OpenCV 4 with Python Blueprints - Second Edition

Converting boundary boxes to observations

In order to pass the boundary boxes to the Kalman filter, we will have to define a transformation function from each boundary box to the observation model, and, in order to use the predicted boundary boxes for object tracking, we need to define a function from a state to a boundary box.

Let's start with a transformation function from a boundary box to an observation:

1. First, we calculate the center coordinates of the boundary box:

def bbox_to_observation(bbox):    x, y = (bbox[0:2] + bbox[2:4]) / 2

2. Next, we calculate the width and height of the box, which we will use to calculate the size (that is, the area) and the scale:

    w, h = bbox[2:4] - bbox[0:2]

3. Then, we calculate the size of bbox, that is, ...