1. 规则矩形框的IoU计算
有些目标检测中,预测的边界框为规则的矩形,则只需要知道矩形的左上角和右下角的坐标信息,就可以得到矩形框所有想要的信息。对于这种情况,IoU的python实现如下(python3.5)
def IoU(box1, box2): ''' 计算两个矩形框的交并比 :param box1: list,第一个矩形框的左上角和右下角坐标 :param box2: list,第二个矩形框的左上角和右下角坐标 :return: 两个矩形框的交并比iou ''' x1 = max(box1[0], box2[0]) # 交集左上角x x2 = min(box1[2], box2[2]) # 交集右下角x y1 = max(box1[1], box2[1]) # 交集左上角y y2 = min(box1[3], box2[3]) # 交集右下角y overlap = max(0., x2-x1) * max(0., y2-y1) union = (box1[2]-box1[0]) * (box1[3]-box1[1]) \ + (box2[2]-box2[0]) * (box2[3]-box2[1]) \ - overlap return overlap/unionif __name__ == '__main__': # box = [左上角x1,左上角y1,右下角x2,右下角y2] box1 = [10, 0, 15, 10] box2 = [12, 5, 20, 15] iou = IoU(box1, box2)
2. 非矩形框IoU计算
在有些目标检测中,检测框并不是规则的矩形框,例如自然场景下的文本检测,有些呈现平行四边形,梯形等情况,这时计算IoU时,就比较复杂一些。这时可以借助于python的一些库实现多边形的面积计算。
import shapelyimport numpy as npfrom shapely.geometry import Polygon, MultiPoint # 多边形def bbox_iou_eval(box1, box2): ''' 利用python的库函数实现非矩形的IoU计算 :param box1: list,检测框的四个坐标[x1,y1,x2,y2,x3,y3,x4,y4] :param box2: lsit,检测框的四个坐标[x1,y1,x2,y2,x3,y3,x4,y4] :return: IoU ''' box1 = np.array(box1).reshape(4, 2) # 四边形二维坐标表示 # python四边形对象,会自动计算四个点,并将四个点重新排列成 # 左上,左下,右下,右上,左上(没错左上排了两遍) poly1 = Polygon(box1).convex_hull box2 = np.array(box2).reshape(4, 2) poly2 = Polygon(box2).convex_hull if not poly1.intersects(poly2): # 如果两四边形不相交 iou = 0 else: try: inter_area = poly1.intersection(poly2).area # 相交面积 iou = float(inter_area) / (poly1.area + poly2.area - inter_area) except shapely.geos.TopologicalError: print('shapely.geos.TopologicalError occured, iou set to 0') iou = 0 return iouif __name__ == '__main__': # box = [四个点的坐标,顺序无所谓] box3 = [10, 0, 15, 0, 15, 10, 10, 10] # 左上,右上,右下,左下 box4 = [12, 5, 20, 2, 20, 15, 12, 15] iou = bbox_iou_eval(box3, box4) print(iou)
References
IOU交并比的计算