让我来详细讲解如何通过Python实现IOU(Intersection over Union,交并比)计算代码实例。
IOU是目标检测中常用的一种指标,用于评价模型预测框和真实标注框之间的重合程度。IOU计算公式如下:
IOU = Area of Overlap / Area of Union
其中,OverLap指的是预测框和标注框的交集,Union指的是预测框和标注框的并集。
首先,我们需要读取两个矩形框的坐标信息,然后使用Opencv库计算其IOU。
import cv2
def get_iou(box1, box2):
"""
Calculate IOU between two boxes
:param box1: [x1, y1, x2, y2]
:param box2: [x1, y1, x2, y2]
:return: IOU
"""
x1 = max(box1[0], box2[0])
y1 = max(box1[1], box2[1])
x2 = min(box1[2], box2[2])
y2 = min(box1[3], box2[3])
inter_area = (x2 - x1 + 1) * (y2 - y1 + 1)
box1_area = (box1[2] - box1[0] + 1) * (box1[3] - box1[1] + 1)
box2_area = (box2[2] - box2[0] + 1) * (box2[3] - box2[1] + 1)
iou = inter_area / float(box1_area + box2_area - inter_area)
return iou
假设我们有N个矩形框,我们可以通过双重循环计算任意两个矩形框之间的IOU。
# 计算所有矩形框之间的iou
iou_matrix = []
for i in range(N):
iou_row = []
for j in range(N):
iou = get_iou(boxes[i], boxes[j])
iou_row.append(iou)
iou_matrix.append(iou_row)
以上就是如何通过Python实现IOU计算的代码实例,希望对你有所帮助。
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