目标检测-IOU

IOU，Intersection over Union，并交比，是目标检测中常见的评价标准。
主要是衡量模型生成的预测检测框（Predicted Bounding Box）和标注框（Ground Truth Box）之间的重叠程度。

假设预测检测框为A，标注框为B，则IOU计算公式为：

一维IOU计算示例：

两个线段之间的IOU

import numpy as np

def iou(set_a, set_b):
    '''
    一维 iou 的计算
    :param set_a: 第一段的起始和终点坐标
    :param set_b: 第二段的起始和终点坐标
    :return:
    '''
    '''
    '''
    x1, x2 = set_a  # (left, right)
    y1, y2 = set_b  # (left, right)

    low = max(x1, y1)
    high = min(x2, y2)
    # intersection
    if high - low < 0:
        inter = 0
    else:
        inter = high - low
    # union
    union = (x2 - x1) + (y2 - y1) - inter
    # iou
    iou = inter / union
    return iou


if __name__ == "__main__":
    r = iou((1, 3), (2, 4))
    print(r)

二维IOU计算示例：

两个图形之间的IOU

import numpy as np

def IOU(box1, box2, wh=False):
    if wh == False:
        xmin1, ymin1, xmax1, ymax1 = box1
        xmin2, ymin2, xmax2, ymax2 = box2
    else:
        xmin1, ymin1 = int(box1[0] - box1[2] / 2.0), int(box1[1] - box1[3] / 2.0)
        xmax1, ymax1 = int(box1[0] + box1[2] / 2.0), int(box1[1] + box1[3] / 2.0)
        xmin2, ymin2 = int(box2[0] - box2[2] / 2.0), int(box2[1] - box2[3] / 2.0)
        xmax2, ymax2 = int(box2[0] + box2[2] / 2.0), int(box2[1] + box2[3] / 2.0)
    # 获取矩形框交集对应的左上角和右下角的坐标（intersection）
    xx1 = np.max([xmin1, xmin2])
    yy1 = np.max([ymin1, ymin2])
    xx2 = np.min([xmax1, xmax2])
    yy2 = np.min([ymax1, ymax2])
    # 计算两个矩形框面积
    area1 = (xmax1 - xmin1) * (ymax1 - ymin1)
    area2 = (xmax2 - xmin2) * (ymax2 - ymin2)
    inter_area = (np.max([0, xx2 - xx1])) * (np.max([0, yy2 - yy1]))  # 计算交集面积
    iou = inter_area / (area1 + area2 - inter_area + 1e-6)  # 计算交并比

    return iou


if __name__ == "__main__":
    r = IOU([1, 1, 3, 3], [2, 2, 4, 4])
    print(r)

参考：

• IoU、GIoU、DIoU、CIoU损失函数的那点事儿 - Error的文章 - 知乎
  https://zhuanlan.zhihu.com/p/94799295

• https://blog.csdn.net/u014061630/article/details/82818112