Baumer Industrial Camera How Baumer Industrial Camera combines NEOAPI SDK and OpenCV to convert camera images into Mat image format (C#)

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Baumer industrial cameras

Baumer Industrial Cameras Baumer cameras are high-performance, high-quality industrial cameras that can be used in a variety of applications such as object detection, counting and recognition, motion analysis and image processing.

Baumer's 10GbE cameras have excellent image processing performance and can transmit high-resolution images in real time. In addition, the camera features fast data transfer, low power consumption, easy integration, and high scalability.
​Baumer
Industrial Camera NEOAPI SDK is the latest software development kit (SDK) for Baumer industrial cameras. It provides developers with a series of APIs and tools for communicating and controlling Baumer industrial cameras. The control method is extremely convenient, similar to Halcon's camera assistant control method. ​

When using industrial vision software to integrate industrial cameras, it is often necessary to integrate some functions in the industrial camera SDK into the image processing software to facilitate the advancement of the project; for example, convert the image data collected in the SDK into a suitable image format such as Bitmap, etc. or Opencv Mat image data format, and then perform image processing to start the image processing task;

Note: This article is based on Baumer's NEOAPI SDK and OpenCV using C# language to convert camera images into Mat image format.

Technical background of converting images from Baumer industrial cameras to OpenCV Mat images

The conversion of industrial camera images into OpenCV Mat images involves the collection, processing and storage of image data. Here’s the technical background:

1. Image acquisition: Industrial cameras use image sensors to capture real-world optical information and convert it into digital image data. The data can be grayscale images (single channel) or color images (multiple channels).

2. Data format: The image data of industrial cameras can be stored in different formats, such as RAW, RGB, YUV, etc. These formats reflect the arrangement of pixel values ​​and the representation of color information.

3. OpenCV Library: OpenCV is an open source computer vision library widely used for processing image and video data. It provides a rich set of functions and tools for loading, processing and storing image data.

4. Mat object: In OpenCV, image data is usually represented as a Mat object. The Mat object contains the pixel value of the image and related metadata, such as image size, number of channels, etc.

5. Data conversion: Converting industrial camera image data to OpenCV Mat images usually involves parsing and converting data formats to ensure that the image data can be loaded and processed correctly. This may require conversion and processing that takes into account the number of channels, bit depth, color space, etc. of the image.

Therefore, converting industrial camera image data to OpenCV's Mat image requires understanding the format and characteristics of industrial camera image data, and using the functions and tools provided by OpenCV for appropriate data parsing and conversion.

Convert camera images to Mat image format in NEOAPI SDK

After the camera is connected, the camera image can be converted into Mat image format in the NEOAPI SDK. The C# calling code is as follows:

using System;
using System.IO;
using System.Threading;
using NeoAPI;
using System;
using System.Collections.Generic;
using OpenCvSharp;
using co = System.Console;

NeoAPI.Cam camera = new NeoAPI.Cam();
camera.Connect();                                       // connect to a camera

MatType type;
bool isColor = true;
Feature pixelformat = new Feature();
if ((camera.f.PixelFormat.GetEnumValueList().TryGetValue("BGR8", out pixelformat))
    && pixelformat.IsAvailable)
{
    
    
    camera.f.PixelFormat.ValueString = "BGR8";
    type = MatType.CV_8UC3;
}
else if ((camera.f.PixelFormat.GetEnumValueList().TryGetValue("Mono8", out pixelformat))
    && pixelformat.IsAvailable)
{
    
    
    camera.f.PixelFormat.ValueString = "Mono8";
    type = MatType.CV_8UC1;
    isColor = false;
}
else
{
    
    
    type = MatType.CV_8UC1;
    System.Console.Write("no supported pixel format");
    result = 0;
}
camera.f.ExposureTime.Value = 10000;
for (int count = 0; count < 200; ++count)
{
    
    
     using (Image image = camera.GetImage())
     {
    
    
           var img = new Mat((int)image.Height, (int)image.Width, type,image.ImageData);
           Cv2.NamedWindow(windowName, WindowMode.Normal);
           Cv2.ImShow(windowName, img);          
    }
   
}
Cv2.DestroyWindow(windowName);
camera.Dispose();

Combined with OpenCV to convert camera images into Mat image format test demonstration diagram

The test uses NEOAPI to convert images into Mat image format as follows:

Industrial cameras realize the advantages of converting camera images into Mat image format through OpenCV

Industrial cameras converting camera images into Mat image format through OpenCV has multiple advantages:

1. Convenient data processing: OpenCV provides a wealth of functions and methods that can easily load, process and save image data. The Mat object can be used to easily perform various image processing operations, such as filtering, rotation, cropping, etc.

2. Cross-platform: OpenCV is a cross-platform computer vision library that can run on a variety of operating systems, including Windows, Linux, Mac, etc. This means that industrial cameras can be integrated with OpenCV libraries on different platforms to achieve a wider range of Applications.

3. Rich functions: OpenCV provides a wealth of image processing and computer vision functions, including feature detection, target tracking, three-dimensional reconstruction, etc. After the industrial camera is converted to Mat image format, these functions of OpenCV can be directly used for more complex image processing and analysis. .

4. Community support: OpenCV has a large developer community and rich documentation resources. Industrial camera developers can obtain support and solve problems from the community, and can make full use of relevant functional modules contributed by the community.

5. High efficiency: Converting camera images to Mat image format through OpenCV can achieve efficient image processing and data storage, allowing industrial camera applications to have higher performance and response speed.

In summary, industrial cameras use OpenCV to convert camera images into Mat image format, which has many advantages such as convenient data processing, cross-platform, rich functions, community support and higher efficiency.

Industrial cameras use OpenCV to convert camera images into Mat image format for industrial applications

Industrial cameras use OpenCV to convert camera images into Mat image format. Industry applications include but are not limited to:

1. Manufacturing industry: Industrial cameras can be used for product quality inspection, component size measurement, defect detection and other applications in the manufacturing industry through OpenCV. After converting camera images to Mat image format, you can take advantage of OpenCV's rich features for image analysis and quality control.

2. Medical industry: In the medical industry, industrial cameras combined with OpenCV can be used for medical image analysis and diagnosis, such as X-ray image processing, medical ultrasound image processing, etc., which helps to improve the efficiency of analysis and diagnosis of medical imaging data.

3. Agricultural field: The image conversion achieved by industrial cameras through OpenCV can be applied to crop growth monitoring, fruit and vegetable quality inspection, pest and disease detection and other fields in the agricultural field. The image processing function of OpenCV can help achieve efficient data collection and analysis in the agricultural field.

4. Intelligent transportation: Industrial cameras combined with OpenCV can be used in vehicle recognition, license plate recognition, traffic monitoring and other scenarios in intelligent transportation systems to achieve real-time collection and analysis of traffic data.

5. Intelligent manufacturing: In the field of intelligent manufacturing, the image conversion achieved by industrial cameras through OpenCV can be used for production process monitoring, product quality analysis, intelligent robot visual navigation and other applications to improve the intelligence and automation of manufacturing production.

In summary, industrial cameras use OpenCV to convert camera images into Mat image format, which plays a key role in many industry applications such as manufacturing, medical care, agriculture, intelligent transportation, and intelligent manufacturing, providing efficient image collection for these industries. , processing and analysis solutions.