yolov8学习以及地下城数据标注训练
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yololv5的教学可以参考下面这个炮哥的文章，讲的很详细，我就是从这里学会的跳转
下面分享一下我学习(ultralytics) yolov8的目标检测过程
1.首先就是下载源码 yolov8
2.解压进入根目录新建main.py
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.yaml") # build a new model from scratch
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
# Use the model
model.train(data="coco128.yaml", epochs=3) # train the model
metrics = model.val() # evaluate model performance on the validation set
results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
path = model.export(format="onnx") # export the model to ONNX format
上面为官网的代码配置文件在目录中有说明 链接
3.安装依赖pip3 install -r requirements.txt
4.将上面代码保存运行python main.py, 结果打印得到保存的目录，目录下有best.pt则是基于yolov8n.pt训练的。
该流程为加载模型配置 yolov8n.yaml， 加载权重yolov8n.pt，训练数据，验证数据，推理，导出onnx (导出一般用不到)
上面内容为官网的示例，python，gpu环境安装参考yolov5的文章，下面就可以自己操作，比如训练一个地下城的目标检测
1.录制一个游戏视频 保存D:/ChromeCoreDownloads/py/video/test.mp4
2.录制完讲视频切割帧
代码根目录为ultralytics的话
新建 /ultralytics/VOCdevkit/biaozhu/Annotations/
新建 /ultralytics/VOCdevkit/biaozhu/JPEGImages/
新建 /ultralytics/VOCdevkit/biaozhu/predefined_classes.txt
新建 /ultralytics/VOCdevkit/VOC2007/
这里有一些标注的标签提示，将他们复制到 predefined_classes.txt 中，此处演示删减了一些分类
character
money
item
epic
open_door
close_door
guai_wu
在下面的tool.py中， 自行修改参数
视频目录 videoDir='D:/ChromeCoreDownloads/py/video/'
代码根目录 root="E:/ideaworkspace3/ultralytics/"
标注文件根目录 biaozhuRoot = root+"VOCdevkit/biaozhu/"
xml格式的待切割的数据根目录 vocRoot = root+"VOCdevkit/VOC2007/"
标签 classes = ["character", "money", "item", "epic", "open_door", "close_door", "guai_wu"]
运行 mp4ToPic， 将视频转为图片保存到标注目录下（不会运行的右转 https://www.python.org/）
2. 上面的操作完不出意外的话标注目录下会有图片，接着下载标注软件https://github.com/HumanSignal/labelImg
下载labelImg标注工具，cmd启动，指定两个一个图片目录和提示文件
labelImg.exe D:\\ultralytics\\VOCdevkit\\biaozhu\\JPEGImages D:\\ultralytics\\VOCdevkit\\biaozhu\\predefined_classes.txt
3.上面完成回车应该打开软件，打不开软件？管理员运行或者自行百度。
设置自动保存，修改标注的xml的保存目录，完成后开始画框标注，这里应该会出现predefined_classes.txt内的提示标签
4.标注完后检查 标注的Annotations和JPEGImages文件是否齐全
5.上一步确认没问题，接着开始转换，分割数据集，用于训练
在下面的tool.py中 biaozhuTovoc2007 函数用于将标注的xml和图片转移到VOC2007目录下的对应文件夹，并将xml配置转换为yolo8的txt配置，同时分割90%为训练，10%为验证， 结果保存到 VOCdevkit/images和labels
6.代码根目录新建data.yaml和model.yaml, model.yaml中的nc值应该等于data.yaml中的names的总数，这里的model.yaml还可以自定义转换函数，提升模型精度，自己摸索吧
# data.yaml
train: "VOCdevkit/images/train"
val: "VOCdevkit/images/val"
test:
names:
0: character
1: money
2: item
3: epic
4: open_door
5: close_door
6: guai_wu
# model.yaml
nc: 7
depth_multiple: 0.33 # model depth multiple
width_multiple: 0.50 # layer channel multiple
anchors:
- [10,13, 16,30, 33,23] # P3/8
- [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
# YOLOv8.0n backbone
backbone:
# [from, repeats, module, args]
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
- [-1, 3, C2f, [128, True]]
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
- [-1, 6, C2f, [256, True]]
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
- [-1, 6, C2f, [512, True]]
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
- [-1, 3, C2f, [1024, True]]
