mediapipe/mediapipe/util/sequence

MediaSequence keys and functions reference

The documentation below will first provide an overview of using MediaSequence for machine learning tasks. Then, the documentation will describe the function prototypes used in MediaSequence for storing multimedia data in SequenceExamples. Finally, the documentation will describe the specific keys for storing specific types of data.

Overview of MediaSequence for machine learning

The goal of MediaSequence is to provide a tool for transforming annotations of multimedia into input examples ready for use with machine learning models in TensorFlow. The most semantically appropriate data type for this task that can be easily parsed in TensorFlow is tensorflow.train.SequenceExamples/tensorflow::SequenceExamples. Using SequenceExamples enables quick integration of new features into TensorFlow pipelines, easy open sourcing of models and data, reasonable debugging, and efficient TensorFlow decoding. For many machine learning tasks, TensorFlow Examples are capable of fulfilling that role. However, Examples can become unwieldy for sequence data, particularly when the number of features per timestep varies, creating a ragged struction. Video object detection is one example task that requires this ragged structure because the number of detections per frame varies. SequenceExamples can easily encode this ragged structure. Sequences naturally match the semantics of video as a sequence of frames or other common media patterns. The interpretable semantics simplify debugging and decoding of potentially complicated data. One potential disadvantage of SequenceExamples is that keys and formats can vary widely. The MediaSequence library provides tools for consistently manipulating and decoding SequenceExamples in Python and C++ in a consistent format. The consistent format enables creating a pipeline for processing data sets. A goal of MediaSequence as a pipeline is that users should only need to specify the metadata (e.g. videos and labels) for their task. The pipeline will turn the metadata into training data.

The pipeline has two stages. First, users must generate the metadata describing the data and applicable labels. This process is straightforward and described in the next section. Second, users run MediaPipe graphs with the UnpackMediaSequenceCalculator and PackMediaSequenceCalculators to extract the relevant data from multimedia files. A sequence of graphs can be chained together in this second stage to achieve complex processing such as first extracting a subset of frames from a video and then extracting deep features or object detections for each extracted frame. As MediaPipe is built to simply and reproducibly process media files, the two stage approach separates and simplifies data management.

Creating metadata for a new data set

Generating examples for a new data set typically only requires defining the metadata. MediaPipe graphs can interpret this metadata to fill out the SequenceExamples using the UnpackMediaSequenceCalculator and PackMediaSequenceCalculator. This section will list the metadata required for different types of tasks and provide a limited descripiton for the data filled by MediaPipe. The input media will be referred to as video because that is a common case, but audio files or other sequences could be supported. The function calls in the Python API will be used in examples, and the equivalent C++ calls are described below.

The video metadata is a way to access the video, using set_clip_data_path to define the path on disk, and the time span to include using set_clip_start_timestamp and set_clip_end_timestamp. The data path can be absolute or can be relative to a root directory passed to the UnpackMediaSequenceCalculator. The start and end timestamps should be valid MediaPipe timestamps in microseconds. Given this information, the pipeline can extract the portion of the media between the start and end timestamps. If you do not specify a start time, the video is decoded from the beginning. If you do not specify an end time, the entire video is decoded. The start and end times are not filled if left empty.

The features extracted from the video depends on the MediaPipe graph that is run. The documentation of keys below and in PackMediaSequenceCalculator provide the best description.

The annotations including labels should be added as metadata. They will be passed through the MediaPipe pipeline unchanged. The label format will vary depending on the task you want to do. Several examples are included below. In general, the MediaPipe processing is independent of any labels that you provide: only the clip data path, start time, and end time matter.

Clip classification

For clip classification, e.g. is this video clip about basketball?, you should use set_clip_label_index with the integer index of the correct class and set_clip_label_string with the human readable version of the correct class. The index is often used when training the model and the string is used for human readable debugging. The same number of indices and strings need to be provided. The association between the two is just their relative positions in the list.

