Eight Essential OpenCV Examples for Image Processing

OpenCV is a widely used library for computer‑vision tasks, offering many algorithms for image and video processing. Below are eight common examples that demonstrate basic operations.

01. Read Image

Read an image from disk using cv2.imread():

import cv2
image = cv2.imread('image.jpg')

02. Display Image

Show the loaded image in a window and wait for a key press:

import cv2
image = cv2.imread('image.jpg')
cv2.imshow('Image', image)
cv2.waitKey(0)

03. Grayscale Conversion

Convert the image to grayscale before displaying:

import cv2
image = cv2.imread('image.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.imshow('Image', gray)
cv2.waitKey(0)

04. Edge Detection

Detect edges using the Canny algorithm:

import cv2
image = cv2.imread('image.jpg')
edges = cv2.Canny(image, 100, 200)
cv2.imshow('Edges', edges)
cv2.waitKey(0)

05. Resize Image

Resize the image to a fixed size (500×500) with area interpolation:

import cv2
image = cv2.imread('image.jpg')
resized = cv2.resize(image, (500, 500), interpolation=cv2.INTER_AREA)
cv2.imshow('Resized Image', resized)
cv2.waitKey(0)

06. Gaussian Blur

Apply a Gaussian blur to smooth the image:

import cv2
image = cv2.imread('image.jpg')
blur = cv2.GaussianBlur(image, (5,5), 0)
cv2.imshow('Blurred Image', blur)
cv2.waitKey(0)

07. Face Detection

Detect faces using a Haar cascade classifier and draw rectangles around them:

import cv2
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
image = cv2.imread('image.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
    cv2.rectangle(image, (x, y), (x+w, y+h), (255, 0, 0), 2)
cv2.imshow('Faces', image)
cv2.waitKey(0)

08. Conclusion

OpenCV is a powerful tool for a wide range of image and video processing tasks. The eight examples above illustrate just a fraction of its capabilities; they can be combined and extended to meet specific project requirements.