Phenopype: a Phenotyping Pipeline for Python

Digital images are an intuitive way to capture, store and analyse organismal phenotypes. Many biologists are taking images to collect high-dimensional phenotypic information from specimens to investigate complex ecological, evolutionary and developmental phenomena, such as relationships between trait diversity and ecosystem function, multivariate natural selection or developmental plasticity. As a consequence, images are being collected at ever-increasing rates, but extraction of the contained phenotypic information poses a veritable analytical bottleneck. phenopype is a high-throughput phenotyping pipeline for the programming language Python that aims at alleviating this bottleneck. The package facilitates immediate extraction of high-dimensional phenotypic data from digital images with low levels of background noise and complexity. At the core, phenopype provides functions for rapid signal processing-based image preprocessing and segmentation, data extraction, as well as visualization and data export. This functionality is provided by wrapping low-level computer vision libraries (such as OpenCV) into accessible functions to facilitate scientific image analysis. In addition, phenopype provides a project management ecosystem to streamline data collection and to increase reproducibility. phenopype offers two different workflows that support users during different stages of scientific image analysis. The low-throughput workflow uses regular Python syntax and has greater flexibility at the cost of reproducibility, which is suitable for prototyping during the initial stages of a research project. The high-throughput workflow allows users to specify and store image-specific settings for analysis in human-readable YAML format, and then execute all functions in one step by means of an interactive parser. This approach facilitates rapid program-user interactions during batch processing, and greatly increases scientific reproducibility. Overall, phenopype intends to make the features of powerful but technically involved low-level CV libraries available to biologists with little or no Python coding experience. Therefore, phenopype is aiming to augment, rather than replace the utility of existing Python CV libraries, allowing biologists to focus on rapid and reproducible data collection. Furthermore, image annotations produced by phenopype can be used as training data, thus presenting a stepping stone towards the application of deep learning architectures.