52 lines
1.5 KiB
Python
52 lines
1.5 KiB
Python
|
# vim: expandtab:ts=4:sw=4
|
||
|
|
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, label, feature, mask=None):
|
||
|
|
self.tlwh = np.asarray(tlwh, dtype=np.float32)
|
||
|
|
self.confidence = float(confidence)
|
||
|
|
self.cls = int(label)
|
||
|
|
self.feature = np.asarray(feature, dtype=np.float32)
|
||
|
|
self.mask = mask
|
||
|
|
|
||
|
|
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
|