#include <basic/util/app/AppPreference.hpp>
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#include "DnDetect.h"
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DnDetect::DnDetect::DnDetect(const int gpuIndex) : m_net(nullptr), m_thresh(0.5), m_hier_thresh(0.5), m_nms(0.45),
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names(nullptr), alphabet(nullptr), m_thdInit(init, this),
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m_bInitThd(false) {
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}
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DnDetect::DnDetect::~DnDetect() {
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}
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std::vector<DnDetect::YoloObjInfo> DnDetect::DnDetect::detect(cv::Mat &img) {
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std::lock_guard<std::mutex> dataGuard(dataMtx);
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ClockTimer cl("DnDetect::DnDetect");
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std::vector<YoloObjInfo> objInfos;
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if (!m_bInitThd) return objInfos;
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double bttime = what_time_is_it_now();
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image im = matToImg(img);
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// DBG("matToImg : "<<what_time_is_it_now()-bttime);
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// printf("matToImg %f seconds.\n", what_time_is_it_now()-bttime);
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image sized = letterbox_image(im, m_net->w, m_net->h);
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layer l = m_net->layers[m_net->n - 1];
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float *X = sized.data;
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//attime=what_time_is_it_now();p->
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network_predict(m_net, X);
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//printf("Predicted in %f seconds.\n", what_time_is_it_now()-attime);
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int nboxes = 0;
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detection *dets;
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{
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// std::lock_guard<std::mutex> dataGuard(dataMtx);
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// dataMtx.lock();
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dets = get_network_boxes(m_net, im.w, im.h, m_thresh, m_hier_thresh, 0, 1, &nboxes);
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if (nboxes > 100) {
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// dataMtx.unlock();
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return objInfos;
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}
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if (m_nms) do_nms_sort(dets, nboxes, l.classes, m_nms);
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// dataMtx.unlock();
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}
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// draw_detections(im, dets, nboxes, m_thresh, names, alphabet, l.classes);
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for (int i = 0; i < nboxes; i++) {
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YoloObjInfo objInfo;
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std::vector<float> vec(80);
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memcpy(&vec[0], dets[i].prob, sizeof(float) * 80);
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int type = -1;
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for (int j = 0; j < l.classes; ++j) {
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//#todo new func in list out bool
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if (j != 0) {
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continue;
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}
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//#todo get score
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if (dets[i].prob[j] > 0.0f) {
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if (type < 0) {
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type = j;
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objInfo.prob = dets[i].prob[j];
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} else {
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}
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} else {
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}
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}
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if (type >= 0) {
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// if(type != 0){
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// continue;
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// }
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objInfo.type = type;
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objInfo.rcObj.left = (dets[i].bbox.x - dets[i].bbox.w / 2.);
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objInfo.rcObj.top = (dets[i].bbox.y - dets[i].bbox.h / 2.);
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objInfo.rcObj.right = (dets[i].bbox.x + dets[i].bbox.w / 2.);
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objInfo.rcObj.bottom = (dets[i].bbox.y + dets[i].bbox.h / 2.);
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objInfos.push_back(objInfo);
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}
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}
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free_detections(dets, nboxes);
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// show_image(im, "Video");
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// cv::waitKey(10);
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free_image(im);
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free_image(sized);
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//printf("all time use %f seconds.\n", what_time_is_it_now()-bttime);
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return objInfos;
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}
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image DnDetect::DnDetect::matToImg(cv::Mat &RefImg) {
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CV_Assert(RefImg.depth() == CV_8U);
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int h = RefImg.rows;
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int w = RefImg.cols;
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int channels = RefImg.channels();
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image im = make_image(w, h, 3);
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int count = 0;
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switch (channels) {
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case 1: {
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cv::MatIterator_<unsigned char> it, end;
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for (it = RefImg.begin<unsigned char>(), end = RefImg.end<unsigned char>(); it != end; ++it) {
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im.data[count] = im.data[w * h + count] = im.data[w * h * 2 + count] = (float) (*it) / 255.0;
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++count;
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}
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break;
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}
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case 3: {
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float *desData = im.data;
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uchar *srcData = RefImg.data;
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int size = w * h;
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int size2 = size * 2;
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for (int i = 0; i < size; i++) {
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*(desData) = *(srcData + 2) / 255.0f;
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*(desData + size) = *(srcData + 1) / 255.0f;
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*(desData + size2) = *(srcData) / 255.0f;
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desData++;
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srcData += 3;
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}
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break;
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}
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default:
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printf("Channel number not supported.\n");
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break;
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}
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return im;
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}
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int DnDetect::DnDetect::init(void *arg) {
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DnDetect *p = (DnDetect *) arg;
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p->m_thresh = appPref.getFloatData("thresh.detect");
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cuda_set_device(appPref.getIntData("gpu.index"));
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char *datacfg = "cfg/coco.data";
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char *cfgfile = "cfg/yolov3.cfg";
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char *weightfile = "./yolov3.weights";
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double loadtime = what_time_is_it_now();
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list *options = read_data_cfg(datacfg);
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char *name_list = option_find_str(options, "names", "data/names.list");
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p->names = get_labels(name_list);
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p->alphabet = load_alphabet();
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p->m_net = load_network(cfgfile, weightfile, 0);
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set_batch_network(p->m_net, 1);
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printf("load mod use %f seconds.\n", what_time_is_it_now() - loadtime);
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srand(2222222);
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p->m_bInitThd = true;
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return 0;
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}
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