Loading autocnet/camera/sensor_model.py 0 → 100644 +485 −0 Original line number Diff line number Diff line """ A set of classes that represent a sensor mode. Each class implements the calculate_sample_line and sampline2xyz methods for computing image coordinate location from lon/lat and xyz location from sample/line, respectively. """ import abc import collections import logging from subprocess import CalledProcessError from numbers import Number from autocnet.transformation.spatial import og2oc, og2xyz, xyz2og, xyz2oc from autocnet.io import isis import numpy as np import pvl try: import kalasiris as isis except Exception as exception: from autocnet.utils.utils import FailedImport isis = FailedImport(exception) from knoten.csm import create_csm # set up the logger file log = logging.getLogger(__name__) class BaseSensor: def __init__(self, data_path, dem): self.data_path = data_path self.dem = dem self.semi_major = dem.a self.semi_minor = dem.b def _check_arg(self, x, y, z=None): if isinstance(x, collections.abc.Sequence) and isinstance(y, collections.abc.Sequence) and (isinstance(z, collections.abc.Sequence) or z is None): if len(x) != len(y) or (z is not None and len(x) != len(y) != len(z)): raise IndexError( f"Sequences given to x and y must be of the same length." ) x_coords = x y_coords = y if z is not None: z_coords = z elif isinstance(x, Number) and isinstance(y, Number) and (isinstance(z, Number) or z is None): x_coords = [x, ] y_coords = [y, ] if z is not None: z_coords = [z, ] elif isinstance(x, np.ndarray) and isinstance(y, np.ndarray) and (isinstance(z, np.ndarray) or z is None): if not all((x.ndim == 1, y.ndim == 1)) and (z is not None and all((x.ndim ==1, y.ndim == 1, z.ndim == 1))): if z is None: raise IndexError( f"If they are numpy arrays, x and y must be one-dimensional, " f"they were: {x.ndim} and {y.ndim}" ) else: raise IndexError( f"If they are numpy arrays, x and y must be one-dimensional, " f"they were: {x.ndim}, {y.ndim} and {z.ndim}" ) if x.shape != y.shape: raise IndexError( f"Numpy arrays given to x and y must be of the same shape." ) x_coords = x y_coords = y if z is not None: z_coords = z else: raise TypeError( f"The values of x and y were neither Sequences nor individual numbers" f"numbers, they were: {x} and {y}" ) if z is not None: return x_coords, y_coords, z_coords else: return x_coords, y_coords @abc.abstractmethod def lonlat2xyz(self, lon, lat, height): return @abc.abstractmethod def xyz2sampline(self, x, y, z): return @abc.abstractmethod def lonlat2sampline(self, lon, lat): return @abc.abstractmethod def sampline2xyz(self, sample, line): return @abc.abstractmethod def sampline2lonlat(sample, line): return class ISISSensor(BaseSensor): """ ISIS defaults to ocentric latitudes for everything. """ sensor_type = "isis" def _point_info( self, x, y, point_type: str, allowoutside=False ): """ Returns a pvl.collections.MutableMappingSequence object or a Sequence of MutableMappingSequence objects which contain keys and values derived from the output of ISIS campt or mappt on the *cube_path*. If x and y are single numbers, then a single MutableMappingSequence object will be returned. If they are Sequences or Numpy arrays, then a Sequence of MutableMappingSequence objects will be returned, such that the first MutableMappingSequence object of the returned Sequence will correspond to the result of *x[0]* and *y[0]*, etc. Raises subprocess.CalledProcessError if campt or mappt have failures. May raise ValueError if campt completes, but reports errors. Parameters ---------- cube_path : os.PathLike Path to the input cube. x : Number, Sequence of Numbers, or Numpy Array Point(s) in the x direction. Interpreted as either a sample or a longitude value determined by *point_type*. y : Number, Sequence of Numbers, or Numpy Array Point(s) in the y direction. Interpreted as either a line or a latitude value determined by *point_type*. point_type : str Options: {"image", "ground"} Pass "image" if x,y are in image space (sample, line) or "ground" if in ground space (longitude, latitude) allowoutside: bool Defaults to False, this parameter is passed to campt or mappt. Please read the ISIS documentation to learn more about this