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https://github.com/pjreddie/darknet.git
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repair tabs spaces
This commit is contained in:
Tino Hager
@ -22,338 +22,340 @@ extern "C" {
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#define FRAMES 3
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int max_objects() { return C_SHARP_MAX_OBJECTS; }
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//static Detector* detector = NULL;
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static std::unique_ptr<Detector> detector;
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int init(const char *configurationFilename, const char *weightsFilename, int gpu)
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{
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detector.reset(new Detector(configurationFilename, weightsFilename, gpu));
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return 1;
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detector.reset(new Detector(configurationFilename, weightsFilename, gpu));
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return 1;
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}
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int detect_image(const char *filename, bbox_t_container &container)
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{
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std::vector<bbox_t> detection = detector->detect(filename);
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for (size_t i = 0; i < detection.size() && i < C_SHARP_MAX_OBJECTS; ++i)
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container.candidates[i] = detection[i];
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return detection.size();
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std::vector<bbox_t> detection = detector->detect(filename);
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for (size_t i = 0; i < detection.size() && i < C_SHARP_MAX_OBJECTS; ++i)
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container.candidates[i] = detection[i];
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return detection.size();
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}
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int detect_mat(const uint8_t* data, const size_t data_length, bbox_t_container &container) {
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#ifdef OPENCV
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std::vector<char> vdata(data, data + data_length);
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cv::Mat image = imdecode(cv::Mat(vdata), 1);
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std::vector<char> vdata(data, data + data_length);
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cv::Mat image = imdecode(cv::Mat(vdata), 1);
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std::vector<bbox_t> detection = detector->detect(image);
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for (size_t i = 0; i < detection.size() && i < C_SHARP_MAX_OBJECTS; ++i)
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container.candidates[i] = detection[i];
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return detection.size();
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std::vector<bbox_t> detection = detector->detect(image);
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for (size_t i = 0; i < detection.size() && i < C_SHARP_MAX_OBJECTS; ++i)
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container.candidates[i] = detection[i];
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return detection.size();
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#else
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return -1;
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return -1;
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#endif // OPENCV
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}
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int dispose() {
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//if (detector != NULL) delete detector;
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//detector = NULL;
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detector.reset();
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return 1;
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//if (detector != NULL) delete detector;
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//detector = NULL;
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detector.reset();
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return 1;
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}
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#ifdef GPU
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void check_cuda(cudaError_t status) {
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if (status != cudaSuccess) {
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const char *s = cudaGetErrorString(status);
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printf("CUDA Error Prev: %s\n", s);
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}
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if (status != cudaSuccess) {
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const char *s = cudaGetErrorString(status);
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printf("CUDA Error Prev: %s\n", s);
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}
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}
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#endif
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struct detector_gpu_t {
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network net;
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image images[FRAMES];
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float *avg;
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float *predictions[FRAMES];
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int demo_index;
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unsigned int *track_id;
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network net;
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image images[FRAMES];
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float *avg;
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float *predictions[FRAMES];
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int demo_index;
