目标检测：yolov9训练自己的数据集，新手小白也能学会训练模型，一看就会

conda create -n labelimg python=3.8

conda activate labelimg

pip install labelimg -i https://pypi.tuna.tsinghua.edu.cn/simple

labelimg JPEGImages classes.txt

import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import joindef convert(size, box):x_center = (box[0] + box[1]) / 2.0y_center = (box[2] + box[3]) / 2.0x = x_center / size[0]y = y_center / size[1]w = (box[1] - box[0]) / size[0]h = (box[3] - box[2]) / size[1]return (x, y, w, h)def convert_annotation(xml_files_path, save_txt_files_path, classes):xml_files = os.listdir(xml_files_path)print(xml_files)for xml_name in xml_files:print(xml_name)xml_file = os.path.join(xml_files_path, xml_name)out_txt_path = os.path.join(save_txt_files_path, xml_name.split('.')[0] + '.txt')out_txt_f = open(out_txt_path, 'w')tree = ET.parse(xml_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'):difficult = obj.find('difficult').textcls = 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))# b=(xmin, xmax, ymin, ymax)print(w, h, b)bb = convert((w, h), b)out_txt_f.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')if __name__ == "__main__":# 需要转换的类别，需要一一对应classes1 = ['hat','nohat']# 2、voc格式的xml标签文件路径xml_files1 = r'f:\data\Annotations'# 3、转化为yolo格式的txt标签文件存储路径save_txt_files1 = r'f:\data\labels'convert_annotation(xml_files1, save_txt_files1, classes1)

import os
import shutil
import randomrandom.seed(0)def split_data(file_path,xml_path, new_file_path, train_rate, val_rate, test_rate):each_class_image = []each_class_label = []for image in os.listdir(file_path):each_class_image.append(image)for label in os.listdir(xml_path):each_class_label.append(label)data=list(zip(each_class_image,each_class_label))total = len(each_class_image)random.shuffle(data)each_class_image,each_class_label=zip(*data)train_images = each_class_image[0:int(train_rate * total)]val_images = each_class_image[int(train_rate * total):int((train_rate + val_rate) * total)]test_images = each_class_image[int((train_rate + val_rate) * total):]train_labels = each_class_label[0:int(train_rate * total)]val_labels = each_class_label[int(train_rate * total):int((train_rate + val_rate) * total)]test_labels = each_class_label[int((train_rate + val_rate) * total):]for image in train_images:print(image)old_path = file_path + '/' + imagenew_path1 = new_file_path + '/' + 'train' + '/' + 'images'if not os.path.exists(new_path1):os.makedirs(new_path1)new_path = new_path1 + '/' + imageshutil.copy(old_path, new_path)for label in train_labels:print(label)old_path = xml_path + '/' + labelnew_path1 = new_file_path + '/' + 'train' + '/' + 'labels'if not os.path.exists(new_path1):os.makedirs(new_path1)new_path = new_path1 + '/' + labelshutil.copy(old_path, new_path)for image in val_images:old_path = file_path + '/' + imagenew_path1 = new_file_path + '/' + 'val' + '/' + 'images'if not os.path.exists(new_path1):os.makedirs(new_path1)new_path = new_path1 + '/' + imageshutil.copy(old_path, new_path)for label in val_labels:old_path = xml_path + '/' + labelnew_path1 = new_file_path + '/' + 'val' + '/' + 'labels'if not os.path.exists(new_path1):os.makedirs(new_path1)new_path = new_path1 + '/' + labelshutil.copy(old_path, new_path)for image in test_images:old_path = file_path + '/' + imagenew_path1 = new_file_path + '/' + 'test' + '/' + 'images'if not os.path.exists(new_path1):os.makedirs(new_path1)new_path = new_path1 + '/' + imageshutil.copy(old_path, new_path)for label in test_labels:old_path = xml_path + '/' + labelnew_path1 = new_file_path + '/' + 'test' + '/' + 'labels'if not os.path.exists(new_path1):os.makedirs(new_path1)new_path = new_path1 + '/' + labelshutil.copy(old_path, new_path)if __name__ == '__main__':file_path = r"f:\data\JPEGImages"txt_path = r'f:\data\labels'new_file_path = r"f:\VOCdevkit"split_data(file_path,txt_path, new_file_path, train_rate=0.8, val_rate=0.2, test_rate=0.0)

train: f:/VOCdevkit/train/images  # train images (relative to 'path') 128 images
val: f:/VOCdevkit/val/images  # val images (relative to 'path') 128 imagesnc: 2# Classes
names: ['hat','nohat']