video_source
VideoConsumer
¶
This class should be consumed as part of internal implementation. It provides abstraction around stream consumption strategies.
Source code in inference/core/interfaces/camera/video_source.py
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VideoSource
¶
Source code in inference/core/interfaces/camera/video_source.py
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__next__()
¶
Method allowing to use VideoSource
convenient to read frames
Returns: VideoFrame
Example
source = VideoSource.init(video_reference="./some.mp4")
source.start()
for frame in source:
pass
Source code in inference/core/interfaces/camera/video_source.py
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frame_ready()
¶
Method to check if decoded frame is ready for consumer
Returns: boolean flag indicating frame readiness
Source code in inference/core/interfaces/camera/video_source.py
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get_state()
¶
Method to get current state of the VideoSource
Returns: StreamState
Source code in inference/core/interfaces/camera/video_source.py
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init(video_reference, buffer_size=DEFAULT_BUFFER_SIZE, status_update_handlers=None, buffer_filling_strategy=None, buffer_consumption_strategy=None, adaptive_mode_stream_pace_tolerance=DEFAULT_ADAPTIVE_MODE_STREAM_PACE_TOLERANCE, adaptive_mode_reader_pace_tolerance=DEFAULT_ADAPTIVE_MODE_READER_PACE_TOLERANCE, minimum_adaptive_mode_samples=DEFAULT_MINIMUM_ADAPTIVE_MODE_SAMPLES, maximum_adaptive_frames_dropped_in_row=DEFAULT_MAXIMUM_ADAPTIVE_FRAMES_DROPPED_IN_ROW, video_source_properties=None, source_id=None)
classmethod
¶
This class is meant to represent abstraction over video sources - both video files and
on-line streams that are possible to be consumed and used by other components of inference
library.
Before digging into details of the class behaviour, it is advised to familiarise with the following concepts and implementation assumptions:
- Video file can be accessed from local (or remote) storage by the consumer in a pace dictated by its processing capabilities. If processing is faster than the frame rate of video, operations may be executed in a time shorter than the time of video playback. In the opposite case - consumer may freely decode and process frames in its own pace, without risk for failures due to temporal dependencies of processing - this is classical offline processing example.
- Video streams, on the other hand, usually need to be consumed in a pace near to their frame-rate - in other words - this is on-line processing example. Consumer being faster than incoming stream frames cannot utilise its resources to the full extent as not-yet-delivered data would be needed. Slow consumer, however, may not be able to process everything on time and to keep up with the pace of stream - some frames would need to be dropped. Otherwise - over time, consumer could go out of sync with the stream causing decoding failures or unpredictable behavior.
To fit those two types of video sources, VideoSource
introduces the concept of buffered decoding of
video stream (like at the YouTube - player buffers some frames that are soon to be displayed).
The way on how buffer is filled and consumed dictates the behavior of VideoSource
.
Starting from BufferFillingStrategy
- we have 3 basic options:
* WAIT: in case of slow video consumption, when buffer is full - VideoSource
will wait for
the empty spot in buffer before next frame will be processed - this is suitable in cases when
we want to ensure EACH FRAME of the video to be processed
* DROP_OLDEST: when buffer is full, the frame that sits there for the longest time will be dropped -
this is suitable for cases when we want to process the most recent frames possible
* DROP_LATEST: when buffer is full, the newly decoded frame is dropped - useful in cases when
it is expected to have processing performance drops, but we would like to consume portions of
video that are locally smooth - but this is probably the least common use-case.
On top of that - there are two ADAPTIVE strategies: ADAPTIVE_DROP_OLDEST and ADAPTIVE_DROP_LATEST, which are equivalent to DROP_OLDEST and DROP_LATEST with adaptive decoding feature enabled. The notion of that mode will be described later.
Naturally, decoded frames must also be consumed. VideoSource
provides a handy interface for reading
a video source frames by a SINGLE consumer. Consumption strategy can also be dictated via
BufferConsumptionStrategy
:
* LAZY - consume all the frames from decoding buffer one-by-one
* EAGER - at each readout - take all frames already buffered, drop all of them apart from the most recent
In consequence - there are various combinations of BufferFillingStrategy
and BufferConsumptionStrategy
.
