From Pixels to Art: Mastering Edges in CV 🌟
Tags: computervision, tutorial, learning, python
Published: 2023-10-11 13:10:25

## The Magic of Edges in Computer Vision 🌟

Computer vision is the bridge between machines and the visual world. In this blog, we'll unravel the magic of **Edges** in computer vision using OpenCV.

> Imagine teaching a computer to identify object edges or enhance image details – these are just a glimpse of what's possible. We'll guide you from basics to practical applications, equipping you to harness the power of computer vision. ✨

## What are Edges? 🤔

![A Table Edge](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/9ioldmza6vmmvyomvmh4.jpeg)

Edges in images are like the outlines of objects in a coloring book 🖍️. They represent sudden changes in pixel intensity and help us distinguish one object from another. Imagine a photo of a cat against a white background: the edges of the cat's body define its shape and separate it from the background.

## Types of Edges 📏

Edges come in various flavors, just like the diverse shapes they outline. Here are a few common types:

![Types of Edges](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/hi26wynjsqtvdodj1kov.png)

| Step Edges | Ramp Edges | Roof Edges |
|------------|------------|------------|
| These are sharp, sudden transitions, similar to a black object on a white background. The edge is crisp, like a distinct line. ⚡ | Ramp edges depict a gradual and consistent change in intensity. They are like a gentle slope, where the transition is steady and continuous, resembling a ramp or incline. 🌄 | Roof-like edges have a gradual slope, like the slope of a roof. They represent smoother transitions in intensity, such as the edge of a shadow. 🏠 |

## Why Edges Matter 🌟

Edges are fundamental in computer vision for several reasons:

![Why edges matter](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/3xg53bzg5hnm8hv2iujm.png)

| Object Detection | Image Segmentation | Feature Extraction |
|------------------|---------------------|---------------------|
| Edges help identify objects in images. For example, in self-driving cars, detecting edges can help recognize pedestrians or other vehicles on the road. 🚗 | They assist in breaking down an image into smaller, manageable regions. In medical imaging, edges help separate organs or anomalies from the surrounding tissue. 🏥 | Edges are rich sources of information. In facial recognition systems, detecting the edges of facial features aids in identifying individuals. 👤 |

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## 🕵️‍♂️ Edge Detection: Sobel Operator 🌟

> The Sobel operator is one of the simplest and widely used edge detection techniques. It relies on convolution with a pair of 3x3 kernels, one for detecting vertical edges and the other for horizontal edges. By calculating the gradient in both directions, it highlights the areas of the image where intensity changes are most significant.

**Real-life Example**: Imagine you're processing a medical X-ray image. The Sobel operator can help identify the edges of bones and organs, making it easier for doctors to diagnose and locate issues within the body.

### Demo

Checkout the python code on how the algorithm works 

{% embed https://colab.research.google.com/drive/1CnABA274EJOzTquQGup-vnhuzTliSCDl?usp=sharing %}

### How does it work? 📐

The Sobel Operator uses two small grids (3x3 matrices) called kernels. 

![vertical and horizontal kernel](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/ko3om8pijqci3jp61ov6.png)

- The horizontal kernel emphasizes changes in brightness from left to right.
- The vertical kernel emphasizes changes in brightness from top to bottom.

It slides these kernels over each pixel in the image and calculates a new value for each pixel based on how much the colors change in these two directions.

### Let's take an example

Here are what the intensity values might look like after the grayscale conversion. 

![Example intensity](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/sbkb6p280dzii3734ei1.png)

1. Matrix and kernel are given (e.g., 3x3 kernel).

2. Position the kernel over the matrix, aligning the kernel's anchor with the target cell.

3. Multiply kernel (Both vertical & horizontal) values with overlapping matrix cells.

4. Sum the products and have the value as Gx and Gy
Get the magnitude(G) = square_root(Gx^2 + Gy^2) and direction of the gradient Ɵ = atan(Gy / Gx)

![vertical](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/v0qhgxxc3dc3wwird2qs.png)

![horizontal](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/53wmcfrmja6bwcuqvtbt.png)

Slide the kernel, repeat steps 3 and 4 for the entire matrix.

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## Conclusion 🚀

In a world of pixels, edges are like the secret agents of computer vision. They define the cool contours of objects, helping machines see and recognize stuff. So, whether it's spotting cats in photos or cruising in self-driving cars, edges are the VIPs of the visual world. Stay sharp! 😎👁️ #EdgeOfPossibility