【Datasets】LaRa Traffic Light Dataset Analysis

Purpose: Parse the LaRa traffic light dataset into VOC format.
Divided into four categories: 'Traffic_Light_go', 'Traffic_Light_stop', 'Traffic_Light_ambiguous', 'Traffic_Light_warning'
data set download link: http://www.lara.prd.fr/benchmarks/trafficlightsrecognition

Open the official website, as shown in the figure below, and download the data set, a total of 9168 pictures.

Save the label information below in the label_old.txt folder.

1. There are only two files downloaded and copied from the official website, one is the original image compression package, and the other is the copied label information.
   

2. First, extract the label information in the label_old.tx file, and correspond each picture with its label information one by one. Each picture corresponds to a txt file. [Note that the file path is changed] preprocess.pyThe generated txt file is stored in the labels folder, and the original images corresponding to each other are stored in the Images folder.

# -*- coding: utf-8 -*-
import os
import shutil
from os import getcwd

#标签信息
classes = ['Traffic_Light_go', 'Traffic_Light_stop', 'Traffic_Light_ambiguous', 'Traffic_Light_warning']

width = 640
hight = 480
size = [width,hight]

def process(file_path, out_path, image_in_path, image_out_path):
    #generate labels
    with open(file_path) as f:
        i = 0
        pic_indexs = []
        if os.path.exists(out_path):
            shutil.rmtree(out_path)
        os.mkdir(out_path)
        for line in f.readlines():
            strs = str.split(line)
            pic_index = strs[2]
            pic_indexs.append(pic_index)
            x1 = float(strs[3])
            y1 = float(strs[4])
            x2 = float(strs[5])
            y2 = float(strs[6])
            box = [x1, x2, y1, y2]
            x,y,w,h = convert(size, box)
            label = strs[-3][1:]+'_'+strs[-2][:-1]+'_'+strs[-1][1:-1]
            class_ = classes.index(label)
            with open(out_path+'/%s.txt'%(str(i).zfill(6)), 'w') as out:
                out.write('%s %s %s %s %s\n'%(class_, x, y, w, h))
            i = i+1
    
    #reorder pictures
    i = 0
    if os.path.exists(image_out_path):        
        shutil.rmtree(image_out_path)
    os.mkdir(image_out_path)
    for index in pic_indexs:
        pic_name = 'frame_'+index.zfill(6)+'.jpg'  #fill left null with zeros
        new_pic = str(i).zfill(6)+'.jpg'
        new_pic_path = image_out_path + '/' + new_pic
        old_pic_path = image_in_path + '/' + pic_name
        print(old_pic_path+' -> '+new_pic_path)
        shutil.copyfile(old_pic_path, new_pic_path)
        i = i+1
            

def convert(size, box):
    dw = 1./(size[0])
    dh = 1./(size[1])
    x = (box[0] + box[1])/2.0 - 1
    y = (box[2] + box[3])/2.0 - 1
    w = box[1] - box[0]
    h = box[3] - box[2]
    x = x*dw
    w = w*dw
    y = y*dh
    h = h*dh
    return (x,y,w,h)

wd = getcwd()
file_path = wd + '/label_old.txt'                   #复制的标签信息，放在label_old.txt文件中
out_path = wd +'/labels'                            #提取出标签坐标信息的txt存放文件夹
image_in_path = wd + '/Lara3D_UrbanSeq1_JPG'        #下载下来的原始图片存储位置
image_out_path = wd + '/Images'                     #存放提取的信号灯原始图片
process(file_path, out_path, image_in_path, image_out_path)

Open the txt file, the content is as follows:

3. Then, convert the txt file into an xml file. [Note to modify to your own path and category] txt2xml.pyThe generated xml file is saved in the Annotations folder.

import cv2
import os
xml_head = '''<annotation>
    <folder>VOC2007</folder>
    <filename>{}</filename>
    <source>
        <database>The VOC2007 Database</database>
        <annotation>PASCAL VOC2007</annotation>
        <image>flickr</image>
        <flickrid>325991873</flickrid>
    </source>
    <owner>
        <flickrid>null</flickrid>
        <name>null</name>
    </owner>    
    <size>
        <width>{}</width>
        <height>{}</height>
        <depth>{}</depth>
    </size>
    <segmented>0</segmented>
    '''
xml_obj = '''
    <object>        
        <name>{}</name>
        <pose>Rear</pose>
        <truncated>0</truncated>
        <difficult>0</difficult>
        <bndbox>
            <xmin>{}</xmin>
            <ymin>{}</ymin>
            <xmax>{}</xmax>
            <ymax>{}</ymax>
        </bndbox>
    </object>
    '''
xml_end = '''
</annotation>'''
 
