A directory of classification code for the iSeaTree project
image-classification folder
- detect-a-tree.py A python code built using CNN and tensorflow to detect the presence of a tree in an image. The directory structure of training and testing images are as follows:
basedata -> train, test train -> tree, nottree test -> tree, nottree
- redwood-vs-birch.py A python code built using CNN, Tensorflow and keras to classify images based on barktypes. The 2 barktypes classified here are coastal redwood and paper birch The current accuracy rate is 67%, because only 100 images are used to train The directory structure of training and testing images are as follows: dataset_redwood_vs_birch -> train, test train -> birch, redwood test -> birch, redwood
other
- count-features.py - counts all the trees based on color, shape, direction, needle traits (if conifer) leaftype, barktype (if broadleaf)
- find-an-entry.py - A menu-driven program to return the plant ID and common name of an entry based on a user-defined feature (present in the json file).