1. 批量重命名文件夹中的图片文件
# -*- coding:utf8 -*-
import os
class BatchRename():
'''
批量重命名文件夹中的图片文件
'''
def __init__(self):
self.path = '/home/zhy/Documents/Perception/camera_data/mine_obstacle_image/image/6/' # 表示需要命名处理的文件夹
def rename(self):
filelist = os.listdir(self.path) # 获取文件路径
filelist.sort()
total_num = len(filelist) # 获取文件长度（个数）
i = 3172# 表示文件的命名是从1开始的
for item in filelist:
if item.endswith('.jpg'): # 初始的图片的格式为jpg格式的（或者源文件是png格式及其他格式，后面的转换格式就可以调整为自己需要的格式即可）
src = os.path.join(os.path.abspath(self.path), item)
#dst = os.path.join(os.path.abspath(self.path), '' + str(i) + '.txt') # 处理后的格式也为jpg格式的，当然这里可以改成png格式
dst = os.path.join(os.path.abspath(self.path),
'00' + format(str(i), '0>2s') + '.jpg') # 这种情况下的命名格式为0000000.jpg形式，可以自主定义想要的格式
try:
os.rename(src, dst)
print('converting %s to %s ...' % (src, dst))
i = i + 1
except:
continue
print('total %d to rename & converted %d txt' % (total_num, i))
if __name__ == '__main__':
demo = BatchRename()
demo.rename()
2.图像保存格式转换
# -*- coding:utf8 -*-
import os
import cv2 as cv
image_path = '/home/zhy/Documents/Perception/标注/data1/images/val' # 设置图片读取的路径
save_path = '/home/zhy/Documents/Perception/标注/data1/images/1' # 设置图片保存的路径
if not os.path.exists(save_path): # 判断路径是否正确，如果正确就打开
os.makedirs(save_path)
image_file = os.listdir(image_path)
for image in image_file:
if image.split('.')[-1] in ['bmp', 'jpg', 'jpeg', 'png', 'JPG', 'PNG']:
str = image.rsplit(".", 1) # 从右侧判断是否有符号“.”，并对image的名称做一次分割。如112345.jpeg分割后的str为["112345","jpeg"]
output_img_name = str[0] + ".jpg" # 取列表中的第一个字符串与“.jpg”放在一起。
src = cv.imread(os.path.join(image_path, image))
newimg = cv.imwrite(save_path + '/' + output_img_name, src)
print('FINISHED')
3. kitti数据集txt转xml
# -*- coding:utf8 -*-
# 根据一个给定的XML Schema，使用DOM树的形式从空白文件生成一个XML
from xml.dom.minidom import Document
import cv2
import os
def generate_xml(name, split_lines, img_size, class_ind):
doc = Document() # 创建DOM文档对象
annotation = doc.createElement('annotation')
doc.appendChild(annotation)
title = doc.createElement('folder')
title_text = doc.createTextNode('KITTI')
title.appendChild(title_text)
annotation.appendChild(title)
img_name = name + '.png'
title = doc.createElement('filename')
title_text = doc.createTextNode(img_name)
title.appendChild(title_text)
annotation.appendChild(title)
source = doc.createElement('source')
annotation.appendChild(source)
title = doc.createElement('database')
title_text = doc.createTextNode('The KITTI Database')
title.appendChild(title_text)
source.appendChild(title)
title = doc.createElement('annotation')
title_text = doc.createTextNode('KITTI')
title.appendChild(title_text)
source.appendChild(title)
size = doc.createElement('size')
annotation.appendChild(size)
title = doc.createElement('width')
title_text = doc.createTextNode(str(img_size[1]))
title.appendChild(title_text)
size.appendChild(title)
title = doc.createElement('height')
title_text = doc.createTextNode(str(img_size[0]))
title.appendChild(title_text)
size.appendChild(title)
title = doc.createElement('depth')
title_text = doc.createTextNode(str(img_size[2]))
title.appendChild(title_text)
size.appendChild(title)
for split_line in split_lines:
line = split_line.strip().split()
if line[0] in class_ind:
object = doc.createElement('object')
annotation.appendChild(object)
title = doc.createElement('name')
title_text = doc.createTextNode(line[0])
title.appendChild(title_text)
object.appendChild(title)
bndbox = doc.createElement('bndbox')
object.appendChild(bndbox)
title = doc.createElement('xmin')
title_text = doc.createTextNode(str(int(float(line[4]))))
title.appendChild(title_text)
bndbox.appendChild(title)
title = doc.createElement('ymin')
title_text = doc.createTextNode(str(int(float(line[5]))))
title.appendChild(title_text)
bndbox.appendChild(title)
title = doc.createElement('xmax')
title_text = doc.createTextNode(str(int(float(line[6]))))
title.appendChild(title_text)
bndbox.appendChild(title)
title = doc.createElement('ymax')
