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: https://edge-detection-u7f3.onrender.com 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>
    
    <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>
    
    <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. Resources GitHub Repository: Edge Detection App 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: https://edge-detection-u7f3.onrender.com https://edge-detection-u7f3.onrender.com Prerequisites Python 3.x installed on your machine. Basic knowledge of Python programming. Familiarity with HTML and CSS is helpful but not required. 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 pip install opencv-python numpy Flask 2. Create the Project Directory mkdir edge_detection_app
cd edge_detection_app 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: 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 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: Code Implementation: # Apply Canny edge detector
edges = cv2.Canny(image, threshold1=100, threshold2=200) # 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 : 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) 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: 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') @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) 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) @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) 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 upload.html Create a templates directory and add upload.html : templates upload.html <!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>Edge Detection App</title>
    
    <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> <!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>Edge Detection App</title>
    
    <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 result.html Create result.html in the templates directory: result.html templates <!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>Edge Detection Results</title>
    
    <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> <!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>Edge Detection Results</title>
    
    <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 python app.py 2. Access the Application Open your web browser and navigate to http://localhost:5000 . http://localhost:5000 Upload an image and click Upload and Process. View the edge detection results. Upload an image and click Upload and Process . 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. Enhance the Application : Add more edge detection options or allow parameter adjustments. Enhance the Application Improve the UI : Incorporate more Bootstrap components for a better user experience. Improve the UI Explore Further : Deploy the app on other platforms like Heroku or AWS. Explore Further Resources GitHub Repository: Edge Detection App GitHub Repository : Edge Detection App GitHub Repository Edge Detection App

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Implementing Edge Detection with Python and OpenCV: A Step-by-Step Guide

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