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104 lines
5.4 KiB
Markdown
104 lines
5.4 KiB
Markdown
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# Yolo-Windows v2
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# "You Only Look Once: Unified, Real-Time Object Detection (version 2)"
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A yolo windows version (for object detection)
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Contributtors: https://github.com/pjreddie/darknet/graphs/contributors
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This repository is forked from Linux-version: https://github.com/pjreddie/darknet
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More details: http://pjreddie.com/darknet/yolo/
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##### Requires:
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* **MS Visual Studio 2015 (v140)**: https://www.microsoft.com/download/details.aspx?id=48146
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* **CUDA 8.0 for Windows x64**: https://developer.nvidia.com/cuda-downloads
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* **OpenCV 2.4.9**: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/2.4.9/opencv-2.4.9.exe/download
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- To compile without OpenCV - remove define OPENCV from: Visual Studio->Project->Properties->C/C++->Preprocessor
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- 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.
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- With OpenCV will show image or video detection in window
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##### Pre-trained models for different cfg-files can be downloaded from (smaller -> faster & lower quality):
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* `yolo.cfg` (256 MB) - require 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo-voc.weights
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* `yolo-tiny.cfg` (60 MB) - require 1 GB GPU-RAM: http://pjreddie.com/media/files/tiny-yolo-voc.weights
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Put it near compiled: darknet.exe
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##### Examples of results:
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[](https://www.youtube.com/watch?v=VOC3huqHrss "Everything Is AWESOME")
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Others: https://www.youtube.com/channel/UC7ev3hNVkx4DzZ3LO19oebg
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### How to use:
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##### Example of usage in cmd-files from `build\darknet\x64\`:
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* `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
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* `darknet_net_cam_voc.cmd` - initialization with 256 MB model, play video from network video-camera mjpeg-stream (also from you phone)
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How to use from command line with 256 MB model: `darknet.exe yolo demo yolo-voc.cfg yolo-voc.weights test.mp4 -i 0`
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##### For using network video-camera mjpeg-stream with any Android smartphone:
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1. Download for Android phone mjpeg-stream soft: IP Webcam / Smart WebCam
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Smart WebCam - preferably: https://play.google.com/store/apps/details?id=com.acontech.android.SmartWebCam
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IP Webcam: https://play.google.com/store/apps/details?id=com.pas.webcam
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2. Connect your Android phone to computer by WiFi (through a WiFi-router) or USB
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3. Start Smart WebCam on your phone
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4. Replace the address below, on shown in the phone application (Smart WebCam) and launch:
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```
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darknet.exe yolo demo yolo-voc.cfg yolo-voc.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0
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```
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##### How to use COCO instead of VOC:
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* Get synset names from `build\darknet\x64\data\coco.names`: https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/data/coco.names
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* And change list `char *voc_names[] = ` to COCO-names in file `yolo.c`: https://github.com/AlexeyAB/darknet/blob/master/src/yolo.c#L30
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### How to compile:
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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
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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
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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
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3.1 (right click on project) -> properties -> C/C++ -> General -> Additional Include Directories
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3.2 (right click on project) -> properties -> Linker -> General -> Additional Library Directories
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4. If you have other version of OpenCV 3.x (not 2.4.x) then you should change many places in code by yourself.
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### How to compile (custom):
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Also, you can to create your own `darknet.sln` & `darknet.vcxproj`, this example for CUDA 8.0 and OpenCV 2.4.9
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Then add to your created project:
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- (right click on project) -> properties -> C/C++ -> General -> Additional Include Directories, put here:
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`C:\opencv_2.4.9\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include`
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- 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
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- add to project all .c & .cu files from yolo-windows\src
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- (right click on project) -> properties -> Linker -> General -> Additional Library Directories, put here:
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`C:\opencv_2.4.9\opencv\build\x64\vc12\lib;$(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories)`
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- (right click on project) -> properties -> Linker -> Input -> Additional dependecies, put here:
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`..\..\3rdparty\lib\x64\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;cudnn.lib;%(AdditionalDependencies)`
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- (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions
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`OPENCV;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)`
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- compile to .exe (X64 & Release) and put .dll`s near with .exe:
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`pthreadVC2.dll, pthreadGC2.dll` from yolo-windows\3rdparty\dll\x64
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`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
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