Howto

💡 Problem Formulation: In image processing, filters such as Gaussian and Laplacian are commonly used for blurring, sharpening, and edge detection. To analyze their frequency components, we can compute the Fourier Transforms of these filters. This article demonstrates how to find the Fourier Transforms of Gaussian and Laplacian filters in OpenCV using Python, with the … Read more

💡 Problem Formulation: In computer vision tasks, finding the extreme points (topmost, bottommost, rightmost, and leftmost) of an object in an image is crucial for object tracking, shape analysis, and image cropping. Suppose we have an input image with a clear object contrasted against the background. We would like to locate the extreme points of … Read more

💡 Problem Formulation: In image processing, the Fourier Transform is vital for frequency domain analysis. It allows us to visualize the spatial frequencies present in an image. The task is to convert an image from its spatial domain to frequency domain, which reveals periodicities and directions of patterns within the image. For instance, if we … Read more

💡 Problem Formulation: This article addresses the problem of calculating and visualizing the distribution of pixel intensities (histogram) within a specified region of interest (ROI) in an image using OpenCV with Python. For instance, suppose you have a photograph and you wish to analyze the grayscale value distribution of a specific area. The desired output … Read more

💡 Problem Formulation: We often need to analyze the intensity distribution or color profiles in images for various computer vision tasks. A 2D histogram is a graphical representation of this distribution where two features are considered simultaneously. This article will guide you through methods on how to compute and visualize 2D histograms (such as color … Read more

💡 Problem Formulation: In image processing, it’s often necessary to convert colored images into a binary format – where each pixel is either black or white. This is a fundamental step for various applications like document scanning and edge detection. Given a standard colored image, we aim to transform it into a binary (black and … Read more

💡 Problem Formulation: In various computer vision tasks, we are often required to calculate the shortest distance from a given point to a contour within an image. This operation is critical in applications like object tracking, collision avoidance, and area measurement. For example, given the coordinates of a point and a binary image where the … Read more

💡 Problem Formulation: When creating a line chart using Python’s Plotly library, you might find that your dataset’s variable names aren’t suitable for direct display in the legend, either because they are not descriptive enough or because they use naming conventions that are not user-friendly. You want to be able to change the names displayed … Read more

💡 Problem Formulation: Data visualization is a critical aspect of data analysis, allowing for a clear understanding of trends and comparisons. This article solves the problem of visualizing multiple datasets as distinct lines within a single chart using Plotly Express in Python. For instance, consider having two sets of time-series data representing sales over time … Read more