def IoU(bbox, gt):
"""
:param bbox: (n, 4)
:param gt: (m, 4)
:return: (n, m)
numpy 广播机制 从后向前对齐。 维度为1 的可以重复等价为任意维度
eg: (4,3,2) (3,2) (3,2)会扩充为(4,3,2)
(4,1,2) (3,2) (4,1,2) 扩充为(4, 3, 2) (3, 2)扩充为(4, 3,2) 扩充的方法为重复
广播会在numpy的函数 如sum, maximun等函数中进行
pytorch同理。
扩充维度的方法：
eg: a a.shape: (3,2) a[:, None, :] a.shape: (3, 1, 2) None 对应的维度相当于newaxis
"""
lt = np.maximum(bbox[:, None, :2], gt[:, :2]) # left_top (x, y)
rb = np.minimum(bbox[:, None, 2:], gt[:, 2:]) # right_bottom (x, y)
wh = np.maximum(rb - lt + 1, 0) # inter_area (w, h)
inter_areas = wh[:, :, 0] * wh[:, :, 1] # shape: (n, m)
box_areas = (bbox[:, 2] - bbox[:, 0] + 1) * (bbox[:, 3] - bbox[:, 1] + 1)
gt_areas = (gt[:, 2] - gt[:, 0] + 1) * (gt[:, 3] - gt[:, 1] + 1)
IoU = inter_areas / (box_areas[:, None] + gt_areas - inter_areas)
return IoU
