def get_mask(ds, dem_fn):
#Mask glaciers, vegetated slopes
static_mask = dem_mask.get_lulc_mask(ds,
mask_glaciers=True,
filter='not_forest',
bareground_thresh=60)
#Mask glaciers only
#static_mask = dem_mask.get_icemask(ds)
#Top-of-atmosphere reflectance threshold (requires orthoimage and output from toa.sh)
toa_fn = dem_mask.get_toa_fn(dem_fn)
if toa_fn is not None:
toa_ds = warplib.memwarp_multi_fn([
toa_fn,
],
res=ds,
extent=ds,
t_srs=ds,
r='cubicspline')[0]
toa_mask = dem_mask.get_toa_mask(toa_ds)
static_mask = np.logical_and(static_mask, toa_mask)
#Return final mask, ready to be applied
return ~(static_mask)
def main():
parser = getparser()
args = parser.parse_args()
if args.seedmode == 'existing_velocity':
if args.vx_fn is None or args.vy_fn is None:
parser.error('"-seedmode existing_velocity" requires "-vx_fn" and "-vy_fn"')
print('\n%s' % datetime.now())
print('%s UTC\n' % datetime.utcnow())
align = args.align
seedmode = args.seedmode
spr = args.refinement
erode = args.erode
#Correlator tile timeout
#With proper seeding, correlation should be very fast
#timeout = 360
timeout = 1200
threads = args.threads
kernel = (args.kernel, args.kernel)
#SGM correlator
if spr > 3:
#kernel = (7,7)
kernel = (11,11)
erode = 0
#Smooth the output F.tif
smoothF = args.filter
res = args.tr
#Resample input to something easier to work with
#res = 4.0
#Open input files
fn1 = args.fn1
fn2 = args.fn2
if not iolib.fn_check(fn1) or not iolib.fn_check(fn2):
sys.exit("Unable to locate input files")
if args.outdir is not None:
outdir = args.outdir
else:
outdir = '%s__%s_vmap_%sm_%ipx_spm%i' % (os.path.splitext(os.path.split(fn1)[1])[0], \
os.path.splitext(os.path.split(fn2)[1])[0], res, kernel[0], spr)
#Note, can encounter filename length issues in boost, just use vmap prefix
outprefix = '%s/vmap' % (outdir)
if not os.path.exists(outdir):
os.makedirs(outdir)
#Check to see if inputs have geolocation and projection information
ds1 = iolib.fn_getds(fn1)
ds2 = iolib.fn_getds(fn2)
if geolib.srs_check(ds1) and geolib.srs_check(ds2):
ds1_clip_fn = os.path.join(outdir, os.path.splitext(os.path.basename(fn1))[0]+'_warp.tif')
ds2_clip_fn = os.path.join(outdir, os.path.splitext(os.path.basename(fn2))[0]+'_warp.tif')
if not os.path.exists(ds1_clip_fn) or not os.path.exists(ds2_clip_fn):
#This should write out files to new subdir
ds1_clip, ds2_clip = warplib.diskwarp_multi_fn([fn1, fn2], extent='intersection', res=res, r='average', outdir=outdir)
ds1_clip = None
ds2_clip = None
#However, if inputs have identical extent/res/proj, then link to original files
if not os.path.exists(ds1_clip_fn):
os.symlink(os.path.abspath(fn1), ds1_clip_fn)
if not os.path.exists(ds2_clip_fn):
os.symlink(os.path.abspath(fn2), ds2_clip_fn)
align = 'None'
#Mask support - limit correlation only to rock/ice surfaces, no water/veg
#This masks input images - guarantee we won't waste time correlating over vegetation
#TODO: Add support to load arbitrary raster or shp mask
if args.mask_input:
ds1_masked_fn = os.path.splitext(ds1_clip_fn)[0]+'_masked.tif'
ds2_masked_fn = os.path.splitext(ds2_clip_fn)[0]+'_masked.tif'
if not os.path.exists(ds1_masked_fn) or not os.path.exists(ds2_masked_fn):
#Load NLCD or bareground mask
from demcoreg.dem_mask import get_lulc_mask
ds1_clip = iolib.fn_getds(ds1_clip_fn)
lulc_mask_fn = os.path.join(outdir, 'lulc_mask.tif')
#if not os.path.exists(nlcd_mask_fn):
lulc_mask = get_lulc_mask(ds1_clip, mask_glaciers=False, filter='not_forest')
iolib.writeGTiff(lulc_mask, lulc_mask_fn, ds1_clip)
ds1_clip = None
#Now apply to original images
#This could be problematic for huge inputs, see apply_mask.py
#lulc_mask = lulc_mask.astype(int)
for fn in (ds1_clip_fn, ds2_clip_fn):
ds = iolib.fn_getds(fn)
a = iolib.ds_getma(ds)
