Exporting data

To download your labeled data, you need to create a release on the Releases tab. A release is a snapshot of your dataset at a specific point in time.

By clicking the download link of a release, you obtain a release file in JSON format. This release file contains all information about the dataset, tasks, samples, and labels in the release.

Note that the segmentation masks are encoded as a png image that appears black when opened in an image viewer. The png image contains all necessary information though. Read more below.

Exporting the release file to COCO format

You can export the release file to COCO format with the Python SDK. Set export_format to coco-instance for the object detection format, or to coco-panoptic for the panoptic segmentation format. For bounding box datasets, you can set the export format to yolo.

# pip install segments-ai
from segments import SegmentsDataset
from segments.utils import export_dataset
# Initialize a SegmentsDataset from the release file
release_file = 'flowers-v1.0.json'
dataset = SegmentsDataset(release_file, labelset='ground-truth', filter_by=['labeled', 'reviewed'])
# Export to COCO panoptic format
export_dataset(dataset, export_format='coco-panoptic')

Alternatively, you can use the initialized SegmentsDataset to loop through the samples and labels, and visualize or process them in any way you please:

import matplotlib.pyplot as plt
from segments.utils import get_semantic_bitmap
for sample in dataset:
# Print the sample name and list of labeled objects
print(sample['name'])
print(sample['annotations'])
# Show the image
plt.imshow(sample['image'])
plt.show()
# Show the instance segmentation label
plt.imshow(sample['segmentation_bitmap'])
plt.show()
# Show the semantic segmentation label
semantic_bitmap = get_semantic_bitmap(sample['segmentation_bitmap'], sample['annotations'])
plt.imshow(semantic_bitmap)
plt.show()

Structure of the release file

The general structure of the release file is as follows:

{
"name": "first release",
"description": "This is a first release of Segments.ai playground dataset",
"created_at": "2020-07-09 10:20:19.888887+00:00",
"dataset": {
"name": "flowers",
"task_type": "segmentation-bitmap",
"task_attributes": {...} # the categories etc.
"labelsets": [
** list of labelsets **
],
"samples": {
** list of samples **
}
}
}

Label set

Each labelset entry contains the labelset's name and description:

{
"name": "ground-truth",
"description": ""
}

Sample

Each sample entry contains information about the sample (name, image URL, ...) and a list of labels.

{
"name": "donuts.jpg",
"attributes": {
"image": {
"url": "https://segmentsai-prod.s3.eu-west-2.amazonaws.com/assets/segments/3b8b3da2-f09a-494b-999e-37250dfbf5b6.jpg"
}
},
"labels": {
/** list of labels, indexed by labelset **/
}
}

Label

Each label contains basic information such as the time it was created, the user who created it, its status (e.g. LABELED). The attributes field contains all info about the labeled objects. Its contents depend on the labeling type (segmentation or bounding boxes) and are described in more detail here.

{
"label_status": "LABELED",
"attributes": {
"format_version": "0.1",
"annotations": [
{
"id": 1,
"category_id": 1
},
{
"id": 2,
"category_id": 1
},
{
"id": 3,
"category_id": 1
},
{
"id": 4,
"category_id": 1
}
],
"segmentation_bitmap": {
"url": "https://segmentsai-prod.s3.eu-west-2.amazonaws.com/assets/segments/504e7633-ef51-49c3-8b0e-d4eb9100532d.png"
}
}
}

Please refer to this blog post for an example of training a model on exported data.