更新入侵检测的功能（存在15s延迟需优化）
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parent
3d07422520
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07e692970f
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@ -1,9 +1,9 @@
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#include <stdio.h>
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#include <mutex>
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#include <chrono> // 用于计时
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#include <string> // 使用 std::string
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#include <vector> // 使用 std::vector
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#include <algorithm> // 使用 std::min/max
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// #include <mutex> // 已移除
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// #include <chrono> // 已移除
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#include <string>
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#include <vector>
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// #include <algorithm> // 已移除
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#include "postprocess.h"
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#include "preprocess.h"
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@ -15,19 +15,7 @@
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#include "opencv2/imgproc/imgproc.hpp"
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#include "rknn/rknn_api.h"
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// 报警接口函数 (目前只打印信息)
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void trigger_alarm(int person_id, const cv::Rect& box) {
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printf("[ALARM] Intrusion detected! Person ID: %d at location (%d, %d, %d, %d)\n",
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person_id, box.x, box.y, box.width, box.height);
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// TODO: 在这里实现真正的报警逻辑，例如发送网络消息、写入数据库等。
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}
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// 获取当前时间的函数 (返回秒)
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double get_current_time_seconds() {
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return std::chrono::duration_cast<std::chrono::duration<double>>(
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std::chrono::high_resolution_clock::now().time_since_epoch()
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).count();
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}
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// trigger_alarm 和 get_current_time_seconds 已被移至 video_service.cc
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static void dump_tensor_attr(rknn_tensor_attr *attr)
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{
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@ -108,17 +96,10 @@ rkYolov5s::rkYolov5s(const std::string &model_path)
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nms_threshold = NMS_THRESH;
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box_conf_threshold = BOX_THRESH;
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// 初始化跟踪器和入侵检测参数
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next_track_id = 1;
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intrusion_time_threshold = 3.0; // 报警时间阈值：3秒
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// 默认设置一个无效的入侵区域，将在第一帧时根据图像大小初始化
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intrusion_zone = cv::Rect(0, 0, 0, 0);
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// 跟踪器相关的初始化已全部移除
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}
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void rkYolov5s::set_intrusion_zone(const cv::Rect& zone) {
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std::lock_guard<std::mutex> lock(mtx);
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this->intrusion_zone = zone;
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}
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// set_intrusion_zone 已被移除
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int rkYolov5s::init(rknn_context *ctx_in, bool share_weight)
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{
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@ -224,97 +205,18 @@ rknn_context *rkYolov5s::get_pctx()
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return &ctx;
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}
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void rkYolov5s::update_tracker(detect_result_group_t &detect_result_group)
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// update_tracker 函数已完全移除 (移至 video_service.cc)
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// 关键修改：
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// 1. 函数签名改变
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// 2. 移除了 lock_guard
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// 3. 输入参数变为 const cv::Mat&
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// 4. 移除了所有 update_tracker 和 绘图(cv::rectangle/putText) 逻辑
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// 5. 返回值变为 detect_result_group_t
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detect_result_group_t rkYolov5s::infer(const cv::Mat &orig_img)
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{
