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https://github.com/uehwan/csharp-yolo-video
C# Yolo for Video
https://github.com/uehwan/csharp-yolo-video
csharp object-detection tutorial windows-form-application windows-forms windows-forms-csharp windows-presentation-foundation wpf wpf-application yolo
Last synced: 3 months ago
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C# Yolo for Video
- Host: GitHub
- URL: https://github.com/uehwan/csharp-yolo-video
- Owner: Uehwan
- License: mit
- Created: 2020-01-23T11:25:05.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2020-01-23T12:09:08.000Z (almost 5 years ago)
- Last Synced: 2024-09-22T17:02:11.658Z (3 months ago)
- Topics: csharp, object-detection, tutorial, windows-form-application, windows-forms, windows-forms-csharp, windows-presentation-foundation, wpf, wpf-application, yolo
- Size: 11.7 KB
- Stars: 10
- Watchers: 2
- Forks: 5
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# CSharp-Yolo-Video
Although [the C# wrapper for Darknet](https://github.com/AlturosDestinations/Alturos.Yolo) exists, I went through a hard time figuring out how to apply the wrapper for videos. For later use for myself and saving others' time, I summarize how to apply the Yolo wrapper on videos.
## Getting Started
The following instructions will lead you to setting the environment for using the Yolo wrapper in your project.
### System requriements
- .NET Framework 4.6.1
- [Microsoft Visual C++ Redistributable for Visual Studio 2017 x64](https://aka.ms/vs/16/release/vc_redist.x64.exe)
### Install Alturos.Yolo using [NuGet](https://www.nuget.org/packages/Alturos.Yolo) as follows or you can use the NuGet GUI instead. If you're using NuGet GUI, search for "Alturos.Yolo".
```
PM> install-package Alturos.Yolo (C# wrapper and C++ dlls 28MB)
PM> install-package Alturos.YoloV2TinyVocData (YOLOv2-tiny Pre-Trained Dataset 56MB)
```
### (Optional) For GPU support, install and download the followings.
1) Install the latest Nvidia driver for your graphic device.
2) [Install Nvidia CUDA Toolkit 10.1](https://developer.nvidia.com/cuda-downloads) (must be installed add a hardware driver for cuda support)
3) [Download Nvidia cuDNN v7.6.3 for CUDA 10.1](https://developer.nvidia.com/rdp/cudnn-download)
4) Copy the `cudnn64_7.dll` from the output directory of cdDNN v7.6.3. into the `x64` folder of your project.
### Install [OpenCvSharp3-AnyCPU](https://github.com/shimat/opencvsharp) over NuGet as follows or search for "OpenCvSharp3-AnyCPU". Although the package name contains CvSharp3, it is actually an OpenCv 4.x wrapper.
```
PM> install-package OpenCvSharp3-AnyCPU
```
### Download pretrained weights and place it in your project directory. For more information, visit [Alturos.Yolo](https://github.com/AlturosDestinations/Alturos.Yolo/blob/master/README.md#pre-trained-dataset)
Model | Processing Resolution | Cfg | Weights | Names |
--- | --- | --- | --- | --- |
YOLOv3 | 608x608 | [yolov3.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3.cfg) | [yolov3.weights](https://pjreddie.com/media/files/yolov3.weights) | [coco.names](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/coco.names) |
YOLOv3-tiny | 416x416 | [yolov3-tiny.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-tiny.cfg) | [yolov3-tiny.weights](https://pjreddie.com/media/files/yolov3-tiny.weights) | [coco.names](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/coco.names) |
YOLOv2 | 608x608 | [yolov2.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov2.cfg) | [yolov2.weights](https://pjreddie.com/media/files/yolov2.weights) | [coco.names](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/coco.names) |
YOLOv2-tiny | 416x416 | [yolov2-tiny.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov2-tiny.cfg) | [yolov2-tiny.weights](https://pjreddie.com/media/files/yolov2-tiny.weights) | [voc.names](https://raw.githubusercontent.com/pjreddie/darknet/master/data/voc.names) |
yolo9000 | 448x448 | [yolo9000.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolo9000.cfg) | [yolo9000.weights](https://github.com/philipperemy/yolo-9000/tree/master/yolo9000-weights) | [9k.names](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/9k.names) |
## Write Codes for Video Object Recognition
The following is the minimum code for running the Yolo wrapper on a video file. For running the code, set the solution platform as "x64"!
```cs
using OpenCvSharp;
using OpenCvSharp.Extensions;
using Alturos.Yolo;
private void VideoObjectDetection()
{
// YOLO setting
int yoloWidth = 608, yoloHeight = 608;
var configurationDetector = new ConfigurationDetector();
var config = configurationDetector.Detect();
YoloWrapper yoloWrapper = new YoloWrapper(config);
// OpenCV & WPF setting
VideoCapture videocapture;
Mat image = new Mat();
WriteableBitmap wb = new WriteableBitmap(yoloWidth, yoloHeight, 96, 96, PixelFormats.Bgr24, null);
byte[] imageInBytes = new byte[(int)(yoloWidth * yoloHeight * image.Channels())];
// Read a video file and run object detection over it!
using (videocapture = new VideoCapture(address))
{
using(Mat imageOriginal = new Mat())
{
// read a single frame and convert the frame into a byte array
videocapture.Read(imageOriginal);
image = imageOriginal.Resize(new OpenCvSharp.Size(yoloWidth, yoloHeight));
imageInBytes = image.ToBytes();
// conduct object detection and display the result
var items = yolowrapper.Detect(imageInBytes);
foreach(var item in items)
{
var x = item.X;
var y = item.Y;
var width = item.Width;
var height = item.Height;
var type = item.Type; // class name of the object
// draw a bounding box for the detected object
// you can set different colors for different classes
Cv2.Rectangle(image, new OpenCvSharp.Rect(x, y, width, height), Scalar.Green, 3);
}
// display the detection result
WriteableBitmapConverter.ToWriteableBitmap(image, wb);
/* WPF component: videoViewer
*/
videoViewer.Source = wb;
}
}
}
```