Yolo-Windows v2

"You Only Look Once: Unified, Real-Time Object Detection (version 2)"

A yolo windows version (for object detection)

Contributtors: https://github.com/pjreddie/darknet/graphs/contributors

This repository is forked from Linux-version: https://github.com/pjreddie/darknet

More details: http://pjreddie.com/darknet/yolo/

Requires:

• MS Visual Studio 2015 (v140): https://www.microsoft.com/download/details.aspx?id=48146
• CUDA 8.0 for Windows x64: https://developer.nvidia.com/cuda-downloads
• OpenCV 2.4.9: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/2.4.9/opencv-2.4.9.exe/download
• To compile without OpenCV - remove define OPENCV from: Visual Studio->Project->Properties->C/C++->Preprocessor
• To compile with different OpenCV version - change in file yolo.c each string look like #pragma comment(lib, "opencv_core249.lib") from 249 to required version.
• With OpenCV will show image or video detection in window

Pre-trained models for different cfg-files can be downloaded from (smaller -> faster & lower quality):

• yolo.cfg (256 MB) - require 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo-voc.weights
• yolo-tiny.cfg (60 MB) - require 1 GB GPU-RAM: http://pjreddie.com/media/files/tiny-yolo-voc.weights

Put it near compiled: darknet.exe

Examples of results:

Others: https://www.youtube.com/channel/UC7ev3hNVkx4DzZ3LO19oebg

How to use:

Example of usage in cmd-files from build\darknet\x64\:

• darknet_demo_voc.cmd - initialization with 256 MB model yolo-voc.weights & yolo-voc.cfg and play your video file which you must rename to: test.mp4
• darknet_net_cam_voc.cmd - initialization with 256 MB model, play video from network video-camera mjpeg-stream (also from you phone)

How to use from command line with 256 MB model: darknet.exe yolo demo yolo-voc.cfg yolo-voc.weights test.mp4 -i 0

For using network video-camera mjpeg-stream with any Android smartphone:

1. Download for Android phone mjpeg-stream soft: IP Webcam / Smart WebCam

Smart WebCam - preferably: https://play.google.com/store/apps/details?id=com.acontech.android.SmartWebCam
IP Webcam: https://play.google.com/store/apps/details?id=com.pas.webcam

1. Connect your Android phone to computer by WiFi (through a WiFi-router) or USB
2. Start Smart WebCam on your phone
3. Replace the address below, on shown in the phone application (Smart WebCam) and launch:

darknet.exe yolo demo yolo-voc.cfg yolo-voc.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0

How to use COCO instead of VOC:

• Get synset names from build\darknet\x64\data\coco.names: https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/data/coco.names
• And change list char *voc_names[] = to COCO-names in file yolo.c: https://github.com/AlexeyAB/darknet/blob/master/src/yolo.c#L30

How to compile:

1. If you have CUDA 8.0, OpenCV 2.4.9 (C:\opencv_2.4.9) and MSVS 2015 then start MSVS, open yolo-windows\build\darknet\darknet.sln and do the: Build -> Build darknet

2. If you have other version of CUDA (not 8.0) then open darknet\build\darknet\darknet.vcxproj by using Notepad, find 2 places with "CUDA 8.0" and change it to your CUDA-version, then do step 1

3. If you have other version of OpenCV 2.4.x (not 2.4.9) then you should change pathes after \darknet.sln is opened

3.1 (right click on project) -> properties -> C/C++ -> General -> Additional Include Directories

3.2 (right click on project) -> properties -> Linker -> General -> Additional Library Directories

1. If you have other version of OpenCV 3.x (not 2.4.x) then you should change many places in code by yourself.

How to compile (custom):

Also, you can to create your own darknet.sln & darknet.vcxproj, this example for CUDA 8.0 and OpenCV 2.4.9

Then add to your created project:
- (right click on project) -> properties -> C/C++ -> General -> Additional Include Directories, put here:

C:\opencv_2.4.9\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include
- right click on project -> Build dependecies -> Build Customizations -> set check on CUDA 8.0 or what version you have - for example as here: http://devblogs.nvidia.com/parallelforall/wp-content/uploads/2015/01/VS2013-R-5.jpg
- add to project all .c & .cu files from yolo-windows\src
- (right click on project) -> properties -> Linker -> General -> Additional Library Directories, put here:

C:\opencv_2.4.9\opencv\build\x64\vc12\lib;$(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories)
- (right click on project) -> properties -> Linker -> Input -> Additional dependecies, put here:

..\..\3rdparty\lib\x64\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;cudnn.lib;%(AdditionalDependencies)
- (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions

OPENCV;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)
- compile to .exe (X64 & Release) and put .dll`s near with .exe:

pthreadVC2.dll, pthreadGC2.dll from yolo-windows\3rdparty\dll\x64

cusolver64_80.dll, curand64_80.dll, cudart64_80.dll, cublas64_80.dll - 80 for CUDA 8.0 or your version, from C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\bin