完成GStreamer工作流最小核心，采集视频数据
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@ -4,7 +4,11 @@
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"name": "Linux",
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"includePath": [
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"${workspaceFolder}/**",
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"/usr/include"
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"/usr/include",
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"/usr/include/gstreamer-1.0",
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"/usr/include/glib-2.0",
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"/usr/lib/aarch64-linux-gnu/glib-2.0/include"
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// (GStreamer 依赖 GLib, GLib的这个 .include 路径是必须的)
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],
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"defines": [
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"CROW_USE_BOOST"
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@ -14,7 +14,6 @@ find_package(SQLite3 REQUIRED)
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find_package(PkgConfig REQUIRED)
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pkg_check_modules(GST REQUIRED gstreamer-1.0 gstreamer-app-1.0)
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find_package(OpenCV REQUIRED)
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# 在包含 Crow 子目录之前，设置一个 CMake 选项
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# 这会告诉 Crow 的构建脚本去使用 Boost 库中包含的 Asio
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add_subdirectory(src/vendor/crow)
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@ -117,42 +116,23 @@ target_link_libraries(test PRIVATE
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)
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# 1. 定义新的可执行文件 (我们将把代码放在 src/streamer/ 目录下)
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# add_executable(edge_streamer
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# src/streamer/main.cpp
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# src/streamer/StreamManager.cpp
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# )
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add_executable(edge_streamer
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src/streamer/main.cpp
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)
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# # 2. 为新服务链接所有必需的库
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# target_link_libraries(edge_streamer PRIVATE
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# # GStreamer (来自我们新增的 pkg_check_modules)
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# ${GST_LIBRARIES}
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# 链接新服务所需的所有库
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target_link_libraries(edge_streamer PRIVATE
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# --- GStreamer (来自 pkg_check_modules) ---
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${GST_LIBRARIES}
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# Crow
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# nlohmann_json
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# PahoMqttCpp::paho-mqttpp3
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# Boost::system
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# Boost::thread
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# rknn_api
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# rknnrt
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# pthread
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# ssl
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# crypto
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# )
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pthread
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)
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# # 3. 为新服务设置头文件包含路径
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# target_include_directories(edge_streamer PRIVATE
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# # 新服务的私有头文件
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# ${CMAKE_CURRENT_SOURCE_DIR}/src/streamer/include
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# ${CMAKE_CURRENT_SOURCE_DIR}/src/vendor
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# # GStreamer 的头文件
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# ${GST_INCLUDE_DIRS}
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# ${Boost_INCLUDE_DIRS}
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# # RKNN C-API 的头文件 (来自我们 Dockerfile 的 /usr/local/include)
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# /usr/local/include
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# )
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# 为新服务设置头文件包含路径
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target_include_directories(edge_streamer PRIVATE
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# 新服务的私有头文件
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${CMAKE_CURRENT_SOURCE_DIR}/src/streamer/include
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# --- GStreamer (来自 pkg_check_modules) ---
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${GST_INCLUDE_DIRS}
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)
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@ -0,0 +1,133 @@
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/*
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* 文件: src/streamer/main.cpp
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* 目标: 阶段 0.1 - 最小硬件采集
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* 职责: 连接RTSP, 使用MPP硬件解码, 并在C++回调中接收原始帧。
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*/
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#include <gst/gst.h>
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#include <gst/app/gstappsink.h> // 包含 appsink 的头文件
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#include <glib.h>
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// 这是一个简单的结构体，用于在回调间传递数据
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typedef struct {
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GMainLoop *loop;
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GstElement *pipeline;
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} AppData;
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/**
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* @brief 这是核心回调函数
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* * 当 GStreamer 的 'appsink' 元件成功从管线中拉出一个解码后的帧时，
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* 此函数将被调用。
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* * @param appsink 触发信号的 appsink 元件
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* @param user_data 我们传递的自定义数据
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* @return GstFlowReturn 告诉管线上游是否继续发送数据
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*/
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static GstFlowReturn
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on_new_sample (GstAppSink * appsink, gpointer user_data)
