pywayne-cv-camera-model

To load camera model parameters from a YAML configuration file in Python, instantiate a CameraModel wrapper and pass the YAML file path. This uses pybind11 bindings to parse configurations and expose properties like model type, image size, and distortion parameters for computer vision workloads.

To project 3D space coordinates onto an image plane, use the space_to_plane projection method provided by the camera model wrapper. This maps 3D spatial points directly to 2D image coordinates using the loaded YAML parameters and distortion properties.

The lift_projective operation in camera model projections maps 2D image coordinates back to 3D rays. It reverses the projection process, allowing you to lift normalized image points into 3D space using the camera model's intrinsic parameters loaded from YAML.

Yes, you can export camera parameters as a dictionary for ML data pipelines. The Python wrapper provides functionality to extract all loaded camera model properties, including distortion parameters and model type, into a standard dictionary format for downstream integration.

The Python camera model wrapper relies on pybind11 bindings to interface with the underlying C++ camera_models library. Users need an environment that supports pybind11 compilation to successfully build and utilize the projection methods and YAML loading functionalities.

After loading a YAML configuration, you can inspect camera properties including the model type, image size, and distortion parameters. These properties are accessible directly through the Python wrapper to facilitate projection computations and parameter management.