README

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cam2model

cam2model provides a workflow for preparing camera trap images for annotation and model training.

Recommended workflow

Quick reference by stage

End-to-end example (temporary paths)

Stage	Minimum input	Main output	File operations
`rename_images()`	Directory containing images	Result tibble (or a list with `plan` and `result`)	Creates a site folder in `output_dir` and copies images
`process_metadata()`	Directory of renamed images	Metadata tibble	Writes the `output_file` CSV
`generate_plot()`	Data frame with the columns used for aesthetics	`ggplot` object	No files written
`process_training_images()`	CSV with `Directory` and `FileName`	Sample tibble	Creates `dest_dir`, writes two CSV files, and optionally copies images
`copy_in_batches()`	Data frame with `id` and `path`	Per-file report data frame	Creates batch subfolders and copies files (or simulates the operation with `dry_run`)

library(cam2model)

# 1) Set up temporary paths
raw_dir <- tempfile("cam2model_raw_")
renamed_dir <- tempfile("cam2model_renamed_")
train_dir <- tempfile("cam2model_train_")
batch_dir <- tempfile("cam2model_batches_")

dir.create(raw_dir, recursive = TRUE)
dir.create(renamed_dir, recursive = TRUE)
dir.create(train_dir, recursive = TRUE)

# Copy real camera trap images into raw_dir before running the next stages
# file.copy("path_to_real_image.jpg", file.path(raw_dir, "cam1", "image.jpg"), recursive = TRUE)

# 2) Rename and flatten (requires real images)
# renamed <- rename_images(input_dir = raw_dir, output_dir = renamed_dir, site_name = "demo")

# 3) Extract metadata (requires real EXIF data)
# metadata <- process_metadata(output_dir = renamed_dir, output_file = "metadata.csv", UserComment = FALSE)
# metadata_csv <- file.path(renamed_dir, "metadata.csv")

# 4) Self-contained training example without real EXIF data
src_dir <- tempfile("cam2model_src_")
dir.create(src_dir, recursive = TRUE)
file_a <- file.path(src_dir, "a.jpg")
file_b <- file.path(src_dir, "b.jpg")
writeBin(charToRaw("a"), file_a)
writeBin(charToRaw("b"), file_b)

metadata_tbl <- data.frame(
  Directory = c(src_dir, src_dir),
  FileName = c("a.jpg", "b.jpg"),
  File_hour = c(8, 11),
  stringsAsFactors = FALSE
)
metadata_csv <- tempfile("cam2model_metadata_", fileext = ".csv")
readr::write_csv(metadata_tbl, metadata_csv)

sampled <- process_training_images(
  metadata_file = metadata_csv,
  dest_dir = train_dir,
  output_sample_file = "sampled_images.csv",
  output_annotation_file = "annotation_data.csv",
  sample_proportion = 1,
  copy_images = FALSE,
  show_progress = FALSE
)

# 5) Reorganize into batches (dry run)
batch_report <- copy_in_batches(
  df = data.frame(
    id = sprintf("img_%03d", seq_len(nrow(sampled))),
    path = sampled$file_path,
    stringsAsFactors = FALSE
  ),
  output_dir = batch_dir,
  batch_size = 250L,
  dry_run = TRUE,
  verbose = FALSE
)

head(batch_report)

Workflow vignette

See vignettes/cam2model-workflow.Rmd for a step-by-step guide with a table of inputs, outputs, and generated files.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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