Example lines creating metadata for clip classification

# Python: functions from media_sequence.py as ms
sequence = tf.train.SequenceExample()
ms.set_clip_data_path(b"path_to_video", sequence)
ms.set_clip_start_timestamp(1000000, sequence)
ms.set_clip_end_timestamp(6000000, sequence)
ms.set_clip_label_index((4, 3), sequence)
ms.set_clip_label_string((b"run", b"jump"), sequence)

// C++: functions from media_sequence.h
tensorflow::SequenceExample sequence;
SetClipDataPath("path_to_video", &sequence);
SetClipStartTimestamp(1000000, &sequence);
SetClipEndTimestamp(6000000, &sequence);
SetClipLabelIndex({4, 3}, &sequence);
SetClipLabelString({"run", "jump"}, &sequence);

Temporal detection

For temporal event detection or localization, e.g. classify regions in time where people are playing a sport, the labels are referred to as segments. You need to set the segment timespans with set_segment_start_timestamp and set_segment_end_timestamp and labels with set_segment_label_index and set_segment_label_string. All of these are repeated fields so you can provide multiple segments for each clip. The label index and string have the same meaning as for clip classification. Only the start and end timestamps need to be provided. (The pipeline will automatically call set_segment_start_index to the index of the image frame under the image/timestamp key that is closest in time, and similarly for set_segment_end_index. Allowing the pipeline to fill in the indices corrects for frame rate changes automatically.) The same number of values must be present in each field. If the same segment would have multiple labels, the segment start and end time must be duplicated.

Example lines creating metadata for temporal detection

# Python: functions from media_sequence.py as ms
sequence = tf.train.SequenceExample()
ms.set_clip_data_path(b"path_to_video", sequence)
ms.set_clip_start_timestamp(1000000, sequence)
ms.set_clip_end_timestamp(6000000, sequence)

ms.set_segment_start_timestamp((2000000, 4000000), sequence)
ms.set_segment_end_timestamp((3500000, 6000000), sequence)
ms.set_segment_label_index((4, 3), sequence)
ms.set_segment_label_string((b"run", b"jump"), sequence)

// C++: functions from media_sequence.h
tensorflow::SequenceExample sequence;
SetClipDataPath("path_to_video", &sequence);
SetClipStartTimestamp(1000000, &sequence);
SetClipEndTimestamp(6000000, &sequence);

SetSegmentStartTimestamp({2000000, 4000000}, &sequence);
SetSegmentEndTimestamp({3500000, 6000000}, &sequence);
SetSegmentLabelIndex({4, 3}, &sequence);
SetSegmentLabelString({"run", "jump"}, &sequence);

Tracking and spatiotemporal detection

For object tracking or detection in videos, e.g. classify regions in time and space, the labels are typically bounding boxes. Unlike previous tasks, the annotations are provided as a FeatureList instead of in a context Feature because they occur in multiple frames. Set up a detection task with add_bbox, add_bbox_timestamp, add_bbox_label_string, and add_bbox_label_index. Only add metadata for annotated frames. The pipeline will add empty features to each feature list to align the box annotations with the nearest image frame. add_bbox_is_annotated distinguishes between annotated frames and frames added as padding. 1 is added if the frame was annotated and 0 otherwise. It is automatically maintained in PackMediaSequenceCalculator. Other fields can be used for tracking tasks: add_bbox_track_string identifies instances over time and add_bbox_class_string can be concatenated to the track string if track ids are not already unique. If track ids are unique across classes, you do not need to fill out the class information.

Example lines creating metadata for spatiotemporal detection or tracking

# Python: functions from media_sequence.py as ms
sequence = tf.train.SequenceExample()
ms.set_clip_data_path(b"path_to_video", sequence)
ms.set_clip_start_timestamp(1000000, sequence)
ms.set_clip_end_timestamp(6000000, sequence)

# For an object tracking task with action labels:
loctions_on_frame_1 = np.array([[0.1, 0.2, 0.3 0.4],
                                [0.2, 0.3, 0.4, 0.5]])
add_bbox(locations_on_frame_1, sequence)
add_bbox_timestamp(3000000, sequence)
add_bbox_label_index((4, 3), sequence)
add_bbox_label_string((b"run", b"jump"), sequence)
add_bbox_track_string((b"id_0", b"id_1"), sequence)
# add_bbox_class_string(("cls_0", "cls_0"), sequence)  # if required
locations_on_frame_2 = locations_on_frame_1[0]
add_bbox(locations_on_frame_2, sequence)
add_bbox_timestamp(5000000, sequence)
add_bbox_label_index((3), sequence)
add_bbox_label_string((b"jump",), sequence)
add_bbox_track_string((b"id_0",), sequence)
# add_bbox_class_string(("cls_0",), sequence)  # if required

// C++: functions from media_sequence.h
tensorflow::SequenceExample sequence;
SetClipDataPath("path_to_video", &sequence);
SetClipStartTimestamp(1000000, &sequence);
SetClipEndTimestamp(6000000, &sequence);

