|
|
| import numpy as np
|
|
|
|
|
| class Detection(object):
|
| """
|
| This class represents a bounding box detection in a single image.
|
| Parameters
|
| ----------
|
| tlwh : array_like
|
| Bounding box in format `(x, y, w, h)`.
|
| confidence : float
|
| Detector confidence score.
|
| feature : array_like
|
| A feature vector that describes the object contained in this image.
|
| Attributes
|
| ----------
|
| tlwh : ndarray
|
| Bounding box in format `(top left x, top left y, width, height)`.
|
| confidence : ndarray
|
| Detector confidence score.
|
| feature : ndarray | NoneType
|
| A feature vector that describes the object contained in this image.
|
| """
|
|
|
| def __init__(self, tlwh, confidence, feature):
|
| self.tlwh = np.asarray(tlwh, dtype=np.float)
|
| self.confidence = float(confidence)
|
| self.feature = np.asarray(feature, dtype=np.float32)
|
|
|
| def to_tlbr(self):
|
| """Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
|
| `(top left, bottom right)`.
|
| """
|
| ret = self.tlwh.copy()
|
| ret[2:] += ret[:2]
|
| return ret
|
|
|
| def to_xyah(self):
|
| """Convert bounding box to format `(center x, center y, aspect ratio,
|
| height)`, where the aspect ratio is `width / height`.
|
| """
|
| ret = self.tlwh.copy()
|
| ret[:2] += ret[2:] / 2
|
| ret[2] /= ret[3]
|
| return ret
|
|
|
