OpenCV is a cross-platform library that is concerned with video processing, video capture, and analysis. It consists of features that support face and object detection. OpenCV can be used to develop real-time computer applications. This article focusses on the basics of the OpenCV library. Java Programming Language has been used in all the examples. This means that you must have at least the basic exposure to Java to benefit from this article.
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Computer vision is a discipline that focusses on how to reconstruct, interpret, and understand a 3D scene from its 2D images. It does this while considering the properties of the structure present in the scene. Computer vision handles modelling and replicating human vision using both computer software and hardware.
· Image processing – This area is concerned with image manipulation
· Pattern recognition – This field deals with the various techniques that are used to classify patterns
· Photogrammetry - It deals with the process of obtaining accurate measurements from images.
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Image processing, as we have already said, is concerned with an image to image transformation. Meaning, both the input and output of image processing are images. On the other hand, computer vision constructs explicit and meaningful descriptions of physical objects from their image. The interpretation and description of 3D scene structures is the output of computer vision.
Application of Computer Vision
Computer vision is applied in several major domains. Our experts have outlined some of these fields below:
· Localization – This involves automatically determining robot location
· Avoiding obstacles
· Assembling robots (welding, peg-in-hole, painting)
· HRI (Human-Robot Interaction)
· Segmentation of 2D/3D
· Detection and Classification
· Reconstruction of human organs in 3D (Ultrasound or MRI)
· Robotics surgery that is vision-guided
· Inspection to detect defects
· Package label and barcode reading
· Sorting of objects
· Understanding of Objects such as OCR
· Autonomous vehicle
· The driver vigilance monitoring
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The OpenCV library supports the following:
· Reading and writing images
· Capturing and saving images
· Filtering, transforming and general processing of images
· Performing feature detection
· Image detection
· Analyzing videos
Originally, the OpenCV library was created in C++. Later on, bindings of Python and Java were provided. OpenCV is cross-platform and can run on a variety of operating systems including Windows, OS X. Linux, Net BSD, FreeBSD, Open BSD, etc.
The main library modules of OpenCV are:
Basic data structures like Scalar, Range, and Point are covered in this module. These are the data structures that are used to build applications in OpenCV. Also, the core functionality module includes Mat (Multidimensional array), which is used to store the images. This model is included as a package under the name org.opencv.core in the Java library of OpenCV. Get our OpenCV project help for immediate assistance with assignments on core functionality.
This module is responsible for several image processing operations that include geometrical image transformations, image filtering, histograms, colour space conversion, etc. The image processing module is included as a package under the name org.opencv.imgproc in the Java library of OpenCV.
This is where video analysis concepts such as motion estimation, object tracking, and background estimation are done. The module comes as a package in the java library of OpenCv under the name org.opencv.video.
This module uses the OpenCV library to explain video capturing and codecs. It is included in the Java library of OpenCV as a package with the name org.opencv.videoio.
The calib3d module consists of algorithms related to basic multiple-view geometry, single and stereo camera calibration, object pose estimation, elements of 3D reconstruction, and stereo correspondence. It is included as a package in the Java library of open CV with the name org. opencv. calb3D.
The features2D module has concepts of feature detection and description. It is included as a package in the Java library of OpenCV under the name org. opencv. features2d.
This module is responsible for detecting objects and instances of the predefined classes like eyes, faces, mugs, cars, people, etc. You can find this module in OpenCV’s Java library under the name org. opencv.objdetect.
This module has simple UI capabilities and is easy to use. It is also included in OpenCV’s Java library in two different packages, org.opencv.imgcodecs and org.opencv.videoio.
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If you feel that this whole process is much of a hassle for you, do not hesitate to take our OpenCV project help. First, you need to download OpenCV onto your system using the steps below:
· Go to OpenCV official website and click on the download link. This will direct you to the download page of OpenCV.
· Click on the file named OpenCV-3.1.0.exe and your download will begin. Once the download is complete, you can extract the file to generate an OpenCV’s folder in your system.
· Next, open the folder OpenCV, then build and then Java. You will find an OpenCV file named OpenCV-310.jar. This file should be saved in a separate folder for further use.
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The JAR files downloaded in the previous section should now be embedded in an Eclipse environment. To do this, set the Build Path to these files by using pom.xml.
· Make sure that Eclipse is running in your system. If not, go to Eclipse’s official website to download and install it in your system.
· Next, start eclipse and create a new project by clicking on the file, new, and then open a new project.
· You will be led to a new project wizard. Choose Java Project and click on the “Next” button.
· You should be able to see a New Java Project wizard. Proceed to create a new project and click on next.
· Right-click on your newly created java project and select Build Path. Then, click on the configure Build Path.
· When you click on the Build Path option, you will be directed to the Java Build path wizard. Next, click on the “Add External JARs button.
· I hope you still remember the path where you saved the OpenCV-310.jar file for further use because it will be needed in this step. Choose that path
· Click on the open button and the file will be added to your library.
· Finally, click OK. You have successfully added the required JAR file to your current project. To verify the added library, expand the referenced libraries folder and you will see it.
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