# Train models for forest detection As input data, the function needs two folders- Nonforestdir with images of non-forest and Forestdir with ones of forest. train() uses all images in both folders to train a model. Putting an image into an incorrect folder is equivalent to tagging the image incorrectly. ```r train( n_pts, model = c("fr_Non-Param", "fr_Param"), Forestdir, Nonforestdir, train_method = c("cv", "train"), k_folds, parallel = FALSE ) ``` ## Arguments - `n_pts`: matters only when train_method='cv'. Defines the size of the square sub-frames into which images would be split during cross-validation. - `model`: which model to train - `Forestdir`: path to the directory with (only) forest images - `Nonforestdir`: path to the directory with (only) non-forest images - `train_method`: how to train the model: simple training, cross-validation. - `k_folds`: matters only when train_method='cv'. The number of folds in the k-fold cross-validation setup. - `parallel`: matters only when train_method='cv'. Boolean. whether or not use a parallel setting during cross-validation ## Returns object of class ForestTrain potentially with a sub-class. See `Class_ForestTrain`. ## Details Currently, both fr_Non-Param and fr_Param use parameter n_pts only in the testing part of cross-validation, not during training. Training is always done on whole original images in the training folders. ## Examples ```r library(deforestable) n_pts <- 20 # Choosing folders with training data Forestdir <- system.file('extdata/Forest/', package = "deforestable") Nonforestdir <- system.file('extdata/Non-forest/', package = "deforestable") k_folds=3; #### Read the target image #### tg_dir <- system.file('extdata/', package = "deforestable") test_image <- read_data_raster('smpl_1.jpeg', dir = tg_dir) #### Models #### # Simple training of the non-parametric model Model_nonP_tr <- train(model='fr_Non-Param', Forestdir=Forestdir, Nonforestdir=Nonforestdir, train_method='train', parallel=FALSE) res <- classify(data=test_image, Model=Model_nonP_tr, n_pts=n_pts, parallel=FALSE, progress = 'text') tmp_d <- tempdir(); tmp_d jpeg::writeJPEG(image=res, target=paste(tmp_d,'Model_nonP_tr.jpeg', sep='/')) # Cross-validation of the non-parametric model Model_nonP_cv <- train(n_pts=n_pts, model='fr_Non-Param', Forestdir=Forestdir, Nonforestdir=Nonforestdir, train_method='cv', k_folds=k_folds, parallel=FALSE) res <- classify(data=test_image, Model=Model_nonP_cv, n_pts=n_pts, parallel=FALSE, progress = 'text') tmp_d <- tempdir(); tmp_d jpeg::writeJPEG(image=res, target=paste(tmp_d,'Model_nonP_cv.jpeg', sep='/')) # Cross-validation of the parametric model Model_P_cv <- train(n_pts=n_pts, model='fr_Param', Forestdir=Forestdir, Nonforestdir=Nonforestdir, train_method='cv', k_folds=k_folds, parallel=FALSE) res <- classify(data=test_image, Model=Model_P_cv, n_pts=n_pts, parallel=FALSE, progress = 'text') tmp_d <- tempdir(); tmp_d jpeg::writeJPEG(image=res, target=paste(tmp_d,'Model_P_cv.jpeg', sep='/')) # Simple training of the parametric model Model_P_tr <- train(model='fr_Param', Forestdir=Forestdir, Nonforestdir=Nonforestdir, train_method='train', parallel=FALSE) res <- classify(data=test_image, Model=Model_P_tr, n_pts=n_pts, parallel=FALSE, progress = 'text') tmp_d <- tempdir(); tmp_d jpeg::writeJPEG(image=res, target=paste(tmp_d,'Model_P_tr.jpeg', sep='/')) ```

train function