如何使用 opencv 加载 darknet yolo 预训练模型?
如何使用 opencv 加载 darknet yolo 预训练模型?
opencv 版本 > 3.4 以上
constexpr const char *image_path = "darknet.jpg";//待检测图片
constexpr const char *darknet_cfg = "darknet.cfg";//网络文件
constexpr const char *darknet_weights = "darknet.weights";//训练模型
const std::vector<std::string> class_labels = {"darknet","yolo"};//类标签
void darknetDetection(const std::string &path,const std::string &darknet_cfg,const std::string &darknet_weights,std::vector<std::string> class_labels,float confidenceThreshold)
{
// 加载模型
cv::dnn::Net net = cv::dnn::readNetFromDarknet(darknet_cfg,darknet_weights);
// 加载标签集
std::vector<std::string> classLabels = class_labels;
// 读取待检测图片
cv::Mat img = cv::imread(path);
cv::Mat blob = cv::dnn::blobFromImage(img,1.0/255.0,{416,416},0.00392,true);
net.setInput(blob,"data");
// 检测
cv::Mat detectionMat = net.forward("detection_out");// 6 845 1 W x H x C
// 获取网络每层的用时并获取总用时
std::vector<double> layersTimings;
double freq = cv::getTickFrequency() / 1000;
double time = net.getPerfProfile(layersTimings) / freq;
std::ostringstream ss;
ss << "detection time: " << time << " ms";
// 绘制总用时至原始图片
cv::putText(img, ss.str(), cv::Point(20, 20), 0, 0.5, cv::Scalar(0, 0, 255));
// 遍历所有结果集
for(int i = 0;i < detectionMat.rows;++i){
const int probability_index = 5;
const int probability_size = detectionMat.cols - probability_index;
float *prob_array_ptr = &detectionMat.at<float>(i, probability_index);
size_t objectClass = std::max_element(prob_array_ptr, prob_array_ptr + probability_size) - prob_array_ptr;
float confidence = detectionMat.at<float>(i, (int)objectClass + probability_index);
// 比较置信度并绘制满足条件的置信度
if (confidence > confidenceThreshold)
{
float x = detectionMat.at<float>(i, 0);
float y = detectionMat.at<float>(i, 1);
float width = detectionMat.at<float>(i, 2);
float height = detectionMat.at<float>(i, 3);
int xLeftBottom = static_cast<int>((x - width / 2) * img.cols);
int yLeftBottom = static_cast<int>((y - height / 2) * img.rows);
int xRightTop = static_cast<int>((x + width / 2) * img.cols);
int yRightTop = static_cast<int>((y + height / 2) * img.rows);
cv::Rect object(xLeftBottom, yLeftBottom,xRightTop - xLeftBottom,yRightTop - yLeftBottom);//x y w h
cv::rectangle(img, object, cv::Scalar(0, 0, 255), 2, 8);
// 判断类id是否符合标签范围并绘制该标签,也就是矩阵的下标索引
if (objectClass < classLabels.size())
{
cv::String label = cv::String(classLabels[objectClass]) + ": " + std::to_string(confidence);
int baseLine = 0;
cv::Size labelSize = cv::getTextSize(label,cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
cv::rectangle(img, cv::Rect(cv::Point(xLeftBottom, yLeftBottom),cv::Size(labelSize.width, labelSize.height + baseLine)),cv::Scalar(255, 255, 255), cv::FILLED);
cv::putText(img, label, cv::Point(xLeftBottom, yLeftBottom + labelSize.height),cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0));
}
}
}
// 显示图片
cv::imshow("Darknet",img);
cv::waitKey(0);
}
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