Intel® DL Streamer – Edge AI Extension

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This project will no longer be maintained by Intel.

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Intel has ceased development and contributions including, but not limited to, maintenance, bug fixes, new releases, or updates, to this project.

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The Intel® DL Streamer – Edge AI Extension is a microservice based on DL Streamer Pipeline Server that provides video analytics pipelines built with OpenVINO™ DL Streamer. Developers can send decoded video frames to the AI Extension which performs detection, classification, or tracking and returns the results. The AI Extension exposes gRPC and HTTP protocol APIs that are compatible with Azure Video Analyzer (AVA). Powered by OpenVINO™ toolkit, the AI Extension enables developers to build, optimize and deploy deep learning inference workloads for the best performance across Intel® architectures.

This document assumes a working knowledge of DL Streamer Pipeline Server.

Highlights

Spatial analytics features: Object Line Crossing and Object Zone Count similar to Azure Video Analyzer Spatial Analysis
Scalable, high-performance solution for serving video analytics pipelines on Intel® architectures
gRPC extension enables fast data transfer rate and low latency
HTTP extension provides a simpler API with inference results as JSON response
Supported Configuration: Pre-built Ubuntu Linux container for CPU, iGPU, Intel® Neural Compute Stick 2 (NCS2) and HDDL-R inference accelerators
Pre-loaded Object Detection, Object Classification, Object Tracking and Action Recognition pipelines to get started quickly
Pre-loaded models - see table below.

Name	Version	Model
object_detection	person_vehicle_bike	person-vehicle-bike-detection-crossroad-0078
object_detection	person	person-detection-retail-0013
object_detection	vehicle	vehicle-detection-0202
object_classification	vehicle_attributes	vehicle-attributes-recognition-barrier-0039
action_recognition	decoder	action-recognition-0001-decoder
action_recognition	encoder	action-recognition-0001-encoder

What's New

Added Support for AVA HTTP extension protocol

IMPORTANT: DL Streamer Edge AI Extension is provided as a sample. It is not intended to be deployed into production environments without modification. Developers deploying DL Streamer Edge AI Extension should review it against their production requirements.

Getting Started

The Intel® DL Streamer – Edge AI Extension can run as a standalone microservice or as a module within an Azure Video Analyzer graph. For more information on deploying the module as part of an Azure Video Analyzer graph please see Configuring the AI Extension for Azure Video Analyzer and refer to the Azure Video Analyzer documentation. The following instructions demonstrate building and running the microservice and test client outside of Azure Video Analyzer.

Supported Hardware

Refer to OpenVINO System Requirements for supported development and target runtime platforms and the OpenVINO Container Release Notes for details on providing access to accelerator devices.

Building the Edge AI Extension Image

Prerequisites

Building the image requires a modern Linux distro with the following packages installed:


Docker	Edge AI Extension requires Docker for its build, development, and runtime environments. Please install the latest for your platform. Docker.
bash	Edge AI Extension's build and run scripts require bash and have been tested on systems using versions greater than or equal to: `GNU bash, version 4.3.48(1)-release (x86_64-pc-linux-gnu)`. Most users shouldn't need to update their version but if you run into issues please install the latest for your platform. Instructions for macOS®* users here.

Building the Image

Run the docker image build script.

./docker/build.sh

Resulting image name is dlstreamer-edge-ai-extension:0.7.1

Running the Edge AI Extension

gRPC Extension Protocol

To run the Edge AI Extension gRPC server as a standalone microservice use the run_server.sh script with default options. For examples of additional options see Edge AI Extension Server Options.

./docker/run_server.sh

<snip>
{"levelname": "INFO", "asctime": "2021-12-01 21:54:13,049", "message": "Starting GRPC DL Streamer Edge AI Extension on port: 5001", "module": "grpc_server"}

To send a test frame to the microservice and receive object_detection results use the run_client.sh script.

./docker/run_client.sh

[AIXC] [2021-12-01 21:54:33,661] [MainThread  ] [INFO]: Extension Configuration: {'pipeline': {'name': 'object_detection', 'version': 'person_vehicle_bike'}}
[AIXC] [2021-12-01 21:54:33,665] [Thread-2    ] [INFO]: MediaStreamDescriptor request #1
[AIXC] [2021-12-01 21:54:33,668] [MainThread  ] [INFO]: [Received] AckNum: 1
[AIXC] [2021-12-01 21:54:34,034] [MainThread  ] [INFO]: ENTITY - person (1.00) [0.30, 0.47, 0.09, 0.39] []
[AIXC] [2021-12-01 21:54:34,034] [MainThread  ] [INFO]: ENTITY - person (0.97) [0.36, 0.40, 0.05, 0.24] []
[AIXC] [2021-12-01 21:54:34,035] [MainThread  ] [INFO]: ENTITY - person (0.94) [0.44, 0.42, 0.08, 0.43] []
[AIXC] [2021-12-01 21:54:34,035] [MainThread  ] [INFO]: ENTITY - person (0.92) [0.57, 0.38, 0.05, 0.25] []
[AIXC] [2021-12-01 21:54:34,035] [MainThread  ] [INFO]: ENTITY - person (0.91) [0.69, 0.56, 0.12, 0.43] []
[AIXC] [2021-12-01 21:54:34,035] [MainThread  ] [INFO]: ENTITY - person (0.90) [0.68, 0.42, 0.04, 0.24] []
[AIXC] [2021-12-01 21:54:34,035] [MainThread  ] [INFO]: ENTITY - person (0.82) [0.64, 0.36, 0.05, 0.27] []
[AIXC] [2021-12-01 21:54:34,035] [MainThread  ] [INFO]: ENTITY - person (0.60) [0.84, 0.44, 0.05, 0.29] []
[AIXC] [2021-12-01 21:54:34,036] [MainThread  ] [INFO]: Start Time: 1638395673.6689658 End Time: 1638395674.0366473 Frames: Tx 1 Rx 1 FPS: 2.7197451898885205
[AIXC] [2021-12-01 21:54:34,038] [MainThread  ] [INFO]: Client finished execution

