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Real-Time Sketch Generation using Adaptive Thresholding

📌 Project Overview

This Python project demonstrates real-time sketch generation from a webcam feed using OpenCV. The program captures video frames, processes them using image processing techniques (e.g., grayscale conversion, Gaussian blur, and Canny edge detection), and generates a sketch-like output.

🚀 Key Features

  1. Grayscale Conversion and Blur:
    • Each frame is converted to grayscale to simplify processing and the grayscale image is blurred to reduce noise and smooth edges.
  2. Edge Detection:
    • Edges are detected using intensity gradients, highlighting key features.
  3. Thresholding and Output:
    • Edges are thresholded and displays live webcam feed with processed sketch like output.

🔍 How It Works

  1. Grayscale Conversion:
    • Converts frames to grayscale using cv2.cvtColor(), reducing complexity and focusing on intensity.
  2. Gaussian Blur:
    • Applies a Gaussian filter to reduce noise and improve edge detection.
  3. Canny Edge Detection:
    • Detects edges using cv2.Canny() based on intensity gradients.
  4. Adaptive Thresholding:
    • Converts edges into a binary image using cv2.adaptiveThreshold() for enhanced sketch effects.

🛠 System Requirements

Dependencies

  • Python 3.8+
  • Libraries: opencv-python

📄 License

This project is licensed under the MIT License. See the LICENSE file for details.