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Merged
tkswanson merged 13 commits into from
Feb 4, 2026
+351 −206
Merged

animl 3.2.0 #97

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3257884
move install outside of .onLoad
tkswanson Jan 28, 2026
e2c627b
venv instead of conda, change arg names
tkswanson Jan 28, 2026
ee06ac4
Merge pull request #95 from conservationtechlab/install_attempt2
tkswanson Jan 29, 2026
eeb53fa
change to load_animl() and update to 3.2.0
tkswanson Jan 29, 2026
ab8b2eb
add best param to single_classification
tkswanson Jan 29, 2026
26fcf17
update args and docstrings
tkswanson Jan 29, 2026
da57b80
change anml_py variable access
tkswanson Feb 2, 2026
9f73e12
Merge branch 'animl_3.2.0' of github.com:conservationtechlab/animl-r …
tkswanson Feb 2, 2026
77ac49f
update docs
tkswanson Feb 2, 2026
be0827e
Merge pull request #96 from conservationtechlab/animl_3.2.0
tkswanson Feb 2, 2026
9647e3e
update onAttach message
tkswanson Feb 2, 2026
1f4a2c3
increase default num_workers for video processing to 4
tkswanson Feb 4, 2026
ac58289
update default value for num_workers
tkswanson Feb 4, 2026
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1 change: 1 addition & 0 deletions .gitignore
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Expand Up @@ -7,3 +7,4 @@ inst/doc
.ipynb
animl-py/src/animl/__pycache__/**
animl-py/src/animl/.ipynb_checkpoints/**
examples/**
2 changes: 1 addition & 1 deletion DESCRIPTION
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@@ -1,6 +1,6 @@
Package: animl
Title: A Collection of ML Tools for Conservation Research
Version: 3.1.1
Version: 3.2.0
Authors@R: c(person(given="Kyra", family="Swanson",email="tswanson@sdzwa.org", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-1496-3217")), person(given="Mathias",family="Tobler",role = "aut"))
Description: Functions required to classify subjects within camera trap field data. The package can handle both images and videos. The authors recommend a two-step approach using Microsoft's 'MegaDector' model and then a second model trained on the classes of interest.
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3 changes: 2 additions & 1 deletion NAMESPACE
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Expand Up @@ -2,6 +2,7 @@

export(WorkingDirectory)
export(animl_install)
export(animl_install_instructions)
export(build_file_manifest)
export(check_python)
export(classify)
Expand All @@ -24,7 +25,7 @@ export(get_animals)
export(get_empty)
export(get_frame_as_image)
export(list_models)
export(load_animl_py)
export(load_animl)
export(load_class_list)
export(load_classifier)
export(load_data)
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33 changes: 26 additions & 7 deletions R/classification.R
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@@ -1,3 +1,22 @@
#' Save model state weights
#'
#' @param model pytorch model
#' @param out_dir directory to save model to
#' @param epoch current training epoch
#' @param stats performance metrics of current epoch
#' @param optimizer pytorch optimizer (optional)
#' @param scheduler pytorch scheduler (optional)
#'
#' @returns None
#' @export
#'
#' @examples
#' \dontrun{save_classifier(model, 'models/', 10, list(acc = 0.85))}
save_classifier <- function(model, out_dir, epoch, stats, optimizer=NULL, scheduler=NULL){
animl_py <- get("animl_py", envir = parent.env(environment()))
animl_py$save_classifier(model, out_dir, epoch, reticulate::r_to_py(stats), optimizer=optimizer, scheduler=scheduler)
}

#' Load a Classifier Model and Class_list
#'
#' @param model_path path to model
Expand All @@ -13,8 +32,7 @@
#' classes <- load_class_list('sdzwa_andes_v1_classes.csv')
#' andes <- load_classifier('andes_v1.pt', nrow(classes))}
load_classifier <- function(model_path, classes, device=NULL, architecture="CTL"){
animl_py <- get("animl_py", envir = parent.env(environment()))

animl_py <- .animl_internal$animl_py
if(is.numeric(classes)){ classes = as.integer(classes)}
animl_py$load_classifier(model_path, classes, device=device, architecture=architecture)
}
Expand All @@ -35,7 +53,7 @@ load_classifier <- function(model_path, classes, device=NULL, architecture="CTL"
#' @examples
#' \dontrun{save_classifier(model, 'models/', 10, list(acc = 0.85))}
save_classifier <- function(model, out_dir, epoch, stats, optimizer=NULL, scheduler=NULL){
animl_py <- get("animl_py", envir = parent.env(environment()))
animl_py <- .animl_internal$animl_py
animl_py$save_classifier(model, out_dir, epoch, reticulate::r_to_py(stats), optimizer=optimizer, scheduler=scheduler)
}