title_text = doc.createTextNode(str(int(float(line[7]))))
title.appendChild(title_text)
bndbox.appendChild(title)
# 将DOM对象doc写入文件
f = open('/home/zhy/Documents/Perception/标注/training/Annotations/' + name + '.xml', 'w')
f.write(doc.toprettyxml(indent=''))
f.close()
if __name__ == '__main__':
class_ind = ('Cyclist', 'Car', 'train', 'tipperTruck', 'person')
# cur_dir=os.getcwd()
cur_dir = '/home/zhy/Documents/Perception/标注/training'
labels_dir = os.path.join(cur_dir, 'label_2')
for parent, dirnames, filenames in os.walk(labels_dir): # 分别得到根目录，子目录和根目录下文件
for file_name in filenames:
full_path = os.path.join(parent, file_name) # 获取文件全路径
f = open(full_path)
split_lines = f.readlines()
name = file_name[:-4] # 后四位是扩展名.txt，只取前面的文件名
img_name = name + '.png'
img_path = os.path.join('/home/zhy/Documents/Perception/标注/training/image_2/', img_name) # 路径需要自行修改
img_size = cv2.imread(img_path).shape
generate_xml(name, split_lines, img_size, class_ind)
print('all txts has converted into xmls')
4. 将原来的类合并
# -*- coding:utf8 -*-
import os
# modify_annotations_txt.py
#将原来的8类物体转换为我们现在需要的3类：Car，Pedestrian，Cyclist。
#我们把原来的Car、Van、Truck，Tram合并为Car类，把原来的Pedestrian，Person(sit-ting)合并为现在的Pedestrian，原来的Cyclist这一类保持不变。
import glob
import string
txt_list = glob.glob('/home/zhy/Documents/Perception/标注/training/label_txt/*.txt')
def show_category(txt_list):
category_list= []
for item in txt_list:
try:
with open(item) as tdf:
for each_line in tdf:
labeldata = each_line.strip().split(' ') # 去掉前后多余的字符并把其分开
category_list.append(labeldata[0]) # 只要第一个字段，即类别
except IOError as ioerr:
print('File error:'+str(ioerr))
print(set(category_list)) # 输出集合
def merge(line):
each_line=''
for i in range(len(line)):
if i!= (len(line)-1):
each_line=each_line+line[i]+' '
else:
each_line=each_line+line[i] # 最后一条字段后面不加空格
each_line=each_line+'\n'
return (each_line)
print('before modify categories are:\n')
show_category(txt_list)
for item in txt_list:
new_txt=[]
try:
with open(item, 'r') as r_tdf:
for each_line in r_tdf:
labeldata = each_line.strip().split(' ')
if labeldata[0] == '3': # 合并行人类
labeldata[0] = labeldata[0].replace(labeldata[0],'0')
# if labeldata[0] in ['Truck','Van']: # 合并重卡
# labeldata[0] = labeldata[0].replace(labeldata[0],'tipperTruck')
# if labeldata[0] in ['Person_sitting','Pedestrian']: # 合并行人类
# labeldata[0] = labeldata[0].replace(labeldata[0],'person')
# if labeldata[0] == 'Tram': # 合并行人类
# labeldata[0] = labeldata[0].replace(labeldata[0],'train')
# if labeldata[0] in ['Truck','Van','Tram']: # 合并汽车类
# labeldata[0] = labeldata[0].replace(labeldata[0],'Car')
# if labeldata[0] == 'Person_sitting': # 合并行人类
# labeldata[0] = labeldata[0].replace(labeldata[0],'Pedestrian')
# if labeldata[0] == 'DontCare': # 忽略Dontcare类
# continue
# if labeldata[0] == 'Misc': # 忽略Misc类
# continue
new_txt.append(merge(labeldata)) # 重新写入新的txt文件
with open(item,'w+') as w_tdf: # w+是打开原文件将内容删除，另写新内容进去
for temp in new_txt:
w_tdf.write(temp)
except IOError as ioerr:
print('File error:'+str(ioerr))
print('\nafter modify categories are:\n')
show_category(txt_list)
5. 去除相似度高的图片
# -*- coding:utf8 -*-
import os
import shutil
import cv2
import numpy as np
def calc_similarity(img1_path, img2_path):
img1 = cv2.imdecode(np.fromfile(img1_path, dtype=np.uint8), -1)
H1 = cv2.calcHist([img1], [1], None, [256], [0, 256]) # 计算图直方图
H1 = cv2.normalize(H1, H1, 0, 1, cv2.NORM_MINMAX, -1) # 对图片进行归一化处理
img2 = cv2.imdecode(np.fromfile(img2_path, dtype=np.uint8), -1)
H2 = cv2.calcHist([img2], [1], None, [256], [0, 256]) # 计算图直方图
H2 = cv2.normalize(H2, H2, 0, 1, cv2.NORM_MINMAX, -1) # 对图片进行归一化处理
similarity1 = cv2.compareHist(H1, H2, 0) # 相似度比较
print('similarity:', similarity1)
if similarity1 > 0.98: # 0.98是阈值，可根据需求调整
return True
else:
return False
# 去除相似度高的图片
def is_image(file_name):
pass
def filter_similar(dir_path):
filter_dir = os.path.join(os.path.dirname(dir_path), 'filter_similar')
if not os.path.exists(filter_dir):
os.mkdir(filter_dir)
filter_number = 0
