使用隊列來緩衝視頻流和神經網絡

在使用神經網絡進行視頻的結構化的時候，當視頻流的個數足夠大的時候，需要使用隊列來起到緩衝的作用。下面的代碼就是我實現的，在SSD模型檢測時，視頻流的個數多的時候，視頻流產生數據速度遠遠高於檢測圖片的速度，所以需要隊列

#include <caffe/caffe.hpp>
#ifdef USE_OPENCV
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#endif
#include <algorithm>
#include <iomanip>
#include <iosfwd>
#include <memory>
#include <string>
#include <utility>
#include <vector>
#include <iostream>
#include <caffe/layers/my_video_data_layer.h>
#include <caffe/util/bbox_util.hpp>
#include <csignal>
#include <boost/algorithm/string/classification.hpp>
#include <boost/algorithm/string/split.hpp>
#include <sys/stat.h>
#include <dirent.h>
#include <uuid/uuid.h>
#include <time.h>


using namespace std;
#ifdef USE_OPENCV
using namespace caffe;


//初始化視頻參數結構體
struct thread_data{
    int thread_id;
    string message;
    MyQueue<queue_data> *myQueue;
};

class Detector{
public:
    Detector(const string& model_file,
            const string& weights_file,
            const string& mean_value);

    std::vector<vector<float> > Detect(const cv::Mat& img);

private:
    void SetMean(const string& mean_value);

    void WrapInputLayer(std::vector<cv::Mat>* input_channels);

    void Preprocess(const cv::Mat& img,
                        std::vector<cv::Mat>* input_channels);

    shared_ptr<Net<float> > net_;

    cv::Size input_geometry_;

    int num_channels_;

    cv::Mat mean_;
};

DEFINE_string(gpu, "0",
              "Optional; run in GPU mode on given device IDs separated by ','."
              "Use '-gpu all' to run on all available GPUs. The effective training "
              "batch size is multiplied by the number of devices.");
DEFINE_string(mean_value, "104,117,123",
              "If specified, can be one value or can be same as image channels"
              " - would subtract from the corresponding channel). Separated by ','."
              "Either mean_file or mean_value should be provided, not both.");
DEFINE_double(confidence_threshold, 0.3,
              "Only store detections with score higher than the threshold.");
DEFINE_string(rtsp_file, "list.txt",
              "If provided, store the detection results in the rtsp_file.");
//獲取可用的GPU設備
static void get_gpus(vector<int>* gpus){
    if (FLAGS_gpu == "all"){
        int count = 0;
#ifndef CPU_ONLY
        CUDA_CHECK(cudaGetDeviceCount(&count));
#else
        NO_GPU;
#endif
        for (int i = 0; i < count; ++i){
            gpus->push_back(i);
        }
    }else if (FLAGS_gpu.size()){
        vector<string> strings;
        boost::split(strings, FLAGS_gpu, boost::is_any_of(","));
        for (int i = 0; i < strings.size();++i){
            gpus->push_back(boost::lexical_cast<int>(strings[i]));
        }
    }else{
        CHECK_EQ(gpus->size(), 0);
    }
}

Detector::Detector(const string &model_file, const string &weights_file,
                   const string &mean_value) {
    //設置GPU的編號和模型
    vector<int> gpus;
    get_gpus(&gpus);
    if (gpus.size() != 0){
        LOG(INFO) << "Use GPU with device ID " << gpus[0];
#ifndef CPU_ONLY
        cudaDeviceProp device_prop;
        cudaGetDeviceProperties(&device_prop, gpus[0]);
        LOG(INFO) << "GPU device name: " << device_prop.name;
#endif
        Caffe::SetDevice(gpus[0]);
        Caffe::set_mode(Caffe::GPU);
    } else {
        LOG(INFO) << "Use CPU.";
        Caffe::set_mode(Caffe::CPU);
    }

    //加載網絡
    net_.reset(new Net<float >(model_file, TEST));
    net_->CopyTrainedLayersFrom(weights_file);

    CHECK_EQ(net_->num_inputs(), 1) << "Network should have exactly one input.";
    CHECK_EQ(net_->num_outputs(), 1) << "Netwarok should have exactly one output.";

    Blob<float>* input_layer = net_->input_blobs()[0];
    num_channels_ = input_layer->channels();
    CHECK(num_channels_ == 3 || num_channels_ == 1)
        << "Input layer should have 1 or 3 channels.";
    input_geometry_ = cv::Size(input_layer->width(), input_layer->height());

    SetMean(mean_value);
}

//物體檢測
std::vector<vector<float> > Detector::Detect(const cv::Mat &img) {
    Blob<float >* input_layer = net_->input_blobs()[0];
    input_layer->Reshape(1, num_channels_,
                            input_geometry_.height, input_geometry_.width);

    net_->Reshape();

    std::vector<cv::Mat> input_channels;

    WrapInputLayer(&input_channels);

    Preprocess(img, &input_channels);

    net_->Forward();

