OpenCV officially provides both C++ and Python APIs for developers. Most of the time, developers just need to use one kind of programming languages to read, write and process images with hundreds of computer vision algorithms. However, if you want to use OpenCV Python APIs with an extended C/C++ library, it will be tricky to pass the data. In this article, I will share how to read camera stream with OpenCV-Python and detect barcode with Dynamsoft C/C++ Barcode SDK.
In reality, we can always see some photos that have low brightnesses and low contrast. To make objects recognizable in pictures, we need to process the photo with Illumination Compensation. There are many algorithms used for Illumination Compensation such as Histogram equalization, Color similarity measure, Gamma Correction and so on. In this tutorial, I will introduce Gamma Correction and show you how to use it with OpenCV.
In this tutorial, let’s see how easy to find all contours in an image with OpenCV APIs.
In this tutorial, let’s learn how to use Hough line transformation with OpenCV to make line detection in an Image.
Changes or discontinuities of amplitude attribute, such as luminance value, are fundamentally important primitive characteristics of an image. They often provide an indication of the physical extent of objects. Local Discontinuities of image luminance that from one level to another are called luminance edges. In this post, I’ll share how to make image edge detection with OpenCV.
OpenCV (Open Source Computer Vision Library) is a powerful open source library of computer vision algorithms. It is widely used by many technologies, such as image acquiring (e.g. Webcam capture), image processing (e.g. noise reduction), image detection (e.g. face detection), image recognition (e.g. OCR), and so on. Since all OpenCV source code is on GitHub, let’s get the copy and build the source code ourselves for fun.