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std::vector<cv::Rect> current_detections;
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for (int i = 0; i < detect_result_group.count; i++) {
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detect_result_t *det_result = &(detect_result_group.results[i]);
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if (strcmp(det_result->name, "person") == 0) {
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current_detections.push_back(cv::Rect(
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det_result->box.left, det_result->box.top,
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det_result->box.right - det_result->box.left,
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det_result->box.bottom - det_result->box.top));
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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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double max_iou = 0.3; // IoU阈值，用于判断是否为同一目标
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for (auto const& [id, person] : tracked_persons) {
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// 计算交并比 (Intersection over Union)
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double iou = (double)(det_box & person.box).area() / (double)(det_box | person.box).area();
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if (iou > max_iou) {
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max_iou = iou;
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best_match_id = id;
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}
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}
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if (best_match_id != -1) {
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// 匹配成功，更新目标信息
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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 = next_track_id++;
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new_person.box = det_box;
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new_person.entry_time = 0;
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new_person.is_in_zone = false;
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new_person.alarm_triggered = false;
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new_person.frames_unseen = 0;
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tracked_persons[new_person.id] = new_person;
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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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// 判断人员包围盒是否与入侵区域有交集
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bool currently_in_zone = (intrusion_zone & person.box).area() > 0;
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if (currently_in_zone) {
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if (!person.is_in_zone) {
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// 刚进入区域
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person.is_in_zone = true;
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person.entry_time = current_time;
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} else {
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// 已在区域内，检查是否超时
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if (!person.alarm_triggered && (current_time - person.entry_time) > intrusion_time_threshold) {
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person.alarm_triggered = true;
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trigger_alarm(person.id, person.box);
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}
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}
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} else {
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// 不在区域内，重置状态
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person.is_in_zone = false;
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person.entry_time = 0;
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person.alarm_triggered = false;
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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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if (it->second.frames_unseen > 20) { // 超过20帧未见则移除
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it = tracked_persons.erase(it);
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} else {
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++it;
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}
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}
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}
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cv::Mat rkYolov5s::infer(cv::Mat &orig_img)
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{
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std::lock_guard<std::mutex> lock(mtx);
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// std::lock_guard<std::mutex> lock(mtx); // 已移除
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cv::Mat img;
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cv::cvtColor(orig_img, img, cv::COLOR_BGR2RGB);
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img_width = img.cols;
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@ -369,33 +271,12 @@ cv::Mat rkYolov5s::infer(cv::Mat &orig_img)