// For an object tracking task with action labels:
std::vector<mediapipe::Locations> locations_on_frame_1;
AddBBox(locations_on_frame_1, &sequence);
AddBBoxTimestamp(3000000, &sequence);
AddBBoxLabelIndex({4, 3}, &sequence);
AddBBoxLabelString({"run", "jump"}, &sequence);
AddBBoxTrackString({"id_0", "id_1"}, &sequence);
// AddBBoxClassString({"cls_0", "cls_0"}, &sequence); // if required
std::vector<mediapipe::Locations> locations_on_frame_2;
AddBBox(locations_on_frame_2, &sequence);
AddBBoxTimestamp(5000000, &sequence);
AddBBoxLabelIndex({3}, &sequence);
AddBBoxLabelString({"jump"}, &sequence);
AddBBoxTrackString({"id_0"}, &sequence);
// AddBBoxClassString({"cls_0"}, &sequence); // if required

Running a MediaSequence through MediaPipe

UnpackMediaSequenceCalculator and PackMediaSequenceCalculator

MediaSequence utilizes MediaPipe for processing by providing two special calculators. The UnpackMediaSequenceCalculator is used to extract data from SequenceExamples. This will often be the metadata, such as the path to the video file, and the clip start and end times. However, after storing images in a SequenceExample, the images themselves can also be unpacked for further processing, such as computing optical flow. Whatever data is extracted during processing is added to the SequenceExample by the PackMediaSequenceCalculator. The values that are unpacked and packed into these calculators are determined by the tags on the streams in the MediaPipe calculator graph. (Tags are required to be all capitals and underscores. To encode prefixes for feature keys as tags, prefixes for feature keys should follow the same convention.) The documentation for these two calculators describes the variety of data they support. Any other MediaPipe processing can be used between these calculators to extract features.

Adding data and reconciling metadata

In general, the pipeline will decode the specified media between the clip start and end timestamps and store any requested features. A common feature to request is JPEG encoded images, so this will be used it as an example. Each image between the clip start and end timestamps (generally inclusive) is added to the SequenceExample's feature list with add_image_encoded and the corresponding timestamp it arrived at is added with add_image_timestamp. At the end of the image stream, the pipeline will determine and store what metadata it can about the stream. For images, it will store the height and width of the image as well as the number of channels and encoding format. Similar storage and metadata computation is done when adding audio, float feature vectors, or encoded optical flow to the SequenceExample. The code that reconciles the metadata is in media_sequence.cc.

Automatically aligning bounding boxes to images

At the time of writing, the image/timestamp is also used to update the closest timestamp for segment/start/index and segment/end/index and bounding box data. Segment indices are relative to the start of the clip (i.e. only reference data within the SequenceExample), while timestamps are absolute times within the video. Bounding box data is aligned to the image/timestamps by inserting empty bounding box annotations and indicating this with add_bbox_is_annotated. If images are stored at a lower rate than the bounding box data, then only the nearest annotation to each frame is retained and any others are dropped. Be careful when downsampling frame rates with bounding box annotations; downsampling bounding box annotations is the only time annotations will be lost in the MediaPipe pipeline.

Chaining processing graphs

A common use case is to derive deep features from frames in a video when those features are too expensive to compute during training. For example, extracting ResNet-50 features on each frame of video. In the MediaSequence pipeline, the way to generate these features is to first extract the images to the SequenceExample in one MediaPipe graph. Then create a second MediaPipe graph that unpacks the images from the SequenceExample and appends the new features to a copy of that SequenceExample. This chaining behavior makes it easy to incrementally add features in a modular way and makes debugging easier because you can identify the anomalous stage more easily. Once the pipeline is complete, any unnecessary features can be removed. Be aware that the number of derived feature timestamps may be different than the number of input features, e.g. optical flow can't be estimated for the last frame of a video clip, so it adds one less frame of data. With the exception of aligning bounding boxes, the pipeline does nothing to require consistent timestamps between features.

Function prototypes for each data type

MediaSequence provides accessors to store common data patterns in SequenceExamples. The exact functions depend on the type of data and the key, but the patterns are similar. Each function has a name related to the key, so we will document the functions with a generic name, Feature. Note that due to different conventions for Python and C++ code, the capitalization and parameter order varies, but the functionality should be equivalent.

Each function takes an optional prefix parameter. Prefixes enable storing semantically identical data without collisions. For example, it is possible to store predicted and ground truth bounding boxes by using different prefixes. To minimize burdening the API and documentation, eschew using prefixes unless necessary. For some common cases, such as storing instance segmentation labels along with images, named versions with prefixes baked in provided as documented below. Lastly, generic features and audio streams should almost always use a prefix because storing multiple features or transformed audio streams is common.

