#include "CenterTrack.h"


namespace tk { namespace dnn {


bool CenterTrack::init(const std::string& tensor_path, const int n_classes, const int n_batches, 
                                    const float conf_thresh, const bool mode_3d, const std::vector<cv::Mat>& k_calibs) {
    netRT         = new tk::dnn::NetworkRT(NULL, (tensor_path).c_str() );
    dim           = netRT->input_dim;
    dim.c         = 3;
    nBatches      = n_batches;
    confThreshold = conf_thresh;
    mode3D = mode_3d;
    inputCalibs   = k_calibs;
    init_preprocessing();
    init_pre_inf();
    init_postprocessing();
    init_visualization(n_classes);
    return true;
}

bool CenterTrack::init_preprocessing(){
    //image transformation
    src      = cv::Mat(cv::Size(2,3), CV_32F);
    dst      = cv::Mat(cv::Size(2,3), CV_32F);
    dst2     = cv::Mat(cv::Size(2,3), CV_32F);
    trans    = cv::Mat(cv::Size(3,2), CV_32F);
    trans2   = cv::Mat(cv::Size(3,2), CV_32F);
    transOut = cv::Mat(cv::Size(3,2), CV_32F);

    dst2.at<float>(0,0) = width * 0.5;
    dst2.at<float>(0,1) = width * 0.5;
    dst2.at<float>(1,0) = width * 0.5;
    dst2.at<float>(1,1) = width * 0.5 +  width * -0.5; 
    dst2.at<float>(2,0) = dst2.at<float>(1,0) + (-dst2.at<float>(0,1)+dst2.at<float>(1,1) );
    dst2.at<float>(2,1) = dst2.at<float>(1,1) + (dst2.at<float>(0,0)-dst2.at<float>(1,0) );

    for(int bi=0; bi<nBatches; bi++) {
        szOld.push_back(cv::Size(0,0));
    }
    
#ifdef OPENCV_CUDACONTRIB
    std::cout<<"OPENCV CPMTROB\n";
    checkCuda( cudaMalloc(&mean_d, 3 * sizeof(float)) );
    checkCuda( cudaMalloc(&stddev_d, 3 * sizeof(float)) );
    float mean[3]   = {0.40789655, 0.44719303, 0.47026116};
    float stddev[3] = {0.2886383, 0.27408165, 0.27809834};
    
    checkCuda( cudaMemcpy(mean_d, mean, 3*sizeof(float), cudaMemcpyHostToDevice));
    checkCuda( cudaMemcpy(stddev_d, stddev, 3*sizeof(float), cudaMemcpyHostToDevice));
#else
    std::cout<<"NO OPENCV CPMTROB\n";
    checkCuda( cudaMallocHost(&input, sizeof(dnnType)*dim.tot() * nBatches));
    mean    << 0.40789655, 0.44719303, 0.47026116;
    stddev  << 0.2886383, 0.27408165, 0.27809834;
    
#endif

    checkCuda( cudaMalloc(&input_d, sizeof(dnnType)*netRT->input_dim.tot() * nBatches));
    checkCuda( cudaMalloc(&input_pre_inf_d, sizeof(dnnType)*dim.tot()));
    checkCuda( cudaMalloc(&d_ptrs, dim.tot() * sizeof(float)) );
    return true;
}

bool CenterTrack::init_pre_inf(){
    // initial steps: the first part of the network
    const char *pre_img_conv1_bin = "dla34_ctrack/layers/base-pre_img_layer-0.bin";
    const char *pre_hm_conv1_bin  = "dla34_ctrack/layers/base-pre_hm_layer-0.bin";
    const char *conv1_bin         = "dla34_ctrack/layers/base-base_layer-0.bin";
    const char *conv2_bin         = "dla34_ctrack/layers/base-level0-0.bin";
    dim_in0 = tk::dnn::dataDim_t(1, 3, 512, 512, 1);
    dim_in1 = tk::dnn::dataDim_t(1, 1, 512, 512, 1);
    
    checkCuda( cudaMalloc(&out_d, netRT->input_dim.tot()*sizeof(dnnType)) );
    checkCuda( cudaMalloc(&img_d, dim_in0.tot()*sizeof(dnnType)) );
    checkCuda( cudaMalloc(&hm_d, dim_in1.tot()*sizeof(dnnType)) );
    // init to zeros hm
    dnnType *hm_h;
    checkCuda( cudaMallocHost(&hm_h, 1 * dim.h * dim.w*sizeof(dnnType)) ); 
    for(int i=0; i<1 * dim.h * dim.w; i++)
        hm_h[i] = 0.0f;
    checkCuda( cudaMemcpy(hm_d, hm_h, 1 * dim.h * dim.w * sizeof(dnnType), cudaMemcpyHostToDevice) );
    checkCuda( cudaFreeHost(hm_h) );
    dnnType *i0_h, *i1_h, *i2_h;
    // dnnType *i0_d, *i1_d, *i2_d;

    // const char *input_bin = "dla34_ctrack/debug/input.bin";
    // const char *pre_img_bin = "dla34_ctrack/debug/pre_imgages.bin";
    // const char *pre_hm_bin = "dla34_ctrack/debug/pre_hms.bin";
    // readBinaryFile(pre_img_bin, dim_in0.tot(), &i0_h, &img_d);
    // readBinaryFile(pre_hm_bin, dim_in1.tot(), &i1_h, &hm_d);
    // readBinaryFile(input_bin, dim_in0.tot(), &i2_h, &input_pre_inf_d);

    pre_phase_net = new tk::dnn::Network(dim_in0);
    //pre-img
    tk::dnn::Input      *in_pre_img    = new tk::dnn::Input(pre_phase_net, dim_in0, img_d);
    tk::dnn::Conv2d     *pre_img_conv1 = new tk::dnn::Conv2d(pre_phase_net, 16, 7, 7, 1, 1, 3, 3, pre_img_conv1_bin, true);
    tk::dnn::Activation *pre_img_relu  = new tk::dnn::Activation(pre_phase_net, CUDNN_ACTIVATION_RELU);
    //pre-hm
    tk::dnn::Input      *in_pre_hm     = new tk::dnn::Input(pre_phase_net, dim_in1, hm_d);
    tk::dnn::Conv2d     *pre_hm_conv1  = new tk::dnn::Conv2d(pre_phase_net, 16, 7, 7, 1, 1, 3, 3, pre_hm_conv1_bin, true);
    tk::dnn::Activation *pre_hm_relu   = new tk::dnn::Activation(pre_phase_net, CUDNN_ACTIVATION_RELU);
    // image input
    tk::dnn::Input      *input_image   = new tk::dnn::Input(pre_phase_net, dim_in0, input_pre_inf_d);
    tk::dnn::Conv2d     *conv1         = new tk::dnn::Conv2d(pre_phase_net, 16, 7, 7, 1, 1, 3, 3, conv1_bin, true);
    tk::dnn::Activation *relu1         = new tk::dnn::Activation(pre_phase_net, CUDNN_ACTIVATION_RELU);
   
