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Implementing Edge Detection with Python and OpenCV: A Step-by-Step Guide
Implementing Edge Detection with Python and OpenCV: A Step-by-Step Guide
Introduction
Edge detection is fundamental in computer vision, allowing us to identify object boundaries within images. In this tutorial, we'll implement edge detection using the Sobel operator and the Canny edge detector with Python and OpenCV. We'll then create a simple web application using Flask, styled with Bootstrap, to allow users to upload images and view the results.
DEMO LINK: Edge Detection Demo
Prerequisites
- Python 3.x installed on your machine.
- Basic knowledge of Python programming.
- Familiarity with HTML and CSS is helpful but not required.
Setting Up the Environment
1. Install Required Libraries
Open your terminal or command prompt and run:
pip install opencv-python numpy Flask
2. Create the Project Directory
mkdir edge_detection_app cd edge_detection_app
Implementing Edge Detection
1. The Sobel Operator
The Sobel operator calculates the gradient of image intensity, emphasizing edges.
Code Implementation:
import cv2
# Load the image in grayscale
image = cv2.imread('input_image.jpg', cv2.IMREAD_GRAYSCALE)
if image is None:
print("Error loading image")
exit()
# Apply Sobel operator
sobelx = cv2.Sobel(image, cv2.CV_64F, 1, 0, ksize=5) # Horizontal edges
sobely = cv2.Sobel(image, cv2.CV_64F, 0, 1, ksize=5) # Vertical edges
2. The Canny Edge Detector
The Canny edge detector is a multi-stage algorithm for detecting edges.
Code Implementation:
# Apply Canny edge detector edges = cv2.Canny(image, threshold1=100, threshold2=200)
Creating a Flask Web Application
1. Set Up the Flask App
Create a file named app.py:
from flask import Flask, request, render_template, redirect, url_for import cv2 import os app = Flask(__name__) UPLOAD_FOLDER = 'static/uploads/' OUTPUT_FOLDER = 'static/outputs/' app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER app.config['OUTPUT_FOLDER'] = OUTPUT_FOLDER # Create directories if they don't exist os.makedirs(UPLOAD_FOLDER, exist_ok=True) os.makedirs(OUTPUT_FOLDER, exist_ok=True)
2. Define Routes
Upload Route:
@app.route('/', methods=['GET', 'POST'])
def upload_image():
if request.method == 'POST':
file = request.files.get('file')
if not file or file.filename == '':
return 'No file selected', 400
filepath = os.path.join(app.config['UPLOAD_FOLDER'], file.filename)
file.save(filepath)
process_image(file.filename)
return redirect(url_for('display_result', filename=file.filename))
return render_template('upload.html')
Process Image Function:
def process_image(filename):
image_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
# Apply edge detection
sobelx = cv2.Sobel(image, cv2.CV_64F, 1, 0, ksize=5)
edges = cv2.Canny(image, 100, 200)
# Save outputs
cv2.imwrite(os.path.join(app.config['OUTPUT_FOLDER'], 'sobelx_' + filename), sobelx)
cv2.imwrite(os.path.join(app.config['OUTPUT_FOLDER'], 'edges_' + filename), edges)
Result Route:
@app.route('/result/<filename>')
def display_result(filename):
return render_template('result.html',
original_image='uploads/' + filename,
sobelx_image='outputs/sobelx_' + filename,
edges_image='outputs/edges_' + filename)
3. Run the App
if __name__ == '__main__':
app.run(debug=True)
Styling the Web Application with Bootstrap
Include Bootstrap CDN in your HTML templates for styling.
1. upload.html
Create a templates directory and add upload.html:
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>Edge Detection App</title>
<!-- Bootstrap CSS CDN -->
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.5.2/css/bootstrap.min.css">
</head>
<body>
<div class="container mt-5">
<h1 class="text-center mb-4">Upload an Image for Edge Detection</h1>
<div class="row justify-content-center">
<div class="col-md-6">
<form method="post" enctype="multipart/form-data" class="border p-4">
<div class="form-group">
<label for="file">Choose an image:</label>
<input type="file" name="file" accept="image/*" required class="form-control-file" id="file">
</div>
<button type="submit" class="btn btn-primary btn-block">Upload and Process</button>
</form>
</div>
</div>
</div>
</body>
</html>
2. result.html
Create result.html in the templates directory:
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>Edge Detection Results</title>
<!-- Bootstrap CSS CDN -->
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.5.2/css/bootstrap.min.css">
</head>
<body>
<div class="container mt-5">
<h1 class="text-center mb-5">Edge Detection Results</h1>
<div class="row">
<div class="col-md-6 mb-4">
<h4 class="text-center">Original Image</h4>
<img src="{{ url_for('static', filename=original_image) }}" alt="Original Image" class="img-fluid rounded mx-auto d-block">
</div>
<div class="col-md-6 mb-4">
<h4 class="text-center">Sobel X</h4>
<img src="{{ url_for('static', filename=sobelx_image) }}" alt="Sobel X" class="img-fluid rounded mx-auto d-block">
</div>
<div class="col-md-6 mb-4">
<h4 class="text-center">Canny Edges</h4>
<img src="{{ url_for('static', filename=edges_image) }}" alt="Canny Edges" class="img-fluid rounded mx-auto d-block">
</div>
</div>
<div class="text-center mt-4">
<a href="{{ url_for('upload_image') }}" class="btn btn-secondary">Process Another Image</a>
</div>
</div>
</body>
</html>
Running and Testing the Application
1. Run the Flask App
python app.py
2. Access the Application
Open your web browser and navigate to http://localhost:5000.
- Upload an image and click Upload and Process.
- View the edge detection results.
SAMPLE RESULT
Conclusion
We've built a simple web application that performs edge detection using the Sobel operator and the Canny edge detector. By integrating Python, OpenCV, Flask, and Bootstrap, we've created an interactive tool that allows users to upload images and view edge detection results.
Next Steps
- Enhance the Application: Add more edge detection options or allow parameter adjustments.
- Improve the UI: Incorporate more Bootstrap components for a better user experience.
- Explore Further: Deploy the app on other platforms like Heroku or AWS.
GitHub Repository: Edge Detection App
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