Exporting the release file to different formats
You can export the release file 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-panoptic | |
yolo | |
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()