phenopype

phenopype extracts phenotypic measurements from digital images to support quantitative analyses in ecology and evolutionary biology.

Key Features:

• Computer vision integration: Leverages OpenCV (Open Source Computer Vision Library) for image preprocessing and segmentation.
• Dual workflow system: Provides a low-throughput workflow using native Python syntax for prototyping and a high-throughput workflow that stores image-specific settings in human-readable YAML for batch processing via an interactive parser.
• Project management: Includes a project management system for organizing images and associated metadata to support reproducible data collection.
• Trait extraction: Performs segmentation-based extraction of morphological and phenotypic traits from images.
• Data export and visualization: Produces exportable data and visualization outputs for downstream analysis.
• Annotation generation for deep learning: Generates annotations that can serve as training datasets for deep learning models.

Scientific Applications:

• Trait diversity and ecosystem function: Quantifies trait diversity for studies of ecosystem function.
• Multivariate natural selection: Measures multiple phenotypic traits to analyze multivariate natural selection.
• Developmental plasticity: Extracts phenotypic measurements relevant to studies of developmental plasticity.
• High-dimensional phenotypic analyses: Enables extraction of high-dimensional phenotypic data for ecological and evolutionary research.

Methodology:

Uses OpenCV for image preprocessing and segmentation; extracts traits via segmentation and measurement functions; stores image-specific settings in YAML for reproducible batch processing via an interactive parser; generates annotations for deep learning and produces exportable data and visualization outputs.