From 29f2d731f59eb9e8182e496aa634a717b322f468 Mon Sep 17 00:00:00 2001 From: gitttt-1234 Date: Wed, 18 Sep 2024 21:47:36 +0000 Subject: [PATCH] deploy: 3c7f5afa8dd952fdee8ef3c8ac94b0689dac9cdb --- develop/_sources/api.rst | 12 +-- develop/_sources/guides/cli.md | 2 +- develop/_sources/guides/proofreading.md | 2 + develop/api.html | 36 ++++----- develop/api/sleap.nn.inference.html | 76 +++++++++---------- develop/api/sleap.nn.tracker.components.html | 36 +++++---- develop/api/sleap.nn.tracking.html | 49 ++++++------ develop/genindex.html | 2 + develop/guides/cli.html | 2 +- develop/guides/proofreading.html | 2 + develop/objects.inv | Bin 20437 -> 20444 bytes develop/searchindex.js | 2 +- 12 files changed, 118 insertions(+), 103 deletions(-) diff --git a/develop/_sources/api.rst b/develop/_sources/api.rst index 28313416b..0328c4f91 100644 --- a/develop/_sources/api.rst +++ b/develop/_sources/api.rst @@ -64,6 +64,12 @@ Developer API sleap.nn.training sleap.nn.utils sleap.nn.viz + sleap.nn.config.data + sleap.nn.config.model + sleap.nn.config.optimization + sleap.nn.config.outputs + sleap.nn.config.training_job + sleap.nn.config.utils sleap.nn.architectures.common sleap.nn.architectures.encoder_decoder sleap.nn.architectures.hourglass @@ -75,12 +81,6 @@ Developer API sleap.nn.architectures.upsampling sleap.nn.tracker.components sleap.nn.tracker.kalman - sleap.nn.config.data - sleap.nn.config.model - sleap.nn.config.optimization - sleap.nn.config.outputs - sleap.nn.config.training_job - sleap.nn.config.utils sleap.nn.data.augmentation sleap.nn.data.confidence_maps sleap.nn.data.dataset_ops diff --git a/develop/_sources/guides/cli.md b/develop/_sources/guides/cli.md index 03b806903..134461c60 100644 --- a/develop/_sources/guides/cli.md +++ b/develop/_sources/guides/cli.md @@ -207,7 +207,7 @@ optional arguments: --tracking.clean_iou_threshold TRACKING.CLEAN_IOU_THRESHOLD IOU to use when culling instances *after* tracking. (default: 0) --tracking.similarity TRACKING.SIMILARITY - Options: instance, centroid, iou (default: instance) + Options: instance, normalized_instance, object_keypoint, centroid, iou (default: instance) --tracking.match TRACKING.MATCH Options: hungarian, greedy (default: greedy) --tracking.robust TRACKING.ROBUST diff --git a/develop/_sources/guides/proofreading.md b/develop/_sources/guides/proofreading.md index fea1c5ebc..941b85154 100644 --- a/develop/_sources/guides/proofreading.md +++ b/develop/_sources/guides/proofreading.md @@ -50,6 +50,8 @@ There are currently three methods for matching instances in frame N against thes - “**centroid**” measures similarity by the distance between the instance centroids - “**iou**” measures similarity by the intersection/overlap of the instance bounding boxes - “**instance**” measures similarity by looking at the distances between corresponding nodes in the instances, normalized by the number of valid nodes in the candidate instance. +- “**normalized_instance**” measures similarity by looking at the distances between corresponding nodes in the instances, normalized by the number of valid nodes in the candidate instance and the keypoints normalized by the image size. +- “**object_keypoint**” measures similarity by measuring the distance between each keypoints from a reference instance and a query instance, takes the exp(-d**2), sum for all the keypoints and divide by the number of visible keypoints in the reference instance. Once SLEAP has measured the similarity between all the candidates and the instances in frame N, you need to choose a way to pair them up. You can do this either by picking the best match, and the picking the best remaining match for each remaining instance in turn—this is “**greedy**” matching—or you can find the way of matching identities which minimizes the total cost (or: maximizes the total similarity)—this is “**Hungarian**” matching. diff --git a/develop/api.html b/develop/api.html index 98e78e0b8..ec017f27f 100644 --- a/develop/api.html +++ b/develop/api.html @@ -473,6 +473,24 @@

Developer API

sleap.nn.viz

Visualization and plotting utilities.

+

sleap.nn.config.data

+

+ +

sleap.nn.config.model

+

+ +

sleap.nn.config.optimization

+

+ +

sleap.nn.config.outputs

+

+ +

sleap.nn.config.training_job

+

Serializable configuration classes for specifying all training job parameters.

+ +

sleap.nn.config.utils

+

Utilities for config building and validation.

+

sleap.nn.architectures.common

Common utilities for architecture and model building.

@@ -506,24 +524,6 @@

Developer API

sleap.nn.tracker.kalman

Module to use Kalman filters for tracking instance identities.

-

sleap.nn.config.data

-

- -

sleap.nn.config.model

-

- -

sleap.nn.config.optimization

-

- -

sleap.nn.config.outputs

-

- -

sleap.nn.config.training_job

-

Serializable configuration classes for specifying all training job parameters.

- -

sleap.nn.config.utils

-

Utilities for config building and validation.

-

sleap.nn.data.augmentation

Transformers for applying data augmentation.

diff --git a/develop/api/sleap.nn.inference.html b/develop/api/sleap.nn.inference.html index 06224a382..315507f8e 100644 --- a/develop/api/sleap.nn.inference.html +++ b/develop/api/sleap.nn.inference.html @@ -637,7 +637,7 @@

Contents

function which provides a simplified interface for creating `Predictor`s.

-class sleap.nn.inference.BottomUpInferenceLayer(*args, **kwargs)[source]#
+class sleap.nn.inference.BottomUpInferenceLayer(*args, **kwargs)[source]#

Keras layer that predicts instances from images using a trained model.

This layer encapsulates all of the inference operations required for generating predictions from a centered instance confidence map model. This includes @@ -760,7 +760,7 @@

Contents

-call(data)[source]#
+call(data)[source]#

Predict instances for one batch of images.

Parameters:
@@ -794,13 +794,13 @@

Contents

-find_peaks(cms, offsets)[source]#
+find_peaks(cms, offsets)[source]#

Run peak finding on predicted confidence maps.

-forward_pass(data)[source]#
+forward_pass(data)[source]#

Run preprocessing and model inference on a batch.

@@ -808,7 +808,7 @@

Contents

-class sleap.nn.inference.BottomUpInferenceModel(*args, **kwargs)[source]#
+class sleap.nn.inference.BottomUpInferenceModel(*args, **kwargs)[source]#

Bottom-up instance prediction model.

This model encapsulates the bottom-up approach where points are first detected by local peak detection and then grouped into instances by connectivity scoring using @@ -822,7 +822,7 @@

Contents

-call(example)[source]#
+call(example)[source]#

Predict instances for one batch of images.

Parameters:
@@ -855,7 +855,7 @@

Contents

-class sleap.nn.inference.BottomUpMultiClassInferenceLayer(*args, **kwargs)[source]#
+class sleap.nn.inference.BottomUpMultiClassInferenceLayer(*args, **kwargs)[source]#

Keras layer that predicts instances from images using a trained model.

This layer encapsulates all of the inference operations required for generating predictions from a centered instance confidence map model. This includes @@ -961,7 +961,7 @@

Contents

-call(data)[source]#
+call(data)[source]#

Predict instances for one batch of images.

Parameters:
@@ -992,13 +992,13 @@

Contents

-find_peaks(cms, offsets)[source]#
+find_peaks(cms, offsets)[source]#

Run peak finding on predicted confidence maps.

-forward_pass(data)[source]#
+forward_pass(data)[source]#

Run preprocessing and model inference on a batch.

@@ -1006,7 +1006,7 @@

Contents

-class sleap.nn.inference.BottomUpMultiClassInferenceModel(*args, **kwargs)[source]#
+class sleap.nn.inference.BottomUpMultiClassInferenceModel(*args, **kwargs)[source]#

Bottom-up multi-class instance prediction model.

This model encapsulates the bottom-up multi-class approach where points are first detected by local peak finding and then grouped into instances by their identity @@ -1020,7 +1020,7 @@

Contents

-call(example)[source]#
+call(example)[source]#

Predict instances for one batch of images.

Parameters:
@@ -1053,7 +1053,7 @@

Contents

-class sleap.nn.inference.BottomUpMultiClassPredictor(config: TrainingJobConfig, model: Model, inference_model: Optional[BottomUpMultiClassInferenceModel] = None, peak_threshold: float = 0.2, batch_size: int = 4, integral_refinement: bool = True, integral_patch_size: int = 5, tracks: Optional[List[Track]] = None, *, verbosity: str = 'rich', report_rate: float = 2.0, model_paths: List[str] = _Nothing.NOTHING)[source]#
+class sleap.nn.inference.BottomUpMultiClassPredictor(config: TrainingJobConfig, model: Model, inference_model: Optional[BottomUpMultiClassInferenceModel] = None, peak_threshold: float = 0.2, batch_size: int = 4, integral_refinement: bool = True, integral_patch_size: int = 5, tracks: Optional[List[Track]] = None, *, verbosity: str = 'rich', report_rate: float = 2.0, model_paths: List[str] = _Nothing.NOTHING)[source]#

Bottom-up multi-instance predictor.

This high-level class handles initialization, preprocessing and tracking using a trained bottom-up multi-instance SLEAP model.

@@ -1172,7 +1172,7 @@

Contents

-classmethod from_trained_models(model_path: str, batch_size: int = 4, peak_threshold: float = 0.2, integral_refinement: bool = True, integral_patch_size: int = 5, resize_input_layer: bool = True) → BottomUpMultiClassPredictor[source]#
+classmethod from_trained_models(model_path: str, batch_size: int = 4, peak_threshold: float = 0.2, integral_refinement: bool = True, integral_patch_size: int = 5, resize_input_layer: bool = True) → BottomUpMultiClassPredictor[source]#

Create predictor from a saved model.