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unsigned int *track_id;
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};
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YOLODLL_API Detector::Detector(std::string cfg_filename, std::string weight_filename, int gpu_id) : cur_gpu_id(gpu_id)
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{
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wait_stream = 0;
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int old_gpu_index;
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wait_stream = 0;
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int old_gpu_index;
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#ifdef GPU
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check_cuda(cudaGetDevice(&old_gpu_index));
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check_cuda(cudaGetDevice(&old_gpu_index));
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#endif
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detector_gpu_ptr = std::make_shared<detector_gpu_t>();
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detector_gpu_t &detector_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
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detector_gpu_ptr = std::make_shared<detector_gpu_t>();
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detector_gpu_t &detector_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
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#ifdef GPU
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//check_cuda( cudaSetDevice(cur_gpu_id) );
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cuda_set_device(cur_gpu_id);
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printf(" Used GPU %d \n", cur_gpu_id);
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//check_cuda( cudaSetDevice(cur_gpu_id) );
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cuda_set_device(cur_gpu_id);
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printf(" Used GPU %d \n", cur_gpu_id);
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#endif
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network &net = detector_gpu.net;
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net.gpu_index = cur_gpu_id;
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//gpu_index = i;
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network &net = detector_gpu.net;
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net.gpu_index = cur_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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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_custom(cfgfile, 1);
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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 = cur_gpu_id;
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fuse_conv_batchnorm(net);
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net = parse_network_cfg_custom(cfgfile, 1);
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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 = cur_gpu_id;
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fuse_conv_batchnorm(net);
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layer l = net.layers[net.n - 1];
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int j;
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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.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.track_id = (unsigned int *)calloc(l.classes, sizeof(unsigned int));
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for (j = 0; j < l.classes; ++j) detector_gpu.track_id[j] = 1;
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detector_gpu.track_id = (unsigned int *)calloc(l.classes, sizeof(unsigned int));
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for (j = 0; j < l.classes; ++j) detector_gpu.track_id[j] = 1;
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#ifdef GPU
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check_cuda(cudaSetDevice(old_gpu_index));
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check_cuda(cudaSetDevice(old_gpu_index));
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#endif
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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 = *static_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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detector_gpu_t &detector_gpu = *static_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.track_id);
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free(detector_gpu.track_id);
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free(detector_gpu.avg);
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for (int j = 0; j < FRAMES; ++j) free(detector_gpu.predictions[j]);
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for (int j = 0; j < FRAMES; ++j) if (detector_gpu.images[j].data) free(detector_gpu.images[j].data);
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free(detector_gpu.avg);
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for (int j = 0; j < FRAMES; ++j) free(detector_gpu.predictions[j]);
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for (int j = 0; j < FRAMES; ++j) if (detector_gpu.images[j].data) free(detector_gpu.images[j].data);
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int old_gpu_index;
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int old_gpu_index;
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#ifdef GPU
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cudaGetDevice(&old_gpu_index);
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cuda_set_device(detector_gpu.net.gpu_index);
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cudaGetDevice(&old_gpu_index);
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cuda_set_device(detector_gpu.net.gpu_index);
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#endif
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free_network(detector_gpu.net);
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free_network(detector_gpu.net);
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#ifdef GPU
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cudaSetDevice(old_gpu_index);
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cudaSetDevice(old_gpu_index);
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#endif
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}
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YOLODLL_API int Detector::get_net_width() const {
