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On this page
  • Table of contents
  • Step 1 — Videos Annotator?
  • Step 2 — Trackers
  • Step 3 — Creating the APP
  • Step 4 — Results

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  1. App development
  2. Advanced
  3. Tutorial - App Engine v1
  4. Chapter 1 Headless

Part 5 — Integrate custom tracker into Videos Annotator tool [OpenCV Tracker]

In this part, we will learn how to integrate any tracker into Videos Annotator.

PreviousPart 4 — SDK Preview [Lemons counter app]NextChapter 2 Modal Window

Last updated 2 years ago

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Table of contents

Step 1 — Videos Annotator?

Ok. This is the video annotation tool available in Supervisely. To launch the Videos Annotator, click on the videos dataset. Done!

Step 2 — Trackers

Supervisely has two types of tracking algorithms:

  1. Predefined — our base tracking solutions

  2. Apps — custom tracking solutions

They become available when you select the annotated object. In this part, we will integrate our own tracker (Apps).

Step 3 — Creating the APP

1. Add session tag to config

config.json (partially)

"session_tags": [
  "sly_video_tracking"
]

2. Handle track command

src/main.py (partially)

@g.my_app.callback("track")
@sly.timeit
@send_error_data
def track(api: sly.Api, task_id, context, state, app_logger):
    tracker = TrackerContainer(context)
    tracker.track()

Step 4 — Results

In order for the Videos Annotator to see our application, we link it through the sessions tags space. Only through the sly_video_tracking tag will Videos Annotator see our application. So:

How to handle commands — . The most important thing is to write a handler for the track command.

The OpenCV tracker logic is described . You can replace it with your own code (your own tracker).

🔥
see here
here
Videos Annotator?
Trackers
Creating the APP
Results
Integrate custom tracker into Videos Annotation tool
Videos Project
Videos Annotation Tool
Track Settings
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