image_crowns: Compute evaluation statistics for one plot

In weecology/NeonTreeEvaluation_package: Evaluate Individual Tree Segmentation Algorithms against Benchmark Data

Compute evaluation statistics for one plot

Description

Compute evaluation statistics for one plot

Compute evaluation statistics for one plot of image-annotated crowns

Usage

image_crowns(predictions, show = TRUE, project_boxes = TRUE)

image_crowns(predictions, show = TRUE, project_boxes = TRUE)

Arguments

predictions	The format of the submission is either a csv with 5 columns: plot_name, xmin, ymin, xmax, ymax or a sf polygon object with poylgons in image coordinates. Each row contains information for one predicted bounding box. The plot column should be named the same as the files in the dataset (e.g. SJER_021), not the path to the file.
show	Logical. Plot the overlayed annotations for each plot?
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)
submission	A five column dataframe in the order plot_name, xmin, xmax, ymin, ymax. Each row is a predicted bounding box.
compute_PR	Logical. Should the average precision and recall be computed?

Value

If compute_PR=T, the recall and precision scores for the plot, if False, the intersection-over-union scores for each prediction.

If compute_PR=T, the recall and precision scores for the plot, if False, the intersection-over-union scores for each prediction.

weecology/NeonTreeEvaluation_package documentation built on Aug. 27, 2024, 10:53 a.m.