| | |
| | | |
| | | darknet.exe detector demo data/coco.data yolov3.cfg yolov3.weights -i 0 -thresh 0.25 test.mp4 |
| | | darknet.exe detector demo data/coco.data yolov3.cfg yolov3.weights -i 0 -thresh 0.25 -ext_output test.mp4 |
| | | |
| | | |
| | | pause |
| | |
| | | avg_loss = avg_loss*.9 + loss*.1; |
| | | |
| | | i = get_current_batch(net); |
| | | printf("\n %d: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), (what_time_is_it_now()-time), i*imgs); |
| | | printf("\n %d: %f, %f avg loss, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), (what_time_is_it_now()-time), i*imgs); |
| | | |
| | | #ifdef OPENCV |
| | | if(!dont_show) |
| | |
| | | const int best_class = selected_detections[i].best_class; |
| | | printf("%s: %.0f%%", names[best_class], selected_detections[i].det.prob[best_class] * 100); |
| | | if (ext_output) |
| | | printf("\t(left: %4.0f top: %4.0f w: %4.0f h: %4.0f)\n", |
| | | printf("\t(left_x: %4.0f top_y: %4.0f width: %4.0f height: %4.0f)\n", |
| | | (selected_detections[i].det.bbox.x - selected_detections[i].det.bbox.w / 2)*im.w, |
| | | (selected_detections[i].det.bbox.y - selected_detections[i].det.bbox.h / 2)*im.h, |
| | | selected_detections[i].det.bbox.w*im.w, selected_detections[i].det.bbox.h*im.h); |
| | |
| | | |
| | | cvRectangle(show_img, pt1, pt2, color, width, 8, 0); |
| | | if (ext_output) |
| | | printf(" (left: %4.0f top: %4.0f w: %4.0f h: %4.0f)\n", |
| | | printf("\t(left_x: %4.0f top_y: %4.0f width: %4.0f height: %4.0f)\n", |
| | | (float)left, (float)right, b.w*show_img->width, b.h*show_img->height); |
| | | else |
| | | printf("\n"); |
| | |
| | | #include "route_layer.h" |
| | | #include "shortcut_layer.h" |
| | | #include "yolo_layer.h" |
| | | #include "upsample_layer.h" |
| | | #include "parser.h" |
| | | |
| | | network *load_network_custom(char *cfg, char *weights, int clear, int batch) |