foreword
Recently, I encountered a problem in target detection. The image is very large, with several thousand x several thousand pixels. The effect of direct input to the network training is not good. Usually, the input images of target detection are of fixed size, such as: yolo series, image input network It will be resized to 416*416 before. If a large image is directly resized, it will lose a lot of information, so the training effect is good, so we overlap and cut the image and the label at the same time, and then input the network training.
code
The code contains both label and image processing, and the label format is 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'))
References: https://blog.csdn.net/m0_37615398/article/details/84982384