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

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YOLO DNNs {#tutorial_dnn_yolo}
===============================
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.
![Picture example](images/yolo.jpg)
Examples
--------
VIDEO DEMO:
@youtube{NHtRlndE2cg}
Source Code
-----------
The latest version of sample source code can be downloaded [here](https://github.com/opencv/opencv/blob/master/samples/dnn/yolo_object_detection.cpp).
@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.