    tk::dnn::Shortcut   *s0_input      = new tk::dnn::Shortcut(pre_phase_net, pre_img_relu);
    tk::dnn::Shortcut   *s1_input      = new tk::dnn::Shortcut(pre_phase_net, pre_hm_relu);
    // output data
    out_d = s1_input->dstData;
    //print network model
    pre_phase_net->print();

    iter0=true; // in the first iteration the last input is equal to the current input.
    return true;
}

bool CenterTrack::init_postprocessing(){
    srand(0); //seed = 0 for random colors

    dim_hm              = tk::dnn::dataDim_t(1, 10, 128, 128, 1);
    dim_wh              = tk::dnn::dataDim_t(1, 2, 128, 128, 1);
    dim_reg             = tk::dnn::dataDim_t(1, 2, 128, 128, 1);
    dim_track           = tk::dnn::dataDim_t(1, 2, 128, 128, 1);
    dim_dep             = tk::dnn::dataDim_t(1, 1, 128, 128, 1);
    dim_rot             = tk::dnn::dataDim_t(1, 8, 128, 128, 1);
    dim_dim             = tk::dnn::dataDim_t(1, 3, 128, 128, 1);
    dim_amodel_offset   = tk::dnn::dataDim_t(1, 2, 128, 128, 1);

    checkCuda( cudaMalloc(&topk_scores, dim_hm.c * K *sizeof(float)) );
    checkCuda( cudaMalloc(&topk_inds_, dim_hm.c * K *sizeof(int)) );      
    checkCuda( cudaMalloc(&topk_ys_, dim_hm.c * K *sizeof(float)) );      
    checkCuda( cudaMalloc(&topk_xs_, dim_hm.c * K *sizeof(float)) );    
    checkCuda( cudaMalloc(&ids_d, dim_hm.c * dim_hm.h * dim_hm.w*sizeof(int)) );
    checkCuda( cudaMallocHost(&ids_, dim_hm.c * dim_hm.h * dim_hm.w*sizeof(int)) );
    for(int i=0; i<dim_hm.c * dim_hm.h * dim_hm.w; i++){
        ids_[i] = i;
    }
    
    checkCuda( cudaMalloc(&ones, dim_dep.c * dim_dep.h * dim_dep.w * sizeof(float)) );
    float *ones_h;
    checkCuda( cudaMallocHost(&ones_h, dim_dep.c * dim_dep.h * dim_dep.w * sizeof(float)) );
    for(int i=0; i<dim_dep.c * dim_dep.h * dim_dep.w; i++)
        ones_h[i] = 1.0f;
    checkCuda( cudaMemcpy(ones, ones_h, dim_dep.c * dim_dep.h * dim_dep.w * sizeof(float), cudaMemcpyHostToDevice) );
    checkCuda( cudaFreeHost(ones_h) );

    checkCuda( cudaMallocHost(&scores, K *sizeof(float)) );
    checkCuda( cudaMalloc(&scores_d, K *sizeof(float)) );

    checkCuda( cudaMallocHost(&clses, K *sizeof(int)) );
    checkCuda( cudaMalloc(&clses_d, K *sizeof(int)) );

    checkCuda( cudaMalloc(&topk_inds_d, K *sizeof(int)) );
    checkCuda( cudaMalloc(&topk_ys_d, K *sizeof(float)) );     
    checkCuda( cudaMalloc(&topk_xs_d, K *sizeof(float)) ); 
    checkCuda( cudaMalloc(&inttopk_ys_d, K *sizeof(int)) );
    checkCuda( cudaMalloc(&inttopk_xs_d, K *sizeof(int)) );

    checkCuda( cudaMallocHost(&bbx0, K * sizeof(float)) ); 
    checkCuda( cudaMallocHost(&bby0, K * sizeof(float)) ); 
    checkCuda( cudaMallocHost(&bbx1, K * sizeof(float)) ); 
    checkCuda( cudaMallocHost(&bby1, K * sizeof(float)) ); 
    checkCuda( cudaMalloc(&bbx0_d, K * sizeof(float)) ); 
    checkCuda( cudaMalloc(&bby0_d, K * sizeof(float)) ); 
    checkCuda( cudaMalloc(&bbx1_d, K * sizeof(float)) ); 
    checkCuda( cudaMalloc(&bby1_d, K * sizeof(float)) );

    checkCuda( cudaMallocHost(&intxs, K  * sizeof(int)) ); 
    checkCuda( cudaMallocHost(&intys, K  * sizeof(int)) ); 

    checkCuda( cudaMallocHost(&track, K * dim_track.c * sizeof(float)) );
    checkCuda( cudaMallocHost(&dep, K * dim_dep.c * sizeof(float)) ); 
    checkCuda( cudaMallocHost(&rot, K * dim_rot.c * sizeof(float)) ); 
    checkCuda( cudaMallocHost(&dim_, K * dim_dim.c * sizeof(float)) ); 
    checkCuda( cudaMallocHost(&wh, K * dim_wh.c * sizeof(float)) );
    checkCuda( cudaMallocHost(&amodel_offset, K * dim_amodel_offset.c * sizeof(float)) );
    checkCuda( cudaMalloc(&track_d, K * dim_track.c * sizeof(float)) ); 
    checkCuda( cudaMalloc(&dep_d, K * dim_dep.c * sizeof(float)) ); 
    checkCuda( cudaMalloc(&rot_d, K * dim_rot.c * sizeof(float)) ); 
    checkCuda( cudaMalloc(&dim_d, K * dim_dim.c * sizeof(float)) ); 
    checkCuda( cudaMalloc(&wh_d, K * dim_wh.c * sizeof(float)) );  
    checkCuda( cudaMalloc(&amodel_offset_d, K * dim_amodel_offset.c * sizeof(float)) ); 

    checkCuda( cudaMallocHost(&target_coords, 4 * K *sizeof(float)) );

    for(int bi=0; bi<nBatches; bi++) {
        cv::Mat calibs_ = cv::Mat::zeros(cv::Size(4,3), CV_32F);        
        if(inputCalibs.size() == 0 || inputCalibs[bi].empty()) {
            calibs_.at<float>(0,0) = 633.0;
            calibs_.at<float>(1,1) = 633.0;
            calibs_.at<float>(2,2) = 1.0;
        }
        calibs_.at<float>(2,2) = 1.0;
        calibs.push_back(calibs_);
    }

