12/27/2023 0 Comments Easy screen ocr zooming inThis kernel is going to slide from left-to-right and from top-to-bottom for each and every pixel in our input image. To accomplish our average blur, we’ll actually be convolving our image with an normalized filter where both and are both odd integers. Remember when we discussed kernels and convolutions? Well, it turns out that we can use kernels for not only edge detection and gradients, but for averaging as well! This allows us to reduce noise and the level of detail, simply by relying on the average. The first blurring method we are going to explore is averaging.Īn average filter does exactly what you think it might do - takes an area of pixels surrounding a central pixel, averages all these pixels together, and replaces the central pixel with the average.īy taking the average of the region surrounding a pixel, we are smoothing it and replacing it with the value of its local neighborhood. The second Python script, bilateral.py, will demonstrate how to use OpenCV to apply a bilateral blur to our input image. Our first script, blurring.py, will show you how to apply an average blur, Gaussian blur, and median blur to an image ( adrian.png) using OpenCV. Start by accessing the “Downloads” section of this tutorial to retrieve the source code and example image: $ tree. Gain access to Jupyter Notebooks for this tutorial and other PyImageSearch guides that are pre-configured to run on Google Colab’s ecosystem right in your web browser! No installation required.Īnd best of all, these Jupyter Notebooks will run on Windows, macOS, and Linux! Project structureīefore we can learn how to apply blurring with OpenCV, let’s first review our project directory structure. Then join PyImageSearch University today! Ready to run the code right now on your Windows, macOS, or Linux systems?.Wanting to skip the hassle of fighting with the command line, package managers, and virtual environments?.Learning on your employer’s administratively locked system?.Having problems configuring your development environment?įigure 1: Having trouble configuring your dev environment? Want access to pre-configured Jupyter Notebooks running on Google Colab? Be sure to join PyImageSearch University - you’ll be up and running with this tutorial in a matter of minutes. If you need help configuring your development environment for OpenCV, I highly recommend that you read my pip install OpenCV guide - it will have you up and running in a matter of minutes. Luckily, OpenCV is pip-installable: $ pip install opencv-contrib-python To follow this guide, you need to have the OpenCV library installed on your system. In the rest of this lesson we’ll be discussing the four main smoothing and blurring options that you’ll often use in your own projects: While this may sound counter-intuitive, by reducing the detail in an image we can more easily find objects that we are interested in.įurthermore, this allows us to focus on the larger structural objects in the image. By smoothing an image prior to applying techniques such as edge detection or thresholding we are able to reduce the amount of high-frequency content, such as noise and edges (i.e., the “detail” of an image). Smoothing and blurring is one of the most important preprocessing steps in all of computer vision and image processing. Why is smoothing and blurring such an important preprocessing operation? In both these examples the smaller details in the image are smoothed out and we are left with more of the structural aspects of the image.Īs we’ll see through this series of tutorials, many image processing and computer vision functions, such as thresholding and edge detection, perform better if the image is first smoothed or blurred. We also apply smoothing to aid us in finding our marker when measuring the distance from an object to our camera. In fact, smoothing and blurring is one of the most common preprocessing steps in computer vision and image processing.įor example, we can see that blurring is applied when building a simple document scanner on the PyImageSearch blog. While this effect is usually unwanted in our photographs, it’s actually quite helpful when performing image processing tasks. This “mixture” of pixels in a neighborhood becomes our blurred pixel. Practically, this means that each pixel in the image is mixed in with its surrounding pixel intensities. The goal here is to use a low-pass filter to reduce the amount of noise and detail in an image. Sharper regions in the image lose their detail. Visually, it’s what happens when your camera takes a picture out of focus. I’m pretty sure we all know what blurring is. Looking for the source code to this post? Jump Right To The Downloads Section OpenCV Smoothing and Blurring
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