A Collection of ML Tools for Conservation Research

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Documentation for package ‘animl’ version 3.2.0

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animl_install Load animl-py if available
animl_install_instructions Installation Instructions for animl-r Python dependencies
build_file_manifest File Management Module
check_file Check for files existence and prompt user if they want to load
check_python Check that the python version is compatible with the current version of animl-py
classify Infer Species for Given Detections
compute_batched_distance_matrix Computes the distance matrix in a batched manner to save memory.
compute_distance_matrix A wrapper function for computing distance matrix.
cosine_distance Computes cosine distance of two sets of vectors
create_pyenv Install python if necessary and create the environment animl_env
delete_pyenv Delete the animl_env environment
detect Apply MegaDetector to a Given Batch of Images
download_model Download specified model to the given directory.
euclidean_squared_distance Computes euclidean squared distance of two sets of vectors
export_camtrapR Export data into sorted folders organized by station
export_coco Converts the .csv file to a COCO-formatted .json file.
export_folders Create SymLink Directories and Sort Classified Images
export_megadetector Converts the .csv file to the MD-formatted .json file.
export_timelapse Converts the Manifests to a csv file that contains columns needed for TimeLapse conversion in later step
extract_frames Extract frames from video for classification
extract_miew_embeddings Extract Embeddings from MiewID
get_animals Return a dataframe of only MD animals
get_empty Return MD empty, vehicle and human images in a dataframe
get_frame_as_image Given a video path, return a specific frame as an RGB image
list_models List available models for download.
load_animl Load animl-py if available
load_classifier Load a Classifier Model and Class_list
load_class_list Load class list .csv file
load_data Load .csv or .Rdata file
load_detector Load an Object Detector
load_json Load data from a JSON file.
load_miew Load MiewID model
parse_detections Parse MD results into a simple dataframe
plot_all_bounding_boxes Plot all bounding boxes in a manifest
plot_box Plot bounding boxes on image from md results
remove_diagonal Removes the diagonal elements from a square matrix.
remove_link Remove Sorted Links
save_classifier Save model state weights
save_data Save Data to Given File
save_json Save data to a JSON file.
sequence_classification Leverage sequences to classify images
single_classification Get Maximum likelihood label for each Detection
test_main Test a model with a Config file
train_main Model Training
train_val_test Splits the manifest into training validation and test datasets for training
update_animl_py Update animl-py version for the given environment
update_labels_from_folders Udate Results from File Browser
WorkingDirectory Set Working Directory and Save File Global Variables