How to implement machine vision algorithms and object recognition in C++?

How to implement machine vision algorithm and object recognition in C?

Introduction:
With the continuous development and application of artificial intelligence, machine vision technology has been widely used in various fields, such as autonomous driving, security monitoring, medical imaging, etc. Among them, C, as a widely used programming language, has the characteristics of high compilation efficiency and strong flexibility, and has gradually become the preferred language for the implementation of machine vision algorithms. This article will introduce how to implement machine vision algorithms and object recognition through C, and attach code examples, hoping to provide some help to readers.

1. Implementation of machine vision algorithm
1.1 Image processing
Image processing is an important part of the machine vision algorithm, mainly including image reading, display, saving and common image processing operations (Such as image binarization, filtering, edge detection, etc.). Next, we will introduce how to use C to implement machine vision algorithms through a simple image processing example.

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>

int main() {
    // 读取图像
    cv::Mat image = cv::imread("lena.jpg", cv::IMREAD_COLOR);

    // 图像二值化
    cv::Mat grayImage;
    cv::cvtColor(image, grayImage, cv::COLOR_BGR2GRAY);

    cv::Mat binaryImage;
    cv::threshold(grayImage, binaryImage, 128, 255, cv::THRESH_BINARY);

    // 显示图像
    cv::imshow("Binary Image", binaryImage);

    // 保存图像
    cv::imwrite("binary.jpg", binaryImage);

    // 等待按键退出
    cv::waitKey(0);

    return 0;
}

In this example, we used the OpenCV library to read and process images. First, we read the image named "lena.jpg" through the cv::imread function. Then, we convert the color image into a grayscale image and perform a binarization operation on the grayscale image through the cv::threshold function. Finally, we display the binarized image through the cv::imshow function, and use the cv::imwrite function to save the binary image to a file named "binary.jpg" in the file.

1.2 Feature extraction and description
Feature extraction and description is one of the core tasks in machine vision algorithms. It is the process of extracting representative features from images and describing them. In this section we will use the OpenCV library to implement an example of the SIFT (Scale Invariant Feature Transform) algorithm.

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/features2d/features2d.hpp>

int main() {
    // 读取图像
    cv::Mat image = cv::imread("lena.jpg", cv::IMREAD_COLOR);

    // 使用SIFT算法检测图像中的关键点
    cv::Ptr<cv::SIFT> sift = cv::SIFT::create();
    std::vector<cv::KeyPoint> keypoints;
    sift->detect(image, keypoints);

    // 绘制关键点
    cv::Mat keypointImage;
    cv::drawKeypoints(image, keypoints, keypointImage, cv::Scalar::all(-1), cv::DrawMatchesFlags::DRAW_RICH_KEYPOINTS);

    // 显示图像
    cv::imshow("Keypoints", keypointImage);

    // 等待按键退出
    cv::waitKey(0);

    return 0;
}

In this example, we use the cv::SIFT class in the OpenCV library to implement the SIFT algorithm. First, we read the image named "lena.jpg" through the cv::imread function. Then, we created a cv::SIFT objectsift and used the sift->detect function to detect key points in the image. Next, we draw the key points on the image through the cv::drawKeypoints function, and use the cv::imshow function to display the results.

2. Implementation of object recognition
Object recognition is one of the important applications in machine vision. It completes the object recognition task by matching objects in images with pre-trained models. . In this section we will use the DNN (deep neural network) module in the OpenCV library to implement an example of object recognition.

#include <opencv2/core/utility.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/dnn/dnn.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>

int main() {
    // 加载模型及相应的配置文件
    std::string model = "MobileNetSSD_deploy.caffemodel";
    std::string config = "MobileNetSSD_deploy.prototxt";
    cv::dnn::Net net = cv::dnn::readNetFromCaffe(config, model);

    // 加载图像
    cv::Mat image = cv::imread("person.jpg", cv::IMREAD_COLOR);

    // 对图像进行预处理
    cv::Mat blob = cv::dnn::blobFromImage(image, 1.0, cv::Size(300, 300), cv::Scalar(127.5, 127.5, 127.5), true, false);

    // 将blob输入到网络中进行推理
    net.setInput(blob);

    // 获取检测结果
    cv::Mat detection = net.forward();

    // 解析检测结果
    cv::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 x1 = static_cast<int>(detectionMat.at<float>(i, 3) * image.cols);
            int y1 = static_cast<int>(detectionMat.at<float>(i, 4) * image.rows);
            int x2 = static_cast<int>(detectionMat.at<float>(i, 5) * image.cols);
            int y2 = static_cast<int>(detectionMat.at<float>(i, 6) * image.rows);

            // 绘制边界框
            cv::rectangle(image, cv::Point(x1, y1), cv::Point(x2, y2), cv::Scalar(0, 255, 0), 2);
        }
    }

    // 显示结果
    cv::imshow("Detection", image);

    // 等待按键退出
    cv::waitKey(0);

    return 0;
}

In this example, we use the cv::dnn::Net class in the OpenCV library to load the model and configuration file, and use cv::imreadThe function reads the image named "person.jpg". Next, we preprocess the image through the cv::dnn::blobFromImage function, and then input the processed data into the network for inference. Finally, we parse the detection results and draw the detected bounding box using the cv::rectangle function.

Conclusion:
Through the introduction of this article, we have learned how to use C to implement machine vision algorithms and object recognition. From image processing to feature extraction and description, to object recognition, the C and OpenCV libraries provide a wealth of tools and functions to help us implement machine vision algorithms efficiently. I hope this article can provide readers with some help and inspiration in implementing machine vision algorithms and object recognition in C.

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