vpi.KLTFeatureTracker
- class vpi.KLTFeatureTracker(img_template: vpi.Image, in_boxes: vpi.Array, *, backend: vpi.Backend = vpi.Backend.DEFAULT) vpi.KLTFeatureTracker
- class vpi.KLTFeatureTracker(img_template: vpi.Image, in_boxes: vpi.Array, *, max_template_count: int, max_template_size: Tuple[int, int], backend: vpi.Backend = vpi.Backend.DEFAULT) vpi.KLTFeatureTracker
Creates the basis for the KLT algorithm.
Note
This class allocates all resources needed by the Kanade-Lucas-Tomasi (KLT) feature tracker algorithm. An object of this class is able to run the KLT algorithm via its
call operator
.- Parameters
img_template (vpi.Image) – Template image.
in_boxes (vpi.Array) – Input bounding boxes.
max_template_count (int, optional) – Maximum number of templates to be tracked.
max_template_size (Tuple[int, int], optional) – Maximum width/height of each tracked template.
backend (vpi.Backend, optional) – The backend to be used by the algorithm.
- Returns
The main object of the KLT algorihtm.
- Return type
Caution
Restrictions to several arguments may apply. Check the C API references of the create payload function and the group concepts for more details.
Methods
__call__
(img_reference, *[, ...])Runs a KLT Feature tracker on two images.
add_boxes
(self, bboxes)Add input bounding boxes to be tracked.
default_update
(in_boxes, in_preds, ...)Get the default update function.
in_predictions
(self)Get current input predictions.
out_estimations
(self)Get current output estimations.