def get_footprint_mask(shpfile, geoimg, filelist, fprint_out=False):
imlist = [im.split('OIS-Reech_')[-1].split('.tif')[0] for im in filelist]
footprints_shp = gpd.read_file(shpfile)
fp = footprints_shp[footprints_shp.ID.isin(imlist)]
fprint = cascaded_union(fp.to_crs(
epsg=geoimg.epsg).geometry.values[1:-1]).minimum_rotated_rectangle
tmp_gdf = gpd.GeoDataFrame(columns=['geometry'])
tmp_gdf.loc[0, 'geometry'] = fprint
tmp_gdf.crs = {'init': 'epsg:{}'.format(geoimg.epsg)}
tmp_gdf.to_file('tmp_fprint.shp')
maskout = create_mask_from_shapefile(geoimg, 'tmp_fprint.shp')
for f in glob('tmp_fprint.*'):
os.remove(f)
if fprint_out:
return maskout, fprint
else:
return maskout
def get_initial_transformation(img1, img2, landmask=None, footmask=None):
im2_lowres = reshape_geoimg(img2, 800, 800)
im2_eq = match_hist(im2_lowres.img, np.array(img1))
im1_mask = 255 * np.ones(img1.shape, dtype=np.uint8)
im1_mask[img1 == 0] = 0 # nodata from ortho
im2_mask = 255 * np.ones(im2_eq.shape, dtype=np.uint8)
im2_mask[im2_eq == 0] = 0
if landmask is not None:
mask_ = create_mask_from_shapefile(im2_lowres, landmask)
im2_mask[~mask_] = 0
if footmask is not None:
mask_ = get_footprint_mask(footmask, im2_lowres, glob('OIS*.tif'))
im2_mask[~mask_] = 0
kp, des, matches = get_matches(img1,
im2_eq,
mask1=im1_mask,
mask2=im2_mask)
src_pts = np.float32([kp[0][m.queryIdx].pt
for m in matches]).reshape(-1, 1, 2)
dst_pts = np.float32([kp[1][m.trainIdx].pt
for m in matches]).reshape(-1, 1, 2)
aff_matrix, good_mask = cv2.estimateAffine2D(src_pts,
dst_pts,
ransacReprojThreshold=25)
# check that the transformation was successful by correlating the two images.
im1_tfm = cv2.warpAffine(
img1, aff_matrix, (im2_lowres.img.shape[1], im2_lowres.img.shape[0]))
im1_pad = np.zeros(np.array(im1_tfm.shape) + 2, dtype=np.uint8)
im1_pad[1:-1, 1:-1] = im1_tfm
res = cv2.matchTemplate(im2_eq, im1_pad, cv2.TM_CCORR_NORMED)
print(res[1, 1])
success = res[1, 1] > 0.6
return aff_matrix, success, im2_eq.shape
[rect_u[0] + rect_u[2], rect_u[1] + rect_u[3]],
[rect_u[0], rect_u[1] + rect_u[3]],
[rect_u[0], rect_u[1]]])
def cerc_units_to_verts(cerc_u):
xy, rad = cerc_u
theta = np.linspace(0, 2 * np.pi, 100)
verts = np.vstack([np.sin(theta), np.cos(theta)]).T
return verts * rad + xy
img = GeoImg(fn_hs)
land_mask = create_mask_from_shapefile(img, fn_land)
ds = gdal.Open(fn_hs)
hs = ds.ReadAsArray()
hs = hs.astype(float)
def stretch_hs(hs, stretch_factor=1.):
max_hs = 255
min_hs = 0
hs_s = (hs -
(max_hs - min_hs) / 2) * stretch_factor + (max_hs - min_hs) / 2
return hs_s
def create_stable_mask(img, mask1, mask2):
"""
Create mask representing stable terrain, given exclusion (i.e., glacier) and inclusion (i.e., land) masks.
:param img: GeoImg to pull extents from
:param mask1: filename for shapefile representing pixels to exclude from stable terrain (i.e., glaciers)
:param mask2: filename for shapefile representing pixels to include in stable terrain (i.e., land)
:type img: pybob.GeoImg
:type mask1: str
:type mask2: str
:returns stable_mask: boolean array representing stable terrain
"""
# if we have no masks, just return an array of true values
if mask1 is None and mask2 is None:
return np.ones(
img.img.shape) == 0 # all false, so nothing will get masked.
elif mask1 is not None and mask2 is None: # we have a glacier mask, not land
if mask1.split('.')[-1] == 'tif':
mask_rast = myRaster = gdal.Open(mask1)
transform = myRaster.GetGeoTransform()
dx = transform[1]
dy = transform[5]
Xsize = myRaster.RasterXSize
Ysize = myRaster.RasterYSize
mask = myRaster.ReadAsArray(0, 0, Xsize, Ysize)
else:
mask = create_mask_from_shapefile(img, mask1)
return mask # returns true where there's glacier, false everywhere else
elif mask1 is None and mask2 is not None:
if mask2.split('.')[-1] == 'tif':
mask_rast = myRaster = gdal.Open(mask2)
transform = myRaster.GetGeoTransform()
dx = transform[1]
dy = transform[5]
Xsize = myRaster.RasterXSize
Ysize = myRaster.RasterYSize
mask = myRaster.ReadAsArray(0, 0, Xsize, Ysize)
else:
mask = create_mask_from_shapefile(img, mask2)
return np.logical_not(
mask) # false where there's land, true where there isn't
else: # if none of the above, we have two masks.
