目标检测数据集处理脚本

古城微笑少年丶 2021-12-24 00:53 325阅读 0赞

#### 功能一：将图像文件和标注文件对应，删去无标注文件 ####

import os
    import sys
    input_folder=r'C:\Users\rockhuang\Desktop\rider'
    
    training_data=[]    
    a=[]
    for root, dirs, files in os.walk(input_folder):
        for filename in (x for x in files if x.endswith('.jpg')):
            filepath = os.path.join(root, filename) 
            #E:\pic\20180729\000006.jpg
            object_class = filename.split('.')[0]
            a.append(object_class)
        print(a)
    
    input_xml=r'C:\Users\rockhuang\Desktop\rider_xml'
    xml_data=[]
    b=[]
    for root, dirs, files in os.walk(input_xml):
        for filename1 in (x for x in files if x.endswith('.xml')):
            filepath = os.path.join(root, filename1)
            #print(filename1)
            object_class = filename1.split('.')[0]
            b.append(object_class)
            print(object_class)
    
    for i in a:
        if i not in b:
            
            os.remove(input_folder+'\\'+i+'.jpg')

--------------------

#### 功能二 ：linux生成目标检测中的trainval.txt ,test.txt 切记win下的txt一定要转为unix格式，具体notepad++ 打开，视图----》显示符号-------》显示所有字符，然后：编辑-----》文档格式转换--------》转换为unix格式，否则ubuntu下读不了这种txt，会报错。 ####

import os, sys
    import glob
     
    trainval_dir = r"C:\Users\rockhuang\Desktop\train"   #train图像保存路径
    #test_dir = r"C:\Users\rockhuang\Desktop\test" #test图片保存路径
     
    trainval_img_lists = glob.glob(trainval_dir + '/*.jpg')    #获取trainval中所有.png的文件
    trainval_img_names = []    #获取名称
    for item in trainval_img_lists:
        temp1, temp2 = os.path.splitext(os.path.basename(item))
        trainval_img_names.append(temp1)
    
    #test_img_lists = glob.glob(test_dir + '/*.png') #获取test中所有.png文件
    #test_img_names = []
    #for item in test_img_lists:
     # temp1, temp2 = os.path.splitext(os.path.basename(item))
    # test_img_names.append(temp1)
    #dist_img_dir1 = "INRIA_TRAIN_part/PNGImages/trainval"
    #dist_img_dir2 = "INRIA_TRAIN_part/PNGImages/test"
    dist_img_dir = r"VOC2012/JPEGImages"    #需要写入txt的trainval和test路径，因为我们在PNGImges目录下除了有trainval和test文件夹外还有所有159张图片，而文件夹trainval和test文件夹只是用于生成txt之用
    dist_anno_dir = r"VOC2012/Annotations" #需要写入的xml路径
     
    trainval_fd = open(r"C:\Users\rockhuang\Desktop\trainval1.txt", 'w')
    #test_fd = open(r"C:\Users\rockhuang\Desktop\test.txt", 'w')
     
    for item in trainval_img_names:
        trainval_fd.write(dist_img_dir + '/' + str(item) + '.jpg' + ' ' + dist_anno_dir + '/' + str(item) + '.xml\n')
    #for item in test_img_names:
    # test_fd.write(dist_img_dir + '/' + str(item) + '.png' + ' ' + dist_anno_dir + '/' + str(item) + '.xml\n')

--------------------

#### 功能三：生成 test\_name\_size.txt ####

import os, sys
    import glob
    from PIL import Image
     
    img_dir = r'C:\Users\rockhuang\Desktop\yest'
     
    img_lists = glob.glob(img_dir + '\*.jpg')
     
    test_name_size = open(r'C:\Users\rockhuang\Desktop\test_name_size.txt', 'w')
     
    for item in img_lists:
        img = Image.open(item)
        width, height = img.size
        temp1, temp2 = os.path.splitext(os.path.basename(item))
        test_name_size.write(temp1 + ' ' + str(height) + ' ' + str(width) + '\n')

--------------------

#### 功能四：将视频文件每一帧保存 ####

import cv2
    # 使用opencv按一定间隔截取视频帧，并保存为图片
    import glob
    a=r'C:\Users\rockhuang\Desktop\rider1\\'
    vc_list= glob.glob(a+ '/*.MOV') 
    c = 900000
    for vc1 in vc_list:
        vc = cv2.VideoCapture(vc1) #读取视频文件
    
