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Python | Working with PNG Images using Matplotlib
  • Last Updated : 15 Apr, 2019

Matplotlib is an amazing visualization library in Python for 2D plots of arrays. Matplotlib is a multi-platform data visualization library built on NumPy arrays and designed to work with the broader SciPy stack. It was introduced by John Hunter in the year 2002.
One of the greatest benefits of visualization is that it allows us visual access to huge amounts of data in easily digestible visuals. Matplotlib consists of several plots like line, bar, scatter, histogram etc.

In this article, we will see how can we work with PNG images using Matplotlib.

Code #1: Read a PNG image using Matplotlib




# importing pyplot and image from matplotlib
import matplotlib.pyplot as plt
import matplotlib.image as img
  
# reading png image file
im = img.imread('imR.png')
  
# show image
plt.imshow(im)

Output:

Code #2: Applying pseudocolor to image



Pseudocolor is useful for enhancing contrast of image.




# importing pyplot and image from matplotlib
import matplotlib.pyplot as plt
import matplotlib.image as img
  
# reading png image
im = img.imread('imR.png')
  
# applying pseudocolor 
# default value of colormap is used.
lum = im[:, :, 0]
  
# show image
plt.imshow(lum)

Output:

Code #3: We can provide another value to colormap with colorbar.




# importing pyplot and image from matplotlib
import matplotlib.pyplot as plt
import matplotlib.image as img
  
# reading png image
im = img.imread('imR.png')
lum = im[:, :, 0]
  
# setting colormap as hot
plt.imshow(lum, cmap ='hot')
plt.colorbar()

Output:

Interpolation Schemes:
Interpolation calculates what the color or value of a pixel “should” be and this needed when we resize the image but want the same information. There’s missing space when you resize image because pixels are discrete and interpolation is how you fill that space.

Code # 4: Interpolation




# importing PIL and matplotlib
from PIL import Image 
import matplotlib.pyplot as plt
  
# reading png image  file
img = Image.open('imR.png')
  
# resizing the image
img.thumbnail((50, 50), Image.ANTIALIAS)
imgplot = plt.imshow(img)

Output:

Code #6: Here, ‘bicubic’ value is used for interpolation.




# importing pyplot from matplotlib
import matplotlib.pyplot as plt
  
# importing image from PIL
from PIL import Image 
  
# reading image
img = Image.open('imR.png')
  
img.thumbnail((30, 30), Image.ANTIALIAS) 
  
# bicubic used for interpolation
imgplot = plt.imshow(img, interpolation ='bicubic')

Output:

Code #7: ‘sinc’ value is used for interpolation.




# importing PIL and matplotlib
from PIL import Image 
import matplotlib.pyplot as plt
  
# reading image
img = Image.open('imR.png')
  
img.thumbnail((30, 30), Image.ANTIALIAS)
  
# sinc used for interpolation
imgplot = plt.imshow(img, interpolation ='sinc')

Output:

 
Reference: https://matplotlib.org/gallery/images_contours_and_fields/interpolation_methods.html

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