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OpenCV (Open source computer vision)
OpenCV is an open-source computer vision library that contains over 2500 algorithms for various applications in industry and academia. It supports programming in C, C++, and Python and is known for its speed and efficiency in image processing compared to MATLAB, though it is less user-friendly. The library includes several modules for functionalities such as image processing, video analysis, and object detection, with installation instructions varied by operating system.
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OpenCV is an open-source computer vision library with over 2500 algorithms, widely used in various applications. Developed by Intel, it supports C/C++/Python and is cross-platform.
OpenCV's applications include object recognition, automated inspection, medical image analysis, and video search. It is versatile for tasks like gesture recognition and navigation.
Key functionalities of OpenCV include image/video processing, object detection, computer vision geometry, computational photography, machine learning, and CUDA acceleration.
OpenCV outperforms Matlab in speed and resource efficiency, providing extensive functions for computer vision, which expedites tasks such as motion and object detection.
Although powerful, OpenCV is less user-friendly than Matlab, with more complex memory management and longer code for similar functionalities.
OpenCV has a modular structure with various modules for core functionality, image processing, video analysis, object detection, and calibration among others.
Detailed installation steps for OpenCV on Windows, iOS, and Ubuntu are provided, including project setup, package downloads, and verification.
Links to the OpenCV Wiki, modules, and external resources like NVIDIA Developer guides, providing further learning on computer vision.
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OpenCV (Open source computer vision)
- 1. OpenCV (open source computervision) Chetan Allapur-OpenCV
- 2. What is OpenCV? OpenCV(Open Source Computer Vision Library: http://opencv.org) is an open-source BSD-licensed library that includes several hundreds of computer vision algorithms to be used by industry and academia for computer vision applications and research. By Chetan Allapur Chetan Allapur-OpenCV
- 3. ❏ OpenCV isoriginally developed by Intel. ❏ It has more than 2500 optimized algorithms. ❏ It has C/C++/Python Application Programming Interface. ● It is written natively in C++ ❏ OpenCV is a Cross-platform. ● Also available for Android and iOS. ❏ Released under BSD license. ❏ Initial release: June 2000; 20 years ago. ❏ Current release version: 4.3.0 / 3 April 2020. Chetan Allapur-OpenCV
- 4. Applications of OpenCV.... ●Object,face and Gesture Recognition ● Street view image stitching ● Automated inspection and surveillance ● Robot and driver-less car navigation and control ● Medical image analysis ● Video/image search and retrieval ● Movies - 3D structure from motion ● ….and so on. Chetan Allapur-OpenCV
- 5. Functionalities of OpenCV: ●Image/video I/O, processing, display (core, imgprocessing, highgui) ● Object/feature detection (objdetect, features2d, nonfree) ● Geometry-based monocular or stereo computer vision (calib3d, stitching, video stabilization) ● Computational photography (photo, video, superres) ● Machine learning & clustering (ml, flann-Fast Library for Approximate Nearest Neighbors) ● CUDA acceleration (gpu) Chetan Allapur-OpenCV
- 6. Why we useOpenCV the most? ❖ OpenCV is one of the best competitor When compared with Matlab. ❖ OpenCV has more functions for computer vision than Matlab. In general C++ OpenCV code runs faster than Matlab code. ❖ OpenCV is very powerful when it comes to Image Processing. ❖ Few day to day tasks such as Motion Detection, Object Detection, People Detection becomes matter of minutes with OpenCV. Chetan Allapur-OpenCV
- 7. Pros and Cons: Competitorhere: Matlab ❖ Pros: ● With BSD license for OpenCV, you get all the features for free. Also, optimized code runs faster than MATLAB! ● Speed: OpenCV uses high frames processed per sec in real time image processing Than Matlab. ● OpenCV is more efficient than Matlab as Matlab needs more system resources than OpenCV. ● There are many OpenCV wrappers like SimpleCV, JavaCV, EmguCV,JavacvPro,..etc Chetan Allapur-OpenCV
- 8. Pros and Cons: Competitorhere: Matlab ❖ Cons: ● OpenCV does not provide the same ease of use when compared to MATLAB ● OpenCV lacks in memory management. ● Matlab is much easier to write lines of code. Similar functionality might be just 1–2 lines of code compared to OpenCV. Chetan Allapur-OpenCV
- 9. OpenCV has amodular structure, which means that the package includes several shared or static libraries. The following modules are available: Chetan Allapur-OpenCV
- 10. Modules: ● Core functionality(core): basic structures and algorithms. ● Image Processing (imgproc): Image processng algorithms (such as image filtering, image transformation,etc) ● Video Analysis (video): Video analysis (such as ,otion estimation and object tracking) ● High-level GUI (highgui): It has built-in UI and in addition, we use Qt(free and open-source widget toolkit for creating graphical user interfaces) Chetan Allapur-OpenCV
- 11. Modules: ● Camera Calibrationand 3D Reconstruction (calib3d): camera calibration and 3d reconstruction ● 2D Features Framework (features2d): 2D features framework (feature detectors, descriptors and descriptor matchers) ● Object Detection (objdetect): detection of objects and other items(e.g., faces, eyes, people, cars, etc,.) ● Video I/O (videoio): an easy-to-use interface to video capturing and video codecs. Chetan Allapur-OpenCV
- 12. Installation steps inwindows: 1. Step 1: Install Visual Studio. ... 2. Step 2: Install CMake. ... 3. Step 3: Install Anaconda (a python distribution) ... 4. Step 4: Download and extract opencv-3.3. ... 5. Step 5: Generate Visual Studio project using CMake. ... 6. Step 6: Compile OpenCV. ... 7. Step 7: Update System Environment Variables. ... 8. Step 8: Testing C++ code. (for more detailed explanation - Link) Chetan Allapur-OpenCV
- 13. Installation steps iniOS: 1. Download OpenCV 3 from www.opencv.org. 2. Create iOS project by Xcode (Xcode 9.1 (9B55)). 3. Import opencv2. framework to project in General>Linked Frameworks and Libraries. 4. Set correct path in Build Settings>Framework Search Paths. 5. Import header before any module or framework of Cocoa. (For more detailed explanation - Link) Chetan Allapur-OpenCV
- 14. Installation steps inUbuntu: 1. Refresh the packages index and install the OpenCV package by typing: sudo apt update sudo apt install python3-opencv. ... 2. To verify the installation, import the cv2 module and print the OpenCV version (this is the installation of OenCV from Ubuntu Repository, for installng CV from the source - link) Chetan Allapur-OpenCV
- 15. Resources: ➢ OpenCV Wiki-Link ➢ OpenCV: OpenCV modules - Link ➢ Introduction to Computer Vision - Link (Edge AI and Vision Alliance) ➢ OpenCV | NVIDIA Developer - Link (NVIDIA Developer) Chetan Allapur-OpenCV