yolo8+ c++opencv
时间: 2023-08-31 17:02:21 浏览: 65
YOLO(You Only Look Once)是一种实时目标检测算法,它使用深度学习模型在一次前向传递中同时对图像进行了对象检测和分类。YOLO算法因其高效性和准确性而受到广泛关注。
YOLO算法中的数字8可能是指YOLOv3(YOLO的第三个版本),这是最常用和流行的YOLO版本。YOLOv3采用了Darknet-53神经网络作为基础架构,并在该基础上使用了多个尺度的特征图来检测不同大小的对象。此外,它还通过使用卷积层和跳跃连接来提高特征的表示能力和检测的准确性。
C语言是一种广泛使用的编程语言,可以用于实现YOLO算法。C语言的高效性和与硬件底层的紧密结合使得它成为实时目标检测算法的理想选择。开发者可以使用C语言编写YOLO算法的推理代码,以便在不同平台上进行高效的目标检测任务。
OpenCV是一种开源的计算机视觉库,提供了许多用于图像处理和计算机视觉任务的函数和工具。通过结合OpenCV和YOLO算法,开发者可以方便地实现实时目标检测,包括加载YOLO模型、对图像或视频进行对象检测、绘制边界框以及执行对象分类等任务。
综上所述,YOLO算法是一种实时目标检测算法,YOLOv3是其中最常用和流行的版本。C语言可以用于实现YOLO算法,而OpenCV是一种计算机视觉库,可以方便地实现与YOLO算法相关的图像处理和目标检测任务。
相关问题
opencv+c++YOLO
YOLO (You Only Look Once) is an object detection algorithm that uses deep neural networks to detect objects in real-time. OpenCV is a popular computer vision library that provides various tools and functions for image processing and computer vision tasks.
To use YOLO with OpenCV in C++, you need to follow these steps:
1. Download the YOLOv3 weights and configuration files from the official website.
2. Load the model using OpenCV's dnn module.
3. Read the input image and preprocess it.
4. Pass the image through the model to get the output.
5. Postprocess the output to get the object detection results.
6. Draw bounding boxes around the detected objects and display the output image.
Here's a sample code that demonstrates how to use YOLO with OpenCV in C++:
```
#include <iostream>
#include <opencv2/opencv.hpp>
using namespace std;
using namespace cv;
int main()
{
// Load YOLOv3 model
String modelWeights = "yolov3.weights";
String modelConfiguration = "yolov3.cfg";
dnn::Net net = dnn::readNetFromDarknet(modelConfiguration, modelWeights);
// Read input image
Mat image = imread("input.jpg");
// Preprocess input image
Mat blob = dnn::blobFromImage(image, 1/255.0, Size(416, 416), Scalar(0,0,0), true, false);
// Set input for the network
net.setInput(blob);
// Get output from the network
vector<Mat> outs;
net.forward(outs, net.getUnconnectedOutLayersNames());
// Postprocess the output
float confThreshold = 0.5;
vector<int> classIds;
vector<float> confidences;
vector<Rect> boxes;
for (size_t i = 0; i < outs.size(); ++i)
{
// Extract information from the output
Mat output = outs[i];
for (int j = 0; j < output.rows; ++j)
{
Mat scores = output.row(j).colRange(5, output.cols);
Point classId;
double confidence;
minMaxLoc(scores, 0, &confidence, 0, &classId);
if (confidence > confThreshold)
{
// Get bounding box coordinates
int centerX = static_cast<int>(output.at<float>(j, 0) * image.cols);
int centerY = static_cast<int>(output.at<float>(j, 1) * image.rows);
int width = static_cast<int>(output.at<float>(j, 2) * image.cols);
int height = static_cast<int>(output.at<float>(j, 3) * image.rows);
int left = centerX - width / 2;
int top = centerY - height / 2;
// Save detection results
classIds.push_back(classId.x);
confidences.push_back(static_cast<float>(confidence));
boxes.push_back(Rect(left, top, width, height));
}
}
}
// Draw bounding boxes around the detected objects
vector<String> classNames = {"person", "car", "bus", "truck"};
vector<Scalar> colors = {Scalar(0, 0, 255), Scalar(255, 0, 0), Scalar(0, 255, 0), Scalar(255, 255, 0)};
for (size_t i = 0; i < boxes.size(); ++i)
{
rectangle(image, boxes[i], colors[classIds[i]], 2);
String label = format("%.2f", confidences[i]);
label = classNames[classIds[i]] + ":" + label;
int baseLine;
Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
rectangle(image, Point(boxes[i].x, boxes[i].y - labelSize.height - baseLine), Point(boxes[i].x + labelSize.width, boxes[i].y), colors[classIds[i]], FILLED);
putText(image, label, Point(boxes[i].x, boxes[i].y - baseLine), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(255,255,255));
}
// Display the output image
imshow("YOLO Object Detection", image);
waitKey(0);
return 0;
}
```
Note that this sample code is for YOLOv3 and may need to be modified for other versions of YOLO. Also, make sure to download the correct weights and configuration files for the version you are using.