a = np.ma.array(a, mask=~(lulc_mask))
if a.count() > 0:
out_fn = os.path.splitext(fn)[0]+'_masked.tif'
iolib.writeGTiff(a,out_fn,ds)
a = None
else:
sys.exit("No unmasked pixels over bare earth")
ds1_clip_fn = ds1_masked_fn
ds2_clip_fn = ds2_masked_fn
else:
ds1_clip_fn = fn1
ds2_clip_fn = fn2
#Now let user specify alignment methods as option - don't hardcode
#align = 'Homography'
#align = 'AffineEpipolar'
ds1 = None
ds2 = None
#Should have extra kwargs option here
stereo_opt = get_stereo_opt(threads=threads, kernel=kernel, timeout=timeout, \
erode=erode, spr=spr, align=align)
#Stereo arguments
#Latest version of ASP should accept tif without camera models
#stereo_args = [ds1_clip_fn, ds2_clip_fn, outprefix]
#Nope - still need to provide dummy camera models, and they must be unique files
#Use the dummy.tsai file bundled in the vmap repo
dummy_tsai = os.path.join(os.path.split(os.path.realpath(__file__))[0], 'dummy.tsai')
dummy_tsai2 = os.path.splitext(dummy_tsai)[0]+'2.tsai'
if not os.path.exists(dummy_tsai2):
dummy_tsai2 = os.symlink(dummy_tsai, os.path.splitext(dummy_tsai)[0]+'2.tsai')
stereo_args = [ds1_clip_fn, ds2_clip_fn, dummy_tsai, dummy_tsai2, outprefix]
#Run stereo_pprc
if not os.path.exists(outprefix+'-R_sub.tif'):
run_cmd('stereo_pprc', stereo_opt+stereo_args, msg='0: Preprocessing')
#Copy proj info to outputs, this should happen automatically now?
for ext in ('L', 'R', 'L_sub', 'R_sub', 'lMask', 'rMask', 'lMask_sub', 'rMask_sub'):
geolib.copyproj(ds1_clip_fn, '%s-%s.tif' % (outprefix,ext))
#Prepare seeding for stereo_corr
#TODO: these are untested after refactoring
if not os.path.exists(outprefix+'_D_sub.tif'):
#Don't need to do anything for default seed-mode 1
if seedmode == 'sparse_disp':
#Sparse correlation of full-res images
stereo_opt.extend(['--corr-seed-mode', '3'])
sparse_disp_opt = []
sparse_disp_opt.extend(['--Debug', '--coarse', '512', '--fine', '256', '--no_epipolar_fltr'])
sparse_disp_opt.extend(['-P', str(threads)])
sparse_disp_args = [outprefix+'-L.tif', outprefix+'-R.tif', outprefix]
run_cmd('sparse_disp', sparse_disp_opt+sparse_disp_args, msg='0.5: D_sub generation')
elif seedmode == 'existing_velocity':
#User-input low-res velocity maps for seeding
#TODO: Add functions that fetch best available velocities for Ant/GrIS or user-defined low-res velocities
#Automatically query GoLive velocities here
vx_fn = args.vx_fn
vy_fn = args.vy_fn
#Check for existence
#HMA seeding
vdir = '/nobackup/deshean/rpcdem/hma/velocity_jpl_amaury_2013-2015'
vx_fn = os.path.join(vdir, 'PKH_WRS2_B8_2013_2015_snr5_n1_r170_res12.x_vel.TIF')
vy_fn = os.path.join(vdir, 'PKH_WRS2_B8_2013_2015_snr5_n1_r170_res12.y_vel.TIF')
if os.path.exists(vx_fn) and os.path.exists(vy_fn):
ds1_clip = iolib.fn_getds(ds1_clip_fn)
ds1_res = geolib.get_res(ds1_clip, square=True)[0]
#Compute L_sub res - use this for output dimensions
L_sub_fn = outprefix+'-L_sub.tif'
L_sub_ds = gdal.Open(L_sub_fn)
L_sub_x_scale = float(ds1_clip.RasterXSize) / L_sub_ds.RasterXSize
L_sub_y_scale = float(ds1_clip.RasterYSize) / L_sub_ds.RasterYSize
L_sub_scale = np.max([L_sub_x_scale, L_sub_y_scale])
L_sub_res = ds1_res * L_sub_scale
#Since we are likely upsampling here, use cubicspline
vx_ds_clip, vy_ds_clip = warplib.memwarp_multi_fn([vx_fn, vy_fn], extent=ds1_clip, \
t_srs=ds1_clip, res=L_sub_res, r='cubicspline')
ds1_clip = None
#Get vx and vy arrays
vx = iolib.ds_getma(vx_ds_clip)
vy = iolib.ds_getma(vy_ds_clip)
#Determine time interval between inputs
#Use to scaling of known low-res velocities
t_factor = get_t_factor_fn(ds1_clip_fn, ds2_clip_fn, ds=vx_ds_clip)
if t_factor is not None:
#Compute expected offset in scaled pixels
dx = (vx*t_factor)/L_sub_res
dy = (vy*t_factor)/L_sub_res
#Note: Joughin and Rignot's values are positive y up!
