import copy
import cv2
import json
import matplotlib.pyplot as plt
import numpy as np
import os
import shapely.geometry
from shapely import ops, wkt
from shapely.geometry import Polygon, LineString, Point
from matplotlib import patches
from pyproj import CRS, Transformer
from pyproj.exceptions import CRSError
from typing import Any, Dict, List, Optional, Tuple, Union
from .. import cv, helpers
[docs]class CameraConfig:
"""
Camera configuration containing information about the perspective of the camera with respect to real world
coordinates
"""
def __str__(self):
return str(self.to_json())
def __repr__(self):
return self.to_json()
[docs] def __init__(
self,
height: int,
width: int,
crs: Optional[Any] = None,
window_size: int = 10,
resolution: float = 0.05,
bbox: Optional[Union[shapely.geometry.Polygon, str]] = None,
camera_matrix: Optional[List[List[float]]] = None,
dist_coeffs: Optional[List[List[float]]] = None,
lens_position: Optional[List[float]] = None,
corners: Optional[List[List[float]]] = None,
gcps: Optional[Dict[str, Union[List, float]]] = None,
lens_pars: Optional[Dict[str, float]] = None,
calibration_video: Optional[str] = None,
is_nadir: Optional[bool] = False,
stabilize: Optional[List[List]] = None
):
"""
Parameters
----------
height : int
height of frame in pixels
width : int
width of frame in pixels
crs : int, dict or str, optional
Coordinate Reference System. Accepts EPSG codes (int or str) proj (str or dict) or wkt (str). Only used if
the data has no native CRS.
window_size : int
pixel size of interrogation window (default: 15)
resolution : float, optional
resolution in m. of projected pixels (default: 0.01)
bbox : shapely.geometry.Polygon, optional
bounding box in geographical coordinates
lens_position : list of floats (3),
x, y, z coordinate of lens position in CRS
corners : list of lists of floats (2)
[x, y] coordinates defining corners of area of interest in camera cols/rows, bbox will be computed from this
gcps : dict
Can contain "src": list of lists, with column, row locations in objective of control points,
"dst": list of lists, with x, y or x, y, z locations (local or global coordinate reference system) of
control points,
"h_ref": float, measured water level [m] in local reference system (e.g. from staff gauge or pressure gauge)
during gcp survey,
"z_0": float, water level [m] in global reference system (e.g. from used GPS system CRS). This must be in
the same vertical reference as the measured bathymetry and other survey points,
"crs": int, str or CRS object, CRS in which "dst" points are measured. If None, a local coordinate system is
assumed (e.g. from spirit level).
lens_pars (deprecated) : dict, optional
Lens parameters, containing: "k1": float, barrel lens distortion parameter (default: 0.),
"c": float, optical center (default: 2.),
"focal_length": float, focal length (default: width of image frame)
calibration_video : str, optional
local path to video file containing a checkerboard pattern. Must be 9x6 if called directly, otherwise use
``.calibrate_camera`` explicitly and provide ``chessboard_size`` explicitly. When used, an automated camera
calibration on the video file will be attempted.