The code generating these functions resides in media_sequence.h/.cc/.py and media_sequence_util.h/.cc/.py. The media_sequence files generally defines the API that should be used directly by developers. The media_sequence_util files provide the function generation code used to define new features. If you require additional features not supplied in the media_sequence files, use the functions in media_sequence_util to create more in the appropriate namespace / module_dict in your own files and import those as well.

In these prototypes, the prefix is optional as indicated by [ ]s. The C++ types are abbreviated. The code and test cases are recommended for understanding the exact types. The purpose of these example is to provide an illustration of the pattern.

Singular Context Features

python call	c++ call	description
has_feature(example [, prefix])	HasFeature([const string& prefix,] const tf::SE& example)	Returns a boolean if the feature is present.
get_feature(example [, prefix])	GetFeature([const string& prefix,] const tf::SE& example)	Returns a single feature of the appropriate type (string, int64, float).
clear_feature(example [, prefix])	ClearFeature([const string& prefix,] tf::SE* example)	Clears the feature.
set_feature(value, example [, prefix])	SetFeature([const string& prefix,], const TYPE& value, tf::SE* example)	Clears and stores the feature of the appropriate type.
get_feature_key([prefix])	GetFeatureKey([const string& prefix])	Returns the key used by related functions.
get_feature_default_parser()		Returns the tf.io.FixedLenFeature for this type. (Python only.)

List Context Features

python call	c++ call	description
has_feature(example [, prefix])	HasFeature([const string& prefix,] const tf::SE& example)	Returns a boolean if the feature is present.
get_feature(example [, prefix])	GetFeature([const string& prefix,] const tf::SE& example)	Returns a sequence feature of the appropriate type (comparable to list/vector of string, int64, float).
clear_feature(example [, prefix])	ClearFeature([const string& prefix,] tf::SE* example)	Clears the feature.
set_feature(values, example [, prefix])	SetFeature([const string& prefix,], const vector<TYPE>& values, tf::SE* example)	Clears and stores the list of features of the appropriate type.
get_feature_key([prefix])	GetFeatureKey([const string& prefix])	Returns the key used by related functions.
get_feature_default_parser()		Returns the tf.io.VarLenFeature for this type. (Python only.)

Singular Feature Lists

python call	c++ call	description
has_feature(example [, prefix])	HasFeature([const string& prefix,] const tf::SE& example)	Returns a boolean if the feature is present.
get_feature_size(example [, prefix])	GetFeatureSize([const string& prefix,] const tf::SE&(example)	Returns the number of features under this key. Will be 0 if the feature is absent.
get_feature_at(index, example [, prefix])	GetFeatureAt([const string& prefix,] const tf::SE& example, const int index)	Returns a single feature of the appropriate type (string, int64, float) at position index of the feature list.
clear_feature(example [, prefix])	ClearFeature([const string& prefix,] tf::SE* example)	Clears the entire feature.
add_feature(value, example [, prefix])	AddFeature([const string& prefix,], const TYPE& value, tf::SE* example)	Appends a feature of the appropriate type to the feature list.
get_feature_key([prefix])	GetFeatureKey([const string& prefix])	Returns the key used by related functions.
get_feature_default_parser()		Returns the tf.io.FixedLenSequenceFeature for this type. (Python only.)

List Feature Lists

python call	c++ call	description
has_feature(example [, prefix])	HasFeature([const string& prefix,] const tf::SE& example)	Returns a boolean if the feature is present.
get_feature_size(example [, prefix])	GetFeatureSize([const string& prefix,] const tf::SE& example)	Returns the number of feature sequences under this key. Will be 0 if the feature is absent.
get_feature_at(index, example [, prefix])	GetFeatureAt([const string& prefix,] const tf::SE& example, const int index)	Returns a repeated feature of the appropriate type (comparable to list/vector of string, int64, float) at position index of the feature list.
clear_feature(example [, prefix])	ClearFeature([const string& prefix,] tf::SE* example)	Clears the entire feature.
add_feature(value, example [, prefix])	AddFeature([const string& prefix,], const vector<TYPE>& value, tf::SE* example)	Appends a sequence of features of the appropriate type to the feature list.
get_feature_key([prefix])	GetFeatureKey([const string& prefix])	Returns the key used by related functions.
get_feature_default_parser()		Returns the tf.io.VarLenFeature for this type. (Python only.)

