def test_calculation_of_tie_point_grid_float_coords(self):
# NOTE: This does not test against unequaly sized output of get_image_windows_to_match().
# overwrite gt and prj
ref = GeoArray(self.ref_path)
ref.to_mem()
ref.filePath = None
tgt = GeoArray(self.tgt_path)
tgt.to_mem()
tgt.filePath = None
ref.gt = [
330000.19999996503, 10.00000001, 0.0, 5862000.7999997628, 0.0,
-10.00000001
]
# ref.gt = [330000.1, 10.1, 0.0, 5862000.1, 0.0, -10.1]
tgt.gt = [
335440.19999996503, 10.00000001, 0.0, 5866490.7999997628, 0.0,
-10.00000001
]
# tgt.gt = [330000.1, 10.1, 0.0, 5862000.1, 0.0, -10.1]
# get instance of COREG_LOCAL object
CRL = COREG_LOCAL(ref, tgt, **dict(CPUs=32, **self.coreg_kwargs))
CRL.calculate_spatial_shifts()
def test_calculation_of_tie_point_grid(self):
# get instance of COREG_LOCAL object
CRL = COREG_LOCAL(self.ref_path, self.tgt_path, **self.coreg_kwargs)
# calculate tie point grid
CRL.calculate_spatial_shifts()
# test tie point grid visualization
with warnings.catch_warnings():
warnings.filterwarnings(
'ignore',
category=UserWarning,
message='Matplotlib is currently using agg, '
'which is a non-GUI backend, so cannot show the figure.')
CRL.view_CoRegPoints(hide_filtered=True)
CRL.view_CoRegPoints(hide_filtered=False)
CRL.view_CoRegPoints(shapes2plot='vectors')
CRL.view_CoRegPoints_folium()
# test shift correction and output writer
CRL.correct_shifts()
self.assertTrue(
os.path.exists(self.coreg_kwargs['path_out']),
'Output of local co-registration has not been written.')
def processAlgorithm(self, parameters, context, feedback):
"""
Here is where the processing itself takes place.
"""
try:
from arosics import COREG_LOCAL
except:
if platform.system() != 'Windows':
# load/install extra python dependencies
from Coregistration.utils.extra_deps import load_install_extra_deps
feedback.pushInfo("Installing dependencies...")
for msg_type, msg_val in load_install_extra_deps():
print(str(msg_val))
try:
from arosics import COREG_LOCAL
except:
msg = "\nError loading Arosics, this plugin requires additional Python packages to work. " \
"For Windows users, download and reinstall the plugin with this zip all-in-one with all " \
"the libs and dependencies. Read the install instructions here:\n\n" \
"https://github.com/SMByC/Coregistration-Qgis-processing#installation\n\n"
feedback.reportError(msg, fatalError=True)
return {}
def get_inputfilepath(layer):
return os.path.realpath(layer.source().split("|layername")[0])
img_ref = get_inputfilepath(self.parameterAsRasterLayer(parameters, self.IMG_REF, context))
img_tgt = get_inputfilepath(self.parameterAsRasterLayer(parameters, self.INPUT, context))
align_grids = self.parameterAsBoolean(parameters, self.ALIGN_GRIDS, context)
match_gsd = self.parameterAsBoolean(parameters, self.MATCH_GSD, context)
grid_res = self.parameterAsInt(parameters, self.GRID_RES, context)
window_size = self.parameterAsInt(parameters, self.WINDOW_SIZE, context)
max_shift = self.parameterAsInt(parameters, self.MAX_SHIFT, context)
resampling_method = self.resampling_methods[self.parameterAsEnum(parameters, self.RESAMPLING, context)][1]
output_file = self.parameterAsOutputLayer(parameters, self.OUTPUT, context)
feedback.pushInfo("Image to image Co-Registration:")
feedback.pushInfo("\nProcessing file: " + img_tgt)
feedback.pushInfo("\nPerform automatic subpixel co-registration with Arosics...")
