video_processor.cpp

// video_processor.cpp - High-performance C++ video processing service
#include <opencv2/opencv.hpp>
#include <thread>
#include <queue>
#include <mutex>
#include <condition_variable>
#include <chrono>
#include <iostream>
#include <vector>
#include <atomic>

class VideoProcessor {
private:
    std::queue<cv::Mat> frameQueue;
    std::mutex queueMutex;
    std::condition_variable queueCondition;
    std::atomic<bool> running{true};

    // GPU-accelerated processing
    cv::cuda::GpuMat gpuFrame, gpuProcessed;
    cv::Ptr<cv::cuda::CLAHE> clahe;
    cv::Ptr<cv::BackgroundSubtractor> backgroundSubtractor;

public:
    VideoProcessor() {
        // Initialize CUDA components for GPU acceleration
        clahe = cv::cuda::createCLAHE(2.0, cv::Size(8, 8));
        backgroundSubtractor = cv::createBackgroundSubtractorMOG2();
    }

    void processVideoStream(const std::string& rtspUrl) {
        cv::VideoCapture cap(rtspUrl);
        if (!cap.isOpened()) {
            std::cerr << "Error: Cannot open video stream: " << rtspUrl << std::endl;
            return;
        }

        // Configure capture properties for optimal performance
        cap.set(cv::CAP_PROP_BUFFERSIZE, 1);
        cap.set(cv::CAP_PROP_FPS, 30);

        cv::Mat frame;
        auto lastFrameTime = std::chrono::high_resolution_clock::now();

        while (running && cap.read(frame)) {
            // Calculate frame rate for performance monitoring
            auto currentTime = std::chrono::high_resolution_clock::now();
            auto deltaTime = std::chrono::duration_cast<std::chrono::milliseconds>(
                currentTime - lastFrameTime).count();

            if (deltaTime < 33) { // Limit to ~30 FPS
                std::this_thread::sleep_for(std::chrono::milliseconds(33 - deltaTime));
            }

            // Upload frame to GPU for accelerated processing
            gpuFrame.upload(frame);

            // Apply preprocessing (noise reduction, contrast enhancement)
            cv::cuda::cvtColor(gpuFrame, gpuProcessed, cv::COLOR_BGR2GRAY);
            clahe->apply(gpuProcessed, gpuProcessed);

            // Download processed frame back to CPU
            cv::Mat processedFrame;
            gpuProcessed.download(processedFrame);

            // Add to processing queue (thread-safe)
            {
                std::lock_guard<std::mutex> lock(queueMutex);
                if (frameQueue.size() < 10) { // Prevent memory overflow
                    frameQueue.push(processedFrame.clone());
                    queueCondition.notify_one();
                }
            }

            lastFrameTime = currentTime;
        }
    }

    cv::Mat getNextFrame() {
        std::unique_lock<std::mutex> lock(queueMutex);
        queueCondition.wait(lock, [this] { return !frameQueue.empty() || !running; });

        if (!frameQueue.empty()) {
            cv::Mat frame = frameQueue.front();
            frameQueue.pop();
            return frame;
        }
        return cv::Mat();
    }

    void stop() {
        running = false;
        queueCondition.notify_all();
    }

    // Motion detection for preliminary threat assessment
    std::vector<cv::Rect> detectMotion(const cv::Mat& frame) {
        cv::Mat foregroundMask;
        backgroundSubtractor->apply(frame, foregroundMask);

        // Find contours in the foreground mask
        std::vector<std::vector<cv::Point>> contours;
        cv::findContours(foregroundMask, contours, cv::RETR_EXTERNAL, cv::CHAIN_APPROX_SIMPLE);

        std::vector<cv::Rect> motionRects;
        for (const auto& contour : contours) {
            double area = cv::contourArea(contour);
            if (area > 500) { // Minimum area threshold
                motionRects.push_back(cv::boundingRect(contour));
            }
        }

        return motionRects;
    }
};

// High-performance network streaming using WebRTC
class StreamingServer {
private:
    VideoProcessor* processor;
    std::atomic<bool> streaming{true};

public:
    StreamingServer(VideoProcessor* proc) : processor(proc) {}

    void startStreaming(int port = 8001) {
        // Implementation would include WebRTC or custom UDP streaming
        // This is a simplified version showing the concept

        std::thread streamThread([this, port]() {
            while (streaming) {
                cv::Mat frame = processor->getNextFrame();
                if (!frame.empty()) {
                    // Encode frame (H.264 hardware encoding)
                    std::vector<uchar> buffer;
                    cv::imencode(".jpg", frame, buffer, 
                        std::vector<int>{cv::IMWRITE_JPEG_QUALITY, 85});

                    // Send encoded frame to clients (WebSocket/WebRTC)
                    sendFrameToClients(buffer);
                }
                std::this_thread::sleep_for(std::chrono::milliseconds(33));
            }
        });

        streamThread.detach();
    }

private:
    void sendFrameToClients(const std::vector<uchar>& encodedFrame) {
        // Implementation would send to WebSocket clients or WebRTC peers
        // This demonstrates the high-performance approach
        std::cout << "Streaming frame of size: " << encodedFrame.size() << " bytes" << std::endl;
    }
};

// Main application entry point
int main(int argc, char* argv[]) {
    if (argc < 2) {
        std::cerr << "Usage: " << argv[0] << " <rtsp_url1> [rtsp_url2] ..." << std::endl;
        return -1;
    }

    std::vector<std::unique_ptr<VideoProcessor>> processors;
    std::vector<std::thread> processingThreads;

    // Create processors for multiple camera streams
    for (int i = 1; i < argc; ++i) {
        auto processor = std::make_unique<VideoProcessor>();
        auto streamingServer = std::make_unique<StreamingServer>(processor.get());

        // Start video processing in separate thread
        processingThreads.emplace_back([&processor, argv, i]() {
            processor->processVideoStream(std::string(argv[i]));
        });

        // Start streaming server
        streamingServer->startStreaming(8001 + i - 1);

        processors.push_back(std::move(processor));
    }

    std::cout << "ARTEMIS Video Ingestion Service started with " 
              << processors.size() << " camera streams" << std::endl;

    // Keep application running
    std::this_thread::sleep_for(std::chrono::seconds(3600)); // Run for 1 hour

    // Cleanup
    for (auto& processor : processors) {
        processor->stop();
    }

    for (auto& thread : processingThreads) {
        if (thread.joinable()) {
            thread.join();
        }
    }

    return 0;
}