Parameters:
@@ -1211,7 +1211,7 @@

Contents

-class sleap.nn.inference.BottomUpPredictor(bottomup_config: TrainingJobConfig, bottomup_model: Model, inference_model: Optional[BottomUpInferenceModel] = None, peak_threshold: float = 0.2, batch_size: int = 4, integral_refinement: bool = True, integral_patch_size: int = 5, max_edge_length_ratio: float = 0.25, dist_penalty_weight: float = 1.0, paf_line_points: int = 10, min_line_scores: float = 0.25, max_instances: Optional[int] = None, *, verbosity: str = 'rich', report_rate: float = 2.0, model_paths: List[str] = _Nothing.NOTHING)[source]#
+class sleap.nn.inference.BottomUpPredictor(bottomup_config: TrainingJobConfig, bottomup_model: Model, inference_model: Optional[BottomUpInferenceModel] = None, peak_threshold: float = 0.2, batch_size: int = 4, integral_refinement: bool = True, integral_patch_size: int = 5, max_edge_length_ratio: float = 0.25, dist_penalty_weight: float = 1.0, paf_line_points: int = 10, min_line_scores: float = 0.25, max_instances: Optional[int] = None, *, verbosity: str = 'rich', report_rate: float = 2.0, model_paths: List[str] = _Nothing.NOTHING)[source]#

Bottom-up multi-instance predictor.

This high-level class handles initialization, preprocessing and tracking using a trained bottom-up multi-instance SLEAP model.

@@ -1395,7 +1395,7 @@

Contents

-classmethod from_trained_models(model_path: str, batch_size: int = 4, peak_threshold: float = 0.2, integral_refinement: bool = True, integral_patch_size: int = 5, max_edge_length_ratio: float = 0.25, dist_penalty_weight: float = 1.0, paf_line_points: int = 10, min_line_scores: float = 0.25, resize_input_layer: bool = True, max_instances: Optional[int] = None) → BottomUpPredictor[source]#
+classmethod from_trained_models(model_path: str, batch_size: int = 4, peak_threshold: float = 0.2, integral_refinement: bool = True, integral_patch_size: int = 5, max_edge_length_ratio: float = 0.25, dist_penalty_weight: float = 1.0, paf_line_points: int = 10, min_line_scores: float = 0.25, resize_input_layer: bool = True, max_instances: Optional[int] = None) → BottomUpPredictor[source]#

Create predictor from a saved model.

Parameters:
@@ -2065,7 +2065,7 @@

Contents

-class sleap.nn.inference.MoveNetInferenceLayer(*args, **kwargs)[source]#
+class sleap.nn.inference.MoveNetInferenceLayer(*args, **kwargs)[source]#

Inference layer for applying single instance models.

This layer encapsulates all of the inference operations requires for generating predictions from a single instance confidence map model. This includes @@ -2115,7 +2115,7 @@

Contents

-call(ex)[source]#
+call(ex)[source]#

Call the model with preprocessed data.

Parameters:
@@ -2131,7 +2131,7 @@

Contents

-class sleap.nn.inference.MoveNetInferenceModel(*args, **kwargs)[source]#
+class sleap.nn.inference.MoveNetInferenceModel(*args, **kwargs)[source]#

MoveNet prediction model.

This model encapsulates the basic MoveNet approach. The images are passed to a model which is trained to detect all body parts (17 joints in total).

@@ -2148,7 +2148,7 @@

Contents

-call(x)[source]#
+call(x)[source]#

Calls the model on new inputs and returns the outputs as tensors.

In this case call() just reapplies all ops in the graph to the new inputs @@ -2182,7 +2182,7 @@

Contents

-class sleap.nn.inference.MoveNetPredictor(inference_model: Optional[MoveNetInferenceModel] = None, peak_threshold: float = 0.2, batch_size: int = 1, model_name: str = 'lightning', *, verbosity: str = 'rich', report_rate: float = 2.0, model_paths: List[str] = _Nothing.NOTHING)[source]#
+class sleap.nn.inference.MoveNetPredictor(inference_model: Optional[MoveNetInferenceModel] = None, peak_threshold: float = 0.2, batch_size: int = 1, model_name: str = 'lightning', *, verbosity: str = 'rich', report_rate: float = 2.0, model_paths: List[str] = _Nothing.NOTHING)[source]#

MoveNet predictor.

This high-level class handles initialization, preprocessing and tracking using a trained MoveNet model. @@ -2249,7 +2249,7 @@

Contents

-classmethod from_trained_models(model_name: str, peak_threshold: float = 0.2) → MoveNetPredictor[source]#
+classmethod from_trained_models(model_name: str, peak_threshold: float = 0.2) → MoveNetPredictor[source]#

Create the predictor from a saved model.

Parameters:
@@ -2804,7 +2804,7 @@

Contents

-class sleap.nn.inference.TopDownMultiClassFindPeaks(*args, **kwargs)[source]#
+class sleap.nn.inference.TopDownMultiClassFindPeaks(*args, **kwargs)[source]#

Keras layer that predicts and classifies peaks from images using a trained model.

This layer encapsulates all of the inference operations required for generating predictions from a centered instance confidence map and multi-class model. This @@ -2912,7 +2912,7 @@

Contents

-call(inputs: Union[Dict[str, Tensor], Tensor]) → Dict[str, Tensor][source]#
+call(inputs: Union[Dict[str, Tensor], Tensor]) → Dict[str, Tensor][source]#

Predict confidence maps and infer peak coordinates.

This layer can be chained with a CentroidCrop layer to create a top-down inference function from full images.

@@ -2973,7 +2973,7 @@

Contents

-class sleap.nn.inference.TopDownMultiClassInferenceModel(*args, **kwargs)[source]#
+class sleap.nn.inference.TopDownMultiClassInferenceModel(*args, **kwargs)[source]#

Top-down instance prediction model.

This model encapsulates the top-down approach where instances are first detected by local peak detection of an anchor point and then cropped. These instance-centered @@ -2998,7 +2998,7 @@

Contents

-call(example: Union[Dict[str, Tensor], Tensor]) → Dict[str, Tensor][source]#
+call(example: Union[Dict[str, Tensor], Tensor]) → Dict[str, Tensor][source]#

Predict instances for one batch of images.

Parameters:
@@ -3030,7 +3030,7 @@

Contents

-export_model(save_path: str, signatures: str = 'serving_default', save_traces: bool = True, model_name: Optional[str] = None, tensors: Optional[Dict[str, str]] = None, unrag_outputs: bool = True)[source]#
+export_model(save_path: str, signatures: str = 'serving_default', save_traces: bool = True, model_name: Optional[str] = None, tensors: Optional[Dict[str, str]] = None, unrag_outputs: bool = True)[source]#

Save the frozen graph of a model.

Parameters:
@@ -3061,7 +3061,7 @@

Contents

-class sleap.nn.inference.TopDownMultiClassPredictor(centroid_config: Optional[TrainingJobConfig] = None, centroid_model: Optional[Model] = None, confmap_config: Optional[TrainingJobConfig] = None, confmap_model: Optional[Model] = None, inference_model: Optional[TopDownMultiClassInferenceModel] = None, batch_size: int = 4, peak_threshold: float = 0.2, integral_refinement: bool = True, integral_patch_size: int = 5, tracks: Optional[List[Track]] = None, *, verbosity: str = 'rich', report_rate: float = 2.0, model_paths: List[str] = _Nothing.NOTHING)[source]#
+class sleap.nn.inference.TopDownMultiClassPredictor(centroid_config: Optional[TrainingJobConfig] = None, centroid_model: Optional[Model] = None, confmap_config: Optional[TrainingJobConfig] = None, confmap_model: Optional[Model] = None, inference_model: Optional[TopDownMultiClassInferenceModel] = None, batch_size: int = 4, peak_threshold: float = 0.2, integral_refinement: bool = True, integral_patch_size: int = 5, tracks: Optional[List[Track]] = None, *, verbosity: str = 'rich', report_rate: float = 2.0, model_paths: List[str] = _Nothing.NOTHING)[source]#

Top-down multi-instance predictor with classification.

This high-level class handles initialization, preprocessing and tracking using a trained top-down multi-instance classification SLEAP model.

@@ -3220,7 +3220,7 @@

Contents

-export_model(save_path: str, signatures: str = 'serving_default', save_traces: bool = True, model_name: Optional[str] = None, tensors: Optional[Dict[str, str]] = None, unrag_outputs: bool = True, max_instances: Optional[int] = None)[source]#
+export_model(save_path: str, signatures: str = 'serving_default', save_traces: bool = True, model_name: Optional[str] = None, tensors: Optional[Dict[str, str]] = None, unrag_outputs: bool = True, max_instances: Optional[int] = None)[source]#

Export a trained SLEAP model as a frozen graph. Initializes model, creates a dummy tracing batch and passes it through the model. The frozen graph is saved along with training meta info.

@@ -3249,7 +3249,7 @@

Contents

-classmethod from_trained_models(centroid_model_path: Optional[str] = None, confmap_model_path: Optional[str] = None, batch_size: int = 4, peak_threshold: float = 0.2, integral_refinement: bool = True, integral_patch_size: int = 5, resize_input_layer: bool = True) → TopDownMultiClassPredictor[source]#
+classmethod from_trained_models(centroid_model_path: Optional[str] = None, confmap_model_path: Optional[str] = None, batch_size: int = 4, peak_threshold: float = 0.2, integral_refinement: bool = True, integral_patch_size: int = 5, resize_input_layer: bool = True) → TopDownMultiClassPredictor[source]#

Create predictor from saved models.

