对于一些高分辨率的图片，需要分块处理。但有时我们的数据集已经在原图上标注好了，再分块处理可能面临重新标注分块后的图片，这样是很麻烦的，所以提供代码：不需要重新标记分块后的图片，只需要对原数据集进行处理就可以得到，分块后的图片，以及含有目标的块图片的xml文件（VOC格式数据集）。但是注意：这个xml文件没有图片的Size信息。

#把图片切块并且得到xml文件
import torch
import pickle
import sys
import os
import cv2
import numpy as np
import os.path
import torch.utils.data as data
import torchvision.transforms as transforms
from PIL import Image
from xml.dom.minidom import Document
from tqdm import tqdm
if sys.version_info[0] ==2:
    import xml.etree.cElementTree as ET
else:
    import xml.etree.ElementTree as ET
origin_dir = '/home/dlut/网络/make_database/数据集处理/JPEGImages'
target_dir1 = '/home/dlut/网络/make_database/数据集处理/JpGImages_img/'
annota_dir = '/home/dlut/网络/make_database/数据集处理/Annotations'
target_dir2 = '/home/dlut/网络/make_database/数据集处理/Annotations_xml/'
def clip_img(No, oriname):
    from_name = os.path.join(origin_dir, oriname+'.jpg')
    img = cv2.imread(from_name)
    h_ori,w_ori, _ =img.shape#保存原图的大小
    #img = cv2.resize(img, (2048, 2048))#可以resize也可以不resize，看情况而定
    h, w, _ = img.shape
    xml_name = os.path.join(annota_dir, oriname+'.xml')#读取每个原图像的xml文件
    xml_ori = ET.parse(xml_name).getroot()
    res = np.empty((0,5))#存放坐标的四个值和类别
    for obj in xml_ori.iter('object'):
        difficult = int(obj.find('difficult').text) == 1
        if difficult:
            continue
        name = obj.find('name').text.lower().strip()
        bbox = obj.find('bndbox')
        pts = ['xmin', 'ymin', 'xmax', 'ymax']
        bndbox = []
        for i, pt in enumerate(pts):
            cur_pt = int(bbox.find(pt).text) - 1
            cur_pt = int(cur_pt*h/h_ori) if i%2==1 else int(cur_pt * w / w_ori)
            bndbox.append(cur_pt)
        #label_idx = self.class_to_ind[name]
        bndbox.append(name)
        res = np.vstack((res, bndbox))
    i = 0
    win_size = 960#分块的大小
    stride = 960#重叠的大小，设置这个可以使分块有重叠  stride =win_size 说明我设置的分块没有重叠
    for r in range(0, h - win_size, stride):
        for c in range(0, w - win_size, stride):
            flag = np.zeros([1, len(res)])  ## 修改flag = np.zeros([1, len(res)])
            youwu = False
            xiefou = True
            tmp = img[r: r+win_size, c: c+win_size]
            for re in range(res.shape[0]):
                xmin,ymin,xmax,ymax,label = res[re]
                if int(xmin)>=c and int(xmax) <=c+win_size and int(ymin)>=r and int(ymax)<=r+win_size:
                    flag[0][re] = 1
                    youwu = True
                elif int(xmin)<c or int(xmax) >c+win_size or int(ymin) < r or int(ymax) > r+win_size:
                    pass
                else:
                    xiefou = False
                    break;
            if xiefou:#如果物体被分割了，则忽略不写入
                if youwu:#有物体则写入xml文件
                    doc = Document()
                    annotation = doc.createElement('annotation')
                    doc.appendChild(annotation)
                    for re in range(res.shape[0]):
                        xmin,ymin,xmax,ymax,label = res[re]
                        xmin=int(xmin)
                        ymin=int(ymin)
                        xmax=int(xmax)
                        ymax=int(ymax)
                        if flag[0][re] == 1:
                            xmin=str(xmin-c)
                            ymin=str(ymin-r)
                            xmax=str(xmax-c)
                            ymax=str(ymax-r)
                            object_charu = doc.createElement('object')
                            annotation.appendChild(object_charu)
                            name_charu = doc.createElement('name')
                            name_charu_text = doc.createTextNode(label)
                            name_charu.appendChild(name_charu_text)
                            object_charu.appendChild(name_charu)
                            dif = doc.createElement('difficult')
                            dif_text = doc.createTextNode('0')
                            dif.appendChild(dif_text)
                            object_charu.appendChild(dif)
                            bndbox = doc.createElement('bndbox')
                            object_charu.appendChild(bndbox)
                            xmin1 = doc.createElement('xmin')
                            xmin_text = doc.createTextNode(xmin)
                            xmin1.appendChild(xmin_text)
                            bndbox.appendChild(xmin1)
                            ymin1 = doc.createElement('ymin')
                            ymin_text = doc.createTextNode(ymin)
                            ymin1.appendChild(ymin_text)
                            bndbox.appendChild(ymin1)
                            xmax1 = doc.createElement('xmax')
                            xmax_text = doc.createTextNode(xmax)
                            xmax1.appendChild(xmax_text)
                            bndbox.appendChild(xmax1)
                            ymax1 = doc.createElement('ymax')
                            ymax_text = doc.createTextNode(ymax)
                            ymax1.appendChild(ymax_text)
                            bndbox.appendChild(ymax1)
                        else:
                            continue
                    xml_name = oriname+'_%3d.xml' % (i)
                    to_xml_name = os.path.join(target_dir2, xml_name)
                    with open(to_xml_name, 'wb+') as f:
                        f.write(doc.toprettyxml(indent="\t", encoding='utf-8'))
                    #name = '%02d_%02d_%02d_.bmp' % (No, int(r/win_size), int(c/win_size))
                    img_name = oriname+'_%3d.jpg' %(i)
                    to_name = os.path.join(target_dir1, img_name)
                    i = i+1
                    cv2.imwrite(to_name, tmp)
for No, name in tqdm(enumerate(os.listdir(origin_dir))):
    clip_img(No, name.rstrip('.jpg'))

还有一个代码，只是将图片进行分块，并没有得到相对应的xml文件。如下：

#将图片分成了mxn块
# -*- coding:utf-8 -*-
import os
import matplotlib.pyplot as plt
import cv2
import numpy as np
 
def divide_img(img_path, img_name, save_path):
   imgg=img_path+img_name
   img = cv2.imread(imgg)
#   img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
   h = img.shape[0]
   w = img.shape[1]
   n=int(np.floor(h*1.0/1000))+1
   m=int(np.floor(w*1.0/1000))+1
   print('h={},w={},n={},m={}'.format(h,w,n,m))
   dis_h=int(np.floor(h/n))
   dis_w=int(np.floor(w/m))
   num=0
   for i in range(n):
     for j in range(m):
       num+=1
       print('i,j={}{}'.format(i,j))
       sub=img[dis_h*i:dis_h*(i+1),dis_w*j:dis_w*(j+1),:]
       cv2.imwrite(save_path+'{}_{}.jpg'.format(name,num),sub)
   
if __name__ == '__main__':
 
  img_path = 'C:/Users/na/Desktop/img/'#输入图片地址
  save_path = 'C:/Users/na/Desktop/div/' #输出图片的地址
  img_list = os.listdir(img_path)
  for name in img_list:
    divide_img(img_path,name,save_path)

参考：

https://blog.csdn.net/m0_37615398/article/details/84982384
https://blog.csdn.net/gusui7202/article/details/87969534