[人工智能]目标检测中，计算IoU值

1. # 计算IoU，矩形的坐标形式为xyxy

def box_iou_xyxy(box1, box2):
    # 获取box1左上角和右下角的坐标
    x1min, y1min, x1max, y1max = box1[0], box1[1], box1[2], box1[3]
    # 计算box1的面积
    s1 = (y1max - y1min + 1.) * (x1max - x1min + 1.)
    # 获取box2左上角和右下角的坐标
    x2min, y2min, x2max, y2max = box2[0], box2[1], box2[2], box2[3]
    # 计算box2的面积
    s2 = (y2max - y2min + 1.) * (x2max - x2min + 1.)
   

    # 计算相交矩形的坐标
    xmin = np.maximum(x1min, x2min)
    ymin = np.maximum(y1min, y2min)
    xmax = np.minimum(x1max, x2max)
    ymax = np.minimum(y1max, y2max) 

    # 计算相交矩形框的高度、宽度、面积
    inter_h = np.maximum(ymax - ymin + 1., 0.)
    inter_w = np.maximum(xmax - xmin + 1., 0.)
    intersection = inter_h * inter_w
    # 计算相并的面积
    union = s1 + s2 - intersection
    # 计算交并比
    iou = intersection / union
    return iou

bbox1 = [100., 100., 200., 200.] # xmin,ymin,xmax,ymax
bbox2 = [120., 120., 220., 220.]
iou = box_iou_xyxy(bbox1, bbox2)
print('IoU is {}'.format(iou)) 

2. # 计算IoU，矩形框的坐标形式为xywh

def box_iou_xywh(box1, box2):
    x1min, y1min = box1[0] - box1[2]//2.0, box1[1] - box1[3]//2.0
    x1max, y1max = box1[0] + box1[2]//2.0, box1[1] + box1[3]//2.0
    s1 = box1[2] * box1[3]

    x2min, y2min = box2[0] - box2[2]//2.0, box2[1] - box2[3]//2.0
    x2max, y2max = box2[0] + box2[2]//2.0, box2[1] + box2[3]//2.0
    s2 = box2[2] * box2[3]

    xmin = np.maximum(x1min, x2min)
    ymin = np.maximum(y1min, y2min)
    xmax = np.minimum(x1max, x2max)
    ymax = np.minimum(y1max, y2max)
    inter_h = np.maximum(ymax - ymin, 0.)
    inter_w = np.maximum(xmax - xmin, 0.)
    intersection = inter_h * inter_w

    union = s1 + s2 - intersection
    iou = intersection / union
    return iou

bbox1 = [100., 100., 200., 200.] # x,y,w,h
bbox2 = [120., 120., 220., 220.]
iou = box_iou_xywh(bbox1, bbox2)
print('IoU is {}'.format(iou))