HTTP Extension Protocol

To run the Edge AI Extension gRPC server as a standalone microservice use the run_server.sh script with --protocol http argument, this starts server on default HTTP port 8000. For examples of additional options see Edge AI Extension Server Options.

./docker/run_server.sh --protocol http

<snip>
{"levelname": "INFO", "asctime": "2021-12-01 21:55:34,882", "message": "Starting HTTP DL Streamer Edge AI Extension on port: 8000", "module": "http_server"}

To send a test frame to the microservice and receive object_detection results use the run_client.sh script with --protocol http option.

./docker/run_client.sh --protocol http

[AIXC] [2021-12-01 21:57:06,947] [MainThread  ] [INFO]: ENTITY - person (1.00) [0.30, 0.46, 0.09, 0.39] []
[AIXC] [2021-12-01 21:57:06,948] [MainThread  ] [INFO]: ENTITY - person (0.97) [0.36, 0.40, 0.05, 0.24] []
[AIXC] [2021-12-01 21:57:06,948] [MainThread  ] [INFO]: ENTITY - person (0.96) [0.43, 0.42, 0.09, 0.44] []
[AIXC] [2021-12-01 21:57:06,948] [MainThread  ] [INFO]: ENTITY - person (0.95) [0.69, 0.55, 0.13, 0.44] []
[AIXC] [2021-12-01 21:57:06,948] [MainThread  ] [INFO]: ENTITY - person (0.90) [0.57, 0.38, 0.05, 0.25] []
[AIXC] [2021-12-01 21:57:06,948] [MainThread  ] [INFO]: ENTITY - person (0.80) [0.64, 0.36, 0.05, 0.27] []
[AIXC] [2021-12-01 21:57:06,948] [MainThread  ] [INFO]: ENTITY - person (0.73) [0.68, 0.41, 0.04, 0.25] []
[AIXC] [2021-12-01 21:57:06,948] [MainThread  ] [INFO]: ENTITY - person (0.66) [0.84, 0.44, 0.05, 0.27] []
[AIXC] [2021-12-01 21:57:06,952] [MainThread  ] [INFO]: Start Time: 1638395826.5733032 End Time: 1638395826.9528449 Frames: Tx 1 Rx 1 FPS: 2.63475705016543
[AIXC] [2021-12-01 21:57:06,954] [MainThread  ] [INFO]: Client finished execution

Video Analytics Pipelines

The following pipelines are included in the AI Extension:

Name	Version	Definition
object_detection	person_vehicle_bike	definition
object_detection	person	definition
object_detection	vehicle	definition
object_classification	vehicle_attributes	definition
object_tracking	person_vehicle_bike	definition
spatial_analytics	object_zone_count	definition
spatial_analytics	object_line_crossing	definition
action_recognition	general	definition

There are three versions of the object zone count pipeline. They are all based on the same pipeline design but use different detection models.

Pipeline Version	Model
object_zone_count	person-vehicle-bike-detection-crossroad-0078
object_zone_count_person	person-detection-retail-0013
object_zone_count_vehicle	vehicle-detection-0202

Edge AI Extension Server Options

The server can be configured using command line options or environment variables (command line options take precedence).

Setting	Command line option	Environment variable	Default value
gRPC port	--grpc-port	GRPC_PORT	5001
HTTP port	--http-port	HTTP_PORT	8000
Extension Protocol	--protocol	PROTOCOL	grpc
RTSP Re-Streaming	--enable-rtsp	ENABLE_RTSP	false
Logging Level	--log-level	EXTENSION_LOG_LEVEL	INFO

Command line arguments that are not supported by the server are passed to VA Serving. See vaserving/arguments.py.

The run_server.sh script handles container specific arguments (e.g. volume mounting).

Enabling Inference Accelerator

See Enabling Hardware Accelerators for details on configuring docker resources for supported accelerators. The run server script will automatically detect installed accelerators and provide access to their resources.

