python实现交并比IOU教程

交并比（Intersection-over-Union，IoU），目标检测中使用的一个概念，是产生的候选框（candidate bound）与原标记框（ground truth bound）的交叠率，即它们的交集与并集的比值。最理想情况是完全重叠，即比值为1。

计算公式：

Python实现代码：

def cal_iou(box1, box2):
 """
 :param box1: = [xmin1, ymin1, xmax1, ymax1]
 :param box2: = [xmin2, ymin2, xmax2, ymax2]
 :return: 
 """
 xmin1, ymin1, xmax1, ymax1 = box1
 xmin2, ymin2, xmax2, ymax2 = box2
 # 计算每个矩形的面积
 s1 = (xmax1 - xmin1) * (ymax1 - ymin1) # C的面积
 s2 = (xmax2 - xmin2) * (ymax2 - ymin2) # G的面积
 
 # 计算相交矩形
 xmin = max(xmin1, xmin2)
 ymin = max(ymin1, ymin2)
 xmax = min(xmax1, xmax2)
 ymax = min(ymax1, ymax2)
 
 w = max(0, xmax - xmin)
 h = max(0, ymax - ymin)
 area = w * h # C∩G的面积
 iou = area / (s1 + s2 - area)
 return iou

# -*-coding: utf-8 -*-
"""
  @Project: IOU
  @File  : IOU.py
  @Author : panjq
  @E-mail : [email protected]
  @Date  : 2018-10-14 10:44:06
"""
def calIOU_V1(rec1, rec2):
  """
  computing IoU
  :param rec1: (y0, x0, y1, x1), which reflects
      (top, left, bottom, right)
  :param rec2: (y0, x0, y1, x1)
  :return: scala value of IoU
  """
  # 计算每个矩形的面积
  S_rec1 = (rec1[2] - rec1[0]) * (rec1[3] - rec1[1])
  S_rec2 = (rec2[2] - rec2[0]) * (rec2[3] - rec2[1])
 
  # computing the sum_area
  sum_area = S_rec1 + S_rec2
 
  # find the each edge of intersect rectangle
  left_line = max(rec1[1], rec2[1])
  right_line = min(rec1[3], rec2[3])
  top_line = max(rec1[0], rec2[0])
  bottom_line = min(rec1[2], rec2[2])
 
  # judge if there is an intersect
  if left_line >= right_line or top_line >= bottom_line:
    return 0
  else:
    intersect = (right_line - left_line) * (bottom_line - top_line)
    return intersect/(sum_area - intersect)
 
def calIOU_V2(rec1, rec2):
  """
  computing IoU
  :param rec1: (y0, x0, y1, x1), which reflects
      (top, left, bottom, right)
  :param rec2: (y0, x0, y1, x1)
  :return: scala value of IoU
  """
  # cx1 = rec1[0]
  # cy1 = rec1[1]
  # cx2 = rec1[2]
  # cy2 = rec1[3]
  # gx1 = rec2[0]
  # gy1 = rec2[1]
  # gx2 = rec2[2]
  # gy2 = rec2[3]
  cx1,cy1,cx2,cy2=rec1
  gx1,gy1,gx2,gy2=rec2
  # 计算每个矩形的面积
  S_rec1 = (cx2 - cx1) * (cy2 - cy1) # C的面积
  S_rec2 = (gx2 - gx1) * (gy2 - gy1) # G的面积
 
  # 计算相交矩形
  x1 = max(cx1, gx1)
  y1 = max(cy1, gy1)
  x2 = min(cx2, gx2)
  y2 = min(cy2, gy2)
 
  w = max(0, x2 - x1)
  h = max(0, y2 - y1)
  area = w * h # C∩G的面积
 
  iou = area / (S_rec1 + S_rec2 - area)
  return iou
 
if __name__=='__main__':
  rect1 = (661, 27, 679, 47)
  # (top, left, bottom, right)
  rect2 = (662, 27, 682, 47)
  iou1 = calIOU_V1(rect1, rect2)
  iou2 = calIOU_V2(rect1, rect2)
  print(iou1)
  print(iou2)
 

参考：https://www.jb51.net/article/184542.htm

补充知识：Python计算多分类的混淆矩阵，Precision、Recall、f1-score、mIOU等指标

直接上代码，一看很清楚

import os
import numpy as np
from glob import glob
from collections import Counter
 
def cal_confu_matrix(label, predict, class_num):
  confu_list = []
  for i in range(class_num):
    c = Counter(predict[np.where(label == i)])
    single_row = []
    for j in range(class_num):
      single_row.append(c[j])
    confu_list.append(single_row)
  return np.array(confu_list).astype(np.int32)
 
 
def metrics(confu_mat_total, save_path=None):
  '''
  :param confu_mat: 总的混淆矩阵
  backgound：是否干掉背景
  :return: txt写出混淆矩阵, precision，recall，IOU，f-score
  '''
  class_num = confu_mat_total.shape[0]
  confu_mat = confu_mat_total.astype(np.float32) + 0.0001
  col_sum = np.sum(confu_mat, axis=1) # 按行求和
  raw_sum = np.sum(confu_mat, axis=0) # 每一列的数量
 
  '''计算各类面积比，以求OA值'''
  oa = 0
  for i in range(class_num):
    oa = oa + confu_mat[i, i]
  oa = oa / confu_mat.sum()
 
  '''Kappa'''
  pe_fz = 0
  for i in range(class_num):
    pe_fz += col_sum[i] * raw_sum[i]
  pe = pe_fz / (np.sum(confu_mat) * np.sum(confu_mat))
  kappa = (oa - pe) / (1 - pe)
 
  # 将混淆矩阵写入excel中
  TP = [] # 识别中每类分类正确的个数
 
  for i in range(class_num):
    TP.append(confu_mat[i, i])
 
  # 计算f1-score
  TP = np.array(TP)
  FN = col_sum - TP
  FP = raw_sum - TP
 
  # 计算并写出precision，recall, f1-score，f1-m以及mIOU
 
  f1_m = []
  iou_m = []
  for i in range(class_num):
    # 写出f1-score
    f1 = TP[i] * 2 / (TP[i] * 2 + FP[i] + FN[i])
    f1_m.append(f1)
    iou = TP[i] / (TP[i] + FP[i] + FN[i])
    iou_m.append(iou)
 
  f1_m = np.array(f1_m)
  iou_m = np.array(iou_m)
  if save_path is not None:
    with open(save_path + 'accuracy.txt', 'w') as f:
      f.write('OA:\t%.4f\n' % (oa*100))
      f.write('kappa:\t%.4f\n' % (kappa*100))
      f.write('mf1-score:\t%.4f\n' % (np.mean(f1_m)*100))
      f.write('mIou:\t%.4f\n' % (np.mean(iou_m)*100))
 
      # 写出precision
      f.write('precision:\n')
      for i in range(class_num):
        f.write('%.4f\t' % (float(TP[i]/raw_sum[i])*100))
      f.write('\n')
 
      # 写出recall
      f.write('recall:\n')
      for i in range(class_num):
        f.write('%.4f\t' % (float(TP[i] / col_sum[i])*100))
      f.write('\n')
 
      # 写出f1-score
      f.write('f1-score:\n')
      for i in range(class_num):
        f.write('%.4f\t' % (float(f1_m[i])*100))
      f.write('\n')
 
      # 写出 IOU
      f.write('Iou:\n')
      for i in range(class_num):
        f.write('%.4f\t' % (float(iou_m[i])*100))
      f.write('\n')

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