Use the GPU with your openCV code, get performance gains… Not!
Several times I’ve been drawn to the siren call of using key openCV calls on the GPU. Most recently for the matchTemplate call — before that for calls relating to visual odometry. Ah, disappointment! The situation now is that I have 31 possible images (or templates) to match against the image from the sensor. I want to find out which one fits best — anywhere on the big image. Using the openCV call matchTemplate for each of the 31 images, I get a score for the best match — the highest score is the winner. I plan on running this on an nVidia single board computer — whether nano, Xavier or whatever is appropriate for cost and performance.
In this article, we'll talk about Drawing with OpenCV. If you haven't done so already, be sure to read the previous article on setting pixels with Python
Step by step instructions to bind OpenCV libraries with CUDA drivers to enable GPU processing on OpenCV codes. I am renting an EC2 instance with a p3.8xlarge instance in the AWS, which has 4 Nvidia GPUs.
Processing an image in order to derive some meaningful information from the image is known as image processing. It can be called a scientific study where we apply different methods or functions on images to find out what are its different features. We can enhance the image or degrade the image in order to extract unique features.
This article is about the basic concepts behind a digital image, the processing of it, and hence, also the fundaments of CV. In the end, you can find a simple code implementation with Python using OpenCV. Understanding the Basics of Digital Image Processing and Computer Vision using OpenCV
In this video, I will be showing you the powerful library Jimp for image manipulation in Node.js 🔴 Subscribe for more https://www.youtube.com/channel/UCMA8gV...