很多时候，在小目标检测中，对于一副高分辨率图像，我们很难直接输入一整幅大图来进行目标检测，特别是对于one-stage的方法，如SSD的输入尺寸是300和512， YOLO的输入尺寸是416，而高分辨率图像通常有几千×几千像素。所以我在此分享将一副高分辨率图像分块同时写入对应目标的boundingbox改变后的坐标到xml文件中。
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 = '原图像存放地址'
target_dir1 = '分块图像存放地址'
annota_dir = '原boundingbox的xml文件存放地址'
target_dir2 = '分块boundingbox的xml文件存放地址'
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 = 256#分块的大小
stride = 128#重叠的大小，设置这个可以使分块有重叠
for r in range(0, h - win_size, stride):
for c in range(0, w - win_size, stride):
flag = np.zeros([1,10])
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'))
这样就将一个大图分块且保存了坐标xml文件。
注意xml文件的key视情况而定，不是通用的。