Spatial labels on videos

How to create bounding boxes, masks on video frames in Python

Introduction

In this tutorial, you will learn how to programmatically create classes, objects and figures for video frames and upload them to Supervisely platform.

Supervisely supports different types of shapes / geometries for video annotation:

• bounding box (rectangle)

• mask (also known as bitmap)

• polygon - will be covered in other tutorials

• polyline - will be covered in other tutorials

• point - will be covered in other tutorials

• keypoints (also known as graph, skeleton, landmarks) - will be covered in other tutorials

Learn more about Supervisely Annotation JSON format here.

Bounding box and masks

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 demo data and create Virtual Environment.

git clone https://github.com/supervisely-ecosystem/video-figures
cd video-figures
./create_venv.sh

Step 3. Open repository directory in Visual Studio Code.

code -r .

Step 4. change ✅ workspace ID ✅ in local.env file by copying the ID from the context menu of the workspace. A new project with annotated videos will be created in the workspace you define:

WORKSPACE_ID=507 # ⬅️ change value

Copy workspace ID from context menu

Step 5. Start debugging src/main.py

Debug tutorial in Visual Studio Code

Python Code

Import libraries

import os
from os.path import join

from dotenv import load_dotenv

import supervisely as sly

Init API client

Init api for communicating with Supervisely Instance. First, we load environment variables with credentials and workspace ID:

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

With next lines we will check the you did everything right - API client initialized with correct credentials and you defined the correct workspace ID in local.env.

workspace_id = sly.env.workspace_id()
workspace = api.workspace.get_info_by_id(workspace_id)
if workspace is None:
    print("you should put correct workspaceId value to local.env")
    raise ValueError(f"Workspace with id={workspace_id} not found")

Create project

Create empty project with name "Demo" with one dataset "orange & kiwi" in your workspace on server. If the project with the same name exists in your dataset, it will be automatically renamed (Demo_001, Demo_002, etc ...) to avoid name collisions.

project = api.project.create(
    workspace.id,
    name="Demo",
    type=sly.ProjectType.VIDEOS,
    change_name_if_conflict=True,
)
dataset = api.dataset.create(project.id, name="orange & kiwi")
print(f"Project has been sucessfully created, id={project.id}")

Upload video to the dataset on server

video_path = "data/orange_kiwi.mp4"
video_name = sly.fs.get_file_name_with_ext(video_path)
video_info = api.video.upload_path(dataset.id, video_name, video_path)
print(f"Video has been sucessfully uploaded, id={video_info.id}")

Create annotation classes and update project meta

Color will be automatically generated if the class was created without color argument.

kiwi_obj_cls = sly.ObjClass("kiwi", sly.Rectangle, color=[0, 0, 255])
orange_obj_cls = sly.ObjClass("orange", sly.Bitmap, color=[255, 255, 0])

The next step is to create ProjectMeta - a collection of annotation classes and tags that will be available for labeling in the project.

project_meta = sly.ProjectMeta(obj_classes=[kiwi_obj_cls, orange_obj_cls])

And finally, we need to set up classes in our project on server:

api.project.update_meta(project.id, project_meta.to_json())

Prepare source data

masks_dir = "data/masks"

# prepare rectangle points for 10 demo frames
points = [
    [136, 632, 350, 817],
    [139, 655, 355, 842],
    [145, 672, 361, 864],
    [158, 700, 366, 885],
    [153, 700, 367, 885],
    [156, 724, 375, 914],
    [164, 745, 385, 926],
    [177, 770, 396, 944],
    [189, 793, 410, 966],
    [199, 806, 417, 980],
]

Create video objects

orange = sly.VideoObject(orange_obj_cls)
kiwi = sly.VideoObject(kiwi_obj_cls)

Create masks, rectangles, frames and figures

We are going to create ten masks from the following black and white images:

Ten black-and-white masks for every orange

Mask has to be the same size as the video

Supervisely SDK allows creating masks from NumPy arrays with the following values:

• 0 - nothing, 1 - pixels of target mask

• 0 - nothing, 255 - pixels of target mask

• False - nothing, True - pixels of target mask

frames = []
for mask in os.listdir(masks_dir):
    fr_index = int(sly.fs.get_file_name(mask).split("_")[-1])
    mask_path = join(masks_dir, mask)

    # orange will be labeled with a masks.
    # supports masks with values (0, 1) or (0, 255) or (False, True)
    bitmap = sly.Bitmap.from_path(mask_path)

    # kiwi will be labeled with a bounding box.
    bbox = sly.Rectangle(*points[fr_index])

    mask_figure = sly.VideoFigure(orange, bitmap, fr_index)
    bbox_figure = sly.VideoFigure(kiwi, bbox, fr_index)

    frame = sly.Frame(fr_index, figures=[mask_figure, bbox_figure])
    frames.append(frame)

Create VideoObjectCollection and FrameCollection

objects = sly.VideoObjectCollection([kiwi, orange])
frames = sly.FrameCollection(frames)

Get video file info

frame_size, vlength = sly.video.get_image_size_and_frames_count(video_path)

Create VideoAnnotation

Learn more about VideoAnnotation JSON format.

video_ann = sly.VideoAnnotation(
    img_size=frame_size,
    frames_count=vlength,
    objects=objects,
    frames=frames,
)

Upload annotation to the video on server

api.video.annotation.append(video_info.id, video_ann)
print(f"Annotation has been sucessfully uploaded to the video {video_name}")

Download video annotation from server

# download JSON annotation from server
video_ann_json = api.video.annotation.download(video_info.id)

# convert to Python objects
key_id_map = sly.KeyIdMap()
video_ann = sly.VideoAnnotation.from_json(video_ann_json, project_meta, key_id_map)

Note: key_id_map is required to convert annotation downloaded from server from JSON format to Python objects.

Learn more how to download video from Supervisely to local directory by id.

In the GitHub repository for this tutorial, you will find the full python script.

Recap

In this tutorial we learned how to

• quickly configure python development for Supervisely

• how to create a project and dataset with classes of different shapes

• how to initialize rectangles, masks for video frames

• how to construct Supervisely annotation and upload it with an videos to server