From 6e0d293aa608d100b9695aee490ff828ec0c90cd Mon Sep 17 00:00:00 2001 From: Edmond Yoo <hj3yoo@uwaterloo.ca> Date: Thu, 06 Sep 2018 16:52:14 +0000 Subject: [PATCH] Resetting README --- README.md | 240 ----------------------------------------------------------- 1 files changed, 2 insertions(+), 238 deletions(-) diff --git a/README.md b/README.md index 7b7abdb..8d0c8a0 100644 --- a/README.md +++ b/README.md @@ -1,240 +1,4 @@ -|  |  https://arxiv.org/abs/1612.08242 | -|---|---| +# Magic: The Gathering Card Detection Model - -# 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: - -[](https://www.youtube.com/watch?v=VOC3huqHrss "Everything Is AWESOME") - -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 MSVS 2015, CUDA 8.0 and OpenCV 2.4.9 (with paths: `C:\opencv_2.4.9\opencv\build\include` & `C:\opencv_2.4.9\opencv\build\x64\vc14\lib`), then start MSVS, open `build\darknet\darknet.sln`, set **x64** and **Release**, 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 - - 3.3 Open file: `\src\yolo.c` and change 3 lines to your OpenCV-version - `249` (for 2.4.9), `2413` (for 2.4.13), ... : - - * `#pragma comment(lib, "opencv_core249.lib")` - * `#pragma comment(lib, "opencv_imgproc249.lib")` - * `#pragma comment(lib, "opencv_highgui249.lib")` - - -4. If you have other version of OpenCV 3.x (not 2.4.x) then you should change many places in code by yourself. - -5. If you want to build with CUDNN to speed up then: - - * download and install CUDNN: https://developer.nvidia.com/cudnn - - * add Windows system variable `cudnn` with path to CUDNN: https://hsto.org/files/a49/3dc/fc4/a493dcfc4bd34a1295fd15e0e2e01f26.jpg - - * open `\darknet.sln` -> (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and add at the beginning of line: `CUDNN;` - -### 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 - -- open file: `\src\yolo.c` and change 3 lines to your OpenCV-version - `249` (for 2.4.9), `2413` (for 2.4.13), ... : - - * `#pragma comment(lib, "opencv_core249.lib")` - * `#pragma comment(lib, "opencv_imgproc249.lib")` - * `#pragma comment(lib, "opencv_highgui249.lib")` - -`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 - - -## How to train (Pascal VOC Data): - -1. Download pre-trained weights for the convolutional layers (76 MB): http://pjreddie.com/media/files/darknet19_448.conv.23 and put to the directory `build\darknet\x64` - -2. Download The Pascal VOC Data and unpack it to directory `build\darknet\x64\data\voc`: http://pjreddie.com/projects/pascal-voc-dataset-mirror/ will be created file `voc_label.py` and `\VOCdevkit\` dir - -3. Download and install Python for Windows: https://www.python.org/ftp/python/3.5.2/python-3.5.2-amd64.exe - -4. Run command: `python build\darknet\x64\data\voc\voc_label.py` (to generate files: 2007_test.txt, 2007_train.txt, 2007_val.txt, 2012_train.txt, 2012_val.txt) - -5. Run command: `type 2007_train.txt 2007_val.txt 2012_*.txt > train.txt` - -6. Start training by using `train_voc.cmd` or by using the command line: `darknet.exe detector train data/voc.data yolo-voc.cfg darknet19_448.conv.23` - -If required change pathes in the file `build\darknet\x64\data\voc.data` - -More information about training by the link: http://pjreddie.com/darknet/yolo/#train-voc - -## How to train with multi-GPU: - -1. Train it first on 1 GPU for like 1000 iterations: `darknet.exe detector train data/voc.data yolo-voc.cfg darknet19_448.conv.23` - -2. Then stop and by using partially-trained model `/backup/yolo-voc_1000.weights` run training with multigpu (up to 4 GPUs): `darknet.exe detector train data/voc.data yolo-voc.cfg yolo-voc_1000.weights -gpus 0,1,2,3` - -https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ - -## How to train (to detect your custom objects): - -1. Create