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detector_gpu_t &detector_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
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return detector_gpu.net.w;
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detector_gpu_t &detector_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
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return detector_gpu.net.w;
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}
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YOLODLL_API int Detector::get_net_height() const {
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detector_gpu_t &detector_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
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return detector_gpu.net.h;
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detector_gpu_t &detector_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
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return detector_gpu.net.h;
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}
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YOLODLL_API int Detector::get_net_color_depth() const {
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detector_gpu_t &detector_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
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return detector_gpu.net.c;
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detector_gpu_t &detector_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
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return detector_gpu.net.c;
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}
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YOLODLL_API std::vector<bbox_t> Detector::detect(std::string image_filename, float thresh, bool use_mean)
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{
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std::shared_ptr<image_t> image_ptr(new image_t, [](image_t *img) { if (img->data) free(img->data); delete img; });
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*image_ptr = load_image(image_filename);
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return detect(*image_ptr, thresh, use_mean);
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std::shared_ptr<image_t> image_ptr(new image_t, [](image_t *img) { if (img->data) free(img->data); delete img; });
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*image_ptr = load_image(image_filename);
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return detect(*image_ptr, thresh, use_mean);
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}
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static image load_image_stb(char *filename, int channels)
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{
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int w, h, c;
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unsigned char *data = stbi_load(filename, &w, &h, &c, channels);
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if (!data)
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throw std::runtime_error("file not found");
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if (channels) c = channels;
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int i, j, k;
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image im = make_image(w, h, c);
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for (k = 0; k < c; ++k) {
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for (j = 0; j < h; ++j) {
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for (i = 0; i < w; ++i) {
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int dst_index = i + w * j + w * h*k;
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int src_index = k + c * i + c * w*j;
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im.data[dst_index] = (float)data[src_index] / 255.;
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}
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}
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}
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free(data);
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return im;
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int w, h, c;
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unsigned char *data = stbi_load(filename, &w, &h, &c, channels);
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if (!data)
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throw std::runtime_error("file not found");
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if (channels) c = channels;
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int i, j, k;
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image im = make_image(w, h, c);
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for (k = 0; k < c; ++k) {
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for (j = 0; j < h; ++j) {
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for (i = 0; i < w; ++i) {
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int dst_index = i + w * j + w * h*k;
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int src_index = k + c * i + c * w*j;
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im.data[dst_index] = (float)data[src_index] / 255.;
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}
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}
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}
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free(data);
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return im;
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}
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YOLODLL_API image_t Detector::load_image(std::string image_filename)
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{
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char *input = const_cast<char *>(image_filename.data());
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image im = load_image_stb(input, 3);
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char *input = const_cast<char *>(image_filename.data());
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image im = load_image_stb(input, 3);
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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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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 img;
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return img;