parameter. """ point_type = point_type.casefold() valid_types = {"image", "ground"} if point_type not in valid_types: raise ValueError( f'{point_type} is not a valid point type, valid types are ' f'{valid_types}' ) x_coords, y_coords = self._check_arg(x, y) results = [] if pvl.load(self.data_path).get("IsisCube").get("Mapping"): # We have a projected image, and must use mappt mappt_common_args = dict(allowoutside=allowoutside, type=point_type) for xx, yy in zip(x_coords, y_coords): mappt_args = { "ground": dict( longitude=xx, latitude=yy, coordsys="UNIVERSAL" ), "image": dict( # Convert PLIO pixels to ISIS pixels sample=xx+0.5, line=yy+0.5 ) } for k in mappt_args.keys(): mappt_args[k].update(mappt_common_args) mapres = pvl.loads(isis.mappt(self.data_path, **mappt_args[point_type]).stdout)["Results"] # convert from ISIS pixels to PLIO pixels mapres['Sample'] = mapres['Sample'] - 0.5 mapres['Line'] = mapres['Line'] - 0.5 results.append(mapres) else: # Not projected, use campt if point_type == "ground": # campt uses lat, lon for ground but sample, line for image. # So swap x,y for ground-to-image calls p_list = [f"{lat}, {lon}" for lon, lat in zip(x_coords, y_coords)] else: p_list = [ f"{samp+0.5}, {line+0.5}" for samp, line in zip(x_coords, y_coords) ] # ISIS's campt needs points in a file with isis.fromlist.temp(p_list) as f: cp = isis.campt( self.data_path, coordlist=f, allowoutside=allowoutside, usecoordlist=True, coordtype=point_type ) camres = pvl.loads(cp.stdout) if 'campt' in camres.keys(): camres = camres['campt'] for r in camres.getall("GroundPoint"): if r['Error'] is None: # convert all pixels to PLIO pixels from ISIS r["Sample"] -= .5 r["Line"] -= .5 results.append(r) else: r["Sample"] = None r["Line"] = None results.append(r) return self._flatten_results(x, results) def _flatten_results(self, x, results): if isinstance(x, (collections.abc.Sequence, np.ndarray)): return results else: return results[0] def _get_value(self, obj): """Returns *obj*, unless *obj* is of type pvl.collections.Quantity, in which case, the .value component of the object is returned.""" if isinstance(obj, pvl.collections.Quantity): return obj.value else: return obj def xyz2sampline(self, x, y, z): lon, lat = xyz2oc(x, y, z, self.semi_major, self.semi_minor) return self.lonlat2sampline(lon, lat) def lonlat2sampline(self, lon, lat, allowoutside=False): """ Returns a two-tuple of numpy arrays or a two-tuple of floats, where the first element of the tuple is the sample(s) and the second element are the lines(s) that represent the coordinate(s) of the input *lon* and *lat* in *cube_path*. If *lon* and *lat* are single numbers, then the returned two-tuple will have single elements. If they are Sequences, then the returned two-tuple will contain numpy arrays. Raises the same exceptions as _point_info(). Parameters ---------- lon: Number or Sequence of Numbers Longitude coordinate(s). lat: Number or Sequence of Numbers Latitude coordinate(s). """ # ISIS is expecting ocentric latitudes. Convert from ographic before passing. lonoc, latoc = og2oc(lon, lat, self.semi_major, self.semi_minor) res = self._point_info(lonoc, latoc, "ground", allowoutside=allowoutside) if isinstance(lon, (collections.abc.Sequence, np.ndarray)): samples, lines = np.asarray([[r["Sample"], r["Line"]] for r in res]).T else: samples, lines = res["Sample"], res["Line"] return samples, lines def sampline2xyz(self, sample, line): """ Convert a line and sample into and x,y,z point for isis camera model Parameters ---------- node: obj autocnet object containing image information sample: int sample of point line: int lint of point Returns ------- x,y,z : int(s) x,y,z coordinates of the point """ try: p = self._point_info(sample, line, point_type='image') except CalledProcessError as e: if 'Requested position does not project in camera model' in e.stderr: log.debug(f"Image coordinates {sample}, {line} do not project fot image {self.data_path}.") try: x, y, z = p["BodyFixedCoordinate"].value except: x, y, z = p["BodyFixedCoordinate"] if getattr(p["BodyFixedCoordinate"], "units", "None").lower() == "km": x = x * 1000 y = y * 1000 z = z * 1000 return x,y,z def