#ASP is positive y down, so need to multiply these values by -1
#dy = -(vy*t_factor)/L_sub_res
#Should smooth/fill dx and dy
#If absolute search window is only 30x30
#Don't seed, just use fixed search window
#search_window_area_thresh = 900
search_window_area_thresh = 0
search_window = np.array([dx.min(), dy.min(), dx.max(), dy.max()])
dx_p = calcperc(dx, perc=(0.5, 99.5))
dy_p = calcperc(dy, perc=(0.5, 99.5))
search_window = np.array([dx_p[0], dy_p[0], dx_p[1], dy_p[1]])
search_window_area = (search_window[2]-search_window[0]) * (search_window[3]-search_window[1])
if search_window_area < search_window_area_thresh:
stereo_opt.extend(['--corr-seed-mode', '0'])
stereo_opt.append('--corr-search')
stereo_opt.extend([str(x) for x in search_window])
#pad_perc=0.1
#stereo_opt.extend(['--corr-sub-seed-percent', str(pad_perc)]
#Otherwise, generate a D_sub map from low-res velocity
else:
stereo_opt.extend(['--corr-seed-mode', '3'])
#This is relative to the D_sub scaled disparities
d_sub_fn = L_sub_fn.split('-L_sub')[0]+'-D_sub.tif'
gen_d_sub(d_sub_fn, dx, dy)
#If the above didn't generate a D_sub.tif for seeding, run stereo_corr to generate Low-res D_sub.tif
if not os.path.exists(outprefix+'-D_sub.tif'):
newopt = ['--compute-low-res-disparity-only',]
run_cmd('stereo_corr', newopt+stereo_opt+stereo_args, msg='1.1: Low-res Correlation')
#Copy projection info to D_sub
geolib.copyproj(outprefix+'-L_sub.tif', outprefix+'-D_sub.tif')
#Mask D_sub to limit correlation over bare earth surfaces
#This _should_ be a better approach than masking input images, but stereo_corr doesn't honor D_sub
#Still need to mask input images before stereo_pprc
#Left this in here for reference, or if this changes in ASP
if False:
D_sub_ds = gdal.Open(outprefix+'-D_sub.tif', gdal.GA_Update)
#Mask support - limit correlation only to rock/ice surfaces, no water/veg
from demcoreg.dem_mask import get_nlcd, mask_nlcd
nlcd_fn = get_nlcd()
nlcd_ds = warplib.diskwarp_multi_fn([nlcd_fn,], extent=D_sub_ds, res=D_sub_ds, t_srs=D_sub_ds, r='near', outdir=outdir)[0]
#validmask = mask_nlcd(nlcd_ds, valid='rock+ice')
validmask = mask_nlcd(nlcd_ds, valid='not_forest', mask_glaciers=False)
nlcd_mask_fn = os.path.join(outdir, 'nlcd_validmask.tif')
iolib.writeGTiff(validmask, nlcd_mask_fn, nlcd_ds)
#Now apply to D_sub (band 3 is valid mask)
#validmask = validmask.astype(int)
for b in (1,2,3):
dsub = iolib.ds_getma(D_sub_ds, b)
dsub = np.ma.array(dsub, mask=~(validmask))
D_sub_ds.GetRasterBand(b).WriteArray(dsub.filled())
D_sub_ds = None
#OK, finally run stereo_corr full-res integer correlation with appropriate seeding
if not os.path.exists(outprefix+'-D.tif'):
run_cmd('stereo_corr', stereo_opt+stereo_args, msg='1: Correlation')
geolib.copyproj(ds1_clip_fn, outprefix+'-D.tif')
#Run stereo_rfne
if spr > 0:
if not os.path.exists(outprefix+'-RD.tif'):
run_cmd('stereo_rfne', stereo_opt+stereo_args, msg='2: Refinement')
geolib.copyproj(ds1_clip_fn, outprefix+'-RD.tif')
d_fn = make_ln(outdir, outprefix, '-RD.tif')
else:
ln_fn = outprefix+'-RD.tif'
if os.path.lexists(ln_fn):
os.remove(ln_fn)
os.symlink(os.path.split(outprefix)[1]+'-D.tif', ln_fn)
#Run stereo_fltr
if not os.path.exists(outprefix+'-F.tif'):
