目标检测基础——IOU计算

1. 什么是IOU？

      IoU 的全称为交并比（Intersection over Union），通过这个名称我们大概可以猜到 IoU 的计算方法。IoU 计算的是 “预测的边框” 和 “真实的边框” 的交集和并集的比值。该值越大，说明”预测框“越接近”真实框“。

     iou定义如下：

2. IOU计算

      直接上代码：

# --*-- coding:utf-8 -*-
import cv2
 
 
def draw_box(img, box):
    x,y,x1,y1 = box
    cv2.rectangle(img, (x,y), (x1, y1), (0,0,255), 2)
    return img
 
def iou(bbox1, bbox2):
    """
    Calculates the intersection-over-union of two bounding boxes.
    """
    bbox1 = [float(x) for x in bbox1]
    bbox2 = [float(x) for x in bbox2]
    (x0_1, y0_1, x1_1, y1_1) = bbox1
    (x0_2, y0_2, x1_2, y1_2) = bbox2
    # get the overlap rectangle
    overlap_x0 = max(x0_1, x0_2)
    overlap_y0 = max(y0_1, y0_2)
    overlap_x1 = min(x1_1, x1_2)
    overlap_y1 = min(y1_1, y1_2)
    # check if there is an overlap
    if overlap_x1 - overlap_x0 <= 0 or overlap_y1 - overlap_y0 <= 0:
        return 0
    # if yes, calculate the ratio of the overlap to each ROI size and the unified size
    size_1 = (x1_1 - x0_1) * (y1_1 - y0_1)
    size_2 = (x1_2 - x0_2) * (y1_2 - y0_2)
    size_intersection = (overlap_x1 - overlap_x0) * (overlap_y1 - overlap_y0)
    size_union = size_1 + size_2 - size_intersection
    return size_intersection / size_union
 
if __name__ == "__main__":
    img_path = "test.jpg"
    img = cv2.imread(img_path)
 
    person_box = [210, 33, 328, 191]
    horse_box = [65, 73, 403, 310]
 
    img = draw_box(img, person_box)
    img = draw_box(img, horse_box)
 
    iou_result = iou(person_box, horse_box)
    print("The IOU of person and horse is:%s" % iou_result)
    cv2.imwrite("result.jpg", img)

python iou.py

    输出：The IOU of person and horse is:0.16414778487727819

    test.jpg和result.jpg如下图：

       人和马的IOU为：0.164

       IOU为目标检测的基础，从这里开始吧！