Parameters:
@@ -3292,7 +3292,7 @@

Contents

-make_pipeline(data_provider: Optional[Provider] = None) → Pipeline[source]#
+make_pipeline(data_provider: Optional[Provider] = None) → Pipeline[source]#

Make a data loading pipeline.

Parameters:
@@ -3314,7 +3314,7 @@

Contents

-class sleap.nn.inference.TopDownPredictor(centroid_config: Optional[TrainingJobConfig] = None, centroid_model: Optional[Model] = None, confmap_config: Optional[TrainingJobConfig] = None, confmap_model: Optional[Model] = None, inference_model: Optional[TopDownInferenceModel] = None, batch_size: int = 4, peak_threshold: float = 0.2, integral_refinement: bool = True, integral_patch_size: int = 5, max_instances: Optional[int] = None, *, verbosity: str = 'rich', report_rate: float = 2.0, model_paths: List[str] = _Nothing.NOTHING)[source]#
+class sleap.nn.inference.TopDownPredictor(centroid_config: Optional[TrainingJobConfig] = None, centroid_model: Optional[Model] = None, confmap_config: Optional[TrainingJobConfig] = None, confmap_model: Optional[Model] = None, inference_model: Optional[TopDownInferenceModel] = None, batch_size: int = 4, peak_threshold: float = 0.2, integral_refinement: bool = True, integral_patch_size: int = 5, max_instances: Optional[int] = None, *, verbosity: str = 'rich', report_rate: float = 2.0, model_paths: List[str] = _Nothing.NOTHING)[source]#

Top-down multi-instance predictor.

This high-level class handles initialization, preprocessing and tracking using a trained top-down multi-instance SLEAP model.

@@ -3475,7 +3475,7 @@

Contents

-export_model(save_path: str, signatures: str = 'serving_default', save_traces: bool = True, model_name: Optional[str] = None, tensors: Optional[Dict[str, str]] = None, unrag_outputs: bool = True, max_instances: Optional[int] = None)[source]#
+export_model(save_path: str, signatures: str = 'serving_default', save_traces: bool = True, model_name: Optional[str] = None, tensors: Optional[Dict[str, str]] = None, unrag_outputs: bool = True, max_instances: Optional[int] = None)[source]#

Export a trained SLEAP model as a frozen graph. Initializes model, creates a dummy tracing batch and passes it through the model. The frozen graph is saved along with training meta info.

@@ -3627,13 +3627,13 @@

Contents

-sleap.nn.inference.export_cli(args: Optional[list] = None)[source]#
+sleap.nn.inference.export_cli(args: Optional[list] = None)[source]#

CLI for sleap-export.

-sleap.nn.inference.export_model(model_path: Union[str, List[str]], save_path: str = 'exported_model', signatures: str = 'serving_default', save_traces: bool = True, model_name: Optional[str] = None, tensors: Optional[Dict[str, str]] = None, unrag_outputs: bool = True, max_instances: Optional[int] = None)[source]#
+sleap.nn.inference.export_model(model_path: Union[str, List[str]], save_path: str = 'exported_model', signatures: str = 'serving_default', save_traces: bool = True, model_name: Optional[str] = None, tensors: Optional[Dict[str, str]] = None, unrag_outputs: bool = True, max_instances: Optional[int] = None)[source]#

High level export of a trained SLEAP model as a frozen graph.

Parameters:
@@ -3728,7 +3728,7 @@

Contents

-sleap.nn.inference.load_model(model_path: Union[str, List[str]], batch_size: int = 4, peak_threshold: float = 0.2, refinement: str = 'integral', tracker: Optional[str] = None, tracker_window: int = 5, tracker_max_instances: Optional[int] = None, disable_gpu_preallocation: bool = True, progress_reporting: str = 'rich', resize_input_layer: bool = True, max_instances: Optional[int] = None) → Predictor[source]#
+sleap.nn.inference.load_model(model_path: Union[str, List[str]], batch_size: int = 4, peak_threshold: float = 0.2, refinement: str = 'integral', tracker: Optional[str] = None, tracker_window: int = 5, tracker_max_instances: Optional[int] = None, disable_gpu_preallocation: bool = True, progress_reporting: str = 'rich', resize_input_layer: bool = True, max_instances: Optional[int] = None) → Predictor[source]#

Load a trained SLEAP model.

Parameters:
@@ -3787,7 +3787,7 @@

Contents

-sleap.nn.inference.main(args: Optional[list] = None)[source]#
+sleap.nn.inference.main(args: Optional[list] = None)[source]#

Entrypoint for sleap-track CLI for running inference.

Parameters:
@@ -3798,7 +3798,7 @@

Contents

-sleap.nn.inference.make_model_movenet(model_name: str) → Model[source]#
+sleap.nn.inference.make_model_movenet(model_name: str) → Model[source]#

Load a MoveNet model by name.

Parameters:
diff --git a/develop/api/sleap.nn.tracker.components.html b/develop/api/sleap.nn.tracker.components.html index dd41f9543..cdb3ecc3b 100644 --- a/develop/api/sleap.nn.tracker.components.html +++ b/develop/api/sleap.nn.tracker.components.html @@ -332,6 +332,7 @@

Contents

  • instance_iou()
  • instance_similarity()
  • nms_fast()
    • +
  • normalized_instance_similarity()
    • @@ -355,7 +356,7 @@

    Contents

    -class sleap.nn.tracker.components.FrameMatches(matches: List[Match], cost_matrix: ndarray, unmatched_instances: List[InstanceType] = _Nothing.NOTHING)[source]#
    +class sleap.nn.tracker.components.FrameMatches(matches: List[Match], cost_matrix: ndarray, unmatched_instances: List[InstanceType] = _Nothing.NOTHING)[source]#

    Calculates (and stores) matches for a frame.

    This class encapsulates the logic to generate matches (using a custom matching function) from a cost matrix. One key feature is that it retains @@ -401,7 +402,7 @@

    Contents

    -classmethod from_candidate_instances(untracked_instances: List[InstanceType], candidate_instances: List[InstanceType], similarity_function: Callable, matching_function: Callable, robust_best_instance: float = 1.0)[source]#
    +classmethod from_candidate_instances(untracked_instances: List[InstanceType], candidate_instances: List[InstanceType], similarity_function: Callable, matching_function: Callable, robust_best_instance: float = 1.0)[source]#

    Calculates (and stores) matches for a frame from candidate instance.

    Parameters:
    @@ -433,13 +434,13 @@

    Contents

    -class sleap.nn.tracker.components.Match(track: Track, instance: Instance, score: Optional[float] = None, is_first_choice: bool = False)[source]#
    +class sleap.nn.tracker.components.Match(track: Track, instance: Instance, score: Optional[float] = None, is_first_choice: bool = False)[source]#

    Stores a match between a specific instance and specific track.

    -sleap.nn.tracker.components.centroid_distance(ref_instance: InstanceType, query_instance: InstanceType, cache: dict = {}) → float[source]#
    +sleap.nn.tracker.components.centroid_distance(ref_instance: InstanceType, query_instance: InstanceType, cache: dict = {}) → float[source]#

    Returns the negative distance between the centroids of two instances.

    Uses cache dictionary (created with function so it persists between calls) since without cache this method is significantly slower than others.

    @@ -447,7 +448,7 @@

    Contents

    -sleap.nn.tracker.components.connect_single_track_breaks(frames: List[LabeledFrame], instance_count: int) → List[LabeledFrame][source]#
    +sleap.nn.tracker.components.connect_single_track_breaks(frames: List[LabeledFrame], instance_count: int) → List[LabeledFrame][source]#

    Merges breaks in tracks by connecting single lost with single new track.

    Parameters:
    @@ -464,7 +465,7 @@

    Contents

    -sleap.nn.tracker.components.cull_frame_instances(instances_list: List[InstanceType], instance_count: int, iou_threshold: Optional[float] = None) → List[LabeledFrame][source]#
    +sleap.nn.tracker.components.cull_frame_instances(instances_list: List[InstanceType], instance_count: int, iou_threshold: Optional[float] = None) → List[LabeledFrame][source]#

    Removes instances (for single frame) over instance per frame threshold.

    Parameters:
    @@ -484,7 +485,7 @@

    Contents

    -sleap.nn.tracker.components.cull_instances(frames: List[LabeledFrame], instance_count: int, iou_threshold: Optional[float] = None)[source]#
    +sleap.nn.tracker.components.cull_instances(frames: List[LabeledFrame], instance_count: int, iou_threshold: Optional[float] = None)[source]#

    Removes instances from frames over instance per frame threshold.

    Parameters:
    @@ -504,7 +505,7 @@

    Contents

    -sleap.nn.tracker.components.factory_object_keypoint_similarity(keypoint_errors: Optional[Union[List, int, float]] = None, score_weighting: bool = False, normalization_keypoints: str = 'all') → Callable[source]#
    +sleap.nn.tracker.components.factory_object_keypoint_similarity(keypoint_errors: Optional[Union[List, int, float]] = None, score_weighting: bool = False, normalization_keypoints: str = 'all') → Callable[source]#

    Factory for similarity function based on object keypoints.

    Parameters:
    @@ -533,41 +534,47 @@

    Contents

    -sleap.nn.tracker.components.first_choice_matching(cost_matrix: ndarray) → List[Tuple[int, int]][source]#
    +sleap.nn.tracker.components.first_choice_matching(cost_matrix: ndarray) → List[Tuple[int, int]][source]#

    Returns match indices where each row gets matched to best column.

    The means that multiple rows might be matched to the same column.

    -sleap.nn.tracker.components.greedy_matching(cost_matrix: ndarray) → List[Tuple[int, int]][source]#
    +sleap.nn.tracker.components.greedy_matching(cost_matrix: ndarray) → List[Tuple[int, int]][source]#

    Performs greedy bipartite matching.

