Spatial labels on images

How to create bounding boxes, polygons, masks, points and polylines in Python

Introduction

In this tutorial, you will learn how to programmatically create classes and labels of different shapes and upload them to Supervisely platform. Supervisely supports different types of shapes / geometries for image annotation:

• bounding box (rectangle)

• polygon

• multipolygon

• mask (also known as bitmap)

• polyline

• point

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

• cuboids - will be covered in other tutorials

Learn more about Supervisely Annotation JSON format here.

Bounding box, polygon and masks

Points and polyline

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/spatial-labels
cd spatial-labels
./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 images will be created in the workspace you define:

WORKSPACE_ID=506 # ⬅️ 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
import cv2
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 and workspace ID:

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

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 "berries" 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", change_name_if_conflict=True)
dataset = api.dataset.create(project.id, name="berries")
print(f"Project has been sucessfully created, id={project.id}")

Create annotation classes

strawberry = sly.ObjClass("strawberry", sly.Rectangle, color=[0, 0, 255])
raspberry = sly.ObjClass("raspberry", sly.Polygon, color=[0, 255, 0])
blackberry = sly.ObjClass("blackberry", sly.Bitmap, color=[255, 255, 0])
berry_center = sly.ObjClass("berry_center", sly.Point, color=[0, 255, 255])
separator = sly.ObjClass("separator", sly.Polyline)  # color will be generated randomly

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

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=[strawberry, raspberry, blackberry, berry_center, separator]
)

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

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

Create rectangle

Strawberry will be labeled with a bounding box.

bbox = sly.Rectangle(top=127, left=1726, bottom=1087, right=2560)
label1 = sly.Label(geometry=bbox, obj_class=strawberry)

Create polygon

Raspberry will be labeled with a polygon.

polygon = sly.Polygon(
    exterior=[
        [941, 663],
        [976, 874],
        [934, 1096],
        [819, 1196],
        [698, 1228],
        [527, 1081],
        [439, 1090],
        [331, 980],
        [359, 808],
        [452, 698],
        [549, 612],
        [762, 564],
        [879, 605],
    ]
)
label2 = sly.Label(geometry=polygon, obj_class=raspberry)

Create masks

Every blackberry will be labeled with a mask. So we are going to create three masks from the following black and white images:

Three black-and-white masks for every blackberry

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

Mask has to be the same size as the image

labels_masks = []
for mask_path in [
    "data/masks/Blackberry_01.png",
    "data/masks/Blackberry_02.png",
    "data/masks/Blackberry_03.png",
]:
    # read only first channel of an image
    image_black_and_white = cv2.imread(mask_path)[:, :, 0]
    
    # supports masks with values (0, 1) or (0, 255) or (False, True)
    mask = sly.Bitmap(image_black_and_white)
    label = sly.Label(geometry=mask, obj_class=blackberry)
    labels_masks.append(label)

Create image annotation

image_path = "data/berries-01.jpg"
height, width = cv2.imread(image_path).shape[0:2]

# result image annotation
all_labels = [label1, label2]
all_labels.extend(labels_masks)
ann = sly.Annotation(img_size=[height, width], labels=all_labels)

Upload image with annotation

Upload image to the dataset on server:

image_name = sly.fs.get_file_name_with_ext(image_path)
image_info = api.image.upload_path(dataset.id, image_name, image_path)
print(f"Image has been sucessfully uploaded, id={image_info.id}")

Upload annotation to the image on server:

api.annotation.upload_ann(image_info.id, ann)
print(f"Annotation has been sucessfully uploaded to the image {image_name}")

Create points

Let's create points for every berry on the second image and place them to the centers of the berries.

labels_points = []
for [row, col] in [
    [1313, 313],
    [1714, 1061],
    [1318, 1851],
    [554, 1912],
    [190, 808],
    [941, 1094],
]:
    point = sly.Point(row, col)
    label = sly.Label(geometry=point, obj_class=berry_center)
    labels_points.append(label)

Create polyline

polyline = sly.Polyline(
    [[883, 443], [1360, 803], [1395, 1372], [928, 1676], [458, 1372], [552, 554]]
)
label_line = sly.Label(geometry=polyline, obj_class=separator)

Upload the second image with annotation

image_path = "data/berries-02.jpg"
height, width = cv2.imread(image_path).shape[0:2]

# result image annotation
ann = sly.Annotation(img_size=[height, width], labels=[*labels_points, label_line])

# upload image to the dataset on server
image_name = sly.fs.get_file_name_with_ext(image_path)
image_info = api.image.upload_path(dataset.id, image_name, image_path)
print(f"Image has been sucessfully uploaded, id={image_info.id}")

# upload annotation to the image on server
api.annotation.upload_ann(image_info.id, ann)
print(f"Annotation has been sucessfully uploaded to the image {image_name}")

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, polygons, polylines, and points

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