deepsort源码解读(一)

deepsort项目地址

---

目录
deepsort原理及解析汇总

---

detection.py文件

# vim: expandtab:ts=4:sw=4
import numpy as np


# 用于保存通过目标检测器得到的一个检测框
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
        .
    feature : ndarray | NoneTypeDetector confidence score
        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)

    # 将坐标转换成左上顶点与右下顶点
    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

    # 将坐标转换成中心坐标，宽/高，高
    def to_xyah(self):
        """Convert bounding box to format `(center x, center y, aspect ratio,
        height)`, where the aspect ratio is `width / height`.
        """
        # 原检测框的坐标为左上角坐标与宽高
        # 转换成中心坐标与宽/高，高
        ret = self.tlwh.copy()
        ret[:2] += ret[2:] / 2
        ret[2] /= ret[3]
        return ret