Keys

These keys are broadly useful for covering the range of multimedia based machine learning tasks. The key itself should be human interpretable, and descriptions are provided for elaboration.

Keys related to the entire example

key	type	python call / c++ call	description
example/id	context bytes	set_example_id / SetExampleId	A unique identifier for each example.
example/dataset_name	context bytes	set_example_dataset_name / SetExampleDatasetName	The name of the data set, including the version.

Keys related to a clip

key	type	python call / c++ call	description
clip/data_path	context bytes	set_clip_data_path / SetClipDataPath	The relative path to the data on disk from some root directory.
clip/start/timestamp	context int	set_clip_start_timestamp / SetClipStartTimestamp	The start time, in microseconds, for the start of the clip in the media.
clip/end/timestamp	context int	set_clip_end_timestamp / SetClipEndTimestamp	The end time, in microseconds, for the end of the clip in the media.
clip/label/index	context int list	set_clip_label_index / SetClipLabelIndex	A list of label indices for this clip.
clip/label/string	context string list	set_clip_label_string / SetClipLabelString	A list of label strings for this clip.
clip/label/confidence	context float list	set_clip_label_confidence / SetClipLabelConfidence	A list of label confidences for this clip.
clip/media_id	context bytes	set_clip_media_id / SetClipMediaId	Any identifier for the media beyond the data path.
clip/alternative_media_id	context bytes	set_clip_alternative_media_id / SetClipAlternativeMediaId	Yet another alternative identifier.
clip/encoded_media_bytes	context bytes	set_clip_encoded_media_bytes / SetClipEncodedMediaBytes	The encoded bytes for storing media directly in the SequenceExample.
clip/ encoded_media_start_timestamp	context int	set_clip_encoded_media_start_timestamp / SetClipEncodedMediaStartTimestamp	The start time for the encoded media if not preserved during encoding.

Keys related to segments of clips

key	type	python call / c++ call	description
segment/start/timestamp	context int list	set_segment_start_timestamp / SetSegmentStartTimestamp	A list of segment start times in microseconds.
segment/start/index	context int list	set_segment_start_index / SetSegmentstartIndex	A list of indices marking the first frame index >= the start time.
segment/end/timestamp	context int list	set_segment_end_timestamp / SetSegmentEndTimestamp	A list of segment end times in microseconds.
segment/end/index	context int list	set_segment_end_index / SetSegmentEndIndex	A list of indices marking the last frame index <= the end time.
segment/label/index	context int list	set_segment_label_index / SetSegmentLabelIndex	A list with the label index for each segment. Multiple labels for the same segment are encoded as repeated segments.
segment/label/string	context bytes list	set_segment_label_string / SetSegmentLabelString	A list with the label string for each segment. Multiple labels for the same segment are encoded as repeated segments.
segment/label/confidence	context float list	set_segment_label_confidence / SetSegmentLabelConfidence	A list with the label confidence for each segment. Multiple labels for the same segment are encoded as repeated segments.

Keys related to spatial regions (e.g. bounding boxes)

Prefixes are used to distinguish betwen different semantic meanings of regions. This practice is so common, that the BBox version of function calls will be provided. Each call accepts an optional prefix to avoid name collisions. "Region" is used in the keys because of the similar semantic meaning between different types of regions.

A few special accessors are provided to work with multiple keys at once.

Regions can be given identifiers for labels, tracks, and classes. Although similar information can be stored in each identifier, the intended use is different. Labels should be used when predicting a label for a region (such as the class of the bounding box or action performed by a person). Tracks should be used to uniquely identify regions over sequential frames. Classes are only intended to be used to disambiguate track ids if those ids are not unique across object labels. The recommendation is to prefer label fields for classification tasks and tracking (or class) fields for tracking information.

