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Monocular

MonocularCalibratorBase()

Bases: ABC, LogMixin

Abstract base class for monocular camera calibrators.

Source code in src/compas_camcal/calibration/mono.py 
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def __init__(self) -> None:
    super().__init__()

calibrate(correspondences) abstractmethod

Calibrate from the provided correspondences.

Parameters:

Name Type Description Default
correspondences Iterable[Correspondence]

correspondences to calibrate the camera with.

 required

Returns:

Type Description
CameraBase

Camera
MonocularCalibrationStats

Statistics
Source code in src/compas_camcal/calibration/mono.py 
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@abstractmethod
def calibrate(
    self, correspondences: Iterable[Correspondence]
) -> Tuple[CameraBase, MonocularCalibrationStats]:
    """
    Calibrate from the provided correspondences.

    Args:
        correspondences: correspondences to calibrate the camera with.

    Returns:
        Camera
        Statistics
    """
    pass

OpenCVFisheyeMonocularCalibrator(image_width, image_height, flags)

Bases: MonocularCalibratorBase

Initialize the OpenCVFisheyeMonocularCalibrator.

Parameters:

Name Type Description Default
image_width int

width of the images.

 required
image_height int

height of the images.

 required
flags int

flags for the calibration, passed to cv2.fisheye.calibrate.

 required
Source code in src/compas_camcal/calibration/mono.py 
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def __init__(self, image_width: int, image_height: int, flags: int) -> None:
    """
    Initialize the OpenCVFisheyeMonocularCalibrator.

    Args:
        image_width: width of the images.
        image_height: height of the images.
        flags: flags for the calibration, passed to cv2.fisheye.calibrate.
    """
    super().__init__()

    self._image_width = image_width
    self._image_height = image_height
    self._flags = flags

size property

Image size as (width, height).

calibrate(correspondences)

Calibrate from the provided correspondences.

Parameters:

Name Type Description Default
correspondences Iterable[Correspondence]

correspondences to calibrate the camera with.

 required

Returns:

Type Description
FisheyeCamera

Camera
MonocularCalibrationStats

Statistics
Source code in src/compas_camcal/calibration/mono.py 
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def calibrate(
    self, correspondences: Iterable[Correspondence]
) -> Tuple[FisheyeCamera, MonocularCalibrationStats]:
    """
    Calibrate from the provided correspondences.

    Args:
        correspondences: correspondences to calibrate the camera with.

    Returns:
        Camera
        Statistics
    """
    # Pack the correspondences into a single arrays of object and image points
    object_points = np.array(
        [correspondence.object_points for correspondence in correspondences]
    )
    object_points = np.expand_dims(object_points, -2)
    image_points = np.array([correspondence.image_points for correspondence in correspondences])

    # Perform calibration
    self.logger.info(f"Calibrating camera with {len(correspondences)} correspondences")
    _, camera_matrix, distortion_coefficients, rvecs, tvecs = cv2.fisheye.calibrate(
        object_points, image_points, self.size, None, None, flags=self._flags
    )

    # Compute reprojection errors
    reprojection_errors = compute_reprojection_errors_opencv_fisheye(
        camera_matrix, distortion_coefficients, rvecs, tvecs, object_points, image_points
    )
    mean_reprojection_error = np.mean(reprojection_errors)

    self.logger.debug(f"Mean reprojection error: {mean_reprojection_error}")
    self.logger.debug(f"Camera matrix: \n{camera_matrix}")
    self.logger.debug(f"Distortion coefficients: \n{distortion_coefficients}")

    # Collect camera
    intrinsics = CameraIntrinsics.from_matrix(camera_matrix)
    distortion = FisheyeDistortionCoefficients.from_vector(distortion_coefficients)
    camera = FisheyeCamera(intrinsics=intrinsics, distortion=distortion)

    # Collect stats
    poses = [
        Pose3D(rotation=Rotation3D_Rodrigues(rvec), translation=Translation3D.from_vector(tvec))
        for rvec, tvec in zip(rvecs, tvecs)
    ]
    stats = MonocularCalibrationStats(reprojection_errors=reprojection_errors, poses=poses)

    return camera, stats

OpenCVMonocularCalibrator(image_width, image_height, flags)

Bases: MonocularCalibratorBase

Monocular calibrator using OpenCV calibrateCamera.

Initialize the OpenCVMonocularCalibrator.

Parameters:

Name Type Description Default
image_width int

width of the images.

 required
image_height int

height of the images.

 required
flags int

flags for the calibration, passed to cv2.calibrateCamera.

 required
Source code in src/compas_camcal/calibration/mono.py 
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def __init__(self, image_width: int, image_height: int, flags: int) -> None:
    """
    Initialize the OpenCVMonocularCalibrator.

    Args:
        image_width: width of the images.
        image_height: height of the images.
        flags: flags for the calibration, passed to cv2.calibrateCamera.
    """
    super().__init__()

    self._image_width = image_width
    self._image_height = image_height
    self._flags = flags

size property

Image size as (width, height).

calibrate(correspondences)

Calibrate from the provided correspondences.

Parameters:

Name Type Description Default
correspondences Iterable[Correspondence]

correspondences to calibrate the camera with.

 required

Returns:

Type Description
Camera

Camera
MonocularCalibrationStats

Statistics
Source code in src/compas_camcal/calibration/mono.py 
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def calibrate(
    self, correspondences: Iterable[Correspondence]
) -> Tuple[Camera, MonocularCalibrationStats]:
    """
    Calibrate from the provided correspondences.

    Args:
        correspondences: correspondences to calibrate the camera with.

    Returns:
        Camera
        Statistics
    """
    # Pack the correspondences into a single arrays of object and image points
    object_points = [correspondence.object_points for correspondence in correspondences]
    image_points = [correspondence.image_points for correspondence in correspondences]

    # Perform calibration
    self.logger.info(f"Calibrating camera with {len(correspondences)} correspondences")
    _, camera_matrix, distortion_coefficients, rvecs, tvecs = cv2.calibrateCamera(
        object_points, image_points, self.size, None, None, flags=self._flags
    )

    # Compute reprojection errors
    reprojection_errors = compute_reprojection_errors_opencv(
        camera_matrix, distortion_coefficients, rvecs, tvecs, object_points, image_points
    )
    mean_reprojection_error = np.mean(reprojection_errors)

    self.logger.debug(f"Mean reprojection error: {mean_reprojection_error}")
    self.logger.debug(f"Camera matrix: \n{camera_matrix}")
    self.logger.debug(f"Distortion coefficients: \n{distortion_coefficients}")

    # Collect camera
    intrinsics = CameraIntrinsics.from_matrix(camera_matrix)
    distortion = DistortionCoefficients.from_vector(distortion_coefficients)
    camera = Camera(intrinsics=intrinsics, distortion=distortion)

    # Collect stats
    poses = [
        Pose3D(rotation=Rotation3D_Rodrigues(rvec), translation=Translation3D.from_vector(tvec))
        for rvec, tvec in zip(rvecs, tvecs)
    ]
    stats = MonocularCalibrationStats(reprojection_errors=reprojection_errors, poses=poses)

    return camera, stats