"""
assert(isinstance(height, int)), 'height must be provided as type "int"'
assert(isinstance(width, int)), 'width must be provided as type "int"'
assert (isinstance(window_size, int)), 'window_size must be of type "int"'
self.height = height
self.width = width
self.is_nadir = is_nadir
if crs is not None:
try:
crs = CRS.from_user_input(crs)
except CRSError:
raise CRSError(f'crs "{crs}" is not a valid Coordinate Reference System')
assert (crs.is_geographic == 0), "Prodstvided crs must be projected with units like [m]"
self.crs = crs.to_wkt()
if resolution is not None:
self.resolution = resolution
if lens_position is not None:
self.set_lens_position(*lens_position)
else:
self.lens_position = None
if gcps is not None:
self.set_gcps(**gcps)
if camera_matrix is None or dist_coeffs is None:
if self.is_nadir:
# with nadir, no perspective can be constructed, hence, camera matrix and dist coeffs will be set to default values
self.camera_matrix = cv._get_cam_mtx(self.height, self.width)
self.dist_coeffs = cv.DIST_COEFFS
# camera pars are incomplete and need to be derived
else:
self.set_intrinsic(
camera_matrix=camera_matrix,
lens_pars=lens_pars
)
else:
# camera matrix and dist coeffs can also be set hard, this overrules the lens_pars option
self.camera_matrix = camera_matrix
self.dist_coeffs = dist_coeffs
if calibration_video is not None:
self.set_lens_calibration(calibration_video, plot=False)
if bbox is not None:
self.bbox = bbox
if window_size is not None:
self.window_size = window_size
# override the transform and bbox with the set corners
if corners is not None:
self.set_bbox_from_corners(corners)
if stabilize is not None:
self.stabilize = stabilize
@property
def bbox(self):
"""
Returns geographical bbox fitting around corners points of area of interest in camera perspective
Returns
-------
bbox : shapely.geometry.Polygon
bbox of area of interest
"""
return self._bbox
@bbox.setter
def bbox(self, pol):
if isinstance(pol, str):
self._bbox = wkt.loads(pol)
else:
self._bbox = pol
@property
def camera_matrix(self):
return self._camera_matrix
@camera_matrix.setter
def camera_matrix(self, camera_matrix):
self._camera_matrix = camera_matrix.tolist() if isinstance(camera_matrix, np.ndarray) else camera_matrix
@property
def dist_coeffs(self):
return self._dist_coeffs
@dist_coeffs.setter
def dist_coeffs(self, dist_coeffs):
self._dist_coeffs = dist_coeffs.tolist() if isinstance(dist_coeffs, np.ndarray) else dist_coeffs
@property
def gcps_dest(self):
"""
Returns
-------
dst : np.ndarray
destination coordinates of ground control point. z-coordinates are parsed from z_0 if necessary
"""
if hasattr(self, "gcps"):
if "dst" in self.gcps:
return np.array(self.gcps["dst"] if len(self.gcps["dst"][0]) == 3 else np.c_[self.gcps["dst"], np.ones(4)*self.gcps["z_0"]])
# if conditions are not yet met, then return None
return None
@property
def gcps_dest_bbox(self):
"""
Returns
-------
dst : np.ndarray
Destination coordinates measured as column, row in the intended bounding box with the intended resolution
"""
return np.array(cv.transform_to_bbox(self.gcps_dest, self.bbox, self.resolution))
@property
def gcps_bbox_reduced(self):
"""
Returns
-------
dst : np.ndarray
Destination coordinates in col, row in the intended bounding box, reduced with their mean coordinate
"""
return self.gcps_dest_bbox - self.gcps_dest_bbox.mean(axis=0)
@property
def gcps_reduced(self):
"""
Get the location of gcp destination points, reduced with their mean for better local projection
Returns
-------
dst : np.ndarray
Reduced coordinate (x, y) or (x, y, z) of gcp destination points
"""
return np.array(self.gcps_dest - self.gcps_mean)
@property
def gcps_mean(self):
"""
Get the mean location of gcp destination points
Returns
-------
dst_mean : np.ndarray
mean coordinate (x, y) or (x, y, z) of gcp destination points
"""
return np.array(self.gcps_dest).mean(axis=0)
@property
def gcps_dims(self):
"""
Returns
-------
dims : int
amount of dimensions of gcps (can be 2 or 3)
"""
return len(self.gcps["dst"][0]) if hasattr(self, "gcps") else None
@property
def is_nadir(self):
"""
Returns if the camera configuration belongs to nadir video
Returns
-------
is_nadir : bool
False if not nadir, True if nadir
"""
return self._is_nadir
@is_nadir.setter
def is_nadir(
self,
nadir_prop: bool
):
self._is_nadir = nadir_prop
@property
def pnp(self):
return cv.solvepnp(
self.gcps_reduced,
self.gcps["src"],
self.camera_matrix,
self.dist_coeffs
)
@property
def shape(self):
"""
Returns rows and columns in projected frames from ``Frames.project``
Returns
-------
rows : int
Amount of rows in projected frame
cols : int
Amount of columns in projected frame
"""
cols, rows = cv._get_shape(
self.bbox,
resolution=self.resolution,
round=1
)
return rows, cols
@property
def stabilize(self):
"""
Return stabilization polygon (anything outside is used for stabilization
Returns
-------
coords : list of lists
coordinates in original image frame comprising the polygon for use in stabilization
"""
return self._stabilize
@stabilize.setter
def stabilize(
self,
coords: List[List[float]]
):
self._stabilize = coords
@property
def transform(self):
"""
Returns Affine transform of projected frames from ``Frames.project``
Returns
-------
transform : rasterio.transform.Affine object
"""
return cv._get_transform(self.bbox, resolution=self.resolution)
[docs] def set_lens_calibration(
self,
fn: str,
chessboard_size: Optional[Tuple] = (9, 6),
max_imgs: Optional[int] = 30,
plot: Optional[bool] = True,
progress_bar: Optional[bool] = True,
**kwargs
):
"""
Calibrates and sets the properties ``camera_matrix`` and ``dist_coeffs`` using a video of a chessboard pattern.