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}
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YOLODLL_API void Detector::free_image(image_t m)
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{
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if (m.data) {
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free(m.data);
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}
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if (m.data) {
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free(m.data);
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}
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}
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YOLODLL_API std::vector<bbox_t> Detector::detect(image_t img, float thresh, bool use_mean)
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{
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detector_gpu_t &detector_gpu = *static_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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detector_gpu_t &detector_gpu = *static_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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#ifdef GPU
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cudaGetDevice(&old_gpu_index);
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if (cur_gpu_id != old_gpu_index)
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cudaSetDevice(net.gpu_index);
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cudaGetDevice(&old_gpu_index);
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if (cur_gpu_id != old_gpu_index)
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cudaSetDevice(net.gpu_index);
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net.wait_stream = wait_stream; // 1 - wait CUDA-stream, 0 - not to wait
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net.wait_stream = wait_stream; // 1 - wait CUDA-stream, 0 - not to wait
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#endif
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//std::cout << "net.gpu_index = " << net.gpu_index << std::endl;
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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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//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 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;
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image sized;
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if (net.w == im.w && net.h == im.h) {
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sized = make_image(im.w, im.h, im.c);
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memcpy(sized.data, im.data, im.w*im.h*im.c * sizeof(float));
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}
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else
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sized = resize_image(im, net.w, net.h);
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if (net.w == im.w && net.h == im.h) {
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sized = make_image(im.w, im.h, im.c);
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memcpy(sized.data, im.data, im.w*im.h*im.c * sizeof(float));
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}
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else
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sized = resize_image(im, net.w, net.h);
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layer l = net.layers[net.n - 1];
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layer l = net.layers[net.n - 1];
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float *X = sized.data;
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float *X = sized.data;
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float *prediction = network_predict(net, X);
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float *prediction = network_predict(net, X);
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if (use_mean) {
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memcpy(detector_gpu.predictions[detector_gpu.demo_index], prediction, l.outputs * sizeof(float));
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mean_arrays(detector_gpu.predictions, FRAMES, l.outputs, detector_gpu.avg);
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l.output = detector_gpu.avg;
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detector_gpu.demo_index = (detector_gpu.demo_index + 1) % FRAMES;
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}
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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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if (use_mean) {
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memcpy(detector_gpu.predictions[detector_gpu.demo_index], prediction, l.outputs * sizeof(float));
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mean_arrays(detector_gpu.predictions, FRAMES, l.outputs, detector_gpu.avg);
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l.output = detector_gpu.avg;
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detector_gpu.demo_index = (detector_gpu.demo_index + 1) % FRAMES;
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}
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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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int nboxes = 0;
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int letterbox = 0;
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float hier_thresh = 0.5;
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detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letterbox);
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if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
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int nboxes = 0;
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int letterbox = 0;
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float hier_thresh = 0.5;
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detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letterbox);
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if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
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std::vector<bbox_t> bbox_vec;
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std::vector<bbox_t> bbox_vec;
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for (size_t i = 0; i < nboxes; ++i) {
