Howto

💡 Problem Formulation: Distance transformations are powerful tools in image processing used to calculate the minimum distance from each pixel to a certain feature, typically edges in a binary image. For instance, given a binary image of a printed circuit board (PCB), one may need to find the distance from each pixel to the nearest … Read more

💡 Problem Formulation: Implementing a square box filter operation in OpenCV with Python is a common image processing task. It involves smoothing an image by averaging the pixel values within a square ‘window’ that slides across the image. A typical input could be a noisy image, and the desired output is a filtered image where … Read more

💡 Problem Formulation: When working with image processing tasks, one common problem is the detection of lines within an image. Users need to be able to input an image with linear shapes and extract the precise mathematical representations of these lines. The probabilistic Hough Transform in OpenCV Python is a technique used to detect lines … Read more

💡 Problem Formulation: Adding borders to images can be essential in various applications such as photography, graphic design, or when preparing images for machine learning tasks to maintain a consistent size. For instance, you might have an image of 200×300 pixels and want to add a 50-pixel-wide border to all sides, resulting in a final … Read more

💡 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: When displaying two datasets with different scales together on a single chart, it’s essential to create a secondary y-axis for clear and accurate data representation. This is often required when combining datasets like temperature and precipitation or stock prices with volume traded, where each dataset varies drastically in scale and requires separate … Read more

💡 Problem Formulation: When visualizing data using Plotly in Python, we often want to display multiple pieces of text information on a graph to enhance data comprehension. For instance, given a DataFrame with columns for sales, profit, and product names, how can we create a plot that displays all these attributes as text labels on … Read more

💡 Problem Formulation: Users often require the capability to display multiple data visualizations simultaneously on a single web page within a Plotly Dash app. This is particularly useful for comparing different datasets, monitoring various metrics, or simply providing a comprehensive dashboard. We aim to tackle this by detailing multiple approaches to inserting several Plotly graphs … Read more