1.6 KiB
YOLO DNNs
Introduction
In this text you will learn how to use opencv_dnn module using yolo_object_detection (Sample of using OpenCV dnn module in real time with device capture, video and image).
We will demonstrate results of this example on the following picture.
Examples
VIDEO DEMO: @youtube{NHtRlndE2cg}
Source Code
The latest version of sample source code can be downloaded here.
@include dnn/yolo_object_detection.cpp
How to compile in command line with pkg-config
@code{.bash}
g++ pkg-config --cflags opencv pkg-config --libs opencv yolo_object_detection.cpp -o yolo_object_detection
@endcode
Execute in webcam:
@code{.bash}
$ yolo_object_detection -camera_device=0 -cfg=[PATH-TO-DARKNET]/cfg/yolo.cfg -model=[PATH-TO-DARKNET]/yolo.weights -class_names=[PATH-TO-DARKNET]/data/coco.names
@endcode
Execute with image:
@code{.bash}
$ yolo_object_detection -source=[PATH-IMAGE] -cfg=[PATH-TO-DARKNET]/cfg/yolo.cfg -model=[PATH-TO-DARKNET]/yolo.weights -class_names=[PATH-TO-DARKNET]/data/coco.names
@endcode
Execute in video file:
@code{.bash}
$ yolo_object_detection -source=[PATH-TO-VIDEO] -cfg=[PATH-TO-DARKNET]/cfg/yolo.cfg -model=[PATH-TO-DARKNET]/yolo.weights -class_names=[PATH-TO-DARKNET]/data/coco.names
@endcode
Questions and suggestions email to: Alessandro de Oliveira Faria cabelo@opensuse.org or OpenCV Team.