    // Alloc array used in the kernel 
    checkCuda( cudaMalloc(&src_out, K *sizeof(float)) );
    checkCuda( cudaMalloc(&ids_out, K *sizeof(int)) );

    trRes.resize(nBatches);
    countTr.resize(nBatches, 0);
    trackId.resize(nBatches, 0);
    return true;
}

bool CenterTrack::init_visualization(const int n_classes){
    classes = n_classes;
    // const char *kitti_class_name[] = {
    //         "person", "car", "bicycle"};
    // classesNames = std::vector<std::string>(kitti_class_name, std::end( kitti_class_name));
    
    const char *class_name[] = {"car", "truck", "bus", "trailer", "construction_vehicle", "pedestrian", 
                                        "motorcycle", "bicycle", "traffic_cone", "barrier"};
    classesNames = std::vector<std::string>(class_name, std::end( class_name));
    
    // const char *coco_class_name[] = {
    //         "person", "bicycle", "car", "motorcycle", "airplane", 
    //         "bus", "train", "truck", "boat", "traffic light", "fire hydrant",
    //         "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse",
    //         "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack",
    //         "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis",
    //         "snowboard", "sports ball", "kite", "baseball bat", "baseball glove",
    //         "skateboard", "surfboard", "tennis racket", "bottle", "wine glass",
    //         "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich",
    //         "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake",
    //         "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv",
    //         "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave",
    //         "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase",
    //         "scissors", "teddy bear", "hair drier", "toothbrush"
    //         };
    // classesNames = std::vector<std::string>(coco_class_name, std::end( coco_class_name));

    for(int c=0; c<classes; c++) {
        int offset  = c*123457 % classes;
        float r     = getColor(2, offset, classes);
        float g     = getColor(1, offset, classes);
        float b     = getColor(0, offset, classes);
        colors[c]   = cv::Scalar(int(255.0*b), int(255.0*g), int(255.0*r));
    }
    for(int c=0; c<256; c++) {
        int offset  = c * 123457 % 256;
        float r     = getColor(2, offset, 256);
        float g     = getColor(1, offset, 256);
        float b     = getColor(0, offset, 256);
        trColors[c] = cv::Scalar(int(255.0*b), int(255.0*g), int(255.0*r));
    }

    r = cv::Mat(cv::Size(3,3), CV_32F);
    r.at<float>(0,1) = 0.0;
    r.at<float>(1,0) = 0.0;
    r.at<float>(1,1) = 1.0;
    r.at<float>(1,2) = 0.0;
    r.at<float>(2,1) = 0.0;
                        
    corners = cv::Mat(cv::Size(8,3), CV_32F);
    corners.at<float>(1,0) = 0.0;
    corners.at<float>(1,1) = 0.0;
    corners.at<float>(1,2) = 0.0;
    corners.at<float>(1,3) = 0.0;

    pts3DHomo = cv::Mat(cv::Size(8,4), CV_32F);
    pts3DHomo.at<float>(3,0) = 1.0;
    pts3DHomo.at<float>(3,1) = 1.0;
    pts3DHomo.at<float>(3,2) = 1.0;
    pts3DHomo.at<float>(3,3) = 1.0;
    pts3DHomo.at<float>(3,4) = 1.0;
    pts3DHomo.at<float>(3,5) = 1.0;
    pts3DHomo.at<float>(3,6) = 1.0;
    pts3DHomo.at<float>(3,7) = 1.0;

    faceId.push_back({0,1,5,4});
    faceId.push_back({1,2,6, 5});
    faceId.push_back({3,0,4,7});
    faceId.push_back({2,3,7,6});
    // ([[0,1,5,4], [1,2,6, 5], [2,3,7,6], [3,0,4,7]]);

    return true;
}

void CenterTrack::_get_additional_inputs(){
    //None no additional input
}

void CenterTrack::pre_inf(const int bi){
    TKDNN_TSTART
    tk::dnn::dataDim_t dim_aus;
    pre_phase_net->infer(dim_aus, nullptr);
    TKDNN_TSTOP
    checkCuda( cudaDeviceSynchronize() );
    checkCuda( cudaMemcpy(input_d+ netRT->input_dim.tot()*bi, pre_phase_net->layers[pre_phase_net->num_layers-1]->dstData, netRT->input_dim.tot()*sizeof(dnnType), cudaMemcpyDeviceToDevice) );
    checkCuda( cudaDeviceSynchronize() );
}

void CenterTrack::preprocess(cv::Mat &frame, const int bi){
    cv::Size sz = originalSize[bi];
    // float scale = 1.0;
    float new_height = dim.h;//sz.height * scale;
    float new_width = dim.w;//sz.width * scale;
    if(sz.height != szOld[bi].height && sz.width != szOld[bi].width){
        if(inputCalibs.size() == 0 || inputCalibs[bi].empty()) {
            calibs[bi].at<float>(0,2) = new_width / 2.0f;
            calibs[bi].at<float>(1,2) = new_height /2.0f;
        }
        else {
            calibs[bi].at<float>(0,0) = inputCalibs[bi].at<float>(0,0) * dim.w / sz.width;
            calibs[bi].at<float>(0,2) = inputCalibs[bi].at<float>(0,2) * dim.w / sz.width;
            calibs[bi].at<float>(1,1) = inputCalibs[bi].at<float>(1,1) * dim.h / sz.height;
            calibs[bi].at<float>(1,2) = inputCalibs[bi].at<float>(1,2) * dim.h / sz.height;
        }
        
        float c[] = {new_width / 2.0f, new_height /2.0f};
        float s[] = {static_cast<float>(dim.w), static_cast<float>(dim.h)};
        // float s = new_width >= new_height ? new_width : new_height;
        // ----------- get_affine_transform
        // rot_rad = pi * 0 / 100 --> 0
        //dim.print();
        src.at<float>(0,0) = c[0];
        src.at<float>(0,1) = c[1];
        src.at<float>(1,0) = c[0];
        src.at<float>(1,1) = c[1] + s[0] * -0.5;
        dst.at<float>(0,0) = dim.w * 0.5;
        dst.at<float>(0,1) = dim.h * 0.5;
        dst.at<float>(1,0) = dim.w * 0.5;
        dst.at<float>(1,1) = dim.h * 0.5 +  dim.w * -0.5; 
        