feedback.pushInfo("\n(To check the complete log of the process, open the Python Console)...")
if platform.system() == "Windows":
feedback.reportError("\nWarning: in Windows due to restrictions to enable multiprocessing inside Qgis, "
"the process could take longer. Continue with one core ...\n", fatalError=False)
CRL = COREG_LOCAL(img_ref, img_tgt, path_out=output_file, align_grids=align_grids, match_gsd=match_gsd,
grid_res=grid_res, window_size=(window_size, window_size),
resamp_alg_deshift=resampling_method, max_shift=max_shift, max_iter=15,
fmt_out="GTiff", out_crea_options=["WRITE_METADATA=NO"],
CPUs=1 if platform.system() == "Windows" else None)
CRL.correct_shifts()
feedback.pushInfo("DONE\n")
return {self.OUTPUT: output_file}
def test_coreg_init_from_inMem_GeoArray(self):
# get GeoArray instances
self.ref_gA = GeoArray(self.ref_path)
self.tgt_gA = GeoArray(self.tgt_path)
# assure the raster data are in-memory
self.ref_gA.to_mem()
self.tgt_gA.to_mem()
# get instance of COREG_LOCAL object
self.CRL = COREG_LOCAL(self.ref_gA, self.tgt_gA, **self.coreg_kwargs)
def Arosics_test_local(ref_img, tgt_img, kwargs):
"""
testing function for local registration in Arosics
:param ref_img: str; full path and filename of the reference image
:param tgt_img: str; full path and filename of the target image
:param kwargs: dict; dictionary containing the detailed arguments for local correction.
:return:
"""
from arosics import COREG_LOCAL
start = default_timer()
CRL = COREG_LOCAL(ref_img, tgt_img, **kwargs)
CRL.correct_shifts()
end = default_timer()
print(end - start)
return CRL
def setUp(cls):
CRL = COREG_LOCAL(test_cases['INTER1']['ref_path'], test_cases['INTER1']['tgt_path'],
**test_cases['INTER1']['kwargs_local'])
cls.TPG = Tie_Point_Grid(CRL.COREG_obj, CRL.grid_res,
max_points=100, # limit to 100 to reduce computational load
outFillVal=CRL.outFillVal,
resamp_alg_calc=CRL.rspAlg_calc,
tieP_filter_level=CRL.tieP_filter_level,
outlDetect_settings=dict(
min_reliability=CRL.min_reliability,
rs_max_outlier=CRL.rs_max_outlier,
rs_tolerance=CRL.rs_tolerance),
dir_out=CRL.projectDir,
CPUs=CRL.CPUs,
progress=CRL.progress,
v=CRL.v,
q=CRL.q)
def _compute_tie_points(self):
CRL = COREG_LOCAL(self.cfg.path_reference_image,
self._EnMAP_band,
grid_res=40,
max_shift=5,
nodata=(self._ref_Im.nodata, 0),
footprint_poly_tgt=reproject_shapelyGeometry(
self._EnMAP_Im.meta.vnir.ll_mapPoly, 4326,
self._EnMAP_band.epsg),
mask_baddata_tgt=self._EnMAP_mask)
TPG = CRL.tiepoint_grid
# CRL.view_CoRegPoints(shapes2plot='vectors', hide_filtered=False, figsize=(20, 20),
# savefigPath='/home/gfz-fe/scheffler/temp/EnPT/Archachon_AROSICS_tiepoints.png')
valid_tiepoints = TPG.CoRegPoints_table[
TPG.CoRegPoints_table.OUTLIER.__eq__(False)].copy()
return valid_tiepoints
def run_arosics(ref_geoArr,
targ_geoArr,
grid_res,
window_size,
path_out,
projectDir,
shp_output=None):
"""
Coregister two images using Arosics package.