Expand Down Expand Up @@ -78,7 +96,7 @@ classify <- function(model, detections,
batch_size=1, num_workers=1,
device=NULL, out_file=NULL){

animl_py <- get("animl_py", envir = parent.env(environment()))
animl_py <- .animl_internal$animl_py
animl_py$classify(model, detections,
resize_width=as.integer(resize_width),
resize_height=as.integer(resize_height),
Expand All @@ -95,13 +113,14 @@ classify <- function(model, detections,
#' @param empty manifest of md human, vehicle and empty images
#' @param predictions_raw softmaxed likelihoods from predict_species
#' @param class_list list of class labels
#' @param best whether to return one prediction per file
#'
#' @returns dataframe with prediction and confidence columns
#' @export
#'
#' @examples
#' \dontrun{animals <- single_classification(animals, empty, pred_raw, class_list)}
single_classification <- function(animals, empty, predictions_raw, class_list){
animl_py <- get("animl_py", envir = parent.env(environment()))
animl_py$single_classification(animals, empty, predictions_raw, class_list)
single_classification <- function(animals, empty, predictions_raw, class_list, best=FALSE){
animl_py <- .animl_internal$animl_py
animl_py$single_classification(animals, empty, predictions_raw, class_list, best=best)
}
8 changes: 4 additions & 4 deletions R/detection.R
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Expand Up @@ -11,7 +11,7 @@
#' \dontrun{md_py <- megadetector("/mnt/machinelearning/megaDetector/md_v5a.0.0.pt",
#' model_type='mdv5', device='cuda:0')}
load_detector <- function(model_path, model_type, device=NULL){
# first check if animl-py is loaded
animl_py <- .animl_internal$animl_py
animl_py$load_detector(model_path, model_type=model_type, device=device)
}

Expand Down Expand Up @@ -40,7 +40,8 @@ detect <- function(detector, image_file_names, resize_width, resize_height,
letterbox=TRUE, confidence_threshold=0.1, file_col='filepath',
batch_size=1, num_workers=1, device=NULL,
checkpoint_path=NULL, checkpoint_frequency=-1){
animl_py <- get("animl_py", envir = parent.env(environment()))

animl_py <- .animl_internal$animl_py
animl_py$detect(detector, image_file_names,
as.integer(resize_width), as.integer(resize_height),
letterbox=letterbox, confidence_threshold=confidence_threshold,
Expand All @@ -67,8 +68,7 @@ detect <- function(detector, image_file_names, resize_width, resize_height,
#' mdresults <- parseMD(mdres)
#' }
parse_detections <- function(results, manifest=NULL, out_file=NULL, threshold=0, file_col="filepath") {
animl_py <- get("animl_py", envir = parent.env(environment()))
animl_py <- .animl_internal$animl_py
animl_py$parse_detections(results, manifest=manifest, out_file=out_file,
threshold=threshold, file_col=file_col)
}

24 changes: 12 additions & 12 deletions R/export.R
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@@ -1,9 +1,9 @@
#' Create SymLink Directories and Sort Classified Images
#'
#' @param manifest DataFrame of classified images
#' @param out_dir Destination directory for symlinks
#' @param out_dir Destination directory for species folders
#' @param out_file if provided, save the manifest to this file
#' @param label_col (str): specify 'prediction' for species or 'category' for megadetector class
#' @param label_col specify 'prediction' for species or 'category' for megadetector class
#' @param file_col Colun containing file paths
#' @param unique_name Unique image name identifier
#' @param copy Toggle to determine copy or hard link, defaults to link
Expand All @@ -18,7 +18,7 @@
export_folders <- function(manifest, out_dir, out_file=NULL,
label_col="prediction", file_col="filepath",
unique_name='uniquename', copy=FALSE) {
animl_py <- get("animl_py", envir = parent.env(environment()))
animl_py <- .animl_internal$animl_py
manifest <- animl_py$export_folders(manifest, out_dir, out_file,
label_col=label_col, file_col=file_col,
unique_name=unique_name, copy=copy)
Expand Down Expand Up @@ -49,7 +49,7 @@ remove_link <- function(manifest, link_col='link'){
#'
#' @param manifest dataframe containing file data and predictions
#' @param export_dir directory to sort files into
#' @param unique_name column name indicating a unique file name for each row
#' @param unique_name column containing unique file names
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@matobler matobler Feb 4, 2026

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is there a reason there is a default value for unique_name since this is a required parameters?

