Synthetic Particle Image Dataset (SPID)

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.SPID数据集是一个综合性数据集，由合成粒子图像测速（Particle Image Velocimetry，PIV）图像对及其对应的精确光流计算结果构成，为该领域的研究人员与从业者提供了宝贵的研究资源。该数据集划分为训练集、验证集与测试集三个子集，分布比例分别为70%、15%与15%。 SPID的每个子集均包含名为“x”的输入数据与名为“y”的输出数据：输入数据由合成图像对组成，可为光流计算提供必要的场景上下文；输出数据则代表各图像对对应的精确光流结果。值得注意的是，数据集中的图像均为单通道图像，且光流被分解为u与v两个分量。 SPID输入子集的维度格式为（样本数、帧数、图像宽度、图像高度、通道数），用于表征输入数据的维度；输出子集的维度格式则为（样本数、速度分量数、图像宽度、图像高度），对应光流数据的维度结构。 需特别说明的是，SPID数据集是原始合成粒子图像数据集（Raw Synthetic Particle Image Dataset，RSPID）的预处理版本，大幅提升了数据集的可用性与可靠性。此外，该数据集以NumPy压缩NPZ文件格式进行打包，以train、validation、test作为访问密钥，将输入与输出数据分别存储于对应的NPZ文件中。这种格式简化了数据提取流程，便于集成至机器学习框架与库中，实现数据集的便捷使用。 SPID数据集纳入了多种影响PIV分析的因素，以实现全面且贴近真实场景的模拟。该数据集的图像对尺寸为665像素宽、630像素高，采用8位深度，可保证较高的细节水平与计算精度。其包含多种粒子半径（1、2、3、4像素）与粒子密度（15、17、20、23、25、32个粒子），可覆盖多样化的粒子配置场景。 为模拟真实世界场景，SPID通过delta x因子引入位移变化，取值范围为0.05%至0.25%；同时加入了1、5、10、15四个等级的噪声水平，以模拟不同噪声程度下的实际PIV测量场景。此外，数据集还纳入了面外运动效应，通过0.01、0.025与0.05三个标准差参数，用于评估其对光流计算精度的影响。 该数据集涵盖了流体动力学领域中常见的多种流场模式，包括Rankine均匀流、Rankine涡、抛物流、驻点流、剪切流与衰减涡旋流，可支持在不同场景下对PIV算法进行全面的测试与评估。 借助SPID数据集，研究人员可在多种挑战性条件下开发并验证PIV算法与相关技术。其对粒子图像测速场景的真实化、多样化模拟，使其成为推动该领域发展、提升光流计算准确性与可靠性的宝贵工具。