- [-1, 1, SPPF, [1024, 5]] # 9
# YOLOv8.0n head
head:
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 3, C2f, [512]] # 12
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 12], 1, Concat, [1]] # cat head P4
- [-1, 3, C2f, [512]] # 18 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 9], 1, Concat, [1]] # cat head P5
- [-1, 3, C2f, [1024]] # 21 (P5/32-large)
- [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5)
7.开始训练
到这里根目录下应该存在 data.yaml, model.yaml best.pt以及切割好的数据集图片
新建train.py
if __name__ == '__main__':
wd = os.getcwd()
model = YOLO(os.path.join(wd, "model.yaml"))
model = YOLO(os.path.join(wd, "best.pt"))
model.train(data=os.path.join(wd, "data.yaml"), epochs=1000, patience=200, batch=-1, imgsz=640, device=0, workers=2)
metrics = model.val()
大概意思你们应该懂了， 其他进阶参数自己研究去吧，我也在学习中
epochs=1000（训练次数）
patience=200（超过多少次没进展终止）
batch=-1（每次加载多少张图到内存，建议自动-1
workers=2 （电脑带不动设置为0，带得动设置8）
device=0（gpu训练，不会安装pytorch gpu的参考yolo5的文章，cuda跳转 https://developer.nvidia.com/）
8.到这里整体的一个训练流程完成了，下面你们可以开始狱来狱勇了，充了钱尽头还不是搬砖（笑死）
用到的工具类 tool.py
import os
import random
import shutil
import time
import xml.etree.ElementTree as ET
from shutil import copyfile
from xml.dom.minidom import Document
import cv2
videoDir='D:/ChromeCoreDownloads/py/video/'
root="E:/ideaworkspace3/ultralytics/"
biaozhuRoot = root+"VOCdevkit/biaozhu/"
vocRoot = root+"VOCdevkit/VOC2007/"
classes = ["character", "money", "item", "epic", "open_door", "close_door", "guai_wu",
"shang_ren", "fan_pai", "purple_card"]
TRAIN_RATIO = 90
def mp4ToPic():
biaozhuImgDir=biaozhuRoot + "JPEGImages/"
clearVocDir(biaozhuImgDir)
files = os.listdir(videoDir)
for file in files:
file_path = os.path.join(videoDir, file)
if os.path.isfile(file_path) and file.endswith(".mp4"):
cap = cv2.VideoCapture(file_path)
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
frame_rate = int(cap.get(cv2.CAP_PROP_FPS))
target_interval = 10
if frame_rate < target_interval:
target_interval = frame_rate
c = 1
cut_num = 0
while c <= frame_count:
ok, frame = cap.read()
if not ok:
break
if c % target_interval == 0 or c == frame_count:
writePath = biaozhuImgDir + str(int(time.time() * 1000000)) + ".jpg"
cv2.imwrite(writePath, frame)
cut_num += 1
c += 1
cap.release()
cv2.destroyAllWindows()
print("mp4 task -> total: %d, cut: %d" % (frame_count, cut_num))
def biaozhuTovoc2007():
clearVocDir(vocRoot+"Annotations")
clearVocDir(vocRoot+"JPEGImages")
clearVocDir(vocRoot+"YOLOLabels")
# clearVocDir(root+"VOCdevkit/images")
# clearVocDir(root+"VOCdevkit/labels")
arr = list()
files = os.listdir(biaozhuRoot + "Annotations")
for file in files:
xml = os.path.join(biaozhuRoot + "Annotations/", file)
xmlw = os.path.join(vocRoot + "Annotations/", file)
jpg = os.path.join(biaozhuRoot + "JPEGImages/", file.replace(".xml", ".jpg"))
jpgw = os.path.join(vocRoot + "JPEGImages/", file.replace(".xml", ".jpg"))
if os.path.exists(jpg):
shutil.copy(xml, xmlw)
shutil.copy(jpg, jpgw)
arr.append(xml)
arr.append(jpg)
for p in arr:
os.remove(p)
vocToTxt()
print("biaozhuTovoc2007 end!")
