OpenCV in Python: A Full Tutorial for Beginners (With Examples)

Computer vision powers everything from facial recognition to autonomous vehicles — and OpenCV (Open Source Computer Vision Library) is the most widely used open-source tool for this field. With Python, OpenCV becomes incredibly accessible and flexible.

This tutorial will take you from installing OpenCV to performing advanced tasks like image processing, face detection, and real-time video analysis — all with Python.

What is OpenCV?

OpenCV (Open Source Computer Vision Library) is an open-source toolkit for real-time computer vision, developed initially by Intel. It supports:

Image and video processing
Object and face detection
Machine learning models for visual data
Real-time video analysis

It’s fast, written in C/C++, but has bindings for Python, Java, and other languages.

Installing OpenCV in Python

You can install OpenCV using pip:

pip install opencv-python

For advanced features like video codecs or GUI, install the full version:

pip install opencv-contrib-python

Then import it in your Python script:

import cv2

Loading and Displaying Images

import cv2

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

# Show the image
cv2.imshow('My Image', img)

# Wait for key press and close window
cv2.waitKey(0)
cv2.destroyAllWindows()

Convert to Grayscale

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imshow('Gray Image', gray)

Saving Images

cv2.imwrite('gray_cat.jpg', gray)

Reading and Displaying Video

cap = cv2.VideoCapture(0)  # 0 for webcam

while True:
    ret, frame = cap.read()
    cv2.imshow('Live Video', frame)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

Image Resizing, Cropping, and Drawing

Resize Image

resized = cv2.resize(img, (300, 300))

Crop Image

cropped = img[50:200, 100:300]  # y1:y2, x1:x2

Draw Shapes and Text

cv2.rectangle(img, (50, 50), (200, 200), (0, 255, 0), 2)
cv2.circle(img, (150, 150), 40, (255, 0, 0), 3)
cv2.putText(img, 'Hello OpenCV', (50, 300), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)

Image Processing Techniques

1. Blurring

blurred = cv2.GaussianBlur(img, (7, 7), 0)

2. Edge Detection (Canny)

edges = cv2.Canny(img, 100, 200)

3. Thresholding

ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)

4. Erosion and Dilation

kernel = np.ones((5, 5), np.uint8)
dilated = cv2.dilate(thresh, kernel, iterations=1)
eroded = cv2.erode(thresh, kernel, iterations=1)

Face Detection with Haar Cascades

face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

img = cv2.imread('face.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)

for (x, y, w, h) in faces:
    cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)

cv2.imshow('Face Detection', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

Contours and Shape Detection

contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(img, contours, -1, (0, 255, 0), 2)

Color Detection in Images

hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

# Define blue color range
lower_blue = np.array([110,50,50])
upper_blue = np.array([130,255,255])

mask = cv2.inRange(hsv, lower_blue, upper_blue)
result = cv2.bitwise_and(img, img, mask=mask)

cv2.imshow('Detected Blue', result)

Useful OpenCV Functions Summary

Function	Description
`cv2.imread()`	Load image
`cv2.imshow()`	Display image
`cv2.resize()`	Resize image
`cv2.cvtColor()`	Convert color space
`cv2.VideoCapture()`	Capture video stream
`cv2.Canny()`	Detect edges
`cv2.threshold()`	Apply binary threshold
`cv2.findContours()`	Detect shapes
`cv2.putText()`	Add text to image

OpenCV + AI: Go Further

Once you’ve mastered the basics, OpenCV pairs beautifully with:

TensorFlow / PyTorch for deep learning
MediaPipe for hand and face tracking
YOLO / OpenVINO for real-time object detection

Final Thoughts

OpenCV + Python unlocks a powerful combination for anyone interested in:

Image editing
Real-time computer vision apps
Surveillance, robotics, or AR

Whether you’re a beginner or building your first face detection system, this tutorial gives you a solid foundation.

What to Build Next?

Face mask detector
Motion detection camera
Real-time barcode/QR scanner
Image filters (like Instagram!)
Handwriting recognition app