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Python Data Analysis

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

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Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781789955248
Length 478 pages
Edition 3rd Edition
Languages
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Authors (2):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
Avinash Navlani Avinash Navlani
Author Profile Icon Avinash Navlani
Avinash Navlani
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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...

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