Follows methods described on https://docs.opencv.org/4.x/dc/dbb/tutorial_py_calibration.html
Parameters
----------
fn : str
path to video file
df : int, optional
amount of frames to skip after a valid frame with corner points is found, defaults to fps of video.
chessboard_size : tuple, optional
amount of internal corner points expected on chessboard pattern, default is (9, 6).
max_imgs : int, optional
maximum amount of images to use for calibration (default: 30).
tolerance : float, optional
error tolerance alowed for reprojection of corner points (default: 0.1, if set to None, no filtering will
be done). images that exceed the tolerance are excluded from calibration. This is to remove images with
spuriously defined points, or blurry images.
plot : bool, optional
if set, chosen frames will be plotted for the user to inspect on-=the-fly with a one-second delay
(default: True).
progress_bar : bool, optional
if set, a progress bar going through the frames is plotted (default: True).
"""
assert(os.path.isfile(fn)), f"Video calibration file {fn} not found"
camera_matrix, dist_coeffs = cv.calibrate_camera(
fn,
chessboard_size,
max_imgs,
plot,
progress_bar,
**kwargs
)
self.camera_matrix = camera_matrix
self.dist_coeffs = dist_coeffs
def get_bbox(
self,
camera: Optional[bool] = False,
h_a: Optional[float] = None,
redistort: Optional[bool] = False
) -> Polygon:
"""
Parameters
----------
camera : bool, optional
If set, the bounding box will be returned as row and column coordinates in the camera perspective.
In this case ``h_a`` may be set to provide the right water level, to estimate the bounding box for.
h_a : float, optional
If set with ``camera=True``, then the bbox coordinates will be transformed to the camera perspective,
using h_a as a present water level. In case a video with higher (lower) water levels is used, this
will result in a different perspective plane than the control video.
redistort : bool, optional
If set in combination with ``camera``, the bbox will be redistorted in the camera objective using the
distortion coefficients and camera matrix. Not used in orthorectification because this occurs by default
on already undistorted images. Typically only used for plotting purposes on original frames.
Returns
-------
A bounding box, that in the used CRS is perfectly rectangular, and aligned in the up/downstream direction.
It can (and certainly will) be rotated with respect to a typical bbox with xlim, ylim coordinates.
If user sets ``camera=True`` then the geographical bounding box will be converted into a camera perspective,
using the homography belonging to the available ground control points and current water level.
This can then be used to reconstruct the grid for velocimetry calculations.
"""
bbox = self.bbox
if camera:
coords = np.array(bbox.exterior.coords)
z_a = self.get_z_a(h_a)
if redistort:
# typically only done for plotting on original frame, expand number of points to be able to see distortion
coords_expand = np.zeros((0, 2))
for n in range(0, len(coords)-1):
new_coords = np.linspace(coords[n], coords[n + 1], 100)
coords_expand = np.r_[coords_expand, new_coords]
coords = coords_expand
coords = np.c_[coords, np.ones(len(coords))*z_a]
corners = self.project_points(coords)
bbox = Polygon(corners)
return bbox
[docs] def get_depth(
self,
z: List[float],
h_a: Optional[float] = None
) -> List[float]:
"""
Retrieve depth for measured bathymetry points using the camera configuration and an actual water level,
measured in local reference (e.g. staff gauge).
