Cityscapes
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This converter allows to import images with .json annotations in Cityscapes format.
⚠️ Note: images must have suffix _leftImg8bit and annotations suffix _gtFine_polygons and .json extension. Check the example of the file structure below.
Enterprise users have access to "Import as links" option, which supports import of this format with annotations. This option might be beneficial in many cases, as it allows data import to Supervisely platform without re-uploading, maintaining a single source and speeding up import process.
To step up import speed even further you can compress all annotation files (.json's) into an archive and import it together with the images. (Note: This method is format-dependent and may not apply to all formats.)
Supported image formats: .jpg, .jpeg, .mpo, .bmp, .png, .webp, .tiff, .tif, .jfif, .avif, .heic, and .heif
With annotations: yes
Supported annotation file extension: .json.
Grouped by: any structure (uploaded to a single dataset)\
Example data: download ⬇️\
Recommended directory structure:
In order to import custom annotations for the images, you need to provide a class_to_id.json file. This file should contain a list with dictionaries. Each dictionary should contain information about the class with the following fields:
name - the name of the class. It should be unique.
id - the ID of the class. From 1 to N-1, where N is the number of classes.
color - the color of the class in RGB format. If not specified, the color will be generated randomly
Annotation file should contain the following fields:
imgHeight - the height of the image
imgWidth - the width of the image
objects - a list of dictionaries, each containing information about the object
label - the name of the class
polygon - a list of points that form the polygon of the object
Example of the annotation file from provided sample data:
