(
"Could create backup copy of header in same destination as source "
"- possibly due to folder permissions. Aborting",
file=sys.stderr)
except Exception:
raise
envi_header.write_envi_header(args.targetheader[0], target_header_dict)
print("Copied items to {0}, origional header "
"saved as {0}.bak".format(args.targetheader[0]))
def get_bands_from_wavelengths(input_image, wavelengths=DEFAULT_WAVELENGTHS):
"""
Get bands for a list of wavelengths (useful for creating three band
composites).
Function adapted from one in createimage.py
"""
input_header = envi_header.find_hdr_file(input_image)
if input_header is None:
raise Exception("Need to use ENVI header to get bands")
header_dict = envi_header.read_hdr_file(input_header)
image_wavelengths = header_dict['wavelength'].split(',')
out_bands = []
for wavelength in wavelengths:
band = min(range(len(image_wavelengths)),
key=lambda x:abs(float(image_wavelengths[x])-wavelength))
out_bands.append(band)
return out_bands
def get_bands_from_wavelengths(input_image, wavelengths=DEFAULT_WAVELENGTHS):
"""
Get bands for a list of wavelengths (useful for creating three band
composites).
Function adapted from one in createimage.py
"""
input_header = envi_header.find_hdr_file(input_image)
if input_header is None:
raise Exception("Need to use ENVI header to get bands")
header_dict = envi_header.read_hdr_file(input_header)
image_wavelengths = header_dict['wavelength'].split(',')
out_bands = []
for wavelength in wavelengths:
band = min(list(range(len(image_wavelengths))),
key=lambda x: abs(float(image_wavelengths[x]) - wavelength))
out_bands.append(band)
return out_bands
def apply_elc(input_image, output_image, image_spectra_file,
field_spectra_file,
scale_factor=DEFAULT_SCALE_FACTOR):
"""
Derive coefficients for an emprical line calibration and apply
to a BIL file.
"""
print("Getting coefficients")
elc_coefficients = get_elc_coefficients(image_spectra_file,
field_spectra_file)
print("Applying to image")
# Read ENVI header
input_header_file = envi_header.find_hdr_file(input_image)
input_header_dict = envi_header.read_hdr_file(input_header_file)
# Open input file
in_data = numpy_bin_reader.BilReader(input_image)
# Open file for output
out_file = open(output_image, "wb")
for i, line in enumerate(in_data):
print(" Line {0:05}/{1:05}".format(i, in_data.lines), end="\r")
# Apply coefficients
elc_line = elc_coefficients[:,0] * line.transpose() \
+ elc_coefficients[:,1]
elc_line = elc_line.transpose()
# Set any negative values to 0
elc_line = numpy.where(elc_line < 0, 0, elc_line)
elc_line = elc_line * scale_factor
# For default scale factor is over 1000 express as integer
if scale_factor >= 1000:
elc_line = elc_line.astype(numpy.uint16)
else:
elc_line = elc_line.astype(numpy.float32)
elc_line.tofile(out_file)
print(" Line {0:05}/{0:05}".format(in_data.lines), end="\n")
output_header_dict = input_header_dict
# See if need to change data type
if scale_factor >= 1000:
output_header_dict["data type"] = 12
else:
output_header_dict["data type"] = 4
# Remove radiance data units
try:
output_header_dict.pop("radiance data units")
except KeyError:
pass
# Add scale factor
output_header_dict["reflectance scale factor"] = scale_factor
procesing_comment = """\nConverted to reflectance using empirical line method. Field spectra: {}, Image spectra: {}\n""".format(field_spectra_file, image_spectra_file)
try:
output_header_dict["_comments"] = output_header_dict["_comments"] + \
procesing_comment
except KeyError:
output_header_dict["_comments"] = procesing_comment
output_header_dict["description"] = "BIL file created by {} on {}".format(__file__,
time.strftime("%Y-%m-%d %H:%M:%S"))
# Copy header
envi_header.write_envi_header(output_image + ".hdr",
output_header_dict)
# Close files
out_file.close()
in_data = None
dates = []
start_times = []
stop_times = []
for each_hdr in args.inputfiles:
# Check a header file has been provided.
if os.path.splitext(each_hdr)[-1].lower() != '.hdr':
print('Must provide header file as input (ending with ".hdr")',
file=sys.stderr)
sys.exit(1)
if not os.path.isfile(each_hdr):
print('Could not open input header ({})'.format(each_hdr),
file=sys.stderr)
sys.exit(1)
header_dict = envi_header.read_hdr_file(each_hdr)
# Get supplimentary info for csv if available
if 'acquisition date' in header_dict.keys():
dates.append(header_dict['acquisition date'].split(' ')[-1])
else:
dates.append(float('nan'))
if 'gps start time' in header_dict.keys():
start_times.append(header_dict['gps start time'].split(' ')[-1])
else:
start_times.append(float('nan'))
if 'gps stop time' in header_dict.keys():
stop_times.append(header_dict['gps stop time'].split(' ')[-1])
else:
stop_times.append(float('nan'))
if "bands" in header_dict.keys():
dates = []
start_times = []
stop_times = []
for each_hdr in args.inputfiles:
