Learn Opencv - 10 Code Examples & CST Typing Practice Test

OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning library that provides tools for real-time image and video processing across multiple platforms.

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OpenCV Simple Image Read and Display OpenCV Convert Image to Grayscale OpenCV Resize Image OpenCV Draw Shapes on Image OpenCV Image Thresholding OpenCV Edge Detection OpenCV Video Capture OpenCV Image Blurring OpenCV Face Detection with Haar Cascades OpenCV Video Frame Processing

Learn OPENCV with Real Code Examples

Updated Nov 24, 2025

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Code Sample Descriptions

OpenCV Simple Image Read and Display

import cv2

img = cv2.imread('example.jpg')
cv2.imshow('Image', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

A minimal OpenCV example reading an image and displaying it in a window.

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OpenCV Convert Image to Grayscale

import cv2

img = cv2.imread('example.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imshow('Gray Image', gray)
cv2.waitKey(0)
cv2.destroyAllWindows()

Reads an image and converts it to grayscale.

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OpenCV Resize Image

import cv2

img = cv2.imread('example.jpg')
resized = cv2.resize(img,(200,200))
cv2.imshow('Resized', resized)
cv2.waitKey(0)
cv2.destroyAllWindows()

Resizes an image to a specific width and height.

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OpenCV Draw Shapes on Image

import cv2
import numpy as np

img = np.zeros((300,300,3), dtype=np.uint8)
cv2.rectangle(img,(50,50),(250,250),(0,255,0),2)
cv2.circle(img,(150,150),50,(255,0,0),2)
cv2.line(img,(0,0),(300,300),(0,0,255),2)
cv2.imshow('Shapes', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

Draws rectangle, circle, and line on an image.

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OpenCV Image Thresholding

import cv2

img = cv2.imread('example.jpg',0)
_,thresh = cv2.threshold(img,127,255,cv2.THRESH_BINARY)
cv2.imshow('Threshold', thresh)
cv2.waitKey(0)
cv2.destroyAllWindows()

Applies binary thresholding to a grayscale image.

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OpenCV Edge Detection

import cv2

img = cv2.imread('example.jpg',0)
edges = cv2.Canny(img,100,200)
cv2.imshow('Edges', edges)
cv2.waitKey(0)
cv2.destroyAllWindows()

Detects edges using the Canny algorithm.

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OpenCV Video Capture

import cv2

cap = cv2.VideoCapture(0)
while True:
    ret, frame = cap.read()
    cv2.imshow('Webcam', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

Captures video from webcam and displays it.

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OpenCV Image Blurring

import cv2

img = cv2.imread('example.jpg')
blur = cv2.GaussianBlur(img,(5,5),0)
cv2.imshow('Blurred', blur)
cv2.waitKey(0)
cv2.destroyAllWindows()

Applies Gaussian blur to an image.

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OpenCV Face Detection with Haar Cascades

import cv2

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

Detects faces in an image using Haar cascades.

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OpenCV Video Frame Processing

import cv2

cap = cv2.VideoCapture(0)
while True:
    ret, frame = cap.read()
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    cv2.imshow('Gray Video', gray)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

Processes video frame by frame and converts to grayscale.

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Frequently Asked Questions about Opencv

What is Opencv?

OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning library that provides tools for real-time image and video processing across multiple platforms.

What are the primary use cases for Opencv?

Image and video processing (filtering, transformations, enhancement). Object detection and recognition. Facial recognition and emotion detection. Motion tracking and optical flow analysis. Machine learning integration for vision-based applications

What are the strengths of Opencv?

Open-source with active community. Extensive documentation and tutorials. High performance for real-time applications. Wide range of algorithms for classical CV tasks. Cross-language support for developers

What are the limitations of Opencv?

Steeper learning curve for beginners. Limited high-level deep learning features compared to frameworks. Sometimes inconsistent API between C++ and Python. GPU support requires setup with CUDA or OpenCL. Not ideal for large-scale training from scratch

How can I practice Opencv typing speed?

CodeSpeedTest offers 10+ real Opencv code examples for typing practice. You can measure your WPM, track accuracy, and improve your coding speed with guided exercises.

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Code Sample Descriptions

OpenCV Simple Image Read and Display

import cv2

img = cv2.imread('example.jpg')
cv2.imshow('Image', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

A minimal OpenCV example reading an image and displaying it in a window.

Let’s Try →

OpenCV Convert Image to Grayscale

import cv2

img = cv2.imread('example.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imshow('Gray Image', gray)
cv2.waitKey(0)
cv2.destroyAllWindows()

Reads an image and converts it to grayscale.

Let’s Try →

OpenCV Resize Image

import cv2

img = cv2.imread('example.jpg')
resized = cv2.resize(img,(200,200))
cv2.imshow('Resized', resized)
cv2.waitKey(0)
cv2.destroyAllWindows()

Resizes an image to a specific width and height.

Let’s Try →

OpenCV Draw Shapes on Image

import cv2
import numpy as np

img = np.zeros((300,300,3), dtype=np.uint8)
cv2.rectangle(img,(50,50),(250,250),(0,255,0),2)
cv2.circle(img,(150,150),50,(255,0,0),2)
cv2.line(img,(0,0),(300,300),(0,0,255),2)
cv2.imshow('Shapes', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

Draws rectangle, circle, and line on an image.

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OpenCV Image Thresholding

import cv2

img = cv2.imread('example.jpg',0)
_,thresh = cv2.threshold(img,127,255,cv2.THRESH_BINARY)
cv2.imshow('Threshold', thresh)
cv2.waitKey(0)
cv2.destroyAllWindows()

Applies binary thresholding to a grayscale image.

Let’s Try →

OpenCV Edge Detection

import cv2

img = cv2.imread('example.jpg',0)
edges = cv2.Canny(img,100,200)
cv2.imshow('Edges', edges)
cv2.waitKey(0)
cv2.destroyAllWindows()

Detects edges using the Canny algorithm.

Let’s Try →

OpenCV Video Capture

import cv2

cap = cv2.VideoCapture(0)
while True:
    ret, frame = cap.read()
    cv2.imshow('Webcam', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

Captures video from webcam and displays it.

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OpenCV Image Blurring

import cv2

img = cv2.imread('example.jpg')
blur = cv2.GaussianBlur(img,(5,5),0)
cv2.imshow('Blurred', blur)
cv2.waitKey(0)
cv2.destroyAllWindows()

Applies Gaussian blur to an image.

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OpenCV Face Detection with Haar Cascades

import cv2

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

Detects faces in an image using Haar cascades.

Let’s Try →

OpenCV Video Frame Processing

import cv2

cap = cv2.VideoCapture(0)
while True:
    ret, frame = cap.read()
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    cv2.imshow('Gray Video', gray)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

Processes video frame by frame and converts to grayscale.

Let’s Try →

Frequently Asked Questions about Opencv

What is Opencv?

OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning library that provides tools for real-time image and video processing across multiple platforms.

What are the primary use cases for Opencv?

What are the strengths of Opencv?

What are the limitations of Opencv?

How can I practice Opencv typing speed?

CodeSpeedTest offers 10+ real Opencv code examples for typing practice. You can measure your WPM, track accuracy, and improve your coding speed with guided exercises.