The full form of OpenCV C# is Open Source Computer Vision which is a library of programming functions. Open Source Computer Vision is the field of computer science that is mainly used to enable real-time computer vision.

It is a cross-platform library where computer vision applications can be developed. OpenCV was formed to give a clear idea and information about computer vision applications.

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What Is Computer Vision?

Computer vision mainly originated from the image processing field, and image processing comes from signal processing. 

It deals with creating and duplicating human vision using computer software and also hardware. OpenCV mainly concentrates on image processing, video capture, and analysis. It also includes features like object detection and face detection. 

Computer Vision Also Overlaps With the Following Fields:

  • Image Processing - It focuses on image manipulation.
  • Pattern Recognition − It classifies patterns with the help of various techniques.
  • Photogrammetry − Image measurements are calculated and procured with great accuracy using Photogrammetry. 

Applications of Computer Vision

  • It is used for object tracking, which can locate items in a picture and then follow them around the frame.
  • It is used for image reconstruction, where numerous frames are passed in at the same time and the image attributes are reconstructed using the samples provided.
  • It is used for video processing, which is comparable to object tracking in that it analyses video frames for objects and then tracks them in the frame.

How to Use OpenCV C#?

The OpenCV library was built using C and C++ programming languages. So, to be able to use OpenCV using C#, we need to use a wrapper written using C#, which allows us to communicate with the OpenCV library and all its underlying APIs.

The most popular wrapper is Emgu CV, and we are going to discuss how to use OpenCV C# using this wrapper.

The downloading and installation guide to installing the Emgu CV is given here, which has different installation guides according to the Operating System that we use on our systems. Therefore, we are going to skip to the application part directly by discussing an example.

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Examples

Below is an example that thoroughly demonstrates some of the basics of OpenCV using C# and the wrapper Emgu CV.

Example 1

Aim: To display text and features using OpenCV C#.

File name: HelloWorld.cs

The following code will create a 400x200 image with green text saying “Hello, world” on the foreground and blue as the background color.

Code

using Emgu.CV;

using Emgu.CV.CvEnum;

using Emgu.CV.Structure;

using System.Drawing;

// creating window with a specific name

CvInvoke.cvNamedWindow(win1);

// creating a 400x200 Blue color image.

using (Image<Bgr, Byte> img = new Image<Bgr, byte>(400, 200, new Bgr(255, 0, 0))) 

  {

 // assigning font

   MCvFont f = new MCvFont(CvEnum.FONT.CV_FONT_HERSHEY_COMPLEX, 1.0, 1.0);

   // drawing on the image using the specified font

   img.Draw("Hello, world", ref f, new Point(10, 80), new Bgr(0, 255, 0));

   // displaying the image

   CvInvoke.cvShowImage(win1, img.Ptr);

}

Output

OpenCV.

Uses of OpenCV C#

OpenCV C# uses image processing technology in order to process images in nanoseconds. The five main ways in which OpenCV C# is used are :

  • Object Detection 

Using Object Detection, we can detect any objects such as detecting faces, detecting human bodies, and also detecting animals. It is done using a pattern of geometry technique.

  • Object Recognition 

Object Recognition is similar to object detection. In object recognition, we need not detect the objects, but we need to match the results to those stored in the database previously.

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  • Object Tracking 

Object Tracking is based on video analysis. It means that we can get the recordings or recorded videos from the webcam. And then, by performing image processing techniques on the photo itself, we can learn in detail about the photo and objects in the image.

  • Image Reconstruction 

Using Image Reconstruction, we can get the reconstruction of the image properties. This can be done by using the samples provided by passing them into multiple frames.

  • Video Processing 

Video Processing can be comparable to object tracking. It is because of this process that we also need to analyze by seeing the video frames in order to detect the objects in the image. This is followed by tracking them in the video frames. The speed of the object’s movement can also be tracked using this.

Here Are Some External Uses of OpenCV

OpenCV can be used for medical purposes in the form of classifying and detecting diseases. For instance, we can detect brain tumors using OpenCV. 

  • It can be used for security reasons, like biometrics and surveillance. Through surveillance, suspicious activities or behaviors can be detected.
  • It can also be used in Robotics applications. Robotics applications mean we can automatically determine a robot’s location and also use it for manipulation, navigation, and avoid all kinds of obstacles.

Additional Features of OpenCV Overlapping With Image Processing 

  • Edge detection - This is used to process and detect edges from images directly from the webcam and to convert them to greyscale as well.
  • Histogram equalization - This technique has several purposes, one being enhancing the contrast of MATLAB images.
  • Grayscale Images - This is used to create and support creating and processing grayscale images.
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Conclusion

OpenCV is a popular Computer Vision library to develop applications built using C++ and C. It has several uses like Object Detection and Video Processing. Computer Vision overlaps with fields like Image Processing, Photogrammetry, and Pattern Recognition. A popular wrapper Emgu CV is used to run OpenCV using C#.

To understand and apply OpenCV in-depth, we need to refer to other resources and study material as well to get the most out of this concept. 

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About the Author

Ravikiran A SRavikiran A S

Ravikiran A S works with Simplilearn as a Research Analyst. He an enthusiastic geek always in the hunt to learn the latest technologies. He is proficient with Java Programming Language, Big Data, and powerful Big Data Frameworks like Apache Hadoop and Apache Spark.

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