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# 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 # 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++ // 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 # 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++ // 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 tim