How to use C++ for efficient image classification and image recognition?

How to use C for efficient image classification and image recognition?

Introduction: Image classification and image recognition are important research directions in the field of computer vision, among which C is a commonly used programming language. This article will introduce how to use C for efficient image classification and image recognition, and attach relevant code examples.

1. Environment setup
Before using C for image classification and image recognition, we first need to build the corresponding development environment. The following are the steps to build the environment:

1. Install the OpenCV library: OpenCV is a powerful computer vision library that provides many tools and functions in image processing and image recognition. We can download the installation package from the official website (https://opencv.org/releases/) and install it according to the official documentation.
2. Configure the compiler: In order to use OpenCV, we need to configure it with our C compiler. For specific configuration methods, please refer to the official documentation of OpenCV and set them according to different compilers and operating systems.

2. Image Classification
Image classification refers to dividing the input image into different categories. For example, dividing a cat image into the "cat" category and the "non-cat" category. The following is a code example using C for image classification:

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

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

    // 创建分类器
    cv::CascadeClassifier classifier;
    
    // 加载分类器模型
    classifier.load("model.xml");

    // 对图像进行分类
    std::vector<cv::Rect> objects;
    classifier.detectMultiScale(image, objects);

    // 输出分类结果
    for (int i = 0; i < objects.size(); i++) {
        cv::Rect object = objects[i];
        cv::rectangle(image, object, cv::Scalar(0, 255, 0), 2);
    }

    // 显示图像
    cv::imshow("Classification", image);
    cv::waitKey(0);

    return 0;
}

The above code first uses the imread function to read the input image, and then loads the trained classifier model (model .xml), then use the detectMultiScale function to classify the image, and finally mark and display the classification result on the image.

3. Image recognition
Image recognition refers to identifying the input image as a specific object or scene, such as identifying an image of a car as the "car" category. The following is a code example for image recognition using C:

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

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

    // 创建识别器
    cv::dnn::Net net = cv::dnn::readNetFromTensorflow("model.pb");

    // 对图像进行预处理
    cv::Mat inputBlob = cv::dnn::blobFromImage(image, 1.0, cv::Size(224, 224), cv::Scalar(104, 117, 123));

    // 设置网络的输入
    net.setInput(inputBlob);

    // 运行前馈网络
    cv::Mat outputBlob = net.forward();

    // 解析输出结果
    cv::Mat outputProbabilities = outputBlob.reshape(1, 1);

    // 输出识别结果
    cv::Point classIdPoint;
    double confidence;
    cv::minMaxLoc(outputProbabilities, 0, &confidence, 0, &classIdPoint);

    // 显示识别结果
    std::string className = "Unknown";
    cv::imshow("Recognition", image);
    cv::waitKey(0);

    return 0;
}

The above code first uses the imread function to read the input image, and then loads the trained recognizer model (model .pb), then use the blobFromImage function to preprocess the image, and use the preprocessed image as the input of the network. Then use the forward function to run the feedforward network to get the output results, and finally parse the output results and display the recognition results.

Conclusion:
This article introduces how to use C for efficient image classification and image recognition, and gives relevant code examples. By setting up a development environment and following the steps of the sample code, we can implement image classification and image recognition functions in C. I hope this article will be helpful to your study and practice.

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