Parameters:
ref_geoArr (GeoArray): reference image.
targ_geoArr (GeoArray): target image (to be registered).
grid_res (int): arosics parameter: grid resolution.
window_size ((int, int)): arosics parameter: moving window size.
path_out (str): output path of registered image.
projectDir (str): project directory.
shp_output (str): output .shp directory.
"""
kwargs = {
'grid_res': grid_res,
'window_size': window_size,
'path_out': path_out,
'projectDir': projectDir,
'q': False,
}
CRL = COREG_LOCAL(ref_geoArr, targ_geoArr, **kwargs)
CRL.correct_shifts()
###Visualize tie point grid with INITIAL shifts present in your input target image
## import matplotlib
## matplotlib.use("TkAgg")
# CRL.view_CoRegPoints(figsize=(15,15), backgroundIm='ref')
###Visualize tie point grid with shifts present AFTER shift correction
# CRL_after_corr = COREG_LOCAL(img_reference.format('nir'), CRL.path_out, **kwargs)
# CRL_after_corr.view_CoRegPoints(figsize=(15,15),backgroundIm='ref')
if shp_output is not None:
CRL.tiepoint_grid.to_PointShapefile(path_out=shp_output)
return
def run_local_coreg(args):
CRL = COREG_LOCAL(
args.path_ref,
args.path_tgt,
path_out=args.path_out,
fmt_out=args.fmt_out,
grid_res=args.grid_res,
max_points=args.max_points,
r_b4match=args.br,
s_b4match=args.bs,
window_size=args.ws,
max_iter=args.max_iter,
max_shift=args.max_shift,
tieP_filter_level=args.tieP_filter_level,
min_reliability=args.min_reliability,
rs_max_outlier=args.rs_max_outlier,
rs_tolerance=args.rs_tolerance,
# align_grids=args.align_grids,
# match_gsd=args.match_gsd,
# out_gsd=args.out_gsd,
resamp_alg_calc=args.rsp_alg_deshift,
resamp_alg_deshift=args.rsp_alg_calc,
data_corners_ref=args.cor0,
data_corners_tgt=args.cor1,
nodata=args.nodata,
calc_corners=args.calc_cor,
mask_baddata_ref=args.mask_ref,
mask_baddata_tgt=args.mask_tgt,
CPUs=None if args.mp else 1,
force_quadratic_win=args.quadratic_win,
binary_ws=args.bin_ws,
progress=args.progress,
v=args.v,
q=args.q,
)
CRL.correct_shifts()
def test_calculation_of_tie_point_grid(self):
# get instance of COREG_LOCAL object
CRL = COREG_LOCAL(self.ref_path, self.tgt_path, **self.coreg_kwargs)
# use the getter of the CoRegPoints_table to calculate tie point grid
# noinspection PyStatementEffect
CRL.CoRegPoints_table
# test tie point grid visualization
if find_loader(
'mpl_toolkits.basemap'): # only works if basemap is installed
CRL.view_CoRegPoints(hide_filtered=True)
CRL.view_CoRegPoints(hide_filtered=False)
CRL.view_CoRegPoints(shapes2plot='vectors')
if find_loader('folium') and find_loader('geojson'):
CRL.view_CoRegPoints_folium()
# test shift correction and output writer
CRL.correct_shifts()
self.assertTrue(
os.path.exists(self.coreg_kwargs['path_out']),
'Output of local co-registration has not been written.')
