YOLOv10GPU训练自己的数据（含网络结构图） (草履虫都能看懂系列)_yolov10 gpu

conda create -n yolov10 python=3.9

conda activate yolov10

pip install -r requirements.txt

conda install cudatoolkit=11.7

import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
 
 
def convert(size, box):
    # size=(width, height)  b=(xmin, xmax, ymin, ymax)
    # x_center = (xmax+xmin)/2        y_center = (ymax+ymin)/2
    # x = x_center / width            y = y_center / height
    # w = (xmax-xmin) / width         h = (ymax-ymin) / height
 
    x_center = (box[0] + box[1]) / 2.0
    y_center = (box[2] + box[3]) / 2.0
    x = x_center / size[0]
    y = y_center / size[1]
 
    w = (box[1] - box[0]) / size[0]
    h = (box[3] - box[2]) / size[1]
 
    # print(x, y, w, h)
    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').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 = (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__":
    # 把forklift_pallet的voc的xml标签文件转化为yolo的txt标签文件
    # 1、需要转化的类别
    classes = ['fish']
    # 2、voc格式的xml标签文件路径
    xml_files1 = r'D:\TestMain\yolov9-main\datasets\fish\Annotations'
    # 3、转化为yolo格式的txt标签文件存储路径
    save_txt_files1 = r'D:\TestMain\yolov9-main\datasets\fish\lables'
 
    convert_annotation(xml_files1, save_txt_files1, classes)
 

import os, shutil, random
from tqdm import tqdm
 
"""
标注文件是yolo格式（txt文件）
训练集：验证集：测试集 （7：2：1） 
"""
def split_img(img_path, label_path, split_list):
    try:
        Data = 'D:/TestMain/yolov9-main/datasets/fish/ImageSets'
        # Data是你要将要创建的文件夹路径（路径一定是相对于你当前的这个脚本而言的）
        # os.mkdir(Data)
 
        train_img_dir = Data + '/images/train'
        val_img_dir = Data + '/images/val'
        test_img_dir = Data + '/images/test'
 
        train_label_dir = Data + '/labels/train'
        val_label_dir = Data + '/labels/val'
        test_label_dir = Data + '/labels/test'
 
        # 创建文件夹
        os.makedirs(train_img_dir)
        os.makedirs(train_label_dir)
        os.makedirs(val_img_dir)
        os.makedirs(val_label_dir)
        os.makedirs(test_img_dir)
        os.makedirs(test_label_dir)
 
    except:
        print('文件目录已存在')
 
    train, val, test = split_list
    all_img = os.listdir(img_path)
    all_img_path = [os.path.join(img_path, img) for img in all_img]
    # all_label = os.listdir(label_path)
    # all_label_path = [os.path.join(label_path, label) for label in all_label]
    train_img = random.sample(all_img_path, int(train * len(all_img_path)))
    train_img_copy = [os.path.join(train_img_dir, img.split('\\')[-1]) for img in train_img]
    train_label = [toLabelPath(img, label_path) for img in train_img]
    train_label_copy = [os.path.join(train_label_dir, label.split('\\')[-1]) for label in train_label]
    for i in tqdm(range(len(train_img)), desc='train ', ncols=80, unit='img'):
        _copy(train_img[i], train_img_dir)
        _copy(train_label[i], train_label_dir)
        all_img_path.remove(train_img[i])
    val_img = random.sample(all_img_path, int(val / (val + test) * len(all_img_path)))
    val_label = [toLabelPath(img, label_path) for img in val_img]
    for i in tqdm(range(len(val_img)), desc='val ', ncols=80, unit='img'):
        _copy(val_img[i], val_img_dir)
        _copy(val_label[i], val_label_dir)
        all_img_path.remove(val_img[i])
    test_img = all_img_path
    test_label = [toLabelPath(img, label_path) for img in test_img]
    for i in tqdm(range(len(test_img)), desc='test ', ncols=80, unit='img'):
        _copy(test_img[i], test_img_dir)
        _copy(test_label[i], test_label_dir)
 
 
def _copy(from_path, to_path):
    shutil.copy(from_path, to_path)
 
 
def toLabelPath(img_path, label_path):
    img = img_path.split('\\')[-1]
    label = img.split('.jpg')[0] + '.txt'
    return os.path.join(label_path, label)
 
 
if __name__ == '__main__':
    img_path = 'D:\TestMain\yolov9-main\datasets\\fish\images'  # 你的图片存放的路径（路径一定是相对于你当前的这个脚本文件而言的）
    label_path = 'D:\TestMain\yolov9-main\datasets\\fish\lables'  # 你的txt文件存放的路径（路径一定是相对于你当前的这个脚本文件而言的）
    split_list = [0.7, 0.2, 0.1]  # 数据集划分比例[train:val:test]
    split_img(img_path, label_path, split_list)

#path: D:/TestMain/yolov9-main/datasets/fish # dataset root dir
train: D:\TestMain\yolov9-main\datasets\fish1\ImageSets\images\train  # train images (relative to 'path') 118287 images
val: D:\TestMain\yolov9-main\datasets\fish1\ImageSets\images\val  # val images (relative to 'path') 5000 images
test: D:\TestMain\yolov9-main\datasets\fish1\ImageSets\images\test  # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
 
 
# Classes
names:
  0: fish
#放你数据集类型对应的类别，多个就0,1,2,3往后排
 
 
# stuff names
stuff_names: [
  'fish',
  # other
  'other',
  # unlabeled
  'unlabeled'
]
 
 
# Download script/URL (optional)
download: |
  from utils.general import download, Path
 
 
  # Download labels
  #segments = True  # segment or box labels
  #dir = Path(yaml['path'])  # dataset root dir
  #url = 'https://github.com/WongKinYiu/yolov7/releases/download/v0.1/'
  #urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')]  # labels
  #download(urls, dir=dir.parent)
 
  # Download data
  #urls = ['http://images.cocodataset.org/zips/train2017.zip',  # 19G, 118k images
  #        'http://images.cocodataset.org/zips/val2017.zip',  # 1G, 5k images
  #        'http://images.cocodataset.org/zips/test2017.zip']  # 7G, 41k images (optional)
  #download(urls, dir=dir / 'images', threads=3)