    Blob<float >* result_blob = net_->output_blobs()[0];
    const float* result = result_blob->cpu_data();
    const int num_det = result_blob->height();
    vector<vector<float> > detections;
    for (int k = 0; k < num_det; ++k){
        if (result[0] == -1){
            result += 7;
            continue;
        }
        vector<float > detection(result, result + 7);
        detections.push_back(detection);
        result += 7;
    }
    return detections;
}

//設置均值
void Detector::SetMean(const string &mean_value) {
    cv::Scalar channel_mean;
    if (!mean_value.empty()){
        stringstream ss(mean_value);
        vector<float > values;
        string item;
        while (getline(ss, item, ',')){
            float value = std::atof(item.c_str());
            values.push_back(value);
        }
        CHECK(values.size() == 1 || values.size() == num_channels_) <<
            "Specify either 1 mean_value or as many as channels: "<< num_channels_;

        std::vector<cv::Mat> channels;
        for (int i = 0; i < num_channels_; ++i){
            cv::Mat channel(input_geometry_.height, input_geometry_.width, CV_32FC1,
                    cv::Scalar(values[i]));
            channels.push_back(channel);
        }
        cv::merge(channels, mean_);
    }
}

//將網絡的輸入層包裝成單獨的cv::Mat對象（每個通道一個）。
// 通過這種方式，我們保存了一個memcpy操作，並且不需要依賴cudaMemcpy2D。最後的預處理操作將把單獨的通道直接寫入輸入層。
void Detector::WrapInputLayer(std::vector<cv::Mat> *input_channels) {
    Blob<float>* input_layer = net_->input_blobs()[0];

    int width = input_layer->width();
    int height = input_layer->height();
    float* input_data = input_layer->mutable_cpu_data();
    for (int i = 0; i < input_layer->channels();++i){
        cv::Mat channel(height,width, CV_32FC1, input_data);
        input_channels->push_back(channel);
        input_data += width * height;
    }
}

void Detector::Preprocess(const cv::Mat &img, std::vector<cv::Mat> *input_channels) {
    cv::Mat sample;
    if (img.channels() == 3 && num_channels_ == 1)
        cv::cvtColor(img, sample, cv::COLOR_BGR2GRAY);
    else if (img.channels() == 4 && num_channels_ == 1)
        cv::cvtColor(img, sample, cv::COLOR_BGRA2GRAY);
    else if (img.channels() == 4 && num_channels_ == 3)
        cv::cvtColor(img, sample, cv::COLOR_BGRA2BGR);
    else if (img.channels() == 1 && num_channels_ == 3)
        cv::cvtColor(img, sample, cv::COLOR_GRAY2BGR);
    else
        sample = img;

    cv::Mat sample_resized;
    if (sample.size() != input_geometry_)
        cv::resize(sample, sample_resized, input_geometry_);
    else
        sample_resized = sample;

    cv::Mat sample_float;
    if (num_channels_ == 3)
        sample_resized.convertTo(sample_float, CV_32FC3);
    else
        sample_resized.convertTo(sample_float, CV_32FC1);

    cv::Mat sample_normalized;
    cv::subtract(sample_float, mean_, sample_normalized);

    //該操作將把單獨的BGR平面直接寫入網絡的輸入層，因爲它由input_channels中的cv::Mat對象包裝。
    cv::split(sample_normalized, *input_channels);

    CHECK(reinterpret_cast<float*>(input_channels->at(0).data)
        == net_->input_blobs()[0]->cpu_data())
        <<"Input channels are not wrapping the input layer of the network.";
}


//初始化視頻和解碼視頻
void videoDecode(void *threaddata){
    struct thread_data *my_data;
    my_data = (struct thread_data *) threaddata;
    int result;
    Video *video1 = new Video((my_data->message).c_str(),0);
    result = video1->Init();
    //大於0就是初始化成功
    if (result >= 0 ){
            int ret;
            queue_data queueData;
            cv::Mat img;
            while(1){
                ret= video1->Decode(img);
                if (ret == 1){
                    queueData.thread_id = my_data->thread_id;
                    queueData.img = img;
                    my_data->myQueue->push(queueData);
                }
            }
    }
    else if (result < 0){
        video1->~Video();
        cout << "監聽視頻失敗："<<my_data->message<<endl;
        return;
    }

}

//啓動多線程倆初始化視頻參數
void queue_video(MyQueue<queue_data> *myQueue_){
    std::ifstream infile(FLAGS_rtsp_file.c_str());
    std::string file;
    boost::thread_group tg;
    list<shared_ptr<thread_data> > list_td;
    int i = 0;
    while (infile >> file) {
        thread_data *td = new thread_data;
       //輪流啓動一個個rtsp的線程
        td->thread_id = i;
        td->message =file;
        td->myQueue = myQueue_;
        tg.create_thread(boost::bind(&videoDecode,td));
        list_td.push_back(shared_ptr<thread_data>(td));
        ++i;
    }
    tg.join_all();