     
     
        if vc.isOpened():#判断是否正常打开
     
            rval,frame = vc.read()
        else:
            rval = False
        timeF = 8#视频帧计数间隔频率
     
        while(rval): #循环读取视频帧
            rval,frame = vc.read()
            if (c%timeF == 0) :#每隔timeF帧进行存储操作
                cv2.imwrite(r'C:\Users\rockhuang\Desktop\rider1\\'+'la'+str(c)+'.jpg',frame) # 存储为图像
     
            c = c + 1
        cv2.waitKey(1)
        vc.release()

--------------------

#### 功能五：原图像显示标注框bounding box ####

注：这段代码原来用于处理DETRAC数据集，一些函数没用到注销了

import xml.etree.ElementTree as ET
    from xml.dom.minidom import Document
    import os
    import cv2
    import time
    
    def ConvertVOCXml(file_path="",file_name=""):
        tree = ET.parse(file_name)
        root = tree.getroot()
        # print(root.tag)
     
        num=0 #计数
        #读xml操作
     
        frame_lists=[]
        output_file_name=""
        for child in root:
            if(child.tag=="frame"):
              # 创建dom文档
                doc = Document()
                # 创建根节点
                annotation = doc.createElement('annotation')
                # 根节点插入dom树
                doc.appendChild(annotation)
       
                #print(child.tag, child.attrib["num"])
                pic_id= child.attrib["num"].zfill(5)
                #print(pic_id)
                output_file_name=root.attrib["name"]+"__img"+pic_id+".xml"
               # print(output_file_name)
       
                folder = doc.createElement("folder")
                folder.appendChild(doc.createTextNode("VOC2007"))
                annotation.appendChild(folder)
       
                filename = doc.createElement("filename")
                pic_name=root.attrib["name"]+"__img"+pic_id+".jpg"
                filename.appendChild(doc.createTextNode(pic_name))
                annotation.appendChild(filename)
       
                sizeimage = doc.createElement("size")
                imagewidth = doc.createElement("width")
                imageheight = doc.createElement("height")
                imagedepth = doc.createElement("depth")
       
                imagewidth.appendChild(doc.createTextNode("960"))
                imageheight.appendChild(doc.createTextNode("540"))
                imagedepth.appendChild(doc.createTextNode("3"))
       
                sizeimage.appendChild(imagedepth)
                sizeimage.appendChild(imagewidth)
                sizeimage.appendChild(imageheight)
                annotation.appendChild(sizeimage)
       
                target_list=child.getchildren()[0]  #获取target_list
                #print(target_list.tag)
                object=None
                for target in target_list:
                    if(target.tag=="target"):
                        #print(target.tag)
                        object = doc.createElement('object')
                        bndbox = doc.createElement("bndbox")
       
                        for target_child in target:
                            if(target_child.tag=="box"):
                                xmin = doc.createElement("xmin")
                                ymin = doc.createElement("ymin")
                                xmax = doc.createElement("xmax")
                                ymax = doc.createElement("ymax")
                                xmin_value=int(float(target_child.attrib["left"]))
                                ymin_value=int(float(target_child.attrib["top"]))
                                box_width_value=int(float(target_child.attrib["width"]))
                                box_height_value=int(float(target_child.attrib["height"]))
                                xmin.appendChild(doc.createTextNode(str(xmin_value)))
                                ymin.appendChild(doc.createTextNode(str(ymin_value)))
                                if(xmin_value+box_width_value>960):
                                    xmax.appendChild(doc.createTextNode(str(960)))
                                else:
                                    xmax.appendChild(doc.createTextNode(str(xmin_value+box_width_value)))
                                if(ymin_value+box_height_value>540):
                                    ymax.appendChild(doc.createTextNode(str(540)))
                                else:
                                    ymax.appendChild(doc.createTextNode(str(ymin_value+box_height_value)))
       
                            if(target_child.tag=="attribute"):
                                name = doc.createElement('name')
                                pose=doc.createElement('pose')
                                truncated=doc.createElement('truncated')
                                difficult=doc.createElement('difficult')
       
                                name.appendChild(doc.createTextNode("car"))
                                pose.appendChild(doc.createTextNode("Left"))  #随意指定
                                truncated.appendChild(doc.createTextNode("0"))  #随意指定
                                difficult.appendChild(doc.createTextNode("0"))  #随意指定
       