sampline2lonlat( self, sample, line, lontype="PositiveEast360Longitude", lattype="PlanetographicLatitude", allowoutside=False ): """ Returns a two-tuple of numpy arrays or a two-tuple of floats, where the first element of the tuple is the longitude(s) and the second element are the latitude(s) that represent the coordinate(s) of the input *sample* and *line* in *cube_path*. If *sample* and *line* are single numbers, then the returned two-tuple will have single elements. If they are Sequences, then the returned two-tuple will contain numpy arrays. Raises the same exceptions as point_info(). Parameters ---------- cube_path : os.PathLike Path to the input cube. sample : Number or Sequence of Numbers Sample coordinate(s). line : Number or Sequence of Numbers Line coordinate(s). lontype: str Name of key to query in the campt or mappt return to get the returned longitudes. Defaults to "PositiveEast360Longitude", but other values are possible. Please see the campt or mappt documentation. lattype: str Name of key to query in the campt or mappt return to get the returned latitudes. Defaults to "PlanetocentricLatitude", but other values are possible. Please see the campt or mappt documentation. """ res = self._point_info(sample, line, "image", allowoutside=allowoutside) if isinstance(sample, (collections.abc.Sequence, np.ndarray)): lon_list = list() lat_list = list() for r in res: lon_list.append(self._get_value(r[lontype])) lat_list.append(self._get_value(r[lattype])) lons = np.asarray(lon_list) lats = np.asarray(lat_list) else: lons = self._get_value(res[lontype]) lats = self._get_value(res[lattype]) return lons, lats def lonlat2xyz(self, lon, lat, height=0): xyz = og2xyz(lon, lat, height, self.semi_major, self.semi_minor) return xyz class CSMSensor(BaseSensor): """ The CSM sensor model works in ographic latitudes. """ sensor_type = "csm" def __init__(self, data_path, dem): super().__init__(data_path,dem) # Create a sensor model object using knoten here. self.sensor = create_csm(data_path) self.dem = dem def xyz2sampline(self, x, y, z): x_coords, y_coords, z_coords = self._check_args(x, y, z) results = [] for coord in zip(x_coords, y_coords, z_coords): ecef = csmapi.EcefCoord(coord[0], coord[1], coord[2]) imagept = self.sensor.groundToImage(ecef) results+=[imagept.sample, imagept.line] return self._flatten_results(results) def lonlat2sampline(self, lon, lat): """ Calculate the sample and line for an csm camera sensor Parameters ---------- node: obj autocnet object containing image information lon: int longitude of point lat: int latitude of point Returns ------- sample: int sample of point line: int lint of point """ semi_major = self.dem.a semi_minor = self.dem.b x_coords, y_coords = self._check_args(lat, lon) heights = [self.get.get_height(i[0], i[1]) for i in zip(x_coords, y_coords)] x,y,z = og2xyz(lon, lat, heights, semi_major, semi_minor) return self.xyz2linesamp(x,y,z) def sampline2xyz(self, sample, line): """ Convert a line and sample into and x,y,z point for csm camera model Parameters ---------- node: obj autocnet object containing image information sample: int sample of point line: int lint of point kwargs: dict Contain information to be used if the csm sensor model is passed csm_kwargs = { 'semi_major': int, 'semi_minor': int, 'ncg': obj, } Returns ------- x,y,z : int(s) x,y,z coordinates of the point """ semi_major = self.dem.a semi_minor = self.dem.b image_coord = csmapi.ImageCoord(sample, line) pcoord = self.sensor.imageToGround(image_coord) return pcoord.x, pcoord.y, pcoord.z def sampline2lonlat(self, sample, line): x,y,z = self.sampline2xyz(sample, line) lon, lat = xyz2og(x, y, z,self.semi_major, self.semi_minor) return lon, lat def create_sensor(sensor_type, cam_path, dem=None): sensor_type = sensor_type.lower() sensor_classes = { "isis": ISISSensor, "csm": CSMSensor } if sensor_type in sensor_classes: return sensor_classes[sensor_type](cam_path, dem) else: raise Exception(f"Unsupported sensor type: {sensor_type}, accept 'isis' or 'csm'") No newline at end of file autocnet/spatial/tests/test_isis.py→autocnet/camera/tests/test_sensor_model.py +255 −0 File changed and moved.Preview size limit exceeded, changes collapsed. Show changes autocnet/graph/network.py +1 −1 