Multi-view images
Introduction
This easy-to-follow tutorial will show you how to upload multi-view images to Supervisely using Python SDK and get the advantage of the multi-view image annotaion in the Supervisely Labeling Toolbox, which allows you to label images quickly and efficiently on one screen. You will learn how to enable multi-view in the project settings, upload multi-view images and explore the multi-view in the labeling interface.
In this tutorial, we will show you how to do it programmatically using Python, but you can also do it manually in the Web UI using Import Images Groups app from Supervisely Ecosystem or using our Import Wizard in the Web UI. Here is an illustrated example of how to do it:
How to debug this tutorial
Everything you need to reproduce this tutorial is on GitHub: source code and additional app files.
Step 1. Prepare ~/supervisely.env
file with credentials. Learn more here.
Step 2. Clone the repository with source code and demo data and create a Virtual Environment.
Step 3. Open the repository directory in Visual Studio Code.
Step 4. Change the Workspace ID in the local.env
file by copying the ID from the context menu.
Step 5. Start debugging src/main.py
.
Supervisely instance version >= 6.9.14 Supervisely SDK version >= 6.72.214
In the tutorial, Supervisely Python SDK version is not directly defined in the requirements.txt. But when developing your app, we recommend defining the SDK version in the requirements.txt.
Import libraries
Load environment variables
Init API client
Explore the directory with images
here is the structure of the directory with images (src/images
):
Create a new project and dataset
Enable multi-view in the project settings
You can also enable multi-view in the Image Labeling Tool interface:
And now we're ready to upload images.
How to upload multi-view images
In this tutorial, we'll be using the api.image.upload_multiview_images
method to upload multi-view images to Supervisely.
Parameters | Type | Description |
---|---|---|
dataset_id | int | ID of the dataset to upload |
group_name | str | Name of the group (tag value) |
paths | List[str] (paths to the images) | List of paths to the images |
metas | List[Dict] (metas of the images) | List of image metas (optional) |
progress_cb | Optional[Union[tqdm, Callable]] | Function for tracking upload progress. |
So, the method uploads images to Supervisely and returns a list of ImageInfo
objects.
Upload multi-view images
Grouped view in the labeling interface
So now, that we've uploaded all the images, let's take a look at the labeling interface.
As you can see, the images in the Labeling tool are grouped in the same way as in your images in folders (images from one folder are combined into one group). When importing, each image from the folders will be assigned tags with the same values, which allows them to be grouped into one group.
Multi-view labeling can be very useful when annotating objects of multiple classes simultaneously on several images. You don't need to shift your attention to find the necessary class every time you switch between images, allowing you to increase efficiency and save time and effort.
Summary
In this tutorial, you learned how to upload multi-view images to Supervisely using Python SDK and get the advantage of the multi-view image annotation in the labeling interface, which allows you to label images quickly and efficiently on one screen. Let's recap the steps we did:
Create a new project and dataset.
Set multi-view settings for the project using the
api.project.set_multiview_settings
method.Upload images using the
api.image.upload_multiview_images
method.
And that's it! Now you can upload your multi-view images to Supervisely using Python SDK.
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