# -*- encoding:utf-8 -*-
# @Time : 2019/4/18 21:21
# @Author : gfjiang
# @Site :
# @File : iou.py
# @Software: PyCharm
import numpy as np
[文档]def bbox_overlaps(bboxes1, bboxes2, mode='iou'):
"""Calculate the ious between each bbox of bboxes1 and bboxes2.
Args:
bboxes1(ndarray): shape (n, 4)
bboxes2(ndarray): shape (k, 4)
mode(str): iou (intersection over union) or iof (intersection
over foreground)
Returns:
ious(ndarray): shape (n, k)
"""
assert mode in ['iou', 'iof']
bboxes1 = bboxes1.astype(np.float32)
bboxes2 = bboxes2.astype(np.float32)
rows = bboxes1.shape[0]
cols = bboxes2.shape[0]
ious = np.zeros((rows, cols), dtype=np.float32)
if rows * cols == 0:
return ious
exchange = False
if bboxes1.shape[0] > bboxes2.shape[0]:
bboxes1, bboxes2 = bboxes2, bboxes1
ious = np.zeros((cols, rows), dtype=np.float32)
exchange = True
area1 = (bboxes1[:, 2] - bboxes1[:, 0] + 1) * (
bboxes1[:, 3] - bboxes1[:, 1] + 1)
area2 = (bboxes2[:, 2] - bboxes2[:, 0] + 1) * (
bboxes2[:, 3] - bboxes2[:, 1] + 1)
for i in range(bboxes1.shape[0]):
x_start = np.maximum(bboxes1[i, 0], bboxes2[:, 0])
y_start = np.maximum(bboxes1[i, 1], bboxes2[:, 1])
x_end = np.minimum(bboxes1[i, 2], bboxes2[:, 2])
y_end = np.minimum(bboxes1[i, 3], bboxes2[:, 3])
overlap = np.maximum(x_end - x_start + 1, 0) * np.maximum(
y_end - y_start + 1, 0)
if mode == 'iou':
union = area1[i] + area2 - overlap
else:
union = area1[i] if not exchange else area2
ious[i, :] = overlap / union
if exchange:
ious = ious.T
return ious
# 两种box形式均已测试
[文档]def box_ious(b1, b2, center=False):
"""Return iou tensor
Parameters
----------
b1: array, shape=(i1,...,iN, 4), xywh or x1y1x2y2
b2: array, shape=(j, 4), xywh or x1y1x2y2
center: box format
Returns
-------
iou: array, shape=(i1,...,iN, j)"""
if not isinstance(b1, np.ndarray):
b1 = np.array([b1])
if not isinstance(b2, np.ndarray):
b2 = np.array([b2])
# 这是学习数组广播一个非常好的例子
# 当两个数组维度无法广播时，不要尝试手动扩展复制数据，
# 而应该在需要广播的方向插入一个维度，这样数据就会顺着这个插入的1维度进行广播
# Expand dim to apply broadcasting.
b1 = b1[:, np.newaxis, :]
b1_wh = b1[..., [2, 3]] - b1[..., [0, 1]]
b1_mins = b1[..., :2]
b1_maxes = b1[..., 2:4]
if center:
b1_wh = b1[..., 2:4]
b1_mins = b1[..., :2] - b1_wh / 2.
b1_maxes = b1[..., :2] + b1_wh / 2.
# Expand dim to apply broadcasting.
b2 = b2[np.newaxis, :, :]
b2_wh = b2[..., [2, 3]] - b2[..., [0, 1]]
b2_mins = b2[..., :2]
b2_maxes = b2[..., 2:4]
if center:
b2_wh = b2[..., 2:4]
b2_mins = b2[..., :2] - b2_wh / 2.
b2_maxes = b2[..., :2] + b2_wh / 2.
intersect_mins = np.maximum(b1_mins, b2_mins)
intersect_maxes = np.minimum(b1_maxes, b2_maxes)
intersect_wh = np.maximum(intersect_maxes - intersect_mins, 0.)
intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1]
b1_area = b1_wh[..., 0] * b1_wh[..., 1]
b2_area = b2_wh[..., 0] * b2_wh[..., 1]
iou = intersect_area / (b1_area + b2_area - intersect_area)
return iou
if __name__ == "__main__":
BB = np.array([[100, 200, 120, 240], [140, 220, 150, 280], [180, 260, 200, 300]])
BBGT = np.array([[108, 220, 130, 260], [130, 210, 145, 270]])
# center = False
# [100, 200, 120, 240] VS [108, 220, 130, 260]
# intersect_x1y1x2y2 = 108, 220, 120, 240 intersect_wh = 12, 20
# BB_area = 20*40, BBGT_area = 22*40
# iou = 240 / (800 + 880 - 240) = 240 / 1440 = 0.16666
# [140, 220, 150, 280] VS [130, 210, 145, 270]
# intersect_x1y1x2y2 = 140, 220, 145, 270 intersect_wh = 5, 50
# BB_area = 10*60, BBGT_area = 15*60
# iou = 250 / (600 + 900 - 250) = 250 / 1250 = 0.2
# center = True
# [100, 200, 120, 240] VS [108, 220, 130, 260]
# convert to xiyix2y2 format [40, 80, 160, 320] VS [43, 90, 173, 350]
# intersect_x1y1x2y2 = 43, 90, 160, 320 intersect_wh = 117, 230
# BB_area = 120*240, BBGT_area = 130*260
# iou = 117*230 / (120*240 + 130*260 - 117*230) = 0.7539
# [140, 220, 150, 280] VS [130, 210, 145, 270]
# convert to xiyix2y2 format [65, 80, 215, 360] VS [57.5, 75, 202.5, 345]
# intersect_x1y1x2y2 = 65, 80, 202.5, 345 intersect_wh = 137.5, 265
# BB_area = 150*280, BBGT_area = 145*270
# iou = 137.5*265 / (150*280 + 145*270 - 137.5*265) = 36437.5 / 44712.5 = 0.8149
box_ious(BB, BBGT, center=True)