计算两个矩形的交并比,通常在检测任务里面可以作为一个检测指标。你的预测bbox和groundtruth之间的差异,就可以通过IOU来体现。


#!/usr/bin/env python
# encoding: utf-8

import numpy as np

'''
函数说明:计算两个框的重叠面积
输入:
rec1 第一个框xmin ymin xmax ymax
rec2 第二个框xmin ymin xmax ymax
输出:
iouv 重叠比例 0 没有
'''
def compute_iou(rec1, rec2):

# computing area of each rectangles
S_rec1 = (rec1[2] - rec1[0]) * (rec1[3] - rec1[1]) # H1*W1
S_rec2 = (rec2[2] - rec2[0]) * (rec2[3] - rec2[1]) # H2*W2

# computing the sum_area
sum_area = S_rec1 + S_rec2 #总面积

# find the each edge of intersect rectangle
left_line = max(rec1[0], rec2[0])
right_line = min(rec1[2], rec2[2])
top_line = max(rec1[1], rec2[1])
bottom_line = min(rec1[3], rec2[3])

# judge if there is an intersect
if left_line >= right_line or top_line >= bottom_line:
#print("没有重合区域")
return 0
else:
#print("有重合区域")
intersect = (right_line - left_line) * (bottom_line - top_line)
iouv=(float(intersect) / float(sum_area - intersect))*1.0

return iouv

'''
函数说明:获取两组匹配结果
输入:
rectA 车位
rectB 车辆
threod 重叠面积最小数值界限 默认0.6
输出:
CarUse 一维数组保存是否占用 1 占用 0 没有

'''
def TestCarUse(rectA,rectB,threod=0.6,debug=0):
#threod=0.8#设定最小值
ALength=len(rectA)
BLength=len(rectB)

#创建保存匹配结果的矩阵
recIOU=np.zeros((ALength,BLength),dtype=float,order='C')
#用于记录车位能够使否占用
CarUse=np.zeros((1,ALength),dtype=int,order='C')

for i in range(0,ALength):
for j in range(0,BLength):
iou = compute_iou(rectA[i], rectB[j])
recIOU[i][j]=format(iou,'.3f')
if iou>=threod:
CarUse[0,i]=1 #有一个超过匹配认为车位i被占用
if debug==1:
print('----匹配矩阵----')
print(recIOU)
'''
print('----车位占用情况----')
for i in range(0,ALength):
msg='车位'+str(i)+"-"+str(CarUse[0][i])
print(msg)
'''
return CarUse



if __name__=='__main__':
#A代表车位
rectA1 = (30, 10, 70, 20)
rectA2 = (70, 10, 80, 20)
rectA =[rectA1,rectA2]
#B代表检测车辆
rectB1 = (20, 10, 35, 20)
rectB2 = (30, 15, 70, 25)
rectB3 = (70, 10, 80, 20)
rectB =[rectB1,rectB2,rectB3]

#获取车位占用情况 rectA车位 rectB车辆 0.6占面积最小比
CarUse=TestCarUse(rectA,rectB,0.6,1)

print('----车位占用情况----')
for i in range(0,len(CarUse)+1):
msg='车位'+str(i)+"-"+str(CarUse[0][i])
print(msg)