Parameters
----------
z : list of floats
measured bathymetry point depths
h_a : float, optional
actual water level measured [m], if not set, assumption is that a single video
is processed and thus changes in water level are not relevant. (default: None)
Returns
-------
depths : list of floats
"""
if h_a is None:
h_a = self.gcps["h_ref"]
z_pressure = np.maximum(self.gcps["z_0"] - self.gcps["h_ref"] + h_a, z)
return z_pressure - z
def get_dist_shore(
self,
x: List[float],
y: List[float],
z: List[float],
h_a: Optional[float] = None
) -> List[float]:
"""
Retrieve depth for measured bathymetry points using the camera configuration and an actual water level, measured
in local reference (e.g. staff gauge).
Parameters
----------
x : list of floats
measured bathymetry point x-coordinates
y : list of floats
measured bathymetry point y-coordinates
z : list of floats
measured bathymetry point depths
h_a : float, optional
actual water level measured [m], if not set, assumption is that a single video
is processed and thus changes in water level are not relevant. (default: None)
Returns
-------
depths : list of floats
"""
# retrieve depth
depth = self.get_depth(z, h_a=h_a)
if h_a is None:
assert(self.gcps["h_ref"] is None), "No actual water level is provided, but a reference water level is " \
"provided "
# h_a = 0.
# h_ref = 0.
# else:
# h_ref = self.gcps["h_ref"]
z_dry = depth <= 0
z_dry[[0, -1]] = True
# compute distance to nearest dry points with Pythagoras
dist_shore = np.array([(((x[z_dry] - _x) ** 2 + (y[z_dry] - _y) ** 2) ** 0.5).min() for _x, _y, in zip(x, y)])
return dist_shore
def get_dist_wall(
self,
x: List[float],
y: List[float],
z: List[float],
h_a: Optional[float] = None
) -> List[float]:
"""
Retrieve distance to wall for measured bathymetry points using the camera configuration and an actual water
level, measured in local reference (e.g. staff gauge).
Parameters
----------
x : list of floats
measured bathymetry point x-coordinates
y : list of floats
measured bathymetry point y-coordinates
z : list of floats
measured bathymetry point depths
h_a : float, optional
actual water level measured [m], if not set, assumption is that a single video
is processed and thus changes in water level are not relevant. (default: None)
Returns
-------
distance : list of floats
"""
depth = self.get_depth(z, h_a=h_a)
dist_shore = self.get_dist_shore(x, y, z, h_a=h_a)
dist_wall = (dist_shore**2 + depth**2)**0.5
return dist_wall
[docs] def z_to_h(
self,
z: float
) -> float:
"""Convert z coordinates of bathymetry to height coordinates in local reference (e.g. staff gauge)
Parameters
----------
z : float
measured bathymetry point
Returns
-------
h : float
"""
h_ref = 0 if self.gcps["h_ref"] is None else self.gcps["h_ref"]
h = z + h_ref - self.gcps["z_0"]
return h
[docs] def get_M(
self,
h_a: Optional[float] = None,
to_bbox_grid: Optional[bool] = False,
reverse: Optional[bool] = False
) -> np.ndarray:
"""Establish a transformation matrix for a certain actual water level `h_a`. This is done by mapping where the
ground control points, measured at `h_ref` will end up with new water level `h_a`, given the lens position.
