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 and store result to: test_dnn_out.avi

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

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

Put it near compiled: darknet.exe

You can get cfg-files by path: darknet/cfg/

Examples of results:

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

How to use:

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

• darknet_voc.cmd - initialization with 256 MB VOC-model yolo-voc.weights & yolo-voc.cfg and waiting for entering the name of the image file
• darknet_demo_voc.cmd - initialization with 256 MB VOC-model yolo-voc.weights & yolo-voc.cfg and play your video file which you must rename to: test.mp4, and store result to: test_dnn_out.avi
• darknet_net_cam_voc.cmd - initialization with 256 MB VOC-model, play video from network video-camera mjpeg-stream (also from you phone) and store result to: test_dnn_out.avi
• darknet_web_cam_voc.cmd - initialization with 256 MB VOC-model, play video from Web-Camera number #0 and store result to: test_dnn_out.avi

How to use on the command line:

• 256 MB COCO-model - image: darknet.exe detector test data/coco.data yolo.cfg yolo.weights -i 0 -thresh 0.2
• Alternative method 256 MB COCO-model - image: darknet.exe detect yolo.cfg yolo.weights -i 0 -thresh 0.2
• 256 MB VOC-model - image: darknet.exe detector test data/voc.data yolo-voc.cfg yolo-voc.weights -i 0
• 256 MB COCO-model - video: darknet.exe detector demo data/coco.data yolo.cfg yolo.weights test.mp4 -i 0
• 256 MB VOC-model - video: darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights test.mp4 -i 0
• Alternative method 256 MB VOC-model - video: darknet.exe yolo demo yolo-voc.cfg yolo-voc.weights test.mp4 -i 0
• 60 MB VOC-model for video: darknet.exe detector demo data/voc.data tiny-yolo-voc.cfg tiny-yolo-voc.weights test.mp4 -i 0
• 256 MB COCO-model for net-videocam - Smart WebCam: darknet.exe detector demo data/coco.data yolo.cfg yolo.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0
• 256 MB VOC-model for net-videocam - Smart WebCam: darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0
• 256 MB VOC-model - WebCamera #0: darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights -c 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

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

• 256 MB COCO-model: darknet.exe detector demo data/coco.data yolo.cfg yolo.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0
• 256 MB VOC-model: darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0

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 build\darknet\darknet.sln and do the: Build -> Build darknet

2. If you have other version of CUDA (not 8.0) then open 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

4. 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 \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 \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