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post_process((int8_t *)outputs[0].buf, (int8_t *)outputs[1].buf, (int8_t *)outputs[2].buf, height, width,
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box_conf_threshold, nms_threshold, pads, scale_w, scale_h, out_zps, out_scales, &detect_result_group);
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// 更新跟踪器状态
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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(orig_img.cols / 4, orig_img.rows / 4, orig_img.cols / 2, orig_img.rows / 2);
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}
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update_tracker(detect_result_group);
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// 所有 跟踪器(update_tracker) 和 绘图(cv::rectangle/putText) 逻辑均已移除
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// 绘制入侵区域
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cv::rectangle(orig_img, 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] : 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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cv::rectangle(orig_img, person.box, box_color, line_thickness);
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std::string label = "Person " + std::to_string(id);
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if (person.is_in_zone) {
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label += " (In Zone)";
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}
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cv::putText(orig_img, label, cv::Point(person.box.x, person.box.y - 10),
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cv::FONT_HERSHEY_SIMPLEX, 0.5, box_color, 2);
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}
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ret = rknn_outputs_release(ctx, io_num.n_output, outputs);
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return orig_img;
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// 返回原始检测结果
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return detect_result_group;
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}
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rkYolov5s::~rkYolov5s()
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@ -3,11 +3,9 @@
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#include "rknn_api.h"
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#include "opencv2/core/core.hpp"
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#include <map> // 用于存储跟踪目标
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#include <mutex> // 确保线程安全
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#include <string> // 使用 std::string
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#include <vector> // 使用 std::vector
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#include "postprocess.h"
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#include <string>
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#include <vector>
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#include "postprocess.h" // 包含 detect_result_group_t 的定义
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// 前置声明
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static void dump_tensor_attr(rknn_tensor_attr *attr);
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@ -15,22 +13,13 @@ static unsigned char *load_data(FILE *fp, size_t ofst, size_t sz);
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static unsigned char *load_model(const char *filename, int *model_size);
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static int saveFloat(const char *file_name, float *output, int element_size);
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// 用于跟踪单个目标的结构体
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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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};
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// 注意：TrackedPerson 结构体已被移至 video_service.h
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class rkYolov5s
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{
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private:
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int ret;
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std::mutex mtx;
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// std::mutex mtx; // 已移除，推理应是无状态的
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std::string model_path;
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unsigned char *model_data;
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@ -45,24 +34,18 @@ private:
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float nms_threshold, box_conf_threshold;
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// 入侵检测和跟踪相关成员
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cv::Rect intrusion_zone; // 入侵区域
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std::map<int, TrackedPerson> tracked_persons; // 存储所有被跟踪的人
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int next_track_id; // 用于分配新的唯一ID
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double intrusion_time_threshold; // 入侵时间阈值 (秒)
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// 跟踪逻辑的私有方法
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void update_tracker(detect_result_group_t &detect_result_group);