key	type	python call / c++ call	description
region/bbox/ymin	feature list float list	add_bbox_ymin / AddBBoxYMin	A list of normalized minimum y values of bounding boxes in a frame.
region/bbox/xmin	feature list float list	add_bbox_xmin / AddBBoxXMin	A list of normalized minimum x values of bounding boxes in a frame.
region/bbox/ymax	feature list float list	add_bbox_ymax / AddBBoxYMax	A list of normalized maximum y values of bounding boxes in a frame.
region/bbox/xmax	feature list float list	add_bbox_xmax / AddBBoxXMax	A list of normalized maximum x values of bounding boxes in a frame.
region/bbox/\*	special	add_bbox / AddBBox	Operates on ymin,xmin,ymax,xmax with a single call.
region/point/x	feature list float list	add_bbox_point_x / AddBBoxPointX	A list of normalized x values for points in a frame.
region/point/y	feature list float list	add_bbox_point_y / AddBBoxPointY	A list of normalized y values for points in a frame.
region/point/\*	special	add_bbox_point / AddBBoxPoint	Operates on point/x,point/y with a single call.
region/point/radius	feature list float list	add_bbox_point_radius / AddBBoxPointRadius	A list of radii for points in a frame.
region/timestamp	feature list int	add_bbox_timestamp / AddBBoxTimestamp	The timestamp in microseconds for the region annotations.
region/num_regions	feature list int	add_bbox_num_regions / AddBBoxNumRegions	The number of boxes or other regions in a frame. Should be 0 for unannotated frames.
region/is_annotated	feature list int	add_bbox_is_annotated / AddBBoxIsAnnotated	1 if this timestep is annotated. 0 otherwise. Distinguishes empty from unannotated frames.
region/is_generated	feature list int list	add_bbox_is_generated / AddBBoxIsGenerated	For each region, 1 if the region is procedurally generated for this frame.
region/is_occluded	feature list int list	add_bbox_is_occluded / AddBBoxIsOccluded	For each region, 1 if the region is occluded in the current frame.
region/label/index	feature list int list	add_bbox_label_index / AddBBoxLabelIndex	For each region, lists the integer label. Multiple labels for one region require duplicating the region.
region/label/string	feature list bytes list	add_bbox_label_string / AddBBoxLabelString	For each region, lists the string label. Multiple labels for one region require duplicating the region.
region/label/confidence	feature list float list	add_bbox_label_confidence / AddBBoxLabelConfidence	For each region, lists the confidence or weight for the label. Multiple labels for one region require duplicating the region.
region/track/index	feature list int list	add_bbox_track_index / AddBBoxTrackIndex	For each region, lists the integer track id. Multiple track ids for one region require duplicating the region.
region/track/string	feature list bytes list	add_bbox_track_string / AddBBoxTrackString	For each region, lists the string track id. Multiple track ids for one region require duplicating the region.
region/track/confidence	feature list float list	add_bbox_track_confidence / AddBBoxTrackConfidence	For each region, lists the confidence or weight for the track. Multiple track ids for one region require duplicating the region.
region/class/index	feature list int list	add_bbox_class_index / AddBBoxClassIndex	For each region, lists the integer class. Multiple classes for one region require duplicating the region.
region/class/string	feature list bytes list	add_bbox_class_string / AddBBoxClassString	For each region, lists the string class. Multiple classes for one region require duplicating the region.
region/class/confidence	feature list float list	add_bbox_class_confidence / AddBBoxClassConfidence	For each region, lists the confidence or weight for the class. Multiple classes for one region require duplicating the region.
region/embedding/float	feature list float list	add_bbox_embedding_floats / AddBBoxEmbeddingFloats	For each region, provide an embedding as sequence of floats.
region/parts	context bytes list	set_bbox_parts / SetBBoxParts	The list of region parts expected in this example.
region/embedding/ dimensions_per_region	context int list	set_bbox_embedding_dimensions_per_region / SetBBoxEmbeddingDimensionsPerRegion	Provide the dimensions for each embedding.
region/embedding/format	context string	set_bbox_embedding_format / SetBBoxEmbeddingFormat	Provides the encoding format, if any, for region embeddings.
region/embedding/encoded	feature list bytes list	add_bbox_embedding_encoded / AddBBoxEmbeddingEncoded	For each region, provide an encoded embedding.
region/embedding/confidence	feature list float list	add_bbox_embedding_confidence / AddBBoxEmbeddingConfidence	For each region, provide a confidence for the embedding.
region/unmodified_timestamp	feature list int	add_bbox_unmodified_timestamp / AddBBoxUnmodifiedTimestamp	Used to store the original timestamps if procedurally aligning timestamps to image frames.