# Check a header file has been provided.
if os.path.splitext(each_hdr)[-1].lower() != '.hdr':
print('Must provide header file as input (ending with ".hdr")',
file=sys.stderr)
sys.exit(1)
if not os.path.isfile(each_hdr):
print('Could not open input header ({})'.format(each_hdr),
file=sys.stderr)
sys.exit(1)
header_dict = envi_header.read_hdr_file(each_hdr)
# Get supplimentary info for csv if available
if 'acquisition date' in list(header_dict.keys()):
dates.append(header_dict['acquisition date'].split(' ')[-1])
else:
dates.append(float('nan'))
if 'gps start time' in list(header_dict.keys()):
start_times.append(header_dict['gps start time'].split(' ')[-1])
else:
start_times.append(float('nan'))
if 'gps stop time' in list(header_dict.keys()):
stop_times.append(header_dict['gps stop time'].split(' ')[-1])
else:
stop_times.append(float('nan'))
if "bands" in list(header_dict.keys()):
Latest version available from https://github.com/pmlrsg/arsf_tools/.''')
parser.add_argument("inputfile", nargs=1,type=str,
help="Input header")
parser.add_argument("-o","--outcsv", required=False, type=str,
help="Output file to store values for each band to")
args = parser.parse_args()
# Check a header file has been provided.
if os.path.splitext(args.inputfile[0])[-1].lower() != '.hdr':
print('Must provide header file as input (ending with ".hdr")', file=sys.stderr)
sys.exit(1)
if not os.path.isfile(args.inputfile[0]):
print('Could not open input header ({})'.format(args.inputfile[0]), file=sys.stderr)
sys.exit(1)
header_dict = envi_header.read_hdr_file(args.inputfile[0])
band_info_dict = {}
nbands = int(header_dict['bands'])
print('The header contains the following information:\n')
for item in header_dict.keys():
# Check if there are the same number of items as bands, if so don't print
# them out, just the number of values
if nbands != 1 and header_dict[item].count(',') >= nbands - 1:
print(' {} : {} values'.format(item, header_dict['bands']))
item_list = header_dict[item].split(',')
band_info_dict[item] = item_list
else:
print(' {} : {}'.format(item, header_dict[item]))
def _run():
parser = argparse.ArgumentParser(description="Calculates temperature and "
"emissivity from owl data",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("-f", "--file", help="Full path to processed file", required = True)
parser.add_argument("-o", "--outdir", help="Full path to output directory", required = True)
parser.add_argument("-p", "--percentile", help="The percentile of the "
"spectra used to determine the temperature. Should be set "
"to ignore spikes, but not too low that good data are ignored. "
"Ideally spikes should be masked and percentile set high.",
default = 99)
parser.add_argument("-s", "--start_band", help="Start band to use. Bands at "
"the edges of the detector are more noisy.", default = 5,
type = int)
parser.add_argument("-e", "--end_band", help="End band to use. Bands at "
"the edges of the detector are more noisy.", default = 95,
type = int)
args = parser.parse_args()
# get filename without path and extension
infile = os.path.splitext(os.path.split(args.file)[1])[0]
# set output files
temp_outfile = os.path.join(args.outdir, infile + "_temperature.bil")
em_outfile = os.path.join(args.outdir, infile + "_emissivity.bil")
error_outfile = os.path.join(args.outdir, infile + "_temp_error.bil")
temp_hdr = temp_outfile[:-4] + ".hdr"
em_hdr = em_outfile[:-4] + ".hdr"
error_hdr = error_outfile[:-4] + ".hdr"
# Read in wavelengths
hdr = envi_header.find_hdr_file(args.file)
hdrData = envi_header.read_hdr_file(hdr)
wavelengths = numpy.asarray(hdrData["wavelength"].split(","), dtype = float)/1000
# check bands are correct
if args.start_band < 0 or args.start_band > len(wavelengths):
print("Start band specified is out of range ({} bands)".format(hdrData["bands"]))
sys.exit()
if args.end_band < 0 or args.end_band > len(wavelengths):
print("End band specified is out of range ({} bands)".format(hdrData["bands"]))
sys.exit()
# Open bil file reader
in_file = numpy_bin_reader.BilReader(args.file)
# open output files
temp_out = open(temp_outfile,"wb")
em_out = open(em_outfile,"wb")
error_out = open(error_outfile,"wb")
# Read in data
for line, dataline in enumerate(in_file):
sys.stdout.write("\r working on line {}".format(line))
# calculate temperature by line
spectral_T = planck_T(wavelengths, dataline)
# calculate a single temperture from specified bands, ignoring noise
line_temp = numpy.percentile(spectral_T[args.start_band:args.end_band],
args.percentile, axis = 0).astype(numpy.float32)
