numpy批量iou

import random
import time

import numpy as np

"""
#单个iou
"""
def compute_iou2(rec1, rec2):
    areas1 = (rec1[3] - rec1[1]) * (rec1[2] - rec1[0])
    areas2 = (rec2[3] - rec2[1]) * (rec2[2] - rec2[0])
    left = max(rec1[1],rec2[1])
    right = min(rec1[3],rec2[3])
    top = max(rec1[0], rec2[0])
    bottom = min(rec1[2], rec2[2])
    w = max(0, right-left)
    h = max(0, bottom-top)
    return w*h/(areas2+areas1-w*h)

def Ious(bbox, gt):
    assert bbox.shape[0]>0, 'bbox len must>0'
    assert gt.shape[0]>0, 'bbox len must>0'
    """
    :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
    """
    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 , 0)                # inter_area (w, h)
    inter_areas = wh[:, :, 0] * wh[:, :, 1]        # shape: (n, m)
    box_areas = (bbox[:, 2] - bbox[:, 0] ) * (bbox[:, 3] - bbox[:, 1] )
    gt_areas = (gt[:, 2] - gt[:, 0] ) * (gt[:, 3] - gt[:, 1] )
    IoU = inter_areas / (box_areas[:, None] + gt_areas - inter_areas)
    return IoU

if __name__ == '__main__':


    data_a=[[1,1,3,3]]
    data_a=[]
    data_b=[[1,1,2,2],[2,2,4,4],[1,1,3,3],[3,3,4,4]]

    ious= Ious(np.array(data_a), np.array(data_b))
    print('IoU res')
    print(ious.max())

    ious=[]
    start = time.time()
    for data in data_b:
        iou = compute_iou2(data_a[0], data)
        ious.append(iou)
    print('compute_iou2 res')
    print(ious)