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// 所有的跟踪和入侵检测成员变量已被移除
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public:
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rkYolov5s(const std::string &model_path);
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int init(rknn_context *ctx_in, bool isChild);
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rknn_context *get_pctx();
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cv::Mat infer(cv::Mat &ori_img);
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detect_result_group_t infer(const cv::Mat &ori_img);
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~rkYolov5s();
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// 用于从外部设置入侵区域的公共方法
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void set_intrusion_zone(const cv::Rect& zone);
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};
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#endif // RKYOLOV5S_H
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@ -5,6 +5,22 @@
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#include "rknn/rkYolov5s.hpp"
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#include "rknn/rknnPool.hpp"
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#include "spdlog/spdlog.h"
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#include <chrono> // (新增) 用于计时
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#include <algorithm> // (新增)
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// (新增) 报警接口函数 (从 rkYolov5s.cc 移入)
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void VideoService::trigger_alarm(int person_id, const cv::Rect& box) {
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printf("[ALARM] Intrusion detected! Person ID: %d at location (%d, %d, %d, %d)\n",
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person_id, box.x, box.y, box.width, box.height);
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// TODO: 在这里实现真正的报警逻辑，例如发送网络消息、写入数据库等。
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}
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// (新增) 获取当前时间的函数 (从 rkYolov5s.cc 移入)
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double VideoService::get_current_time_seconds() {
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return std::chrono::duration_cast<std::chrono::duration<double>>(
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std::chrono::high_resolution_clock::now().time_since_epoch()
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).count();
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}
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VideoService::VideoService(std::string model_path,
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int thread_num,
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@ -16,6 +32,11 @@ VideoService::VideoService(std::string model_path,
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output_rtsp_url_(output_rtsp_url),
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running_(false)
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{
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// (新增) 初始化跟踪器状态
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next_track_id_ = 1;
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intrusion_time_threshold_ = 3.0; // 3秒
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intrusion_zone_ = cv::Rect(0, 0, 0, 0); // 默认无效
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printf("VideoService created. Input: %s, Output: %s\n", input_url_.c_str(), output_rtsp_url_.c_str());
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}
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@ -27,30 +48,29 @@ VideoService::~VideoService() {
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bool VideoService::start() {
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// 1. 初始化 rknnPool (来自旧的 main)
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rknn_pool_ = std::make_unique<rknnPool<rkYolov5s, cv::Mat, cv::Mat>>(model_path_.c_str(), thread_num_);
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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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return false;
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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 (来自旧的 main)
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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 (使用成员变量)
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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. (关键) 获取输入视频的属性
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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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// 很多RTSP流不提供FPS，给一个默认值
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if (frame_fps_ <= 0) frame_fps_ = 25.0;
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printf("RTSP stream opened successfully! (%dx%d @ %.2f FPS)\n", frame_width_, frame_height_, frame_fps_);
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@ -61,18 +81,18 @@ bool VideoService::start() {
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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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"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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printf("Using GStreamer output pipeline: %s\n", gst_pipeline.c_str());
