Never in the same shape
OpenCV, an acronym for Open Source Computer Vision Library, is an open source programming library developed primarily for image processing and computer vision. It was originally developed by Intel in 1999 and has since grown to become one of the most popular and powerful tools for computer vision, with widespread adoption in fields ranging from academic research to industry.
Among the fundamental image processing techniques in Image Analysis is Image Thresholding. This technique processes a grayscale image by dividing it into two areas: one white and one black. Which parts of the image should be white or black will depend on a series of factors, more or less complex depending on the technique, all based on a threshold value.
If you are using OpenCV in a Python environment, you will find that the matplotlib library is the perfect tool for viewing the images produced during the Image Analysis task. In fact, this library is very flexible and allows you to make customizations and display modes that OpenCV’s internal tools do not have. We will see with a couple of examples how to view images both individually and in groups using plots and subplots.
In OpenCV, Feature Detection is a technique used to identify distinctive key points or features in an image. These keypoints are locally unique and can be used to compare and match different images, facilitating operations such as object recognition, motion tracking, 3D reconstruction, and other computer vision applications.
In Image Analysis we often always operate on the same image, keeping its size constant. However, there are some particular processes that require the generation of the same image in a series of versions at different resolutions. It is often possible to notice differences in the behavior of the techniques used, such as object recognition, at different resolutions. This technique is called Image Pyramid.
Image processing is a crucial field in multiple disciplines, from medical applications to industrial automation. OpenCV (Open Source Computer Vision Library) represents a powerful tool in this context, offering a wide range of features for image manipulation and analysis. Among the most used techniques are morphological transformations, which allow you to model the shape of objects in an image.