def main():
# dir_in = r'Z:\townsenduser-rw\HyspexPro\Output\Cheesehead_V3\CHEESEHEAD_20190626\CHEESEHEAD_20190626_02_quicklooks'
dir_in = r'Z:\townsenduser-rw\HyspexPro\Output\Cheesehead_V3\CHEESEHEAD_20190806'
dest_dir = dir_in + '/Deshift'
img = '/CHEESEHEAD_20190806_03_Refl'
# ref_img = dir_in + '/Merge' + img
dest_file = dest_dir + img
header = read_envi_header(dir_in + '/Merge' + img + '.hdr')
band_vnir, band_swir, idx_vnir, idx_swir = band_select(header)
# hyspex_extract(dir_in + '/Merge' + img, [186], dest_dir + img + '_refband')
# hyspex_extract(dir_in + '/Merge' + img, [185], dest_dir + img + '_swir_test')
ref_img = dest_dir + img + '_refband'
tgt_img = dest_dir + img + '_swir_test'
grid_ls = [350] #[300, 350, 400, 450]
windows = [150] #[128, 150, 264, 280]
for i in range(len(grid_ls)):
out = dest_dir + '/shifted_local_03_{}_{}'.format(
grid_ls[i], windows[i])
g = grid_ls[i]
w = windows[i]
kwargs = kwargs = {
'grid_res': g,
'window_size': (w, w),
'path_out': out,
'nodata': (0, 0),
'r_b4match': 1,
's_b4match': 1,
'max_iter': 10,
'max_shift': 10,
# 'min_reliability': 25,
# 'tieP_filter_level': 1,
'resamp_alg_calc': 'nearest',
'q': False
}
dest_img = dest_file + '_deshift'
# calculate the shift info
try:
CRL = COREG_LOCAL(ref_img, tgt_img, **kwargs)
CRL.calculate_spatial_shifts()
# CRL.correct_shifts()
except:
continue
# apply the shift to a single band:
band = 185
# shift_arg = {
# 'band2process': int(band + 1),
# 'resamp_alg': 'nearest',
# 'nodata': 0,
# 'align_grids': True,
# 'q': True
# }
result = DESHIFTER(dir_in + '/Merge' + img,
CRL.coreg_info,
band2process=int(band + 1),
align_grids=True,
match_gsd=True,
nodata=0,
resamp_alg='cubic',
q=True).correct_shifts()
modify_hyspex_band(dest_img, band, result['arr_shifted'])
def test_coreg_init_from_disk(self):
self.CRL = COREG_LOCAL(self.ref_path, self.tgt_path,
**self.coreg_kwargs)
def main(): # argv
# set up directories
yrs = ['2015'] #['2013', '2014', '2015', '2016', '2017', '2018']
boxes = [
'f150602'
] #['f130612', 'f140603', 'f150601', 'f160621', 'f170607', 'f180622']
box_name = 'Yosemite_NEON'
dir_landsat = r'Y:\CA_timeseries\LandSAT'
dir_avs = r'Z:\townsenduser-rw\CA_project\Raw'
dir_out = r'G:\My Drive\Projects_ongoing\HyspIRI_Validation\TraitValidation\CA_TimeSeries\GCPs'
gcp_save_flag = True
nodata_tgt = -9999
# two for loops: 1. each box; 2. each flight line
for i, box in enumerate(boxes):
yr = yrs[i]
ref_img = glob.glob(f'{dir_landsat}/*{yr}*_sr_brdf.tif')[0]
# flightline list for the box:
dir_l = f'{dir_avs}/{yr}/{box_name}/{box}/L2'
flights = sorted(glob.glob(f'{dir_l}/{box}*_img'))
# only use the green band for ref:
ref_ds = gdal.Open(ref_img)
translate_option = gdal.TranslateOptions(
bandList=[2], outputType=gdalconst.GDT_Float32)
# ref_filename = '/vsimem/ref.tif'
ref_filename = './ref.tif'
gdal.Translate(ref_filename, ref_ds, options=translate_option)
# loop 2: each flightline in the box:
for j, flight in enumerate(flights):