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writer_.open(gst_pipeline,
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cv::CAP_GSTREAMER, // 使用 GStreamer 后端
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0, // fourcc, GStreamer不需要
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frame_fps_, // FPS
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cv::Size(frame_width_, frame_height_), // 帧大小
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true); // 是彩色图像
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cv::CAP_GSTREAMER,
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0,
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frame_fps_,
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cv::Size(frame_width_, frame_height_),
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true);
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if (!writer_.isOpened()) {
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printf("Error: Could not open VideoWriter with GStreamer pipeline.\n");
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@ -99,7 +119,6 @@ void VideoService::stop() {
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}
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printf("Processing thread joined.\n");
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// 释放资源
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if (capture_.isOpened()) {
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capture_.release();
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}
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||||
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|
@ -111,11 +130,127 @@ void VideoService::stop() {
|
|||
}
|
||||
|
||||
|
||||
// (新增) 从 rkYolov5s.cc 移入并修改
|
||||
// 这是现在唯一的跟踪器逻辑，在主线程中串行调用
|
||||
void VideoService::update_tracker(detect_result_group_t &detect_result_group, const cv::Size& frame_size)
|
||||
{
|
||||
// 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)
|
||||
if (intrusion_zone_.width == 0 || intrusion_zone_.height == 0) {
|
||||
intrusion_zone_ = cv::Rect(frame_size.width / 4, frame_size.height / 4, frame_size.width / 2, frame_size.height / 2);
|
||||
}
|
||||
|
||||
std::vector<cv::Rect> current_detections;
|
||||
for (int i = 0; i < detect_result_group.count; i++) {
|
||||
detect_result_t *det_result = &(detect_result_group.results[i]);
|
||||
if (strcmp(det_result->name, "person") == 0) {
|
||||
current_detections.push_back(cv::Rect(
|
||||
det_result->box.left, det_result->box.top,
|
||||
det_result->box.right - det_result->box.left,
|
||||
det_result->box.bottom - det_result->box.top));
|
||||
}
|
||||
}
|
||||
|
||||
// 1. 对于已有的跟踪目标，增加其未见帧数
|
||||
for (auto it = tracked_persons_.begin(); it != tracked_persons_.end(); ++it) {
|
||||
it->second.frames_unseen++;
|
||||
}
|
||||
|
||||
// 2. 将当前帧的检测结果与已有的跟踪目标进行匹配
|
||||
for (const auto& det_box : current_detections) {
|
||||
bool is_matched = false;
|
||||
int best_match_id = -1;
|
||||
double max_iou = 0.3; // IoU阈值
|
||||
|
||||
for (auto const& [id, person] : this->tracked_persons_) {
|
||||
double iou = (double)(det_box & person.box).area() / (double)(det_box | person.box).area();
|
||||
if (iou > max_iou) {
|
||||
max_iou = iou;
|
||||
best_match_id = id;
|
||||
}
|
||||
}
|
||||
|
||||
if (best_match_id != -1) {
|
||||
// 匹配成功
|
||||
tracked_persons_[best_match_id].box = det_box;
|
||||
tracked_persons_[best_match_id].frames_unseen = 0;
|
||||
is_matched = true;
|
||||
} else {
|
||||
// 匹配失败，创建新的跟踪目标
|
||||
TrackedPerson new_person;
|
||||
new_person.id = this->next_track_id_++; // 使用成员变量
|
||||
new_person.box = det_box;
|
||||
new_person.entry_time = 0;
|
||||
new_person.is_in_zone = false;
|
||||
new_person.alarm_triggered = false;
|
||||
new_person.frames_unseen = 0;
|
||||
tracked_persons_[new_person.id] = new_person; // 使用成员变量
|
||||
}
|
||||
}
|
||||
|
||||
// 3. 处理和更新每个目标的状态
|
||||
double current_time = get_current_time_seconds();
|
||||
for (auto it = tracked_persons_.begin(); it != tracked_persons_.end(); ++it) {
|
||||
TrackedPerson& person = it->second;
|
||||
// 使用成员变量
|
||||
bool currently_in_zone = (this->intrusion_zone_ & person.box).area() > 0;
|
||||
|
||||
if (currently_in_zone) {
|
||||
if (!person.is_in_zone) {
|
||||
person.is_in_zone = true;
|
||||
person.entry_time = current_time;
|
||||
} else {
|
||||
// 使用成员变量
|
||||
if (!person.alarm_triggered && (current_time - person.entry_time) > this->intrusion_time_threshold_) {
|
||||
person.alarm_triggered = true;
|
||||
trigger_alarm(person.id, person.box); // 调用成员函数
|
||||
}
|
||||
}
|
||||
} else {
|
||||
person.is_in_zone = false;
|
||||
person.entry_time = 0;
|
||||
person.alarm_triggered = false;
|
||||
}
|
||||
}
|
||||
|
||||
// 4. 移除消失太久的目标
|
||||
for (auto it = tracked_persons_.begin(); it != tracked_persons_.end(); ) {
|
||||
// (建议) 增加到50帧 (约2秒) 提高鲁棒性，减少ID切换
|
||||
if (it->second.frames_unseen > 50) {
|
||||
it = tracked_persons_.erase(it);
|
||||
} else {
|
||||
++it;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// (新增) 绘图辅助函数，从 rkYolov5s::infer 移入
|
||||
void VideoService::draw_results(cv::Mat& frame)
|
||||
{
|
||||
// 绘制入侵区域
|
||||
cv::rectangle(frame, this->intrusion_zone_, cv::Scalar(255, 255, 0), 2); // 黄色