Parameters
----------
h_a : float, optional
actual water level [m] (Default: None)
to_bbox_grid : bool, optional
if set, the M will be computed in row, column values of the target bbox, with set resolution
reverse : bool, optional
if True, the reverse matrix is prepared, which can be used to transform projected
coordinates back to the original camera perspective. (Default: False)
Returns
-------
M : np.ndarray
2x3 transformation matrix
"""
src = cv.undistort_points(self.gcps["src"], self.camera_matrix, self.dist_coeffs)
if to_bbox_grid:
dst_a = self.gcps_bbox_reduced
else:
dst_a = self.gcps_reduced
# compute the water level in the coordinate system reduced with the mean gcp coordinate
z_a = self.get_z_a(h_a)
z_a -= self.gcps_mean[-1]
# treating 3D homography
print(dst_a)
# print(z_a)
return cv.get_M_3D(
src=src,
dst=dst_a,
camera_matrix=self.camera_matrix,
dist_coeffs=cv.DIST_COEFFS, # self.dist_coeffs,
z=z_a,
reverse=reverse
)
def get_z_a(
self,
h_a: Optional[float] = None
) -> float:
"""
h_a : float, optional
actual water level measured [m], if not set, assumption is that a single video
is processed and thus changes in water level are not relevant. (default: None)
Returns
-------
Actual locations of control points (in case these are only x, y) given the current set water level and
the camera location
"""
if h_a is None:
return self.gcps["z_0"]
else:
return self.gcps["z_0"] + (h_a - self.gcps["h_ref"])
[docs] def set_bbox_from_corners(
self,
corners: List[List[float]]
):
"""
Establish bbox based on a set of camera perspective corner points Assign corner coordinates to camera
configuration
Parameters
----------
corners : list of lists (4)
[columns, row] coordinates in original camera perspective without any undistortion applied
"""
assert (np.array(corners).shape == (4,
2)), f"a list of lists of 4 coordinates must be given, resulting in (4, " \
f"2) shape. Current shape is {corners.shape} "
# get homography
corners_xyz = self.unproject_points(corners, np.ones(4)*self.gcps["z_0"])
bbox = cv.get_aoi(
corners_xyz,
resolution=self.resolution
)
self.bbox = bbox
def set_intrinsic(
self,
camera_matrix: Optional[List[List]] = None,
dist_coeffs: Optional[List[List]] = None,
lens_pars: Optional[Dict[str, float]] = None
):
# first set a default estimate from pose if 3D gcps are available
self.set_lens_pars() # default parameters use width of frame
if hasattr(self, "gcps"):
if len(self.gcps["src"]) >= 4:
# if self.gcp_dims == 3:
self.camera_matrix, self.dist_coeffs, err = cv.optimize_intrinsic(
self.gcps["src"],
self.gcps_dest,
# self.gcps["dst"],
self.height,
self.width,
lens_position=self.lens_position
)
if lens_pars is not None:
# override with lens parameter set by user
self.set_lens_pars(**lens_pars)
if camera_matrix is not None and dist_coeffs is not None:
# override with
self.camera_matrix = camera_matrix
self.dist_coeffs = dist_coeffs
[docs] def set_lens_pars(
self,
k1: Optional[float] = 0.,
c: Optional[float] = 2.,
focal_length: Optional[float] = None
):
"""Set the lens parameters of the given CameraConfig
Parameters
----------
k1 : float, optional
lens curvature [-], zero (default) means no curvature
c : float, optional
optical centre [1/n], where n is the fraction of the lens diameter, 2.0 (default) means in the
centre.
f : float, optional
focal length [mm], typical values could be 2.8, or 4 (default).
"""
assert (isinstance(k1, (int, float))), "k1 must be a float"
assert (isinstance(c, (int, float))), "c must be a float"
if focal_length is not None:
assert (isinstance(focal_length, (int, float, None))), "f must be a float"
self.dist_coeffs = cv._get_dist_coefs(k1)
self.camera_matrix = cv._get_cam_mtx(self.height, self.width, c=c, focal_length=focal_length)
[docs] def set_gcps(
self,
src: List[List],
dst: List[List],
z_0: float,
h_ref: Optional[float] = None,
crs: Optional[Any] = None
):
"""
Set ground control points for the given CameraConfig
Parameters
----------
src : list of lists (2, 4 or 6+)
[x, y] pairs of columns and rows in the frames of the original video
dst : list of lists (2, 4 or 6+)
[x, y] or [x, y, z] pairs of real world coordinates in the given coordinate reference system.
z_0 : float
Water level measured in global reference system such as a geoid or ellipsoid used
by a GPS device. All other surveyed points (lens position and cross section) must have the same vertical
reference.
h_ref : float, optional
Water level, belonging to the 4 control points in `dst`. This is the water level
as measured by a local reference (e.g. gauge plate) during the surveying of the control points. Control
points must be taken on the water surface. If a single movie is processed, h_ref can be left out.
