intrinsic_parameters.py

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from __future__ import annotations
 
from typing import Any
 
import numpy as np
 
from ids_peak_icv.datatypes.binning_factor import BinningFactor
from ids_peak_common.datatypes.geometry.point import Point
from ids_peak_common.datatypes.geometry.size import Size
from ids_peak_icv.backend.datatypes import peak_icv_intrinsic_parameters, ctypes_objects_equal
from ids_peak_icv.exceptions import NotPossibleException
 
 
class IntrinsicParameters:
    """!
    @brief
    Class for intrinsic parameters.
 
    The intrinsic parameters contain the necessary information to use the camera calibration.
 
    This class contains only the intrinsic parameters. These parameters are essential for image
    operations like point cloud generation, image undistortion, and other calibration-dependent operations.
 
    @ingroup ids_peak_icv_python_calibration
    @since ids_peak_icv 1.1
    """
 
    def __init__(self, *args: Any, **kwargs: Any) -> None:
        """!
        @brief
        In order to create IntrinsicParameters, please refer to the classmethods.
 
        @since ids_peak_icv 1.1
        """
        try:
            self._parameters: peak_icv_intrinsic_parameters = kwargs["_parameters"]
        except KeyError:
            raise NotPossibleException("{} shall not be initialized".format(self.__class__.__name__))
 
    @property
    def principle_point(self) -> Point:
        """!
        @brief
        Returns the principle point.
 
        @return Point representing the principle point.
 
        @since ids_peak_icv 1.1
        """
        return Point(
            self._parameters.principle_point.x, self._parameters.principle_point.y)
 
    @property
    def distortion_coefficients(self) -> np.ndarray:
        """!
        @brief
        Returns the distortion coefficients.
 
        @return A numpy array of size 14 containing the distortion coefficients.
 
        @since ids_peak_icv 1.1
        """
 
        coefficients = (list(self._parameters.distortion_coefficients.radial_distortion.coefficients)
                        + list(self._parameters.distortion_coefficients.tangential_distortion.coefficients)
                        + list(self._parameters.distortion_coefficients.prism_distortion.coefficients)
                        + list(self._parameters.distortion_coefficients.tilt_distortion.coefficients))
 
        return np.array(coefficients, dtype=np.float32)
 
    @property
    def focal_length_pixel_size_ratio(self) -> Point:
        """!
        @brief
        Returns the focal length pixel size ratio.
 
        @return Point representing the focal length pixel size ratio.
 
        @since ids_peak_icv 1.1
        """
        return Point(
            self._parameters.focal_length_pixel_size_ratio.x, self._parameters.focal_length_pixel_size_ratio.y)
 
    @property
    def image_size(self) -> Size:
        """!
        @brief
        Returns image size
 
        @return Size representing the image size.
 
        @since ids_peak_icv 1.1
        """
        return Size(
            self._parameters.image_size.width, self._parameters.image_size.height)
 
    @property
    def binning_factor(self) -> BinningFactor:
        """!
        @brief
        Returns binning factor
 
        @return Binning factor of the image.
 
        @since ids_peak_icv 1.1
        """
        return BinningFactor(self._parameters.binning_factor.x, self._parameters.binning_factor.y)
 
    def __eq__(self, other: object) -> bool:
        if not isinstance(other, IntrinsicParameters):
            return False
        return ctypes_objects_equal(self._parameters, other._parameters)