    -sleap.nn.tracker.components.hungarian_matching(cost_matrix: ndarray) → List[Tuple[int, int]][source]#
    +sleap.nn.tracker.components.hungarian_matching(cost_matrix: ndarray) → List[Tuple[int, int]][source]#

    Wrapper for Hungarian matching algorithm in scipy.

    -sleap.nn.tracker.components.instance_iou(ref_instance: InstanceType, query_instance: InstanceType, cache: dict = {}) → float[source]#
    +sleap.nn.tracker.components.instance_iou(ref_instance: InstanceType, query_instance: InstanceType, cache: dict = {}) → float[source]#

    Computes IOU between bounding boxes of instances.

    -sleap.nn.tracker.components.instance_similarity(ref_instance: InstanceType, query_instance: InstanceType) → float[source]#
    +sleap.nn.tracker.components.instance_similarity(ref_instance: InstanceType, query_instance: InstanceType) → float[source]#

    Computes similarity between instances.

    -sleap.nn.tracker.components.nms_fast(boxes, scores, iou_threshold, target_count=None) → List[int][source]#
    +sleap.nn.tracker.components.nms_fast(boxes, scores, iou_threshold, target_count=None) → List[int][source]#

    https://www.pyimagesearch.com/2015/02/16/faster-non-maximum-suppression-python/

    +
    +
    +sleap.nn.tracker.components.normalized_instance_similarity(ref_instance: InstanceType, query_instance: InstanceType, img_hw: Tuple[int]) → float[source]#
    +

    Computes similarity between instances with normalized keypoints.

    +
    + @@ -620,6 +627,7 @@

    Contents

  • instance_iou()
  • instance_similarity()
  • nms_fast()
    • +
  • normalized_instance_similarity()
    • diff --git a/develop/api/sleap.nn.tracking.html b/develop/api/sleap.nn.tracking.html index 4b02fead7..1f6a942dd 100644 --- a/develop/api/sleap.nn.tracking.html +++ b/develop/api/sleap.nn.tracking.html @@ -383,13 +383,13 @@

    Contents

    Tracking tools for linking grouped instances over time.

    -class sleap.nn.tracking.BaseTracker[source]#
    +class sleap.nn.tracking.BaseTracker[source]#

    Abstract base class for tracker.

    -class sleap.nn.tracking.FlowCandidateMaker(min_points: int = 0, img_scale: float = 1.0, of_window_size: int = 21, of_max_levels: int = 3, save_shifted_instances: bool = False, track_window: int = 5, shifted_instances: Dict[Tuple[int, int], List[ShiftedInstance]] = _Nothing.NOTHING)[source]#
    +class sleap.nn.tracking.FlowCandidateMaker(min_points: int = 0, img_scale: float = 1.0, of_window_size: int = 21, of_max_levels: int = 3, save_shifted_instances: bool = False, track_window: int = 5, shifted_instances: Dict[Tuple[int, int], List[ShiftedInstance]] = _Nothing.NOTHING)[source]#

    Class for producing optical flow shift matching candidates.

    @@ -466,7 +466,7 @@

    Contents

    -static flow_shift_instances(ref_instances: List[InstanceType], ref_img: ndarray, new_img: ndarray, min_shifted_points: int = 0, scale: float = 1.0, window_size: int = 21, max_levels: int = 3) → List[ShiftedInstance][source]#
    +static flow_shift_instances(ref_instances: List[InstanceType], ref_img: ndarray, new_img: ndarray, min_shifted_points: int = 0, scale: float = 1.0, window_size: int = 21, max_levels: int = 3) → List[ShiftedInstance][source]#

    Generates instances in a new frame by applying optical flow displacements.

    Parameters:
    @@ -499,7 +499,7 @@

    Contents

    -get_shifted_instances(ref_instances: List[InstanceType], ref_img: ndarray, ref_t: int, img: ndarray, t: int) → List[ShiftedInstance][source]#
    +get_shifted_instances(ref_instances: List[InstanceType], ref_img: ndarray, ref_t: int, img: ndarray, t: int) → List[ShiftedInstance][source]#

    Returns a list of shifted instances and save shifted instances if needed.

    Parameters:
    @@ -516,7 +516,7 @@

    Contents

    -get_shifted_instances_from_earlier_time(ref_t: int, ref_img: ~numpy.ndarray, ref_instances: ~typing.List[~sleap.nn.tracker.components.InstanceType], t: int) -> (<class 'numpy.ndarray'>, typing.List[~InstanceType])[source]#
    +get_shifted_instances_from_earlier_time(ref_t: int, ref_img: ~numpy.ndarray, ref_instances: ~typing.List[~sleap.nn.tracker.components.InstanceType], t: int) -> (<class 'numpy.ndarray'>, typing.List[~InstanceType])[source]#

    Generate shifted instances and corresponding image from earlier time.

    Parameters:
    @@ -532,7 +532,7 @@

    Contents

    -prune_shifted_instances(t: int)[source]#
    +prune_shifted_instances(t: int)[source]#

    Prune the shifted instances older than self.track_window.

    If self.save_shifted_instances is False, do nothing.

    @@ -548,17 +548,17 @@

    Contents

    -class sleap.nn.tracking.FlowMaxTracker(max_tracks: ~typing.Optional[int] = None, track_window: int = 5, similarity_function: ~typing.Optional[~typing.Callable] = <function instance_similarity>, matching_function: ~typing.Callable = <function greedy_matching>, candidate_maker: object = _Nothing.NOTHING, max_tracking: bool = False, cleaner: ~typing.Optional[~typing.Callable] = None, target_instance_count: int = 0, pre_cull_function: ~typing.Optional[~typing.Callable] = None, post_connect_single_breaks: bool = False, robust_best_instance: float = 1.0, min_new_track_points: int = 0, track_matching_queue: ~typing.Deque[~sleap.nn.tracking.MatchedFrameInstances] = _Nothing.NOTHING, track_matching_queue_dict: ~typing.Dict[~sleap.instance.Track, ~typing.Deque[~sleap.nn.tracking.MatchedFrameInstance]] = _Nothing.NOTHING, spawned_tracks: ~typing.List[~sleap.instance.Track] = _Nothing.NOTHING, save_tracked_instances: bool = False, tracked_instances: ~typing.Dict[int, ~typing.List[~sleap.nn.tracker.components.InstanceType]] = _Nothing.NOTHING, last_matches: ~typing.Optional[~sleap.nn.tracker.components.FrameMatches] = None)[source]#
    +class sleap.nn.tracking.FlowMaxTracker(max_tracks: ~typing.Optional[int] = None, track_window: int = 5, similarity_function: ~typing.Optional[~typing.Callable] = <function instance_similarity>, matching_function: ~typing.Callable = <function greedy_matching>, candidate_maker: object = _Nothing.NOTHING, max_tracking: bool = False, cleaner: ~typing.Optional[~typing.Callable] = None, target_instance_count: int = 0, pre_cull_function: ~typing.Optional[~typing.Callable] = None, post_connect_single_breaks: bool = False, robust_best_instance: float = 1.0, min_new_track_points: int = 0, track_matching_queue: ~typing.Deque[~sleap.nn.tracking.MatchedFrameInstances] = _Nothing.NOTHING, track_matching_queue_dict: ~typing.Dict[~sleap.instance.Track, ~typing.Deque[~sleap.nn.tracking.MatchedFrameInstance]] = _Nothing.NOTHING, spawned_tracks: ~typing.List[~sleap.instance.Track] = _Nothing.NOTHING, save_tracked_instances: bool = False, tracked_instances: ~typing.Dict[int, ~typing.List[~sleap.nn.tracker.components.InstanceType]] = _Nothing.NOTHING, last_matches: ~typing.Optional[~sleap.nn.tracker.components.FrameMatches] = None)[source]#

    Pre-configured tracker to use optical flow shifted candidates with max tracks.

    -matching_function() → List[Tuple[int, int]][source]#
    +matching_function() → List[Tuple[int, int]][source]#

    Performs greedy bipartite matching.

    -similarity_function(query_instance: InstanceType) → float[source]#
    +similarity_function(query_instance: InstanceType) → float[source]#

    Computes similarity between instances.

    @@ -566,7 +566,7 @@

    Contents

    -class sleap.nn.tracking.FlowMaxTracksCandidateMaker(min_points: int = 0, img_scale: float = 1.0, of_window_size: int = 21, of_max_levels: int = 3, save_shifted_instances: bool = False, track_window: int = 5, shifted_instances: Dict[Tuple[int, int], List[ShiftedInstance]] = _Nothing.NOTHING, max_tracks: Optional[int] = None)[source]#
    +class sleap.nn.tracking.FlowMaxTracksCandidateMaker(min_points: int = 0, img_scale: float = 1.0, of_window_size: int = 21, of_max_levels: int = 3, save_shifted_instances: bool = False, track_window: int = 5, shifted_instances: Dict[Tuple[int, int], List[ShiftedInstance]] = _Nothing.NOTHING, max_tracks: Optional[int] = None)[source]#

    Class for producing optical flow shift matching candidates with maximum tracks.

    @@ -581,7 +581,7 @@

    Contents

    -static get_ref_instances(ref_t: int, ref_img: ndarray, track_matching_queue_dict: Dict[Track, Deque[MatchedFrameInstance]]) → List[InstanceType][source]#
    +static get_ref_instances(ref_t: int, ref_img: ndarray, track_matching_queue_dict: Dict[Track, Deque[MatchedFrameInstance]]) → List[InstanceType][source]#

    Generates a list of instances based on the reference time and image.