# estimate the error in temperature
line_temp_error = 2.5*numpy.std(spectral_T[args.start_band:args.end_band]
, axis = 0).astype(numpy.float32)
# if error is more than 3 degrees, black and grey bodies are bad approximations
# and the errors are also underestimates
# calculate emissivity
emissivity = calc_em(wavelengths, dataline, line_temp)
# convert NaN to 0s (may be in mapped files)
emissivity = numpy.nan_to_num(emissivity).astype(numpy.float32)
# write out temp and emissivity bil files
line_temp.tofile(temp_out)
emissivity.tofile(em_out)
line_temp_error.tofile(error_out)
sys.stdout.write("\r all lines complete \n")
#close input file
dataline = None
#close output file
temp_out.close()
em_out.close()
error_out.close()
# write headers
hdr_dict = {"samples": int(hdrData["samples"]), "lines": int(hdrData["lines"]),
"bands": 1, "data type": 4}
hdr_dict["_comments"] = "file written by owl_temp_emissivity.py"
# if mapped file, map info might be useful to keep
if "map info" in hdrData:
hdr_dict["map info"] = hdrData["map info"]
envi_header.write_envi_header(temp_hdr, hdr_dict)
envi_header.write_envi_header(error_hdr, hdr_dict)
hdr_dict["bands"] = int(hdrData["bands"])
hdr_dict["interleave"] = "bil"
hdr_dict["wavelength"] = hdrData["wavelength"]
hdr_dict["band names"] = hdrData["wavelength"]
envi_header.write_envi_header(em_hdr, hdr_dict)
def apply_elc(input_image, output_image, image_spectra_file,
field_spectra_file,
scale_factor=DEFAULT_SCALE_FACTOR):
"""
Derive coefficients for an emprical line calibration and apply
to a BIL file.
"""
print("Getting coefficients")
elc_coefficients = get_elc_coefficients(image_spectra_file,
field_spectra_file)
print("Applying to image")
# Read ENVI header
input_header_file = envi_header.find_hdr_file(input_image)
input_header_dict = envi_header.read_hdr_file(input_header_file)
# Open input file
in_data = numpy_bin_reader.BilReader(input_image)
# Open file for output
out_file = open(output_image, "wb")
for i, line in enumerate(in_data):
print(" Line {0:05}/{1:05}".format(i, in_data.lines), end="\r")
# Apply coefficients
elc_line = elc_coefficients[:,0] * line.transpose() \
+ elc_coefficients[:,1]
elc_line = elc_line.transpose()
# Set any negative values to 0
elc_line = numpy.where(elc_line < 0, 0, elc_line)
elc_line = elc_line * scale_factor
# For default scale factor is over 1000 express as integer
if scale_factor >= 1000:
elc_line = elc_line.astype(numpy.uint16)
else:
elc_line = elc_line.astype(numpy.float32)
elc_line.tofile(out_file)
print(" Line {0:05}/{0:05}".format(in_data.lines), end="\n")
output_header_dict = input_header_dict
# See if need to change data type
if scale_factor >= 1000:
output_header_dict["data type"] = 12
else:
output_header_dict["data type"] = 4
# Remove radiance data units
try:
output_header_dict.pop("radiance data units")
except KeyError:
pass
# Add scale factor
output_header_dict["reflectance scale factor"] = scale_factor
procesing_comment = """\nConverted to reflectance using empirical line method. Field spectra: {}, Image spectra: {}\n""".format(field_spectra_file, image_spectra_file)
try:
output_header_dict["_comments"] = output_header_dict["_comments"] + \
procesing_comment
except KeyError:
output_header_dict["_comments"] = procesing_comment
output_header_dict["description"] = "BIL file created by {} on {}".format(__file__,
time.strftime("%Y-%m-%d %H:%M:%S"))
# Copy header
envi_header.write_envi_header(output_image + ".hdr",
output_header_dict)
# Close files
out_file.close()
in_data = None
required=False,
type=str,
help="Output file to store values for each band to")
args = parser.parse_args()
# Check a header file has been provided.
if os.path.splitext(args.inputfile[0])[-1].lower() != '.hdr':
print('Must provide header file as input (ending with ".hdr")',
file=sys.stderr)
sys.exit(1)
if not os.path.isfile(args.inputfile[0]):
print('Could not open input header ({})'.format(args.inputfile[0]),
file=sys.stderr)
sys.exit(1)
header_dict = envi_header.read_hdr_file(args.inputfile[0])
band_info_dict = {}
nbands = int(header_dict['bands'])
print('The header contains the following information:\n')
for item in list(header_dict.keys()):
# Check if there are the same number of items as bands, if so don't print
# them out, just the number of values
if nbands != 1 and header_dict[item].count(',') >= nbands - 1:
print(' {} : {} values'.format(item, header_dict['bands']))
item_list = header_dict[item].split(',')
band_info_dict[item] = item_list
else:
print(' {} : {}'.format(item, header_dict[item]))