Original line number Diff line number Diff line Loading @@ -57,7 +57,7 @@ from autocnet.matcher import subpixel from autocnet.matcher import cross_instrument_matcher as cim from autocnet.vis.graph_view import plot_graph, cluster_plot from autocnet.control import control from autocnet.spatial.isis import point_info #from autocnet.spatial.isis import point_info from autocnet.spatial.surface import GdalDem, EllipsoidDem from autocnet.transformation.spatial import reproject, og2oc Loading autocnet/graph/node.py +3 −4 Original line number Diff line number Diff line Loading @@ -10,18 +10,17 @@ from csmapi import csmapi import numpy as np import pandas as pd from plio.io.io_gdal import GeoDataset from plio.io.isis_serial_number import generate_serial_number from skimage.transform import resize import shapely from knoten.csm import generate_latlon_footprint, generate_vrt, create_camera, generate_boundary from knoten.csm import generate_latlon_footprint, generate_boundary from autocnet.matcher import cpu_extractor as fe from autocnet.matcher import cpu_outlier_detector as od from autocnet.cg import cg from autocnet.io.db.model import Images, Keypoints, Matches, Cameras, Base, Overlay, Edges, Costs, Points, Measures from autocnet.io.db.connection import Parent from autocnet.io import keypoints as io_keypoints from autocnet.io.geodataset import AGeoDataset from autocnet.vis.graph_view import plot_node from autocnet.utils import utils Loading Loading @@ -167,7 +166,7 @@ class Node(dict, MutableMapping): @property def geodata(self): if not hasattr(self, '_geodata'): self._geodata = GeoDataset(self['image_path']) self._geodata = AGeoDataset(self['image_path']) return self._geodata @property Loading autocnet/io/db/controlnetwork.py +1 −1 Original line number Diff line number Diff line Loading @@ -6,7 +6,7 @@ import numpy as np import shapely.wkb as swkb from plio.io import io_controlnetwork as cnet from autocnet.io.db.model import Measures from autocnet.spatial.isis import isis2np_types from autocnet.io.isis import isis2np_types from ... import sql from sqlalchemy import text Loading Loading
autocnet/camera/sensor_model.py 0 → 100644 +485 −0 Original line number Diff line number Diff line """ A set of classes that represent a sensor mode. Each class implements the calculate_sample_line and sampline2xyz methods for computing image coordinate location from lon/lat and xyz location from sample/line, respectively. """ import abc import collections import logging from subprocess import CalledProcessError from numbers import Number from autocnet.transformation.spatial import og2oc, og2xyz, xyz2og, xyz2oc from autocnet.io import isis import numpy as np import pvl try: import kalasiris as isis except Exception as exception: from autocnet.utils.utils import FailedImport isis = FailedImport(exception) from knoten.csm import create_csm # set up the logger file log = logging.getLogger(__name__) class BaseSensor: def __init__(self, data_path, dem): self.data_path = data_path self.dem = dem self.semi_major = dem.a self.semi_minor = dem.b def _check_arg(self, x, y, z=None): if isinstance(x, collections.abc.Sequence) and isinstance(y, collections.abc.Sequence) and (isinstance(z, collections.abc.Sequence) or z is None): if len(x) != len(y) or (z is not None and len(x) != len(y) != len(z)): raise IndexError( f"Sequences given to x and y must be of the same length." ) x_coords = x y_coords = y if z is not None: z_coords = z elif isinstance(x, Number) and isinstance(y, Number) and (isinstance(z, Number) or z is None): x_coords = [x, ] y_coords = [y, ] if z is not None: z_coords = [z, ] elif isinstance(x, np.ndarray) and isinstance(y, np.ndarray) and (isinstance(z, np.ndarray) or z is None): if not all((x.ndim == 1, y.ndim == 1)) and (z is not None and all((x.ndim ==1, y.ndim == 1, z.ndim == 1))): if z is None: raise IndexError( f"If they are numpy arrays, x and y must be one-dimensional, " f"they were: {x.ndim} and {y.ndim}" ) else: raise IndexError( f"If they are numpy arrays, x and y must be one-dimensional, " f"they were: {x.ndim}, {y.ndim} and {z.ndim}" ) if x.shape != y.shape: raise IndexError( f"Numpy arrays given to x and y must be of the same shape." ) x_coords = x y_coords = y if z is not None: z_coords = z else: raise TypeError( f"The values of x and y were neither Sequences nor individual numbers" f"numbers, they were: {x} and {y}" ) if z is not None: return x_coords, y_coords, z_coords else: return x_coords, y_coords @abc.abstractmethod def lonlat2xyz(self, lon, lat, height): return @abc.abstractmethod def xyz2sampline(self, x, y, z): return @abc.abstractmethod def lonlat2sampline(self, lon, lat): return @abc.abstractmethod def sampline2xyz(self, sample, line): return @abc.abstractmethod def sampline2lonlat(sample, line): return class ISISSensor(BaseSensor): """ ISIS defaults to ocentric latitudes for everything. """ sensor_type = "isis" def _point_info( self, x, y, point_type: str, allowoutside=False ): """ Returns a pvl.collections.MutableMappingSequence object or a Sequence of MutableMappingSequence objects which contain keys and values derived from the output of ISIS campt or mappt on the *cube_path*. If x and y are single numbers, then a single MutableMappingSequence object will be returned. If they are Sequences or Numpy arrays, then a Sequence of MutableMappingSequence objects will be returned, such that the first MutableMappingSequence object of the returned Sequence will correspond to the result of *x[0]* and *y[0]*, etc. Raises subprocess.CalledProcessError if campt or mappt have failures. May raise ValueError if campt completes, but reports errors. Parameters ---------- cube_path : os.PathLike Path to the input cube. x : Number, Sequence of Numbers, or Numpy Array Point(s) in the x direction. Interpreted as either a sample or a longitude value determined by *point_type*. y : Number, Sequence of Numbers, or Numpy Array Point(s) in the y direction. Interpreted as either a line or a latitude value determined by *point_type*. point_type : str Options: {"image", "ground"} Pass "image" if x,y are in image space (sample, line) or "ground" if in ground space (longitude, latitude) allowoutside: bool Defaults to False, this parameter is passed to campt or mappt. Please read the ISIS documentation to learn more about this parameter. """ point_type = point_type.casefold() valid_types = {"image", "ground"} if point_type not in valid_types: raise ValueError( f'{point_type} is not a valid point type, valid types are ' f'{valid_types}' ) x_coords, y_coords = self._check_arg(x, y) results = [] if pvl.load(self.data_path).get("IsisCube").get("Mapping"): # We have a projected image, and must use mappt mappt_common_args = dict(allowoutside=allowoutside, type=point_type) for xx, yy in zip(x_coords, y_coords): mappt_args = { "ground": dict( longitude=xx, latitude=yy, coordsys="UNIVERSAL" ), "image": dict( # Convert PLIO pixels to ISIS pixels sample=xx+0.5, line=yy+0.5 ) } for k in mappt_args.keys(): mappt_args[k].update(mappt_common_args) mapres = pvl.loads(isis.mappt(self.data_path, **mappt_args[point_type]).stdout)["Results"] # convert from ISIS pixels to PLIO pixels mapres['Sample'] = mapres['Sample'] - 0.5 mapres['Line'] = mapres['Line'] - 0.5 results.append(mapres) else: # Not projected, use campt if point_type == "ground": # campt uses lat, lon for ground but sample, line for image. # So swap x,y for ground-to-image calls p_list = [f"{lat}, {lon}" for lon, lat in zip(x_coords, y_coords)] else: p_list = [ f"{samp+0.5}, {line+0.5}" for samp, line in zip(x_coords, y_coords) ] # ISIS's campt needs points in a file with isis.fromlist.temp(p_list) as f: cp = isis.campt( self.data_path, coordlist=f, allowoutside=allowoutside, usecoordlist=True, coordtype=point_type ) camres = pvl.loads(cp.stdout) if 'campt' in camres.keys(): camres = camres['campt'] for r in camres.getall("GroundPoint"): if r['Error'] is None: # convert all pixels to PLIO pixels from ISIS r["Sample"] -= .5 r["Line"] -= .5 results.append(r) else: r["Sample"] = None r["Line"] = None results.append(r) return self._flatten_results(x, results) def _flatten_results(self, x, results): if isinstance(x, (collections.abc.Sequence, np.ndarray)): return results else: return results[0] def _get_value(self, obj): """Returns *obj*, unless *obj* is of type pvl.collections.Quantity, in which case, the .value component of the object is returned.""" if