import xml.etree.ElementTree as ET from os import getcwd sets=[('2007', 'train'), ('2007', 'val'), ('2007', 'test')] classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"] def convert_annotation(year, image_id, list_file): in_file = open('VOCdevkit/VOC%s/Annotations/%s.xml'%(year, image_id)) tree=ET.parse(in_file) root = tree.getroot() for obj in root.iter('object'): difficult = obj.find('difficult').text cls = obj.find('name').text if cls not in classes or int(difficult)==1: continue cls_id = classes.index(cls) xmlbox = obj.find('bndbox') b = (int(xmlbox.find('xmin').text), int(xmlbox.find('ymin').text), int(xmlbox.find('xmax').text), int(xmlbox.find('ymax').text)) list_file.write(" " + ",".join([str(a) for a in b]) + ',' + str(cls_id)) wd = getcwd() for year, image_set in sets: image_ids = open('VOCdevkit/VOC%s/ImageSets/Main/%s.txt'%(year, image_set)).read().strip().split() list_file = open('%s_%s.txt'%(year, image_set), 'w') for image_id in image_ids: list_file.write('%s/VOCdevkit/VOC%s/JPEGImages/%s.jpg'%(wd, year, image_id)) convert_annotation(year, image_id, list_file) list_file.write('\n') list_file.close()
理解问题import xml.etree.ElementTree as ET from os import getcwd sets=[('2007', 'train'), ('2007', 'val'), ('2007', 'test')] classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"] def convert_annotation(year, image_id, list_file): in_file = open('VOCdevkit/VOC%s/Annotations/%s.xml'%(year, image_id)) tree=ET.parse(in_file) root = tree.getroot() for obj in root.iter('object'): difficult = obj.find('difficult').text cls = obj.find('name').text if cls not in classes or int(difficult)==1: continue cls_id = classes.index(cls) xmlbox = obj.find('bndbox') b = (int(xmlbox.find('xmin').text), int(xmlbox.find('ymin').text), int(xmlbox.find('xmax').text), int(xmlbox.find('ymax').text)) list_file.write(" " + ",".join([str(a) for a in b]) + ',' + str(cls_id)) wd = getcwd() for year, image_set in sets: image_ids = open('VOCdevkit/VOC%s/ImageSets/Main/%s.txt'%(year, image_set)).read().strip().split() list_file = open('%s_%s.txt'%(year, image_set), 'w') for image_id in image_ids: list_file.write('%s/VOCdevkit/VOC%s/JPEGImages/%s.jpg'%(wd, year, image_id)) convert_annotation(year, image_id, list_file) list_file.write('\n') list_file.close()
已完成理解「import xml.etree.ElementTree as ET from os import getcwd sets=[('2007', 'train'), ('2007', 'val'), ('2007', 'test')] classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"] def convert_annotation(year, image_id, list_file): in_file = open('VOCdevkit/VOC%s/Annotations/%s.xml'%(year, image_id)) tree=ET.parse(in_file) root = tree.getroot() for obj in root.iter('object'): difficult = obj.find('difficult').text cls = obj.find('name').text if cls not in classes or int(difficult)==1: continue cls_id = classes.index(cls) xmlbox = obj.find('bndbox') b = (int(xmlbox.find('xmin').text), int(xmlbox.find('ymin').text), int(xmlbox.find('xmax').text), int(xmlbox.find('ymax').text)) list_file.write(" " + ",".join([str(a) for a in b]) + ',' + str(cls_id)) wd = getcwd() for year, image_set in sets: image_ids = open('VOCdevkit/VOC%s/ImageSets/Main/%s.txt'%(year, image_set)).read().strip().split() list_file = open('%s_%s.txt'%(year, image_set), 'w') for image_id in image_ids: list_file.write('%s/VOCdevkit/VOC%s/JPEGImages/%s.jpg'%(wd, year, image_id)) convert_annotation(year, image_id, list_file) list_file.write('\n') list_file.close()」
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这段Python代码的主要功能是处理PASCAL VOC数据集的注释文件，并将它们转换为一种特定的格式，用于训练机器学习模型。代码使用了xml.etree.ElementTree库来解析XML文件，这是Python标准库中用于处理XML数据的一种方式37。
首先，代码定义了两个列表：sets和classes。sets列表包含了数据集的年份和图像集（训练集、验证集、测试集）的元组，而classes列表则包含了数据集中所有类别的名称1。
convert_annotation函数是代码的核心，它接收三个参数：年份year、图像IDimage_id和列表文件list_file。该函数首先打开指定年份和图像ID的XML注释文件，并使用ET.parse()方法将其解析为一个树对象tree，然后获取其根节点root1。
接下来，函数遍历根节点下所有的object元素。对于每个object元素，它检查difficult标签的文本值和name标签的文本值。如果类别不在classes列表中，或者difficult的值为1（表示难以识别的对象），则跳过当前对象。否则，它将获取该对象的边界框（bndbox）信息，并将其转换为一个元组b，包含xmin、ymin、xmax和ymax的整数值。然后，它将边界框的值和类别ID写入到list_file文件中1。
在主程序中，代码首先获取当前工作目录wd。然后，对于sets列表中的每一个元组，它打开对应的图像ID列表文件，并创建一个新的列表文件用于写入转换后的数据。对于图像ID列表中的每一个图像ID，它首先写入图像的文件路径，然后调用convert_annotation函数来处理注释，并写入相应的转换后数据，最后写入一个换行符以分隔不同的图像记录。当处理完所有的图像ID后，它关闭列表文件1。
总的来说，这段代码是一个数据预处理脚本，用于将PASCAL VOC数据集的XML注释转换为一种适合机器学习模型训练的格式。它展示了如何使用xml.etree.ElementTree库来解析和处理XML文件，以及如何遍历和提取XML文档中的数据137。
