前言
最近做目标检测遇到一个问题，图像很大，几千x几千的像素，直接输入网络训练效果不好，通常目标检测输入的图像都是固定大小的，例如：yolo系列，图片输入网路前会resize到416*416。很大的图片直接做resize会丢失很多信息，因此训练出来的效果好，因此我们重叠剪切，对图像和标签同时做重叠剪切，然后输入网络训练。
代码
代码包含同时对标签和图像处理，标签格式是xml
import sys
import os
import cv2
import numpy as np
import os.path
from xml.dom.minidom import Document
from tqdm import tqdm
import xml.etree.cElementTree as ET
def clip_img(number, oriname):
#------------------------------------------------------------#
# 读入原始图像
# 保存原图的大小
# 可以resize也可以不resize，看情况而定
#------------------------------------------------------------#
from_name = os.path.join(image_path, oriname+'.png')
img = cv2.imread(from_name)
h_ori,w_ori, _ =img.shape
img = cv2.resize(img, (2048, 1536))
h, w, _ = img.shape
#------------------------------------------------------------#
# 输入.xml文件
# 创建存放坐标四个值和类别的列表
#------------------------------------------------------------#
xml_name = os.path.join(label_xml_path, oriname+'.xml')
xml_ori = ET.parse(xml_name).getroot()
res = np.empty((0,5))
#------------------------------------------------------------#
# 找到每个.xml文件中的bbox
# lower().strip()转化小写移除字符串头尾空格
# vstack() 水平堆叠
#------------------------------------------------------------#
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)
bndbox.append(name)
res = np.vstack((res, bndbox))
print('*'*5, res)
#-------------------------------------------------------------#
# 开始剪切 + 写入标签信息
#-------------------------------------------------------------#
i = 0
win_size = 512 # 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, 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]
#------------------------------------------------#
# 判断bb是否在当前剪切的区域内
#------------------------------------------------#
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
# 如果物体被分割了，则忽略不写入
if xiefou:
# 有物体则写入xml文件
if youwu:
#---------------------------------------------------#
# 创建.xml文件 + 写入bb
#---------------------------------------------------#
doc = Document()
width, height, channel = str(win_size), str(win_size), str(3)
annotation = doc.createElement('annotation')
doc.appendChild(annotation)
size_chartu = doc.createElement('size')
annotation.appendChild(size_chartu)
width1 = doc.createElement('width')
width1_text = doc.createTextNode(width)
width1.appendChild(width1_text)
size_chartu.appendChild(width1)
height1 = doc.createElement('height')
height1_text = doc.createTextNode(height)
height1.appendChild(height1_text)
size_chartu.appendChild(height1)
channel1 = doc.createElement('channel')
channel1_text = doc.createTextNode(channel)
channel1.appendChild(channel1_text)
size_chartu.appendChild(channel1)
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(lablel_xml_path, xml_name)
with open(to_xml_name, 'wb+') as f:
f.write(doc.toprettyxml(indent="\t", encoding='utf-8'))
#name = '%02d_%02d_%02d_.bmp' % (number, int(r/win_size), int(c/win_size))
img_name = oriname+'_%3d.png' %(i)
to_name = os.path.join(image_crop_path, img_name)
i = i+1
cv2.imwrite(to_name, tmp)
if __name__ == "__main__":
image_path = r'E:\wcs\neural_new\neural_new\test\images'
label_xml_path = r'E:\wcs\neural_new\neural_new\test\Annotations'
image_crop_path = 'E:\\wcs\\cell\\test\\image\\'
lablel_xml_path = 'E:\\wcs\\cell\\test\\label\\'
if not os.path.exists(image_crop_path):
os.makedirs(image_crop_path)
if not os.path.exists(lablel_xml_path):
os.makedirs(lablel_xml_path)
for i, name in tqdm(enumerate(os.listdir(image_path))):
clip_img(i, name.rstrip('.png'))
参考文献：https://blog.csdn.net/m0_37615398/article/details/84982384