file `yolo-obj.cfg` with the same content as in `yolo-voc.cfg` (or copy `yolo-voc.cfg` to `yolo-obj.cfg)` and: - - * change line `classes=20` to your number of objects - * change line `filters=425` to `filters=(classes + 5)*5` (generally this depends on the `num` and `coords`, i.e. equal to `(classes + coords + 1)*num`) - - For example, for 2 objects, your file `yolo-obj.cfg` should differ from `yolo-voc.cfg` in such lines: - - ``` - [convolutional] - filters=35 - - [region] - classes=2 - ``` - -2. Create file `obj.names` in the directory `build\darknet\x64\data\`, with objects names - each in new line - -3. Create file `obj.data` in the directory `build\darknet\x64\data\`, containing (where **classes = number of objects**): - - ``` - classes= 2 - train = train.txt - valid = test.txt - names = obj.names - backup = backup/ - ``` - -4. Put image-files (.jpg) of your objects in the directory `build\darknet\x64\data\obj\` - -5. Create `.txt`-file for each `.jpg`-image-file - with the same name, but with `.txt`-extension, and put to file: object number and object coordinates on this image, for each object in new line: `<object-class> <x> <y> <width> <height>` - - Where: - * `<object-class>` - integer number of object from `0` to `(classes-1)` - * `<x> <y> <width> <height>` - float values relative to width and height of image, it can be equal from 0.0 to 1.0 - * for example: `<x> = <absolute_x> / <image_width>` or `<height> = <absolute_height> / <image_height>` - * atention: `<x> <y>` - are center of rectangle (are not top-left corner) - - For example for `img1.jpg` you should create `img1.txt` containing: - - ``` - 1 0.716797 0.395833 0.216406 0.147222 - 0 0.687109 0.379167 0.255469 0.158333 - 1 0.420312 0.395833 0.140625 0.166667 - ``` - -6. Create file `train.txt` in directory `build\darknet\x64\data\`, with filenames of your images, each filename in new line, with path relative to `darknet.exe`, for example containing: - - ``` - data/obj/img1.jpg - data/obj/img2.jpg - data/obj/img3.jpg - ``` - -7. Download pre-trained weights for the convolutional layers (76 MB): http://pjreddie.com/media/files/darknet19_448.conv.23 and put to the directory `build\darknet\x64` - -8. Start training by using the command line: `darknet.exe detector train data/obj.data yolo-obj.cfg darknet19_448.conv.23` - -9. After training is complete - get result `yolo-obj_final.weights` from path `build\darknet\x64\backup\` - - * After each 1000 iterations you can stop and later start training from this point. For example, after 2000 iterations you can stop training, and later just copy `yolo-obj_2000.weights` from `build\darknet\x64\backup\` to `build\darknet\x64\` and start training using: `darknet.exe detector train data/obj.data yolo-obj.cfg yolo-obj_2000.weights` - - * Also you can get result earlier than all 45000 iterations, for example, usually sufficient 2000 iterations for each class(object). I.e. for 6 classes to avoid overfitting - you can stop training after 12000 iterations and use `yolo-obj_12000.weights` to detection. - -### Custom object detection: - -Example of custom object detection: `darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_3000.weights` - -|  |  | -|---|---| - -## How to mark bounded boxes of objects and create annotation files: - -Here you can find repository with GUI-software for marking bounded boxes of objects and generating annotation files for Yolo v2: https://github.com/AlexeyAB/Yolo_mark +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). -With example of: `train.txt`, `obj.names`, `obj.data`, `yolo-obj.cfg`, `air`1-6`.txt`, `bird`1-4`.txt` for 2 classes of objects (air, bird) and `train_obj.cmd` with example how to train this image-set with Yolo v2 -- Gitblit v1.10.0