opencv/doc/tutorials/dnn/dnn_yolo/dnn_yolo.markdown

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.