#include "yolo_v2_class.hpp"
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#include "network.h"
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extern "C" {
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#include "detection_layer.h"
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#include "region_layer.h"
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#include "cost_layer.h"
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#include "utils.h"
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#include "parser.h"
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#include "box.h"
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#include "image.h"
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#include "demo.h"
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#include "option_list.h"
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}
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//#include <sys/time.h>
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#include <vector>
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#include <iostream>
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#define FRAMES 3
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#define ROI_PER_DETECTOR 100
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struct detector_gpu_t{
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float **probs;
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box *boxes;
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network net;
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//image det;
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//image det_s;
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image images[FRAMES];
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float *avg;
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float *predictions[FRAMES];
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};
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YOLODLL_API Detector::Detector(std::string cfg_filename, std::string weight_filename, int gpu_id)
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{
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int old_gpu_index;
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cudaGetDevice(&old_gpu_index);
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detector_gpu_ptr = std::make_shared<detector_gpu_t>();
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detector_gpu_t &detector_gpu = *reinterpret_cast<detector_gpu_t *>(detector_gpu_ptr.get());
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cudaSetDevice(gpu_id);
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network &net = detector_gpu.net;
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net.gpu_index = gpu_id;
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//gpu_index = i;
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char *cfgfile = const_cast<char *>(cfg_filename.data());
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char *weightfile = const_cast<char *>(weight_filename.data());
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net = parse_network_cfg(cfgfile);
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if (weightfile) {
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load_weights(&net, weightfile);
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}
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set_batch_network(&net, 1);
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net.gpu_index = gpu_id;
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layer l = net.layers[net.n - 1];
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int j;
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detector_gpu.avg = (float *)calloc(l.outputs, sizeof(float));
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for (j = 0; j < FRAMES; ++j) detector_gpu.predictions[j] = (float *)calloc(l.outputs, sizeof(float));
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for (j = 0; j < FRAMES; ++j) detector_gpu.images[j] = make_image(1, 1, 3);
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detector_gpu.boxes = (box *)calloc(l.w*l.h*l.n, sizeof(box));
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detector_gpu.probs = (float **)calloc(l.w*l.h*l.n, sizeof(float *));
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for (j = 0; j < l.w*l.h*l.n; ++j) detector_gpu.probs[j] = (float *)calloc(l.classes, sizeof(float));
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cudaSetDevice(old_gpu_index);
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}
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YOLODLL_API Detector::~Detector()
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{
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detector_gpu_t &detector_gpu = *reinterpret_cast<detector_gpu_t *>(detector_gpu_ptr.get());
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layer l = detector_gpu.net.layers[detector_gpu.net.n - 1];
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free(detector_gpu.boxes);
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free(detector_gpu.avg);
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free(detector_gpu.predictions);
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for (int j = 0; j < l.w*l.h*l.n; ++j) free(detector_gpu.probs[j]);
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free(detector_gpu.probs);
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}
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YOLODLL_API std::vector<bbox_t> Detector::detect(std::string image_filename, float thresh)
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{
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char *input = const_cast<char *>(image_filename.data());
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image im = load_image_color(input, 0, 0);
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image_t img;
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img.c = im.c;
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img.data = im.data;
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img.h = im.h;
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img.w = im.w;
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return detect(img, thresh);
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}
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YOLODLL_API std::vector<bbox_t> Detector::detect(image_t img, float thresh)
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{
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detector_gpu_t &detector_gpu = *reinterpret_cast<detector_gpu_t *>(detector_gpu_ptr.get());
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network &net = detector_gpu.net;
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int old_gpu_index;
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cudaGetDevice(&old_gpu_index);
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cudaSetDevice(net.gpu_index);
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//std::cout << "net.gpu_index = " << net.gpu_index << std::endl;
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float nms = .4;
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image im;
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im.c = img.c;
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im.data = img.data;
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im.h = img.h;
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im.w = img.w;
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image sized = resize_image(im, net.w, net.h);
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layer l = net.layers[net.n - 1];
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//box *boxes = (box *)calloc(l.w*l.h*l.n, sizeof(box));
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//float **probs = (float **)calloc(l.w*l.h*l.n, sizeof(float *));
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// (int j = 0; j < l.w*l.h*l.n; ++j) probs[j] = (float *)calloc(l.classes, sizeof(float *));
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float *X = sized.data;
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network_predict(net, X);
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get_region_boxes(l, 1, 1, thresh, detector_gpu.probs, detector_gpu.boxes, 0, 0);
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if (nms) do_nms_sort(detector_gpu.boxes, detector_gpu.probs, l.w*l.h*l.n, l.classes, nms);
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//draw_detections(im, l.w*l.h*l.n, thresh, boxes, probs, names, alphabet, l.classes);
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std::vector<bbox_t> bbox_vec;
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for (size_t i = 0; i < (l.w*l.h*l.n); ++i) {
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box b = detector_gpu.boxes[i];
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int const obj_id = max_index(detector_gpu.probs[i], l.classes);
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float const prob = detector_gpu.probs[i][obj_id];
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if (prob > thresh)
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{
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bbox_t bbox;
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bbox.x = (b.x - b.w / 2.)*im.w;
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bbox.y = (b.y - b.h / 2.)*im.h;
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bbox.w = b.w*im.w;
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bbox.h = b.h*im.h;
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bbox.obj_id = obj_id;
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bbox.prob = prob;
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bbox_vec.push_back(bbox);
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}
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}
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cudaSetDevice(old_gpu_index);
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return bbox_vec;
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}
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