}


//初始化模型參數和檢測圖片
void detectImg(MyQueue<queue_data> *myQueue_){
    // Initialize the network.
    const string& model_file = "/models/SSD_300x300/deploy.prototxt";
    const string& weights_file = "/models/SSD_300x300_ft_video/VGG_VOC0712Plus_SSD_300x300_ft_iter_160000.caffemodel";
    const string& mean_value = FLAGS_mean_value;
    const float confidence_threshold = FLAGS_confidence_threshold;

    Detector detector(model_file, weights_file, mean_value);
    cv::Mat img;
    vector<vector<float> > previous_detections;
    vector<vector<float> > current_detections;
    vector<vector<float> > last_detections;
    map<int,vector<vector<float> > > mymap;
    map<int,vector<vector<float> > >::iterator mapIterator;
    const vector<cv::Scalar>& colors = GetColors(21);
    char buffer[50];
    double scale = 1;
    int thickness = 2;
    int fontface = cv::FONT_HERSHEY_SIMPLEX;
    int baseline = 0;
    while(true){
        queue_data queueData = myQueue_->pop();
        img = queueData.img;

        CHECK(!img.empty()) << "Error when read frame";
        std::vector<vector<float> > detections = detector.Detect(img);
        vector<vector<float> >().swap(current_detections);
        for (int i = 0; i < detections.size(); ++i) {
                const vector<float>& d = detections[i];
                // Detection format: [image_id, label, score, xmin, ymin, xmax, ymax].
                CHECK_EQ(d.size(), 7);
                const float score = d[2];
                if (score >= confidence_threshold) {
                    //data format:[label, score, xmin, ymin, xmax, ymax];
                    //保存圖片中的檢測物體
                    vector<float> data(6);
                    data[0] = d[1];
                    data[1] = score;
                    data[2] = d[3];
                    data[3] = d[4];
                    data[4] = d[5];
                    data[5] = d[6];
                }
        }

        vector<vector<float> >().swap(previous_detections);
        mapIterator = mymap.find(queueData.thread_id);
        if (mapIterator != mymap.end()){
            previous_detections = mymap.find(queueData.thread_id)->second;
            last_detections = excludeFixedObjects(&previous_detections,current_detections);
            mymap[queueData.thread_id] = previous_detections;
        }
        else {
            last_detections = current_detections;
            mymap[queueData.thread_id] = last_detections;
        }

        //循環將最後的結果展示出來
        char* cstr = new char[120];
        char* cstr1 = new char[120];
        char buf[1024];
        for (int i = 0; i < last_detections.size(); ++i) {
            const vector<float>& bboxes = last_detections[i];
            const float label = bboxes[0];
            const int width = img.cols;
            const int height = img.rows;
            const cv::Scalar& color = colors[bboxes[1]];
            //data format:[label, score, xmin, ymin, xmax, ymax];
            bbox1.set_xmin(bboxes[2]);
            bbox1.set_ymin(bboxes[3]);
            bbox1.set_xmax(bboxes[4]);
            bbox1.set_ymax(bboxes[5]);
            float bbox1_size = BBoxSize(bbox1) * width * height;
            //刪除小面積的物體
            if (bbox1_size < 4000){
                continue;
            }
            int xmin = (bboxes[2]* width);
            int ymin = (bboxes[3]* height);
            int xmax = (bboxes[4]* width);
            int ymax = (bboxes[5]* height);
            cv::Point top_left_pt(xmin, ymin);
            cv::Point bottom_right_pt(xmax, ymax);
            cv::rectangle(img, top_left_pt, bottom_right_pt, color, 4);
            cv::Point bottom_left_pt(xmin, ymax);
            snprintf(buffer, sizeof(buffer), "%f", bboxes[0]);
            cv::Size text = cv::getTextSize(buffer, fontface, scale, thickness,
                                            &baseline);
            cv::rectangle(
                    img, bottom_left_pt + cv::Point(0, 0),
                    bottom_left_pt + cv::Point(text.width, -text.height-baseline),
                    color, CV_FILLED);
            cv::putText(img, buffer, bottom_left_pt - cv::Point(0, baseline),
                        fontface, scale, CV_RGB(0, 0, 0), thickness, 8);

        }
        stringstream stream;
        stream<<queueData.thread_id;
        cv::imshow(stream.str(), img);
        if (cv::waitKey(1) == 27) {
            return;
        }
    }

}

//啓動兩個線程，一個用來處理視頻數據，一個用來跑網絡
void startThread(MyQueue<queue_data> myQueue_){
    boost::thread thrd(boost::bind(&queue_video,&myQueue_));
    boost::thread thrd1(boost::bind(&detectImg,&myQueue_));
    thrd.join();
    thrd1.join();


}

int main(int argc, char** argv){
   gflags::ParseCommandLineFlags(&argc, &argv, true);
    //存儲隊列的數據
    MyQueue<queue_data> myQueue_;
    //啓動準換數據的線程和模型初始化線程
    startThread(myQueue_);
    return 0;
}
#endif  // USE_OPENCV