LEARN OPENCV in 3 HOURS with Python | Including 3xProjects | Computer Vision

Introduction

This tutorial is designed to get you started with OpenCV in Python, a powerful library for computer vision applications. Whether you are a beginner or looking to enhance your skills, this guide will walk you through the installation process, fundamental concepts, and practical projects to help you gain real-world experience in computer vision.

Step 1: Installation of Python and PyCharm

To begin working with OpenCV, you need to set up your development environment.

1. Install Python

   • Download Python version 3.7.6 from the official website: Python Download.
   • Follow the installation instructions specific to your operating system.

2. Install PyCharm

   • Download the Community Edition of PyCharm from: PyCharm Download.
   • Install PyCharm by following the setup instructions.

Step 2: Understanding Images in OpenCV

Before diving into coding, familiarize yourself with how OpenCV handles images.

• OpenCV represents images as multi-dimensional arrays.
• Basic image operations include reading, displaying, and manipulating pixel values.
• Use the following code snippet to load and display an image:

import cv2

# Load an image
image = cv2.imread('path_to_your_image.jpg')

# Display the image
cv2.imshow('Image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

Step 3: Core Concepts of OpenCV

Understanding the core principles will help you effectively use OpenCV.

• Color Spaces: Learn about different color representations, such as BGR and RGB.
• Image Processing Techniques: Familiarize yourself with resizing, cropping, and filtering images.
• Drawing Functions: Practice using functions to draw shapes and text on images.

Step 4: Project 1 - Color Detection

Create a simple project to detect specific colors in real-time.

1. Set Up the Environment

   • Import necessary libraries.

2. Capture Video

   • Use the following code to capture video from your webcam:

cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    # Add color detection logic here
    cv2.imshow('Video', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

3. Implement Color Detection Logic
   • Convert the frame to HSV color space and apply color filtering.

Step 5: Project 2 - Shape Detection

Enhance your skills by detecting shapes in images.

1. Load Image: Use cv2.imread() to read an image.
2. Convert to Grayscale: Use cv2.cvtColor().
3. Edge Detection: Apply Canny edge detection.
4. Find Contours: Use cv2.findContours() to detect shapes.

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray, 50, 150)
contours, _ = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

Step 6: Project 3 - License Plate Recognition

This project will focus on recognizing vehicle number plates.

1. Capture or Load Video: Use the same webcam setup as in Project 1.
2. Preprocess Frames: Convert to grayscale and apply Gaussian blur.
3. Use OCR: Integrate Tesseract OCR for text recognition.

import pytesseract

# After preprocessing your frame
text = pytesseract.image_to_string(frame)
print(text)

Conclusion

By following these steps, you have learned to install OpenCV, understand fundamental concepts, and create three practical projects. Continue exploring OpenCV's extensive features and consider expanding your projects or integrating machine learning techniques for more advanced applications. Happy coding!