No, this time you are wrong if you also considered this topic to be about a curriculum vitae [CV]. On the contrary, maybe if the skills are sharpened enough, they could be on one’s CV, or maybe not, but that’s not why we are here today.
Intel Corporation, America’s multinational company with the largest chip manufacturing credits by revenue, has created a solution called OpenCV that does beyond what its name suggests. As we proceed, you must bear in mind that this article has been put together by The Watchtower, a web design agency in Dubai and a leading name in the business of web design and development in Dubai.
Today’s topic will center on giving a clear understanding of what OpenCV is about, what it does, and how it can be beneficial to you. Also, I will highlight some of the shortcomings of this project called OpenCV.
What is OpenCV?
OpenCV, which means Open-Source Computer Vision Library, is a library of programming functions aimed mainly at real-time computer vision. OpenCV was created by Intel and later supported by Willow Garage and Itseez.
The library has more than 2500 optimized algorithms for image and video analysis.
These algorithms can be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, and produce 3D point clouds from stereo cameras.
One of its highlights is its ability to stitch images together to produce a high-resolution image of an entire scene. OpenCV finds similar images from an image database, removes red eyes from pictures taken using flash, follows eye movements, recognizes scenery, establishes markers to overlay it with augmented reality, etc.
How can I use OpenCV?
OpenCV can be used in a variety of programming languages, including C++, Python, and Java. Here is an example of how to use OpenCV in Python:
- Install OpenCV: You can install OpenCV using pip by running the command "pip install opencv-python" in your command prompt.
- Import the library: To use OpenCV in your code, you need to import the library using the following line:
- Read an image: You can read an image using the imread() function. The function takes the image path as an argument and returns a numpy array that represents the image.
img = cv2.imread("image.jpg")
- Perform image processing: You can perform various image processing operations on the image, such as image filtering, image thresholding, image transformation, etc.
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
- Display the image: You can display the image using the imshow() function.
- Save the image: You can save the processed image using the imwrite() function.
This is just a basic example of how to use OpenCV in Python. OpenCV provides a wide range of functions for image and video processing, and you can explore the library's documentation to learn more.
What are the shortcomings of OpenCV?
OpenCV is a powerful library for computer vision and image processing, but it does have some limitations. Some of the main shortcomings of OpenCV include:
- Speed: OpenCV can be slow when processing large images or videos, especially when using some of the more complex algorithms.
- Memory usage: OpenCV can be memory-intensive, especially when working with large images or multiple images at once.
- Lack of flexibility: OpenCV provides a wide range of pre-built functions for image processing, but it can be difficult to customize or extend these functions to suit specific needs.
- Limited deep learning support: While OpenCV has some support for deep learning, it is not as robust as other libraries such as TensorFlow or PyTorch.
- Limited resources: OpenCV is a large and complex library, and it can be difficult for new users to find the information and resources they need to get started.
- Limited support for newer architectures and methods: OpenCV is a mature library, it may lack support for the newer architectures and methods in computer vision and deep learning.
It's worth noting that some of the shortcomings above are being worked on by the community and developers. With the recent version of OpenCV 4.5, they have introduced CUDA acceleration to speed up the operations and the OpenCV DNN module to support deep learning.
Jan 21, 2023