webRTC算法无延迟
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29ecc25221
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@ -45,7 +45,6 @@ VideoService::~VideoService() {
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bool VideoService::start() {
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// (关键修改) rknnPool 的模板参数已更新
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rknn_pool_ = std::make_unique<rknnPool<rkYolov5s, cv::Mat, detect_result_group_t>>(model_path_.c_str(), thread_num_);
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if (rknn_pool_->init() != 0) {
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printf("rknnPool init fail!\n");
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@ -53,18 +52,15 @@ bool VideoService::start() {
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}
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printf("rknnPool init success.\n");
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// 2. 设置RTSP传输协议
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setenv("OPENCV_FFMPEG_CAPTURE_OPTIONS", "rtsp_transport;tcp", 1);
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printf("Set RTSP transport protocol to TCP\n");
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// 3. 初始化 VideoCapture
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capture_.open(input_url_, cv::CAP_FFMPEG);
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if (!capture_.isOpened()) {
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printf("Error: Could not open RTSP stream: %s\n", input_url_.c_str());
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return false;
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}
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// 4. 获取输入视频的属性
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frame_width_ = static_cast<int>(capture_.get(cv::CAP_PROP_FRAME_WIDTH));
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frame_height_ = static_cast<int>(capture_.get(cv::CAP_PROP_FRAME_HEIGHT));
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frame_fps_ = capture_.get(cv::CAP_PROP_FPS);
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@ -73,14 +69,15 @@ bool VideoService::start() {
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printf("RTSP stream opened successfully! (%dx%d @ %.2f FPS)\n", frame_width_, frame_height_, frame_fps_);
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std::string gst_pipeline =
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"appsrc ! "
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"video/x-raw,format=BGR ! "
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"videoconvert ! "
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"video/x-raw,format=NV12 ! "
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"mpph265enc gop=25 rc-mode=fixqp qp-init=26 ! " // (备注) 你可以根据需要调整 mpph265enc 参数
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"h265parse ! "
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"rtspclientsink location=" + output_rtsp_url_ + " latency=0 protocols=tcp";
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std::string gst_pipeline =
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"appsrc ! "
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"queue max-size-buffers=2 leaky=downstream ! "
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"video/x-raw,format=BGR ! "
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"videoconvert ! "
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"video/x-raw,format=NV12 ! "
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"mpph265enc gop=25 rc-mode=fixqp qp-init=26 ! "
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"h265parse ! "
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"rtspclientsink location=" + output_rtsp_url_ + " latency=0 protocols=tcp";
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printf("Using GStreamer output pipeline: %s\n", gst_pipeline.c_str());
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@ -98,8 +95,8 @@ bool VideoService::start() {
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}
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printf("VideoWriter opened successfully.\n");
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// 6. 启动处理线程
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running_ = true;
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reading_thread_ = std::thread(&VideoService::reading_loop, this);
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processing_thread_ = std::thread(&VideoService::processing_loop, this);
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printf("Processing thread started.\n");
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@ -111,6 +108,10 @@ void VideoService::stop() {
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printf("Stopping VideoService...\n");
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running_ = false;
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if (reading_thread_.joinable()) {
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reading_thread_.join();
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}
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if (processing_thread_.joinable()) {
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processing_thread_.join();
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}
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@ -127,11 +128,8 @@ void VideoService::stop() {
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}
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// (新增) 从 rkYolov5s.cc 移入并修改
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// 这是现在唯一的跟踪器逻辑，在主线程中串行调用
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void VideoService::update_tracker(detect_result_group_t &detect_result_group, const cv::Size& frame_size)
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{
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// 首次运行时，根据图像尺寸初始化入侵区域 (设定在画面中央)
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if (intrusion_zone_.width == 0 || intrusion_zone_.height == 0) {
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intrusion_zone_ = cv::Rect(frame_size.width / 4, frame_size.height / 4, frame_size.width / 2, frame_size.height / 2);
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}
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@ -147,12 +145,10 @@ void VideoService::update_tracker(detect_result_group_t &detect_result_group, co
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}
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}
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// 1. 对于已有的跟踪目标，增加其未见帧数
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for (auto it = tracked_persons_.begin(); it != tracked_persons_.end(); ++it) {
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it->second.frames_unseen++;
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}
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// 2. 将当前帧的检测结果与已有的跟踪目标进行匹配
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for (const auto& det_box : current_detections) {
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bool is_matched = false;
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int best_match_id = -1;
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@ -167,12 +163,10 @@ void VideoService::update_tracker(detect_result_group_t &detect_result_group, co
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}
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if (best_match_id != -1) {
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// 匹配成功
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tracked_persons_[best_match_id].box = det_box;