        src.at<float>(2,0) = src.at<float>(1,0) + (-src.at<float>(0,1)+src.at<float>(1,1) );
        src.at<float>(2,1) = src.at<float>(1,1) + (src.at<float>(0,0)-src.at<float>(1,0) );
        dst.at<float>(2,0) = dst.at<float>(1,0) + (-dst.at<float>(0,1)+dst.at<float>(1,1) );
        dst.at<float>(2,1) = dst.at<float>(1,1) + (dst.at<float>(0,0)-dst.at<float>(1,0) );


        trans = cv::getAffineTransform( src, dst );
        trans2 = cv::getAffineTransform( dst2, src );
        trans2.convertTo(transOut, CV_32F);
    }
    szOld[bi] = sz;
#ifdef OPENCV_CUDACONTRIB
    cv::cuda::GpuMat im_Orig; 
    cv::cuda::GpuMat imageF1_d, imageF2_d;
        
    im_Orig = cv::cuda::GpuMat(frame);
    cv::cuda::resize (im_Orig, imageF1_d, cv::Size(dim.w, dim.h)); 
    // imageF1_d = im_Orig;
    checkCuda( cudaDeviceSynchronize() );
    
    sz = imageF1_d.size();

    cv::cuda::warpAffine(imageF1_d, imageF2_d, trans, cv::Size(dim.w, dim.h), cv::INTER_LINEAR );
    checkCuda( cudaDeviceSynchronize() );
    
    imageF2_d.convertTo(imageF1_d, CV_32FC3, 1/255.0); 
    checkCuda( cudaDeviceSynchronize() );
    
    dim2 = dim;
    cv::cuda::GpuMat bgr[3]; 
    cv::cuda::split(imageF1_d,bgr);//split source

    for(int i=0; i<dim.c; i++)
        checkCuda( cudaMemcpy(d_ptrs + i*dim.h * dim.w, (float*)bgr[i].data, dim.h * dim.w * sizeof(float), cudaMemcpyDeviceToDevice) );
        
    normalize(d_ptrs, dim.c, dim.h, dim.w, mean_d, stddev_d);

    checkCuda( cudaMemcpy(input_pre_inf_d, d_ptrs, dim2.tot()*sizeof(dnnType), cudaMemcpyDeviceToDevice));
    checkCuda( cudaDeviceSynchronize() );

#else
    cv::Mat imageF;
    resize(frame, imageF, cv::Size(dim.w, dim.h));
    // imageF = frame;
    sz = imageF.size();
    cv::warpAffine(imageF, imageF, trans, cv::Size(dim.w, dim.h), cv::INTER_LINEAR );

    // cv::imshow("warp", imageF);

    sz = imageF.size();
    imageF.convertTo(imageF, CV_32FC3, 1/255.0); 
 
    dim2 = dim;
    //split channels
    cv::Mat bgr[3]; 
    cv::split(imageF,bgr);//split source
    
    for(int i=0; i<3; i++){
        bgr[i] = bgr[i] - mean[i];
        bgr[i] = bgr[i] / stddev[i];
    }
    for(int i=0; i<dim2.c; i++) {
        int idx = i * imageF.rows * imageF.cols;
        int ch = i; 
        memcpy((void*)&input[idx], (void*)bgr[ch].data, imageF.rows*imageF.cols*sizeof(dnnType));
    }
    checkCuda( cudaMemcpyAsync(input_pre_inf_d, input, dim2.tot()*sizeof(dnnType), cudaMemcpyHostToDevice));
    checkCuda( cudaDeviceSynchronize() );

#endif

    if(iter0) {
        checkCuda( cudaMemcpy(img_d, input_pre_inf_d, dim.tot()*sizeof(dnnType), cudaMemcpyDeviceToDevice) );
        checkCuda( cudaDeviceSynchronize() );
        iter0=false;
    }
    pre_inf(bi);

    checkCuda( cudaMemcpy(img_d, input_pre_inf_d, dim.tot()*sizeof(dnnType), cudaMemcpyDeviceToDevice) );
    checkCuda( cudaDeviceSynchronize() );
}

cv::Mat CenterTrack::transform_preds_with_trans(float x1, float x2){
    cv::Mat target_coords(cv::Size(1,3), CV_32F);
    target_coords.at<float>(0,0) = x1;
    target_coords.at<float>(0,1) = x2;
    target_coords.at<float>(0,2) = 1.0;
    return transOut * target_coords;
}

void CenterTrack::tracking(const int bi) {
    std::vector<float> item_size(countDet);
    std::vector<int> item_cl(countDet);
    std::vector<float> dets(2*countDet);
    for(int i=0; i<countDet; i++){
        item_size[i] = (detRes[i].bb1.at<float>(0,0) - detRes[i].bb0.at<float>(0,0)) * 
                       (detRes[i].bb1.at<float>(0,1) - detRes[i].bb0.at<float>(0,1));
        item_cl[i]   =  detRes[i].cl;
        dets[i*2]    =  detRes[i].ct.at<float>(0,0);
        dets[i*2+1]  =  detRes[i].ct.at<float>(0,1);
    }
    
    std::vector<float> track_size(countTr[bi]);
    std::vector<int> track_cl(countTr[bi]);
    std::vector<float> tracks(2*countTr[bi]);
    for(int i=0; i<countTr[bi]; i++){
        track_size[i] = (trRes[bi][i].det_res.bb1.at<float>(0,0) - trRes[bi][i].det_res.bb0.at<float>(0,0)) * 
                        (trRes[bi][i].det_res.bb1.at<float>(0,1) - trRes[bi][i].det_res.bb0.at<float>(0,1));
        track_cl[i]   =  trRes[bi][i].det_res.cl;
        tracks[i*2]   =  trRes[bi][i].det_res.ct.at<float>(0,0);
        tracks[i*2+1] =  trRes[bi][i].det_res.ct.at<float>(0,1);
    } 
    std::vector<float> dist(countTr[bi]*countDet);
    bool invalid;
    for(int i=0; i<countTr[bi]; i++){
        for(int j=0; j<countDet; j++){
            dist[j*countTr[bi]+i] =  pow((tracks[i*2] - dets[j*2]), 2) + 
                                     pow((tracks[i*2+1] - dets[j*2+1]), 2);
            invalid               =  dist[j*countTr[bi]+i] > track_size[i] || 
                                     dist[j*countTr[bi]+i] > item_size[j] || 
                                     item_cl[j] != track_cl[i];
            dist[j*countTr[bi]+i] = dist[j*countTr[bi]+i] + invalid * (1 << 18);
        }
    }
    std::vector<int> matched_indices(2*countTr[bi]);
    float min_tr;
    int min_idtr = -1;
    for(int i=0; i<countTr[bi]; i++) {
        matched_indices[i*2]    = -1;
        matched_indices[i*2+1]  = -1;
    }
    for(int i=0; i<countDet; i++){
        min_tr=(1 << 18);
        for(int j=0; j<countTr[bi]; j++){
            if(dist[i*countTr[bi]+j]<min_tr) {
                min_tr   = dist[i*countTr[bi]+j];
                min_idtr = j;
            }
        }
        if(min_tr < (1<<16)) {
            for(int j=0; j<countDet; j++)
                dist[j*countTr[bi]+min_idtr] = (1 << 18);
            matched_indices[2*min_idtr]   = min_idtr;
            matched_indices[2*min_idtr+1] = i;
        }
    }
    
