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Scientific Computing with Python

You're reading from   Scientific Computing with Python High-performance scientific computing with NumPy, SciPy, and pandas

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Product type Paperback
Published in Jul 2021
Publisher Packt
ISBN-13 9781838822323
Length 392 pages
Edition 2nd Edition
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Authors (4):
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Olivier Verdier Olivier Verdier
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Olivier Verdier
Jan Erik Solem Jan Erik Solem
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Jan Erik Solem
Claus Führer Claus Führer
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Claus Führer
Claus Fuhrer Claus Fuhrer
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Claus Fuhrer
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Table of Contents (23) Chapters Close

Preface 1. Getting Started 2. Variables and Basic Types FREE CHAPTER 3. Container Types 4. Linear Algebra - Arrays 5. Advanced Array Concepts 6. Plotting 7. Functions 8. Classes 9. Iterating 10. Series and Dataframes - Working with Pandas 11. Communication by a Graphical User Interface 12. Error and Exception Handling 13. Namespaces, Scopes, and Modules 14. Input and Output 15. Testing 16. Symbolic Computations - SymPy 17. Interacting with the Operating System 18. Python for Parallel Computing 19. Comprehensive Examples 20. About Packt 21. Other Books You May Enjoy 22. References

14.6 Reading and writing images

The module PIL.Image comes with some functions for handling images. The following will read a JPEG image, print the shape and type, and then create a resized image, and write the new image to a file:

import PIL.Image as pil   # imports the Pillow module

# read image to array
im=pil.open("test.jpg") print(im.size) # (275, 183)
# Number of pixels in horizontal and vertical directions # resize image im_big = im.resize((550, 366)) im_big_gray = im_big.convert("L") # Convert to grayscale

im_array=array(im)
print(im_array.shape)
print(im_array.dtype) # unint 8
# write result to new image file im_big_gray.save("newimage.jpg")

 

PIL creates an image object that can easily be converted to a NumPy array. As an array object, images are stored with pixel values in the range 0...255 as 8-bit unsigned integers (unint8). The third shape value shows how many color channels the image...

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