run_cmd('stereo_fltr', stereo_opt+stereo_args, msg='3: Filtering')
geolib.copyproj(ds1_clip_fn, outprefix+'-F.tif')
d_fn = make_ln(outdir, outprefix, '-F.tif')
if smoothF and not os.path.exists(outprefix+'-F_smooth.tif'):
print('Smoothing F.tif')
from pygeotools.lib import filtlib
#Fill holes and smooth F
F_fill_fn = outprefix+'-F_smooth.tif'
F_ds = gdal.Open(outprefix+'-F.tif', gdal.GA_ReadOnly)
#import dem_downsample_fill
#F_fill_ds = dem_downsample_fill.gdalfill_ds(F_fill_ds)
print('Creating F_smooth.tif')
F_fill_ds = iolib.gtif_drv.CreateCopy(F_fill_fn, F_ds, 0, options=iolib.gdal_opt)
F_ds = None
for n in (1, 2):
print('Smoothing band %i' % n)
b = F_fill_ds.GetRasterBand(n)
b_fill_bma = iolib.b_getma(b)
#b_fill_bma = iolib.b_getma(dem_downsample_fill.gdalfill(b))
#Filter extreme values (careful, could lose areas of valid data with fastest v)
#b_fill_bma = filtlib.perc_fltr(b_fill_bma, perc=(0.01, 99.99))
#These filters remove extreme values and fill data gaps
#b_fill_bma = filtlib.median_fltr_skimage(b_fill_bma, radius=7, erode=0)
#b_fill_bma = filtlib.median_fltr(b_fill_bma, fsize=7, origmask=True)
#Gaussian filter
b_fill_bma = filtlib.gauss_fltr_astropy(b_fill_bma, size=9)
b.WriteArray(b_fill_bma)
F_fill_ds = None
d_fn = make_ln(outdir, outprefix, '-F_smooth.tif')
print('\n%s' % datetime.now())
print('%s UTC\n' % datetime.utcnow())
#If time interval is specified, convert pixel displacements to rates
if args.dt != 'none':
#Check if vm.tif already exists
#Should probably just overwrite by default
#if os.path.exists(os.path.splitext(d_fn)[0]+'_vm.tif'):
# print("\nFound existing velocity magnitude map!\n"
#else:
#Generate output velocity products and figure
#Requires that vmap repo is in PATH
cmd = ['disp2v.py', d_fn]
#Note: this will attempt to automatically determine control surfaces
#disp2v.py will accept arbitrary mask, could pass through here
if args.remove_offsets:
cmd.append('-remove_offsets')
cmd.extend(['-dt', args.dt])
print("Converting disparities to velocities")
print(cmd)
subprocess.call(cmd)
def main():
parser = getparser()
args = parser.parse_args()
t_unit = args.dt
plot = args.plot
remove_offsets = args.remove_offsets
mask_fn = args.mask_fn
if mask_fn is not None:
remove_offsets = True
#Input is 3-band disparity map, extract bands directly
src_fn = args.disp_fn
if not iolib.fn_check(src_fn):
sys.exit("Unable to locate input file: %s" % src_fn)
src_ds = iolib.fn_getds(src_fn)
if src_ds.RasterCount != 3:
sys.exit("Input file must be ASP disparity map (3 bands: x, y, mask)")
#Extract pixel resolution
h_res, v_res = geolib.get_res(src_ds)
#Horizontal scale factor
#If running on disparity_view output (gdal_translate -outsize 5% 5% F.tif F_5.tif)
#h_res /= 20
#v_res /= 20
#Load horizontal and vertical disparities
h = iolib.ds_getma(src_ds, bnum=1)
v = iolib.ds_getma(src_ds, bnum=2)
#ASP output has northward motion as negative values in band 2
v *= -1
t1, t2 = timelib.fn_getdatetime_list(src_fn)
dt = t2 - t1
#Default t_factor is in 1/years
t_factor = timelib.get_t_factor(t1, t2)
#Input timestamp arrays if inputs are mosaics
if False:
t1_fn = ''
t2_fn = ''
if os.path.exists(t1_fn) and os.path.exists(t2_fn):
t_factor = timelib.get_t_factor_fn(t1_fn, t2_fn)
