Images

Introduction

In this tutorial we will focus on working with images using Supervisely SDK.

You will learn how to:

📗 Everything you need to reproduce this tutorial is on GitHub: source code and demo data.

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/tutorial-image.git

cd tutorial-image

./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.

WORKSPACE_ID=654 # ⬅️ change value

Step 5. Start debugging src/main.py.

Import libraries

import os
from dotenv import load_dotenv
import supervisely as sly

Init API client

First, we load environment variables with credentials and init API for communicating with Supervisely Instance.

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

Get variables from environment

In this tutorial, you will need an workspace ID that you can get from environment variables. Learn more here

workspace_id = sly.env.workspace_id()

Create new project and dataset

Create new project.

Source code:

project = api.project.create(workspace_id, "Fruits", change_name_if_conflict=True)

print(f"Project ID: {project.id}")

Output:

# Project ID: 15599

Create new dataset.

Source code:

dataset = api.dataset.create(project.id, "Fruits ds1")

print(f"Dataset ID: {dataset.id}")

Output:

# Dataset ID: 53465

Upload images from local directory to Supervisely

Upload single image.

Source code:

original_dir = "src/images/original"
path = os.path.join(original_dir, "lemons.jpg")
meta = {"my-field-1": "my-value-1", "my-field-2": "my-value-2"}

image = api.image.upload_path(
    dataset.id,
    name="Lemons",
    path=path,
    meta=meta # optional
)

print(f'Image "{image.name}" uploaded to Supervisely with ID:{image.id}')

Output:

# Image "Lemons.jpeg" uploaded to Supervisely platform with ID:17539453

Upload list of images.

✅ Supervisely API allows uploading multiple images in a single request. The code sample below sends fewer requests and it leads to a significant speed-up of our original code.

Source code:

names = [
    "grapes-1.jpg",
    "grapes-2.jpg",
    "oranges-2.jpg",
    "oranges-1.jpg",
]
paths = [os.path.join(original_dir, name) for name in names]

upload_info = api.image.upload_paths(dataset.id, names, paths)

print(f"{len(upload_info)} images successfully uploaded to Supervisely platform")

Output:

# 4 images successfully uploaded to Supervisely platform

Upload images as NumPy matrix

Single image

Source code:

img_np = sly.image.read(path)

np_image_info = api.image.upload_np(dataset.id, name="Lemons-np.jpeg", img=img_np)

print(f"Image successfully uploaded as NumPy matrix to Supervisely (ID: {np_image_info.id})")

Output:

# Image successfully uploaded as NumPy matrix to Supervisely (ID: 17539458)

Upload list of images

Source code:

names_np = [f"np-{name}" for name in names]
images_np = [sly.image.read(img_path) for img_path in paths]

np_images_info = api.image.upload_nps(dataset.id, names_np, images_np)

print(f"{len(images_np)} images successfully uploaded to platform as NumPy matrix")

Output:

# 4 images successfully uploaded to platform as NumPy matrix

Get information about images

Single image

Get information about image from Supervisely by id.

Source code:

image_info = api.image.get_info_by_id(image.id)

print(image_info)

Output:

# ImageInfo(
#     id=17539453,
#     name='Lemons.jpeg',
#     link=None,
#     hash='0jirgXvKGTJ8Yi0I9nCdf9MllQ9jP3Les1fD7/dt+Zk=',
#     mime='image/jpeg',
#     ext='jpeg',
#     size=66066,
#     width=640,
#     height=427,
#     labels_count=0,
#     dataset_id=54008,
#     created_at='2022-12-23T15:57:35.707Z',
#     updated_at='2022-12-23T15:57:35.707Z',
#     meta={},
#     path_original='nAXBxaxQJRARr0Ljkj6FfREj1Fq89.jpg',
#     full_storage_url='https://dev.supervisely.com/h5unublic/images/original/3/e/OK/d8Y7NnEj1Fq89.jpg',
#     tags=[]
# )

You can also get information about image from Supervisely by name.

Source code:

image_name = get_file_name(image.name)

image_info_by_name = api.image.get_info_by_name(dataset.id, image_name)

print(f"image name - {image_info_by_name.name}")

Output:

# image name - Lemons.jpeg

Get all images from dataset.

Get information about image from Supervisely by id.

Source code:

image_info_list = api.image.get_list(dataset.id)

print(f"{len(image_info_list)} images information received.")

Output:

# 10 images information received.

Download images to local directory

Single image

Download image from Supervisely to local directory by id.

Source code:

save_path = os.path.join(result_dir, image_info.name)

api.image.download_path(image_info.id, save_path)

print(f"Image has been successfully downloaded to '{save_path}'")

Output:

# Image has been successfully downloaded to 'src/images/result/Lemons.jpeg'

Download list of images to local directory

Download list of images from Supervisely to local directory by ids.

Source code:

image_ids = [img.id for img in image_info_list]
image_names = [img.name for img in image_info_list]
save_paths = [os.path.join(result_dir, img_name) for img_name in image_names]

api.image.download_paths(dataset.id, image_ids, save_paths)

print(f"{len(image_info_list)} images has been successfully downloaded.")

Output:

# 10 images has been successfully downloaded.

Download images as RGB NumPy matrix

Single image

Download image from Supervisely to local directory by id.

Source code:

image_np = api.image.download_np(image_info.id)

print(f"Image downloaded as RGB NumPy matrix. Image shape: {image_np.shape}")

Output:

# Image downloaded as RGB NumPy matrix. Image shape: (427, 640, 3)

Download list of images as RGB NumPy matrix

Download list of images from Supervisely to local directory by ids.

Source code:

image_np = api.image.download_nps(dataset.id, image_ids)

print(f"{len(image_np)} images downloaded in RGB NumPy matrix.")

Output:

# 10 images downloaded in RGB NumPy matrix.

Get and update image metadata

Get image metadata from server

Source code:

image_info = api.image.get_info_by_id(image.id)
meta = image_info.meta

print(meta)

Output:

# {'my-field-1': 'my-value-1', 'my-field-2': 'my-value-2'}

Update image metadata

Source code:

new_meta = {"my-field-3": "my-value-3", "my-field-4": "my-value-4"}

new_image_info = api.image.update_meta(id=image.id, meta=new_meta)

print(new_image_info["meta"])

Output:

# {'my-field-3': 'my-value-3', 'my-field-4': 'my-value-4'}

Get metadata in Image labeling toolbox

You can also get image metadata in Image labeling toolbox interface

Remove images from Supervisely

Remove one image.

Remove image from Supervisely by id

Source code:

api.image.remove(image.id)

print(f"Image (ID: {image.id}) successfully removed")

Output:

# Image (ID: 17539453) successfully removed

Remove list of images.

Remove list of images from Supervisely by ids.

Source code:

images_to_remove = api.image.get_list(dataset.id)
remove_ids = [img.id for img in images_to_remove]

api.image.remove_batch(remove_ids)

print(f"{len(remove_ids)} images successfully removed.")

Output:

# 9 images successfully removed.

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Last updated 14 days ago