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tracked_persons_[best_match_id].frames_unseen = 0;
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is_matched = true;
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} else {
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// 匹配失败，创建新的跟踪目标
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TrackedPerson new_person;
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new_person.id = this->next_track_id_++; // 使用成员变量
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new_person.box = det_box;
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@ -184,7 +178,6 @@ void VideoService::update_tracker(detect_result_group_t &detect_result_group, co
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}
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}
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// 3. 处理和更新每个目标的状态
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double current_time = get_current_time_seconds();
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for (auto it = tracked_persons_.begin(); it != tracked_persons_.end(); ++it) {
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TrackedPerson& person = it->second;
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@ -209,7 +202,6 @@ void VideoService::update_tracker(detect_result_group_t &detect_result_group, co
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}
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}
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// 4. 移除消失太久的目标
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for (auto it = tracked_persons_.begin(); it != tracked_persons_.end(); ) {
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// (建议) 增加到50帧 (约2秒) 提高鲁棒性，减少ID切换
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if (it->second.frames_unseen > 50) {
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@ -220,15 +212,39 @@ void VideoService::update_tracker(detect_result_group_t &detect_result_group, co
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}
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}
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// (新增) 绘图辅助函数，从 rkYolov5s::infer 移入
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void VideoService::reading_loop() {
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cv::Mat frame;
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spdlog::info("Reading thread started.");
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while (running_) {
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if (!capture_.read(frame)) {
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spdlog::warn("Reading loop: Failed to read frame from capture. Stopping service.");
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running_ = false;
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break;
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}
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if (frame.empty()) {
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continue;
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}
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{
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std::lock_guard<std::mutex> lock(frame_mutex_);
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latest_frame_ = frame;
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new_frame_available_ = true;
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}
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frame_cv_.notify_one();
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}
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frame_cv_.notify_all();
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spdlog::info("Reading loop finished.");
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}
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void VideoService::draw_results(cv::Mat& frame)
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{
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// 绘制入侵区域
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cv::rectangle(frame, this->intrusion_zone_, cv::Scalar(255, 255, 0), 2); // 黄色
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// 绘制框体和报警状态
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for (auto const& [id, person] : this->tracked_persons_) {
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// 根据是否触发报警决定颜色 (BGR: 红色 vs 绿色)
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cv::Scalar box_color = person.alarm_triggered ? cv::Scalar(0, 0, 255) : cv::Scalar(0, 255, 0);
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int line_thickness = person.alarm_triggered ? 3 : 2;
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@ -243,24 +259,36 @@ void VideoService::draw_results(cv::Mat& frame)
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}
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// (关键修改) 彻底重写处理循环为 "一进一出" 模式
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void VideoService::processing_loop() {
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cv::Mat frame;
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detect_result_group_t detection_results;
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while (running_) {
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auto t_start = std::chrono::high_resolution_clock::now();
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if (!capture_.read(frame)) {
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spdlog::warn("VideoService: Failed to read frame from capture. Stopping capture.");
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running_ = false;
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break;
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{
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std::unique_lock<std::mutex> lock(frame_mutex_);
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frame_cv_.wait(lock, [&]{
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return new_frame_available_ || !running_;
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});
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if (!running_) {
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break;
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}
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// 确认是有新帧
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frame = latest_frame_.clone();
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new_frame_available_ = false;
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}
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auto t_read = std::chrono::high_resolution_clock::now();
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if (frame.empty()) {
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continue;
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}
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// [保留] 后续的 AI 推理、跟踪、绘图、推流逻辑完全不变
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if (rknn_pool_->put(frame) != 0) {
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spdlog::error("VideoService: Failed to put frame into rknnPool. Stopping.");
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running_ = false;
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@ -283,18 +311,17 @@ void VideoService::processing_loop() {
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writer_.write(frame);
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}
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// 打印详细耗时
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auto t_write = std::chrono::high_resolution_clock::now();
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// (新增) 打印耗时
