Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Python Data Analysis

You're reading from   Python Data Analysis Perform data collection, data processing, wrangling, visualization, and model building using Python

Arrow left icon
Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781789955248
Length 478 pages
Edition 3rd Edition
Languages
Arrow right icon
Authors (2):
Arrow left icon
Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
Avinash Navlani Avinash Navlani
Author Profile Icon Avinash Navlani
Avinash Navlani
Arrow right icon
View More author details
Toc

Table of Contents (20) Chapters Close

Preface 1. Section 1: Foundation for Data Analysis
2. Getting Started with Python Libraries FREE CHAPTER 3. NumPy and pandas 4. Statistics 5. Linear Algebra 6. Section 2: Exploratory Data Analysis and Data Cleaning
7. Data Visualization 8. Retrieving, Processing, and Storing Data 9. Cleaning Messy Data 10. Signal Processing and Time Series 11. Section 3: Deep Dive into Machine Learning
12. Supervised Learning - Regression Analysis 13. Supervised Learning - Classification Techniques 14. Unsupervised Learning - PCA and Clustering 15. Section 4: NLP, Image Analytics, and Parallel Computing
16. Analyzing Textual Data 17. Analyzing Image Data 18. Parallel Computing Using Dask 19. Other Books You May Enjoy

Changing the brightness

Brightness is a comparative term that is determined by visual perception. Sometimes it is difficult to perceive the brightness. The value of pixel intensity can help us to find a brighter image. For example, if two pixels have the intensity values 110 and 230, then the latter one is brighter.

In OpenCV, adjusting image brightness is a very basic operation. Brightness can be controlled by changing the intensity of each pixel in an image:

# Import cv2 latest version of OpenCV library
import cv2

# Import matplotlib for showing the image
import matplotlib.pyplot as plt

# Magic function to render the figure in a notebook
%matplotlib inline

# Read image
image = cv2.imread('nature.jpeg')

# Convert image color space BGR to RGB
rgb_image=cv2.cvtColor(image,cv2.COLOR_BGR2RGB)

# Display the image
plt.imshow(rgb_image)

This results in the following output:

In the preceding code example, we have read the image and converted the BGR color model-based image into an RGB color...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image