                                
                                object.appendChild(name)
                                object.appendChild(pose)
                                object.appendChild(truncated)
                                object.appendChild(difficult)
                                
                        bndbox.appendChild(xmin)
                        bndbox.appendChild(ymin)
                        bndbox.appendChild(xmax)
                        bndbox.appendChild(ymax)
                        object.appendChild(bndbox)
                        annotation.appendChild(object)
       
       
                file_path_out=os.path.join(file_path,output_file_name)
                f = open(file_path_out, 'w')
                f.write(doc.toprettyxml(indent=' ' * 4))
                f.close()
                num=num+1
        return num
    
    
    
    
    ''' 画方框 '''
    def bboxes_draw_on_img(img, bbox, color=[255, 0, 0], thickness=2):
    
        # Draw bounding box...
        print(bbox)
        p1 = (int(float(bbox["xmin"])), int(float(bbox["ymin"])))
        p2 = (int(float(bbox["xmax"])), int(float(bbox["ymax"])))
        cv2.rectangle(img, p1, p2, color, thickness)
    
    
    def visualization_image(image_name,xml_file_name):
        tree = ET.parse(xml_file_name)
        root = tree.getroot()
    
        object_lists=[]
        for child in root:
            if(child.tag=="folder"):
                print(child.tag, child.text)
            elif (child.tag == "filename"):
                print(child.tag, child.text)
            elif (child.tag == "size"):  #解析size
                for size_child in child:
                    if(size_child.tag=="width"):
                        print(size_child.tag,size_child.text)
                    elif (size_child.tag == "height"):
                        print(size_child.tag, size_child.text)
                    elif (size_child.tag == "depth"):
                        print(size_child.tag, size_child.text)
            elif (child.tag == "object"):  #解析object
                singleObject={ }
                for object_child in child:
                    if (object_child.tag == "name"):
                    # print(object_child.tag,object_child.text)
                        singleObject["name"] = object_child.text
                    elif (object_child.tag == "bndbox"):
                        for bndbox_child in object_child:
                            if (bndbox_child.tag == "xmin"):
                                singleObject["xmin"] = bndbox_child.text
                          # print(bndbox_child.tag, bndbox_child.text)
                            elif (bndbox_child.tag == "ymin"):
                               # print(bndbox_child.tag, bndbox_child.text)
                                singleObject["ymin"] = bndbox_child.text
                            elif (bndbox_child.tag == "xmax"):
                                singleObject["xmax"] = bndbox_child.text
                            elif (bndbox_child.tag == "ymax"):
                                singleObject["ymax"] = bndbox_child.text
                object_length=len(singleObject)
                if(object_length>0):
                    object_lists.append(singleObject)
        img = cv2.imread(image_name)
        for object_coordinate in object_lists:
            bboxes_draw_on_img(img,object_coordinate)
        cv2.imshow("capture", img)
        cv2.waitKey (0)
        cv2.destroyAllWindows()
    
    
    if ( __name__ == "__main__"):
       #print("main")
        ##basePath=r"E:\add_dataset\DETRAC-Train-Annotations-XML"
        #totalxml=os.listdir(basePath)
        total_num=0
        flag=False
        print("正在转换")
        #saveBasePath=r"E:\add_dataset\DETRAC-Train-Annotations-XML\xml_test"
        #if os.path.exists(saveBasePath)==False: #判断文件夹是否存在
        # os.makedirs(saveBasePath)
    
       #ConvertVOCXml(file_path="samplexml",file_name="000009.xml")
       # Start time
        start = time.time()
        log=open("xml_statistical.txt","w") #分析日志，进行排错
       # for xml in totalxml:
        # file_name=os.path.join(basePath,xml)
         # print(file_name)
         # num=ConvertVOCXml(file_path=saveBasePath,file_name=file_name)
          # print(num)
          # total_num=total_num+num
          # log.write(file_name+" "+str(num)+"\n")
       # End time
       # end = time.time()
        #seconds=end-start
        #print( "Time taken : {0} seconds".format(seconds))
       # print(total_num)
       # log.write(str(total_num)+"\n")
        