Keys related to images

key	type	python call / c++ call	description
image/encoded	feature list bytes	add_image_encoded / AddImageEncoded	The encoded image at each timestep.
image/timestamp	feature list int	add_image_timestamp / AddImageTimestamp	The timestamp in microseconds for the image.
image/multi_encoded	feature list bytes list	add_image_multi_encoded / AddImageMultiEncoded	Storing multiple images at each timestep (e.g. from multiple camera views).
image/label/index	feature list int list	add_image_label_index / AddImageLabelIndex	If an image at a specific timestamp should have a label, use this. If a range of time, prefer Segments instead.
image/label/string	feature list bytes list	add_image_label_string / AddImageLabelString	If an image at a specific timestamp should have a label, use this. If a range of time, prefer Segments instead.
image/label/confidence	feature list float list	add_image_label_confidence / AddImageLabelConfidence	If an image at a specific timestamp should have a label, use this. If a range of time, prefer Segments instead.
image/format	context bytes	set_image_format / SetImageFormat	The encoding format of the images.
image/channels	context int	set_image_channels / SetImageChannels	The number of channels in the image.
image/height	context int	set_image_height / SetImageHeight	The height of the image in pixels.
image/width	context int	set_image_width / SetImageWidth	The width of the image in pixels.
image/frame_rate	context float	set_image_frame_rate / SetImageFrameRate	The rate of images in frames per second.
image/data_path	context bytes	set_image_data_path / SetImageDataPath	The path of the image file if it did not come from a media clip.

Keys related to image class segmentation

key	type	python call / c++ call	description
CLASS_SEGMENTATION/image/encoded	feature list bytes	add_class_segmentation_encoded / AddClassSegmentationEncoded	The encoded image of class labels at each timestep.
CLASS_SEGMENTATION/image/timestamp	feature list int	add_class_segmentation_timestamp / AddClassSegmentationTimestamp	The timestamp in microseconds for the class labels.
CLASS_SEGMENTATION/image/multi_encoded	feature list bytes list	add_class_segmentation_multi_encoded / AddClassSegmentationMultiEncoded	Storing multiple segmentation masks in case they overlap.
CLASS_SEGMENTATION/image/format	context bytes	set_class_segmentation_format / SetClassSegmentationFormat	The encoding format of the class label images.
CLASS_SEGMENTATION/image/height	context int	set_class_segmentation_height / SetClassSegmentationHeight	The height of the image in pixels.
CLASS_SEGMENTATION/image/width	context int	set_class_segmentation_width / SetClassSegmentationWidth	The width of the image in pixels.
CLASS_SEGMENTATION/image/class/ label/index	context int list	set_class_segmentation_label_index / SetClassSegmentationLabelIndex	If necessary a mapping from values in the image to class labels.
CLASS_SEGMENTATION/image/class/ label/string	context bytes list	set_class_segmentation_label_string / SetClassSegmentationLabelString	A mapping from values in the image to class labels.

Keys related to image instance segmentation

key	type	python call / c++ call	description
INSTANCE_SEGMENTATION/image/ encoded	feature list bytes	add_instance_segmentation_encoded / AddInstanceSegmentationEncoded	The encoded image of object instance labels at each timestep.
INSTANCE_SEGMENTATION/image/ timestamp	feature list int	add_instance_segmentation_timestamp / AddInstanceSegmentationTimestamp	The timestamp in microseconds for the object instance labels.
INSTANCE_SEGMENTATION/image/multi_encoded	feature list bytes list	add_instance_segmentation_multi_encoded / AddInstanceSegmentationEncoded	Storing multiple segmentation masks in case they overlap.
INSTANCE_SEGMENTATION/image/ format	context bytes	set_instance_segmentation_format / SetInstanceSegmentationFormat	The encoding format of the object instance labels.
INSTANCE_SEGMENTATION/image/ height	context int	set_instance_segmentation_height / SetInstanceSegmentationHeight	The height of the image in pixels.
INSTANCE_SEGMENTATION/image/ width	context int	set_instance_segmentation_width / SetInstanceSegmentationWidth	The width of the image in pixels.
INSTANCE_SEGMENTATION/image/ class/label/index	context int list	set_instance_segmentation_label_index / SetInstanceSegmentationLabelIndex	If necessary a mapping from values in the image to class labels.
INSTANCE_SEGMENTATION/image/ class/label/string	context bytes list	set_instance_segmentation_label_string / SetInstanceSegmentationLabelString	A mapping from values in the image to class labels.
INSTANCE_SEGMENTATION/image/ object/class/index	context int	set_instance_segmentation_object_class_index / SetInstanceSegmentationObjectClassIndex	If necessary a mapping from values in the image to class indices.

