lightning_pose.utils

lightning_pose.utils.cropzoom Module

`generate_cropped_labeled_frames`(...)	Given model predictions, generates a bbox.csv, crops frames, and a cropped csv file.
`generate_cropped_video`(input_video_file, ...)	TODO make consistent with generate_cropped_labeled_frames
`generate_cropped_csv_file`(input_csv_file, ...)	Adjusts coordinates in the input CSV file either by adding or subtracting corresponding values from a bounding box CSV file.

Path handling functions.

`ckpt_path_from_base_path`(base_path, model_name)	Given a path to a hydra output with trained model, extract the model .ckpt file.
`check_if_semi_supervised`([losses_to_use])	Use config file to determine if model is semi-supervised.
`get_keypoint_names`([cfg, csv_file, header_rows])
`return_absolute_path`(possibly_relative_path)	Return absolute path from possibly relative path.
`return_absolute_data_paths`(data_cfg[, ...])	Generate absolute path for our example toy data.
`extract_session_name_from_video`(...)	Extract session name from video filename by removing the view name.
`find_video_files_for_views`(video_dir, view_names)	Search inside a folder to find a list of videos from different sessions and views, split them up and return a list of lists like [[sess0_view0.mp4, sess0_view1.mp4, ...], [sess1_view0.mp4, sess1_view1.mp4, ...], ...]
`get_videos_in_dir`(video_dir[, view_names, ...])	Gather videos to process from a single directory.
`check_video_paths`(video_paths[, view_names])
`get_context_img_paths`(center_img_path)	Given the path to a center image frame, return paths of 5 context frames (n-2, n-1, n, n+1, n+2).
`split_video_files_by_view`(video_paths, ...)	For a list of videos from different sessions and views, split them up and return a list of lists like [[sess0_view0.mp4, sess0_view1.mp4, ...], [sess1_view0.mp4, sess1_view1.mp4, ...], ...]

PCA class to assist with computing PCA losses.

Reformat multiview data so each observation is a single body part across views.

`KeypointPCA`	Class to collect data from a dataloader and compute PCA params.
`ComponentChooser`	Determine the number of PCA components to keep.

Functions for predicting keypoints on labeled datasets and unlabeled videos.

`predict_dataset`(cfg, data_module, preds_file)	Save predicted keypoints for a labeled dataset.
`predict_single_video`(cfg_file, video_file, ...)	This function is deprecated.
`make_dlc_pandas_index`(cfg, keypoint_names)
`get_model_class`(map_type, semi_supervised)	[summary]
`load_model_from_checkpoint`(cfg, ckpt_file[, ...])	Load Lightning Pose model from checkpoint file.
`create_labeled_video`(clip, xs_arr, ys_arr[, ...])	Helper function for creating annotated videos. Args clip xs_arr: shape T x n_joints ys_arr: shape T x n_joints mask_array: shape T x n_joints; timepoints/joints with a False entry will not be plotted dotsize: size of marker dot on labeled video colormap: matplotlib color map for markers fps: None to default to fps of original video output_video_path: video file name start_time: time (in seconds) of video start.
`export_predictions_and_labeled_video`(...[, ...])	Deprecated, use predict_video and generate_labeled_video.

Convert batches of model outputs into a prediction dataframe.

Helper functions to build pipeline components from config dictionary.

`get_imgaug_transform`(cfg)	Create simple and flexible data transform pipeline that augments images and keypoints.
`get_dataset`(cfg, data_dir, imgaug_transform)	Create a dataset that contains labeled data.
`get_data_module`(cfg, dataset[, video_dir])	Create a data module that splits a dataset into train/val/test iterators.
`get_loss_factories`(cfg, data_module)	Create loss factory that orchestrates different losses during training.
`get_model`(cfg, data_module, loss_factories)	Create model: regression or heatmap based, supervised or semi-supervised.
`get_callbacks`(cfg[, early_stopping, ...])
`calculate_steps_per_epoch`(data_module)
`compute_metrics`(cfg, preds_file[, data_module])	Compute various metrics on predictions csv file, potentially for multiple views. Saves metrics to files next to predictions file, in the convention of: {prediction_file_stem}_{metric_name}.csv.