Python for Computer Vision: OpenCV

Computer Vision is a rapidly growing field in the world of technology that has been changing the way we interact with our digital devices. It is a fascinating branch of artificial intelligence that focuses on enabling machines to interpret and understand images and video footage like humans. One of the most popular tools used for computer vision is OpenCV, an open-source library that provides a wide range of functions for image and video processing. In this guide, we will explore how Python, one of the most popular programming languages, can be used with OpenCV to create powerful computer vision applications.

Introduction to Computer Vision

Computer vision is a field of artificial intelligence (AI) that focuses on enabling computers to interpret and understand visual information from the world. It involves the development of algorithms and techniques that allow computers to automatically analyze and process images, videos, or live camera feeds.

Some common applications of computer vision include:

  • Object detection and tracking
  • Image and video classification
  • Facial recognition
  • Optical character recognition (OCR)
  • Augmented reality
  • Medical image analysis

Getting Started with OpenCV

OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning software library. It contains more than 2500 optimized algorithms for real-time computer vision tasks. OpenCV is written in C++ but has Python, Java and Matlab/Octave bindings as well.

To get started with OpenCV in Python, you’ll first need to install the OpenCV package. You can do this using pip:

pip install opencv-python

Once you have OpenCV installed, you can import the required module in your Python script:

import cv2

Basic Image Processing Techniques

In this section, we’ll cover some fundamental image processing techniques using OpenCV.

Loading and Displaying an Image

To load an image using OpenCV, you can use the cv2.imread() function:

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

To display the loaded image, use the cv2.imshow() function:

cv2.imshow('My Image', image)

Resizing and Rotating Images

To resize an image, use the cv2.resize() function:

resized_image = cv2.resize(image, (new_width, new_height))

To rotate an image, first calculate the rotation matrix using the cv2.getRotationMatrix2D() function and then apply the matrix using the cv2.warpAffine() function:

(height, width) = image.shape[:2]
center = (width // 2, height // 2)

rotation_matrix = cv2.getRotationMatrix2D(center, angle, scale)
rotated_image = cv2.warpAffine(image, rotation_matrix, (width, height))

Converting Images to Grayscale

To convert an image to grayscale, use the cv2.cvtColor() function:

gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

Working with Videos

OpenCV makes it easy to work with videos. In this section, we’ll cover how to capture and display video from a webcam and how to read and write video files.

Capturing Video from a Webcam

To capture video from a webcam, use the cv2.VideoCapture() function:

cap = cv2.VideoCapture(0)

To display the captured video frames, use a loop to read and show the frames:

while True:
    ret, frame =
    cv2.imshow('Webcam', frame)

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


Reading and Writing Video Files

To read a video file, use the cv2.VideoCapture() function with the file path:

cap = cv2.VideoCapture('video.mp4')
To write a video file, use the cv2.VideoWriter() function:

fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter('output.avi', fourcc, 20.0, (640, 480))

To read and write video frames, use a loop to process the frames and write them to the output file:

while cap.isOpened():
    ret, frame =
    if ret:
cv2.imshow('Video', frame)

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

Feature Detection and Matching

Feature detection and matching are key techniques in computer vision that help identify and track objects in images and videos.

Feature Detection

Feature detection involves identifying distinctive points or regions in an image, known as features, that are invariant to changes in scale, rotation and illumination. Some popular feature detectors include:

  • SIFT (Scale-Invariant Feature Transform)
  • SURF (Speeded-Up Robust Features)
  • ORB (Oriented FAST and Rotated BRIEF)

To apply feature detection in OpenCV, first create a feature detector object and then use the detect() method to find features in an image:

sift = cv2.SIFT_create()
keypoints = sift.detect(gray_image, None)

Feature Matching

Feature matching involves finding corresponding features between two images, which can be useful for object recognition, tracking and image stitching. Some popular feature-matching algorithms include:

  • Brute-Force Matcher
  • FLANN (Fast Library for Approximate Nearest Neighbors) Matcher

To match features between two images using OpenCV, first, create a feature descriptor object and then create a matcher object. Finally, use the match() method to find matches between the descriptors of the two images:

sift = cv2.SIFT_create()
keypoints1, descriptors1 = sift.detectAndCompute(gray_image1, None)
keypoints2, descriptors2 = sift.detectAndCompute(gray_image2, None)

bf = cv2.BFMatcher()
matches = bf.match(descriptors1, descriptors2)

Object Detection and Tracking

OpenCV provides various techniques for detecting and tracking objects in images and videos, such as Haar cascades, HOG (Histogram of Oriented Gradients) and the Meanshift and Camshift algorithms.

Haar Cascades

Haar cascades are a popular method for object detection, especially for faces and eyes. To use Haar cascades in OpenCV, first, load a pre-trained cascade classifier and then use the detectMultiScale() method to find objects in an image:

face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.1, minNeighbors=5)


HOG is another popular method for object detection, especially for pedestrian detection. To use HOG in OpenCV, first create a HOG object and set the default people detector:

hog = cv2.HOGDescriptor()

Then, use the detectMultiScale() method to find objects in an image:

(rectangles, weights) = hog.detectMultiScale(image, winStride=(4, 4), padding=(8, 8), scale=1.05)

Deep Learning and OpenCV

OpenCV can be used with deep learning frameworks like TensorFlow and Caffe for advanced computer vision tasks, such as object detection, semantic segmentation and pose estimation. To use a deep learning model in OpenCV, first, load the model using the cv2.dnn.readNet() function:

net = cv2.dnn.readNetFromCaffe('deploy.prototxt', 'model.caffemodel')

Then, use the forward() method to perform inference on an image:

blob = cv2.dnn.blobFromImage(image, scalefactor=1.0, size, mean, swapRB=True, crop=False)
output = net.forward()


In this guide, we’ve covered how to get started with OpenCV and Python for various computer vision tasks. We’ve explored basic image processing techniques, working with videos, feature detection and matching, object detection and tracking and using deep learning with OpenCV.

By now, you should have a solid understanding of how to use OpenCV for computer vision tasks and be ready to explore more advanced topics and applications.