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Transform Images into Stunning Pencil Sketches with Python and OpenCV 🎨🖌️

#python #opencv

Have you ever wondered how to transform your photos into beautiful pencil sketches? With Python and the powerful OpenCV library, you can create a script that achieves just that. In this article, I’ll walk you through a project I recently developed that converts images into pencil sketches in just a few lines of code.

Let’s dive into the details of how it works!

🚀 The Goal

The main objective of this project is to take an image as input and process it step-by-step to generate a pencil sketch version of it. The output is a stunning, artistic rendering that looks as though it’s been drawn by hand.

✨ Features:

  • Simple and lightweight script.

  • Uses OpenCV, a popular image processing library.

  • Converts any image to a pencil sketch in seconds.

  • Easily extendable for batch processing or web integration.

🛠️ Tools and Technologies

To build this project, I used:

  • Python: The core programming language for this project.

  • OpenCV: A robust library for computer vision and image processing tasks.

📄 Code Breakdown

Here’s the complete code:

import cv2 def create_sketch(input_image_path, output_image_path): """ Converts an image into a pencil sketch and saves the result. Args: input_image_path (str): Path to the input image file. output_image_path (str): Path to save the resulting sketch. """ # Load the image image = cv2.imread(input_image_path) if image is None: raise FileNotFoundError(f"Image not found at {input_image_path}") # Convert the image to grayscale grey_img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Invert the grayscale image inverted_img = cv2.bitwise_not(grey_img) # Apply Gaussian blur to the inverted image blurred_img = cv2.GaussianBlur(inverted_img, (21, 21), 0) # Invert the blurred image inverted_blurred_img = cv2.bitwise_not(blurred_img) # Create the pencil sketch by dividing the grayscale image by the inverted blurred image sketch = cv2.divide(grey_img, inverted_blurred_img, scale=256.0) # Save the resulting sketch cv2.imwrite(output_image_path, sketch) # Example usage create_sketch('test.jpeg', 'image_coloring.png')
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🧩 Step-by-Step Explanation

  1. Reading the Image:

    • The cv2.imread() function loads the image from the specified path.
    • If the image is not found, an error is raised to prevent further execution.

  2. Converting to Grayscale:

    • Using cv2.cvtColor(), the image is converted into grayscale for simplicity. This reduces the color channels to a single intensity channel.

  3. Inverting the Image:

    • The grayscale image is inverted using cv2.bitwise_not(). This creates a negative of the original grayscale image.

  4. Blurring:

    • A Gaussian blur is applied to the inverted image using cv2.GaussianBlur(). This smooths the image, simulating the effect of a pencil stroke.

  5. Creating the Sketch:

    • The final sketch is generated using cv2.divide(), which divides the grayscale image by the inverted blurred image, adjusting for contrast with the scale parameter.

  6. Saving the Sketch:

    • The processed sketch is saved to the specified output path with cv2.imwrite().

✨ Example Output

Here’s what the process looks like visually:

  1. Original Image:

    Original Image

  2. Pencil Sketch:

    Pencil Sketch

🚀 Next Steps

If you want to extend this project, here are a few ideas:

  1. Batch Processing:

    Process multiple images in a folder and save the sketches automatically.

  2. Web App:

    Build a simple Flask or Django app to upload images and download sketches.

  3. Customization:

    Allow users to tweak parameters like blur intensity or sketch contrast.

  4. Real-Time Sketching:

    Integrate a webcam feed to apply the sketch effect in real-time.

  5. Integration with Social Media:

    Automatically share the generated sketches to platforms like Instagram or Twitter.

💡 Final Thoughts

This pencil sketch project showcases how easy it is to combine Python and OpenCV to achieve amazing results. It’s a great starting point for anyone looking to dive into computer vision or add some artistic flair to their projects.

Whether you're a beginner exploring image processing or a seasoned developer looking to create something creative, this project is a fun and rewarding challenge.

I’d love to hear your thoughts or see your own implementations! Feel free to leave a comment or share your versions below. Let’s keep creating! 🎉

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