tgt_img = flight
# nodata_tgt = args.nodata
print(ref_img, tgt_img, dir_out, gcp_save_flag)
# warp_img = '/vsimem/warp_tmp.tif' # out_dir +'/'+os.path.basename(tgt_img).split('.tif')[0]+'_auto_warp.tif'
warp_img = './warp_tmp.tif'
# extract one band from target img, for AVIRIS-Classic, using band 21, wvl: 560nm
tgt_ds = gdal.Open(tgt_img)
gt = tgt_ds.GetGeoTransform()
translate_option = gdal.TranslateOptions(
bandList=[21], outputType=gdalconst.GDT_Float32)
# translate_option = gdal.TranslateOptions(bandList=[3],outputType=gdalconst.GDT_Float32) #[3] gdalconst.GDT_Int16
dst_filename = '/vsimem/translate.tif'
gdal.Translate(dst_filename, tgt_ds, options=translate_option)
gdal.Warp(warp_img, dst_filename)
gdal.GetDriverByName('GTiff').Delete(dst_filename)
kwargs = {
'grid_res': 80, # 400, # 350
'window_size': (100, 100), # (64, 64), #256, #150
'path_out': None, # out_img,
# 'r_b4match': 187,
# 's_b4match': 186,
'match_gsd': False,
'max_iter': 16,
'max_shift': 205,
'nodata': (0, nodata_tgt), # (0, -0.9998),
'CPUs': 8,
# 'tieP_filter_level': 1, # avoid filter 2 and 3, fix the SSIM filter error
# 'min_reliability': 25,
'resamp_alg_calc': 'nearest',
'resamp_alg_deshift': 'nearest',
'align_grids': False,
'q': True
}
# CRL = COREG_LOCAL(ref_img, warp_img, **kwargs)
CRL = COREG_LOCAL(ref_filename, warp_img, **kwargs)
try:
CRL.calculate_spatial_shifts()
except:
print('{} is not processed properly'.format(tgt_img))
# error_ls.append(tgt_img)
# quit()
continue
new_coreg_info = own_copy(CRL.coreg_info)
rot_mat = np.array([[gt[1], gt[2], gt[0]], [gt[4], gt[5], gt[3]],
[0, 0, 1]])
gt_warp = gdal.Open(warp_img).GetGeoTransform()
warp_rot_mat = np.array([[gt_warp[1], gt_warp[2], gt_warp[0]],
[gt_warp[4], gt_warp[5], gt_warp[3]],
[0, 0, 1]])
inv_rot_mat = inv(rot_mat)
update_gcp_rotation(new_coreg_info, inv_rot_mat, gt, warp_rot_mat,
gcp_save_flag)
gdal.GetDriverByName('GTiff').Delete(warp_img)
print("Totally {} GCPs are found.".format(
len(new_coreg_info['GCPList'])))
if not gcp_save_flag:
out_img = dir_out + '/' + os.path.basename(tgt_img).split(
'.tif')[0] + '_geocorr_rot.bin'
out_pixel_size = np.sqrt(gt[1]**2 + gt[2]**2)
shift_arg = {
'band2process': None, # int(band + 1),
'resamp_alg': 'nearest',
'CPUs': 8,
'out_gsd': [out_pixel_size, out_pixel_size],
'nodata': nodata_tgt,
'path_out': out_img,
'align_grids': False,
'q': True
}
DESHIFTER(tgt_img, new_coreg_info,
**shift_arg).correct_shifts()
else:
# save GCPs and new_coreg_info
out_json = dir_out + '/' + os.path.basename(tgt_img).split(
'.tif')[0] + '_GCPs.json'
with open(out_json, "w") as outfile:
json.dump(new_coreg_info, outfile)
print("JSON file {} for GCP is saved.".format(out_json))
del CRL
gdal.GetDriverByName('GTiff').Delete(ref_filename)
def main():
# dir_avs = r'D:\CA_NEON\AVIRIS\Warped'
# dir_NEON = r'D:\CA_NEON\NEON\Merged'
# dest_dir = dir_avs + '/Deshift'
dir_NEON = r'Z:\townsenduser-rw\CA_project\CA_NEON\NEON\Merged'
