Run a segmentation batch workflow on a directory of images — analyze_segmentation

This is the higher-level segmentation path: predict a folder, save overlays, write per-object measurements, and write per-image summaries in one call. It currently uses direct RF-DETR segmentation inference; revisit once SAHI supports Roboflow segmentation models cleanly.

Usage

analyze_segmentation_dir(
  input_dir,
  model,
  output_dir = "results/segmentation_batch",
  save_visualizations = TRUE,
  save_measurements = TRUE,
  save_summary = TRUE,
  save_population_stats = TRUE
)

Arguments

input_dir: Directory containing images
model: Segmentation PetrographyModel from from_pretrained()
output_dir: Output directory for overlays and tables
save_visualizations: Whether to save overlay images
save_measurements: Whether to write per-object measurements CSV
save_summary: Whether to write per-image summary CSV
save_population_stats: Whether to write a JSON population summary

Value

A list with detections, per-image summary, population stats, and output directory