    Parameters:
    @@ -599,13 +599,13 @@

    Contents

    -class sleap.nn.tracking.FlowTracker(max_tracks: ~typing.Optional[int] = None, track_window: int = 5, max_tracking: bool = False, cleaner: ~typing.Optional[~typing.Callable] = None, target_instance_count: int = 0, pre_cull_function: ~typing.Optional[~typing.Callable] = None, post_connect_single_breaks: bool = False, robust_best_instance: float = 1.0, min_new_track_points: int = 0, track_matching_queue: ~typing.Deque[~sleap.nn.tracking.MatchedFrameInstances] = _Nothing.NOTHING, track_matching_queue_dict: ~typing.Dict[~sleap.instance.Track, ~typing.Deque[~sleap.nn.tracking.MatchedFrameInstance]] = _Nothing.NOTHING, spawned_tracks: ~typing.List[~sleap.instance.Track] = _Nothing.NOTHING, save_tracked_instances: bool = False, tracked_instances: ~typing.Dict[int, ~typing.List[~sleap.nn.tracker.components.InstanceType]] = _Nothing.NOTHING, last_matches: ~typing.Optional[~sleap.nn.tracker.components.FrameMatches] = None, similarity_function: ~typing.Callable = <function instance_similarity>, matching_function: ~typing.Callable = <function greedy_matching>, candidate_maker: object = _Nothing.NOTHING)[source]#
    +class sleap.nn.tracking.FlowTracker(max_tracks: ~typing.Optional[int] = None, track_window: int = 5, max_tracking: bool = False, cleaner: ~typing.Optional[~typing.Callable] = None, target_instance_count: int = 0, pre_cull_function: ~typing.Optional[~typing.Callable] = None, post_connect_single_breaks: bool = False, robust_best_instance: float = 1.0, min_new_track_points: int = 0, track_matching_queue: ~typing.Deque[~sleap.nn.tracking.MatchedFrameInstances] = _Nothing.NOTHING, track_matching_queue_dict: ~typing.Dict[~sleap.instance.Track, ~typing.Deque[~sleap.nn.tracking.MatchedFrameInstance]] = _Nothing.NOTHING, spawned_tracks: ~typing.List[~sleap.instance.Track] = _Nothing.NOTHING, save_tracked_instances: bool = False, tracked_instances: ~typing.Dict[int, ~typing.List[~sleap.nn.tracker.components.InstanceType]] = _Nothing.NOTHING, last_matches: ~typing.Optional[~sleap.nn.tracker.components.FrameMatches] = None, similarity_function: ~typing.Callable = <function instance_similarity>, matching_function: ~typing.Callable = <function greedy_matching>, candidate_maker: object = _Nothing.NOTHING)[source]#

    A Tracker pre-configured to use optical flow shifted candidates.

    -class sleap.nn.tracking.KalmanTracker(init_tracker: Optional[Tracker], init_set: KalmanInitSet, kalman_tracker: BareKalmanTracker, cull_function: Optional[Callable] = None, init_frame_count: int = 10, re_init_cooldown: int = 100, re_init_after: int = 20, init_done: bool = False, pre_tracked: bool = False, last_t: int = 0, last_init_t: int = 0)[source]#
    +class sleap.nn.tracking.KalmanTracker(init_tracker: Optional[Tracker], init_set: KalmanInitSet, kalman_tracker: BareKalmanTracker, cull_function: Optional[Callable] = None, init_frame_count: int = 10, re_init_cooldown: int = 100, re_init_after: int = 20, init_done: bool = False, pre_tracked: bool = False, last_t: int = 0, last_init_t: int = 0)[source]#

    Class for Kalman filter-based tracking pipeline.

    Kalman filters need to be initialized with a certain number of already tracked instances.

    @@ -646,7 +646,7 @@

    Contents

    -classmethod make_tracker(init_tracker: Optional[Tracker], node_indices: List[int], instance_count: int, instance_iou_threshold: float = 0.8, init_frame_count: int = 10)[source]#
    +classmethod make_tracker(init_tracker: Optional[Tracker], node_indices: List[int], instance_count: int, instance_iou_threshold: float = 0.8, init_frame_count: int = 10)[source]#

    Creates KalmanTracker object.

    Parameters:
    @@ -671,7 +671,7 @@

    Contents

    -track(untracked_instances: List[InstanceType], img: Optional[ndarray] = None, t: Optional[int] = None) → List[InstanceType][source]#
    +track(untracked_instances: List[InstanceType], img: Optional[ndarray] = None, t: Optional[int] = None) → List[InstanceType][source]#

    Tracks individual frame, using Kalman filters if possible.

    @@ -679,31 +679,31 @@

    Contents

    -class sleap.nn.tracking.SimpleCandidateMaker(min_points: int = 0)[source]#
    +class sleap.nn.tracking.SimpleCandidateMaker(min_points: int = 0)[source]#

    Class for producing list of matching candidates from prior frames.

    -class sleap.nn.tracking.SimpleMaxTracker(track_window: int = 5, cleaner: ~typing.Optional[~typing.Callable] = None, target_instance_count: int = 0, pre_cull_function: ~typing.Optional[~typing.Callable] = None, post_connect_single_breaks: bool = False, robust_best_instance: float = 1.0, min_new_track_points: int = 0, track_matching_queue: ~typing.Deque[~sleap.nn.tracking.MatchedFrameInstances] = _Nothing.NOTHING, track_matching_queue_dict: ~typing.Dict[~sleap.instance.Track, ~typing.Deque[~sleap.nn.tracking.MatchedFrameInstance]] = _Nothing.NOTHING, spawned_tracks: ~typing.List[~sleap.instance.Track] = _Nothing.NOTHING, save_tracked_instances: bool = False, tracked_instances: ~typing.Dict[int, ~typing.List[~sleap.nn.tracker.components.InstanceType]] = _Nothing.NOTHING, last_matches: ~typing.Optional[~sleap.nn.tracker.components.FrameMatches] = None, similarity_function: ~typing.Callable = <function instance_iou>, matching_function: ~typing.Callable = <function hungarian_matching>, candidate_maker: object = _Nothing.NOTHING, max_tracking: bool = True, *, max_tracks: int)[source]#
    +class sleap.nn.tracking.SimpleMaxTracker(track_window: int = 5, cleaner: ~typing.Optional[~typing.Callable] = None, target_instance_count: int = 0, pre_cull_function: ~typing.Optional[~typing.Callable] = None, post_connect_single_breaks: bool = False, robust_best_instance: float = 1.0, min_new_track_points: int = 0, track_matching_queue: ~typing.Deque[~sleap.nn.tracking.MatchedFrameInstances] = _Nothing.NOTHING, track_matching_queue_dict: ~typing.Dict[~sleap.instance.Track, ~typing.Deque[~sleap.nn.tracking.MatchedFrameInstance]] = _Nothing.NOTHING, spawned_tracks: ~typing.List[~sleap.instance.Track] = _Nothing.NOTHING, save_tracked_instances: bool = False, tracked_instances: ~typing.Dict[int, ~typing.List[~sleap.nn.tracker.components.InstanceType]] = _Nothing.NOTHING, last_matches: ~typing.Optional[~sleap.nn.tracker.components.FrameMatches] = None, similarity_function: ~typing.Callable = <function instance_iou>, matching_function: ~typing.Callable = <function hungarian_matching>, candidate_maker: object = _Nothing.NOTHING, max_tracking: bool = True, *, max_tracks: int)[source]#

    Pre-configured tracker to use simple, non-image-based candidates with max tracks.

    -class sleap.nn.tracking.SimpleMaxTracksCandidateMaker(min_points: int = 0, max_tracks: Optional[int] = None)[source]#
    +class sleap.nn.tracking.SimpleMaxTracksCandidateMaker(min_points: int = 0, max_tracks: Optional[int] = None)[source]#

    Class to generate instances with maximum number of tracks from prior frames.

    -class sleap.nn.tracking.SimpleTracker(max_tracks: ~typing.Optional[int] = None, track_window: int = 5, max_tracking: bool = False, cleaner: ~typing.Optional[~typing.Callable] = None, target_instance_count: int = 0, pre_cull_function: ~typing.Optional[~typing.Callable] = None, post_connect_single_breaks: bool = False, robust_best_instance: float = 1.0, min_new_track_points: int = 0, track_matching_queue: ~typing.Deque[~sleap.nn.tracking.MatchedFrameInstances] = _Nothing.NOTHING, track_matching_queue_dict: ~typing.Dict[~sleap.instance.Track, ~typing.Deque[~sleap.nn.tracking.MatchedFrameInstance]] = _Nothing.NOTHING, spawned_tracks: ~typing.List[~sleap.instance.Track] = _Nothing.NOTHING, save_tracked_instances: bool = False, tracked_instances: ~typing.Dict[int, ~typing.List[~sleap.nn.tracker.components.InstanceType]] = _Nothing.NOTHING, last_matches: ~typing.Optional[~sleap.nn.tracker.components.FrameMatches] = None, similarity_function: ~typing.Callable = <function instance_iou>, matching_function: ~typing.Callable = <function hungarian_matching>, candidate_maker: object = _Nothing.NOTHING)[source]#
    +class sleap.nn.tracking.SimpleTracker(max_tracks: ~typing.Optional[int] = None, track_window: int = 5, max_tracking: bool = False, cleaner: ~typing.Optional[~typing.Callable] = None, target_instance_count: int = 0, pre_cull_function: ~typing.Optional[~typing.Callable] = None, post_connect_single_breaks: bool = False, robust_best_instance: float = 1.0, min_new_track_points: int = 0, track_matching_queue: ~typing.Deque[~sleap.nn.tracking.MatchedFrameInstances] = _Nothing.NOTHING, track_matching_queue_dict: ~typing.Dict[~sleap.instance.Track, ~typing.Deque[~sleap.nn.tracking.MatchedFrameInstance]] = _Nothing.NOTHING, spawned_tracks: ~typing.List[~sleap.instance.Track] = _Nothing.NOTHING, save_tracked_instances: bool = False, tracked_instances: ~typing.Dict[int, ~typing.List[~sleap.nn.tracker.components.InstanceType]] = _Nothing.NOTHING, last_matches: ~typing.Optional[~sleap.nn.tracker.components.FrameMatches] = None, similarity_function: ~typing.Callable = <function instance_iou>, matching_function: ~typing.Callable = <function hungarian_matching>, candidate_maker: object = _Nothing.NOTHING)[source]#

    A Tracker pre-configured to use simple, non-image-based candidates.