The most popular would be:
* BufferFillingStrategy.WAIT
and BufferConsumptionStrategy.LAZY
- to always decode and process each and
every frame of the source (useful while processing video files - and default behaviour enforced by
inference
if there is no explicit configuration)
* BufferFillingStrategy.DROP_OLDEST
and BufferConsumptionStrategy.EAGER
- to always process the most
recent frames of source (useful while processing video streams when low latency [real-time experience]
is required - ADAPTIVE version of this is default for streams)
ADAPTIVE strategies were introduced to handle corner-cases, when consumer hardware is not capable to consume
video stream and process frames at the same time (for instance - Nvidia Jetson devices running processing
against hi-res streams with high FPS ratio). It acts with buffer in nearly the same way as DROP_OLDEST
and DROP_LATEST
strategies, but there are two more conditions that may influence frame drop:
* announced rate of source - which in fact dictate the pace of frames grabbing from incoming stream that
MUST be met by consumer to avoid strange decoding issues causing decoder to fail - if the pace of frame grabbing
deviates too much - decoding will be postponed, and frames dropped to grab next ones sooner
* consumption rate - in resource constraints environment, not only decoding is problematic from the performance
perspective - but also heavy processing. If consumer is not quick enough - allocating more useful resources
for decoding frames that may never be processed is a waste. That's why - if decoding happens more frequently
than consumption of frame - ADAPTIVE mode causes decoding to be done in a slower pace and more frames are just
grabbed and dropped on the floor.
ADAPTIVE mode increases latency slightly, but may be the only way to operate in some cases.
Behaviour of adaptive mode, including the maximum acceptable deviations of frames grabbing pace from source,
reader pace and maximum number of consecutive frames dropped in ADAPTIVE mode are configurable by clients,
with reasonable defaults being set.
VideoSource
emits events regarding its activity - which can be intercepted by custom handlers. Take
into account that they are always executed in context of thread invoking them (and should be fast to complete,
otherwise may block the flow of stream consumption). All errors raised will be emitted as logger warnings only.
VideoSource
implementation is naturally multithreading, with different thread decoding video and different
one consuming it and manipulating source state. Implementation of user interface is thread-safe, although
stream it is meant to be consumed by a single thread only.
ENV variables involved: * VIDEO_SOURCE_BUFFER_SIZE - default: 64 * VIDEO_SOURCE_ADAPTIVE_MODE_STREAM_PACE_TOLERANCE - default: 0.1 * VIDEO_SOURCE_ADAPTIVE_MODE_READER_PACE_TOLERANCE - default: 5.0 * VIDEO_SOURCE_MINIMUM_ADAPTIVE_MODE_SAMPLES - default: 10 * VIDEO_SOURCE_MAXIMUM_ADAPTIVE_FRAMES_DROPPED_IN_ROW - default: 16
As an inference
user, please use .init() method instead of constructor to instantiate objects.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
video_reference |
Union[str, int]
|
Either str with file or stream reference, or int representing device ID |
required |
buffer_size |
int
|
size of decoding buffer |
DEFAULT_BUFFER_SIZE
|
status_update_handlers |
Optional[List[Callable[[StatusUpdate], None]]]
|
List of handlers for status updates |
None
|
buffer_filling_strategy |
Optional[BufferFillingStrategy]
|
Settings for buffer filling strategy - if not given - automatic choice regarding source type will be applied |
None
|
buffer_consumption_strategy |
Optional[BufferConsumptionStrategy]
|
Settings for buffer consumption strategy, if not given - automatic choice regarding source type will be applied |
None
|
adaptive_mode_stream_pace_tolerance |
float
|
Maximum deviation between frames grabbing pace and stream pace that will not trigger adaptive mode frame drop |
DEFAULT_ADAPTIVE_MODE_STREAM_PACE_TOLERANCE
|
adaptive_mode_reader_pace_tolerance |
float
|
Maximum deviation between decoding pace and stream consumption pace that will not trigger adaptive mode frame drop |
DEFAULT_ADAPTIVE_MODE_READER_PACE_TOLERANCE
|
minimum_adaptive_mode_samples |
int
|
Minimal number of frames to be used to establish actual pace of processing, before adaptive mode can drop any frame |
DEFAULT_MINIMUM_ADAPTIVE_MODE_SAMPLES
|
maximum_adaptive_frames_dropped_in_row |
int
|
Maximum number of frames dropped in row due to application of adaptive strategy |
DEFAULT_MAXIMUM_ADAPTIVE_FRAMES_DROPPED_IN_ROW
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video_source_properties |
Optional[dict[str, float]]
|
Optional dictionary with video source properties corresponding to OpenCV VideoCapture properties cv2.CAP_PROP_* to set values for the video source. |
None
|
source_id |
Optional[int]
|
Optional identifier of video source - mainly useful to recognise specific source when multiple ones are in use. Identifier will be added to emitted frames and updates. It is advised to keep it unique within all sources in use. |
None
|
Returns: Instance of VideoSource
class
Source code in inference/core/interfaces/camera/video_source.py
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mute()
¶
Method to be used to mute source consumption. Muting is an equivalent of pause for stream - where frames grabbing is not put on hold, just new frames decoding and buffering is not allowed - causing intermediate frames to be dropped. May be also used against files, although arguably less useful. Eligible to be used in states: [RUNNING] End state: * MUTED
Thread safe - only one transition of states possible at the time.
