OpenCV: Know how to use this AI tool for image processing and real-time computer vision
A couple of days ago, artificial intelligence researchers at Meta said that they have made significant progress in building computer vision models, a new AI architecture that can learn about the world from visual cues instead of logic and data, to help them mimic how humans learn about the world and accelerate their training. This emerging field of AI is known as computer vision and it derives meaningful information from digital images, videos, and other visual inputs. And whether you are already working in this field or plan to, you should know about this cool programming library called OpenCV that will enable you to enhance image processing to work on AI systems in an agile way. Let us check the details.
What is OpenCV
OpenCV (Open Source Computer Vision Library) is a library of programming functions and is mainly used for real-time computer vision. It was originally developed by Intel in 1999, and later it was supported by Willow Garage and Itseez. The programming languages for the tool are C, C++, Python, Java, and assembly language.
Today, OpenCV is used in a wide range of applications including 2D and 3D feature toolkits, Facial recognition system, Gesture recognition, Human–computer interaction (HCI), mobile robotics, Motion video tracking, augmented reality (AR), and more.
How to use OpenCV for image processing
Image processing or image enhancement helps increase the accuracy and reliability of the image when it is being analyzed by the AI system. An image of higher quality will be read better, making computer vision more accurate, and images with low resolution and reduced quality will increase the chances of a corrupt training model and AI hallucination.
So, to avoid such instances, it is better to use OpenCV to clean up the training model images. As long as you have correctly set up your Python virtual environment, the steps are quite simple.
Once done, run the following terminal command to install the OpenCV library:
pip install opencv-python
Then, use Matplotlib to display the first two images and install them using the following command:
pip install matplotlib
The final step involves installing Numpty for numerical operations to enhance the image quality. Use the following command:
pip install numpy
Now, you are ready to begin coding and undertake various operations for image processing. Here’s an example.
To reduce noise in an image run the following code:
# Apply image enhancements
# Denoise the image
denoised_image = cv2.fastNlMeansDenoisingColored(image, None, 10, 10, 7, 21)
Similarly, you can code it to sharpen images, stretch contrast, adjust contrast, apply gamma correction, and more.
A couple of days ago, artificial intelligence researchers at Meta said that they have made significant progress in building computer vision models, a new AI architecture that can learn about the world from visual cues instead of logic and data, to help them mimic how humans learn about the world and accelerate their training. This emerging field of AI is known as computer vision and it derives meaningful information from digital images, videos, and other visual inputs. And whether you are already working in this field or plan to, you should know about this cool programming library called OpenCV that will enable you to enhance image processing to work on AI systems in an agile way. Let us check the details.
What is OpenCV
OpenCV (Open Source Computer Vision Library) is a library of programming functions and is mainly used for real-time computer vision. It was originally developed by Intel in 1999, and later it was supported by Willow Garage and Itseez. The programming languages for the tool are C, C++, Python, Java, and assembly language.
Today, OpenCV is used in a wide range of applications including 2D and 3D feature toolkits, Facial recognition system, Gesture recognition, Human–computer interaction (HCI), mobile robotics, Motion video tracking, augmented reality (AR), and more.
How to use OpenCV for image processing
Image processing or image enhancement helps increase the accuracy and reliability of the image when it is being analyzed by the AI system. An image of higher quality will be read better, making computer vision more accurate, and images with low resolution and reduced quality will increase the chances of a corrupt training model and AI hallucination.
So, to avoid such instances, it is better to use OpenCV to clean up the training model images. As long as you have correctly set up your Python virtual environment, the steps are quite simple.
Once done, run the following terminal command to install the OpenCV library:
pip install opencv-python
Then, use Matplotlib to display the first two images and install them using the following command:
pip install matplotlib
The final step involves installing Numpty for numerical operations to enhance the image quality. Use the following command:
pip install numpy
Now, you are ready to begin coding and undertake various operations for image processing. Here’s an example.
To reduce noise in an image run the following code:
# Apply image enhancements
# Denoise the image
denoised_image = cv2.fastNlMeansDenoisingColored(image, None, 10, 10, 7, 21)
Similarly, you can code it to sharpen images, stretch contrast, adjust contrast, apply gamma correction, and more.
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