Computer Vision with Python: Image Processing and Object Detection
Welcome to Stage 6, where we explore one of the most exciting frontiers of artificial intelligence—computer vision. With Python and the power of libraries like OpenCV and TensorFlow, you can teach machines to see, recognize objects, and even interpret visual data.
In this guide, we’ll cover the fundamentals of image processing and introduce object detection in Python.
1. What is Computer Vision?
Computer vision is the field of AI that enables computers to interpret and understand images and videos.
It’s used in:
- Facial recognition systems
- Medical imaging
- Automated quality inspection in factories
- Surveillance and security
- Augmented reality and gesture control
Computer vision systems mimic human sight—using algorithms to detect, classify, and respond to visual inputs.
2. Getting Started with OpenCV
OpenCV (Open Source Computer Vision Library) is the most widely used toolkit for image and video processing in Python.
✅ Install with:
pip install opencv-python
✅ Load and display an image:
import cv2
image = cv2.imread('cat.jpg')
cv2.imshow('My Image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()
✅ Convert to grayscale:
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.imshow('Grayscale', gray)
You can perform operations like resizing, cropping, blurring, edge detection, and more.
3. Drawing, Filtering, and Face Detection
You can draw on images or apply filters easily:
# Draw a rectangle
cv2.rectangle(image, (50, 50), (200, 200), (255, 0, 0), 2)
# Apply Gaussian blur
blurred = cv2.GaussianBlur(image, (5, 5), 0)
✅ Face detection with Haar Cascades:
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)
for (x, y, w, h) in faces:
cv2.rectangle(image, (x, y), (x+w, y+h), (255, 0, 0), 2)
This lets you detect human faces in images using a pre-trained classifier.
4. Real-Time Object Detection with TensorFlow and YOLO
For advanced applications, you can use pre-trained deep learning models like:
- YOLO (You Only Look Once)
- SSD (Single Shot MultiBox Detector)
- MobileNet + TensorFlow Lite for mobile apps
Using TensorFlow:
pip install tensorflow opencv-python
Load a pre-trained model and detect objects from a webcam feed or video stream.
5. Real-World Applications of Computer Vision in Automation
- Attendance system with face recognition
- License plate detection
- Retail analytics (people counting, shelf monitoring)
- Quality control in manufacturing
- Health diagnostics from X-ray/CT scans
You can integrate computer vision with automation scripts to make systems smarter, faster, and more accurate.
Conclusion: Now Your Code Can See! 👁️
By learning computer vision with Python, you now enable your apps and systems to understand the world visually. From simple filters to face detection and real-time object recognition, you’ve entered the realm of intelligent perception.
👉 Next up: Stage 7 — Natural Language Processing (NLP) with Python for Text-Based AI