Steps for enabling HDDL plugin for AVA

Refer to the following steps to provide HDDL plugin access to AVA compliant gRPC/HTTP extension processor node.

Check if system has user with uid 1001. If present, skip to step 3

id -un -- 1001

Create user(e.g: hddluser) with uid 1001

sudo adduser hddluser --uid 1001

Add user(with uid 1001) to users group

sudo usermod -a -G users $(id -un -- 1001)

If hddldaemon was started by a user with uid not 1001, kill hddldaemon process

kill -9 $(pidof hddldaemon autoboot)
pidof hddldaemon autoboot # Make sure none of them is alive

switch to user with uid 1001

su $(id -un -- 1001)

Restart hddldaemon

source /opt/intel/openvino_2021/bin/setupvars.sh
${HDDL_INSTALL_DIR}/bin/bsl_reset

Change the ownership of hddl files to user with uid 1001

sudo chown -R $(id -un -- 1001) /var/tmp/hddl_*

Additional troubleshooting tips are available at HDDL troubleshooting

Enabling Real Time Streaming Protocol (RTSP) Re-streaming

Pipelines can be configured to visualize input video with superimposed inference results such as bounding boxes, labels etc at request time but the RTSP server must first be enabled.

./docker/run_server.sh --enable-rtsp

Logging

Run the following command to monitor the logs from the docker container

docker logs dlstreamer-edge-ai-extension_0.7.1 -f

Developer Mode

The server run script includes a --dev flag which starts the container in "developer" mode. This mode runs with files from the host, not the container, which is useful for quick iteration and development.

./docker/run_server.sh --dev

Edge AI Extension Requests

The Edge AI Extension supports gRPC and HTTP APIs. This sections explains how the these APIs are mapped to VA Serving requests, which are broken into four categories.

Pipeline name and version
Pipeline parameters - runtime configuration (e.g. selecting inference accelerator)
Frame destination - optional destination of frames (e.g. RTSP)
Extensions - JSON Object specified at the time of request, stored with metadata.

gRPC Requests

The gRPC Server supports the extension_configuration field in the MediaStreamDescriptor message. This field contains a JSON string that must match the extension configuration schema. Note that pipeline name and version fields are required but parameters, frame-destination and extensions are optional. The parameters field matches the parameters JSON in a VA Serving request and the frame-destination field matches the frame property in the destination part of the request. See example below.

{
    "pipeline": {
        "name": "object_detection",
        "version": "person_vehicle_bike",
        "parameters": {
            "inference-device": "GPU"
        },
        "frame-destination": {},
        "extensions":{
            "camera-name": "Camera1"
        }
    }
}

Sending this message starts a pipeline with the specified request. Frames are then supplied as a MediaSample and results are returned in an Inference message. The protobuf Python bindings are used as the bridge between the client and the server.

HTTP Requests

Unlike gRPC the HTTP extension protocol is based on single frame operations. We have introduced the concept of a stream identifier to maintain state between transactions and realize the performance benefits of pipeline execution.

Invocation with Pipeline Name and Version

A POST endpoint in the form hostname:port/<pipeline_name>/<pipeline_version> is used to invoke inference using specified pipeline. The request body contains the image encoded in one of the supported content types shown below. Headers will be as follows:

Key	Value(s)
Accept	application/json
Authorization	Basic, Digest, Bearer (through custom header support)
Content-Type	image/jpeg, image/png, image/bmp
Content-Length	Image data length, in bytes
User-Agent	Azure Media Services

Image format image/x-raw is not supported.

If detections are found then the response is a 200 code and the JSON data that conforms to the AVA inference metadata schema, otherwise a code is returned as per the table below.

Code	HTTP Status	MSFT Description	VA Serving Status
200	OK	Inference results found	Successful inference with detections
204	No Content	No result found by the AI	Successful inference with no detections
400	Bad Request	Not expected	Unsupported pipeline
500	Internal Server Error	Not expected	Pipeline execution error
503	Server Busy	Video Analyzer will back-off based on "Retry-After" header	Not expected

An example invocation would be:

curl -i -X POST "localhost:8000/object_detection/person" -H "Content-Type: image/png" --data-binary @sampleframes/sample01.png

And response:

HTTP/1.0 200 OK
Date: Wed, 01 Dec 2021 23:12:14 GMT
Server: WSGIServer/0.2 CPython/3.8.10
content-type: application/json
content-length: 2434

