162 lines
6.3 KiB
Python
162 lines
6.3 KiB
Python
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# import random
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# import os
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# trainval_percent = 0.9
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# train_percent = 0.9
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# xmlfilepath = '/home/ykn/dataset/PCB_DATASET/Annotationss'
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# txtsavepath = '/home/ykn/dataset/PCB_DATASET/ImageSets'
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# total_xml = os.listdir(xmlfilepath)
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# num = len(total_xml)
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# list = range(num)
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# tv = int(num * trainval_percent)
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# tr = int(tv * train_percent)
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# trainval = random.sample(list, tv)
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# train = random.sample(trainval, tr)
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# ftrainval = open(txtsavepath+'/trainval.txt', 'w')
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# ftest = open(txtsavepath+'/test.txt', 'w')
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# ftrain = open(txtsavepath+'/train.txt', 'w')
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# fval = open(txtsavepath+'/val.txt', 'w')
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# for i in list:
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# name = total_xml[i][:-4] + '\n'
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# if i in trainval:
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# ftrainval.write(name)
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# if i in train:
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# ftrain.write(name)
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# else:
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# fval.write(name)
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# else:
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# ftest.write(name)
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# ftrainval.close()
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# ftrain.close()
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# fval.close()
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# ftest.close()
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# xml解析包
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import xml.etree.ElementTree as ET
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import pickle
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import os
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# os.listdir() 方法用于返回指定的文件夹包含的文件或文件夹的名字的列表
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from os import listdir, getcwd
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from os.path import join
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sets = ['train', 'test', 'val']
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classes = ['missing_hole', 'mouse_bite', 'open_circuit', 'short', 'spur', 'spurious_copper']
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label_path = '/home/ykn/dataset/PCB_DATASET/labels'
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ImageSets = '/home/ykn/dataset/PCB_DATASET/ImageSets'
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images = '/home/ykn/dataset/PCB_DATASET/images'
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# 进行归一化操作
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def convert(size, box): # size:(原图w,原图h) , box:(xmin,xmax,ymin,ymax)
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dw = 1./size[0] # 1/w
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dh = 1./size[1] # 1/h
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x = (box[0] + box[1])/2.0 # 物体在图中的中心点x坐标
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y = (box[2] + box[3])/2.0 # 物体在图中的中心点y坐标
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w = box[1] - box[0] # 物体实际像素宽度
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h = box[3] - box[2] # 物体实际像素高度
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x = x*dw # 物体中心点x的坐标比(相当于 x/原图w)
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w = w*dw # 物体宽度的宽度比(相当于 w/原图w)
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y = y*dh # 物体中心点y的坐标比(相当于 y/原图h)
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h = h*dh # 物体宽度的宽度比(相当于 h/原图h)
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return (x, y, w, h) # 返回 相对于原图的物体中心点的x坐标比,y坐标比,宽度比,高度比,取值范围[0-1]
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# year ='2012', 对应图片的id（文件名）
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def convert_annotation(image_id):
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'''
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将对应文件名的xml文件转化为label文件，xml文件包含了对应的bunding框以及图片长款大小等信息，
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通过对其解析，然后进行归一化最终读到label文件中去，也就是说
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一张图片文件对应一个xml文件，然后通过解析和归一化，能够将对应的信息保存到唯一一个label文件中去
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labal文件中的格式：calss x y w h 同时，一张图片对应的类别有多个，所以对应的ｂｕｎｄｉｎｇ的信息也有多个
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'''
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# 对应的通过year 找到相应的文件夹，并且打开相应image_id的xml文件，其对应bund文件
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in_file = open('/home/ykn/dataset/PCB_DATASET/Annotations /%s.xml' % (image_id), encoding='utf-8')
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# 准备在对应的image_id 中写入对应的label，分别为
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# <object-class> <x> <y> <width> <height>
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out_file = open(label_path+'/%s.txt' % (image_id), 'w', encoding='utf-8')
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# 解析xml文件
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tree = ET.parse(in_file)
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# 获得对应的键值对
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root = tree.getroot()
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# 获得图片的尺寸大小
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size = root.find('size')
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# 如果xml内的标记为空，增加判断条件
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if size != None:
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# 获得宽
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w = int(size.find('width').text)
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# 获得高
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h = int(size.find('height').text)
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# 遍历目标obj
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for obj in root.iter('object'):
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# 获得difficult ？？
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difficult = obj.find('difficult').text
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# 获得类别 =string 类型
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cls = obj.find('name').text
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# 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
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if cls not in classes or int(difficult) == 1:
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continue
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# 通过类别名称找到id
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cls_id = classes.index(cls)
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# 找到bndbox 对象
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xmlbox = obj.find('bndbox')
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# 获取对应的bndbox的数组 = ['xmin','xmax','ymin','ymax']
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b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text),
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float(xmlbox.find('ymax').text))
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print(image_id, cls, b)
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# 带入进行归一化操作
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# w = 宽, h = 高， b= bndbox的数组 = ['xmin','xmax','ymin','ymax']
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bb = convert((w, h), b)
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# bb 对应的是归一化后的(x,y,w,h)
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# 生成 calss x y w h 在label文件中
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out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
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# 返回当前工作目录
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wd = getcwd()
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print(wd)
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for image_set in sets:
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'''
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对所有的文件数据集进行遍历
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做了两个工作：
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１．将所有图片文件都遍历一遍，并且将其所有的全路径都写在对应的txt文件中去，方便定位
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２．同时对所有的图片文件进行解析和转化，将其对应的bundingbox 以及类别的信息全部解析写到label 文件中去
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最后再通过直接读取文件，就能找到对应的label 信息
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'''
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# 先找labels文件夹如果不存在则创建
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if not os.path.exists(label_path):
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os.makedirs(label_path+'/')
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# 读取在ImageSets/Main 中的train、test..等文件的内容
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# 包含对应的文件名称
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image_ids = open(ImageSets+'/%s.txt' % (image_set)).read().strip().split()
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# 打开对应的2012_train.txt 文件对其进行写入准备
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list_file = open('/home/ykn/dataset/PCB_DATASET/%s.txt' % (image_set), 'w')
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# 将对应的文件_id以及全路径写进去并换行
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for image_id in image_ids:
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list_file.write(images+'/%s.jpg\n' % (image_id))
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# 调用 year = 年份 image_id = 对应的文件名_id
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convert_annotation(image_id)
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# 关闭文件
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list_file.close()
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# os.system(‘comand’) 会执行括号中的命令，如果命令成功执行，这条语句返回0，否则返回1
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# os.system("cat 2007_train.txt 2007_val.txt 2012_train.txt 2012_val.txt > train.txt")
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# os.system("cat 2007_train.txt 2007_val.txt 2007_test.txt 2012_train.txt 2012_val.txt > train.all.txt")
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