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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

Blurring an image

Blurring is one of the crucial steps of image preprocessing. In preprocessing, the removal of noise impacts the performance of algorithms. Blurring is the process of reducing noise in image data to achieve better accuracy. Blurring also helps us to take charge of handling pixel intensity.

Let's see an example of blurring an image:

# Import OpenCV module
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('tajmahal.jpg')

# 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 sample, we read the image and converted it from a BGR to RGB based image. Let's blur it using the blur() function. Blur takes two arguments: image and kernel size. The blur() function uses the average blurring method:

# Blur...
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