if t_factor is None:
sys.exit("Unable to determine input timestamps")
if t_unit == 'day':
t_factor *= 365.25
print("Input dates:")
print(t1)
print(t2)
print(dt)
print(t_factor, t_unit)
#Scale values for polar stereographic distortion
srs = geolib.get_ds_srs(src_ds)
proj_scale_factor = 1.0
#Want to scale to get correct distances for polar sterographic
if srs.IsSame(geolib.nps_srs) or srs.IsSame(geolib.sps_srs):
proj_scale_factor = geolib.scale_ps_ds(src_ds)
#Convert disparity values in pixels to m/t_unit
h_myr = h * h_res * proj_scale_factor / t_factor
h = None
v_myr = v * v_res * proj_scale_factor / t_factor
v = None
#Velocity Magnitude
m = np.ma.sqrt(h_myr**2 + v_myr**2)
print("Velocity Magnitude stats")
malib.print_stats(m)
#Remove x and y offsets over control surfaces
offset_str = ''
if remove_offsets:
if mask_fn is None:
from demcoreg.dem_mask import get_lulc_mask
print(
"\nUsing demcoreg to prepare mask of stable control surfaces\n"
)
mask = get_lulc_mask(src_ds,
mask_glaciers=True,
filter='rock+ice+water')
else:
print("\nWarping input raster mask")
#This can be from previous dem_mask.py run (e.g. *rockmask.tif)
mask_ds = warplib.memwarp_multi_fn([
mask_fn,
],
res=src_ds,
extent=src_ds,
t_srs=src_ds)[0]
mask = iolib.ds_getma(mask_ds)
#The default from ds_getma is a masked array, so need to isolate boolean mask
#Assume input is 0 for masked, 1 for unmasked (valid control surface)
mask = mask.filled().astype('bool')
#This should work, as the *rockmask.py is 1 for unmasked, 0 for masked, with ndv=0
#mask = np.ma.getmaskarray(mask)
#Vector mask - untested
if os.path.splitext(mask_fn)[1] == 'shp':
mask = geolib.shp2array(mask_fn, src_ds)
print("\nRemoving median x and y offset over static control surfaces")
h_myr_count = h_myr.count()
h_myr_static_count = h_myr[mask].count()
h_myr_med = malib.fast_median(h_myr[mask])
v_myr_med = malib.fast_median(v_myr[mask])
h_myr_mad = malib.mad(h_myr[mask])
v_myr_mad = malib.mad(v_myr[mask])
print("Static pixel count: %i (%0.1f%%)" %
(h_myr_static_count,
100 * float(h_myr_static_count) / h_myr_count))
print("median (+/-NMAD)")
print("x velocity offset: %0.2f (+/-%0.2f) m/%s" %
(h_myr_med, h_myr_mad, t_unit))
print("y velocity offset: %0.2f (+/-%0.2f) m/%s" %
(v_myr_med, v_myr_mad, t_unit))
h_myr -= h_myr_med
v_myr -= v_myr_med
offset_str = '_offsetcorr_h%0.2f_v%0.2f' % (h_myr_med, v_myr_med)
#Velocity Magnitude
m = np.ma.sqrt(h_myr**2 + v_myr**2)
print("Velocity Magnitude stats after correction")
malib.print_stats(m)
if plot:
fig_fn = os.path.splitext(src_fn)[0] + '.png'
label = 'Velocity (m/%s)' % t_unit
f, ax = make_plot(m, fig_fn, label)
plotvec(h_myr, v_myr)
plt.tight_layout()
plt.savefig(fig_fn,
dpi=300,
bbox_inches='tight',
pad_inches=0,
edgecolor='none')
print("Writing out files")
gt = src_ds.GetGeoTransform()
proj = src_ds.GetProjection()
dst_fn = os.path.splitext(src_fn)[0] + '_vm%s.tif' % offset_str
iolib.writeGTiff(m, dst_fn, create=True, gt=gt, proj=proj)
dst_fn = os.path.splitext(src_fn)[0] + '_vx%s.tif' % offset_str
iolib.writeGTiff(h_myr, dst_fn, create=True, gt=gt, proj=proj)
dst_fn = os.path.splitext(src_fn)[0] + '_vy%s.tif' % offset_str
iolib.writeGTiff(v_myr, dst_fn, create=True, gt=gt, proj=proj)
src_ds = None