def clip_glacier_with_terminus(line):
"""
Clip glacier extent with a terminus.
"""
gpoly = shapely.geometry.Polygon(shell=Glacier())
# Extend western edge past polygon boundary
wpoint = shapely.geometry.Point(line[0])
west_snaps = shapely.ops.nearest_points(wpoint, gpoly.exterior)
if west_snaps[0] != west_snaps[1]:
new_west = west_snaps[1].coords
d = new_west - line[0]
d /= np.linalg.norm(d)
line = np.row_stack((new_west + d, line))
# Extend eastern edge past polygon boundary
epoint = shapely.geometry.Point(line[-1])
east_snaps = shapely.ops.nearest_points(epoint, gpoly.exterior)
if east_snaps[0] != east_snaps[1]:
new_east = east_snaps[1].coords
d = new_east - line[-1]
d /= np.linalg.norm(d)
line = np.row_stack((line, new_east + d))
tline = shapely.geometry.LineString(line)
# Split glacier at terminus
splits = shapely.ops.split(gpoly, tline)
if len(splits) < 2:
raise ValueError('Glacier polygon not split by terminus')
else:
areas = [split.area for split in splits]
return np.asarray(splits[np.argmax(areas)].exterior.coords)
def select_repeat_tracks(runs):
"""
Return Tracks composed of the best track for each initial point.
Selects the track for each point that minimizes the temporal mean standard
deviation for vx + vy, i.e. mean(sqrt(vx_sigma**2 + vy_sigma**2)).
Arguments:
runs (iterable): Tracks objects with identical point and time dimensions
"""
# Compute metric for each track
metric = np.row_stack([
np.nanmean(np.sqrt(np.nansum(run.sigmas[..., 3:5]**2, axis=2)), axis=1)
for run in runs
])
# Choose run with the smallest metric
selected = np.argmin(metric, axis=0)
# Merge runs
means = runs[0].means.copy()
sigmas = runs[0].sigmas.copy()
for i, run in enumerate(runs[1:], start=1):
mask = selected == i
means[mask, ...] = run.means[mask, ...]
sigmas[mask, ...] = run.sigmas[mask, ...]
return glimpse.Tracks(datetimes=runs[0].datetimes,
means=means,
sigmas=sigmas)
cam_args = cg.load_calibrations(image,
station_estimate=True, station=True, merge=True, file_errors=False)
img = glimpse.Image(img_path, cam=cam_args)
img.cam.resize(img_size)
# Select nearest dem and ortho
img_date = datetime.datetime.strptime(cg.parse_image_path(image)['date_str'], '%Y%m%d')
i_dem = np.argmin(np.abs(np.asarray(dem_dates) - img_date))
i_ortho = np.argmin(np.abs(np.asarray(ortho_dates) - img_date))
dem_path = dem_paths[i_dem]
ortho_path = ortho_paths[i_ortho]
# Load raster metadata
dem_grid = glimpse.Grid.read(dem_path, d=grid_size)
ortho_grid = glimpse.Grid.read(ortho_path, d=grid_size)
# Intersect bounding boxes
cam_box = img.cam.viewbox(50e3)[[0, 1, 3, 4]]
box = glimpse.helpers.intersect_boxes(np.row_stack((
cam_box, dem_grid.box2d, ortho_grid.box2d)))
# Read dem and ortho
dem = glimpse.Raster.read(dem_path, xlim=box[0::2], ylim=box[1::2], d=grid_size)
dem.crop(zlim=(0.1, np.inf))
radius = circle_radius.get(image, circle_radius_default)
dem.fill_circle(center=img.cam.xyz, radius=radius)
nbands = gdal.Open(ortho_path).RasterCount
bands = []
for i in range(nbands):
bands.append(glimpse.Raster.read(ortho_path, band=i + 1, d=grid_size,
xlim=box[0::2], ylim=box[1::2]).Z)
orthoZ = np.dstack(bands).astype(float)
if not color:
orthoZ = np.atleast_3d(glimpse.helpers.rgb_to_gray(orthoZ))
orthoZ[orthoZ == 0] = np.nan
# HACK: Clip dem and ortho to same size relative to x, y min
services=services,
snap=SNAP,
use_exif=False,
service_exif=True,
anchors=True,
viewdir=True,
viewdir_as_anchor=True)
# Keep only oriented images
images = np.array([img for img in images if img.anchor])
# Write animation(s)
# HACK: Split at camera changes to use observer.animate()
# HACK: Use k1 to quickly differentiate between cameras
ks = np.array([img.cam.k[0] for img in images])
for k in np.unique(ks):
idx = np.where(ks == k)[0]
sizes = np.row_stack([img.cam.imgsz for img in images[idx]])
unique_sizes = np.unique(sizes, axis=0)
if len(unique_sizes) > 1:
# Standardize image sizes
size = unique_sizes.min(axis=0)
not_size = np.any(sizes != size, axis=1)
f = images[~not_size][0].cam.f
for img in images[not_size]:
img.cam.resize(size, force=True)
# HACK: Fix focal length rounding errors
if any(img.cam.f - f > 0.1):
raise ValueError('Focal lengths cannot be reconciled')
img.cam.f = f
observer = glimpse.Observer(images[idx], cache=False).subset(snap=snap)
path = os.path.join(
'viewdirs-animations',
bw = glimpse.helpers.rgb_to_gray(img.read()).astype(np.uint8)
img.write(path=path, I=clahe.apply(bw))
# Select nearest dem and ortho
img_date = datetime.datetime.strptime(
cg.parse_image_path(image)['date_str'], '%Y%m%d')
i_dem = np.argmin(np.abs(np.asarray(dem_dates) - img_date))
i_ortho = np.argmin(np.abs(np.asarray(ortho_dates) - img_date))
dem_path = dem_paths[i_dem]
ortho_path = ortho_paths[i_ortho]
# Load raster metadata
dem_grid = glimpse.Grid.read(dem_path, d=grid_size)
ortho_grid = glimpse.Grid.read(ortho_path, d=grid_size)
# Intersect bounding boxes
cam_box = img.cam.viewbox(50e3)[[0, 1, 3, 4]]
box = glimpse.helpers.intersect_boxes(
np.row_stack((cam_box, dem_grid.box2d, ortho_grid.box2d)))
# Read dem and ortho
dem = glimpse.Raster.read(dem_path,
xlim=box[0::2],
ylim=box[1::2],
d=grid_size)
dem.crop(zlim=(0.1, np.inf))
radius = circle_radius.get(image, circle_radius_default)
dem.fill_circle(center=img.cam.xyz, radius=radius)
nbands = gdal.Open(ortho_path).RasterCount
bands = []
for i in range(nbands):
bands.append(
glimpse.Raster.read(ortho_path,
band=i + 1,
d=grid_size,