初步完成特征识别

src/test.cpp

@@ -0,0 +1,100 @@
+#include <stdio.h>
+#include "opencv2/core.hpp"
+#include "opencv2/opencv.hpp"
+#include "opencv2/highgui/highgui.hpp"
+#include <opencv2/opencv.hpp>
+#include <vector>
+#include <algorithm>
+
+struct lightbors
+{
+    std::string ID;
+
+};
+
+class FeatureDetector8Classes {
+private:
+    cv::Ptr<cv::ml::SVM> svm;
+    cv::HOGDescriptor hog;
+    cv::Size imgSize = cv::Size(48, 36); // 归一化尺寸，需与训练时一致
+    
+    // 映射表：SVM输出的索引 0~7 对应您的8个特征名称
+    std::vector<std::string> class_names = {"1", "2", "3", "4", "5", "6", "7", "sentinel"};
+ 
+public:
+    FeatureDetector8Classes() {
+        // 初始化HOG参数（低算力优选参数）
+        hog.winSize = imgSize;
+        hog.blockSize = cv::Size(12, 12);
+        hog.blockStride = cv::Size(6, 6);
+        hog.cellSize = cv::Size(6, 6);
+        hog.nbins = 9;
+        
+        // 加载训练好的SVM模型
+        svm = cv::ml::SVM::load("asset/svm_8classes_model.xml");
+        if (svm.empty()) {
+            std::cout <<  "模型加载失败" << svm.empty() << std::endl; 
+        }
+    }
+ 
+    /**
+     * 检测特征并返回特征名称
+     * @param inputImg 输入的局部特征图像 (BGR格式)
+     * @return 返回特征名称，失败返回 "unknown"
+     */
+    std::string detect(const cv::Mat& inputImg) {
+        if (inputImg.empty()) return "unknown";
+ 
+        // ================= 1. 图像归一化处理 =================
+        cv::Mat normalizedImg;
+        // 尺寸归一化
+        resize(inputImg, normalizedImg, imgSize, 0, 0, cv::INTER_LINEAR);
+        // 色彩归一化（转灰度降算力）
+        if (normalizedImg.channels() == 3) {
+            cvtColor(normalizedImg, normalizedImg, cv::COLOR_BGR2GRAY);
+        }
+        // 光照归一化（抗光照干扰）
+        equalizeHist(normalizedImg, normalizedImg);
+        // 数值归一化（映射到0~1）
+        normalizedImg.convertTo(normalizedImg, CV_32F, 1.0 / 255.0);
+        // ====================================================
+ 
+        // ================= 2. 特征提取 =================
+        std::vector<float> descriptors;
+        hog.compute(normalizedImg, descriptors);
+        cv::Mat featureMat(1, descriptors.size(), CV_32FC1, descriptors.data());
+ 
+        // ================= 3. 分类预测 =================
+        cv::Mat response;
+        svm->predict(featureMat, response);
+        
+        // 获取SVM预测的整数索引 (0~7)
+        int predicted_label = static_cast<int>(response.at<float>(0, 0));
+        
+        // 将索引映射为指定的名称并返回
+        if (predicted_label >= 0 && predicted_label < 8) {
+            return class_names[predicted_label];
+        }
+        return "unknown";
+    }
+};
+
+int main(){
+
+    lightbors armor;
+    cv::Mat image = cv::imread("asset/1.jpg", cv::IMREAD_COLOR);
+    
+    cv::Mat gray;
+    cv::cvtColor(image, gray, cv::COLOR_BGR2GRAY);
+    std::vector<std::vector<cv::Point>> counters;
+    cv::Mat Canny_P;
+    cv::Canny(gray, Canny_P, 50, 150);
+    cv::findContours(Canny_P, counters, cv::RETR_EXTERNAL, cv::CHAIN_APPROX_SIMPLE);
+
+    cv::Mat result = image.clone();
+    std::cout << "ID:" << armor.ID << std::endl;
+
+    cv::imshow("Display", result);
+    cv::waitKey(0);
+
+}