def clearVocDir(dir_path):
if os.path.exists(dir_path):
try:
shutil.rmtree(dir_path)
os.mkdir(dir_path)
except Exception as e:
print(f"Error while deleting folder: {e}")
else:
os.mkdir(dir_path)
def vocToTxt():
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, "VOC2007/")
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_test_dir = os.path.join(yolov5_images_dir, "val/")
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_test_dir = os.path.join(yolov5_labels_dir, "val/")
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')
test_file = open(os.path.join(wd, "yolov5_val.txt"), 'w')
train_file.close()
test_file.close()
train_file = open(os.path.join(wd, "yolov5_train.txt"), 'a')
test_file = open(os.path.join(wd, "yolov5_val.txt"), 'a')
list_imgs = os.listdir(image_dir) # list image files
prob = 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 dataset
if os.path.exists(annotation_path):
train_file.write(image_path + '\n')
convert_annotation(nameWithoutExtention) # convert label
copyfile(image_path, yolov5_images_train_dir + voc_path)
copyfile(label_path, yolov5_labels_train_dir + label_name)
else:
if os.path.exists(annotation_path):
test_file.write(image_path + '\n')
convert_annotation(nameWithoutExtention) # convert label
copyfile(image_path, yolov5_images_test_dir + voc_path)
copyfile(label_path, yolov5_labels_test_dir + label_name)
train_file.close()
test_file.close()
def txtToXml(picPath, txtPath, xmlPath): # txt所在文件夹路径，xml文件保存路径，图片所在文件夹路径
"""此函数用于将yolo格式txt标注文件转换为voc格式xml标注文件
在自己的标注图片文件夹下建三个子文件夹，分别命名为picture、txt、xml
"""
dic = {'0': "hat", # 创建字典用来对类型进行转换
'1': "person", # 此处的字典要与自己的classes.txt文件中的类对应，且顺序要一致
}
files = os.listdir(txtPath)
for i, name in enumerate(files):
xmlBuilder = Document()
annotation = xmlBuilder.createElement("annotation") # 创建annotation标签
xmlBuilder.appendChild(annotation)
txtFile = open(txtPath + name)
txtList = txtFile.readlines()
img = cv2.imread(picPath + name[0:-4] + ".jpg")
Pheight, Pwidth, Pdepth = img.shape
folder = xmlBuilder.createElement("folder") # folder标签
foldercontent = xmlBuilder.createTextNode("driving_annotation_dataset")
folder.appendChild(foldercontent)
annotation.appendChild(folder) # folder标签结束
filename = xmlBuilder.createElement("filename") # filename标签
filenamecontent = xmlBuilder.createTextNode(name[0:-4] + ".jpg")
filename.appendChild(filenamecontent)
annotation.appendChild(filename) # filename标签结束
size = xmlBuilder.createElement("size") # size标签
width = xmlBuilder.createElement("width") # size子标签width
widthcontent = xmlBuilder.createTextNode(str(Pwidth))
width.appendChild(widthcontent)
size.appendChild(width) # size子标签width结束
height = xmlBuilder.createElement("height") # size子标签height
heightcontent = xmlBuilder.createTextNode(str(Pheight))
height.appendChild(heightcontent)
size.appendChild(height) # size子标签height结束
depth = xmlBuilder.createElement("depth") # size子标签depth
depthcontent = xmlBuilder.createTextNode(str(Pdepth))
depth.appendChild(depthcontent)
size.appendChild(depth) # size子标签depth结束
annotation.appendChild(size) # size标签结束
