def retrieve_band_information(self, bad_src = ''):
u"""
Retrieve all necessary band information, including identifiers of bands
with known bad quality defined in the given external file.
"""
# retrieving band count
if self.img_src is None:
self.band_count = gdal_utils.get_band_count(self.image_data[0])
else:
self.band_count = gdal_utils.get_band_count(self.img_src)
# retrieving bad bands
if bad_src and os.path.isfile(bad_src):
self.bad_bands = [int(b) for b in np.loadtxt(bad_src)]
else:
self.bad_bands = list()
# retrieving good bands by creating difference set with bad bands
self.good_bands = list(set(range(1, self.band_count + 1)).difference(self.bad_bands))
def retrieve_band_information(self, bad_src=""):
u"""
Retrieve all necessary band information, including identifiers of bands
with known bad quality defined in the given external file.
"""
# retrieving band count
if self.img_src is None:
self.band_count = gdal_utils.get_band_count(self.image_data[0])
else:
self.band_count = gdal_utils.get_band_count(self.img_src)
# retrieving bad bands
if bad_src and os.path.isfile(bad_src):
self.bad_bands = [int(b) for b in np.loadtxt(bad_src)]
else:
self.bad_bands = list()
# retrieving good bands by creating difference set with bad bands
self.good_bands = list(set(range(1, self.band_count + 1)).difference(self.bad_bands))
def retrieve_band_information(self, bad_src = ''):
u"""
Retrieve all necessary band information, including identifiers of bands
with known bad quality defined in the given external file.
"""
# retrieving band count
self.bad_src = bad_src
try:
self.band_count = gdal_utils.get_band_count(self.img_src)
except:
return
# retrieving bad bands
if bad_src and os.path.isfile(bad_src):
self.bad_bands = [int(b) for b in open(bad_src).readlines()[1].split("\t")]
else:
self.bad_bands = list()
# retrieving good bands by creating difference set with bad bands
self.good_bands = list(set(range(1, self.band_count + 1)).difference(self.bad_bands))
def retrieve_band_information(self, bad_src=''):
u"""
Retrieve all necessary band information, including identifiers of bands
with known bad quality defined in the given external file.
"""
# retrieving band count
self.bad_src = bad_src
try:
self.band_count = gdal_utils.get_band_count(self.img_src)
except:
return
# retrieving bad bands
if bad_src and os.path.isfile(bad_src):
self.bad_bands = [
int(b) for b in open(bad_src).readlines()[1].split("\t")
]
else:
self.bad_bands = list()
# retrieving good bands by creating difference set with bad bands
self.good_bands = list(
set(range(1, self.band_count + 1)).difference(self.bad_bands))
tgt_path, ext = os.path.splitext(img_src)
if ext == '.dat':
ext = '.img'
tgt_path = tgt_path + "_good_bands_only" + ext
ds = gdal.Open(img_src)
height = ds.RasterYSize
width = ds.RasterXSize
tgt_type = ds.GetRasterBand(1).DataType
src_gtf = ds.GetGeoTransform()
src_prj = ds.GetProjection()
band_count = gdal_utils.get_band_count(img_src)
bad_bands = [int(b) for b in open(bad_src).readlines()[1].split("\t")]
bad_bands = np.loadtxt(bad_src)
good_bands = list(set(range(1, band_count + 1)).difference(bad_bands))
print "Creating target dataset '%s'..." % tgt_path
drv = gdal.GetDriverByName('ENVI')
drv.Create(tgt_path, width, height, len(good_bands), tgt_type)
tgt_ds = gdal.Open(tgt_path, 1)
tgt_ds.SetGeoTransform(src_gtf)
tgt_ds.SetProjection(src_prj)
print "Extracting good bands..."
strip_id = int(re.search("_(\d)_", f).group(1))
available_strips.append(strip_id)
image_data[strip_id] = f
# retrieving plots
plt_ds = ogr.Open(plt_shp_src)
plt_ly = plt_ds.GetLayer(0)
# caching plots
cached_plots = ogr_utils.cache_locations(plt_ly, ['plot_id'])
# setting up output
output = list()
# iterating over a selection of strip ids
for strip_id in available_strips:
# retrieving band count
band_count = gdal_utils.get_band_count(image_data[strip_id])
# retrieving good bands by calculating the difference between all
# bands and those that have been declared bad
good_bands = list(set(range(1, band_count + 1)).difference(bad_bands))
tgt_file = "".join(
(re.search("(.+)_\d_",
os.path.basename(image_data[strip_id])).group(1),
'_spectra.txt'))
# setting up a list of plots that are linked with the current strip
strip_locations = list()
# setting up the output for current strip id
strip_output = list()
# retrieving plots that are linked with the current strip
for cp in sorted(cached_plots, key=itemgetter('attributes')):
strip_id = int(re.search("_(\d)_", f).group(1))
available_strips.append(strip_id)
image_data[strip_id] = f
# retrieving plots
plt_ds = ogr.Open(plt_shp_src)
plt_ly = plt_ds.GetLayer(0)
# caching plots
cached_plots = ogr_utils.cache_locations(plt_ly, ['plot_id'])
# setting up output
output = list()
# iterating over a selection of strip ids
for strip_id in available_strips:
# retrieving band count
band_count = gdal_utils.get_band_count(image_data[strip_id])
# retrieving good bands by calculating the difference between all
# bands and those that have been declared bad
good_bands = list(set(range(1, band_count + 1)).difference(bad_bands))
tgt_file = "".join((re.search("(.+)_\d_", os.path.basename(image_data[strip_id])).group(1), '_spectra.txt'))
# setting up a list of plots that are linked with the current strip
strip_locations = list()
# setting up the output for current strip id
strip_output = list()
# retrieving plots that are linked with the current strip
for cp in sorted(cached_plots, key = itemgetter('attributes')):
plot_id = cp['attributes']['plot_id']
if plot_id in image_plt_links[strip_id]:
strip_locations.append(cp)