    std::vector<bool> unmatched_dets(countDet);
    for(int i=0; i<countDet; i++)
        unmatched_dets[i] = false;
    std::vector<bool> unmatched_tracks(countTr[bi]);
    for(int i=0; i<countTr[bi]; i++) 
        unmatched_tracks[i] = false;
    for(int i=0; i<countTr[bi]; i++) {
        if(matched_indices[2*i] != -1)
            unmatched_tracks[matched_indices[2*i]]=true;
        if(matched_indices[2*i+1] != -1)
            unmatched_dets[matched_indices[2*i+1]]=true;        
    }

    //match
    for(int i=0; i<countTr[bi]; i++) {
        if(matched_indices[2*i+1] != -1 && matched_indices[2*i] != -1) { //second condition is optional
            int tr_id = matched_indices[2*i];
            int d_id  = matched_indices[2*i+1];
            
            // trRes[tr_id].det_res = detRes[d_id];
            trRes[bi][tr_id].det_res.score  = detRes[d_id].score;
            trRes[bi][tr_id].det_res.cl     = detRes[d_id].cl;
            trRes[bi][tr_id].det_res.ct     = detRes[d_id].ct;
            trRes[bi][tr_id].det_res.tr     = detRes[d_id].tr;
            trRes[bi][tr_id].det_res.bb0    = detRes[d_id].bb0;
            trRes[bi][tr_id].det_res.bb1    = detRes[d_id].bb1;
            trRes[bi][tr_id].det_res.dep    = detRes[d_id].dep;
            trRes[bi][tr_id].det_res.dim[0] = detRes[d_id].dim[0];
            trRes[bi][tr_id].det_res.dim[1] = detRes[d_id].dim[1];
            trRes[bi][tr_id].det_res.dim[2] = detRes[d_id].dim[2];
            trRes[bi][tr_id].det_res.alpha  = detRes[d_id].alpha;
            trRes[bi][tr_id].det_res.x      = detRes[d_id].x;
            trRes[bi][tr_id].det_res.y      = detRes[d_id].y;
            trRes[bi][tr_id].det_res.z      = detRes[d_id].z;
            trRes[bi][tr_id].det_res.rot_y  = detRes[d_id].rot_y;
            // trRes[bi][matched_indices[2*i]].tracking_id = ; is the same 
            // trRes[bi][matched_indices[2*i]].color = ; is the same
            trRes[bi][tr_id].age            = 1; 
            trRes[bi][tr_id].active         = trRes[bi][tr_id].active+1;
        }
    }
    //delete target umatched track
    int new_count_tr = 0;
    for(int i=0; i<countTr[bi]; i++) {
        if(unmatched_tracks[i]) 
            new_count_tr++;
    }
    if(new_count_tr == 0 && countTr[bi] != 0) {        //reset
        trRes[bi].clear();
        countTr[bi] = 0;
    }
    int old_count_tr = countTr[bi];
    if(countTr[bi] != 0 && new_count_tr != countTr[bi]) {
        std::vector<struct trackingRes> new_tr_res;
        int id_new_tr=0;
        for(int i=0; i<countTr[bi]; i++) {
            if(unmatched_tracks[i]) {
                struct trackingRes new_tr_res_;
                // new_tr_res_new_det_res.det_res = trRes[i].det_res;
                new_tr_res_.det_res.score   = trRes[bi][i].det_res.score;
                new_tr_res_.det_res.cl      = trRes[bi][i].det_res.cl;
                new_tr_res_.det_res.ct      = trRes[bi][i].det_res.ct;
                new_tr_res_.det_res.tr      = trRes[bi][i].det_res.tr;
                new_tr_res_.det_res.bb0     = trRes[bi][i].det_res.bb0;
                new_tr_res_.det_res.bb1     = trRes[bi][i].det_res.bb1;
                new_tr_res_.det_res.dep     = trRes[bi][i].det_res.dep;
                new_tr_res_.det_res.dim[0]  = trRes[bi][i].det_res.dim[0];
                new_tr_res_.det_res.dim[1]  = trRes[bi][i].det_res.dim[1];
                new_tr_res_.det_res.dim[2]  = trRes[bi][i].det_res.dim[2];
                new_tr_res_.det_res.alpha   = trRes[bi][i].det_res.alpha;
                new_tr_res_.det_res.x       = trRes[bi][i].det_res.x;
                new_tr_res_.det_res.y       = trRes[bi][i].det_res.y;
                new_tr_res_.det_res.z       = trRes[bi][i].det_res.z;
                new_tr_res_.det_res.rot_y   = trRes[bi][i].det_res.rot_y;
                new_tr_res_.tracking_id     = trRes[bi][i].tracking_id;
                new_tr_res_.age             = trRes[bi][i].age;
                new_tr_res_.active          = trRes[bi][i].active;
                new_tr_res_.color           = trRes[bi][i].color;
                id_new_tr ++;
                new_tr_res.push_back(new_tr_res_);
            }
        }
        
        if(countTr[bi]) {
            trRes[bi].clear();
        }
        countTr[bi] = new_count_tr;
        trRes[bi] = new_tr_res;
    }
    
    int count_tr_ = countTr[bi];
    for(int i=0; i<countDet; i++) {
        if((!unmatched_dets[i]) && detRes[i].score > newThresh) {
            count_tr_ ++;
            struct trackingRes new_tr_res_;
            new_tr_res_.det_res.score   = detRes[i].score;
            new_tr_res_.det_res.cl      = detRes[i].cl;
            new_tr_res_.det_res.ct      = detRes[i].ct;
            new_tr_res_.det_res.tr      = detRes[i].tr;
            new_tr_res_.det_res.bb0     = detRes[i].bb0;
            new_tr_res_.det_res.bb1     = detRes[i].bb1;
            new_tr_res_.det_res.dep     = detRes[i].dep;
            new_tr_res_.det_res.dim[0]  = detRes[i].dim[0];
            new_tr_res_.det_res.dim[1]  = detRes[i].dim[1];
            new_tr_res_.det_res.dim[2]  = detRes[i].dim[2];
            new_tr_res_.det_res.alpha   = detRes[i].alpha;
            new_tr_res_.det_res.x       = detRes[i].x;
            new_tr_res_.det_res.y       = detRes[i].y;
            new_tr_res_.det_res.z       = detRes[i].z;
            new_tr_res_.det_res.rot_y   = detRes[i].rot_y;
            new_tr_res_.tracking_id     = trackId[bi]++; 
            new_tr_res_.age             = 1; 
            new_tr_res_.active          = 1;
            new_tr_res_.color           = rand() % 256;
            if(trRes.size() <= bi) {
                std::vector<struct trackingRes> v_new_tr_res_;
                v_new_tr_res_.push_back(new_tr_res_);    
                trRes.push_back(v_new_tr_res_);
            }
            else
                trRes[bi].push_back(new_tr_res_);
        }
    }

    countTr[bi] = count_tr_;
    //reset the tracker id
    if(trackId[bi] == 1000)
        trackId[bi] = 0;
    detRes.clear();
    
