def getparser():
stat_choices = ['first', 'last', 'min', 'max', 'stddev', 'count', 'median']
parser = argparse.ArgumentParser(
description='Wrapper for dem_mosaic that will only write valid tiles')
parser.add_argument('--tr',
default='min',
help='Output resolution (default: %(default)s)')
parser.add_argument('--t_projwin',
default='union',
help='Output extent (default: %(default)s)')
parser.add_argument('--t_srs',
default='first',
help='Output projection (default: %(default)s)')
parser.add_argument('--georef_tile_size',
type=float,
default=100000.,
help='Output tile width (meters)')
parser.add_argument('--threads',
type=int,
default=iolib.cpu_count(),
help='Number of simultaneous jobs to run')
parser.add_argument('--stat',
type=str,
default=None,
choices=stat_choices,
help='Statistic to use (default: weighted mean)')
parser.add_argument('-o',
type=str,
default=None,
help='Output mosaic prefix')
parser.add_argument('src_fn_list',
type=str,
nargs='+',
help='Input filenames (img1.tif img2.tif ...)')
return parser
def getparser():
parser = argparse.ArgumentParser(description="Generate velocity map via feature-tracking")
parser.add_argument('-outdir', default=None, help='Output directory')
parser.add_argument('-threads', type=int, default=iolib.cpu_count(), help='Number of threads to use(default: %(default)s)')
parser.add_argument('-tr', default='min', help='Output resolution (default: %(default)s)')
#Set correlator kernel size
parser.add_argument('-kernel', type=int, default=35, help='Correlator kernel size. Smaller kernels offer more detail but are prone to more noise. Odd integers required (~9-51 px recommended). (default: %(default)s)')
align_choices = ['AffineEpipolar', 'Homography', 'Epipolar', 'None']
parser.add_argument('-align', default='None', choices=align_choices, help='Alignment method to warp second image to match first image, if not already orthorectified. Provides flexibility for L1B inputs')
#Integer correlator seeding
#D_sub is low-resolution correlation (default), which works well for most situations
#sparse_disp will use sparse seeding from full-res chips, useful for ice sheets with limited low-frequency texture
#existing_velocity will accept existing vx and vy rasters. Useful for limiting search range and limiting blunders. Measures products are useful for ice sheets.
seedmode_choices = ['D_sub', 'sparse_disp', 'existing_velocity']
parser.add_argument('-seedmode', type=str, choices=seedmode_choices, default='D_sub', help='Seeding option (default: %(default)s)')
parser.add_argument('-vx_fn', type=str, default=None, help='Seed E-W velocity map filename')
parser.add_argument('-vy_fn', type=str, default=None, help='Seed N-S velocity map filename')
#Sub-pixel refinement
#0) None, 1) Parabolic, 2) Bayes, 3) AffineAdaptive
#See ASP doc or Shean et al, ISPRS, (2016)
#1 is fast but lower quality
#2 is slow but highest quality,
#3 is a good compromise for speed and quality
refinement_choices = list(range(12))
parser.add_argument('-refinement', type=int, default=1, help='Sub-pixel refinement type (see ASP doc): 0) None, 1) Parabolic, 2) Bayes, 3) AffineAdaptive 4) LK, 5) Bayes w/gamma, 6) SGM Linear, 7) SGM Poly4, 8) SGM Cos, 9) SGM Parabola, 10) SGM None, 11) SGM Blend (default: %(default)s)')
#Numer of gaussian pyramids to use
#Can look at texture in GDAL overviews to make a decision
#If you can see plenty of texture at 1/32 resolution, go with 5
#For featureless areas, limiting to 2 can help, or even 0
parser.add_argument('-pyramid-levels', type=int, default=5, help='Number of pyramid levels for correlation (default: %(default)s)')
#This helps get rid of bogus "islands" in the disparity maps
parser.add_argument('-erode', type=int, default=1024, help='Erode isolated blobs smaller than this many pixels. Set to 0 to disable (default: %(default)s)')
parser.add_argument('-filter', action='store_true', help='Filter the output F.tif, smoothing with Gaussian filter')
#This masks input images to improve performance. Useful for forested areas.