{"inferences": [
  {"type": "ENTITY", "entity": {"tag": {"value": "person", "confidence": 0.9973744}, "box": {"l": 0.83602935, "t": 0.42902693, "w": 0.05125153, "h": 0.28660968}, "attributes": [], "id": ""}, "subtype": "", "inferenceId": "", "relatedInferences": [], "extensions": {}}, {"type": "ENTITY", "entity": {"tag": {"value": "person", "confidence": 0.99314684}, "box": {"l": 0.69950473, "t": 0.5465958, "w": 0.10379219, "h": 0.43978453}, "attributes": [], "id": ""}, "subtype": "", "inferenceId": "", "relatedInferences": [], "extensions": {}},
  {"type": "ENTITY", "entity": {"tag": {"value": "person", "confidence": 0.99254084}, "box": {"l": 0.63955873, "t": 0.35717422, "w": 0.050171494, "h": 0.26419628}, "attributes": [], "id": ""}, "subtype": "", "inferenceId": "", "relatedInferences": [], "extensions": {}},
  {"type": "ENTITY", "entity": {"tag": {"value": "person", "confidence": 0.9918835}, "box": {"l": 0.3021902, "t": 0.45843524, "w": 0.07861191, "h": 0.394611}, "attributes": [], "id": ""}, "subtype": "", "inferenceId": "", "relatedInferences": [], "extensions": {}},
  {"type": "ENTITY", "entity": {"tag": {"value": "person", "confidence": 0.97703844}, "box": {"l": 0.42323923, "t": 0.41540772, "w": 0.070638955, "h": 0.34051108}, "attributes": [], "id": ""}, "subtype": "", "inferenceId": "", "relatedInferences": [], "extensions": {}},
  {"type": "ENTITY", "entity": {"tag": {"value": "person", "confidence": 0.959652}, "box": {"l": 0.3596026, "t": 0.39279896, "w": 0.05030316, "h": 0.26924574}, "attributes": [], "id": ""}, "subtype": "", "inferenceId": "", "relatedInferences": [], "extensions": {}},
  {"type": "ENTITY", "entity": {"tag": {"value": "person", "confidence": 0.8912417}, "box": {"l": 0.67697275, "t": 0.40540057, "w": 0.04417312, "h": 0.25463247}, "attributes": [], "id": ""}, "subtype": "", "inferenceId": "", "relatedInferences": [], "extensions": {}},
  {"type": "ENTITY", "entity": {"tag": {"value": "person", "confidence": 0.76399636}, "box": {"l": 0.5680411, "t": 0.37546125, "w": 0.047278523, "h": 0.24400136}, "attributes": [], "id": ""}, "subtype": "", "inferenceId": "", "relatedInferences": [], "extensions": {}},
  {"type": "ENTITY", "entity": {"tag": {"value": "person", "confidence": 0.6758359}, "box": {"l": 0.44094166, "t": 0.447357, "w": 0.07387778, "h": 0.4110204}, "attributes": [], "id": ""}, "subtype": "", "inferenceId": "", "relatedInferences": [], "extensions": {}}
  ], "timestamp": "0"}

Stream Identifier

When the first request is made, the corresponding pipeline is started. A request is defined as the combination of the pipeline name, version and parameters. A stream identifier, which is supplied as URL parameter stream-id, allows subsequent requests to be routed to that pipeline. There are some special rules for stream identifiers:

If you supply parameters, a stream identifier must also be provided. If not, a 400 error will be returned.
Subsequent requests must match initial request with same stream identifier, otherwise a 400 error will be returned.
If neither parameters nor stream identifer are supplied, a stream identifier will be created based on the pipeline name and version.

Example URL: localhost:8000/object_detection/person?stream-id=stream1

If using curl, ensure the URL is quoted so that parameters with dashes in their name are correctly interpreted.

curl -i -X POST "localhost:8000/object_detection/person?stream-id=stream1" -H "Content-Type: image/png" --data-binary @sampleframes/sample01.png

Pipeline Parameters

Pipeline parameters can be supplied as query parameters. Only basic types are supported: string, float, int and boolean. The server will infer parameter type from its value. Remember to also supply a stream identifier. The following example uses string parameter detection-model-instance-id and float parameter threshold.

Example URL:

curl -i -X POST "localhost:8000/object_detection/person?stream-id=stream2&detection-model-instance-id=id1&threshold=0.75" -H "Content-Type: image/png" --data-binary @sampleframes/sample01.png

RTSP Re-Streaming

RTSP re-streaming is configured using reserved URL parameters frame-destination-type and frame-destination-path. RTSP must be enabled at server start with --enable-rtsp flag. This example specifies an endpoint named person.

Example URL:

curl -i -X POST "localhost:8000/object_detection/person?stream-id=stream3&frame-destination-type=rtsp&frame-destination-path=person" -H "Content-Type: image/png" --data-binary @sampleframes/sample01.png

Extensions

Extensions are tags that are specified in the request and appear in the the extensions field of each inference result. They are specified using URL parameters prepended with extensions- with the remaining part of the parameter acting as the extension key. The following example specifies an extension key image with value sample01.png.

Example URL:

curl -i -X POST "localhost:8000/object_detection/person?stream-id=extension-test&extensions-image=sample01.png" -H "Content-Type: image/png" --data-binary @sampleframes/sample01.png

Selecting Inference Accelerator

Assuming the server is configured to support an accelerator it can be selected by setting the appropriate parameter in the request. The parameter name will be defined in the pipeline definition file, usually detection-device. If the pipeline supports classification or tracking then classification-device and tracking-device can also be set.

See Enabling Hardware Accelerators for details on inference device name for supported accelerators.