isinstance(obj, pvl.collections.Quantity): return obj.value else: return obj def xyz2sampline(self, x, y, z): lon, lat = xyz2oc(x, y, z, self.semi_major, self.semi_minor) return self.lonlat2sampline(lon, lat) def lonlat2sampline(self, lon, lat, allowoutside=False): """ Returns a two-tuple of numpy arrays or a two-tuple of floats, where the first element of the tuple is the sample(s) and the second element are the lines(s) that represent the coordinate(s) of the input *lon* and *lat* in *cube_path*. If *lon* and *lat* are single numbers, then the returned two-tuple will have single elements. If they are Sequences, then the returned two-tuple will contain numpy arrays. Raises the same exceptions as _point_info(). Parameters ---------- lon: Number or Sequence of Numbers Longitude coordinate(s). lat: Number or Sequence of Numbers Latitude coordinate(s). """ # ISIS is expecting ocentric latitudes. Convert from ographic before passing. lonoc, latoc = og2oc(lon, lat, self.semi_major, self.semi_minor) res = self._point_info(lonoc, latoc, "ground", allowoutside=allowoutside) if isinstance(lon, (collections.abc.Sequence, np.ndarray)): samples, lines = np.asarray([[r["Sample"], r["Line"]] for r in res]).T else: samples, lines = res["Sample"], res["Line"] return samples, lines def sampline2xyz(self, sample, line): """ Convert a line and sample into and x,y,z point for isis camera model Parameters ---------- node: obj autocnet object containing image information sample: int sample of point line: int lint of point Returns ------- x,y,z : int(s) x,y,z coordinates of the point """ try: p = self._point_info(sample, line, point_type='image') except CalledProcessError as e: if 'Requested position does not project in camera model' in e.stderr: log.debug(f"Image coordinates {sample}, {line} do not project fot image {self.data_path}.") try: x, y, z = p["BodyFixedCoordinate"].value except: x, y, z = p["BodyFixedCoordinate"] if getattr(p["BodyFixedCoordinate"], "units", "None").lower() == "km": x = x * 1000 y = y * 1000 z = z * 1000 return x,y,z def sampline2lonlat( self, sample, line, lontype="PositiveEast360Longitude", lattype="PlanetographicLatitude", allowoutside=False ): """ Returns a two-tuple of numpy arrays or a two-tuple of floats, where the first element of the tuple is the longitude(s) and the second element are the latitude(s) that represent the coordinate(s) of the input *sample* and *line* in *cube_path*. If *sample* and *line* are single numbers, then the returned two-tuple will have single elements. If they are Sequences, then the returned two-tuple will contain numpy arrays. Raises the same exceptions as point_info(). Parameters ---------- cube_path : os.PathLike Path to the input cube. sample : Number or Sequence of Numbers Sample coordinate(s). line : Number or Sequence of Numbers Line coordinate(s). lontype: str Name of key to query in the campt or mappt return to get the returned longitudes. Defaults to "PositiveEast360Longitude", but other values are possible. Please see the campt or mappt documentation. lattype: str Name of key to query in the campt or mappt return to get the returned latitudes. Defaults to "PlanetocentricLatitude", but other values are possible. Please see the campt or mappt documentation. """ res = self._point_info(sample, line, "image", allowoutside=allowoutside) if isinstance(sample, (collections.abc.Sequence, np.ndarray)): lon_list = list() lat_list = list() for r in res: lon_list.append(self._get_value(r[lontype])) lat_list.append(self._get_value(r[lattype])) lons = np.asarray(lon_list) lats = np.asarray(lat_list) else: lons = self._get_value(res[lontype]) lats = self._get_value(res[lattype]) return lons, lats def lonlat2xyz(self, lon, lat, height=0): xyz = og2xyz(lon, lat, height, self.semi_major, self.semi_minor) return xyz class CSMSensor(BaseSensor): """ The CSM sensor model works in ographic latitudes. """ sensor_type = "csm" def __init__(self, data_path, dem): super().