With ClearML Enterprise, annotations can be applied to video and image frames. Frames support two types of annotations: Frame objects and Frame labels.
Annotation Tasks can be used to efficiently organize the annotation of frames in Hyper-Dataset versions (see Annotations Task Page).
For information about how to view, create, and manage annotations using the WebApp, see Annotating Images and Videos.
Frame objects are labeled Regions of Interest (ROIs), which can be bounded by polygons (including rectangles), ellipses, or key points. These ROIs are useful for object detection, classification, or semantic segmentation.
Frame objects can include ROI labels, confidence levels, and masks for semantic segmentation. In ClearML Enterprise, one or more labels and sources dictionaries can be associated with an ROI (although multiple source ROIs are not frequently used).
Frame labels are applied to an entire frame, not a region in a frame.
To add a frame object annotation to a SingleFrame, use the
box2d_xywh argument specifies the coordinates of the annotation's bounding box, and the
labels argument specifies
a list of labels for the annotation.
When adding an annotation there are a few options for entering the annotation's boundaries, including:
poly2d_xy- A list of floating points (x,y) to create for single polygon, or a list of floating points lists for a complex polygon.
ellipse2d_xyrrt- A List consisting of cx, cy, rx, ry, and theta for an ellipse.
- And more! See
SingleFrame.add_annotationfor further options.
Adding a frame label is similar to creating a frame objects, except that coordinates don't need to be specified, since the whole frame is being referenced.
SingleFrame.add_annotation method, but use only the