Configuring the AI Extension for Azure Video Analyzer

gRPC

Please refer to the Analyze live video with Intel OpenVINO™ DL Streamer – Edge AI Extension tutorial for deployment manifests, topologies or operations files and other details.

HTTP

Configurations for the HTTP protocol can be adapted from Analyze live video using OpenVINO™ Model Server – AI Extension from Intel.

Edge AI Extension Client

A test client is provided to demonstrate the capabilities of the Edge AI Extension. The test client script run_client.sh sends frames(s) to the extension module and prints inference results. Use the --help option to see how to use the script. All arguments are optional.

./docker/run_client.sh --help

All arguments are optional, usage is as follows
  [ -s : gRPC server address, defaults to None]
  [ --server-ip : Specify the server ip to connect to ] (defaults to localhost)
  [ --grpc-port : Specify the grpc server port to connect to ] (defaults to 5001)
  [ --http-port : Specify the http server port to connect to ] (defaults to 8000)
  [ --sample-file-path : Specify the sample file path to run] (defaults to sampleframes/sample01.png)
  [ --loop-count : How many times to loop the source after it finishes ]
  [ --number-of-streams : Specify number of streams (one client process per stream)]
  [ --fps-interval FPS_INTERVAL] (interval between frames in seconds, defaults to 0)
  [ --frame-rate FRAME_RATE] (send frames at given fps, default is no limit)
  [ --frame-queue-size : Max number of frames to buffer in client, defaults to 200]
  [ --shared-memory : Enables and uses shared memory between client and server ] (defaults to off)
  [ --output-file-path : Specify the output file path to save inference results in jsonl format] (defaults to /tmp/results.jsonl)
  [ --extension-config : JSON string or file containing extension configuration]
  [ --pipeline-name : Name of the pipeline to run](defaults to object_detection)
  [ --pipeline-version : Name of the pipeline version to run] (defaults to person_vehicle_bike)
  [ --pipeline-parameters : Pipeline parameters]
  [ --pipeline-extensions : JSON string containing tags to be added to extensions field in results]
  [ --frame-destination : Frame destination for rtsp restreaming]
  [ --http-image-encoding : Type of encoding to use when sending http request] (defaults to jpeg)
  [ --http-url : Complete url path to send http request]
  [ --http-stream-id : stream-id to map pipeline in server, must specify when any of parameters, extensions or frame destination set ]
  [ --dev : Mount local source code] (use for development)

Notes:

When using --extension-config with grpc you must not set any of the following options
- --pipeline-name
- --pipeline-version
- --pipeline-parameters
- --pipeline-extensions
- --frame-destination
Media or log file must be inside container or in volume mounted path
When using without shared memory decoded image frames must be less than 4MB (the maximum gPRC message size)
If you are behind a firewall ensure no_proxy contains localhost in docker config and system settings.

Examples

Setting Parameters with HTTP

Set parameters detection-model-instance-id and threshold

./docker/run_client.sh --protocol http \
 --pipeline-name object_detection --pipeline-version person --http-image-encoding png \
 --pipeline-parameters '{\"detection-model-instance-id\":\"test\",\"threshold\":0.99}' \
 --http-stream-id test

RSTP Re-Streaming with gRPC

Run client with frame destination set. For demonstration, path set as person-detection in example request below.

./docker/run_client.sh --pipeline-name object_detection --pipeline-version person_vehicle_bike --sample-file-path https://github.com/intel-iot-devkit/sample-videos/blob/master/people-detection.mp4?raw=true --frame-destination '{\"type\":\"rtsp\",\"path\":\"person-detection\"}'

Now connect and visualize using VLC network stream with url rtsp://localhost:8554/person-detection.

RSTP Re-Streaming with HTTP

./docker/run_client.sh --protocol http \
 --pipeline-name object_detection --pipeline-version person \
 --http-stream-id rtsp \
 --pipeline-parameters '{\"threshold\":0.99}' \
 --frame-destination '{\"type\":\"rtsp\",\"path\":\"person-detection\"}' \
 --pipeline-extensions '{\"video\":\"people-detection.mp4\"}' \
 -f https://github.com/intel-iot-devkit/sample-videos/blob/master/people-detection.mp4?raw=true

Spatial Analytics Pipelines

Note: HTTP Server doesn't support Spatial Analytics Pipelines.

Object Zone Count

The spatial_analytics/object_zone_count pipeline generates events containing objects detected in zones defined by the extension configuration. For more information on the underlying zone event operation, see object_zone_count README.

Build and Run

Build and run server as normal.