__init__(data_path,dem) # Create a sensor model object using knoten here. self.sensor = create_csm(data_path) self.dem = dem def xyz2sampline(self, x, y, z): x_coords, y_coords, z_coords = self._check_args(x, y, z) results = [] for coord in zip(x_coords, y_coords, z_coords): ecef = csmapi.EcefCoord(coord[0], coord[1], coord[2]) imagept = self.sensor.groundToImage(ecef) results+=[imagept.sample, imagept.line] return self._flatten_results(results) def lonlat2sampline(self, lon, lat): """ Calculate the sample and line for an csm camera sensor Parameters ---------- node: obj autocnet object containing image information lon: int longitude of point lat: int latitude of point Returns ------- sample: int sample of point line: int lint of point """ semi_major = self.dem.a semi_minor = self.dem.b x_coords, y_coords = self._check_args(lat, lon) heights = [self.get.get_height(i[0], i[1]) for i in zip(x_coords, y_coords)] x,y,z = og2xyz(lon, lat, heights, semi_major, semi_minor) return self.xyz2linesamp(x,y,z) def sampline2xyz(self, sample, line): """ Convert a line and sample into and x,y,z point for csm camera model Parameters ---------- node: obj autocnet object containing image information sample: int sample of point line: int lint of point kwargs: dict Contain information to be used if the csm sensor model is passed csm_kwargs = { 'semi_major': int, 'semi_minor': int, 'ncg': obj, } Returns ------- x,y,z : int(s) x,y,z coordinates of the point """ semi_major = self.dem.a semi_minor = self.dem.b image_coord = csmapi.ImageCoord(sample, line) pcoord = self.sensor.imageToGround(image_coord) return pcoord.x, pcoord.y, pcoord.z def sampline2lonlat(self, sample, line): x,y,z = self.sampline2xyz(sample, line) lon, lat = xyz2og(x, y, z,self.semi_major, self.semi_minor) return lon, lat def create_sensor(sensor_type, cam_path, dem=None): sensor_type = sensor_type.lower() sensor_classes = { "isis": ISISSensor, "csm": CSMSensor } if sensor_type in sensor_classes: return sensor_classes[sensor_type](cam_path, dem) else: raise Exception(f"Unsupported sensor type: {sensor_type}, accept 'isis' or 'csm'") No newline at end of file
autocnet/spatial/tests/test_isis.py→autocnet/camera/tests/test_sensor_model.py +255 −0 File changed and moved.Preview size limit exceeded, changes collapsed. Show changes
autocnet/graph/network.py +1 −1 Original line number Diff line number Diff line Loading @@ -57,7 +57,7 @@ from autocnet.matcher import subpixel from autocnet.matcher import cross_instrument_matcher as cim from autocnet.vis.graph_view import plot_graph, cluster_plot from autocnet.control import control from autocnet.spatial.isis import point_info #from autocnet.spatial.isis import point_info from autocnet.spatial.surface import GdalDem, EllipsoidDem from autocnet.transformation.spatial import reproject, og2oc Loading
autocnet/graph/node.py +3 −4 Original line number Diff line number Diff line Loading @@ -10,18 +10,17 @@ from csmapi import csmapi import numpy as np import pandas as pd from plio.io.io_gdal import GeoDataset from plio.io.isis_serial_number import generate_serial_number from skimage.transform import resize import shapely from knoten.csm import generate_latlon_footprint, generate_vrt, create_camera, generate_boundary from knoten.csm import generate_latlon_footprint, generate_boundary from autocnet.matcher import cpu_extractor as fe from autocnet.matcher import cpu_outlier_detector as od from autocnet.cg import cg from autocnet.io.db.model import Images, Keypoints, Matches, Cameras, Base, Overlay, Edges, Costs, Points, Measures from autocnet.io.db.connection import Parent from autocnet.io import keypoints as io_keypoints from autocnet.io.geodataset import AGeoDataset from autocnet.vis.graph_view import plot_node from autocnet.utils import utils Loading Loading @@ -167,7 +166,7 @@ class Node(dict, MutableMapping): @property def geodata(self): if not hasattr(self, '_geodata'): self._geodata = GeoDataset(self['image_path']) self._geodata = AGeoDataset(self['image_path']) return self._geodata @property Loading
autocnet/io/db/controlnetwork.py +1 −1 Original line number Diff line number Diff line Loading @@ -6,7 +6,7 @@ import numpy as np import shapely.wkb as swkb from plio.io import io_controlnetwork as cnet from autocnet.io.db.model import Measures from autocnet.spatial.isis import isis2np_types from autocnet.io.isis import isis2np_types from ... import sql from sqlalchemy import text Loading
