predict_treedamage {rTLsDeep}

Predict post-hurricane individual tree level damage

Description

This function predicts post-hurricane individual tree-level damage from TLS derived 2D images

Usage

predict_treedamage(
  model,
  input_file_path,
  weights,
  target_size = c(256, 256),
  class_list,
  batch_size = 8
)

Arguments

model

A model object output of the get_dl_model function. See [rTLsDeep::get_dl_model()].

input_file_path

A character string describing the path to the images to predict, e.g.: "C:/test_data/".

weights

A character string indicating the filename of the weights to use for prediction.

target_size

A vector of two values describing the image dimensions (Width and height) to be used in the model. Default: c(256,256)

class_list

A character string or numeric value describing the post-hurricane individual tree level damage classes, e.g.: c("1","2","3","4","5","6").

batch_size

A numerical value indicating the number of images to be processed at the same time. Reduce the batch_size if the GPU is giving memory errors.

Value

Returns a character string with the prediction classes.

Examples

# Set directory to tensorflow (python environment)
# This is required if running deep learning local computer with GPU
# Guide to install here: https://doi.org/10.5281/zenodo.3929709
tensorflow_dir = NA

# define model type
model_type = "simple"
#model_type = "vgg"
#model_type = "inception"
#model_type = "resnet"
#model_type = "densenet"
#model_type = "efficientnet"

train_image_files_path = system.file('extdata', 'train', package='rTLsDeep')
test_image_files_path = system.file('extdata', 'validation', package='rTLsDeep')
img_width <- 256
img_height <- 256
class_list_train = unique(list.files(train_image_files_path))
class_list_test = unique(list.files(test_image_files_path))
lr_rate = 0.0001
target_size <- c(img_width, img_height)
channels = 4
batch_size = 8L
epochs = 20L

# get model
model = get_dl_model(model_type=model_type,
                    img_width=img_width,
                    img_height=img_height,
                    lr_rate = lr_rate,
                    tensorflow_dir = tensorflow_dir,
                    channels = channels,
                    class_list = class_list_train)


# train model and return best weights
weights = fit_dl_model(model = model,
                                train_input_path = train_image_files_path,
                                test_input_path = test_image_files_path,
                                target_size = target_size,
                                batch_size = batch_size,
                                class_list = class_list_train,
                                epochs = epochs,
                                lr_rate = lr_rate)


# Predicting post-hurricane damage at the tree-level
tree_damage<-predict_treedamage(model=model,
                           input_file_path=test_image_files_path,
                           weights=weights,
                           target_size = c(256,256),
                           class_list=class_list_test,
                           batch_size = batch_size)

unlink('epoch_history', recursive = TRUE)
unlink('weights', recursive = TRUE)
unlink('weights_r_save', recursive = TRUE)