DOI: 10.5281/zenodo.7935214
title: Synthetic Particle Image Dataset (SPID)

description: SPID is a comprehensive dataset composed of synthetic particle image velocimetry (PIV) image pairs and their corresponding exact optical flow computations. It serves as a valuable resource for researchers and practitioners in the field. The dataset is organized into three subsets: training, validation, and test, distributed in a ratio of 70%, 15%, and 15%, respectively.
Each subset within SPID consists of an input denoted as "x", which comprises synthetic image pairs. These image pairs provide the necessary context for the optical flow computations. Additionally, an output termed "y" is provided, which represents the exact optical flow calculated for each image pair. Notably, the images within the dataset are single-channel, and the optical flow is decomposed into its u and v components.
The shape of the input subsets in SPID is given by (number of samples, number of frames, image width, image height, number of channels), representing the dimensions of the input data. On the other hand, the shape of the output subsets is given by (number of samples, velocity components, image width, image height), denoting the shape of the optical flow data.
It is important to mention that SPID dataset is a preprocessed version of the Raw Synthetic Particle Image Dataset (RSPID), ensuring improved usability and reliability. Moreover, the dataset is packaged as a NumPy compressed NPZ file, which conveniently stores the inputs and outputs as separate NumPy NPZ files with the labels train, validation and test as acess keys. This format simplifies data extraction and integration into machine learning frameworks and libraries, facilitating seamless usage of the dataset.
SPID incorporates various factors that impact PIV analysis to provide a comprehensive and realistic simulation. The dataset includes image pairs with an image width of 665 pixels and an image height of 630 pixels, ensuring a high level of detail and accuracy with an 8-bit depth. It incorporates different particle radii (1, 2, 3, and 4 pixels) and particle densities (15, 17, 20, 23, 25, and 32 particles) to capture diverse particle configurations.
To simulate real-world scenarios, SPID introduces displacement variations through the delta x factor, ranging from 0.05% to 0.25%. Noise levels (1, 5, 10, and 15) are also incorporated to mimic practical PIV measurements with varying degrees of noise. Furthermore, out-of-plane motion effects are considered with standard deviations of 0.01, 0.025, and 0.05 to assess their impact on optical flow accuracy.
The dataset covers a wide range of flow patterns encountered in fluid dynamics. It includes Rankine uniform, Rankine vortex, parabolic, stagnation, shear, and decaying vortex flows, allowing for comprehensive testing and evaluation of PIV algorithms across different scenarios.
By leveraging the SPID dataset, researchers can develop and validate PIV algorithms and techniques under various challenging conditions. Its realistic and diverse simulation of particle image velocimetry scenarios makes it an invaluable tool for advancing the field and improving the accuracy and reliability of optical flow computations.