💡 Problem Formulation: Data visualization often requires the representation of multiple datasets side-by-side for comparison. In Python, using Plotly, one may want to create a single figure containing multiple subplots. This article discusses how to take separate Plotly figures and organize them into subplots within one encompassing figure. The desired output is a cohesive visualization … Read more
💡 Problem Formulation: In computer vision, precisely quantifying the shape of objects within an image is a common task. This article addresses the challenge of computing the area and perimeter of image contours using OpenCV with Python. Imagine you have an image with a single prominent object – your goal is to calculate the size … Read more
💡 Problem Formulation: In the realm of computer vision, quantifying the solidity and equivalent diameter of objects in an image can be crucial for applications like quality control, object sorting, or biological measurements. Solidity is the ratio of contour area to its convex hull area, while the equivalent diameter is the diameter of a circle … Read more
💡 Problem Formulation: When working with image processing in OpenCV using Python, a common task is to determine the size or extent of an object within an image. This involves detecting an object and measuring the area it covers relative to its bounding box. For instance, if we have an image of a sky with … Read more
💡 Problem Formulation: When working with images, determining the aspect ratio of an object within it can be essential for various tasks such as object recognition, resizing, or altering objects proportionally. The aspect ratio is the ratio of the width to the height of an object. This article focuses on extracting this ratio using OpenCV … Read more
💡 Problem Formulation: Applying a bilateral filter to an image involves reducing unwanted noise while keeping edges sharp. In OpenCV, we aim to efficiently perform this task using Python. An example input would be a noisy image, and the desired output is a clear, denoised image with well-preserved edges. Method 1: Standard Bilateral Filtering This … Read more
💡 Problem Formulation: Many computer vision tasks involve identifying and tracking objects within images. Sometimes, these objects are best represented geometrically—a common case being elliptical shapes for objects such as eyes, wheels, or planets. For example, given an image of a car, we may wish to fit an ellipse around one of the wheels to … Read more
💡 Problem Formulation: In the realm of computer vision and image processing, there are situations where combining two images becomes necessary. Specifically, the bitwise OR operation allows for the merging of two images, pixel by pixel, such that each bit is set to 1 if either pixel’s corresponding bit is 1. For instance, when overlaying … Read more
💡 Problem Formulation: Watermarking images can be essential for copyright protection or branding purposes. This article will walk through how to add a watermark to an image using Python’s OpenCV library, starting with a base image (e.g., a landscape photo) and rendering a transparent text or logo overlay as the desired output. Method 1: Overlaying … Read more
💡 Problem Formulation: In data science tasks, often there is a need to convert datasets into dictionary-like objects for further processing or feature extraction. This article explains how to use Python’s Scikit-Learn library to accomplish this, specifically demonstrating how to convert datasets into a format that resembles Python dictionaries, where keys correspond to feature names … Read more
💡 Problem Formulation: In image processing, finding the minimum enclosing circle for an object is a common task that involves identifying the smallest circle that can completely enclose the target object. This problem is relevant in scenarios such as object tracking, shape analysis, and computer vision applications. The input is an image with an object, … Read more
💡 Problem Formulation: Transforming continuous or categorical data into a binary format is often a necessary preprocessing step in machine learning. Binarization turns your feature values into zeros and ones based on a threshold. For example, given an input array [1, 2, 3, 4], you might want to consider values greater than or equal to … Read more