parser.add_argument('-mask_input', action='store_true', help='Mask any vegetation/water in input images. Requires demcoreg')
parser.add_argument('-remove_offsets', action='store_true', help='Remove median horizontal and vertical offsets over stable control surfaces')
parser.add_argument('-dt', type=str, choices=['yr','day','none'], default='yr', help='Time increment (default: %(default)s)')
#Inputs can be images, DEMs, shaded relief maps
#Personal experience suggests multi-directional hillshades with identical illumination work well
#Only 2 input datsets allowed for this - want to stay modular
parser.add_argument('fn1', type=str, help='Raster filename 1')
parser.add_argument('fn2', type=str, help='Raster filename 2')
return parser
def getparser():
stat_choices = [
'first', 'firstindex', 'last', 'lastindex', 'min', 'max', 'mean',
'stddev', 'count', 'median', 'medianindex', 'nmad', 'wmean'
]
parser = argparse.ArgumentParser(
description='Wrapper for dem_mosaic that will only write valid tiles')
parser.add_argument('--tr',
default='min',
help='Output resolution (default: %(default)s)')
parser.add_argument('--t_projwin',
default='union',
help='Output extent (default: %(default)s)')
parser.add_argument('--t_srs',
default='first',
help='Output projection (default: %(default)s)')
parser.add_argument('--georef_tile_size',
type=float,
default=100000.,
help='Output tile width (meters)')
parser.add_argument(
'--threads',
type=int,
default=iolib.cpu_count(logical=False),
help='Number of simultaneous dem_mosaic processes to run')
parser.add_argument('--stat', type=str, nargs='*', default=None, choices=stat_choices, \
help='Specify space-delimited list of output statistics to pass to dem_mosaic (e.g., "count stddev", default: wmean)')
parser.add_argument('-o',
type=str,
default=None,
help='Output mosaic prefix')
#parser.add_argument('-i', type=str, default=None, help='Input file list (e.g., fn_list.txt)')
parser.add_argument('src_fn_list',
type=str,
nargs='+',
help='Input filenames (img1.tif img2.tif ...)')
return parser
mos_fn = os.path.join(
stackdir,
'stack_seasonal_summer/%s_stack_seasonal_summer' %
site_name)
cmd = geolib.get_dem_mosaic_cmd(stack_fn_list,
mos_fn,
tr=res,
t_srs=dst_srs,
t_projwin=site_extent,
threads=8)
dz_cmd_list.append(cmd)
if make_stacks:
from concurrent.futures import ThreadPoolExecutor
#threads = 8
threads = iolib.cpu_count()
iolib.setstripe(outdir, threads)
delay = 3.0
outf = open(os.devnull, 'w')
if mos_cmd_list:
with ThreadPoolExecutor(max_workers=threads) as executor:
for cmd in mos_cmd_list:
#print(cmd)
#executor.submit(subprocess.call, cmd, stdout=outf, stderr=subprocess.STDOUT)
executor.submit(subprocess.call, cmd)
time.sleep(delay)
if std_cmd_list:
with ThreadPoolExecutor(max_workers=threads) as executor:
for cmd in std_cmd_list:
#Filter glacier poly - let's stick with big glaciers for now
#min_glac_area = 0.1 #km^2
min_glac_area = 10. #km^2
#Minimum percentage of glacier poly covered by valid dz
min_valid_area_perc = 0.80
#Write out DEMs and dz map
writeout = True
#Generate figures
mb_plot = True
#Run in parallel, set to False for serial loop
parallel = True
#Verbose for debugging
verbose = False
#Number of parallel processes
nproc = iolib.cpu_count() - 1
#Shortcut to use existing glacfeat_list.p if found
use_existing_glacfeat = True
global z1_date
global z2_date
z1_date = None
z2_date = None
z1_srtm_penetration_corr = False
z2_srtm_penetration_corr = False
if site == 'conus':
#Glacier shp
#glac_shp_fn = os.path.join(topdir,'data/rgi60/regions/rgi60_merge.shp')
#ogr2ogr -t_srs '+proj=aea +lat_1=36 +lat_2=49 +lat_0=43 +lon_0=-115 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs ' 24k_selection_aea.shp 24k_selection_32610.shp
#glac_shp_fn = '/nobackupp8/deshean/conus/shp/24k_selection_aea.shp'