# implement option if either or, or both mask are given as rasters.
if (mask1.split('.')[-1] == 'tif') & (mask2.split('.')[-1] == 'shp'):
mask_rast = myRaster = gdal.Open(mask1)
transform = myRaster.GetGeoTransform()
dx = transform[1]
dy = transform[5]
Xsize = myRaster.RasterXSize
Ysize = myRaster.RasterYSize
gmask = myRaster.ReadAsArray(0, 0, Xsize, Ysize)
lmask = create_mask_from_shapefile(img, mask2)
elif (mask2.split('.')[-1] == 'tif') & (mask1.split('.')[-1] == 'shp'):
mask_rast = myRaster = gdal.Open(mask2)
transform = myRaster.GetGeoTransform()
dx = transform[1]
dy = transform[5]
Xsize = myRaster.RasterXSize
Ysize = myRaster.RasterYSize
lmask = myRaster.ReadAsArray(0, 0, Xsize, Ysize)
gmask = create_mask_from_shapefile(img, mask1)
elif (mask1.split('.')[-1] == 'tif') & (mask2.split('.')[-1] == 'tif'):
mask_rast = myRaster = gdal.Open(mask1)
transform = myRaster.GetGeoTransform()
dx = transform[1]
dy = transform[5]
Xsize = myRaster.RasterXSize
Ysize = myRaster.RasterYSize
gmask = myRaster.ReadAsArray(0, 0, Xsize, Ysize)
mask_rast = myRaster = gdal.Open(mask2)
transform = myRaster.GetGeoTransform()
dx = transform[1]
dy = transform[5]
Xsize = myRaster.RasterXSize
Ysize = myRaster.RasterYSize
lmask = myRaster.ReadAsArray(0, 0, Xsize, Ysize)
else:
gmask = create_mask_from_shapefile(img, mask1)
lmask = create_mask_from_shapefile(img, mask2)
return np.logical_or(
gmask,
np.logical_not(lmask)) # true where there's glacier or water
# load the full mask, which we'll re-project later
mask_full = GeoImg('../southeast_average_corr.tif')
for yr in [2012, 2013]:
print("Loading {} data files.".format(yr))
ifsar = GeoImg('{}/seak.ifsar.{}.dem.30m_adj.tif'.format(yr, yr))
ifsar_srtm = GeoImg('{}/ifsar_srtm_{}_dh.tif'.format(yr, yr))
srtm = GeoImg('{}/SRTM_SE_Alaska_30m_{}IfSAR_adj.tif'.format(yr, yr))
valid_area = np.isfinite(ifsar.img)
glac_shp = '../outlines/01_rgi60_Alaska_GlacierBay_02km_UTM_{}.shp'.format(yr)
glacier_mask = it.create_mask_from_shapefile(ifsar, glac_shp)
mask_geo = mask_full.reproject(ifsar_srtm)
corrs = [35, 50, 70, 80, 90, 95]
for i, corr in enumerate(corrs):
corr_mask = mask_geo.img < corr
masked_ifsar = ifsar.copy()
masked_ifsar.img[corr_mask] = np.nan
masked_ifsar_srtm = ifsar_srtm.copy()
masked_ifsar_srtm.img[corr_mask] = np.nan
print("Linear interpolation of dH")
ifsar_srtm_lin_interp = dt.fill_holes(masked_ifsar_srtm, dt.linear, valid_area=valid_area)
ifsar_srtm_lin_interp.write('ifsar_srtm_{}_dHinterp.tif'.format(yr),
out_folder='filled_ddems/{}/void{}'.format(yr, corr))
# dh_dem
dem2_reproj = dem2.reproject(dem1) # reproject master DEM to slave DEM extent, cell size
dh_dem = dem2.copy(new_raster=dem2_reproj.img - dem1.img)
dh_bedrx = dem2.copy(new_raster=dem2_reproj.img - dem1.img)
# remove outliers
dem_rem = np.absolute(np.nanmean(dh_dem.img))+(np.nanstd(dh_dem.img)*5)
np.seterr(all='ignore')
dh_dem.img[dh_dem.img > dem_rem] = np.nan
dh_dem.img[dh_dem.img < (dem_rem*-1)] = np.nan
dh_bedrx.img[dh_bedrx.img > dem_rem] = np.nan
dh_bedrx.img[dh_bedrx.img < (dem_rem*-1)] = np.nan
# test coregistered bedrx error
bedrx_ma = imtool.create_mask_from_shapefile(dem1,bedrx_poly_mask,buffer=None)
bedrx_mask = np.invert(bedrx_ma) # inverting for mask
dh_bedrx.img = np.ma.masked_where(bedrx_mask, dh_bedrx.img) # masking
fig0 = dh_bedrx.display(fig=plt.figure(figsize=(8,8)), cmap='bwr_r', vmin=-10, vmax=10)
fig0.gca().set_xlabel('utm easting (m)')
fig0.gca().set_ylabel('utm northing (m)')
cb = plt.colorbar(); cb.set_label('elevation difference (m)')
#ts = np.ma.compressed(dh_bedrx.img)
#plt.hist(ts,bins=40,range=[-7.5,7.5])
#plt.show() # to plot histogram
# mask DEMs
s1 = gpd.read_file(odir+'2016_outline1.shp')
s2 = gpd.read_file(odir+'2021_outline.shp')
s1arr_a = imtool.create_mask_from_shapefile(dem1,odir+'2016_outline1.shp',buffer=None)
s1arr = np.invert(s1arr_a)