【计算机视觉】YoloV8-训练与测试教程

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制作数据集

Labelme 数据集

数据集选用自己标注的，可参考以下：

['c17', 'c5', 'helicopter', 'c130', 'f16', 'b2',
'other', 'b52', 'kc10', 'command', 'f15', 'kc135', 'a10',
'b1', 'aew', 'f22', 'p3', 'p8', 'f35', 'f18', 'v22', 'f4',
'globalhawk', 'u2', 'su-27', 'il-38', 'tu-134', 'su-33',
'an-70', 'su-24', 'tu-22', 'il-76']

格式转换

将 Labelme 数据集转为 yolov8 格式的数据集，转换代码如下：

import os
import shutilimport numpy as np
import json
from glob import glob
import cv2
from sklearn.model_selection import train_test_split
from os import getcwddef convert(size, box):dw = 1. / (size[0])dh = 1. / (size[1])x = (box[0] + box[1]) / 2.0 - 1y = (box[2] + box[3]) / 2.0 - 1w = box[1] - box[0]h = box[3] - box[2]x = x * dww = w * dwy = y * dhh = h * dhreturn (x, y, w, h)def change_2_yolo5(files, txt_Name):imag_name = []for json_file_ in files:json_filename = labelme_path + json_file_ + ".json"out_file = open('%s/%s.txt' % (labelme_path, json_file_), 'w')json_file = json.load(open(json_filename, "r", encoding="utf-8"))# image_path = labelme_path + json_file['imagePath']imag_name.append(json_file_ + '.jpg')height, width, channels = cv2.imread(labelme_path + json_file_ + ".jpg").shapefor multi in json_file["shapes"]:points = np.array(multi["points"])xmin = min(points[:, 0]) if min(points[:, 0]) > 0 else 0xmax = max(points[:, 0]) if max(points[:, 0]) > 0 else 0ymin = min(points[:, 1]) if min(points[:, 1]) > 0 else 0ymax = max(points[:, 1]) if max(points[:, 1]) > 0 else 0label = multi["label"].lower()if xmax <= xmin:passelif ymax <= ymin:passelse:cls_id = classes.index(label)b = (float(xmin), float(xmax), float(ymin), float(ymax))bb = convert((width, height), b)out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')# print(json_filename, xmin, ymin, xmax, ymax, cls_id)return imag_namedef image_txt_copy(files, scr_path, dst_img_path, dst_txt_path):""":param files: 图片名字组成的list:param scr_path: 图片的路径:param dst_img_path: 图片复制到的路径:param dst_txt_path: 图片对应的txt复制到的路径:return:"""for file in files:img_path = scr_path + fileprint(file)shutil.copy(img_path, dst_img_path + file)scr_txt_path = scr_path + file.split('.')[0] + '.txt'shutil.copy(scr_txt_path, dst_txt_path + file.split('.')[0] + '.txt')if __name__ == '__main__':classes = ['c17', 'c5', 'helicopter', 'c130', 'f16', 'b2','other', 'b52', 'kc10', 'command', 'f15', 'kc135', 'a10','b1', 'aew', 'f22', 'p3', 'p8', 'f35', 'f18', 'v22', 'f4','globalhawk', 'u2', 'su-27', 'il-38', 'tu-134', 'su-33','an-70', 'su-24', 'tu-22', 'il-76']# 1.标签路径labelme_path = "USA-Labelme/"isUseTest = True  # 是否创建test集# 3.获取待处理文件files = glob(labelme_path + "*.json")files = [i.replace("\\", "/").split("/")[-1].split(".json")[0] for i in files]for i in files:print(i)trainval_files, test_files = train_test_split(files, test_size=0.1, random_state=55)# splittrain_files, val_files = train_test_split(trainval_files, test_size=0.1, random_state=55)train_name_list = change_2_yolo5(train_files, "train")print(train_name_list)val_name_list = change_2_yolo5(val_files, "val")test_name_list = change_2_yolo5(test_files, "test")# 创建数据集文件夹。file_List = ["train", "val", "test"]for file in file_List:if not os.path.exists('./VOC/images/%s' % file):os.makedirs('./VOC/images/%s' % file)if not os.path.exists('./VOC/labels/%s' % file):os.makedirs('./VOC/labels/%s' % file)image_txt_copy(train_name_list, labelme_path, './VOC/images/train/', './VOC/labels/train/')image_txt_copy(val_name_list, labelme_path, './VOC/images/val/', './VOC/labels/val/')image_txt_copy(test_name_list, labelme_path, './VOC/images/test/', './VOC/labels/test/')

运行完成后就得到了yolov8格式的数据集。

本地调试

下载与安装
● Github： GitHub - ultralytics/ultralytics: NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite
● 也可以直接使用命令行：pip install。
● 下载到本地后解压，将生成的yolo数据集放到datasets（需要创建datasets 文件夹）文件夹下面，如下图：

安装库文件

安装必要的库文件，安装命令：
- pip install opencv-python
- pip install numpy==1.23.5
- pip install pyyaml
- pip install tqdm
- pip install matplotlib
注意numpy的版本，如果是 2.0 以上版本一定要把版本降下来。

创建配置文件

在根目录新建 VOC.yaml 文件，添加内容：

train:./VOC/images/train # train images
val:./VOC/images/val # val images
test:./VOC/images/test # test images (optional)names: ['c17', 'c5', 'helicopter', 'c130', 'f16', 'b2','other', 'b52', 'kc10', 'command', 'f15', 'kc135', 'a10','b1', 'aew', 'f22', 'p3', 'p8', 'f35', 'f18', 'v22', 'f4','globalhawk', 'u2', 'su-27', 'il-38', 'tu-134', 'su-33','an-70', 'su-24', 'tu-22', 'il-76']

创建训练脚本：

新建train.py，在train.py添加代码：

from ultralytics import YOLO
if __name__ == '__main__':# 加载模型model = YOLO("ultralytics/cfg/models/v8/yolov8l.yaml")  # 从头开始构建新模型print(model.model)# Use the modelresults = model.train(data="VOC.yaml", epochs=100, device='0', batch=16, workers=0)  # 训练模型

点击run开始运行train.py进行训练。

实时目标检测代码实现

以下是一个使用 Python 和 OpenCV 实现实时目标检测的示例代码：

import cv2
from ultralytics import YOLO# 加载模型
model = YOLO('your_model_path.pt')  # 替换为你的模型路径# 打开摄像头
cap = cv2.VideoCapture(0)  # 0 表示默认摄像头，如果有多个摄像头可以调整这个参数while True:# 读取一帧图像ret, frame = cap.read()if not ret:break# 进行目标检测results = model(frame)# 在图像上绘制检测结果annotated_frame = results[0].plot()# 显示图像cv2.imshow('Real-time Object Detection', annotated_frame)# 按下 'q' 键退出循环if cv2.waitKey(1) & 0xFF == ord('q'):break# 释放摄像头和关闭窗口
cap.release()
cv2.destroyAllWindows()