dir_in = r'Z:\townsenduser-rw\CA_project\CA_NEON\CA_spectra_unmixing\testing'
# img = '/CHEESEHEAD_20190806_03_Refl'
# ref_img = dir_in + '/Merge' + img
# dest_file = dest_dir + img
# img = '/AVIRIS_TEAK_2017_solar4_warp'
# header = read_envi_header(dir_avs + '/AVIRIS_TEAK_2017_solar4_warp.hdr')
# band_vnir, band_swir, idx_vnir, idx_swir = band_select(header)
# hyspex_extract(dir_in + '/Merge' + img, [186], dest_dir + img + '_refband')
# hyspex_extract(dir_avs + '/AVIRIS_TEAK_2017_solar4_warp', [16], dir_avs + img + '_g')
# img_band_extraction(dir_avs+img, 17, dir_avs + img + '_g', nodata=-9999)
# ref_img = dir_NEON + '/TEAK_rgb_2017_v2'
# tgt_img = dir_avs + img + '_g'
ref_img = dir_NEON + '/TEAK_rgb_2017_v2'
tgt_img = dir_in + '/f170607t01p00r16_rfl_v1g_img_topo_brdf_solar_g_TEAK_clipped'
dest_img = dir_in + '/f170607t01p00r16_rfl_v1g_img_topo_brdf_solar_SVDI_v0_TEAK_clipped'
dest_img_deshift = dest_img + '_deshift'
# out = dir_in + '/shifted_global'
grid_ls = [150] #[150, 200, 300, 350]
windows = [70] #[70, 120, 128, 150]
band_idx = [0, 1, 2, 3, 4]
bandnames = ['Substrate', 'Canopy', 'Dark', 'Snow', 'RMSE']
for i in range(len(grid_ls)):
# out = dir_avs + '/shifted_Teak_2017_{}_{}'.format(grid_ls[i], windows[i])
out = dir_in + '/shifted_Teak_2017_{}_{}'.format(
grid_ls[i], windows[i])
g = grid_ls[i]
w = windows[i]
kwargs = kwargs = {
'grid_res': g,
'window_size': (w, w),
'path_out': out,
'nodata': (-9999, -9999),
'r_b4match': 2,
's_b4match': 1,
'max_iter': 10,
'max_shift': 5,
'min_reliability': 25,
# 'tieP_filter_level': 1,
'resamp_alg_calc': 'nearest',
'q': False
}
CRL = COREG_LOCAL(ref_img, tgt_img, **kwargs)
CRL.calculate_spatial_shifts()
# CRL.correct_shifts()
for j, band in enumerate(band_idx):
result = DESHIFTER(dest_img,
CRL.coreg_info,
band2process=int(band + 1),
align_grids=True,
match_gsd=True,
nodata=0,
resamp_alg='cubic',
q=True).correct_shifts()
# modify_hyspex_band(dest_img_deshift, band, result['arr_shifted'])
# Use gdal if the deshifted array has different dimensions compared to the original one.
if j == 0:
driver = gdal.GetDriverByName('ENVI')
dst_raster = driver.Create(dest_img_deshift,
result['arr_shifted'].shape[1],
result['arr_shifted'].shape[0],
len(band_idx), gdal.GDT_Float32)
dst_raster.SetGeoTransform(result['updated geotransform'])
dst_raster.SetProjection(result['updated projection'])
Gdal_write_band(dst_raster,
int(band + 1),
result['arr_shifted'],
bandnames[j],
no_data=0)
del result
#im_reference = r'C:\DEV\FuelMapping\registration\Sample_Images\L2A_T14RMT_20171022T171339_B04_10m.jp2'
#im_target = r'C:\DEV\FuelMapping\registration\Sample_Images\L2A_T14RMT_20170430T171301_B04_10m.jp2'
#shiftedfn = im_target[0:-4] + "_pyshifted" + '.tif'
#print(shiftedfn)
##print(help(CRL.correct_shifts()))
#for full band image
#im_reference = r'c:\DEV\FuelMapping\registration\Multiband_Images\S2B_MSIL2A_T14RMT_N0205_20171022_10m.tif'