#'
#' @return dataframe with new "Species" column that contains the verified species
#' @export
Expand Down Expand Up @@ -87,7 +87,7 @@ update_labels_from_folders <- function(manifest, export_dir, unique_name='unique
#' @examples
#' \dontrun{export_megadetector(manifest, output_file= 'results.json', detector='MDv6')}
export_coco <- function(manifest, class_list, out_file, info=NULL, licenses=NULL){
animl_py <- get("animl_py", envir = parent.env(environment()))
animl_py <- .animl_internal$animl_py
animl_py$export_coco(manifest, class_list, out_file, info=info, licenses=licenses)
}

Expand All @@ -114,7 +114,7 @@ export_coco <- function(manifest, class_list, out_file, info=NULL, licenses=NULL
export_camtrapR <- function(manifest, out_dir, out_file=NULL, label_col='prediction',
file_col="filepath", station_col='station',
unique_name='uniquename', copy=FALSE){
animl_py <- get("animl_py", envir = parent.env(environment()))
animl_py <- .animl_internal$animl_py
animl_py$export_camtrapR(manifest, out_dir, out_file=out_file, label_col=label_col,
file_col=file_col, station_col=station_col,
unique_name=unique_name, copy=copy)
Expand All @@ -123,18 +123,18 @@ export_camtrapR <- function(manifest, out_dir, out_file=NULL, label_col='predict

#' Converts the Manifests to a csv file that contains columns needed for TimeLapse conversion in later step
#'
#' @param results a DataFrame that has entries of anuimal classification
#' @param image_dir location of root directory where all images are stored (can contain subdirectories)
#' @param manifest a DataFrame that has entries of anuimal classification
#' @param out_dir location of root directory where all images are stored (can contain subdirectories)
#' @param only_animal A bool that confirms whether we want only animal detctions or all
#'
#' @returns animals.csv, non-anim.csv, csv_loc
#' @export
#'
#' @examples
#' \dontrun{export_timelapse(animals, empty, '/path/to/images/')}
export_timelapse <- function(results, image_dir, only_animal=TRUE){
animl_py <- get("animl_py", envir = parent.env(environment()))
animl_py$export_timelapse(results, image_dir, only_animal=only_animal)
export_timelapse <- function(manifest, out_dir, only_animal=TRUE){
animl_py <- .animl_internal$animl_py
animl_py$export_timelapse(manifest, out_dir, only_animal=only_animal)
}


Expand All @@ -152,7 +152,7 @@ export_timelapse <- function(results, image_dir, only_animal=TRUE){
#' \dontrun{export_megadetector(manifest, output_file= 'results.json', detector='MDv6')}
export_megadetector <- function(manifest, out_file=NULL,
detector='MegaDetector v5a', prompt=TRUE){
animl_py <- get("animl_py", envir = parent.env(environment()))
animl_py <- .animl_internal$animl_py
animl_py$export_megadetector(manifest, out_file=out_file,
detector=detector, prompt=prompt)
}
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10 changes: 5 additions & 5 deletions R/file_management.R
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Expand Up @@ -22,7 +22,7 @@
#' }
build_file_manifest <- function(image_dir, exif=TRUE, out_file=NULL,
offset=0, recursive=TRUE) {
animl_py <- get("animl_py", envir = parent.env(environment()))
animl_py <- .animl_internal$animl_py
manifest <- animl_py$build_file_manifest(image_dir, exif=exif, out_file=out_file, offset=offset, recursive=recursive)
return(manifest)
}
Expand Down Expand Up @@ -145,7 +145,7 @@ check_file <- function(file, output_type) {
#' save_json(mdresults, 'mdraw.json')
#' }
save_json <- function(data, out_file, prompt=TRUE){
animl_py <- get("animl_py", envir = parent.env(environment()))
animl_py <- .animl_internal$animl_py
animl_py$save_json(data, out_file, prompt=prompt)
}

Expand All @@ -162,7 +162,7 @@ save_json <- function(data, out_file, prompt=TRUE){
#' mdraw <- load_json('mdraw.json')
#' }
load_json <- function(file){
animl_py <- get("animl_py", envir = parent.env(environment()))
animl_py <- .animl_internal$animl_py
animl_py$load_json(file)
}

Expand All @@ -181,7 +181,7 @@ load_json <- function(file){
#' download_model("https://models.com/path/to/model.pt", out_dir='models')
#' }
download_model <- function(model_url, out_dir='models'){
animl_py <- get("animl_py", envir = parent.env(environment()))
animl_py <- .animl_internal$animl_py
animl_py$download_model(model_url, out_dir = out_dir)
}

Expand All @@ -196,6 +196,6 @@ download_model <- function(model_url, out_dir='models'){
#' download_model("https://models.com/path/to/model.pt", out_dir='models')
#' }
list_models <- function(){
animl_py <- get("animl_py", envir = parent.env(environment()))
animl_py <- .animl_internal$animl_py
animl_py$list_models()
}
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