Run client with example extension configuration. The object_zone_count.json extension configuration contains zone definitions to generate object-zone-count events for a media stream. Look for the below events in client output:

./docker/run_client.sh \
  --extension-config /home/edge-ai-extension/client/extension-config/object_zone_count.json

<snip>
[AIXC] [2021-09-09 19:50:45,607] [MainThread  ] [INFO]: ENTITY - person (1.00) [0.30, 0.47, 0.09, 0.39] ['inferenceId: 4ea7a39d41eb4befae87894a48e1ea6a', 'subtype: objectDetection']
[AIXC] [2021-09-09 19:50:45,607] [MainThread  ] [INFO]: ENTITY - person (0.97) [0.36, 0.40, 0.05, 0.24] ['inferenceId: 287b569a93fb4d4386af3cb0871b52ca', 'subtype: objectDetection']
[AIXC] [2021-09-09 19:50:45,607] [MainThread  ] [INFO]: ENTITY - person (0.94) [0.44, 0.42, 0.08, 0.43] ['inferenceId: 4e82d111fccc4649a650fe205f70d079', 'subtype: objectDetection']
[AIXC] [2021-09-09 19:50:45,607] [MainThread  ] [INFO]: ENTITY - person (0.92) [0.57, 0.38, 0.05, 0.25] ['inferenceId: cdc5e1dfa20a41b69bb05d3289e773d5', 'subtype: objectDetection']
[AIXC] [2021-09-09 19:50:45,607] [MainThread  ] [INFO]: ENTITY - person (0.91) [0.69, 0.56, 0.12, 0.43] ['inferenceId: d873d43a9e274e5b8693b1df87764e30', 'subtype: objectDetection']
[AIXC] [2021-09-09 19:50:45,607] [MainThread  ] [INFO]: ENTITY - person (0.90) [0.68, 0.42, 0.04, 0.24] ['inferenceId: ab759106752a45279007bae98eabd032', 'subtype: objectDetection']
[AIXC] [2021-09-09 19:50:45,607] [MainThread  ] [INFO]: ENTITY - person (0.82) [0.64, 0.36, 0.05, 0.27] ['inferenceId: 908960e242334549a52bafb33f6a29a0', 'subtype: objectDetection']
[AIXC] [2021-09-09 19:50:45,607] [MainThread  ] [INFO]: ENTITY - person (0.60) [0.84, 0.44, 0.05, 0.29] ['inferenceId: 1a74f84445cf49cbb517ff2ea83f74c3', 'subtype: objectDetection']
[AIXC] [2021-09-09 19:50:45,608] [MainThread  ] [INFO]: EVENT - Zone2: ['inferenceId: fe65126e0db64e659b6414345d52a96c', 'subtype: object-zone-count', "relatedInferences: ['4ea7a39d41eb4befae87894a48e1ea6a']", "status: ['intersects']", 'zone-count: 1']
[AIXC] [2021-09-09 19:50:45,608] [MainThread  ] [INFO]: EVENT - Zone3: ['inferenceId: 2b1685ebe9914805b962615e19116b87', 'subtype: object-zone-count', "relatedInferences: ['287b569a93fb4d4386af3cb0871b52ca', '4e82d111fccc4649a650fe205f70d079', 'cdc5e1dfa20a41b69bb05d3289e773d5', 'd873d43a9e274e5b8693b1df87764e30', 'ab759106752a45279007bae98eabd032', '908960e242334549a52bafb33f6a29a0', '1a74f84445cf49cbb517ff2ea83f74c3']", "status: ['intersects', 'intersects', 'within', 'intersects', 'within', 'within', 'intersects']", 'zone-count: 7']

Enabling RTSP Output

To get a visual of object_zone_count extension, run with object_zone_count_rendered.json extension configuration which sets enable_watermark and frame-destination parameters for RTSP re streaming.

gvawatermark does not draw the polygon lines but markers/dots showing the boundary of the defined polygon regions, so the viewer must currently "connect the dots" themself.

Build as normal but run server with --enable-rtsp flag.

Run client with example extension configuration, with rendering support:

  ./docker/run_client.sh \
  --extension-config /home/edge-ai-extension/client/extension-config/object_zone_count_rendered.json \
  --sample-file-path https://github.com/intel-iot-devkit/sample-videos/blob/master/person-bicycle-car-detection.mp4?raw=true

Connect and visualize: Re-stream pipeline using VLC network stream with url rtsp://localhost:8554/zone-events.

Object Line Crossing

The spatial_analytics/object_line_crossing pipeline generates events containing objects which crossed lines defined by the extension configuration. For more information on the underlying line crossing operation, see object_line_crossing README.

Build and Run

Build and run server as normal.

Run client with example extension configuration. The line_cross_tracking_config.json extension configuration contains example line definitions needed to generate object_line_crossing events for a media stream. Look for the below events in client output:

  ./docker/run_client.sh \
  --extension-config /home/edge-ai-extension/client/extension-config/object_line_crossing.json \
  --sample-file-path https://github.com/intel-iot-devkit/sample-videos/blob/master/people-detection.mp4?raw=True

<snip>
[AIXC] [2021-05-12 18:57:01,315] [MainThread  ] [INFO]: ENTITY - person (1.00) [0.40, 0.27, 0.12, 0.62] ['inferenceId: d47a4192ca4b4933a6c6c588220f59de', 'subtype: objectDetection', 'id: 1']
[AIXC] [2021-05-12 18:57:01,315] [MainThread  ] [INFO]: EVENT - hallway_bottom: ['inferenceId: 520d7506e5c94f3b9aeb1d157af6311c', 'subtype: lineCrossing', "relatedInferences: ['d47a4192ca4b4933a6c6c588220f59de']", 'counterclockwiseTotal: 1', 'total: 1', 'clockwiseTotal: 0', 'direction: counterclockwise']

Enabling RTSP Output

Adding a configuration parameter to specify the frame-destination enables a secondary workflow, with VA Serving rendering visualization of lines and entity detections/events (shown below).

