Holistically-Nested Edge Detection with OpenCV and Deep Learning

Edge detection is a fundamental image processing task that identifies boundaries within images, making it valuable for applications in computer vision. Holistically-Nested Edge Detection (HED) is a powerful deep learning approach that performs multi-scale edge detection, producing cleaner and more accurate results. In this guide, we’ll build a basic edge detection project using Python, OpenCV, and a deep learning-based method, focusing on how HED works in a simplified context.

We’ll walk through every component, ensuring even beginners can follow along. By the end, you’ll have a functional edge detection GUI that uses adjustable parameters to visualize edges in images.

Table of Contents

1. Prerequisites
2. Setting up the Dataset
3. Loading and Displaying Images
4. Creating a Simple Edge Detector
5. Building the GUI with Widgets
6. FAQs

Introduction to Edge Detection

Edge detection is a fundamental task in computer vision that identifies the boundaries within images. It’s widely used in applications like object detection, segmentation, and image recognition. By detecting edges, we can obtain…