feat(video Service): 完成了测试用的视频加载。

TODO.md

@@ -2,8 +2,6 @@
 
 ## 当前任务 (Doing)
 
--   [ ] 验证 OpenCV 在 RK3588 容器内的调用 (/dev/video0)
--   [ ] 接入摄像头设备 rtsp://admin:123456@192.168.1.57:554/stream0
 -   [ ] 接入 RKNN 模型
 
 ## 待办 (To Do)
@@ -17,3 +15,5 @@
 -   [x] 项目重命名 (EdgeProxy -> RoadCounter)
 -   [x] 解决 Git 权限问题
 -   [x] 跑通 MQTT 上报基础流程
+-   [x] 验证 OpenCV 在 RK3588 容器内的调用 (/dev/video0)
+-   [x] 接入摄像头设备 rtsp://admin:123456@192.168.1.57:554/stream0

src/main.cpp

@@ -74,7 +74,8 @@ void poll_system_metrics(boost::asio::steady_timer& timer, SystemMonitor::System
 
 int main(int argc, char* argv[]) {
 	// TODO: [GYK] DEV#1: 将 URL 放入 config.json 中读取
-	std::string cam_rtsp_input = "rtsp://admin:123456@192.168.1.57:554/stream0";
+	// std::string cam_rtsp_input = "rtsp://admin:123456@192.168.1.57:554/stream0";
+	std::string cam_rtsp_input = "../data/test_car.mp4";
 	// std::string cam_rtsp_input =
 	// "rtsp://admin:hzx12345@192.168.1.10:554/Streaming/Channels/1901";
 
@@ -112,8 +113,6 @@ int main(int argc, char* argv[]) {
 
 		AlarmService alarm_service(g_io_context, mqtt_client);
 
-		// VideoPipeline video_pipeline;
-
 		if (!alarm_service.load_rules(config.getAlarmRulesPath())) {
 			spdlog::error("Failed to load alarm rules. Alarms may be disabled.");
 		}
@@ -180,7 +179,7 @@ int main(int argc, char* argv[]) {
 		web_server.start();
 
 		spdlog::info("Starting Video Pipeline Service...");
-		video_pipeline.Start(cam_rtsp_input, output_stream_url);
+		video_pipeline.StartTest(cam_rtsp_input, output_stream_url);
 		// video_pipeline.Start(cam_rtsp_input, algorithm_rtsp_output);
 
 		boost::asio::signal_set signals(g_io_context, SIGINT, SIGTERM);

src/videoService/video_pipeline.cpp

@@ -14,7 +14,17 @@ void VideoPipeline::Start(const std::string& inputUrl, const std::string& output
 	running_ = true;
 	spdlog::info("Starting VideoPipeline with Input: {}", inputUrl);
 
-	processingThread_ = std::thread(&VideoPipeline::processLoop, this, inputUrl, outputUrl);
+	processingThread_ = std::thread(&VideoPipeline::processLoop, this, inputUrl, outputUrl, false);
+}
+
+void VideoPipeline::StartTest(const std::string& filePath, const std::string& outputUrl) {
+	if (running_)
+		return;
+	running_ = true;
+	spdlog::info("Starting VideoPipeline (File Test Mode) Input: {}", filePath);
+
+	// true 表示是文件源
+	processingThread_ = std::thread(&VideoPipeline::processLoop, this, filePath, outputUrl, true);
 }
 
 void VideoPipeline::Stop() {
@@ -42,7 +52,7 @@ std::vector<DetectionResult> VideoPipeline::mockInference(const cv::Mat& frame)
 	res.y = 200;
 	res.width = 150;
 	res.height = 300;
-	res.label = "EV CAR";
+	res.label = "TEST_CLIP";
 	res.confidence = 0.95f;
 
 	results.push_back(res);
@@ -61,38 +71,25 @@ void VideoPipeline::drawOverlay(cv::Mat& frame, const std::vector<DetectionResul
 				cv::Scalar(0, 0, 255), 2);
 }
 
-void VideoPipeline::processLoop(std::string inputUrl, std::string outputUrl) {
+void VideoPipeline::processLoop(std::string inputUrl, std::string outputUrl, bool isFileSource) {
 	cv::VideoCapture cap;
-
+	cap.open(inputUrl);	 // 文件路径也是通过 open 打开
-	// [MOD] 尝试设置 FFmpeg 后端参数以减少延迟（可选，依赖于 OpenCV 版本）
-	// os.environ["OPENCV_FFMPEG_CAPTURE_OPTIONS"] = "rtsp_transport;tcp" //
-	// C++中通常通过API设置或环境变量
-
-	cap.open(inputUrl);
 
 	if (!cap.isOpened()) {
-		spdlog::error("Failed to open input RTSP stream: {}", inputUrl);
+		spdlog::error("Failed to open input: {}", inputUrl);
 		running_ = false;
 		return;
 	}
 
-	// [MOD] 强制指定 20 FPS
-	// 虽然 cap.get 可能读取到 20，但为了稳健性，我们在输出端强制使用 20
 	const double TARGET_FPS = 20.0;
+	// 计算每帧的目标耗时 (毫秒) -> 1000 / 20 = 50ms
+	const double FRAME_DURATION_MS = 1000.0 / TARGET_FPS;
 
 	int width = cap.get(cv::CAP_PROP_FRAME_WIDTH);
 	int height = cap.get(cv::CAP_PROP_FRAME_HEIGHT);
 