       # for xml in totalxml:
        # file_name=os.path.join(basePath,xml)
         # print(file_name)
         # num=ConvertVOCXml(file_path=saveBasePath,file_name=file_name)
          # print(num)
          # total_num=total_num+num
          # log.write(file_name+" "+str(num)+"\n")
       # End time
       # end = time.time()
        #seconds=end-start
        #print( "Time taken : {0} seconds".format(seconds))
       # print(total_num)
       # log.write(str(total_num)+"\n")
        visualization_image(r"C:\Users\rockhuang\Desktop\rider\xa900048.jpg",r"C:\Users\rockhuang\Desktop\rider_xml\xa900048.xml")

--------------------

#### 功能六：COCO数据集提取某一类别 ####

生成id号

import json
     
    className = { 
        1:'person'
    }
     
    classNum = [1]
     
    def writeNum(Num):
        with open("COCO_train.json","r+") as f:
            f.write(str(Num))
     # with open("instances_val2014.json","r+") as f:
    # data = json.load(f)
        # annData = data["annotations"]
        # print(annData[0])
        # for x in annData[0]:
        # if(x == "image_id"):
        # print(type(x))
        # print(x+ ":" + str(annData[0][x]))
        # if (x == "image_id" or x == "bbox" or x == "category_id"):
        # print(x + ":" + annData[0][x])
        # if (x == "image_id" or x == "bbox" or x == "category_id"):
        # print(x+ ":" + annData[0][x])
     # with open("test.json","w") as f:
    # json.dump(annData, f, ensure_ascii=False)
     
    inputfile = []
    inner = { }
    ##向test.json文件写入内容
    path="E:/add_dataset/annotations_trainval2017/annotations/instances_train2017.json"
    with open(path,"r+") as f:
        allData = json.load(f)
        data = allData["annotations"]
        print(data[1])
        print("read ready")
        
    for i in data:
        if(i['category_id'] in classNum):
            inner = { 
                "filename": str(i["image_id"]).zfill(6),
                "name": className[i["category_id"]],
                "bndbox":i["bbox"]
            } 
            inputfile.append(inner)
    inputfile = json.dumps(inputfile)
    writeNum(inputfile)

写文件

import json
    import os
    import cv2
    nameStr = []
    with open("COCO_train.json", "r+") as f:
        data = json.load(f)
        print("read ready")
    for i in data:
        imgName = '000000' + str(i["filename"]) + ".jpg"
        nameStr.append(imgName)
    nameStr = set(nameStr)
    print(nameStr)
    print(len(nameStr))
     
    path = r'E:/add_dataset/train2017/'
    savePath=r"E:/add_dataset/coco1/"
    count=0
    for file in nameStr:
        img=cv2.imread(path+file)
        cv2.imwrite(savePath+file,img)
        count=count+1
        print('num: '+count.__str__()+' '+file+'\n')

--------------------

#### 功能七：对坐标txt文件转xml ####

这个博主主要是对[https://data.vision.ee.ethz.ch/cvl/aess/dataset/][https_data.vision.ee.ethz.ch_cvl_aess_dataset]里面的数据集做处理，里面标注给的都是txt，需要转化为xml。  
txt格式为（图片名.png a b c d ; d f g h ;…）具体自己调整代码哈

1，生成每个图像的txt文件

import os
    import re
    import numpy as np
    input_folder=open(r'F:\add_person\1\annotations.txt',"r")
    for lines in input_folder:
        lines=lines[:-2]
        img_name=lines.split(":")[0]
        txt_label=lines.split(":")[1]
        list_txt_label="".join(txt_label)
        
        order=list_txt_label[2:-1].split("), (")
       # print(newlines)
        print(order)
        for i in range(len(order)):
            print(order[i])
            
            output_folder=r'F:\add_person\1\2'+'\\'+img_name.split('.')[0]+'.txt'
            f= open(output_folder,"a")
            f.write(order[i])  
            f.write('\n')

2.txt 文件转化为xml格式

#! /usr/bin/python
    # -*- coding:UTF-8 -*-
    import os, sys
    import glob
    from PIL import Image
     
    # VEDAI 图像存储位置
    src_img_dir = r"F:\add_person\5\image"
    # VEDAI 图像的 ground truth 的 txt 文件存放位置
    src_txt_dir = r"F:\add_person\5\txt"
    src_xml_dir = r"F:\add_person\5\xml"
     
    img_Lists = glob.glob(src_img_dir + '\*.png')
     
    img_basenames = [] # e.g. 100.jpg
    for item in img_Lists:
        img_basenames.append(os.path.basename(item))
    
    img_names = [] # e.g. 100
    for item in img_basenames:
        temp1, temp2 = os.path.splitext(item)
        img_names.append(temp1)
    
    for img in img_names:
        im = Image.open((src_img_dir + '\\' + img + '.png'))
        width, height = im.size
     