(Default: None)
crs : int, dict or str, optional
Coordinate Reference System. Accepts EPSG codes (int or str) proj (str or dict) or wkt (str). CRS used to
measure the control points (e.g. 4326 for WGS84 lat-lon). Destination control points will automatically be
reprojected to the local crs of the CameraConfig. (Default: None)
"""
assert (isinstance(src, list)), f"src must be a list of (x, y) or (x, y, z) coordinates"
assert (isinstance(dst, list)), f"dst must be a list of (x, y) or (x, y, z) coordinates"
if np.array(dst).shape[1] == 2:
assert (len(src) in [2, 4]), f"2 or 4 source points are expected in src, but {len(src)} were found"
if len(src) == 4:
assert (len(dst) == 4), f"4 destination points are expected in dst, but {len(dst)} were found"
else:
assert (len(dst) == 2), f"2 destination points are expected in dst, but {len(dst)} were found"
else:
assert(len(src) == len(dst)), f"Amount of (x, y, z) coordinates in src ({len(src)}) and dst ({len(dst)} must be equal"
assert(len(src) >= 6), f"for (x, y, z) points, at least 6 pairs must be available, only {len(src)} provided"
if h_ref is not None:
assert (isinstance(h_ref, (float, int))), "h_ref must contain a float number"
if z_0 is not None:
assert (isinstance(z_0, (float, int))), "z_0 must be provided as type float"
assert (all(isinstance(x, (float, int)) for p in src for x in p)), "src contains non-int parts"
assert (all(isinstance(x, (float, int)) for p in dst for x in p)), "dst contains non-float parts"
if crs is not None:
if not (hasattr(self, "crs")):
raise ValueError(
'CameraConfig does not contain a crs, so gcps also cannot contain a crs. Ensure that the provided '
'destination coordinates are in a locally defined coordinate reference system, e.g. established '
'with a spirit level.')
dst = helpers.xyz_transform(dst, crs, CRS.from_wkt(self.crs))
# if there is no h_ref, then no local gauge system, so set h_ref to zero
# check if 2 points are available
if len(src) == 2:
self.is_nadir = True
src, dst = cv._get_gcps_2_4(src, dst, self.width, self.height)
if h_ref is None:
h_ref = 0.
self.gcps = {
"src": src,
"dst": dst,
"h_ref": h_ref,
"z_0": z_0,
}
[docs] def set_lens_position(
self,
x: float,
y: float,
z: float,
crs: Optional[Any] = None
):
"""Set the geographical position of the lens of current CameraConfig.
Parameters
----------
x : float
x-coordinate
y : float
y-coordinate
z : float
z-coordinate
crs : int, dict or str, optional
Coordinate Reference System. Accepts EPSG codes (int or str) proj (str or dict) or wkt (str). CRS used to
measure the lens position (e.g. 4326 for WGS84 lat-lon). The position's x and y coordinates will
automatically be reprojected to the local crs of the CameraConfig.
