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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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| | | # Magic: The Gathering Card Detection Model |
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| | | Contributtors: https://github.com/pjreddie/darknet/graphs/contributors |
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| | | This is a fork of [Yolo-v3 and Yolo-v2 for Windows and Linux by AlexeyAB](https://github.com/AlexeyAB/darknet#how-to-compile-on-linux) for creating a custom model for [My MTG card detection project](https://github.com/hj3yoo/MTGCardDetector). |
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| | | This repository is forked from Linux-version: https://github.com/pjreddie/darknet |
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| | | ## Day ~0: Sep 6th, 2018 |
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| | | --------------------- |
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| | | More details: http://pjreddie.com/darknet/yolo/ |
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| | | Uploading all the progresses on the model training for the last few days. |
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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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| | | First batch of model training is completed, where I used ~40,000 generated images of MTG cards laid out in one of the pre-defined pattern. |
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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 COCO-model) - require 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo.weights |
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| | | * `yolo-voc.cfg` (256 MB VOC-model) - require 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo-voc.weights |
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| | | * `tiny-yolo.cfg` (60 MB COCO-model) - require 1 GB GPU-RAM: http://pjreddie.com/media/files/tiny-yolo.weights |
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| | | * `tiny-yolo-voc.cfg` (60 MB VOC-model) - require 1 GB GPU-RAM: http://pjreddie.com/media/files/tiny-yolo-voc.weights |
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| | | <img src="https://github.com/hj3yoo/darknet/blob/master/figures/0_training_set_example_1.jpg" width="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/0_training_set_example_2.jpg" width="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/0_training_set_example_3.jpg" width="360"> |
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| | | Put it near compiled: darknet.exe |
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| | | After 5000 training epochs, the model got 88% validation accuracy on the generated test set. |
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| | | You can get cfg-files by path: `darknet/cfg/` |
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| | | <img src="https://github.com/hj3yoo/darknet/blob/master/figures/0_detection_result_1.jpg" width="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/0_detection_result_2.jpg" width="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/0_detection_result_3.jpg" width="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/0_detection_result_4.jpg" width="360"> |
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| | | ##### Examples of results: |
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| | | However, there are some blind spots on the model, notably: |
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| | | [](https://www.youtube.com/watch?v=VOC3huqHrss "Everything Is AWESOME") |
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| | | - Fails to spot some of the obscured cards, where only a fraction of them are shown. |
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| | | - Fairly fragile against any glaring or light variations. |
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| | | - Cannot detect any skewed cards. |
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| | | Others: https://www.youtube.com/channel/UC7ev3hNVkx4DzZ3LO19oebg |
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| | | Example of bad detections: |
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| | | ### How to use: |
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| | | <img src="https://github.com/hj3yoo/darknet/blob/master/figures/0_detection_result_5.jpg" width="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/0_detection_result_6.jpg" width="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/0_detection_result_7.jpg" width="360"> |
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| | | ##### Example of usage in cmd-files from `build\darknet\x64\`: |
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| | | The second and third problems should easily be solved by further augmenting the dataset with random lighting and image skew. I'll have to think more about the first problem, though. |
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| | | * `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 |
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| | | * `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 |
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| | | * `darknet_net_cam_voc.cmd` - initialization with 256 MB VOC-model, play video from network video-camera mjpeg-stream (also from you phone) |
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| | | ## Day 1 |
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| | | ----------------------- |
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| | | ##### How to use on the command line: |
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| | | * 256 MB COCO-model - image: `darknet.exe detector test data/coco.data yolo.cfg yolo.weights -i 0 -thresh 0.2` |
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| | | * 256 MB VOC-model - image: `darknet.exe detector test data/voc.data yolo-voc.cfg yolo-voc.weights -i 0` |
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| | | * 256 MB COCO-model - video: `darknet.exe detector demo data/coco.data yolo.cfg yolo.weights test.mp4 -i 0` |
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| | | * 256 MB VOC-model - video: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights test.mp4 -i 0` |
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| | | * Alternative method 256 MB VOC-model - video: `darknet.exe yolo demo yolo-voc.cfg yolo-voc.weights test.mp4 -i 0` |
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| | | * 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` |
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| | | * 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` |
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| | | * 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` |
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| | | Added several image augmentation techniques to apply to the training set: noise, dropout, light variation, and glaring: |
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| | | ##### For using network video-camera mjpeg-stream with any Android smartphone: |
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| | | <img src="https://github.com/hj3yoo/darknet/blob/master/figures/1_augmented_set_example_1.jpg" width="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/1_augmented_set_example_2.jpg" width="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/1_augmented_set_example_3.jpg" width="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/1_augmented_set_example_4.jpg" width="360"> |
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| | | 1. Download for Android phone mjpeg-stream soft: IP Webcam / Smart WebCam |
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| | | Currently trying to generate enough images to start model training. Hopefully this helps. |
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| | | Recompiled darknet with OpenCV and CUDNN installed, and recalculated anchors. |
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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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| | | ----------------------- |
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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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| | | I've ran a quick training with tiny_yolo configuration with new training data, and Voila! The model performs significantly better than the last iteration, even against some hard images with glaring & skew! The first prediction model can't detect anything from these new test images, so this is a huge improvement to the model :) |
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| | | |
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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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| | | <img src="https://github.com/hj3yoo/darknet/blob/master/figures/1_detection_result_1.jpg" width="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/1_detection_result_2.jpg" width="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/1_detection_result_3.jpg" width="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/1_detection_result_4.jpg" width="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/1_detection_result_5.jpg" width="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/1_detection_result_6.jpg" width="360"> |
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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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| | | <img src="https://github.com/hj3yoo/darknet/blob/master/figures/1_learning_curve.jpg" width="360"> |
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