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# 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.
class_name : ndarray
Detector class.
feature : ndarray | NoneType
A feature vector that describes the object contained in this image.
"""
def __init__(self, tlwh, confidence, class_name, feature):
self.tlwh = np.asarray(tlwh, dtype=np.float32)
self.confidence = float(confidence)
self.class_name = class_name
self.feature = np.asarray(feature, dtype=np.float32)
def get_class(self):
return self.class_name
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 |