YOLOv5将自己数据集划分为训练集 验证集和测试集

在用自己数据集跑YOLOv5代码时候，需要将自己的VOC标签格式数据集转为yolo格式。

首先是要获取自己的数据集，然后再对数据集进行标注，保存为VOC(xml格式)。然后再把标注完的数据集划分为训练集和验证集，这样更加方便模型的训练和测试。首先上划分数据集的代码。这里提供了一份代码将xml格式的标注文件转换为txt格式的标注文件，并按比例划分为训练集、验证集和测试集。代码如下：

classes为自己数据集的类别名称，TRAIN_RATIO为训练集比例，本代码按照6：2：2比例划分为训练集、验证集和测试集，可自行调整。

import xml.etree.ElementTree as ETimport pickleimport osfrom os import listdir, getcwdfrom os.path import joinimport randomfrom shutil import copyfileclasses = ["hens"]# classes = ["hat", "person"]#classes = ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',#'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']TRAIN_RATIO = 60def clear_hidden_files(path):dir_list = os.listdir(path)for i in dir_list:abspath = os.path.join(os.path.abspath(path), i)if os.path.isfile(abspath):if i.startswith("._"):os.remove(abspath)else:clear_hidden_files(abspath)def convert(size, box):dw = 1. / size[0]dh = 1. / size[1]x = (box[0] + box[1]) / 2.0y = (box[2] + box[3]) / 2.0w = box[1] - box[0]h = box[3] - box[2]x = x * dww = w * dwy = y * dhh = h * dhreturn (x, y, w, h)def convert_annotation(image_id):in_file = open('VOCdevkit/VOC/Annotations/%s.xml' % image_id, encoding="utf_8")out_file = open('VOCdevkit/VOC/YOLOLabels/%s.txt' % image_id, 'w', encoding="utf_8")tree = ET.parse(in_file)root = tree.getroot()size = root.find('size')w = int(size.find('width').text)h = int(size.find('height').text)for obj in root.iter('object'):if obj.find('difficult'):difficult = obj.find('difficult').textelse:difficult = 0cls = obj.find('name').textif cls not in classes or int(difficult) == 1:continuecls_id = classes.index(cls)xmlbox = obj.find('bndbox')b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text),float(xmlbox.find('ymax').text))bb = convert((w, h), b)out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')in_file.close()out_file.close()wd = os.getcwd()wd = os.getcwd()data_base_dir = os.path.join(wd, "VOCdevkit/")if not os.path.isdir(data_base_dir):os.mkdir(data_base_dir)work_sapce_dir = os.path.join(data_base_dir, "VOC/")if not os.path.isdir(work_sapce_dir):os.mkdir(work_sapce_dir)annotation_dir = os.path.join(work_sapce_dir, "Annotations/")if not os.path.isdir(annotation_dir):os.mkdir(annotation_dir)clear_hidden_files(annotation_dir)image_dir = os.path.join(work_sapce_dir, "JPEGImages/")if not os.path.isdir(image_dir):os.mkdir(image_dir)clear_hidden_files(image_dir)yolo_labels_dir = os.path.join(work_sapce_dir, "YOLOLabels/")if not os.path.isdir(yolo_labels_dir):os.mkdir(yolo_labels_dir)clear_hidden_files(yolo_labels_dir)yolov5_images_dir = os.path.join(data_base_dir, "images/")if not os.path.isdir(yolov5_images_dir):os.mkdir(yolov5_images_dir)clear_hidden_files(yolov5_images_dir)yolov5_labels_dir = os.path.join(data_base_dir, "labels/")if not os.path.isdir(yolov5_labels_dir):os.mkdir(yolov5_labels_dir)clear_hidden_files(yolov5_labels_dir)yolov5_images_train_dir = os.path.join(yolov5_images_dir, "train/")if not os.path.isdir(yolov5_images_train_dir):os.mkdir(yolov5_images_train_dir)clear_hidden_files(yolov5_images_train_dir)yolov5_images_val_dir = os.path.join(yolov5_images_dir, "val/")if not os.path.isdir(yolov5_images_val_dir):os.mkdir(yolov5_images_val_dir)clear_hidden_files(yolov5_images_val_dir)yolov5_images_test_dir = os.path.join(yolov5_images_dir, "test/")if not os.path.isdir(yolov5_images_test_dir):os.mkdir(yolov5_images_test_dir)clear_hidden_files(yolov5_images_test_dir)yolov5_labels_train_dir = os.path.join(yolov5_labels_dir, "train/")if not os.path.isdir(yolov5_labels_train_dir):os.mkdir(yolov5_labels_train_dir)clear_hidden_files(yolov5_labels_train_dir)yolov5_labels_val_dir = os.path.join(yolov5_labels_dir, "val/")if not os.path.isdir(yolov5_labels_val_dir):os.mkdir(yolov5_labels_val_dir)clear_hidden_files(yolov5_labels_val_dir)yolov5_labels_test_dir = os.path.join(yolov5_labels_dir, "test/")if not os.path.isdir(yolov5_labels_test_dir):os.mkdir(yolov5_labels_test_dir)clear_hidden_files(yolov5_labels_test_dir)train_file = open(os.path.join(wd, "yolov5_train.txt"), 'w')val_file = open(os.path.join(wd, "yolov5_val.txt"), 'w')test_file = open(os.path.join(wd, "yolov5_test.txt"), 'w')train_file.close()val_file.close()test_file.close()train_file = open(os.path.join(wd, "yolov5_train.txt"), 'a')val_file = open(os.path.join(wd, "yolov5_val.txt"), 'a')test_file = open(os.path.join(wd, "yolov5_test.txt"), 'a')list_imgs = os.listdir(image_dir) # list image filesprob = random.randint(1, 100)print("Probability: %d" % prob)for i in range(0, len(list_imgs)):path = os.path.join(image_dir, list_imgs[i])if os.path.isfile(path):image_path = image_dir + list_imgs[i]voc_path = list_imgs[i](nameWithoutExtention, extention) = os.path.splitext(os.path.basename(image_path))(voc_nameWithoutExtention, voc_extention) = os.path.splitext(os.path.basename(voc_path))annotation_name = nameWithoutExtention + '.xml'annotation_path = os.path.join(annotation_dir, annotation_name)label_name = nameWithoutExtention + '.txt'label_path = os.path.join(yolo_labels_dir, label_name)prob = random.randint(1, 100)print("Probability: %d" % prob)if (prob < TRAIN_RATIO): # train datasetif os.path.exists(annotation_path):train_file.write(image_path + '\n')convert_annotation(nameWithoutExtention) # convert labelcopyfile(image_path, yolov5_images_train_dir + voc_path)copyfile(label_path, yolov5_labels_train_dir + label_name)elif (prob > TRAIN_RATIO and prob < 80):if os.path.exists(annotation_path):val_file.write(image_path + '\n')convert_annotation(nameWithoutExtention) # convert labelcopyfile(image_path, yolov5_images_val_dir + voc_path)copyfile(label_path, yolov5_labels_val_dir + label_name)else : # test datasetif os.path.exists(annotation_path):test_file.write(image_path + '\n')convert_annotation(nameWithoutExtention) # convert labelcopyfile(image_path, yolov5_images_test_dir + voc_path)copyfile(label_path, yolov5_labels_test_dir + label_name)train_file.close()test_file.close()运行上述代码后，在VOCdevkit目录下生成images和labels文件夹，文件夹下分别生成了train文件夹、val文件夹和test文件夹，里面分别保存着训练集的照片和txt格式的标签、验证集的照片和txt格式的标签以及测试集的照片和txt格式的标签images文件夹和labels文件夹。在VOCdevkit/VOC目录下还生成了一个YOLOLabels文件夹，里面存放着所有的txt格式的标签文件。

yaml文件按如下路径修改即可，注意将nc调整为自己数据集类别个数，names调整为自己数据集类别名称。