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double read_ms = std::chrono::duration_cast<std::chrono::duration<double, std::milli>>(t_read - t_start).count();
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double infer_ms = std::chrono::duration_cast<std::chrono::duration<double, std::milli>>(t_infer - t_read).count();
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double track_ms = std::chrono::duration_cast<std::chrono::duration<double, std::milli>>(t_track_draw - t_infer).count();
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double write_ms = std::chrono::duration_cast<std::chrono::duration<double, std::milli>>(t_write - t_track_draw).count();
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double total_ms = std::chrono::duration_cast<std::chrono::duration<double, std::milli>>(t_write - t_start).count();
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// [保留] 性能日志
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double read_ms = std::chrono::duration_cast<std::chrono::duration<double, std::milli>>(t_read - t_start).count();
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double infer_ms = std::chrono::duration_cast<std::chrono::duration<double, std::milli>>(t_infer - t_read).count();
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double track_ms = std::chrono::duration_cast<std::chrono::duration<double, std::milli>>(t_track_draw - t_infer).count();
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double write_ms = std::chrono::duration_cast<std::chrono::duration<double, std::milli>>(t_write - t_track_draw).count();
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double total_ms = std::chrono::duration_cast<std::chrono::duration<double, std::milli>>(t_write - t_start).count();
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printf("Loop time: Total=%.1fms [Read=%.1f, Infer=%.1f, Track=%.1f, Write=%.1f]\n",
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total_ms, read_ms, infer_ms, track_ms, write_ms);
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printf("Loop time: Total=%.1fms [Read=%.1f, Infer=%.1f, Track=%.1f, Write=%.1f]\n",
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total_ms, read_ms, infer_ms, track_ms, write_ms);
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}
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spdlog::info("VideoService: Processing loop finished.");
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@ -5,25 +5,26 @@
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#include <thread>
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#include <atomic>
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#include <memory>
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#include <map> // (新增) 用于跟踪
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#include <map>
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#include <mutex>
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#include <condition_variable>
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#include <opencv2/core/core.hpp>
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#include <opencv2/videoio.hpp>
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#include "postprocess.h" // (新增) 需要 detect_result_group_t
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#include "postprocess.h"
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// 向前声明
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template<typename T, typename IN, typename OUT>
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class rknnPool;
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class rkYolov5s;
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// (新增) 从 rkYolov5s.hpp 移动过来的结构体
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struct TrackedPerson
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{
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int id; // 唯一ID
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cv::Rect box; // 当前位置
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double entry_time; // 进入入侵区域的时间戳 (秒)
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bool is_in_zone; // 是否在区域内
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bool alarm_triggered; // 是否已触发报警
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int frames_unseen; // 消失的帧数
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int id;
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cv::Rect box;
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double entry_time;
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bool is_in_zone;
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bool alarm_triggered;
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int frames_unseen;
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};
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class VideoService {
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@ -40,35 +41,36 @@ public:
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private:
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void processing_loop();
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void reading_loop(); // [新增] 读取线程的循环函数
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// (新增) 跟踪和绘图相关的私有方法
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void update_tracker(detect_result_group_t &detect_result_group, const cv::Size& frame_size);
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void draw_results(cv::Mat& frame); // 绘图辅助函数
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void trigger_alarm(int person_id, const cv::Rect& box);
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double get_current_time_seconds();
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// 配置
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std::string model_path_;
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int thread_num_;
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std::string input_url_;
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std::string output_rtsp_url_;
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// 视频属性
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int frame_width_ = 0;
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int frame_height_ = 0;
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double frame_fps_ = 0.0;
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// 资源
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// (关键修改) rknnPool 的输出类型变为 detect_result_group_t
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std::unique_ptr<rknnPool<rkYolov5s, cv::Mat, detect_result_group_t>> rknn_pool_;
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cv::VideoCapture capture_;
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cv::VideoWriter writer_;
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// 线程管理
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std::thread processing_thread_;
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std::thread reading_thread_;
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std::atomic<bool> running_{false};
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// (新增) 跟踪器状态变量 (从 rkYolov5s 移入)
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std::mutex frame_mutex_;
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std::condition_variable frame_cv_;
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cv::Mat latest_frame_;
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bool new_frame_available_{false};
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cv::Rect intrusion_zone_;
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std::map<int, TrackedPerson> tracked_persons_;
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int next_track_id_;
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