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A Python library for reading, writing and visualizing the OMEGA Format, targeted towards storing reference and perception data in the automotive context on an object list basis with a focus on an urban use case.
Both deep learning datasets can be imported in python directly with h5py (HDF5 format). The datasets can be directly imported or converted with a python script.
The program recursively searches through folders for .dat files and images, converting them into datasets within an HDF5 file. It utilizes the NumPy library for data handling and the h5py library for HDF5 file manipulation.
Generates a list that contains all beacons at certain timestamps with their corresponding support vector. The input is expected to be a big JSON file. Intermediate results are stored in a HDF5 file and the end result is stored in a JSON file.
This repository contains a collection of code examples demonstrating various techniques and methods for working with HDF5 (Hierarchical Data Format version 5) files. These examples are designed to help developers and data scientists efficiently manage, process, and analyze large datasets stored in HDF5 format.