This endpoint creates a
segmentannotation task. In this task, one of our labelers will view the given image and classify every pixel of the image according to the labels provided. You will get a full semantic, pixel-wise, dense segmentation of the image.
We also support instance-aware semantic segmentations, also called panoptic segmentation, via LabelDescription objects.
The required parameters for this task are
callback_url is the URL that will be POSTed on task completion and is described in more detail in the Callbacks section. The
attachment is a URL to an image you’d like to be segmented.
labels is an array of strings or LabelDescription objects describing the different types of objects you’d like to be used to segment the image.
You can optionally provide additional markdown-enabled or Google Doc-based instructions via the
You can also optionally set
allow_unlabeled to true, which will allow the existence of unlabeled pixels in the segmentation response - otherwise, all pixels in the image will be classified (in which case it's important that there are labels for everything in the image, to avoid misclassification).
The response you will receive will be a series of images where each pixel's value corresponds to the label, either via a numerical index or a color mapping. You will also get separate masks for each label for convenience.
If successful, Scale will immediately return the generated task object, at which point you could store the
task_id to have a permanent reference to the task.