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CV实战项目----YOLO

article 2024/12/24 12:37:09

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目标检测项目：基于改进YOLOv8 的密集行人检测
目标跟踪：
重识别
姿态检测

Yolov11 网络结构

在这里插入图片描述

划分数据集

# 将图片和标注数据按比例切分为 训练集和测试集
import shutil
import random
import os
 
# 原始路径
image_original_path = "data/images/"
label_original_path = "data/labels/"
 
cur_path = os.getcwd()
# 训练集路径
train_image_path = os.path.join(cur_path, "datasets/images/train/")
train_label_path = os.path.join(cur_path, "datasets/labels/train/")
 
# 验证集路径
val_image_path = os.path.join(cur_path, "datasets/images/val/")
val_label_path = os.path.join(cur_path, "datasets/labels/val/")
 
# 测试集路径
test_image_path = os.path.join(cur_path, "datasets/images/test/")
test_label_path = os.path.join(cur_path, "datasets/labels/test/")
 
# 训练集目录
list_train = os.path.join(cur_path, "datasets/train.txt")
list_val = os.path.join(cur_path, "datasets/val.txt")
list_test = os.path.join(cur_path, "datasets/test.txt")
 
train_percent = 0.8
val_percent = 0.1
test_percent = 0.1
 
 
def del_file(path):
    for i in os.listdir(path):
        file_data = path + "\\" + i
        os.remove(file_data)
 
 
def mkdir():
    if not os.path.exists(train_image_path):
        os.makedirs(train_image_path)
    else:
        del_file(train_image_path)
    if not os.path.exists(train_label_path):
        os.makedirs(train_label_path)
    else:
        del_file(train_label_path)
 
    if not os.path.exists(val_image_path):
        os.makedirs(val_image_path)
    else:
        del_file(val_image_path)
    if not os.path.exists(val_label_path):
        os.makedirs(val_label_path)
    else:
        del_file(val_label_path)
 
    if not os.path.exists(test_image_path):
        os.makedirs(test_image_path)
    else:
        del_file(test_image_path)
    if not os.path.exists(test_label_path):
        os.makedirs(test_label_path)
    else:
        del_file(test_label_path)
 
 
def clearfile():
    if os.path.exists(list_train):
        os.remove(list_train)
    if os.path.exists(list_val):
        os.remove(list_val)
    if os.path.exists(list_test):
        os.remove(list_test)
 
 
def main():
    mkdir()
    clearfile()
 
    file_train = open(list_train, 'w')
    file_val = open(list_val, 'w')
    file_test = open(list_test, 'w')
 
    total_txt = os.listdir(label_original_path)
    num_txt = len(total_txt)
    list_all_txt = range(num_txt)
 
    num_train = int(num_txt * train_percent)
    num_val = int(num_txt * val_percent)
    num_test = num_txt - num_train - num_val
 
    train = random.sample(list_all_txt, num_train)
    # train从list_all_txt取出num_train个元素
    # 所以list_all_txt列表只剩下了这些元素
    val_test = [i for i in list_all_txt if not i in train]
    # 再从val_test取出num_val个元素，val_test剩下的元素就是test
    val = random.sample(val_test, num_val)
 
    print("训练集数目：{}, 验证集数目：{}, 测试集数目：{}".format(len(train), len(val), len(val_test) - len(val)))
    for i in list_all_txt:
        name = total_txt[i][:-4]
 
        srcImage = image_original_path + name + '.jpg'
        srcLabel = label_original_path + name + ".txt"
 
        if i in train:
            dst_train_Image = train_image_path + name + '.jpg'
            dst_train_Label = train_label_path + name + '.txt'
            shutil.copyfile(srcImage, dst_train_Image)
            shutil.copyfile(srcLabel, dst_train_Label)
            file_train.write(dst_train_Image + '\n')
        elif i in val:
            dst_val_Image = val_image_path + name + '.jpg'
            dst_val_Label = val_label_path + name + '.txt'
            shutil.copyfile(srcImage, dst_val_Image)
            shutil.copyfile(srcLabel, dst_val_Label)
            file_val.write(dst_val_Image + '\n')
        else:
            dst_test_Image = test_image_path + name + '.jpg'
            dst_test_Label = test_label_path + name + '.txt'
            shutil.copyfile(srcImage, dst_test_Image)
            shutil.copyfile(srcLabel, dst_test_Label)
            file_test.write(dst_test_Image + '\n')
 
    file_train.close()
    file_val.close()
    file_test.close()

 
if __name__ == "__main__":
    main()

编写data.yaml

在这里插入图片描述

模型文件位置

在这里插入图片描述
在v11目标下的yaml文件修改网络结构

在根目录下创建run脚本（训练与测试），执行不同的任务

# -*- coding: utf-8 -*-
"""

from ultralytics import YOLO

if __name__ == '__main__':

    # Load a model
    model = YOLO(model=r'D:\2-Python\1-YOLO\YOLOv11\ultralytics-8.3.2\yolo11n-seg.pt')  
    model.predict(source=r'D:\2-Python\1-YOLO\YOLOv11\ultralytics-8.3.2\ultralytics\assets\bus.jpg',
                  save=True,
                  show=True,
                  )

在这里插入图片描述

训练
from ultralytics import YOLO
 
if __name__ == '__main__':
    model = YOLO(r'yolov11m.yaml')  # 此处以 m 为例，只需写yolov11m即可定位到m模型
    model.train(data=r'data.yaml',
                imgsz=640,
                epochs=100,
                single_cls=True,  
                batch=16,
                workers=10,
                device='0',
                )

验证
import warnings
warnings.filterwarnings('ignore')
from ultralytics import YOLO
 
if __name__ == '__main__':
    model = YOLO('runs/train/exp/weights/best.pt')
    model.val(data='data.yaml',
                imgsz=640,
                batch=16,
                split='test',
                workers=10,
                device='0',
                )

推理
import warnings
warnings.filterwarnings('ignore')
from ultralytics import YOLO
 
if __name__ == '__main__':
    model = YOLO('runs/train/exp/weights/best.pt')
    model.predict(source='images',
                imgsz=640,
                device='0',
                )