exporting-image-annotations-to-different-formats.md

Exporting image annotations to different formats

Exporting the release file for image datasets to different formats

You can export the release file for image datasets to different formats with the Python SDK. Use the export_datasetutil function for this, setting the export_format parameter to one of the following:

Value	Description
coco-instance	COCO instance segmentation format
coco-panoptic	COCO panoptic segmentation format
yolo	Yolo Darknet object detection format
instance	Grayscale PNGs (16-bit) where the values correspond to instance ids
semantic	Grayscale PNGs (8-bit) where the values correspond to category ids
instance-color	Colored PNGs where the colors correspond to different instances
semantic-color	Colored PNGs where the colors correspond to different categories, with colors as configured in the label editor settings when available
polygon	For exporting segmentation bitmap labels to polygons

Example:

# pip install segments-ai
from segments import SegmentsClient, SegmentsDataset
from segments.utils import export_dataset

# Initialize a SegmentsDataset from the release file
client = SegmentsClient('YOUR_API_KEY')
release = client.get_release('jane/flowers', 'v1.0') # Alternatively: release = 'flowers-v1.0.json'
dataset = SegmentsDataset(release, 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()