C++实现DPM算法

DPM (Deformable Part-based Models) 算法是一种目标检测算法，它采用了分层的结构来描述目标，并通过学习模型参数来实现目标的快速检测。

以下是一个简单的 C++ 实现 DPM 算法的示例代码：

#include <opencv2/opencv.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <iostream>
#include <fstream>
#include <vector>

using namespace cv;
using namespace std;

struct Feature {
    Mat image;
    int x, y, w, h;
};

struct Model {
    vector<Feature> features;
    float score;
};

vector<Model> models;

void load_model(string filename) {
    ifstream file(filename);
    if (!file.is_open()) {
        cerr << "Failed to open file: " << filename << endl;
        exit(1);
    }

    string line;
    while (getline(file, line)) {
        Model model;
        model.score = stof(line);

        while (getline(file, line)) {
            if (line.empty()) break;

            Feature feature;
            stringstream ss(line);
            ss >> feature.x >> feature.y >> feature.w >> feature.h;

            Mat image(feature.h, feature.w, CV_8UC1);
            for (int y = 0; y < feature.h; y++) {
                for (int x = 0; x < feature.w; x++) {
                    int pixel;
                    ss >> pixel;
                    image.at<uchar>(y, x) = pixel;
                }
            }
            feature.image = image;
            model.features.push_back(feature);
        }
        models.push_back(model);
    }
}

float match_feature(const Feature& feature, const Mat& image, int x, int y) {
    float sum = 0;
    for (int j = 0; j < feature.h; j++) {
        for (int i = 0; i < feature.w; i++) {
            int pixel = image.at<uchar>(y + j, x + i);
            sum += pixel * feature.image.at<uchar>(j, i);
        }
    }
    return sum;
}

float match_model(const Model& model, const Mat& image, int x, int y) {
    float sum = model.score;
    for (const auto& feature : model.features) {
        sum += match_feature(feature, image, x + feature.x, y + feature.y);
    }
    return sum;
}

vector<Rect> detect_objects(const Mat& image) {
    vector<Rect> objects;
    for (int y = 0; y < image.rows - 64; y += 8) {
        for (int x = 0; x < image.cols - 64; x += 8) {
            float max_score = -INFINITY;
            for (const auto& model : models) {
                float score = match_model(model, image, x, y);
                if (score > max_score) {
                    max_score = score;
                }
            }
            if (max_score > 0.5) {
                objects.push_back(Rect(x, y, 64, 64));
            }
        }
    }
    return objects;
}

int main() {
    load_model("model.txt");

    Mat image = imread("image.jpg", IMREAD_GRAYSCALE);
    vector<Rect> objects = detect_objects(image);

    Mat result;
    cvtColor(image, result, COLOR_GRAY2BGR);
    for (const auto& object : objects) {
        rectangle(result, object, Scalar(0, 0, 255), 2);
    }

    imshow("Result", result);
    waitKey(0);

    return 0;
}

上述代码中，我们首先定义了 `Feature` 和 `Model` 结构体，分别表示特征和模型。然后，我们通过 `load_model` 函数从文件中加载模型，并将其存储在 `models` 变量中。

接着，我们实现了 `match_feature` 和 `match_model` 函数，用于计算特征和模型之间的相似度得分。最后，我们实现了 `detect_objects` 函数，用于在图像中检测目标。

在 `main` 函数中，我们首先调用 `load_model` 函数加载模型，然后读取待检测的图像，并调用 `detect_objects` 函数来检测目标。最后，我们在图像上绘制检测结果，并显示出来。

需要注意的是，上述代码只是一个简单的示例，实际应用中还需要进行许多优化和改进，以提高检测的准确率和效率。