1. yolov5在模型推理阶段，命令如下：

 python detect.py --weights runs/exp1/weights/best.pt --source inference/images/ --device 0 --save-txt

该命令中save_txt选项用于生成结果的txt标注文件，会生成每张图片对应文件名的txt检测框信息文件，每个txt会生成一行一个目标的信息，信息包括类别序号、xcenter ycenter w h，后面四个为bbox位置，均为归一化数值，如下图：

2. python根据yolov5检测得到的txt文件，截取目标框图片并保存（即从原图中裁剪出检测到的目标物小图），代码如下：

# -*- coding: utf-8 -*-
import os
import cv2

path = "jpg_txt"         # jpg图片和对应的生成结果的txt标注文件，放在一起
path3 = "bboxcut"    # 裁剪出来的小图保存的根目录
w = 100                         # 原始图片resize
h = 100
img_total = []
txt_total = []

file = os.listdir(path)
for filename in file:
    first,last = os.path.splitext(filename)
    if last == ".jpg":                      # 图片的后缀名
        img_total.append(first)
    #print(img_total)
    else:
        txt_total.append(first)

for img_ in img_total:
    if img_ in txt_total:
        filename_img = img_+".jpg"          # 图片的后缀名
        # print('filename_img:', filename_img)
        path1 = os.path.join(path,filename_img)
        img = cv2.imread(path1)
        img = cv2.resize(img,(w,h),interpolation = cv2.INTER_CUBIC)        # resize 图像大小，否则roi区域可能会报错
        filename_txt = img_+".txt"
        # print('filename_txt:', filename_txt)
        n = 1
        with open(os.path.join(path,filename_txt),"r+",encoding="utf-8",errors="ignore") as f:
            for line in f:
                aa = line.split(" ")
                x_center = w * float(aa[1])       # aa[1]左上点的x坐标  
                y_center = h * float(aa[2])       # aa[2]左上点的y坐标
                width = int(w*float(aa[3]))       # aa[3]图片width
                height = int(h*float(aa[4]))      # aa[4]图片height
                lefttopx = int(x_center-width/2.0)
                lefttopy = int(y_center-height/2.0)
                roi = img[lefttopy+1:lefttopy+height+3,lefttopx+1:lefttopx+width+1]   # [左上y:右下y,左上x:右下x] (y1:y2,x1:x2)需要调参，否则裁剪出来的小图可能不太好
                print('roi:', roi)                        # 如果不resize图片统一大小，可能会得到有的roi为[]导致报错         
                filename_last = img_+"_"+str(n)+".jpg"    # 裁剪出来的小图文件名
                # print(filename_last)
                path2 = os.path.join(path3,"roi")           # 需要在path3路径下创建一个roi文件夹
                print('path2:', path2)                    # 裁剪小图的保存位置
                cv2.imwrite(os.path.join(path2,filename_last),roi)
                n = n+1
    else:
        continue

裁剪出来的小图如下：