pytorch 批量 iou
【摘要】 批量max
a = torch.Tensor([[random.randint(0, 20), random.randint(0, 20), random.randint(0, 20)]])b1_x1=torch.Tensor([[random.randint(0, 20),random.randint(0, 20), random.randint(0, 2...
批量max
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a = torch.Tensor([[random.randint(0, 20), random.randint(0, 20), random.randint(0, 20)]])
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b1_x1=torch.Tensor([[random.randint(0, 20),random.randint(0, 20), random.randint(0, 20)]])
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inter_rect_x1 = torch.max(b1_x1, a)
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print(a)
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print(b1_x1)
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print(inter_rect_x1)
批量iou
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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import datetime
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import torch
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from numba import jit
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import numpy as np
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import random
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#面积计算法,前提,坐标都需要转成int类型
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def bbox_iou2(box1, box2, x1y1x2y2=True):
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a= np.zeros((6,6),dtype=np.int)
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a[box1[:,1][0]:box1[:,3][0]+1,box1[:,0][0]:box1[:,2][0]+1]=1
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a[box2[:,1][0]:box2[:,3][0]+1, box2[:,0][0]:box2[:,2][0]+1] += 1
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m = np.sum(a[np.where(a ==1)])
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inner = np.sum(a[np.where(a == 2)])//2
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return inner/(inner+m)
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def bbox_iou(box
文章来源: blog.csdn.net,作者:网奇,版权归原作者所有,如需转载,请联系作者。
原文链接:blog.csdn.net/jacke121/article/details/80817696
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