-	spdlog::info("Video Source: {}x{} | Input FPS: {} | Target Output FPS: {}", width, height,
+	spdlog::info("Source: {}x{} | Mode: {}", width, height,
-				 cap.get(cv::CAP_PROP_FPS), TARGET_FPS);
+				 isFileSource ? "FILE LOOP" : "LIVE STREAM");
-
-	// === [MOD] 优化后的 GStreamer H.264 推流管道 ===
-	// 1. appsrc: OpenCV 数据源
-	// 2. videoconvert: 像素格式转换
-	// 3. capsfilter: 强制转换为 NV12 (RK3588 编码器首选格式) 并 锁定 20/1 帧率
-	// 4. mpph264enc: Rockchip 硬件 H.264 编码器
-	// 5. h264parse: 解析 NALU，对 RTSP 传输至关重要
-	// 6. rtspclientsink: 推流到 MediaMTX
 
 	std::stringstream pipeline;
 	pipeline << "appsrc ! "
@@ -101,35 +98,33 @@ void VideoPipeline::processLoop(std::string inputUrl, std::string outputUrl) {
 			 << ",framerate=20/1 ! "
 			 << "mpph264enc ! "
 			 << "h264parse ! "
-			 << "rtspclientsink location=" << outputUrl
+			 << "rtspclientsink location=" << outputUrl << " protocols=tcp";
-			 << " protocols=tcp";  // [MOD] 使用 TCP 协议推流更稳定
-
-	spdlog::debug("GStreamer Pipeline: {}", pipeline.str());
 
 	cv::VideoWriter writer;
-	// [MOD] 在 open 时传入 TARGET_FPS (20.0)
 	writer.open(pipeline.str(), cv::CAP_GSTREAMER, 0, TARGET_FPS, cv::Size(width, height), true);
 
 	if (!writer.isOpened()) {
-		spdlog::error("Failed to initialize VideoWriter. Check GStreamer plugins.");
+		spdlog::error("Failed to initialize VideoWriter.");
 	}
 
 	cv::Mat frame;
 
-	// [MOD] 帧率控制辅助
-	// 如果摄像头实际输出稍微快于或慢于20帧，OpenCV的阻塞读取会自动对齐
-	// 但如果源断流，我们需要处理
-
 	while (running_) {
-		// [MOD] 记录时间以监测实际处理耗时
+		// 记录循环开始时间
-		auto start = std::chrono::steady_clock::now();
+		auto loop_start = std::chrono::steady_clock::now();
 
 		if (!cap.read(frame)) {
-			spdlog::warn("Frame read failed. Reconnecting...");
+			if (isFileSource) {
-			std::this_thread::sleep_for(std::chrono::seconds(1));
+				spdlog::info("End of file reached, looping...");
-			cap.release();
+				cap.set(cv::CAP_PROP_POS_FRAMES, 0);
-			cap.open(inputUrl);
+				continue;
-			continue;
+			} else {
+				spdlog::warn("Frame read failed. Reconnecting...");
+				std::this_thread::sleep_for(std::chrono::seconds(1));
+				cap.release();
+				cap.open(inputUrl);
+				continue;
+			}
 		}
 
 		if (frame.empty())
@@ -141,18 +136,22 @@ void VideoPipeline::processLoop(std::string inputUrl, std::string outputUrl) {
 		// 2. 绘制叠加
 		drawOverlay(frame, results);
 
-		// 3. 硬件编码推流
+		// 3. 推流
 		if (writer.isOpened()) {
 			writer.write(frame);
 		}
 
-		// 简单监控处理延迟
+		if (isFileSource) {
-		auto end = std::chrono::steady_clock::now();
+			auto loop_end = std::chrono::steady_clock::now();
-		std::chrono::duration<double, std::milli> elapsed = end - start;
+			std::chrono::duration<double, std::milli> elapsed = loop_end - loop_start;
 
-		// 如果处理太快（例如只是简单的画框，几毫秒就完了），
+			double elapsed_ms = elapsed.count();
-		// 这里的 cap.read 会自动阻塞等待下一帧，所以不需要手动 sleep。
+			double wait_ms = FRAME_DURATION_MS - elapsed_ms;
-		// 只要输入是 20fps，这个循环就会被输入流“带”着以 20fps 运行。
+
+			if (wait_ms > 0) {
+				std::this_thread::sleep_for(std::chrono::milliseconds((int)wait_ms));
+			}
+		}
 	}
 
 	cap.release();

src/videoService/video_pipeline.hpp

@@ -25,12 +25,13 @@ public:
 
 	// 启动视频流处理
 	void Start(const std::string& inputUrl, const std::string& outputUrl);
+	void StartTest(const std::string& filePath, const std::string& outputUrl);
 
 	// 停止处理
 	void Stop();
 
 private:
-	void processLoop(std::string inputUrl, std::string outputUrl);
+	void processLoop(std::string inputUrl, std::string outputUrl, bool isFileSource);
 
 	// 占位算法函数：模拟推理
 	std::vector<DetectionResult> mockInference(const cv::Mat& frame);