|
||||
|
||||
// 绘制框体和报警状态
|
||||
for (auto const& [id, person] : this->tracked_persons_) {
|
||||
// 根据是否触发报警决定颜色 (BGR: 红色 vs 绿色)
|
||||
cv::Scalar box_color = person.alarm_triggered ? cv::Scalar(0, 0, 255) : cv::Scalar(0, 255, 0);
|
||||
int line_thickness = person.alarm_triggered ? 3 : 2;
|
||||
|
||||
cv::rectangle(frame, person.box, box_color, line_thickness);
|
||||
std::string label = "Person " + std::to_string(id);
|
||||
if (person.is_in_zone) {
|
||||
label += " (In Zone)";
|
||||
}
|
||||
cv::putText(frame, label, cv::Point(person.box.x, person.box.y - 10),
|
||||
cv::FONT_HERSHEY_SIMPLEX, 0.5, box_color, 2);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
// (关键修改) 彻底重写处理循环为 "一进一出" 模式
|
||||
void VideoService::processing_loop() {
|
||||
cv::Mat frame;
|
||||
cv::Mat processed_frame;
|
||||
int frames_in_pipeline = 0;
|
||||
|
||||
detect_result_group_t detection_results; // (修改) 存储推理结果
|
||||
|
||||
while (running_) {
|
||||
if (!capture_.read(frame)) {
|
||||
spdlog::warn("VideoService: Failed to read frame from capture. Stopping capture.");
|
||||
|
|
@ -127,42 +262,34 @@ void VideoService::processing_loop() {
|
|||
continue;
|
||||
}
|
||||
|
||||
// 1. (并行) 将原始帧放入池中进行推理
|
||||
if (rknn_pool_->put(frame) != 0) {
|
||||
spdlog::error("VideoService: Failed to put frame into rknnPool. Stopping.");
|
||||
running_ = false;
|
||||
break;
|
||||
}
|
||||
|
||||
if (frames_in_pipeline >= thread_num_) {
|
||||
|
||||
if (rknn_pool_->get(processed_frame) != 0) {
|
||||
spdlog::error("VideoService: Failed to get frame from rknnPool. Stopping.");
|
||||
running_ = false;
|
||||
break;
|
||||
}
|
||||
// 2. (串行) 立刻取回该帧的推理结果
|
||||
// 这保证了 跟踪 和 绘图 总是按顺序在主线程中执行
|
||||
if (rknn_pool_->get(detection_results) != 0) {
|
||||
spdlog::error("VideoService: Failed to get frame from rknnPool. Stopping.");
|
||||
running_ = false;
|
||||
break;
|
||||
}
|
||||
|
||||
// (核心) 推流
|
||||
if (writer_.isOpened()) {
|
||||
writer_.write(processed_frame);
|
||||
}
|
||||
} else {
|
||||
frames_in_pipeline++;
|
||||
// 3. (串行) 在主循环中更新唯一的跟踪器
|
||||
this->update_tracker(detection_results, frame.size());
|
||||
|
||||
// 4. (串行) 在主循环中将跟踪结果绘制到帧上
|
||||
this->draw_results(frame);
|
||||
|
||||
// 5. (串行) 将处理和绘制完毕的帧推流
|
||||
if (writer_.isOpened()) {
|
||||
writer_.write(frame);
|
||||
}
|
||||
}
|
||||
|
||||
spdlog::info("VideoService: Processing loop finished. Draining remaining frames...");
|
||||
|
||||
while (true) {
|
||||
if (rknn_pool_->get(processed_frame) != 0) {
|
||||
// 队列已空，排空完成
|
||||
break;
|
||||
}
|
||||
|
||||
// 成功获取一帧，将其推流
|
||||
if (writer_.isOpened()) {
|
||||
writer_.write(processed_frame);
|
||||
}
|
||||
}
|
||||
|
||||
spdlog::info("VideoService: Draining complete.");
|
||||
// (修改) 移除排空循环 (Draining loop)
|
||||
// 新的 "一进一出" 逻辑不需要排空，退出即停止
|
||||
spdlog::info("VideoService: Processing loop finished.");
|
||||
}
|
||||
|
|
@ -5,22 +5,35 @@
|
|||
#include <thread>
|
||||
#include <atomic>
|
||||
#include <memory>
|
||||
#include <map> // (新增) 用于跟踪
|
||||
#include <opencv2/core/core.hpp>
|
||||
#include <opencv2/videoio.hpp>
|
||||
#include "postprocess.h" // (新增) 需要 detect_result_group_t
|
||||
|
||||
// 向前声明
|
||||
template<typename T, typename IN, typename OUT>
|
||||
class rknnPool;
|
||||
class rkYolov5s;
|
||||
|
||||
// (新增) 从 rkYolov5s.hpp 移动过来的结构体
|
||||
struct TrackedPerson
|
||||
{
|
||||
int id; // 唯一ID
|
||||
cv::Rect box; // 当前位置
|
||||
double entry_time; // 进入入侵区域的时间戳 (秒)
|
||||
bool is_in_zone; // 是否在区域内
|
||||
bool alarm_triggered; // 是否已触发报警
|
||||
int frames_unseen; // 消失的帧数
|
||||
};
|
||||
|
||||
class VideoService {
|
||||
public:
|
||||
VideoService(std::string model_path,
|
||||
int thread_num,
|
||||
std::string input_url, // <--- 新增
|
||||
std::string output_rtsp_url); // <--- 名称修改，更明确
|
||||
std::string input_url,
|
||||
std::string output_rtsp_url);
|
||||
|
||||
~VideoService(); // <-- 析构函数将用于调用 stop()
|
||||
~VideoService();
|
||||
|
||||
bool start();
|
||||
void stop();
|
||||
|
|
@ -28,23 +41,36 @@ public:
|
|||
private:
|
||||
void processing_loop();
|
||||
|
||||
// (新增) 跟踪和绘图相关的私有方法
|
||||
void update_tracker(detect_result_group_t &detect_result_group, const cv::Size& frame_size);
|
||||
void draw_results(cv::Mat& frame); // 绘图辅助函数
|
||||
void trigger_alarm(int person_id, const cv::Rect& box);
|
||||
double get_current_time_seconds();
|
||||
|
||||
// 配置
|
||||
std::string model_path_;
|
||||
int thread_num_;
|
||||
std::string input_url_; // <--- 新增
|
||||
std::string output_rtsp_url_; // <--- 新增
|
||||
std::string input_url_;
|
||||
std::string output_rtsp_url_;
|
||||
|
||||
// 视频属性 (重要)
|
||||
// 视频属性
|
||||
int frame_width_ = 0;
|
||||
int frame_height_ = 0;
|
||||
double frame_fps_ = 0.0;
|
||||
|
||||
// 资源
|
||||
std::unique_ptr<rknnPool<rkYolov5s, cv::Mat, cv::Mat>> rknn_pool_;
|
||||
// (关键修改) rknnPool 的输出类型变为 detect_result_group_t
|
||||
std::unique_ptr<rknnPool<rkYolov5s, cv::Mat, detect_result_group_t>> rknn_pool_;
|
||||
cv::VideoCapture capture_;
|
||||
cv::VideoWriter writer_;
|
||||
|
||||
// 线程管理
|
||||
std::thread processing_thread_;
|
||||
std::atomic<bool> running_{false};
|
||||
|
||||
// (新增) 跟踪器状态变量 (从 rkYolov5s 移入)
|
||||
cv::Rect intrusion_zone_;
|
||||
std::map<int, TrackedPerson> tracked_persons_;
|
||||
int next_track_id_;
|
||||
double intrusion_time_threshold_;
|
||||
};
|
||||
Loading…
Reference in New Issue