In the ever-evolving field of computer vision(OpenCV), edge detection is a fundamental technique that plays an important role in various applications like image processing, object detection, and so on. It does not matter whether you are a new data analyst or an experienced data scientist who wants to expand your toolkit, understanding and implementing edge detection is considered to be a valuable skill. In this blog, with the help of OpenCV, we are going to create a tutorial-level program to understand the workings of real-time edge detection using Laplacian and Canny methods.

What is Edge Detection in OpenCV?

Edge detection is a technique that is used to identify boundaries within an image. It highlights significant changes in brightness, which usually mark the edges of objects. By detecting these edges, we can simplify the image and focus on its important structural details.

Let’s get started

Step 1: Libraries installation

In this program, we need to install numpy and opencv and import them as we are going to call functions from these libraries. For that, we have to open the terminal and run

# Go to terminal and run these commands one by one to install both libraries

pip install opencv-python
pip install numpy

Step 2: Import libraries and capture video from the camera.

We need to import both libraries and declare a variable that starts capturing video from the camera.

import cv2 as cv
import numpy as np

camera = cv.VideoCapture(0)        # To access your laptop camera
# camera=cv.VideoCapture('video.mp4')

You are reading OpenCV: Edge detection in 6 steps

Step 3: Real-time video processing.

Now we are going to create an infinity loop to capture video continuously.

while True:
    ret,frame= camera.read()

    cv.imshow('camera',frame)      # It will display the original video.Window name camera

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Step 4: Applying Laplacian Edge Detection

Laplacian is simpler and faster but more sensitive to noise, resulting in potentially more false edges.

# inside the while loop
    laplacian=cv.Laplacian(frame,cv.CV_64F)
    laplacian=np.uint8(laplacian)
    cv.imshow('Laplacian',laplacian)            # window name--- Laplacian

Step 5: Applying Canny Edge Detection

Canny is more complex and computationally intensive but provides cleaner and more accurate edge detection with noise reduction and edge tracking mechanisms.

# inside the while loop
    edges = cv.Canny(frame, 100, 200)
    cv.imshow('Canny', edges)                  # window name----- Canny

You are reading OpenCV: Edge detection in 6 steps

Step 6: To stop the infinity loop and to end all the screens

# inside the loop
    if cv.waitKey(5)==ord('x'):    # press  x and it will end the loop
        break

# Out of the loop
camera.release()
cv.destroyAllWindows()

This program captures video from your webcam, processes each frame using Laplacian and Canny edge detection methods, and displays the original and processed frames in real time. The loop continues until you press the ‘x’ key, at which point it releases the camera and closes the windows.

Why Edge Detection of opencv Matters

Edge detection is a crucial step in many computer vision applications. Here are a few reasons why it’s important:

1. Simplification: By reducing the amount of data in an image, edge detection simplifies an image, making it easier to process later.
   
2. Feature Extraction: Edges are rich sources of information that helps to identify and isolate important features within an image.
   
3. Object Recognition: Detecting edges is often the first step in recognizing and classifying objects within an image.

Where it is used

Edge detection has a wide range of applications across various domains, including:

• Medical Imaging: Enhancing the boundaries of organs and tissues in medical images.
  
• Automated Inspection: Identifying defects or features in manufacturing processes.
  
• Robotics: Enabling robots to understand and navigate their environment.
  
• Image Segmentation: Dividing an image into meaningful regions for analysis.

Edge detection is a foundational technique in computer vision, enabling the identification of significant transitions in intensity that correspond to object boundaries. With OpenCV, you can easily implement real-time edge detection using methods like Laplacian and Canny. This tutorial has provided a hands-on approach to understanding and applying these methods, laying the groundwork for further exploration and application in your data science projects.

By mastering edge detection and other image processing techniques, you’ll be well-equipped to tackle a wide range of computer vision challenges, opening up new possibilities in your data science career.

You were reading OpenCV: Edge detection in 6 steps

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