"""
if crs is not None:
if self.crs is None:
raise ValueError("CameraConfig does not contain a crs, ")
x, y = helpers.xyz_transform([[x, y]], crs, self.crs)[0]
self.lens_position = [x, y, z]
def project_points(
self,
points: List[List]
) -> np.ndarray:
"""
Project real world x, y, z coordinates into col, row coordinates on image
Parameters
----------
points : list of lists or array-like
list of points [x, y, z] in real world coordinates
Returns
-------
points_project : list or array-like
list of points (equal in length as points) with [col, row] coordinates
"""
_, rvec, tvec = self.pnp
# normalize points wrt mean of gcps
points = np.float32(np.array(points) - self.gcps_mean)
points_proj, jacobian = cv2.projectPoints(
points,
rvec,
tvec,
np.array(self.camera_matrix),
np.array(self.dist_coeffs)
)
points_proj = np.array([list(point[0]) for point in points_proj])
return points_proj
def unproject_points(
self,
points: List[List],
zs: List[float]
) -> np.ndarray:
"""
Reverse projects points in [column, row] space to [x, y, z] real world
Parameters
----------
points : List of lists or array-like
Points in [col, row] to unproject
zs : float or list of floats : z-coordinate on which to unproject points
Returns
-------
points_unproject : List of lists or array-like
unprojected points as list of [x, y, z] coordinates
"""
_, rvec, tvec = self.pnp
# reduce zs by the mean of the gcps
_zs = np.atleast_1d(zs) - self.gcps_mean[-1]
dst = cv.unproject_points(
np.array(points),
_zs,
rvec=rvec,
tvec=tvec,
camera_matrix=self.camera_matrix,
dist_coeffs=self.dist_coeffs
)
dst = np.array(dst) + self.gcps_mean
return dst
[docs] def plot(
self,
figsize: Optional[Tuple] = (13, 8),
ax: Optional[plt.Axes] = None,
tiles: Optional[Any] = None,
buffer: Optional[float] = 0.0005,
zoom_level: Optional[int] = 19,
camera: Optional[bool] = False,
tiles_kwargs: Optional[Dict] = {}
) -> plt.Axes:
"""
Plot the geographical situation of the CameraConfig. This is very useful to check if the CameraConfig seems
to be in the right location. Requires cartopy to be installed.
Parameters
----------
figsize : tuple, optional
width and height of figure (Default value = (13)
ax : plt.axes, optional
if not provided, axes is setup (Default: None)
tiles : str, optional
name of tiler service to use (called as attribute from cartopy.io.img_tiles) (Default: None)
buffer : float, optional
buffer in lat-lon around points, used to set extent (default: 0.0005)
zoom_level : int, optional
zoom level of image tiler service (default: 18)
camera : bool, optional
If set to True, all camera config information will be back projected to the original camera objective.
**tiles_kwargs
additional keyword arguments to pass to ax.add_image when tiles are added
8) :
Returns
-------
ax : plt.axes
"""
# initiate transform
transformer = None
# if there is an axes, get the extent
xlim = ax.get_xlim() if ax is not None else None
ylim = ax.get_ylim() if ax is not None else None
# prepare points for plotting
if camera:
points = [Point(x, y) for x, y in self.gcps["src"]]
else:
points = [Point(p[0], p[1]) for p in self.gcps["dst"]]
if not camera:
if self.lens_position is not None and not camera:
#
# if hasattr(self, "lens_position") and not camera:
points.append(Point(self.lens_position[0], self.lens_position[1]))
# transform points in case a crs is provided
if hasattr(self, "crs"):
# make a transformer to lat lon
transformer = Transformer.from_crs(
CRS.from_user_input(self.crs),
CRS.from_epsg(4326),
always_xy=True).transform
points = [ops.transform(transformer, p) for p in points]
xmin, ymin, xmax, ymax = list(np.array(LineString(points).bounds))
extent = [xmin - buffer, xmax + buffer, ymin - buffer, ymax + buffer]
x = [p.x for p in points]
y = [p.y for p in points]
if ax is None:
f = plt.figure(figsize=figsize)
if (hasattr(self, "crs") and not(camera)):
ax = helpers.get_geo_axes(tiles=tiles, extent=extent, zoom_level=zoom_level, **tiles_kwargs)
else:
ax = plt.subplot()
if hasattr(ax, "add_geometries"):
import cartopy.crs as ccrs
plot_kwargs = dict(transform=ccrs.PlateCarree())
else:
plot_kwargs = {}
ax.plot(
x[0:len(self.gcps["dst"])],
y[0:len(self.gcps["dst"])],
".",
label="Control points",
markersize=12,
markeredgecolor="w",
zorder=2,
**plot_kwargs
)
if len(x) > len(self.gcps["dst"]):
ax.plot(
x[-1],
y[-1],
".",
label="Lens position",
markersize=12,
zorder=2,
markeredgecolor="w",
**plot_kwargs
)
patch_kwargs = {
**plot_kwargs,
"alpha": 0.5,
"zorder": 2,
"edgecolor": "w",
"label": "Area of interest",
**plot_kwargs
}
if hasattr(self, "bbox"):
self.plot_bbox(ax=ax, camera=camera, transformer=transformer, **patch_kwargs)
if camera:
# make sure that zero is on the top
ax.set_aspect("equal")
if xlim is not None:
ax.set_xlim(xlim)
ax.set_ylim(ylim)
ax.legend()
return ax
[docs] def plot_bbox(
self,
ax: Optional[plt.Axes] = None,
camera: Optional[bool] = False,
transformer: Optional[Any] = None,
h_a: Optional[float] = None,
redistort: Optional[bool] = True,
**kwargs
):
"""
Plot bounding box for orthorectification in a geographical projection (``camera=False``) or the camera
Field Of View (``camera=True``).