Throws: * StreamOperationNotAllowedError: if executed in context of incorrect state of the source
Source code in inference/core/interfaces/camera/video_source.py
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pause()
¶
Method to be used to pause source consumption. During pause - no new frames are consumed. Used on on-line streams for too long may cause stream disconnection. Eligible to be used in states: [RUNNING] End state: * PAUSED
Thread safe - only one transition of states possible at the time.
Throws: * StreamOperationNotAllowedError: if executed in context of incorrect state of the source
Source code in inference/core/interfaces/camera/video_source.py
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read_frame(timeout=None)
¶
Method to be used by the consumer to get decoded source frame.
Throws: * EndOfStreamError: when trying to get the frame from closed source.
Source code in inference/core/interfaces/camera/video_source.py
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restart(wait_on_frames_consumption=True, purge_frames_buffer=False)
¶
Method to restart source consumption. Eligible to be used in states: [MUTED, RUNNING, PAUSED, ENDED, ERROR]. End state: * INITIALISING - that should change into RUNNING once first frame is ready to be grabbed * ERROR - if it was not possible to connect with source
Thread safe - only one transition of states possible at the time.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
wait_on_frames_consumption |
bool
|
Flag telling if all frames from buffer must be consumed before completion of this operation. |
True
|
Throws: * StreamOperationNotAllowedError: if executed in context of incorrect state of the source * SourceConnectionError: if source cannot be connected
Source code in inference/core/interfaces/camera/video_source.py
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resume()
¶
Method to recover from pause or mute into running state. [PAUSED, MUTED] End state: * RUNNING
Thread safe - only one transition of states possible at the time.
Throws: * StreamOperationNotAllowedError: if executed in context of incorrect state of the source
Source code in inference/core/interfaces/camera/video_source.py
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start()
¶
Method to be used to start source consumption. Eligible to be used in states: [NOT_STARTED, ENDED, (RESTARTING - which is internal state only)] End state: * INITIALISING - that should change into RUNNING once first frame is ready to be grabbed * ERROR - if it was not possible to connect with source
Thread safe - only one transition of states possible at the time.
Throws: * StreamOperationNotAllowedError: if executed in context of incorrect state of the source * SourceConnectionError: if source cannot be connected
Source code in inference/core/interfaces/camera/video_source.py
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terminate(wait_on_frames_consumption=True, purge_frames_buffer=False)
¶
Method to be used to terminate source consumption. Eligible to be used in states: [MUTED, RUNNING, PAUSED, ENDED, ERROR, (RESTARTING - which is internal state only)] End state: * ENDED - indicating success of the process * ERROR - if error with processing occurred
Must be used to properly dispose resources at the end.
Thread safe - only one transition of states possible at the time.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
wait_on_frames_consumption |
bool
|
Flag telling if all frames from buffer must be consumed before completion of this operation. |
True
|
Throws: * StreamOperationNotAllowedError: if executed in context of incorrect state of the source
Source code in inference/core/interfaces/camera/video_source.py
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get_from_queue(queue, timeout=None, on_successful_read=lambda: None, purge=False)
¶
Function is supposed to take element from the queue waiting on the first element to appear using timeout
parameter. One may ask to go to the very last element of the queue and return it - then purge
should be set
to True. No additional wait on new elements to appear happen and the purge stops once queue is free returning last
element consumed.
queue.task_done() and on_successful_read(...) will be called on each received element.
Source code in inference/core/interfaces/camera/video_source.py
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