#' Compute evaluation statistics for one plot of field-annotated crowns
#'
#' @param x submission csv table or polygon shp file
#' @param show Logical. Plot the overlayed annotations for each plot?
#' @param project_boxes Logical. Should the boxes be projected into utm coordinates? This is needed if the box coordinates are given from the image origin (top left is 0,0)
#' @param compute_PR Logical. Should the average precision and recall be computed?
#' @return If compute_PR=T, the recall and precision scores for the plot, if False, the intersection-over-union scores for each prediction.
#' @details The format of the submission is either a csv with 5 columns: plot_name, xmin, ymin, xmax, ymax with #' Each row contains information for one predicted bounding box or a shp file of unprojected polygons.
#' The plot column should be named the same as the files in the dataset (e.g. SJER_021), not the path to the file (e.g. /path/to/SJER_021.tif).
#' @export
#'
field_crowns <- function(x, show = TRUE, project_boxes = TRUE) {
# find ground truth file
plot_name <- unique(x$plot_name)
print(plot_name)
if (!length(plot_name) == 1) {
stop(paste("There are", length(plot_name), "plot names. Please submit a single plot of annotations to this function, to run all plots in a x see evaluate_field_crowns."))
}
ground_truth <- load_field_crown(plot_name, show = FALSE)
ground_truth$crown_id<-1:nrow(ground_truth)
# Read RGB image as projected raster
siteID <- stringr::str_match(plot_name, "(\\w+)_\\d+_")[, 2]
path_to_rgb <-get_data(plot_name, "rgb")
rgb <- raster::stack(path_to_rgb)
# project boxes
#check sf polygon or csv file
is_polygons = any(class(x) == "sf")
#If is_polygons, project must be true
if(is_polygons){
predictions <- sf_to_spatial_polygons(x, rgb)
} else{
predictions <- boxes_to_spatial_polygons(x, rgb, project_boxes = project_boxes)
}
if(show){
raster::plotRGB(rgb)
plot(st_geometry(ground_truth), border = "black", add = TRUE)
plot(st_geometry(predictions), border = "red", add = TRUE)
}
# Create spatial polygons objects
result <- compute_precision_recall(ground_truth, predictions, summarize = FALSE,threshold = 0.4)
return(result)
}
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