Parameters
----------
ax : plt.axes, optional
if not provided, axes is setup (Default: None)
camera : bool, optional
If set to True, all camera config information will be back projected to the original camera objective.
transformer : pyproj transformer transformation function, optional
used to reproject bbox to axes object projection (e.g. lat lon)
h_a : float, optional
If set with ``camera=True``, then the bbox coordinates will be transformed to the camera perspective,
using h_a as a present water level. In case a video with higher (lower) water levels is used, this
will result in a different perspective plane than the control video.
Returns
-------
p : matplotlib.patch mappable
"""
# collect information to plot
bbox = self.get_bbox(camera=camera, h_a=h_a, redistort=redistort)
if camera is False and transformer is not None:
# geographical projection is needed
bbox = ops.transform(transformer, bbox)
bbox_x, bbox_y = bbox.exterior.xy
bbox_coords = list(zip(bbox_x, bbox_y))
patch = patches.Polygon(
bbox_coords,
**kwargs
)
p = ax.add_patch(patch)
return p
[docs] def to_dict(self) -> Dict:
"""Return the CameraConfig object as dictionary
Returns
-------
camera_config_dict : dict
serialized CameraConfig
"""
d = copy.deepcopy(self.__dict__)
# replace underscore keys for keys without underscore
for k in list(d.keys()):
if k[0] == "_":
d[k[1:]] = d.pop(k)
return d
def to_dict_str(self) -> Dict:
d = self.to_dict()
# convert anything that is not string in string
dict_str = {k: v if not(isinstance(v, Polygon)) else v.__str__() for k, v in d.items()}
return dict_str
[docs] def to_file(
self,
fn: str
):
"""Write the CameraConfig object to json structure
Parameters
----------
fn : str
Path to file to write camera config to
"""
with open(fn, "w") as f:
f.write(self.to_json())
[docs] def to_json(self) -> str:
"""Convert CameraConfig object to string
Returns
-------
json_str : str
json string with CameraConfig components
"""
return json.dumps(self, default=lambda o: o.to_dict_str(), indent=4)
depr_warning_height_width = """
Your camera configuration does not have a property "height" and/or "width", probably because your configuration file is
from an older < 0.3.0 version. Please rectify this by editing your .json config file. The top of your file should e.g.
look as follows for a HD video:
{
"height": 1080,
"width": 1920,
"crs": ....
...
}
"""
def get_camera_config(
s: str
) -> CameraConfig:
"""Read camera config from string
Parameters
----------
s : str
json string containing camera config
Returns
-------
cam_config : CameraConfig
"""
d = json.loads(s)
if not "height" in d or not "width" in d:
raise IOError(depr_warning_height_width)
# ensure the bbox is a Polygon object
if "bbox" in d:
if isinstance(d["bbox"], str):
d["bbox"] = wkt.loads(d["bbox"])
return CameraConfig(**d)
def load_camera_config(
fn: str
) -> CameraConfig:
"""Load a CameraConfig from a geojson file.
Parameters
----------
fn : str
path to file with camera config in json format.
Returns
-------
cam_config : CameraConfig
"""
with open(fn, "r") as f:
camera_config = get_camera_config(f.read())
return camera_config
