yolo-fastestv2部署视频检测_综合

训练部署见: https://blog.csdn.net/qq_36758461/article/details/121431614

这是视频检测并保存… 小白部署学习记录
整理下文件依赖思路 :

yolo-fastestv2.h 只做声明，声明了 TargetBox 和 yoloFastestv2 类, 编译后不产生代码;
yolo-fastestv2.cpp 进行代码实现

TargetBox 用在 yolo-fastestv2.cpp 中 vector 数据类型中
yoloFastestv2 类封装了具体的一些函数:

(1) yoloFastestv2::yoloFastestv2() 模型的参数配置
(2) yoloFastestv2::~yoloFastestv2() 退出类
(3) int yoloFastestv2::loadModel() ncnn 模型加载
(4) int yoloFastestv2::detection() 检测 -> 调用 predHandle, nmsHandle

int yoloFastestv2::nmsHandle() NMS处理

int yoloFastestv2::predHandle() 特征图后处理 -> 调用 getCategory

int yoloFastestv2::getCategory() 检测类别分数处理

video_demo.cpp

#include "yolo-fastestv2.h"int main()
{
    static const char* class_names[] = {
    "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light","fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow","elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee","skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard","tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple","sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch","potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone","microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear","hair drier", "toothbrush"};// static const char* class_names[] = {
    
// "airplane", "ship", "storage tank", "baseball diamond", "tennis court",
// "basketball court", "ground track field", "harbor", "bridge", "vehicle"
// };//在yolo-fastestv2.h 中, 有一个类函数 yoloFastestv2yoloFastestv2 api;api.loadModel("./model/yolo-fastestv2-opt.param","./model/yolo-fastestv2-opt.bin");//cv::Mat cvImg = cv::imread("058.jpg");// Vector容器中存放自定义数据类型,存放目标框的信息(cls,score,x,y,w,h,area), 在 yolo-fastestv2.h 有声明std::vector<TargetBox> boxes;cv::VideoCapture capture;capture.open("../../person.mp4");//获取当前 视频信息cv::Size S = cv::Size((int)capture.get(CV_CAP_PROP_FRAME_WIDTH),(int)capture.get(CV_CAP_PROP_FRAME_HEIGHT));// -----------保存视频的检测结果--------------cv:: VideoWriter outputVideo;outputVideo.open("./out.mp4", CV_FOURCC('P','I','M','1'), 30.0, S, true);if (!outputVideo.isOpened()) {
    std::cout << "fail to open!" << std::endl;return -1;}// ---------------------------------cv::Mat frame;while (1) {
    capture >> frame;//读入视频的帧if (frame.empty()) break;// 检测 图像,结果保存在 boxesapi.detection(frame, boxes);// 可视化,绘制框for (int i = 0; i < boxes.size(); i++) {
    std::cout<<boxes[i].x1<<" "<<boxes[i].y1<<" "<<boxes[i].x2<<" "<<boxes[i].y2<<" "<<boxes[i].score<<" "<<boxes[i].cate<<std::endl;char text[256];sprintf(text, "%s %.1f%%", class_names[boxes[i].cate], boxes[i].score * 100);int baseLine = 0;cv::Size label_size = cv::getTextSize(text, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);int x = boxes[i].x1;int y = boxes[i].y1 - label_size.height - baseLine;if (y < 0)y = 0;if (x + label_size.width > frame.cols)x = frame.cols - label_size.width;cv::rectangle(frame, cv::Rect(cv::Point(x, y), cv::Size(label_size.width, label_size.height + baseLine)),cv::Scalar(255, 255, 255), -1);cv::putText(frame, text, cv::Point(x, y + label_size.height),cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0));cv::rectangle (frame, cv::Point(boxes[i].x1, boxes[i].y1),cv::Point(boxes[i].x2, boxes[i].y2), cv::Scalar(255, 255, 0), 2, 2, 0);}cv:: namedWindow("img",CV_WINDOW_NORMAL);cv:: imshow("img", frame);// 保存 视频检测文件outputVideo.write(frame); //把图像写入视频流//按下ESC退出整个程序int c = cv::waitKey(30);if( char(c) == 27) return -1;}
// cv::imwrite("output.png", cvImg);// 关闭释放capture.release();cv::waitKey(0);return 0;
}

yolo-fastestv2部署 视频 检测

video_demo.cpp

yolo-fastestv2部署视频检测