    -class sleap.nn.tracking.TrackCleaner(instance_count: int, iou_threshold: Optional[float] = None)[source]#
    +class sleap.nn.tracking.TrackCleaner(instance_count: int, iou_threshold: Optional[float] = None)[source]#

    Class for merging breaks in the predicted tracks.

    Method: 1. You specify how many instances there should be in each frame. @@ -742,7 +742,7 @@

    Contents

    -class sleap.nn.tracking.Tracker(max_tracks: ~typing.Optional[int] = None, track_window: int = 5, similarity_function: ~typing.Optional[~typing.Callable] = <function instance_similarity>, matching_function: ~typing.Callable = <function greedy_matching>, candidate_maker: object = _Nothing.NOTHING, max_tracking: bool = False, cleaner: ~typing.Optional[~typing.Callable] = None, target_instance_count: int = 0, pre_cull_function: ~typing.Optional[~typing.Callable] = None, post_connect_single_breaks: bool = False, robust_best_instance: float = 1.0, min_new_track_points: int = 0, track_matching_queue: ~typing.Deque[~sleap.nn.tracking.MatchedFrameInstances] = _Nothing.NOTHING, track_matching_queue_dict: ~typing.Dict[~sleap.instance.Track, ~typing.Deque[~sleap.nn.tracking.MatchedFrameInstance]] = _Nothing.NOTHING, spawned_tracks: ~typing.List[~sleap.instance.Track] = _Nothing.NOTHING, save_tracked_instances: bool = False, tracked_instances: ~typing.Dict[int, ~typing.List[~sleap.nn.tracker.components.InstanceType]] = _Nothing.NOTHING, last_matches: ~typing.Optional[~sleap.nn.tracker.components.FrameMatches] = None)[source]#
    +class sleap.nn.tracking.Tracker(max_tracks: ~typing.Optional[int] = None, track_window: int = 5, similarity_function: ~typing.Optional[~typing.Callable] = <function instance_similarity>, matching_function: ~typing.Callable = <function greedy_matching>, candidate_maker: object = _Nothing.NOTHING, max_tracking: bool = False, cleaner: ~typing.Optional[~typing.Callable] = None, target_instance_count: int = 0, pre_cull_function: ~typing.Optional[~typing.Callable] = None, post_connect_single_breaks: bool = False, robust_best_instance: float = 1.0, min_new_track_points: int = 0, track_matching_queue: ~typing.Deque[~sleap.nn.tracking.MatchedFrameInstances] = _Nothing.NOTHING, track_matching_queue_dict: ~typing.Dict[~sleap.instance.Track, ~typing.Deque[~sleap.nn.tracking.MatchedFrameInstance]] = _Nothing.NOTHING, spawned_tracks: ~typing.List[~sleap.instance.Track] = _Nothing.NOTHING, save_tracked_instances: bool = False, tracked_instances: ~typing.Dict[int, ~typing.List[~sleap.nn.tracker.components.InstanceType]] = _Nothing.NOTHING, last_matches: ~typing.Optional[~sleap.nn.tracker.components.FrameMatches] = None)[source]#

    Instance pose tracker.

    Use by instantiated with the desired parameters and then calling the track method for each frame.

    @@ -846,18 +846,19 @@

    Contents

    -final_pass(frames: List[LabeledFrame])[source]#
    +final_pass(frames: List[LabeledFrame])[source]#

    Called after tracking has run on all frames to do any post-processing.

    -track(untracked_instances: List[InstanceType], img: Optional[ndarray] = None, t: Optional[int] = None) → List[InstanceType][source]#
    +track(untracked_instances: List[InstanceType], img_hw: Tuple[int], img: Optional[ndarray] = None, t: Optional[int] = None) → List[InstanceType][source]#

    Performs a single step of tracking.

    Parameters:

    • untracked_instances – List of instances to assign to tracks.

      • +

    • img_hw – (height, width) of the image used to normalize the keypoints.

    • img – Image data of the current frame for flow shifting.

    • t – Current timestep. If not provided, increments from the internal queue.

    @@ -878,7 +879,7 @@

    Contents

    -sleap.nn.tracking.run_tracker(frames: List[LabeledFrame], tracker: BaseTracker) → List[LabeledFrame][source]#
    +sleap.nn.tracking.run_tracker(frames: List[LabeledFrame], tracker: BaseTracker) → List[LabeledFrame][source]#

    Run a tracker on a set of labeled frames.

    Parameters:
    diff --git a/develop/genindex.html b/develop/genindex.html index 1ef6879cd..03b9fef90 100644 --- a/develop/genindex.html +++ b/develop/genindex.html @@ -3351,6 +3351,8 @@

    N

  • nodes_points (sleap.instance.Instance property)
  • normalize_bboxes() (in module sleap.nn.data.instance_cropping) +
    • +
  • normalized_instance_similarity() (in module sleap.nn.tracker.components)
  • Normalizer (class in sleap.nn.data.normalization)
    • diff --git a/develop/guides/cli.html b/develop/guides/cli.html index 2a9f71be9..5af6b70b8 100644 --- a/develop/guides/cli.html +++ b/develop/guides/cli.html @@ -548,7 +548,7 @@

    Inference and Tracking Contents

  • “centroid” measures similarity by the distance between the instance centroids

  • “iou” measures similarity by the intersection/overlap of the instance bounding boxes

  • “instance” measures similarity by looking at the distances between corresponding nodes in the instances, normalized by the number of valid nodes in the candidate instance.

    • +

  • “normalized_instance” measures similarity by looking at the distances between corresponding nodes in the instances, normalized by the number of valid nodes in the candidate instance and the keypoints normalized by the image size.

    • +

  • “object_keypoint” measures similarity by measuring the distance between each keypoints from a reference instance and a query instance, takes the exp(-d**2), sum for all the keypoints and divide by the number of visible keypoints in the reference instance.

    • Once SLEAP has measured the similarity between all the candidates and the instances in frame N, you need to choose a way to pair them up. You can do this either by picking the best match, and the picking the best remaining match for each remaining instance in turn—this is “greedy” matching—or you can find the way of matching identities which minimizes the total cost (or: maximizes the total similarity)—this is “Hungarian” matching.