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box b = dets[i].bbox;
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int const obj_id = max_index(dets[i].prob, l.classes);
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float const prob = dets[i].prob[obj_id];
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for (size_t i = 0; i < nboxes; ++i) {
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box b = dets[i].bbox;
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int const obj_id = max_index(dets[i].prob, l.classes);
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float const prob = dets[i].prob[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 = std::max((double)0, (b.x - b.w / 2.)*im.w);
|
||||
bbox.y = std::max((double)0, (b.y - b.h / 2.)*im.h);
|
||||
bbox.w = b.w*im.w;
|
||||
bbox.h = b.h*im.h;
|
||||
bbox.obj_id = obj_id;
|
||||
bbox.prob = prob;
|
||||
bbox.track_id = 0;
|
||||
if (prob > thresh)
|
||||
{
|
||||
bbox_t bbox;
|
||||
bbox.x = std::max((double)0, (b.x - b.w / 2.)*im.w);
|
||||
bbox.y = std::max((double)0, (b.y - b.h / 2.)*im.h);
|
||||
bbox.w = b.w*im.w;
|
||||
bbox.h = b.h*im.h;
|
||||
bbox.obj_id = obj_id;
|
||||
bbox.prob = prob;
|
||||
bbox.track_id = 0;
|
||||
|
||||
bbox_vec.push_back(bbox);
|
||||
}
|
||||
}
|
||||
bbox_vec.push_back(bbox);
|
||||
}
|
||||
}
|
||||
|
||||
free_detections(dets, nboxes);
|
||||
if (sized.data)
|
||||
free(sized.data);
|
||||
free_detections(dets, nboxes);
|
||||
if (sized.data)
|
||||
free(sized.data);
|
||||
|
||||
#ifdef GPU
|
||||
if (cur_gpu_id != old_gpu_index)
|
||||
cudaSetDevice(old_gpu_index);
|
||||
if (cur_gpu_id != old_gpu_index)
|
||||
cudaSetDevice(old_gpu_index);
|
||||
#endif
|
||||
|
||||
return bbox_vec;
|
||||
return bbox_vec;
|
||||
}
|
||||
|
||||
YOLODLL_API std::vector<bbox_t> Detector::tracking_id(std::vector<bbox_t> cur_bbox_vec, bool const change_history,
|
||||
int const frames_story, int const max_dist)
|
||||
int const frames_story, int const max_dist)
|
||||
{
|
||||
detector_gpu_t &det_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
|
||||
detector_gpu_t &det_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
|
||||
|
||||
bool prev_track_id_present = false;
|
||||
for (auto &i : prev_bbox_vec_deque)
|
||||
if (i.size() > 0) prev_track_id_present = true;
|
||||
bool prev_track_id_present = false;
|
||||
for (auto &i : prev_bbox_vec_deque)
|
||||
if (i.size() > 0) prev_track_id_present = true;
|
||||
|
||||
if (!prev_track_id_present) {
|
||||
for (size_t i = 0; i < cur_bbox_vec.size(); ++i)
|
||||
cur_bbox_vec[i].track_id = det_gpu.track_id[cur_bbox_vec[i].obj_id]++;
|
||||
prev_bbox_vec_deque.push_front(cur_bbox_vec);
|
||||
if (prev_bbox_vec_deque.size() > frames_story) prev_bbox_vec_deque.pop_back();
|
||||
return cur_bbox_vec;
|
||||
}
|
||||
if (!prev_track_id_present) {
|
||||
for (size_t i = 0; i < cur_bbox_vec.size(); ++i)
|
||||
cur_bbox_vec[i].track_id = det_gpu.track_id[cur_bbox_vec[i].obj_id]++;
|
||||
prev_bbox_vec_deque.push_front(cur_bbox_vec);
|
||||
if (prev_bbox_vec_deque.size() > frames_story) prev_bbox_vec_deque.pop_back();
|
||||
return cur_bbox_vec;
|
||||
}
|
||||
|
||||
std::vector<unsigned int> dist_vec(cur_bbox_vec.size(), std::numeric_limits<unsigned int>::max());
|
||||
std::vector<unsigned int> dist_vec(cur_bbox_vec.size(), std::numeric_limits<unsigned int>::max());
|
||||
|
||||
for (auto &prev_bbox_vec : prev_bbox_vec_deque) {
|
||||
for (auto &i : prev_bbox_vec) {
|
||||
int cur_index = -1;
|
||||
for (size_t m = 0; m < cur_bbox_vec.size(); ++m) {
|
||||
bbox_t const& k = cur_bbox_vec[m];
|
||||
if (i.obj_id == k.obj_id) {
|
||||
float center_x_diff = (float)(i.x + i.w / 2) - (float)(k.x + k.w / 2);
|
||||
float center_y_diff = (float)(i.y + i.h / 2) - (float)(k.y + k.h / 2);
|
||||
unsigned int cur_dist = sqrt(center_x_diff*center_x_diff + center_y_diff * center_y_diff);
|
||||
if (cur_dist < max_dist && (k.track_id == 0 || dist_vec[m] > cur_dist)) {
|
||||
dist_vec[m] = cur_dist;
|
||||
cur_index = m;
|
||||
}
|
||||
}
|
||||
}
|
||||
for (auto &prev_bbox_vec : prev_bbox_vec_deque) {
|
||||
for (auto &i : prev_bbox_vec) {
|
||||
int cur_index = -1;
|
||||
for (size_t m = 0; m < cur_bbox_vec.size(); ++m) {
|
||||
bbox_t const& k = cur_bbox_vec[m];
|
||||
if (i.obj_id == k.obj_id) {
|
||||
float center_x_diff = (float)(i.x + i.w / 2) - (float)(k.x + k.w / 2);
|
||||
float center_y_diff = (float)(i.y + i.h / 2) - (float)(k.y + k.h / 2);
|
||||
unsigned int cur_dist = sqrt(center_x_diff*center_x_diff + center_y_diff * center_y_diff);
|
||||
if (cur_dist < max_dist && (k.track_id == 0 || dist_vec[m] > cur_dist)) {
|
||||
dist_vec[m] = cur_dist;
|
||||
cur_index = m;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
bool track_id_absent = !std::any_of(cur_bbox_vec.begin(), cur_bbox_vec.end(),
|
||||
[&i](bbox_t const& b) { return b.track_id == i.track_id && b.obj_id == i.obj_id; });
|
||||
bool track_id_absent = !std::any_of(cur_bbox_vec.begin(), cur_bbox_vec.end(),
|
||||
[&i](bbox_t const& b) { return b.track_id == i.track_id && b.obj_id == i.obj_id; });
|
||||
|
||||
if (cur_index >= 0 && track_id_absent) {
|
||||
cur_bbox_vec[cur_index].track_id = i.track_id;
|
||||
cur_bbox_vec[cur_index].w = (cur_bbox_vec[cur_index].w + i.w) / 2;
|
||||
cur_bbox_vec[cur_index].h = (cur_bbox_vec[cur_index].h + i.h) / 2;
|
||||
}
|
||||
}
|
||||
}
|
||||
if (cur_index >= 0 && track_id_absent) {
|
||||
cur_bbox_vec[cur_index].track_id = i.track_id;
|
||||
cur_bbox_vec[cur_index].w = (cur_bbox_vec[cur_index].w + i.w) / 2;
|
||||
cur_bbox_vec[cur_index].h = (cur_bbox_vec[cur_index].h + i.h) / 2;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
for (size_t i = 0; i < cur_bbox_vec.size(); ++i)
|
||||
if (cur_bbox_vec[i].track_id == 0)
|
||||
cur_bbox_vec[i].track_id = det_gpu.track_id[cur_bbox_vec[i].obj_id]++;
|
||||
for (size_t i = 0; i < cur_bbox_vec.size(); ++i)
|
||||
if (cur_bbox_vec[i].track_id == 0)
|
||||
cur_bbox_vec[i].track_id = det_gpu.track_id[cur_bbox_vec[i].obj_id]++;
|
||||
|
||||
if (change_history) {
|
||||
prev_bbox_vec_deque.push_front(cur_bbox_vec);
|
||||
if (prev_bbox_vec_deque.size() > frames_story) prev_bbox_vec_deque.pop_back();
|
||||
}
|
||||
if (change_history) {
|
||||
prev_bbox_vec_deque.push_front(cur_bbox_vec);
|
||||
if (prev_bbox_vec_deque.size() > frames_story) prev_bbox_vec_deque.pop_back();
|
||||
}
|
||||
|
||||
return cur_bbox_vec;
|
||||
return cur_bbox_vec;
|
||||
}
|
||||
Reference in New Issue
Block a user