        # open the crospronding txt file
        gt = open(src_txt_dir + '\\' + img + '.txt').read().splitlines()
        #gt = open(src_txt_dir + '/gt_' + img + '.txt').read().splitlines()
     
        # write in xml file
        #os.mknod(src_xml_dir + '/' + img + '.xml')
        xml_file = open((src_xml_dir + '\\' + img + '.xml'), 'w')
        #xml_file.write('<?xml version="1.0" ?>\n')
        xml_file.write('<annotation>\n')
        xml_file.write(' <folder>VOC2007</folder>\n')
        xml_file.write(' <filename>' + str(img) + '.png' + '</filename>\n')
        xml_file.write(' <size>\n')
        xml_file.write(' <width>' + str(width) + '</width>\n')
        xml_file.write(' <height>' + str(height) + '</height>\n')
        xml_file.write(' <depth>3</depth>\n')
        xml_file.write(' </size>\n')
     
        # write the region of image on xml file
        for img_each_label in gt:
            spt = img_each_label.split(', ') 
            if(int(spt[0])>int(spt[2])):
                temp=spt[2]
                spt[2]=spt[0]
                spt[0]=temp
            if(int(spt[1])>int(spt[3])):
                temp1=spt[3]
                spt[3]=spt[1]
                spt[1]=temp1#这里如果txt里面是以逗号‘，’隔开的，那么就改为spt = img_each_label.split(',')。
            xml_file.write(' <object>\n')
            xml_file.write(' <name>' + 'person' + '</name>\n')
            xml_file.write(' <pose>Unspecified</pose>\n')
            xml_file.write(' <truncated>0</truncated>\n')
            xml_file.write(' <difficult>0</difficult>\n')
            xml_file.write(' <bndbox>\n')
            xml_file.write(' <xmin>' + str(spt[0]) + '</xmin>\n')
            xml_file.write(' <ymin>' + str(spt[1]) + '</ymin>\n')
            xml_file.write(' <xmax>' + str(spt[2]) + '</xmax>\n')
            xml_file.write(' <ymax>' + str(spt[3]) + '</ymax>\n')
            xml_file.write(' </bndbox>\n')
            xml_file.write(' </object>\n')
     
        xml_file.write('</annotation>\n')

--------------------

功能八：INRIAPerson数据集处理 ，该数据集地址为：[http://pascal.inrialpes.fr/data/human/][http_pascal.inrialpes.fr_data_human]

# -*- coding: UTF-8 -*-
    from xml.dom.minidom import Document
    import os
    import re
    
    list = os.listdir(r"F:\person\INRIAPerson\Train\annotations")
    savePath = r'F:\person\INRIAPerson\Train\xml'
    for oldfilename in list:
        if str(".txt") not in oldfilename:
            continue
        print (oldfilename)
    
        #raw_input(unicode('按回车键退出...','utf-8').encode('gbk'))
    
        fileindex = re.findall('\d+', oldfilename)
    
        print (fileindex)
        #raw_input(unicode('按回车键退出...','utf-8').encode('gbk'))
    
        print(str(int(fileindex[0]))) 
    
        #raw_input(unicode('按回车键退出...','utf-8').encode('gbk'))
        newfilename = os.path.splitext(oldfilename)[0] + ".xml"
    
        #print newfilename
        #raw_input(unicode('按回车键退出...','utf-8').encode('gbk'))
        f = open(os.path.join(r"F:\person\INRIAPerson\Train\annotations",oldfilename), "r")
        print( 'processing:' + f.name)
    
        doc = Document()
        annotation = doc.createElement('annotation')
        doc.appendChild(annotation)
    
        folder = doc.createElement('folder')
        folder.appendChild(doc.createTextNode('VOC2007'))
        annotation.appendChild(folder)
    
        filename = doc.createElement('filename')
        filename.appendChild(doc.createTextNode(oldfilename))
        annotation.appendChild(filename)
    
        source = doc.createElement('source')
        annotation.appendChild(source)
    