    Finally, you have an optional second-pass method which “cleans” the resulting diff --git a/develop/objects.inv b/develop/objects.inv index 106889229f45443472f0737dd11bc08a2744f6c1..aa8af5c502d58c2e63ac2e2bdf03c6fb76110888 100644 GIT binary patch delta 2271 zcmV<52q5>>p8?#T0kDlTe;ow7tgp<;ibCe*ANg2W~)!U)WMii zE|ck4*nA%i9L&N7(%(HgAXAOVe#odn*fggR8M`$RU)CKtCVqU{sWl;Cr~@MLecYf= z!`|%W4p2S9?O(mm%cr-(54w%v;SMMKC~7X*skL^q+)0QsQjV$5e-WOADG7?}JVSNV zW>*Bxn_bTqTRm5TlQnIN_1l%`H;pcNHsb^YUL6YX~q^u zO>Jo)^yP1~s|80Qe=}f@*fC3uBXTEHI`1u=r&?c4V_f>!5AZ%Ci>QD)%K(AQ!WgUp zPbNefDaKp;xBvti202OD8P0CHW0#%fu|#WVh^*5f%VJ*c8k^1@I^ho01A=$T3VMXM z@wB?y{thIiz3n!I)p7#Gj$?tI_#hC-g%sd*dF=48i!Pywe^vknzR>l9U;|j=5fqf0 zCP2F)`+LXxA+pdD;a?r{?dh1uuL+FJa*MQCCp>K0FD0$t$6Q)Pb5QKy9dr0PCf{pp^&B1FIDs zFl3!*dEjb9e@g>cAMfHe{)plf)s0v2OE`8%Pga5E@lhLJ#TB%r`(b)epn{ZP@mM%c z%^sc`JbxG>tm4yv^Ex5AnO96f82SpNc57Um|g`)6r1koD!=}_ z2QvqxbAiLXM$9{l1`HJ8{;1PNx^O`Q^Q#8!rkg4w;GzUdPmDxARh45acRep+0we1n#K@gW 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{"Contributor Covenant Code of Conduct": [[0, "contributor-covenant-code-of-conduct"]], "Our Pledge": [[0, "our-pledge"]], "Our Standards": [[0, "our-standards"]], "Our Responsibilities": [[0, "our-responsibilities"]], "Scope": [[0, "scope"]], "Enforcement": [[0, "enforcement"]], "Attribution": [[0, "attribution"]], "Contributing to SLEAP": [[1, "contributing-to-sleap"]], "Table Of Contents": [[1, "table-of-contents"]], "Code of Conduct": [[1, "code-of-conduct"]], "Contributing": [[1, "contributing"]], "Issues": [[1, "issues"]], "Discussions": [[1, "discussions"]], "Pull Requests": [[1, "pull-requests"]], "Style Guides": [[1, "style-guides"]], "Thank you for contributing to SLEAP!": [[1, "thank-you-for-contributing-to-sleap"]], "Developer API": [[2, "developer-api"]], "sleap.info.align": [[3, "module-sleap.info.align"]], "sleap.info.feature_suggestions": [[4, "module-sleap.info.feature_suggestions"]], "sleap.info.labels": [[5, "module-sleap.info.labels"]], "sleap.info.metrics": [[6, 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files": [[88, "dataset-files"]], "sleap-convert": [[88, "sleap-convert"]], "sleap-inspect": [[88, "sleap-inspect"]], "Rendering": [[88, "rendering"]], "sleap-render": [[88, "sleap-render"]], "Debugging": [[88, "debugging"]], "sleap-diagnostic": [[88, "sleap-diagnostic"]], "Run training and inference on Colab": [[89, "run-training-and-inference-on-colab"]], "About Colab": [[89, "about-colab"]], "Moving Data to/from Colab": [[89, "moving-data-to-from-colab"]], "Creating a custom training profile": [[90, "creating-a-custom-training-profile"]], "Menus": [[91, "menus"]], "File": [[91, "file"]], "Go": [[91, "go"]], "View": [[91, "view"]], "Labels": [[91, "labels"]], "Predict": [[91, "predict"]], "Help": [[91, "help"], [99, "help"]], "Application GUI": [[91, "application-gui"]], "Mouse": [[91, "mouse"]], "Navigation Keys": [[91, "navigation-keys"]], "Selection Keys": [[91, "selection-keys"]], "Seekbar": [[91, "seekbar"]], "Labeling Suggestions": [[91, "labeling-suggestions"]], "Guides": [[92, "guides"]], "General": [[92, "general"]], "Getting better results": [[92, "getting-better-results"]], "Running remotely": [[92, "running-remotely"]], "Importing predictions for labeling": [[93, "importing-predictions-for-labeling"]], "Tracking and proofreading": [[94, "tracking-and-proofreading"]], "Tracking methods": [[94, "tracking-methods"]], "More training data?": [[94, "more-training-data"]], "The \u201ctrack cleaning\u201d script": [[94, "the-track-cleaning-script"]], "Color palettes": [[94, "color-palettes"]], "Proofreading": [[94, "id1"]], "Orientation": [[94, "orientation"]], "Running SLEAP remotely": [[95, "running-sleap-remotely"]], "Remote training": [[95, "remote-training"]], "Remote inference": [[95, "remote-inference"]], "Skeleton design": [[96, "skeleton-design"]], "Tips": [[96, "tips"]], "Training with GUI": [[97, "training-with-gui"]], "Troubleshooting workflows": [[98, "troubleshooting-workflows"]], "Installation": [[99, "installation"], [101, "installation"]], "I can\u2019t get SLEAP to install!": [[99, "i-cant-get-sleap-to-install"]], "Can I install it on a computer without a GPU?": [[99, "can-i-install-it-on-a-computer-without-a-gpu"]], "What if I already have CUDA set up on my system?": [[99, "what-if-i-already-have-cuda-set-up-on-my-system"]], "Usage": [[99, "usage"]], "How do I use SLEAP?": [[99, "how-do-i-use-sleap"]], "Does my data need to be in a particular format?": [[99, "does-my-data-need-to-be-in-a-particular-format"]], "I get strange results where the poses appear to be correct but shifted relative to the image.": [[99, "i-get-strange-results-where-the-poses-appear-to-be-correct-but-shifted-relative-to-the-image"]], "How do I get predictions out?": [[99, "how-do-i-get-predictions-out"]], "What do I do with the output of SLEAP?": [[99, "what-do-i-do-with-the-output-of-sleap"]], "Getting more help": [[99, "getting-more-help"]], "I\u2019ve found a bug or have another problem!": [[99, "ive-found-a-bug-or-have-another-problem"]], "Can I just talk to someone?": [[99, "can-i-just-talk-to-someone"]], "Improving SLEAP": [[99, "improving-sleap"]], "How can I help improve SLEAP?": [[99, "how-can-i-help-improve-sleap"]], "What is usage data?": [[99, "what-is-usage-data"]], "Social LEAP Estimates Animal Poses (SLEAP)": [[100, "social-leap-estimates-animal-poses-sleap"]], "Features": [[100, "features"]], "Get some SLEAP": [[100, "get-some-sleap"]], "Quick install": [[100, "quick-install"]], "Learn to SLEAP": [[100, "learn-to-sleap"]], "References": [[100, "references"]], "Contact": [[100, "contact"]], "Contributors": [[100, "contributors"]], "License": [[100, "license"]], "SLEAP Documentation": [[100, "sleap-documentation"]], "Contents": [[101, "contents"]], "Package Manager": [[101, "package-manager"]], "Miniforge (recommended)": [[101, "miniforge-recommended"]], "Miniconda": [[101, "miniconda"]], "Installation methods": [[101, "installation-methods"]], "Testing that things are working": [[101, "testing-that-things-are-working"]], "GUI support": [[101, "gui-support"]], "Importing": [[101, "importing"]], "GPU support": [[101, "gpu-support"]], "Upgrading and uninstalling": [[101, "upgrading-and-uninstalling"]], "Getting help": [[101, "getting-help"]], "Analysis examples": [[102, "analysis-examples"], [111, "id4"]], "Example analysis data": [[102, "example-analysis-data"]], "Loading the data": [[102, "loading-the-data"]], "Fill missing values": [[102, "fill-missing-values"]], "Visualize thorax movement across video": [[102, "visualize-thorax-movement-across-video"]], "More advanced visualizations": [[102, "more-advanced-visualizations"]], "Visualizing thorax x-y dynamics and velocity for fly 0": [[102, "visualizing-thorax-x-y-dynamics-and-velocity-for-fly-0"]], "Visualize thorax colored by magnitude of fly speed": [[102, "visualize-thorax-colored-by-magnitude-of-fly-speed"]], "Find covariance in thorax velocities between fly-0 and fly-1": [[102, "find-covariance-in-thorax-velocities-between-fly-0-and-fly-1"]], "Clustering": [[102, "clustering"]], "Data structures": [[103, "data-structures"], [111, "id5"]], "1. Setup SLEAP and data": [[103, "setup-sleap-and-data"]], "2. Data structures and inference": [[103, "data-structures-and-inference"]], "Interactive and realtime inference": [[104, "interactive-and-realtime-inference"], [111, "id8"]], "1. Setup SLEAP": [[104, "setup-sleap"], [105, "setup-sleap"], [107, "setup-sleap"]], "2. Setup data": [[104, "setup-data"], [107, "setup-data"]], "3. Interactive inference": [[104, "interactive-inference"]], "4. Realtime performance": [[104, "realtime-performance"]], "Interactive and resumable training": [[105, "interactive-and-resumable-training"], [111, "id7"]], "2. Setup training data": [[105, "setup-training-data"]], "3. Setup training job": [[105, "setup-training-job"]], "4. Training": [[105, "training"]], "5. Continuing training": [[105, "continuing-training"]], "Model evaluation": [[106, "model-evaluation"], [111, "id9"]], "Post-inference tracking": [[107, "post-inference-tracking"], [111, "id6"]], "Install": [[107, "install"]], "Test": [[107, "test"]], "3. Track": [[107, "track"]], "4. Inspect and save": [[107, "inspect-and-save"]], "Training and inference on an example dataset": [[108, "training-and-inference-on-an-example-dataset"], [111, "id2"]], "Install SLEAP": [[108, "install-sleap"], [109, "install-sleap"]], "Download sample training data into Colab": [[108, "download-sample-training-data-into-colab"]], "Train models": [[108, "train-models"]], "Inference": [[108, "inference"], [116, "inference"]], "Training and inference on your own data using Google Drive": [[109, "training-and-inference-on-your-own-data-using-google-drive"], [111, "id3"]], "Import training data into Colab with Google Drive": [[109, "import-training-data-into-colab-with-google-drive"]], "Create and export the training job package": [[109, "create-and-export-the-training-job-package"]], "Upload training job package to Google Drive": [[109, "upload-training-job-package-to-google-drive"]], "Mount your Google Drive": [[109, "mount-your-google-drive"]], "Train a model": [[109, "train-a-model"]], "Note on training profiles": [[109, "note-on-training-profiles"]], "Note on training process": [[109, "note-on-training-process"]], "Run inference to predict instances": [[109, "run-inference-to-predict-instances"]], "Predicting instances in suggested frames": [[109, "predicting-instances-in-suggested-frames"]], "Predicting and tracking instances in uploaded video": [[109, "predicting-and-tracking-instances-in-uploaded-video"]], "Inference with top-down models": [[109, "inference-with-top-down-models"]], "Notebooks": [[111, "notebooks"]], "Basic usage": [[111, "basic-usage"]], "Advanced SLEAPing": [[111, "advanced-sleaping"]], "Overview": [[112, "overview"]], "Export Data For Analysis": [[113, "export-data-for-analysis"]], "MATLAB": [[113, "matlab"]], "Python": [[113, "python"]], "Prediction-assisted labeling": [[114, "prediction-assisted-labeling"]], "Reviewing and fixing predictions": [[114, "reviewing-and-fixing-predictions"]], "Initial Labeling": [[115, "initial-labeling"]], "Selecting frames to label": [[115, "selecting-frames-to-label"]], "Labeling the first frame": [[115, "labeling-the-first-frame"]], "Saving": [[115, "saving"]], "Labeling