Video and object tags

How to create, add, update and remove tags from Video and its objects.

Introduction

In this tutorial, you will learn how to create new tags for Video, its objects or frames and assign them, update its values or remove at all using the Supervisely SDK.

Supervisely supports different types of tags:

• NONE

• ANY_NUMBER

• ANY_STRING

• ONEOF_STRING

And could be applied to:

• ALL

• IMAGES_ONLY - in our case this indicates Videos

• OBJECTS_ONLY

You can find all the information about those types in the Tags in Annotations section and SDK documentation.

You can learn more about working with Video using Supervisely SDK and what Annotations for Video are.

Everything you need to reproduce this tutorial is on GitHub: source code, Visual Studio Code configuration, and a shell script for creating virtual env.

How to debug this tutorial

Step 1. Prepare ~/supervisely.env file with credentials. Learn more here.

Step 2. Clone repository with source code and create Virtual Environment.

git clone https://github.com/supervisely-ecosystem/how-to-work-with-video-object-tags
cd how-to-work-with-video-object-tags
./create_venv.sh

Step 3. Open repository directory in Visual Studio Code.

code -r .

Step 4. Create video project, for example, using this tutorial Spatial labels on videos.

There you see project classes after project initialization.

Project tags metadata after its initialization. This data is empty.

Visualization in Labeling Tool before we starting add tags.

Step 5. Change Workspace ID in local.env file by copying the ID from the context menu of the workspace. Do the same for Project ID and Dataset ID .

WORKSPACE_ID=82841  # ⬅️ change value
PROJECT_ID=240755  # ⬅️ change value
DATASET_ID=778169  # ⬅️ change value

Step 6. Start debugging src/main.py

Python Code

Import libraries

import os
import supervisely as sly
from dotenv import load_dotenv

Init API client

Init api for communicating with Supervisely Instance. First, we load environment variables with credentials, Project and Dataset IDs:

load_dotenv("local.env")
load_dotenv(os.path.expanduser("~/supervisely.env"))
api = sly.Api.from_env()

With next lines we will get values from local.env.

project_id = sly.env.project_id()
dataset_id = sly.env.dataset_id()

By using these IDs, we can retrieve the project metadata and annotations, and define the values needed for the following operations.

video_ids = api.video.get_list(dataset_id)
project_meta_json = api.project.get_meta(project_id)
project_meta = sly.ProjectMeta.from_json(data=project_meta_json)
video_ann_json = api.video.annotation.download(video_ids[0].id)

Define function to work with metadata

This function is used to recreate the source project metadata with new tag metadata. Right after updating the metadata, we need to obtain added metadata again to work with it in the next steps. In case a tag with the tag_name already exists in the metadata, we could just use it if it fits our requirements. If this tag doesn't meet our requirements, it would be better to create a new one with a different name.

def refresh_meta(project_meta, new_tag_meta):
    if not project_meta.tag_metas.has_key(new_tag_meta.name):
        new_tags_collection = project_meta.tag_metas.add(new_tag_meta)
        project_meta = sly.ProjectMeta(
            tag_metas=new_tags_collection, obj_classes=project_meta.obj_classes
        )
        api.project.update_meta(project_id, project_meta)
        new_prject_meta_json = api.project.get_meta(project_id)
        project_meta = sly.ProjectMeta.from_json(data=new_prject_meta_json)
        new_tag_meta = project_meta.tag_metas.get(new_tag_meta.name)
    else:
        tag_values = new_tag_meta.possible_values
        new_tag_meta = project_meta.tag_metas.get(new_tag_meta.name)
        if tag_values:
            if sorted(new_tag_meta.possible_values) != sorted(tag_values):
                sly.logger.warning(
                    f"Tag [{new_tag_meta.name}] already exists, but with another values: {new_tag_meta.possible_values}"
                )
    return new_tag_meta, project_meta

Create new tag metadata for video

Here, we are creating metadata for a video tag and using the function from the previous step to insert it into our project.

video_tag_meta = sly.TagMeta(
    name="fruits",
    value_type=sly.TagValueType.ANY_NUMBER,
    applicable_to=sly.TagApplicableTo.ALL,
)

new_tag_meta, project_meta = refresh_meta(project_meta, video_tag_meta)

Create new tag for video and its frames

When you pass information from tag metadata using its ID to the object, a new tag is created and appended.

To add a tag with value, you must define the value argument with possible values.

If you want to add a tag to frames, you must define the frame_range argument.

api.video.tag.add_tag(new_tag_meta.sly_id, video_ids[0].id, value=3)

tag_info = api.video.tag.add_tag(new_tag_meta.sly_id, video_ids[0].id, value=2, frame_range=[2, 6])

Visualization in Labeling Tool with new tags.

**Update tag value and frame range for video **

Also, if you need to correct tag values or frames, you can easily do so as follows:

api.video.tag.update_value(tag_id=tag_info["id"], tag_value=1)

api.video.tag.update_frame_range(tag_info["id"], [3, 5])

Delete tag

To remove a tag, all you need is its ID.

api.video.tag.remove_from_video(tag_info["id"])

Please note that you are only deleting the tag from the object. To remove a tag from the project (TagMeta), you need to use other SDK methods.

Create new tag metadatas for objects in video

The process is the same as for video, but now we strictly define the applicable_to parameter to specify which entities these tags can be added to. It is not necessary and depends solely on your desire to limit the types other than objects.

orange_object_tag_meta = sly.TagMeta(
    name="orange",
    value_type=sly.TagValueType.ONEOF_STRING,
    applicable_to=sly.TagApplicableTo.OBJECTS_ONLY,
    possible_values=["small", "big"],
)

kiwi_object_tag_meta = sly.TagMeta(
    name="kiwi",
    value_type=sly.TagValueType.ONEOF_STRING,
    applicable_to=sly.TagApplicableTo.OBJECTS_ONLY,
    possible_values=["medium", "small"],
)

orange_new_tag_meta, project_meta = refresh_meta(project_meta, orange_object_tag_meta)

kiwi_new_tag_meta, _ = refresh_meta(project_meta, kiwi_object_tag_meta)

Create new tag for object and frames with this object

There's nothing new that you haven't seen already, just added some lines to handle objects according to their classes. Collects only oranges tag ids for further processing.

project_objects = video_ann_json.get("objects")
created_tag_ids = {}
orange_ids = []
for object in project_objects:
    if object["classTitle"] == "orange":
        tag_id = api.video.object.tag.add(
            orange_new_tag_meta.sly_id, object["id"], value="big", frame_range=[2, 6]
        )
        created_tag_ids[object["id"]] = tag_id
        orange_ids.append(object["id"])
    elif object["classTitle"] == "kiwi":
        api.video.object.tag.add(kiwi_new_tag_meta.sly_id, object["id"], value="medium")

Visualization in Labeling Tool with new tags.

Update tag value and frame range for object

To correct tag values for the first orange in list, do so as follows:

tag_id_to_operate = created_tag_ids.get(orange_ids[0])

api.video.object.tag.update_value(tag_id_to_operate, "small")

api.video.object.tag.update_frame_range(tag_id_to_operate, [3, 5])

Delete tag from object

api.video.object.tag.remove(tag_id_to_operate)