#im_target = r'c:\DEV\FuelMapping\registration\Multiband_Images\S2A_MSIL2A_T14RMT_N0205_20170430_10m.tif'
#shiftedfn = im_target[0:-4] + "_pyfullbandshifted" + '.tif'
#CRL = COREG(im_reference,im_target,shiftedfn,fmt_out='GTiff')
#CRL.correct_shifts()
#shiftedarray.save(shiftedfn,fmt='GTiff',creationOptions="WRITE_METADATA=YES")
#
#import gdal
#print(help(gdal))
#Local
from arosics import COREG_LOCAL
im_reference = r'C:\DEV\FuelMapping\registration\Sample_Images\L2A_T14RMT_20171022T171339_B04_10m.jp2'
im_target = r'C:\DEV\FuelMapping\registration\Sample_Images\L2A_T14RMT_20170430T171301_B04_10m.jp2'
shiftedfn = im_target[0:-4] + "_pylocalshifted" + '.tif'
kwargs = {
'path_out':shiftedfn,
'fmt_out':'GTiff',
'grid_res' : 200,
'window_size' : (64,64),
'q' : False,
}
CRL = COREG_LOCAL(im_reference,im_target,**kwargs)
CRL.correct_shifts()
def main():
# parameters for the geo rectification:
kwargs = {
'grid_res': 400, #450, #400, # 350
'window_size': (256, 256), #280, #256, #150
# 'path_out': out,
'r_b4match': 187,
's_b4match': 186,
'match_gsd': True,
'max_iter': 8,
'max_shift': 10,
'nodata': (0, 0),
'tieP_filter_level':
3, # avoid filter 2 and 3, fix the SSIM filter error
# 'min_reliability': 25,
'resamp_alg_calc': 'nearest',
'q': False
}
# dir_in = r'Z:\townsenduser-rw\HyspexPro\Output\Cheesehead_V3\CHEESEHEAD_20190626\CHEESEHEAD_20190626_02_quicklooks'
dir_in = r'Z:\townsenduser-rw\HyspexPro\Output\Cheesehead_V3\CHEESEHEAD_20190830'
# create deshift folder for deshifted Refl data:
dest_dir = dir_in + '/Deshift'
if not os.path.exists(dest_dir):
os.makedirs(dest_dir)
imgs = glob.glob(dir_in + '/Merge/*_Refl')
imgs.sort()
# get swir bands:
header = read_envi_header(imgs[0] + '.hdr')
band_vnir, band_swir, idx_vnir, idx_swir = band_select(header)
error_ls = []
# for left over imgs:
imgs = [imgs[x] for x in [12]]
for img in imgs:
# # copy file in /Merge to Deshift:
shutil.copy(img, dest_dir)
# file header:
shutil.copy(img + '.hdr', dest_dir)
# rename files:
img_base = os.path.basename(img)
dest_file = os.path.join(dest_dir, img_base)
dest_img = dest_file + '_deshift'
#
os.rename(dest_file, dest_img)
dest_hdr = os.path.join(dest_dir, img_base + '.hdr')
os.rename(dest_hdr, dest_file + '_deshift.hdr')
del dest_file, dest_hdr
# calculate the shift info
CRL = COREG_LOCAL(img, img, **kwargs)
try:
CRL.calculate_spatial_shifts()
except:
print('{} is not processed properly'.format(img))
error_ls.append(img)
continue
# apply the shift to all swir bands:
for band in idx_swir:
print('Now shifting band {}'.format(band))
shift_arg = {
'band2process': int(band + 1),
'resamp_alg': 'cubic',
'nodata': 0,
'align_grids': True,
'q': True
}
result = DESHIFTER(img, CRL.coreg_info,
**shift_arg).correct_shifts()
modify_hyspex_band(dest_img, band, result['arr_shifted'])
del result
del CRL
# If files are not processed properly:
if len(error_ls) > 0:
with open(dest_dir + '/error_files.txt', 'w') as f:
for item in error_ls:
f.write("%s\n" % item)