#CV2 PALETTE SWAP CODE#

One of the most common pitfalls I see computer vision developers make is overlooking lighting and the effect it will have on algorithm performance. So let me say this again, because I really want to make sure it sticks: lighting can mean the difference between success and failure of your computer vision algorithm.

We’ll certainly be able to make our systems more robust in relation to poor lighting conditions, but we’ll never be able to overcome an image that was captured under inferior conditions. Tiny little computers, like the Raspberry Pi, Google Coral, and Jetson Nano can be used to build intricate surveillance systems.Īnd industry is seeing more commercial computer vision applications pushed to market every single day.Īnd while the field is growing, changing, and evolving, I can guarantee you one absolute constant that will never change: every single computer vision algorithm, application, and system ever developed and that will be developed will depend on the quality of images input to the system. We’re seeing deep learning classify images, detect objects, and segment images with astonishingly high accuracy. Every day we are seeing new advances in the field that we once thought were impossible. The field of computer vision is rapidly expanding and evolving. The importance of lighting conditions in image processing and computer vision We’ll wrap up this tutorial with a discussion of our results.

#CV2 PALETTE SWAP CODE#

We’ll then implement Python code to work with four color spaces/models in OpenCV: We’ll then review the three goals you should seek to obtain when working with lighting conditions:įrom there we’ll configure our development environment and review our project directory structure. In the first part of this tutorial, we’ll discuss the importance of lighting conditions in any computer vision and image processing pipeline. Looking for the source code to this post? Jump Right To The Downloads Section OpenCV Color Spaces ( cv2.cvtColor ) To learn about color spaces in OpenCV and the cv2.cvtColor function, just keep reading. We’ll also be discussing color spaces and how we can leverage them to build more robust computer vision applications. In the rest of this article, we’ll be discussing the importance of lighting conditions and the dramatic role it plays in the successful development of a computer vision system. And given this high number of visually similar pills, non-ideal lighting conditions can completely ruin the results of the pill identification algorithm.īelieve it or not, the success of (nearly) all computer vision systems and applications is determined before the developer writes a single line of code. With over 27,000+ prescription pills on the market in the United States (with well over half being round and/or white), there are an incredible number of pills that are nearly identical to each other. I’m talking, of course, about lighting conditions.Ī pill captured in very poor lighting conditions where there is an abundant amount of shadowing, washout, or lack of contrast simply cannot be identified. In fact, whether or not a pill can be successfully identified is determined long before a single line of code is even executed. And a successful pill identification has absolutely nothing to do with my pill identification algorithms that I developed working in the background. It had nothing to do with the computer vision libraries that I built ID My Pill around. Whether or not a pill was identified correctly had nothing to do with the programming language I used to develop it. You simply snapped a photo of your prescription pills and ID My Pill would instantly identify and verify that they are the correct medication using computer vision and machine learning techniques. ID My Pill was an iPhone application and web API that allowed you to identify prescription pills in the snap of your smartphone. To illustrate the importance of lighting conditions, I’d like to start today’s tutorial with a personal story from my time developing an iPhone app called ID My Pill.

Cv2 palette swap