opencv YOLO c++
在 C++ 中使用 OpenCV 和 YOLO 进行目标检测,可以按照以下步骤进行:
1. 下载 YOLO 的权重文件和配置文件,并将它们放在适当的位置。
2. 使用 OpenCV 加载图像或视频,并将其传递给 YOLO 进行目标检测。
3. 遍历 YOLO 的输出,并将检测到的边界框和类别绘制到图像或视频上。
以下是一个简单的 C++ 示例代码,演示了如何使用 OpenCV 和 YOLO 进行目标检测:
```c++
#include <opencv2/opencv.hpp>
#include <iostream>
using namespace cv;
using namespace std;
int main()
{
// 加载 YOLO 的权重文件和配置文件
String modelConfiguration = "yolov3.cfg";
String modelWeights = "yolov3.weights";
Net net = readNetFromDarknet(modelConfiguration, modelWeights);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableTarget(DNN_TARGET_CPU);
// 加载图像或视频
Mat image = imread("test.jpg");
// 将图像传递给 YOLO 进行目标检测
Mat blob = blobFromImage(image, 1 / 255.0, Size(416, 416), Scalar(0, 0, 0), true, false);
net.setInput(blob);
vector<Mat> outs;
net.forward(outs, getOutputsNames(net));
// 遍历 YOLO 的输出,并将检测到的边界框和类别绘制到图像上
float confThreshold = 0.5;
vector<int> classIds;
vector<float> confidences;
vector<Rect> boxes;
for (size_t i = 0; i < outs.size(); ++i)
{
// 获取当前输出层的信息
float* data = (float*)outs[i].data;
for (int j = 0; j < outs[i].rows; ++j, data += outs[i].cols)
{
Mat scores = outs[i].row(j).colRange(5, outs[i].cols);
Point classIdPoint;
double confidence;
minMaxLoc(scores, 0, &confidence, 0, &classIdPoint);
if (confidence > confThreshold)
{
int centerX = (int)(data[0] * image.cols);
int centerY = (int)(data[1] * image.rows);
int width = (int)(data[2] * image.cols);
int height = (int)(data[3] * image.rows);
int left = centerX - width / 2;
int top = centerY - height / 2;
classIds.push_back(classIdPoint.x);
confidences.push_back((float)confidence);
boxes.push_back(Rect(left, top, width, height));
}
}
}
// 非极大值抑制,去除重复的边界框
vector<int> indices;
NMSBoxes(boxes, confidences, confThreshold, 0.4, indices);
// 绘制检测到的边界框和类别
for (size_t i = 0; i < indices.size(); ++i)
{
int idx = indices[i];
Rect box = boxes[idx];
int classId = classIds[idx];
String className = "object";
rectangle(image, box, Scalar(0, 255, 0), 2);
putText(image, className, Point(box.x, box.y - 10), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0), 2);
}
// 显示结果
imshow("YOLO object detection", image);
waitKey(0);
return 0;
}
```
注意,此处示例代码中的模型文件和测试图片需要根据实际情况进行修改。