struct Object { cv::Rect_<float> rect; int label; float prob; };

这是一个名为Object的结构体，包含以下成员变量：

1. cv::Rect_<float> rect：表示目标在图像中的边界框，使用浮点数表示。
2. int label：表示目标的类别标签。
3. float prob：表示目标的置信度或概率。

该结构体用于存储检测到的目标的信息，包括边界框、类别和置信度等。

相关问题

请解释下这段代码namespace cros { // This class interfaces with the Google3 auto-framing library: // http://google3/chromeos/camera/lib/auto_framing/auto_framing_cros.h class AutoFramingClient : public AutoFramingCrOS::Client { public: struct Options { Size input_size; double frame_rate = 0.0; uint32_t target_aspect_ratio_x = 0; uint32_t target_aspect_ratio_y = 0; }; // Set up the pipeline. bool SetUp(const Options& options); // Process one frame. |buffer| is only used during this function call. bool ProcessFrame(int64_t timestamp, buffer_handle_t buffer); // Return the stored ROI if a new detection is available, or nullopt if not. // After this call the stored ROI is cleared, waiting for another new // detection to fill it. std::optional<Rect<uint32_t>> TakeNewRegionOfInterest(); // Gets the crop window calculated by the full auto-framing pipeline. Rect<uint32_t> GetCropWindow(); // Tear down the pipeline and clear states. void TearDown(); // Implementations of AutoFramingCrOS::Client. void OnFrameProcessed(int64_t timestamp) override; void OnNewRegionOfInterest( int64_t timestamp, int x_min, int y_min, int x_max, int y_max) override; void OnNewCropWindow( int64_t timestamp, int x_min, int y_min, int x_max, int y_max) override; void OnNewAnnotatedFrame(int64_t timestamp, const uint8_t* data, int stride) override; private: base::Lock lock_; std::unique_ptr<AutoFramingCrOS> auto_framing_ GUARDED_BY(lock_); std::unique_ptr<CameraBufferPool> buffer_pool_ GUARDED_BY(lock_); std::map<int64_t, CameraBufferPool::Buffer> inflight_buffers_ GUARDED_BY(lock_); std::optional<Rect<uint32_t>> region_of_interest_ GUARDED_BY(lock_); Rect<uint32_t> crop_window_ GUARDED_BY(lock_); }; } // namespace

这段代码是定义了一个名为AutoFramingClient的类，该类继承自AutoFramingCrOS::Client。AutoFramingClient是一个与Google3自动裁剪库进行交互的类，用于视频流的自动裁剪。具体地，AutoFramingClient实现了SetUp、ProcessFrame、TakeNewRegionOfInterest、GetCropWindow和TearDown这几个函数，分别用于设置自动裁剪的参数、处理视频流的一帧、获取新的感兴趣区域（Region of Interest，ROI）、获取自动裁剪的窗口以及清除状态。此外，AutoFramingClient还实现了AutoFramingCrOS::Client中的几个回调函数，用于处理自动裁剪库返回的信息。在AutoFramingClient的实现中，使用了base::Lock进行线程同步，并使用了std::map和std::unique_ptr等C++标准库提供的容器和智能指针来管理内存。

请解释下这段代码namespace cros { // FaceTracker takes a set of face data produced by FaceDetector as input, // filters the input, and produces the bounding rectangle that encloses the // filtered input. class FaceTracker { public: struct Options { // The dimension of the active sensory array in pixels. Used for normalizing // the input face coordinates. Size active_array_dimension; // The dimension of the active stream that will be cropped. Used for // translating the ROI coordinates in the active array space. Size active_stream_dimension; // The threshold in ms for including a newly detected face for tracking. int face_phase_in_threshold_ms = 3000; // The threshold in ms for excluding a face that's no longer detected for // tracking. int face_phase_out_threshold_ms = 2000; // The angle range [|pan_angle_range|, -|pan_angle_range|] in degrees used // to determine if a face is looking at the camera. float pan_angle_range = 30.0f; }; explicit FaceTracker(const Options& options); ~FaceTracker() = default; FaceTracker(FaceTracker& other) = delete; FaceTracker& operator=(FaceTracker& other) = delete; // Callback for when new face data are ready. void OnNewFaceData(const std::vector<human_sensing::CrosFace>& faces); // The all the rectangles of all the detected faces. std::vector<Rect<float>> GetActiveFaceRectangles() const; // Gets the rectangle than encloses all the detected faces. Returns a // normalized rectangle in [0.0, 1.0] x [0.0, 1.0] with respect to the active // stream dimension. Rect<float> GetActiveBoundingRectangleOnActiveStream() const; void OnOptionsUpdated(const base::Value& json_values); private: struct FaceState { Rect<float> normalized_bounding_box = {0.0f, 0.0f, 0.0f, 0.0f}; base::TimeTicks first_detected_ticks; base::TimeTicks last_detected_ticks; bool has_attention = false; }; Options options_; std::vector<FaceState> faces_; }; } // namespace cros

这段代码定义了一个名为 "FaceTracker" 的类，用于对人脸数据进行过滤并生成边界框。该类具有一个嵌套的 "Options" 结构体，用于存储一些选项参数，例如输入坐标的维度、阈值等。类中还定义了一些函数，例如 "OnNewFaceData" 用于处理新的人脸数据， "GetActiveFaceRectangles" 返回所有检测到的人脸的矩形框， "GetActiveBoundingRectangleOnActiveStream" 返回包含所有检测到的人脸的矩形框。同时，它也包含了一些私有成员变量和结构体，例如 "FaceState" 结构体用于存储每个人脸的状态信息。