Landmark assignment

The Plant Computer Vision (PlantCV) software package, is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. 

PlantCV v2 is the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.

PlantCV is composed of modular functions in order to be applicable to a variety of plant types and imaging systems. PlantCV currently supports the analysis of standard RGB color images (aka "VIS"), standard grayscale images (e.g. near-infrared, "NIR"), thermal infrared images, grayscale images from chlorophyll fluorescence imaging systems ("PSII"), and hyperspectral ("ENVI") images. 

A python based tool for registering a template brain (from the Allen Reference Atlas) to the sample image, an extension of aMAP. Once this is complete, any other image in the template space can be aligned with the sample (such as region annotations, for segmentation of the sample image). The template to sample transformation can also be inverted, allowing sample images to be aligned in a common coordinate space. Current registration backend is niftyreg.

Component of BrainGlobe Initiative

MorphoNet is a novel concept of web-based morphodynamic browser to visualise and interact with complex datasets, with applications in research and teaching. 

MorphoNet offers a comprehensive palette of interactions to explore the structure, dynamics and variability of biological shapes and its connection to genetic expressions. 

By handling a broad range of natural or simulated morphological data, it fills a gap which has until now limited the quantitative understanding of morphodynamics and its genetic underpinnings by contributing to the creation of ever-growing morphological atlases.

This workflow predict landmark positions on images by using DMBL landmark detection models.

This workflow trains DMBL landmark detection models from a dataset of annotated images.