By setting enable_watermark and frame-destination parameter for RTSP re streaming, the caller may visualize the output. This added to the line_cross_tracking_config_rtsp.json extension configuration. So following the same instructions above but swapping the extension configuration used will dynamically produce the scene using rudimentary markers/dots showing the start and end points of defined lines. This allows the DL Streamer gvawatermark element (used in the frame-destination) to handle rendering.

To get a visual of object_line_crossing extension, run with object_line_crossing_rendered.json extension configuration which sets enable_watermark and frame-destination parameters for RTSP re streaming.

gvawatermark does not draw the lines, so the viewer must currently "connect the dots" themself.

Build as normal but run server with --enable-rtsp flag

Run client with example extension configuration, with rendering support:

  ./docker/run_client.sh \
  --extension-config /home/edge-ai-extension/client/extension-config/object_line_crossing_rendered.json \
  --sample-file-path https://github.com/intel-iot-devkit/sample-videos/blob/master/people-detection.mp4?raw=True

Connect and visualize: Re-stream pipeline using VLC network stream with url rtsp://localhost:8554/vaserving.

Updating or Changing Detection and Classification Models

Before updating the models used by a pipeline please see the format of pipeline definition files and read the tutorial on changing object detection models.

Most of the steps to change models used by the Extension are the same as for the above tutorial, but it assumes you are working with the REST service and not the AI Extension module. The AI Extension specific steps are called out in the following sections.

Run Existing Object Detection Pipeline

Get baseline results for existing object_detection model person-vehicle-bike-detection-crossroad-0078

./docker/run_server.sh

<snip>
/object_classification/vehicle_attributes/pipeline.json", "module": "pipeline_manager"}
{"levelname": "INFO", "asctime": "2021-01-21 12:10:10,288", "message": "===========================", "module": "pipeline_manager"}
{"levelname": "INFO", "asctime": "2021-01-21 12:10:10,288", "message": "Completed Loading Pipelines", "module": "pipeline_manager"}
{"levelname": "INFO", "asctime": "2021-01-21 12:10:10,289", "message": "===========================", "module": "pipeline_manager"}
{"levelname": "INFO", "asctime": "2021-01-21 12:10:10,292", "message": "Starting DL Streamer Edge AI Extension on port: 5001", "module": "__main__"}

In a separate terminal:

./docker/run_client.sh

<snip>
[AIXC] [2020-11-20 23:29:11,417] [MainThread  ] [INFO]: - person (1.00) [0.30, 0.47, 0.09, 0.39]
[AIXC] [2020-11-20 23:29:11,417] [MainThread  ] [INFO]: - person (0.97) [0.36, 0.40, 0.05, 0.24]
[AIXC] [2020-11-20 23:29:11,417] [MainThread  ] [INFO]: - person (0.94) [0.44, 0.42, 0.08, 0.43]
[AIXC] [2020-11-20 23:29:11,418] [MainThread  ] [INFO]: - person (0.92) [0.57, 0.38, 0.05, 0.25]
[AIXC] [2020-11-20 23:29:11,418] [MainThread  ] [INFO]: - person (0.91) [0.69, 0.56, 0.12, 0.43]
[AIXC] [2020-11-20 23:29:11,418] [MainThread  ] [INFO]: - person (0.90) [0.68, 0.42, 0.04, 0.24]
[AIXC] [2020-11-20 23:29:11,418] [MainThread  ] [INFO]: - person (0.82) [0.64, 0.36, 0.05, 0.27]
[AIXC] [2020-11-20 23:29:11,418] [MainThread  ] [INFO]: - person (0.60) [0.84, 0.44, 0.05, 0.29]
<snip>

Send a request to run classification pipeline

./docker/run_client.sh --pipeline-name object_classification --pipeline-version vehicle_attributes

Send a request to run detection pipeline on GPU

./docker/run_client.sh --pipeline-name object_detection --pipeline-version person_vehicle_bike \
  --pipeline-parameters '{\"detection-device\":\"GPU\"}'

Add New Model to Models List

Copy the existing model list models_list/models.list.yml to models_list/yolo-models.list.yml then add the following entry:

- model: yolo-v2-tiny-tf
  alias: yolo
  version: 1
  precision: [FP16,FP32]

Update Pipeline Definition File to Use New Model

Copy, rename and update the existing object detection pipeline to reference yolo-v2-tiny-tf model:

cp -r pipelines/object_detection/person_vehicle_bike pipelines/object_detection/yolo
sed -i -e s/person_vehicle_bike/yolo/g pipelines/object_detection/yolo/pipeline.json

