C++ opencv深度学习

C++ Opencv是一个开源计算机视觉库，它包含了很多计算机视觉和机器学习的经典算法，如图像处理、特征检测、目标检测、人脸识别等。同时，Opencv也支持深度学习，可以用于图像分类、目标检测、语义分割等任务。以下是一些C++ Opencv深度学习的例子：

1. 使用Opencv进行图像分类

#include <opencv2/opencv.hpp>
#include <opencv2/dnn.hpp>
#include <iostream>

using namespace cv;
using namespace cv::dnn;
using namespace std;

int main()
{
    // 加载模型和标签
    Net net = readNetFromCaffe("deploy.prototxt", "model.caffemodel");
    string label[10] = {"airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"};

    // 加载图像
    Mat img = imread("test.jpg");

    // 图像预处理
    Mat inputBlob = blobFromImage(img, 1.0, Size(32, 32), Scalar(104, 117, 123), false, false);

    // 输入模型进行预测
    net.setInput(inputBlob, "data");
    Mat prob = net.forward("prob");

    // 输出预测结果
    Point classIdPoint;
    double confidence;
    minMaxLoc(prob.reshape(1, 1), 0, &confidence, 0, &classIdPoint);
    int classId = classIdPoint.x;
    cout << "Predicted class: " << label[classId] << endl;
    cout << "Confidence: " << confidence << endl;

    return 0;
}

2. 使用Opencv进行目标检测

#include <opencv2/opencv.hpp>
#include <opencv2/dnn.hpp>
#include <iostream>

using namespace cv;
using namespace cv::dnn;
using namespace std;

int main()
{
    // 加载模型和标签
    Net net = readNetFromCaffe("deploy.prototxt", "model.caffemodel");
    string label[21] = {"background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"};

    // 加载图像
    Mat img = imread("test.jpg");

    // 图像预处理
    Mat inputBlob = blobFromImage(img, 0.007843, Size(300, 300), Scalar(127.5, 127.5, 127.5), false, false);

    // 输入模型进行预测
    net.setInput(inputBlob, "data");
    Mat detection = net.forward("detection_out");

    // 输出预测结果
    Mat detectionMat(detection.size[2], detection.size[3], CV_32F, detection.ptr<float>());
    for (int i = 0; i < detectionMat.rows; i++)
    {
        float confidence = detectionMat.at<float>(i, 2);
        if (confidence > 0.5)
        {
            int classId = (int)(detectionMat.at<float>(i, 1));
            int left = (int)(detectionMat.at<float>(i, 3) * img.cols);
            int top = (int)(detectionMat.at<float>(i, 4) * img.rows);
            int right = (int)(detectionMat.at<float>(i, 5) * img.cols);
            int bottom = (int)(detectionMat.at<float>(i, 6) * img.rows);
            rectangle(img, Point(left, top), Point(right, bottom), Scalar(0, 255, 0), 2);
            putText(img, label[classId], Point(left, top - 5), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0), 2);
        }
    }

    // 显示结果
    imshow("result", img);
    waitKey(0);

    return 0;
}