for j in txtList:
oneline = j.strip().split(" ")
object = xmlBuilder.createElement("object") # object 标签
picname = xmlBuilder.createElement("name") # name标签
namecontent = xmlBuilder.createTextNode(dic[oneline[0]])
picname.appendChild(namecontent)
object.appendChild(picname) # name标签结束
pose = xmlBuilder.createElement("pose") # pose标签
posecontent = xmlBuilder.createTextNode("Unspecified")
pose.appendChild(posecontent)
object.appendChild(pose) # pose标签结束
truncated = xmlBuilder.createElement("truncated") # truncated标签
truncatedContent = xmlBuilder.createTextNode("0")
truncated.appendChild(truncatedContent)
object.appendChild(truncated) # truncated标签结束
difficult = xmlBuilder.createElement("difficult") # difficult标签
difficultcontent = xmlBuilder.createTextNode("0")
difficult.appendChild(difficultcontent)
object.appendChild(difficult) # difficult标签结束
bndbox = xmlBuilder.createElement("bndbox") # bndbox标签
xmin = xmlBuilder.createElement("xmin") # xmin标签
mathData = int(((float(oneline[1])) * Pwidth + 1) - (float(oneline[3])) * 0.5 * Pwidth)
xminContent = xmlBuilder.createTextNode(str(mathData))
xmin.appendChild(xminContent)
bndbox.appendChild(xmin) # xmin标签结束
ymin = xmlBuilder.createElement("ymin") # ymin标签
mathData = int(((float(oneline[2])) * Pheight + 1) - (float(oneline[4])) * 0.5 * Pheight)
yminContent = xmlBuilder.createTextNode(str(mathData))
ymin.appendChild(yminContent)
bndbox.appendChild(ymin) # ymin标签结束
xmax = xmlBuilder.createElement("xmax") # xmax标签
mathData = int(((float(oneline[1])) * Pwidth + 1) + (float(oneline[3])) * 0.5 * Pwidth)
xmaxContent = xmlBuilder.createTextNode(str(mathData))
xmax.appendChild(xmaxContent)
bndbox.appendChild(xmax) # xmax标签结束
ymax = xmlBuilder.createElement("ymax") # ymax标签
mathData = int(((float(oneline[2])) * Pheight + 1) + (float(oneline[4])) * 0.5 * Pheight)
ymaxContent = xmlBuilder.createTextNode(str(mathData))
ymax.appendChild(ymaxContent)
bndbox.appendChild(ymax) # ymax标签结束
object.appendChild(bndbox) # bndbox标签结束
annotation.appendChild(object) # object标签结束
f = open(xmlPath + name[0:-4] + ".xml", 'w')
xmlBuilder.writexml(f, indent='\t', newl='\n', addindent='\t', encoding='utf-8')
f.close()
def 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])/2.0
y = (box[2] + box[3])/2.0
w = box[1] - box[0]
h = box[3] - box[2]
x = x*dw
w = w*dw
y = y*dh
h = h*dh
return (x,y,w,h)
def convert_annotation(image_id):
in_file = open('VOCdevkit/VOC2007/Annotations/%s.xml' %image_id)
out_file = open('VOCdevkit/VOC2007/YOLOLabels/%s.txt' %image_id, 'w')
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'):
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))
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()
if __name__=='__main__':
mp4ToPic()
biaozhuTovoc2007()
# picPath = "VOCdevkit/VOC2007/JPEGImages/" # 图片所在文件夹路径，后面的/一定要带上
# txtPath = "VOCdevkit/VOC2007/YOLO/" # txt所在文件夹路径，后面的/一定要带上
# xmlPath = "VOCdevkit/VOC2007/Annotations/" # xml文件保存路径，后面的/一定要带上
# txtToXml(picPath, txtPath, xmlPath)
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