yolov8 opencv dnn部署自己的模型

源码地址

1. 本人使用的opencv c++ github代码,代码作者非本人

使用github源码结合自己导出的onnx模型推理自己的视频

推理条件

windows 10
Visual Studio 2019
Nvidia GeForce GTX 1070
opencv4.7.0 (opencv4.5.5在别的地方看到不支持yolov8的推理，所以只使用opencv4.7.0)

导出yolov8模型

yolov8版本: version = ‘8.0.110’
首先将default.yaml中的一些配置修改以下，将只修改的部分贴上去，注意下面的batch一定要设置为1

task: detect  # YOLO task, i.e. detect, segment, classify, pose
mode: export  # YOLO mode, i.e. train, val, predict, export, track, benchmark# Train settings -------------------------------------------------------------------------------------------------------
# model: C:\Users\HUST\Desktop\yolov8_ultralytics\ultralytics\models\v8\yolov8.yaml # path to model file, i.e. yolov8n.pt, yolov8n.yaml
model: C:\Users\Administrator\Desktop\yolov8_ultralytics\runs\detect\yolov8n\weights\best.pt # path to model file, i.e. yolov8n.pt, yolov8n.yaml
data: C:\Users\Administrator\Desktop\yolov8_ultralytics/ultralytics/datasets/custom.yaml # path to data file, i.e. coco128.yaml
weights: yolov8n.pt
epochs: 1  # number of epochs to train for
patience: 50  # epochs to wait for no observable improvement for early stopping of training
batch: 1  # number of images per batch (-1 for AutoBatch)

default.yaml中的export部分的配置也需要修改

# Export settings ------------------------------------------------------------------------------------------------------
format: onnx  # format to export to
keras: False  # use Keras
optimize: False  # TorchScript: optimize for mobile
int8: False  # CoreML/TF INT8 quantization
dynamic: False  # ONNX/TF/TensorRT: dynamic axes
simplify: False  # ONNX: simplify model
opset: 12 # ONNX: opset version (optional)
workspace: 4  # TensorRT: workspace size (GB)
nms: False  # CoreML: add NMS

然后直接运行ultralytics/yolo/engine/exporter.py

测试一下导出的best.onnx可不可用，直接正常的val即可

将best.onnx模型放入netron中，onnx的输入和输出如下图1所示

$$图1$$

c++部署

先将源码复制到下图位置中

环境和代码的大致步骤跟yolov5 opencv dnn部署 github代码一样

由于源码中使用的输入尺寸如图2是640 * 480的，我导出模型时使用的模型的输入如图1是640 * 640，所以需要对尺寸的那一部分需要进行修改，修改为640 * 640

const float INPUT_WIDTH = 640.0;
const float INPUT_HEIGHT = 640.0;
const float SCORE_THRESHOLD = 0.45;
const float NMS_THRESHOLD = 0.5;
const float CONFIDENCE_THRESHOLD = 0.25;

$$图2$$在进行修改之后，就可以直接运行yolo.cpp

c++推理结果

yolov8_deploy_fire