}

void CenterTrack::postprocess(const int bi, const bool mAP) {
    dnnType *rt_out[9];
    rt_out[0] = (dnnType *)netRT->buffersRT[1]+ netRT->buffersDIM[1].tot()*bi;
    rt_out[1] = (dnnType *)netRT->buffersRT[2]+ netRT->buffersDIM[2].tot()*bi;
    rt_out[2] = (dnnType *)netRT->buffersRT[3]+ netRT->buffersDIM[3].tot()*bi; 
    rt_out[3] = (dnnType *)netRT->buffersRT[4]+ netRT->buffersDIM[4].tot()*bi; 
    rt_out[4] = (dnnType *)netRT->buffersRT[5]+ netRT->buffersDIM[5].tot()*bi; 
    rt_out[5] = (dnnType *)netRT->buffersRT[6]+ netRT->buffersDIM[6].tot()*bi; 
    rt_out[6] = (dnnType *)netRT->buffersRT[7]+ netRT->buffersDIM[7].tot()*bi; 
    rt_out[7] = (dnnType *)netRT->buffersRT[8]+ netRT->buffersDIM[8].tot()*bi; 
    rt_out[8] = (dnnType *)netRT->buffersRT[9]+ netRT->buffersDIM[9].tot()*bi; 
    
    // ------------------------------------ process --------------------------------------------
    
    activationSIGMOIDForward(rt_out[0], rt_out[0], dim_hm.tot());
    checkCuda( cudaDeviceSynchronize() );    

    // output['dep'] = 1. / (output['dep'].sigmoid() + 1e-6) - 1.
    activationSIGMOIDForward(rt_out[5], rt_out[5], dim_dep.tot());
    checkCuda( cudaDeviceSynchronize() );
    transformDep(ones, ones + dim_dep.tot(), rt_out[5], rt_out[5] + dim_dep.tot());
    checkCuda( cudaDeviceSynchronize() );

    // nms 
    subtractWithThreshold(rt_out[0], rt_out[0] + dim_hm.tot(), rt_out[1], rt_out[0], op);
    
    // ----------- nms end
    // ----------- topk  
      
    if(K > dim_hm.h * dim_hm.w){
        printf ("Error topk (K is too large)\n");
        return;
    }
    
    checkCuda( cudaMemcpy(ids_d, ids_, dim_hm.c * dim_hm.h * dim_hm.w*sizeof(int), cudaMemcpyHostToDevice) );    

    sort(rt_out[0],rt_out[0]+dim_hm.tot(),ids_d);
    checkCuda( cudaDeviceSynchronize() );   

    topk(rt_out[0], ids_d, K, scores_d, topk_inds_d, topk_ys_d, topk_xs_d);
    checkCuda( cudaDeviceSynchronize() );    
    
    checkCuda( cudaMemcpy(scores, scores_d, K *sizeof(float), cudaMemcpyDeviceToHost) );

    topKxyclasses(topk_inds_d, topk_inds_d+K, K, width, dim_hm.w*dim_hm.h, clses_d, inttopk_xs_d, inttopk_ys_d);
    checkCuda( cudaDeviceSynchronize() ); 
    checkCuda( cudaMemcpy(topk_xs_d, (float *)inttopk_xs_d, K*sizeof(float), cudaMemcpyDeviceToDevice) );
    checkCuda( cudaMemcpy(topk_ys_d, (float *)inttopk_ys_d, K*sizeof(float), cudaMemcpyDeviceToDevice) );
    
    checkCuda( cudaMemcpy(intxs, inttopk_xs_d, K * sizeof(int), cudaMemcpyDeviceToHost) ); 
    checkCuda( cudaMemcpy(intys, inttopk_ys_d, K * sizeof(int), cudaMemcpyDeviceToHost) ); 
    
    checkCuda( cudaMemcpy(clses, clses_d, K*sizeof(int), cudaMemcpyDeviceToHost) );
    
    // ----------- topk end 
    
    topKxyAddOffset(topk_inds_d, K, dim_reg.h*dim_reg.w, inttopk_xs_d, inttopk_ys_d, topk_xs_d, topk_ys_d, rt_out[3], src_out, ids_out);
    checkCuda( cudaDeviceSynchronize() );

    bboxes(topk_inds_d, K, dim_wh.h*dim_wh.w, topk_xs_d, topk_ys_d, rt_out[2], bbx0_d, bbx1_d, bby0_d, bby1_d, src_out, ids_out);
    checkCuda( cudaDeviceSynchronize() );
    checkCuda( cudaMemcpy(bbx0, bbx0_d, K * sizeof(float), cudaMemcpyDeviceToHost) ); 
    checkCuda( cudaMemcpy(bby0, bby0_d, K * sizeof(float), cudaMemcpyDeviceToHost) ); 
    checkCuda( cudaMemcpy(bbx1, bbx1_d, K * sizeof(float), cudaMemcpyDeviceToHost) ); 
    checkCuda( cudaMemcpy(bby1, bby1_d, K * sizeof(float), cudaMemcpyDeviceToHost) );

