Train on 3 classes and 9 functions

Takes a directory of images and directories of masks for three different classes, and then trains an SVM three-class model on that data.

wrapper_train_3class_9feat(img_dir, plant_mask_dir, soil_mask_dir,
  other_mask_dir, kernel = "radial", pixels_per_class = 1e+06,
  probability = TRUE)

Arguments

img_dir	Directory where images are located
plant_mask_dir	Directory where masks indicating plant location are located. This directory must contain one file per image on the img_dir file with exactly the same name, and the same dimensions, but wiht only one chanel. Any non-zero value is interpreted as presence of a plant
soil_mask_dir	Directory where masks indicating soil location are located. This directory must contain one file per image on the img_dir file with exactly the same name, and the same dimensions, but wiht only one chanel. Any non-zero value is interpreted as presence of a soil
other_mask_dir	Directory where masks indicating other location are located. This directory must contain one file per image on the img_dir file with exactly the same name, and the same dimensions, but wiht only one chanel. Any non-zero value is interpreted as presence of a other.
kernel	Type of kernel to use for SVM. See documentation from e1071
pixels_per_class	Not all pixels need to be used. Guud accuracy can be achieved with few pixels per class. Specify the number here.
probability	Logical indicating whether the SVM model must include probabilities or just classification

Value

A list with elements m1 which contains the SVM trained model that can be used for prediction, and Dat element which is a data.frame containing the Data used to train the model