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{
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// 帧计数
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static guint frame_count = 0;
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// 从 appsink 中拉取 GstSample
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// GstSample 是 GStreamer 中包含数据 (GstBuffer) 和元数据 (GstCaps) 的容器
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GstSample *sample = gst_app_sink_pull_sample (appsink);
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if (sample == NULL) {
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g_warning ("Failed to pull sample.");
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return GST_FLOW_ERROR;
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}
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// --- 核心采集点 ---
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// 此时，'sample' 变量中就装着解码后的原始视频帧 (NV12 格式)
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// 这是我们未来插入 ONNX/RGA/编码 的地方。
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frame_count++;
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if (frame_count % 100 == 0) {
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g_print ("Hardware decoder acquired frame %u\n", frame_count);
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// (可选的调试: 打印帧的详细信息)
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// GstBuffer *buffer = gst_sample_get_buffer(sample);
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// GstCaps *caps = gst_sample_get_caps(sample);
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// g_print(" Buffer size: %lu, Caps: %s\n",
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// gst_buffer_get_size(buffer),
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// gst_caps_to_string(caps));
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}
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// **重要**: 释放 'sample'
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// 必须释放它，否则将导致内存泄漏。
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gst_sample_unref (sample);
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return GST_FLOW_OK;
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}
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int
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main (int argc, char *argv[])
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{
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AppData data;
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GstElement *appsink;
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// 初始化 GStreamer 和 GMainLoop
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gst_init (&argc, &argv);
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data.loop = g_main_loop_new (NULL, FALSE);
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// 构建采集管线 (Pipeline String)
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// **注意**: 您的摄像头可能不是H.264，如果是H.265，请使用 rtph265depay ! h265parse
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// **关键**: appsink 的配置
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// name=sink: 给它一个名字 "sink"，方便我们后面找到它
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// emit-signals=true: **必须**设置为 true，它才会触发 'new-sample' 信号
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// max-buffers=5: 设置一个小的内部缓冲区
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// drop=true: 如果C++处理不过来 (缓冲区满了)，就丢弃旧的帧 (防止内存溢出)
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const gchar *pipeline_str =
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"rtspsrc location=rtsp://admin:hzx12345@192.168.1.10:554/Streaming/Channels/1301 latency=300 protocols=tcp ! "
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"application/x-rtp, media=(string)video ! "
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"rtpjitterbuffer latency=100 ! "
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"rtph265depay ! h265parse ! "
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"queue ! "
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"mppvideodec ! "
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"video/x-raw,format=NV12 ! "
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"appsink name=sink emit-signals=true max-buffers=5 drop=true";
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g_print ("Using pipeline:\n%s\n", pipeline_str);
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data.pipeline = gst_parse_launch (pipeline_str, NULL);
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if (!data.pipeline) {
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g_printerr ("Failed to parse ingest pipeline.\n");
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return -1;
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}
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// 获取 'appsink' 元件
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// 我们通过在管线字符串中设置的 'name=sink' 来找到它
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appsink = gst_bin_get_by_name (GST_BIN (data.pipeline), "sink");
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if (!appsink) {
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g_printerr("Failed to get appsink element.\n");
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gst_object_unref(data.pipeline);
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return -1;
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}
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// 连接 'new-sample' 信号到我们的 C++ 回调函数
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// 这是整个程序最关键的“粘合”步骤
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g_signal_connect (appsink, "new-sample", G_CALLBACK (on_new_sample), &data);
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// appsink 已经设置好，我们可以释放对它的临时引用
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g_object_unref (appsink);
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// 启动管线
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gst_element_set_state (data.pipeline, GST_STATE_PLAYING);
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g_print ("Acquisition pipeline started. Waiting for frames...\n");
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// 运行主循环
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// GStreamer 的所有工作都在后台线程中进行
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// 主循环会阻塞在这里，直到 g_main_loop_quit() 被调用或程序被中断
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g_main_loop_run (data.loop);
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// 7. (程序退出时) 清理
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g_print ("Exiting acquisition service...\n");
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gst_element_set_state (data.pipeline, GST_STATE_NULL);
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gst_object_unref (data.pipeline);
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g_main_loop_unref (data.loop);
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return 0;
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}
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