        database = doc.createElement('database')
        database.appendChild(doc.createTextNode('PASperson Database'))
        source.appendChild(database)
    
        annotation1 = doc.createElement('annotation')
        annotation1.appendChild(doc.createTextNode('PASperson'))
        source.appendChild(annotation1)
    
        fr = f.readlines()  # 调用文件的 readline()方法一次读取
    
        for line in fr:
            if str(line).__contains__("size"):
                sizes = []
                sizes = re.findall('\d+', line)
                size = doc.createElement('size')
                annotation.appendChild(size)
                width = doc.createElement('width')
                width.appendChild(doc.createTextNode(sizes[0]))
                size.appendChild(width)
                height = doc.createElement('height')
                height.appendChild(doc.createTextNode(sizes[1]))
                size.appendChild(height)
                depth = doc.createElement('depth')
                depth.appendChild(doc.createTextNode(sizes[2]))
                size.appendChild(depth)
    
                segmented = doc.createElement('segmented')
                segmented.appendChild(doc.createTextNode('0'))
                annotation.appendChild(segmented)
            if (str(line).__contains__('Objects')):
                nums = re.findall('\d+', line)
                break
        for index in range(1, int(nums[0])+1):
            for line in fr:
                if str(line).__contains__("Bounding box for object " + str(index)):
                    coordinate = re.findall('\d+', line)
                    object = doc.createElement('object')
                    annotation.appendChild(object)
    
                    name = doc.createElement('name')
                    name.appendChild(doc.createTextNode('person'))
                    object.appendChild(name)
    
                    pose = doc.createElement('pose')
                    pose.appendChild(doc.createTextNode('Unspecified'))
                    object.appendChild(pose)
    
                    truncated = doc.createElement('truncated')
                    truncated.appendChild(doc.createTextNode('0'))
                    object.appendChild(truncated)
    
                    difficult = doc.createElement('difficult')
                    difficult.appendChild(doc.createTextNode('0'))
                    object.appendChild(difficult)
    
                    bndbox = doc.createElement('bndbox')
                    object.appendChild(bndbox)
    
                    #数字中包含序号，下标应从1开始
                    xmin = doc.createElement('xmin')
                    xmin.appendChild(doc.createTextNode(coordinate[1]))
                    bndbox.appendChild(xmin)
                    ymin = doc.createElement('ymin')
                    ymin.appendChild(doc.createTextNode(coordinate[2]))
                    bndbox.appendChild(ymin)
                    xmax = doc.createElement('xmax')
                    xmax.appendChild(doc.createTextNode(coordinate[3]))
                    bndbox.appendChild(xmax)
                    ymax = doc.createElement('ymax')
                    ymax.appendChild(doc.createTextNode(coordinate[4]))
                    bndbox.appendChild(ymax)
        f.close()
        f = open(os.path.join(savePath,newfilename), 'w')
        f.write(doc.toprettyxml(indent="\t"))
        f.close()
        print(str(fileindex) + " compelete") 
    
    print('process compelete')

--------------------

##### 功能九：voc格式数据集生成trainval.txt, text.txt, val.txt #####

import os
    import random
    
    trainval_percent = 0.66
    train_percent = 0.95
    xmlfilepath = 'Annotations'
    txtsavepath = 'ImageSets\Main'
    total_xml = os.listdir(xmlfilepath)
    
    num=len(total_xml)
    list=range(num)
    tv=int(num*trainval_percent)
    tr=int(tv*train_percent)
    trainval= random.sample(list,tv)
    train=random.sample(trainval,tr)
    
    ftrainval = open('ImageSets/Main/trainval.txt', 'w')
    ftest = open('ImageSets/Main/test.txt', 'w')
    ftrain = open('ImageSets/Main/train.txt', 'w')
    fval = open('ImageSets/Main/val.txt', 'w')
    
    for i  in list:
        name=total_xml[i][:-4]+'\n'
        if i in trainval:
            ftrainval.write(name)
            if i in train:
                ftrain.write(name)
            else:
                fval.write(name)
        else:
            ftest.write(name)
    
    ftrainval.close()
    ftrain.close()
    fval.close()
    ftest .close()

[https_data.vision.ee.ethz.ch_cvl_aess_dataset]: https://data.vision.ee.ethz.ch/cvl/aess/dataset/
[http_pascal.inrialpes.fr_data_human]: http://pascal.inrialpes.fr/data/human/