Leaf length/width pipeline
The leaf length/width pipeline for season 6 is running on DDPSC server. This is going to be finished in next week.
The pipeline currently running finds the leaves first instead of plots. So I rewrote the merging to fit this method.
Leaf Curvature
I'm digging into the PCL (Point Cloud Library) to see if we could apply this library to our point cloud data. This library is originally developed on C++. There is an official python binding project under development. But there are not too many activities on that repo for years. (Also there is a API for calculate the curvature is not implemented on this binding.) So should we working on some point cloud problems on C++? If we are going to keep working on the ply data, considering the processing speed for point cloud and the library, this seems like a appropriate and viable way to work with.
Or, at least for the curvature, I could implement the method used in PCL with python. Since we already have the xyz-map. Finding the neiberhood could be faster than on the ply file. Then the curvature could be calculated with some differential geometry methods
PCL curvature: http://docs.pointclouds.org/trunk/group__features.html
Python bindings to the PCL: https://github.com/strawlab/python-pcl
Reverse Phenotyping
Since the ply data are too large (~20 TB) to download(~6 MB/s). I created a new pipeline to find only the cropping position with ply file. So that I can run this on NCSA server and use those information to crop the raw data on our server. This is running on NCSA server now and I'm working on the cropping procedure.
I'm going to try Triplet loss, Hong's NN loss and Magnet loss to train the new data and do what we did before to visualize the result.