more frames": [[115, "labeling-more-frames"]], "Training and Inference": [[116, "training-and-inference"]], "Training Options": [[116, "training-options"]], "Start Training": [[116, "start-training"]], "Creating a project": [[117, "creating-a-project"]], "Starting SLEAP": [[117, "starting-sleap"]], "Opening a video": [[117, "opening-a-video"]], "Creating a Skeleton": [[117, "creating-a-skeleton"]], "Tracking instances across frames": [[118, "tracking-instances-across-frames"]], "Track proofreading": [[118, "track-proofreading"]], "Tutorial": [[119, "tutorial"]]}, "indexentries": {"align_instance_points() (in module sleap.info.align)": [[3, "sleap.info.align.align_instance_points"]], "align_instances() (in module sleap.info.align)": [[3, "sleap.info.align.align_instances"]], "align_instances_on_most_stable() (in module 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"Troubleshooting workflows": [[98, "troubleshooting-workflows"]], "Installation": [[99, "installation"], [101, "installation"]], "I can\u2019t get SLEAP to install!": [[99, "i-cant-get-sleap-to-install"]], "Can I install it on a computer without a GPU?": [[99, "can-i-install-it-on-a-computer-without-a-gpu"]], "What if I already have CUDA set up on my system?": [[99, "what-if-i-already-have-cuda-set-up-on-my-system"]], "Usage": [[99, "usage"]], "How do I use SLEAP?": [[99, "how-do-i-use-sleap"]], "Does my data need to be in a particular format?": [[99, "does-my-data-need-to-be-in-a-particular-format"]], "I get strange results where the poses appear to be correct but shifted relative to the image.": [[99, "i-get-strange-results-where-the-poses-appear-to-be-correct-but-shifted-relative-to-the-image"]], "How do I get predictions out?": [[99, "how-do-i-get-predictions-out"]], "What do I do with the output of SLEAP?": [[99, "what-do-i-do-with-the-output-of-sleap"]], "Getting more help": 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"miniconda"]], "Installation methods": [[101, "installation-methods"]], "Testing that things are working": [[101, "testing-that-things-are-working"]], "GUI support": [[101, "gui-support"]], "Importing": [[101, "importing"]], "GPU support": [[101, "gpu-support"]], "Upgrading and uninstalling": [[101, "upgrading-and-uninstalling"]], "Getting help": [[101, "getting-help"]], "Analysis examples": [[102, "analysis-examples"], [111, "id4"]], "Example analysis data": [[102, "example-analysis-data"]], "Loading the data": [[102, "loading-the-data"]], "Fill missing values": [[102, "fill-missing-values"]], "Visualize thorax movement across video": [[102, "visualize-thorax-movement-across-video"]], "More advanced visualizations": [[102, "more-advanced-visualizations"]], "Visualizing thorax x-y dynamics and velocity for fly 0": [[102, "visualizing-thorax-x-y-dynamics-and-velocity-for-fly-0"]], "Visualize thorax colored by magnitude of fly speed": [[102, "visualize-thorax-colored-by-magnitude-of-fly-speed"]], "Find covariance in thorax velocities between fly-0 and fly-1": [[102, "find-covariance-in-thorax-velocities-between-fly-0-and-fly-1"]], "Clustering": [[102, "clustering"]], "Data structures": [[103, "data-structures"], [111, "id5"]], "1. Setup SLEAP and data": [[103, "setup-sleap-and-data"]], "2. Data structures and inference": [[103, "data-structures-and-inference"]], "Interactive and realtime inference": [[104, "interactive-and-realtime-inference"], [111, "id8"]], "1. Setup SLEAP": [[104, "setup-sleap"], [105, "setup-sleap"], [107, "setup-sleap"]], "2. Setup data": [[104, "setup-data"], [107, "setup-data"]], "3. Interactive inference": [[104, "interactive-inference"]], "4. Realtime performance": [[104, "realtime-performance"]], "Interactive and resumable training": [[105, "interactive-and-resumable-training"], [111, "id7"]], "2. Setup training data": [[105, "setup-training-data"]], "3. Setup training job": [[105, "setup-training-job"]], "4. Training": [[105, "training"]], "5. Continuing training": [[105, "continuing-training"]], "Model evaluation": [[106, "model-evaluation"], [111, "id9"]], "Post-inference tracking": [[107, "post-inference-tracking"], [111, "id6"]], "Install": [[107, "install"]], "Test": [[107, "test"]], "3. Track": [[107, "track"]], "4. Inspect and save": [[107, "inspect-and-save"]], "Training and inference on an example dataset": [[108, "training-and-inference-on-an-example-dataset"], [111, "id2"]], "Install SLEAP": [[108, "install-sleap"], [109, "install-sleap"]], "Download sample training data into Colab": [[108, "download-sample-training-data-into-colab"]], "Train models": [[108, "train-models"]], "Inference": [[108, "inference"], [116, "inference"]], "Training and inference on your own data using Google Drive": [[109, "training-and-inference-on-your-own-data-using-google-drive"], [111, "id3"]], "Import training data into Colab with Google Drive": [[109, "import-training-data-into-colab-with-google-drive"]], "Create and export the training job package": [[109, "create-and-export-the-training-job-package"]], "Upload training job package to Google Drive": [[109, "upload-training-job-package-to-google-drive"]], "Mount your Google Drive": [[109, "mount-your-google-drive"]], "Train a model": [[109, "train-a-model"]], "Note on training profiles": [[109, "note-on-training-profiles"]], "Note on training process": [[109, "note-on-training-process"]], "Run inference to predict instances": [[109, "run-inference-to-predict-instances"]], "Predicting instances in suggested frames": [[109, "predicting-instances-in-suggested-frames"]], "Predicting and tracking instances in uploaded video": [[109, "predicting-and-tracking-instances-in-uploaded-video"]], "Inference with top-down models": [[109, "inference-with-top-down-models"]], "Notebooks": [[111, "notebooks"]], "Basic usage": [[111, "basic-usage"]], "Advanced SLEAPing": [[111, "advanced-sleaping"]], "Overview": [[112, "overview"]], "Export Data For Analysis": [[113, "export-data-for-analysis"]], "MATLAB": [[113, "matlab"]], "Python": [[113, "python"]], "Prediction-assisted labeling": [[114, "prediction-assisted-labeling"]], "Reviewing and fixing predictions": [[114, "reviewing-and-fixing-predictions"]], "Initial Labeling": [[115, "initial-labeling"]], "Selecting frames to label": [[115, "selecting-frames-to-label"]], "Labeling the first frame": [[115, "labeling-the-first-frame"]], "Saving": [[115, "saving"]], "Labeling more frames": [[115, "labeling-more-frames"]], "Training and Inference": [[116, "training-and-inference"]], "Training Options": [[116, "training-options"]], "Start Training": [[116, "start-training"]], "Creating a project": [[117, "creating-a-project"]], "Starting SLEAP": [[117, "starting-sleap"]], "Opening a video": [[117, "opening-a-video"]], "Creating a Skeleton": [[117, "creating-a-skeleton"]], "Tracking instances across frames": [[118, "tracking-instances-across-frames"]], "Track proofreading": [[118, "track-proofreading"]], "Tutorial": [[119, "tutorial"]]}, "indexentries": {"align_instance_points() (in module sleap.info.align)": [[3, "sleap.info.align.align_instance_points"]], "align_instances() (in module sleap.info.align)": [[3, "sleap.info.align.align_instances"]], "align_instances_on_most_stable() (in module sleap.info.align)": [[3, "sleap.info.align.align_instances_on_most_stable"]], "get_instances_points() (in module sleap.info.align)": [[3, "sleap.info.align.get_instances_points"]], "get_mean_and_std_for_points() (in module sleap.info.align)": [[3, "sleap.info.align.get_mean_and_std_for_points"]], "get_most_stable_node_pair() (in module sleap.info.align)": [[3, "sleap.info.align.get_most_stable_node_pair"]], "get_stable_node_pairs() (in module sleap.info.align)": [[3, "sleap.info.align.get_stable_node_pairs"]], "get_template_points_array() (in module sleap.info.align)": [[3, "sleap.info.align.get_template_points_array"]], "module": [[3, "module-sleap.info.align"], [4, "module-sleap.info.feature_suggestions"], [5, "module-sleap.info.labels"], [6, "module-sleap.info.metrics"], [7, "module-sleap.info.summary"], [8, "module-sleap.info.trackcleaner"], [9, "module-sleap.info.write_tracking_h5"], [10, "module-sleap.instance"], [11, "module-sleap.io.convert"], [12, "module-sleap.io.dataset"], 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"sleap.info.feature_suggestions.FrameGroupSet.groupset_data"]], "hog_bag_of_features() (sleap.info.feature_suggestions.itemstack method)": [[4, "sleap.info.feature_suggestions.ItemStack.hog_bag_of_features"]], "item_group (sleap.info.feature_suggestions.framegroupset attribute)": [[4, "sleap.info.feature_suggestions.FrameGroupSet.item_group"]], "items (sleap.info.feature_suggestions.itemstack attribute)": [[4, "sleap.info.feature_suggestions.ItemStack.items"]], "kmeans() (sleap.info.feature_suggestions.itemstack method)": [[4, "sleap.info.feature_suggestions.ItemStack.kmeans"]], "make() (sleap.info.feature_suggestions.parallelfeaturepipeline class method)": [[4, "sleap.info.feature_suggestions.ParallelFeaturePipeline.make"]], "make_sample_group() (sleap.info.feature_suggestions.itemstack method)": [[4, "sleap.info.feature_suggestions.ItemStack.make_sample_group"]], "meta (sleap.info.feature_suggestions.itemstack attribute)": [[4, "sleap.info.feature_suggestions.ItemStack.meta"]], 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"sleap.info.feature_suggestions.ParallelFeaturePipeline.tuples_to_suggestions"]], "sleap.info.labels": [[5, "module-sleap.info.labels"]], "calculate_pairwise_cost() (in module sleap.info.metrics)": [[6, "sleap.info.metrics.calculate_pairwise_cost"]], "compare_instance_lists() (in module sleap.info.metrics)": [[6, "sleap.info.metrics.compare_instance_lists"]], "list_points_array() (in module sleap.info.metrics)": [[6, "sleap.info.metrics.list_points_array"]], "match_instance_lists() (in module sleap.info.metrics)": [[6, "sleap.info.metrics.match_instance_lists"]], "match_instance_lists_nodewise() (in module sleap.info.metrics)": [[6, "sleap.info.metrics.match_instance_lists_nodewise"]], "matched_instance_distances() (in module sleap.info.metrics)": [[6, "sleap.info.metrics.matched_instance_distances"]], "nodeless_point_dist() (in module sleap.info.metrics)": [[6, "sleap.info.metrics.nodeless_point_dist"]], "point_dist() (in module sleap.info.metrics)": [[6, 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