简单的做一个yolov8（cpu）的使用教程。
第一步，python版本必须3.8以上（我用的是3.9）。
第二步，通过pip命令下载ultralytics,也可以直接通过pycharm的包管理工具来下载。哪个库安装失败就单独pip一下。
[Asm] 纯文本查看 复制代码 Requirement already satisfied: ultralytics in d:\pythonproject\venv\lib\site-packages (8.2.18)
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[Python] 纯文本查看 复制代码 pip install ultralytics
第三步，前往github网站：https://github.com/ultralytics/ultralytics下载yolov8n.pt。
第四步，创建一个yolov8的项目，名称为yolo环境，详情如下：
[Asm] 纯文本查看 复制代码 yolo环境
--yolov8n.pt
--官方示例.py
第五步，向官方示例.py文件中添加内容如下：
[Python] 纯文本查看 复制代码 from ultralytics import YOLO
# Create a new YOLO model from scratch
model = YOLO("yolov8n.yaml")
# Load a pretrained YOLO model (recommended for training)
model = YOLO("yolov8n.pt")
# Train the model using the 'coco8.yaml' dataset for 3 epochs
results = model.train(data="coco8.yaml", epochs=3)
# Evaluate the model's performance on the validation set
results = model.val()
# 百度上一个公交车的图片
results = model("https://i2.hdslb.com/bfs/archive/b4815f0dbfd250194b63789d87d66b6f2fd145b9.jpg")
# Export the model to ONNX format
success = model.export(format="onnx")
第六步，运行官方示例.py，完毕后会自动添加一些文件，结构如下：
[Asm] 纯文本查看 复制代码 yolo环境
--runs
----detect
------train
------train2
--名字太长就不写了.jpg
--yolov8n.pt
--官方示例.py
yolo
--datasets
----coco8
------images
--------train
--------val
------labels
--------train
--------val
这些文件自己看看就行，主要是先把官方示例给跑通了，中途可能有个报错onnx这个库没有，自己pip下载就行。
第七步，创建一个新的项目文件取名为yolov8，，把yolov8n.pt移到里面，在里面创建一个文件夹为dataset，在dataset里面创建两个文件夹分别是images和labels。
第八步，在百度上下载50张图片，图片上包含一个人和一个鸟，名字最好重命名一下，把图片放在在images文件夹里。
第九步，下载标注工具，pip install -U label-studio。可能会报错ERROR: Operation cancelled by user，以管理员身份运行pycharm就好了。这个安装的依赖挺多的，慢慢下载。
第十步，执行label-studio start，会提示Starting development server at http://0.0.0.0:8080/，打开链接。
第十一步，注册账号，登录。依次点击create project--Project Name（输入项目名称）--Description（输入描述）--点击Data Import--点击upload files（这里上传图片，把images里面的图片都全选）--全部上传完毕点击Labeling Setup--左边栏保持不变，中间选择第一行第三个飞机画框的那个点击一下--新页面中左边有一个add按钮，在上方输入person，点击add。输入bird，点击add，把按钮右侧无关标签删了。点击标签可以更换颜色。最后点击右上角save。转到新界面。--点击label all tasks--选择标签开始框选--点击框可以调节大小--框选完了点击submit，全部标注完成之后点击最上方的项目名称（这个是你自己创建的项目名），检查一下是否第三列都为1。点击右上角export。选择yolo，然后点击export。会自动下载。
第十二步，将下载到的文件分别替换为dataset里面的images和labels文件夹。在images和labels文件夹里各创建一个train和val文件夹。images的train里面留40个图片，val留10个图片，对应labels的train里面留40个txt，val留10个txt，记得名字要对应，不要乱分。
第十三步，在yolov8文件夹下面创建一个ceshi.yaml文件，内容如下
[Python] 纯文本查看 复制代码 path: 'D:\pythonProject\yolov8\dataset\images'
train: 'train'
val: 'val'
nc: 2 #标签个数
names: [ 'person','bird' ] #添加标签的顺序要一致
第十三步，在yolov8文件夹下面创建一个训练.py，内容如下：
[Asm] 纯文本查看 复制代码 from ultralytics import YOLO
model = YOLO("yolov8n.pt")
model.train(
data="ceshi.yaml",
epochs=100,#次数
imgsz=640,
device='cpu'
)
第十四步，运行训练.py，
第十五步，在yolov8文件夹下面创建一个检测.py，内容如下。同时把训练好的best.pt移动到yolov8文件下，下载一个图片命名为bus.jpg放到yolov8文件下
[Python] 纯文本查看 复制代码 import cv2
from ultralytics import YOLO
model = YOLO("best.pt")
results = model.predict(
source="bus.jpg", # 被检测图片
device='cpu',
save=False,
conf=0.7, # 置信度>=0.7才显示出来
)
# 获取返回值中心坐标
def getRes(results):
res = {}
for r in results:
for i, detection in enumerate(r.boxes.xywh):
label = r.names[int(r.boxes.cls[i])]
x = int(detection[0].item())
y = int(detection[1].item())
if label not in res:
res[label] = []
res[label].append((x, y))
return res
# 将中心点坐标显示到图片中
res = getRes(results)
img = cv2.imread('bus.jpg')
for a in res:
for b in res[a]:
img = cv2.circle(img, b, 5, (255, 0, 0), 5)
cv2.imshow('4556', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
# 框选
def result_show(res):
annotated_frame = res[0].plot()
cv2.imshow("YOLOv8 Inference", annotated_frame)
cv2.waitKey(0)
cv2.destroyAllWindows()
# result_show(results)
第十六步，运行检测.py。可以通过result_show(result)来看到被检测情况。 | 
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发表于 2024-5-23 15:11
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发表于 2024-5-23 17:44
