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Video Analytics Service

The Video Analytics Service exposes a set of RESTful interfaces designed to simplify the deployment and use of hardware optimized video analytics pipelines. It offers RESTful interfaces to customize and execute pre-defined pipelines in either GStreamer or FFmpeg. Each pipeline type defines the semantics of its customizable parameters. Pipeline developers define named and versioned pipelines and expose them to users via simple RESTful interfaces.

Architecture Overview

Interfaces

Path Description
GET /models Return supported models.
GET /pipelines Return supported pipelines
GET /pipelines/{name}/{version} Return pipeline description.
POST /pipelines/{name}/{version} Start new pipeline instance.
DELETE /pipelines/{name}/{version}/{instance_id} Stop pipeline instance.
GET /pipelines/{name}/{version}/{instance_id} Return pipeline instance summary.
GET /pipelines/{name}/{version}/{instance_id}/status Return pipeline instance status.

Example Pipelines

The AD-Insertion E2E sample project contains two sample analytics pipelines.

Pipeline Description Example Request Example Detection
/pipelines/object_detection/1 Object Detection curl localhost:8080/pipelines/object_detection/1 -X POST -H 'Content-Type: application/json' -d '{ "source": { "uri": "https://github.com/intel-iot-devkit/sample-videos/blob/master/bottle-detection.mp4?raw=true", "type": "uri" }, "destination": { "type": "file", "uri": "file:///tmp/results.txt"}}' {"objects": [{"detection": {"bounding_box": {"x_max": 0.8820319175720215, "x_min": 0.7787219285964966, "y_max": 0.8876367211341858, "y_min": 0.3044118285179138}, "confidence": 0.6628172397613525, "label": "bottle", "label_id": 5}}], "resolution": {"height": 360, "width": 640}, "source": "https://github.com/intel-iot-devkit/sample-videos/blob/master/bottle-detection.mp4?raw=true", "timestamp": 7407821229}
/pipelines/emotion_recognition/1 Emotion Recognition curl localhost:8080/pipelines/emotion_recognition/1 -X POST -H 'Content-Type: application/json' -d '{ "source": { "uri": "https://github.com/intel-iot-devkit/sample-videos/blob/master/head-pose-face-detection-male.mp4?raw=true", "type": "uri" }, "destination": { "type": "file", "uri": "file:///tmp/results1.txt"}}' {"objects": [{"detection": {"bounding_box": {"x_max": 0.5859826803207397, "x_min": 0.43868017196655273, "y_max": 0.5278626084327698, "y_min": 0.15201044082641602}, "confidence": 0.9999998807907104, "label": "face", "label_id": 1}, "emotion": {"label": "neutral", "model": {"name": "0003_EmoNet_ResNet10"}}}], "resolution": {"height": 432, "width": 768}, "source": "https://github.com/intel-iot-devkit/sample-videos/blob/master/head-pose-face-detection-male.mp4?raw=true", "timestamp": 133083333333}

Building and Running the Video Analytics Service

The Video Analytis Service is built as a component of the AD-Insertion E2E sample project but can also be built and run as a standalone service.

Building

To build the service as a standalone component execute the following commands from the top level directory.

(1) cd ad-insertion/video-analytics-service  
(2) ./build.sh

Running

After the service is built it can be run using standard docker run commands (volume mounting is required only to see the sample results)

(1) sudo docker run -e http_proxy=$http_proxy -e https_proxy=$https_proxy -p8080:8080 -v/tmp:/tmp -d --rm video_analytics_service_gstreamer

Sample Request

To run a simple pipeline request against a running Video Analytics Service use the following commands.

(1) curl localhost:8080/pipelines/object_detection/1 -X POST -H 'Content-Type: application/json' -d '{ "source": { "uri": "https://github.com/intel-iot-devkit/sample-videos/blob/master/bottle-detection.mp4?raw=true", "type": "uri" }, "destination": { "type": "file", "uri": "file:///tmp/results.txt"}}'

(2) tail -f /tmp/results.txt

Sample Result

{"objects": [{"detection": {"bounding_box": {"x_max": 0.9027906656265259, "x_min": 0.792841911315918, "y_max": 0.8914870023727417, "y_min": 0.3036404848098755}, "confidence": 0.6788424253463745, "label": "bottle", "label_id": 5}}], "resolution": {"height": 360, "width": 640}, "source": "https://github.com/intel-iot-devkit/sample-videos/blob/master/bottle-detection.mp4?raw=true", "timestamp": 39854748603}