    //regression heads
    // ['tracking', 'dep', 'rot', 'dim', 'amodel_offset',
    // 'nuscenes_att', 'velocity']
    getRecordsFromTopKId(topk_inds_d, K, dim_track.c, dim_track.h * dim_track.w, rt_out[4], track_d, ids_out);
    checkCuda( cudaMemcpy(track, track_d, K * dim_track.c * sizeof(float), cudaMemcpyDeviceToHost) );

    getRecordsFromTopKId(topk_inds_d, K, dim_dep.c, dim_dep.h * dim_dep.w, rt_out[5], dep_d, ids_out);
    checkCuda( cudaMemcpy(dep, dep_d, K * dim_dep.c * sizeof(float), cudaMemcpyDeviceToHost) );
    
    getRecordsFromTopKId(topk_inds_d, K, dim_rot.c, dim_rot.h * dim_rot.w, rt_out[6], rot_d, ids_out);
    checkCuda( cudaMemcpy(rot, rot_d, K * dim_rot.c * sizeof(float), cudaMemcpyDeviceToHost) );
    
    getRecordsFromTopKId(topk_inds_d, K, dim_dim.c, dim_dim.h * dim_dim.w, rt_out[7], dim_d, ids_out);
    checkCuda( cudaMemcpy(dim_, dim_d, K * dim_dim.c * sizeof(float), cudaMemcpyDeviceToHost) );

    getRecordsFromTopKId(topk_inds_d, K, dim_amodel_offset.c, dim_amodel_offset.h * dim_amodel_offset.w, rt_out[8], amodel_offset_d, ids_out);
    checkCuda( cudaMemcpy(amodel_offset, amodel_offset_d, K * dim_amodel_offset.c * sizeof(float), cudaMemcpyDeviceToHost) );    
    
    // ---------------------------------- post-process -----------------------------------------
    
    countDet = 0;
    detRes.clear();
    for(int i=0; i<K; i++){
        if(scores[i] < outThresh) 
            break;
        
        countDet ++;
        struct detectionRes new_det_res;
        new_det_res.score = scores[i]; 
        new_det_res.cl = clses[i]+1; 
        // ret_s=scores[i];
        // ret_c=clses[i]+1;
        new_det_res.ct     = transform_preds_with_trans(intxs[i], intys[i]);
        new_det_res.tr     = transform_preds_with_trans(intxs[i] + track[i], intys[i] + track[i+K]);
        new_det_res.tr     = new_det_res.tr -new_det_res.ct;
        new_det_res.bb0    = transform_preds_with_trans(bbx0[i], bby0[i]);
        new_det_res.bb1    = transform_preds_with_trans(bbx1[i], bby1[i]);
        new_det_res.ct     = transform_preds_with_trans(((bbx0[i]+bbx1[i])/2 + amodel_offset[i]), 
                                        ((bby0[i]+bby1[i])/2 + amodel_offset[i+K]));
        new_det_res.dep    = dep[i]; 
        new_det_res.dim[0] = dim_[i];
        new_det_res.dim[1] = dim_[i+K];
        new_det_res.dim[2] = dim_[i+2*K];
        
        // unproject_2d_to_3d
        new_det_res.z = dep[i] - calibs[bi].at<float>(2,3);
        new_det_res.x = ((float)new_det_res.ct.at<float>(0,0) * dep[i] - calibs[bi].at<float>(0,3) - 
                        calibs[bi].at<float>(0,2) * new_det_res.z) / calibs[bi].at<float>(0,0);
        new_det_res.y = ((float)new_det_res.ct.at<float>(0,1) * dep[i] - calibs[bi].at<float>(1,3) - 
                        calibs[bi].at<float>(1,2) * new_det_res.z) / calibs[bi].at<float>(1,1) + (dim_[i] / 2);
        
        // alpha2rot_y
        // idx = rot[:, 1] > rot[:, 5]
        // alpha1 = np.arctan2(rot[:, 2], rot[:, 3]) + (-0.5 * np.pi)
        // alpha2 = np.arctan2(rot[:, 6], rot[:, 7]) + ( 0.5 * np.pi)
        // return alpha1 * idx + alpha2 * (1 - idx)
        if(rot[1*K + i] > rot[5*K + i])
            new_det_res.alpha = std::atan2(rot[2*K + i], rot[3*K + i]) -0.5 * M_PI;
        else
            new_det_res.alpha = std::atan2(rot[6*K + i], rot[7*K + i]) +0.5 * M_PI;
        new_det_res.rot_y = (new_det_res.alpha + std::atan2((float)new_det_res.ct.at<float>(0,0) - calibs[bi].at<float>(0,2), calibs[bi].at<float>(0,0)));
        new_det_res.ct    = new_det_res.ct + new_det_res.tr;   //dest  
        detRes.push_back(new_det_res);    
    }    
    // track step
    tracking(bi);
}

void CenterTrack::draw(std::vector<cv::Mat>& frames) {
    struct trackingRes t;
    float sc;
    int id;
    std::string txt;
    int baseline = 0;
    float font_scale = 0.8;
    int thickness = 2;
    
    for(int bi=0; bi<frames.size(); ++bi) {
        float scale_x = float(originalSize[bi].width)/dim.w;
        float scale_y = float(originalSize[bi].height)/dim.h;
        resize(frames[bi], frames[bi], originalSize[bi]);
        // draw dets
        for(int i=0; trRes.size() != 0 && i<trRes[bi].size(); i++) {
            t = trRes[bi][i];
            id = t.tracking_id;
            txt = classesNames[t.det_res.cl-1]+'-'+std::to_string(id); //forse ha bisogno di cl-1
            cv::Size text_size = getTextSize(txt, cv::FONT_HERSHEY_SIMPLEX, font_scale, thickness, &baseline);
            
            if(t.det_res.score > confThreshold){// && t.active!=0) {
                if(!mode3D) {
                    cv::rectangle(frames[bi],  
                                    cv::Point(t.det_res.bb0.at<float>(0,0) * scale_x, t.det_res.bb0.at<float>(0,1) * scale_y), 
                                    cv::Point(t.det_res.bb1.at<float>(0,0) * scale_x, t.det_res.bb1.at<float>(0,1) * scale_y), 
                                    trColors[t.color], thickness);                      
                    cv::rectangle(frames[bi],  
                                    cv::Point(t.det_res.bb0.at<float>(0,0) * scale_x, t.det_res.bb0.at<float>(0,1) * scale_y - text_size.height - thickness), 
                                    cv::Point(t.det_res.bb0.at<float>(0,0) * scale_x + text_size.width, t.det_res.bb0.at<float>(0,1) * scale_y), 
                                    trColors[t.color], -1);                      
                                        
                    cv::putText(frames[bi], txt, 
                                    cv::Point(t.det_res.bb0.at<float>(0,0) * scale_x, t.det_res.bb0.at<float>(0,1) * scale_y - thickness -1), 
                                    cv::FONT_HERSHEY_SIMPLEX, font_scale, cv::Scalar(255, 255, 255), 1);