Keys related to optical flow

key	type	python call / c++ call	description
FORWARD_FLOW/image/encoded	feature list bytes	add_forward_flow_encoded / AddForwardFlowEncoded	The encoded forward optical flow field at each timestep.
FORWARD_FLOW/image/timestamp	feature list int	add_forward_flow_timestamp / AddForwardFlowTimestamp	The timestamp in microseconds for the optical flow field.
FORWARD_FLOW/image/multi_encoded	feature list bytes list	add_forward_flow_multi_encoded / AddForwardFlowMultiEncoded	Storing multiple optical flow fields at each timestep (e.g. from multiple camera views).
FORWARD_FLOW/image/format	context bytes	set_forward_flow_format / SetForwardFlowFormat	The encoding format of the optical flow field.
FORWARD_FLOW/image/channels	context int	set_forward_flow_channels / SetForwardFlowChannels	The number of channels in the optical flow field.
FORWARD_FLOW/image/height	context int	set_forward_flow_height / SetForwardFlowHeight	The height of the optical flow field in pixels.
FORWARD_FLOW/image/width	context int	set_forward_flow_width / SetForwardFlowWidth	The width of the optical flow field in pixels.
FORWARD_FLOW/image/frame_rate	context float	set_forward_flow_frame_rate / SetForwardFlowFrameRate	The rate of optical flow field in frames per second.
FORWARD_FLOW/image/saturation	context float	set_forward_flow_saturation / SetForwardFlowSaturation	The saturation value used before encoding the flow field to an image.

Keys related to generic features

Storing generic features is powerful, but potentially confusing. The recommendation is to use more specific methods if possible. When using these generic features, always supply a prefix. (The recommended prefix format, enforced by some MediaPipe functions, is all caps with underscores, e.g. MY_FAVORITE_FEATURE.) Following this recommendation, the keys will be listed with a generic PREFIX.

key	type	python call / c++ call	description
PREFIX/feature/floats	feature list float list	add_feature_floats / AddFeatureFloats	A list of floats at a timestep.
PREFIX/feature/bytes	feature list bytes list	add_feature_bytes / AddFeatureBytes	A list of bytes at a timestep. Maybe be encoded.
PREFIX/feature/ints	feature list int list	add_feature_ints / AddFeatureInts	A list of ints at a timestep.
PREFIX/feature/timestamp	feature list int	add_feature_timestamp / AddFeatureTimestamp	A timestamp for a set of features.
PREFIX/feature/duration	feature list int list	add_feature_duration / AddFeatureDuration	It is occasionally useful to indicate that a feature applies to a time range. This should only be used for features and annotations should be provided as Segments.
PREFIX/feature/confidence	feautre list float list	add_feature_confidence / AddFeatureConfidence	The confidence for each generated feature.
PREFIX/feature/dimensions	context int list	set_feature_dimensions / SetFeatureDimensions	A list of integer dimensions for each feature.
PREFIX/feature/rate	context float	set_feature_rate / SetFeatureRate	The rate that features are calculated as features per second.
PREFIX/feature/bytes/format	context bytes	set_feature_bytes_format / SetFeatureBytesFormat	The encoding format if any for features stored as bytes.

Keys related to audio

Audio is a special subtype of generic features with additional data about the audio format. When using audio, always supply a prefix. (The recommended prefix format, enforced by some MediaPipe functions, is all caps with underscores, e.g. MY_FAVORITE_FEATURE.) Following this recommendation, the keys will be listed with a generic PREFIX.

To understand the terminology, it is helpful conceptualize the audio as a list of matrices. The columns of the matrix are called samples. The rows of the matrix are called channels. Each matrix is called a packet. The packet rate is how often packets appear per second. The sample rate is the rate of columns per second. The audio sample rate is used for derived features such as spectrograms where the STFT is computed over audio at some other rate.

key	type	python call / c++ call	description
PREFIX/feature/floats	feature list float list	add_feature_floats / AddFeatureFloats	A list of floats at a timestep.
PREFIX/feature/timestamp	feature list int	add_feature_timestamp / AddFeatureTimestamp	A timestamp for a set of features.
PREFIX/feature/sample_rate	context float	set_feature_sample_rate / SetFeatureSampleRate	The number of features per second. (e.g. for a spectrogram, this is the rate of STFT windows.)
PREFIX/feature/num_channels	context int	set_feature_num_channels / SetFeatureNumChannels	The number of channels of audio in each stored feature.
PREFIX/feature/num_samples	context int	set_feature_num_samples / SetFeatureNumSamples	The number of samples of audio in each stored feature.
PREFIX/feature/packet_rate	context float	set_feature_packet_rate / SetFeaturePacketRate	The number of packets per second.
PREFIX/feature/audio_sample_rate	context float	set_feature_audio_sample_rate / SetFeatureAudioSampleRate	The sample rate of the original audio for derived features.