labels = ['Traffic_Light_go', 'Traffic_Light_stop', 'Traffic_Light_ambiguous', 'Traffic_Light_warning']  #label for datasets
 
cnt = 0
txt_path=os.path.join("F:/ceshi/labels/")       #存放txt的文件目录
image_path=os.path.join("F:/ceshi/Images/")     #存放图片的文件目录
path=os.path.join("F:/ceshi/Annotations/")      #存放生成xml的文件目录
 
for(root,dirname,files) in os.walk(image_path):#遍历图片文件夹
    for ft in files:
        ftxt=ft.replace('jpg','txt')#ft是图片名字+扩展名，将jpg和txt替换
        fxml=ft.replace('jpg','xml')
        xml_path=path+fxml
        obj = ''
		
        img = cv2.imread(root+ft)		
        try:
            img_h,img_w = img.shape[0],img.shape[1]
            head = xml_head.format(str(fxml),str(img_w),str(img_h),3)
        except:
            print(root+ft)
		
        with open(txt_path+ftxt,'r') as f:          #读取对应txt文件内容
            for line in f.readlines():
                yolo_datas = line.strip().split(' ')
                label = int(float(yolo_datas[0].strip()))
                center_x = round(float(str(yolo_datas[1]).strip()) * img_w)
                center_y = round(float(str(yolo_datas[2]).strip()) * img_h)
                bbox_width = round(float(str(yolo_datas[3]).strip()) * img_w)
                bbox_height = round(float(str(yolo_datas[4]).strip()) * img_h)
 
                xmin = str(int(center_x - bbox_width / 2 ))
                ymin = str(int(center_y - bbox_height / 2))
                xmax = str(int(center_x + bbox_width / 2))
                ymax = str(int(center_y + bbox_height / 2))
 
                obj += xml_obj.format(labels[label],xmin,ymin,xmax,ymax)
        with open(xml_path,'w') as f_xml:
            f_xml.write(head+obj+xml_end)
        cnt += 1
        print(cnt)

So far, the dataset has been parsed into VOC format. There are original pictures, which are stored in the Images folder, and the xml files are stored in the Annotations folder.

---

4. However, want to see how this dataset is labeled? Then, next, you need to restore the label coordinate information to the original picture and draw it with a frame.

   Pictures containing frame coordinate information are saved in the label_picture folder. [Pay attention to modify the file path]xml_to_picture.py

# -*- coding: utf-8 -*-

import xml.etree.ElementTree as ET
import os, cv2

# 将路径换成自己的即可
annota_dir = "F:/ceshi/Annotations/"      #xml文件保存的路径
origin_dir = "F:/ceshi/Images/"          #jpg图片保存的路径
target_dir1= "F:/ceshi/label_picture/"          #生成带有标签信息的文件夹路径，即想将转化后的图片保存在哪  

def divide_img(oriname):
    img_file = os.path.join(origin_dir, oriname + '.jpg')
    im = cv2.imread(img_file)

    # 读取每个原图像的xml文件
    xml_file = os.path.join(annota_dir, oriname + '.xml')  
    tree = ET.parse(xml_file)
    root = tree.getroot()

    for object in root.findall('object'):
        object_name = object.find('name').text
        Xmin = int(object.find('bndbox').find('xmin').text)
        Ymin = int(object.find('bndbox').find('ymin').text)
        Xmax = int(object.find('bndbox').find('xmax').text)
        Ymax = int(object.find('bndbox').find('ymax').text)
        color = (4, 250, 7)
        cv2.rectangle(im, (Xmin, Ymin), (Xmax, Ymax), color, 2)
        font = cv2.FONT_HERSHEY_SIMPLEX
        cv2.putText(im, object_name, (Xmin, Ymin - 7), font, 0.5, (6, 230, 230), 2)

    img_name = oriname + '.jpg'
    to_name = os.path.join(target_dir1, img_name)
    cv2.imwrite(to_name, im)

img_list = os.listdir(origin_dir)
for name in img_list:
    divide_img(name.rstrip('.jpg'))
print("转化完成！")  

Oh, that's exactly what it looks like~~