                    cv::arrowedLine(frames[bi], 
                                    cv::Point((int)t.det_res.ct.at<float>(0,0) * scale_x, (int)t.det_res.ct.at<float>(0,1) * scale_y), 
                                    cv::Point((int)(t.det_res.ct.at<float>(0,0) * scale_x + t.det_res.tr.at<float>(0,0) * scale_x),
                                              (int)(t.det_res.ct.at<float>(0,1) * scale_y + t.det_res.tr.at<float>(0,1) * scale_y)),
                                    cv::Scalar(255, 0, 255), 2);
                }
                //3d
                if(mode3D && t.det_res.z > 1){
                    r.at<float>(0,0) = std::cos(t.det_res.rot_y);
                    r.at<float>(0,2) = std::sin(t.det_res.rot_y);
                    r.at<float>(2,0) = -std::sin(t.det_res.rot_y);
                    r.at<float>(2,2) = std::cos(t.det_res.rot_y);

                    corners.at<float>(0,0) = t.det_res.dim[2]/2;
                    corners.at<float>(0,1) = t.det_res.dim[2]/2;
                    corners.at<float>(0,2) = -t.det_res.dim[2]/2;
                    corners.at<float>(0,3) = -t.det_res.dim[2]/2;
                    corners.at<float>(0,4) = t.det_res.dim[2]/2;
                    corners.at<float>(0,5) = t.det_res.dim[2]/2;
                    corners.at<float>(0,6) = -t.det_res.dim[2]/2;
                    corners.at<float>(0,7) = -t.det_res.dim[2]/2;

                    corners.at<float>(1,4) = -t.det_res.dim[0];
                    corners.at<float>(1,5) = -t.det_res.dim[0];
                    corners.at<float>(1,6) = -t.det_res.dim[0];
                    corners.at<float>(1,7) = -t.det_res.dim[0];
                    
                    corners.at<float>(2,0) = t.det_res.dim[1]/2;
                    corners.at<float>(2,1) = -t.det_res.dim[1]/2;
                    corners.at<float>(2,2) = -t.det_res.dim[1]/2;
                    corners.at<float>(2,3) = t.det_res.dim[1]/2;
                    corners.at<float>(2,4) = t.det_res.dim[1]/2;
                    corners.at<float>(2,5) = -t.det_res.dim[1]/2;
                    corners.at<float>(2,6) = -t.det_res.dim[1]/2;
                    corners.at<float>(2,7) = t.det_res.dim[1]/2;
                    
                    cv::Mat aus = r * corners;

                    for(int k=0; k<8; k++) {
                        aus.at<float>(0,k) += t.det_res.x;
                        aus.at<float>(1,k) += t.det_res.y;
                        aus.at<float>(2,k) += t.det_res.z;
                    }
                    
                    // corners.copyTo(pts3DHomo(cv::Rect(0, 0, 8, 3)));
                    for(int k1=0; k1<3; k1++) {
                        for(int k2=0; k2<8; k2++)
                            pts3DHomo.at<float>(k1,k2) = aus.at<float>(k1,k2); 
                    }
                    
                    aus.release();
                    aus = calibs[bi] * pts3DHomo;
                    std::vector<float> res_corners;
                    for(int k=0; k<8; k++) {
                        res_corners.push_back(aus.at<float>(0,k) / aus.at<float>(2,k));
                        res_corners.push_back(aus.at<float>(1,k) / aus.at<float>(2,k));
                    }
                    aus.release();
                    for(int ind_f=3; ind_f>=0; ind_f--) {
                        for(int j=0; j<4; j++) {
                            cv::line(frames[bi], 
                                    cv::Point((int)res_corners.at(faceId.at(ind_f).at(j) * 2) * scale_x, 
                                              (int)res_corners.at(faceId.at(ind_f).at(j) * 2 + 1) * scale_y),
                                    cv::Point((int)res_corners.at(faceId.at(ind_f).at((j+1)%4) * 2) * scale_x, 
                                              (int)res_corners.at(faceId.at(ind_f).at((j+1)%4) * 2 + 1) * scale_y), 
                                     trColors[t.color], 2);
                            if(ind_f == 0 && j==3) {
                                cv::line(frames[bi], 
                                        cv::Point((int)res_corners.at(faceId.at(ind_f).at(0) * 2) * scale_x, 
                                                  (int)res_corners.at(faceId.at(ind_f).at(0) * 2 + 1) * scale_y),
                                        cv::Point((int)res_corners.at(faceId.at(ind_f).at(2) * 2) * scale_x, 
                                                  (int)res_corners.at(faceId.at(ind_f).at(2) * 2 + 1) * scale_y), trColors[t.color], 2);
                                cv::line(frames[bi], 
                                        cv::Point((int)res_corners.at(faceId.at(ind_f).at(1) * 2) * scale_x, 
                                                  (int)res_corners.at(faceId.at(ind_f).at(1) * 2 + 1) * scale_y),
                                        cv::Point((int)res_corners.at(faceId.at(ind_f).at(3) * 2) * scale_x, 
                                                  (int)res_corners.at(faceId.at(ind_f).at(3) * 2 + 1) * scale_y), trColors[t.color], 2);
                            }
                        }
                    }
                    float bb0=(1 << 10), bb1=0, bb2=(1 << 10), bb3=0;
                    for(int k=0; k<8; k++) {
                        if(res_corners[2*k] < bb0)
                            bb0 = res_corners[2*k];
                        if(res_corners[2*k] > bb1)
                            bb1 = res_corners[2*k];
                        if(res_corners[2*k+1] < bb2)
                            bb2 = res_corners[2*k+1];
                        if(res_corners[2*k+1] > bb3)
                            bb3 = res_corners[2*k+1];
                                
                    }
                    // if(not no_bbox):
                    // cv::rectangle(frame,  
                    //             cv::Point(bb0, bb2), 
                    //             cv::Point(bb1, bb3), 
                    //             trColors[t.color], thickness);                      
                    cv::rectangle(frames[bi],  
                                cv::Point(bb0 * scale_x, bb2 * scale_y - text_size.height - thickness), 
                                cv::Point(bb0 * scale_x + text_size.width, bb2 * scale_y), 
                                trColors[t.color], -1);                      
                                        
                    cv::putText(frames[bi], txt, 
                                cv::Point(bb0 * scale_x, bb2 * scale_y - thickness -1), 
                                cv::FONT_HERSHEY_SIMPLEX, font_scale, cv::Scalar(255, 255, 255), 1);

                    cv::arrowedLine(frames[bi], 
                                cv::Point((int)((bb0 + bb1)/2) * scale_x, (int)((bb2 + bb3)/2) * scale_y), 
                                cv::Point((int)((bb0 + bb1)/2 + t.det_res.tr.at<float>(0,0)) * scale_x,
                                          (int)((bb2 + bb3)/2 + t.det_res.tr.at<float>(0,1)) * scale_y),
                                cv::Scalar(255, 0, 255), 2);
                }
            }
        }
    }
}

}}