Rebuild Edge AI Extension with new Model and Pipeline

./docker/build.sh --models models_list/yolo-models.list.yml

The model will now be in models folder in the root of the project:

models
└── yolo
    └── 1
        ├── FP16
        │   ├── yolo-v2-tiny-tf.bin
        │   ├── yolo-v2-tiny-tf.mapping
        │   └── yolo-v2-tiny-tf.xml
        ├── FP32
        │   ├── yolo-v2-tiny-tf.bin
        │   ├── yolo-v2-tiny-tf.mapping
        │   └── yolo-v2-tiny-tf.xml
        └── yolo-v2-tiny-tf.json

Check that expected model and pipeline are present in the built image:

docker run -it --entrypoint /bin/bash dlstreamer-edge-ai-extension:0.7.1

vaserving@82dd59743ca3:~$ ls models
action_recognition object_classification object_detection yolo
vaserving@82dd59743ca3:~$ ls pipelines/object_detection
  person_vehicle_bike person vehicle yolo

Run Edge AI Extension with new Model and Pipeline

Restart service

Restart the service to ensure we are using the image with the yolo-v2-tiny-tf model

docker stop dlstreamer-edge-ai-extension_0.7.1
docker/run_server.sh

Run the client

Note different results due to different model

docker/run_client.sh --pipeline-name object_detection --pipeline-version yolo

<snip>
[AIXC] [2021-01-07 06:51:13,081] [MainThread  ] [INFO]: - person (0.82) [0.63, 0.36, 0.06, 0.24] []
[AIXC] [2021-01-07 06:51:13,081] [MainThread  ] [INFO]: - person (0.78) [0.56, 0.37, 0.06, 0.23] []
[AIXC] [2021-01-07 06:51:13,081] [MainThread  ] [INFO]: - person (0.63) [0.44, 0.43, 0.11, 0.43] []
[AIXC] [2021-01-07 06:51:13,081] [MainThread  ] [INFO]: - person (0.63) [0.31, 0.45, 0.09, 0.23] []
[AIXC] [2021-01-07 06:51:13,081] [MainThread  ] [INFO]: - person (0.62) [0.69, 0.38, 0.06, 0.23] []
[AIXC] [2021-01-07 06:51:13,081] [MainThread  ] [INFO]: - person (0.60) [0.40, 0.44, 0.07, 0.27] []
[AIXC] [2021-01-07 06:51:13,081] [MainThread  ] [INFO]: - person (0.59) [0.45, 0.43, 0.08, 0.29] []
[AIXC] [2021-01-07 06:51:13,082] [MainThread  ] [INFO]: - person (0.57) [0.33, 0.40, 0.07, 0.20] []
[AIXC] [2021-01-07 06:51:13,082] [MainThread  ] [INFO]: - person (0.57) [0.76, 0.46, 0.13, 0.23] []
[AIXC] [2021-01-07 06:51:13,082] [MainThread  ] [INFO]: - person (0.55) [0.41, 0.44, 0.03, 0.10] []
<snip>

Name		Name	Last commit message	Last commit date
Latest commit History 202 Commits
client		client
common		common
contracts		contracts
docker		docker
models_list		models_list
pipeline_diagrams		pipeline_diagrams
pipelines		pipelines
sampleframes		sampleframes
server		server
tests		tests
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
requirements.txt		requirements.txt
third-party-programs.txt		third-party-programs.txt

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Intel® DL Streamer – Edge AI Extension

Highlights

What's New

Getting Started

Supported Hardware

Building the Edge AI Extension Image

Prerequisites

Building the Image

Running the Edge AI Extension

gRPC Extension Protocol

HTTP Extension Protocol

Video Analytics Pipelines

Edge AI Extension Server Options

Enabling Inference Accelerator

Steps for enabling HDDL plugin for AVA

Enabling Real Time Streaming Protocol (RTSP) Re-streaming

Logging

Developer Mode

Edge AI Extension Requests

gRPC Requests

HTTP Requests

Invocation with Pipeline Name and Version

Stream Identifier

Pipeline Parameters

RTSP Re-Streaming

Extensions

Selecting Inference Accelerator

Configuring the AI Extension for Azure Video Analyzer

gRPC

HTTP

Edge AI Extension Client

Examples

Setting Parameters with HTTP

RSTP Re-Streaming with gRPC

RSTP Re-Streaming with HTTP

Spatial Analytics Pipelines

Object Zone Count

Build and Run

Enabling RTSP Output

Object Line Crossing

Build and Run

Enabling RTSP Output

Updating or Changing Detection and Classification Models

Run Existing Object Detection Pipeline

Send a request to run classification pipeline

Send a request to run detection pipeline on GPU

Add New Model to Models List

Update Pipeline Definition File to Use New Model

Rebuild Edge AI Extension with new Model and Pipeline

Run Edge AI Extension with new Model and Pipeline

Restart service

Run the client

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