def angle_between_views(geotiff_path_1, geotiff_path_2, lon=None, lat=None,
z=None, verbose=False):
"""
Compute the view angle difference between two (stereo) images.
Args:
geotiff_path_1 (str): path or url to a GeoTIFF file
geotiff_path_2 (str): path or url to a GeoTIFF file
lon, lat, z (floats): longitude, latitude, altitude of the 3D point
where to compute the angle
Returns:
float: angle between the views, in degrees
"""
rpc1 = rpc_from_geotiff(geotiff_path_1)
rpc2 = rpc_from_geotiff(geotiff_path_2)
if lon is None:
lon = rpc1.lon_offset
if lat is None:
lat = rpc1.lat_offset
if z is None:
z = srtm4.srtm4(lon, lat)
a = utils.viewing_direction(*rpc1.incidence_angles(lon, lat, z))
b = utils.viewing_direction(*rpc2.incidence_angles(lon, lat, z))
angle = np.degrees(np.arccos(np.dot(a, b)))
if verbose:
print('{:.3f}'.format(angle))
return angle
def localization(img_path, x, y, z, crop_path=None, verbose=False):
"""
Conversion of image coordinates to geographic coordinates.
Args:
img_path (str): path or url to a GeoTIFF image with RPC metadata
x (float or list): x coordinate(s) of the input points
y (float or list): y coordinate(s) of the input points
z (float or list): altitude(s) of the input points
crop_path (str): path or url to an image crop produced by rpcm.
Input image coordinates are interpreted wrt this crop.
Returns:
float or list: longitude(s) of the localised point(s)
float or list: latitude(s) of the localised point(s)
"""
if crop_path: # load and apply crop transformation matrix
with rasterio.open(crop_path, 'r') as src:
tags = src.tags()
C = list(map(float, tags['CROP_TRANSFORM'].split()))
C = np.array(C).reshape(3, 3)
h = np.row_stack((x, y, x**0)) # homogeneous coordinates
x, y = np.dot(np.linalg.inv(C), h).squeeze()[:2]
rpc = rpc_from_geotiff(img_path)
lon, lat = rpc.localization(x, y, z)
if verbose:
for p in zip(np.atleast_1d(lon), np.atleast_1d(lat)):
print('{:.8f} {:.8f}'.format(*p))
return lon, lat
def projection(img_path,
lon,
lat,
z=None,
crop_path=None,
svg_path=None,
verbose=False):
"""
Conversion of geographic coordinates to image coordinates.
Args:
img_path (str): path or url to a GeoTIFF image with RPC metadata
lon (float or list): longitude(s) of the input points
lat (float or list): latitude(s) of the input points
z (float or list): altitude(s) of the input points
crop_path (str): path or url to an image crop produced by rpcm.
Projected image coordinates are computed wrt this crop.
svg_path (str): path to an svg file where to plot the projected image
point(s)
Returns:
float or list: x pixel coordinate(s) of the projected point(s)
float or list: y pixel coordinate(s) of the projected point(s)
"""
rpc = rpc_from_geotiff(img_path)
if z is None:
z = srtm4.srtm4(lon, lat)
x, y = rpc.projection(lon, lat, z)
if crop_path: # load and apply crop transformation matrix
with rasterio.open(crop_path, 'r') as src:
tags = src.tags()
C = list(map(float, tags['CROP_TRANSFORM'].split()))
C = np.array(C).reshape(3, 3)
h = np.row_stack((x, y, x**0)) # homogeneous coordinates
x, y = np.dot(C, h).squeeze()[:2]
if svg_path: #TODO
pass
if verbose:
for p in zip(np.atleast_1d(x), np.atleast_1d(y)):
print('{:.4f} {:.4f}'.format(*p))
return x, y
def crop_aoi(geotiff, aoi, z=0):
"""
Crop a geographic AOI in a georeferenced image using its RPC functions.
Args:
geotiff (string): path or url to the input GeoTIFF image file
aoi (geojson.Polygon): GeoJSON polygon representing the AOI
z (float, optional): base altitude with respect to WGS84 ellipsoid (0
by default)
Return:
crop (array): numpy array containing the cropped image
x, y, w, h (ints): image coordinates of the crop. x, y are the
coordinates of the top-left corner, while w, h are the dimensions
of the crop.
"""
x, y, w, h = bounding_box_of_projected_aoi(
rpc_model.rpc_from_geotiff(geotiff), aoi, z)
with rasterio.open(geotiff, 'r') as src:
crop = src.read(window=((y, y + h), (x, x + w)),
boundless=True).squeeze()
return crop, x, y