wrapper_train_3class_9feat: Train on 3 classes and 9 functions
In surh/RosetteDetector: RosetteDetector

Description Usage Arguments Value Author(s)

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

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

`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

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

Sur Herrera Paredes

surh/RosetteDetector documentation built on May 14, 2019, 10:36 a.m.