for root, dirs, files in os.walk(dir_path):
img_files = files
# img_files = [file_name for file_name in files if is_image(file_name)]
filter_list = []
for index in range(len(img_files))[:-4]:
if img_files[index] in filter_list:
continue
for idx in range(len(img_files))[(index + 1):(index + 5)]:
img1_path = os.path.join(root, img_files[index])
img2_path = os.path.join(root, img_files[idx])
if calc_similarity(img1_path, img2_path):
filter_list.append(img_files[idx])
filter_number += 1
for item in filter_list:
src_path = os.path.join(root, item)
shutil.move(src_path, filter_dir)
return filter_number
if __name__ == '__main__':
test_path = "/home/zhy/Documents/Perception/标注/image/8/"
# save_path = "/home/zhy/Documents/Perception/标注/clean_image/1/"
filter_similar(test_path)
6. 数据集的划分
# coding:utf-8
import os
import random
import argparse
parser = argparse.ArgumentParser()
#xml文件的地址，根据自己的数据进行修改 xml一般存放在Annotations下
#parser.add_argument('--xml_path', default='Annotations', type=str, help='input xml label path')
#数据集的划分，地址选择自己数据下的ImageSets/Main
parser.add_argument('--txt_path', default='/home/zhy/Documents/Perception/标注/data/labels', type=str, help='output txt label path')
opt = parser.parse_args()
trainval_percent = 1.0 # 训练集和验证集所占比例。 这里没有划分测试集
train_percent = 0.9 # 训练集所占比例，可自己进行调整
#xmlfilepath = opt.xml_path
txtsavepath = opt.txt_path
total_xml = os.listdir(txtsavepath)
if not os.path.exists(txtsavepath):
os.makedirs(txtsavepath)
num = len(total_xml)
list_index = range(num)
tv = int(num * trainval_percent)
tr = int(tv * train_percent)
trainval = random.sample(list_index, tv)
train = random.sample(trainval, tr)
file_trainval = open(txtsavepath + '/trainval.txt', 'w')
file_test = open(txtsavepath + '/test.txt', 'w')
file_train = open(txtsavepath + '/train.txt', 'w')
file_val = open(txtsavepath + '/val.txt', 'w')
for i in list_index:
name = total_xml[i][:-4] + '\n'
if i in trainval:
file_trainval.write(name)
if i in train:
file_train.write(name)
else:
file_val.write(name)
else:
file_test.write(name)
file_trainval.close()
file_train.close()
file_val.close()
file_test.close()
7. xml转 txt
# -*- coding:utf8 -*-
import os
import xml.etree.ElementTree as ET
import io
find_path = '/home/zhy/Documents/Perception/camera_data/mine_obstacle_image/label/3/' # xml所在的文件
savepath = '/home/zhy/Documents/Perception/camera_data/mine_obstacle_image/label/4/' # 保存文件
classes = ['car','lightTruck','person','tipperTruck', 'construction','tricycle','train','bicycle','bus']
class Voc_Yolo(object):
def __init__(self, find_path):
self.find_path = find_path
def Make_txt(self, outfile):
out = open(outfile, 'w')
print("创建成功：{}".format(outfile))
return out
def Work(self, count):
# 找到文件路径
for root, dirs, files in os.walk(self.find_path):
# 找到文件目录中每一个xml文件
for file in files:
# 记录处理过的文件
count += 1
# 输入、输出文件定义
input_file = find_path + file
outfile = savepath + file[:-4] + '.txt'
# 新建txt文件，确保文件正常保存
out = self.Make_txt(outfile)
# 分析xml树，取出w_image、h_image
tree = ET.parse(input_file)
root = tree.getroot()
size = root.find('size')
w_image = float(size.find('width').text)
h_image = float(size.find('height').text)
# 继续提取有效信息来计算txt中的四个数据
for obj in root.iter('object'):
# 将类型提取出来，不同目标类型不同，本文仅有一个类别->0
classname = obj.find('name').text
# 如果类别不是对应在我们预定好的class文件中，或difficult==1则跳过
if classname not in classes == 1:
continue
# 通过类别名称找到id
cls_id = classes.index(classname)
xmlbox = obj.find('bndbox')
x_min = float(xmlbox.find('xmin').text)
x_max = float(xmlbox.find('xmax').text)
y_min = float(xmlbox.find('ymin').text)
y_max = float(xmlbox.find('ymax').text)
# 计算公式
x_center = ((x_min + x_max) / 2 - 1) / w_image
y_center = ((y_min + y_max) / 2 - 1) / h_image
w = (x_max - x_min) / w_image
h = (y_max - y_min) / h_image
# 文件写入
out.write(
str(cls_id) + " " + str(x_center) + " " + str(y_center) + " " + str(w) + " " + str(h) + '\n')
out.close()
return count
if __name__ == "__main__":
data = Voc_Yolo(find_path)
number = data.Work(0)
print(number)
