Converting a vector image to matrix











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I am playing with the cifar-10 dataset (available here) and for now I would like to plot one of the images of a batch.



The images are represented as vectors when I get them from pickle:




From the cifar-10 documentation:

The first 1024 entries (of an image) contain the red channel values, the next 1024 the green, and the final 1024 the blue. The image is stored in row-major order, so that the first 32 entries of the array are the red channel values of the first row of the image.




What I came up with to plot an image is this:



import numpy as np

import matplotlib.pyplot as plt



# get the dataset

a = unpickle('./cifar-10/data_batch_1')

# get the first image

img = np.array(a[b'data'][0])

# transform it to a 3 x 1024 array, one row per color channel 

# and transpose it to a 1024 x 3 array, one row per rgb pixel

img = img.reshape(3, 1024).T

# reshape it so we can plot it as a 32 x 32 image with 3 color channels

img = img.reshape(32, 32, 3)



# plot

plt.imshow(img)

plt.show()


It's my first attempt at matrix manipulation so even if this is concise, I feel like it could be simpler. What do you guys think?






python image numpy







share|improve this question




























    up vote
    4
    down vote

    favorite
    1












    I am playing with the cifar-10 dataset (available here) and for now I would like to plot one of the images of a batch.



    The images are represented as vectors when I get them from pickle:




    From the cifar-10 documentation:

    The first 1024 entries (of an image) contain the red channel values, the next 1024 the green, and the final 1024 the blue. The image is stored in row-major order, so that the first 32 entries of the array are the red channel values of the first row of the image.




    What I came up with to plot an image is this:



    import numpy as np
    
import matplotlib.pyplot as plt
    

    
# get the dataset
    
a = unpickle('./cifar-10/data_batch_1')
    
# get the first image
    
img = np.array(a[b'data'][0])
    
# transform it to a 3 x 1024 array, one row per color channel 
    
# and transpose it to a 1024 x 3 array, one row per rgb pixel
    
img = img.reshape(3, 1024).T
    
# reshape it so we can plot it as a 32 x 32 image with 3 color channels
    
img = img.reshape(32, 32, 3)
    

    
# plot
    
plt.imshow(img)
    
plt.show()


    It's my first attempt at matrix manipulation so even if this is concise, I feel like it could be simpler. What do you guys think?






    python image numpy







    share|improve this question


























      up vote
      4
      down vote

      favorite
      1









      up vote
      4
      down vote

      favorite
      1






      1





      I am playing with the cifar-10 dataset (available here) and for now I would like to plot one of the images of a batch.



      The images are represented as vectors when I get them from pickle:




      From the cifar-10 documentation:

      The first 1024 entries (of an image) contain the red channel values, the next 1024 the green, and the final 1024 the blue. The image is stored in row-major order, so that the first 32 entries of the array are the red channel values of the first row of the image.




      What I came up with to plot an image is this:



      import numpy as np
      
import matplotlib.pyplot as plt
      

      
# get the dataset
      
a = unpickle('./cifar-10/data_batch_1')
      
# get the first image
      
img = np.array(a[b'data'][0])
      
# transform it to a 3 x 1024 array, one row per color channel 
      
# and transpose it to a 1024 x 3 array, one row per rgb pixel
      
img = img.reshape(3, 1024).T
      
# reshape it so we can plot it as a 32 x 32 image with 3 color channels
      
img = img.reshape(32, 32, 3)
      

      
# plot
      
plt.imshow(img)
      
plt.show()


      It's my first attempt at matrix manipulation so even if this is concise, I feel like it could be simpler. What do you guys think?






      python image numpy







      share|improve this question















      I am playing with the cifar-10 dataset (available here) and for now I would like to plot one of the images of a batch.



      The images are represented as vectors when I get them from pickle:




      From the cifar-10 documentation:

      The first 1024 entries (of an image) contain the red channel values, the next 1024 the green, and the final 1024 the blue. The image is stored in row-major order, so that the first 32 entries of the array are the red channel values of the first row of the image.




      What I came up with to plot an image is this:



      import numpy as np
      
import matplotlib.pyplot as plt
      

      
# get the dataset
      
a = unpickle('./cifar-10/data_batch_1')
      
# get the first image
      
img = np.array(a[b'data'][0])
      
# transform it to a 3 x 1024 array, one row per color channel 
      
# and transpose it to a 1024 x 3 array, one row per rgb pixel
      
img = img.reshape(3, 1024).T
      
# reshape it so we can plot it as a 32 x 32 image with 3 color channels
      
img = img.reshape(32, 32, 3)
      

      
# plot
      
plt.imshow(img)
      
plt.show()


      It's my first attempt at matrix manipulation so even if this is concise, I feel like it could be simpler. What do you guys think?






      python image numpy




      python image numpy






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      edited 27 mins ago









      Jamal♦

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      asked Aug 18 '17 at 9:07









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          One alternative is to transform it to the right shapes, then use moveaxis. I don't know how much simpler this is than what you've got, I guess it avoids one reshaping operation.



          img = img.reshape(3, 32, 32)
          
img = np.moveaxis(img, 0, -1) # move the first axis to the end


          or as a one-liner:



          img = np.moveaxis(img.reshape(3, 32, 32), 0, -1)


          Note that moveaxis returns a view, meaning that no data is copied.






          share|improve this answer





















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            up vote
            0
            down vote













            One alternative is to transform it to the right shapes, then use moveaxis. I don't know how much simpler this is than what you've got, I guess it avoids one reshaping operation.



            img = img.reshape(3, 32, 32)
            
img = np.moveaxis(img, 0, -1) # move the first axis to the end


            or as a one-liner:



            img = np.moveaxis(img.reshape(3, 32, 32), 0, -1)


            Note that moveaxis returns a view, meaning that no data is copied.






            share|improve this answer

























              up vote
              0
              down vote













              One alternative is to transform it to the right shapes, then use moveaxis. I don't know how much simpler this is than what you've got, I guess it avoids one reshaping operation.



              img = img.reshape(3, 32, 32)
              
img = np.moveaxis(img, 0, -1) # move the first axis to the end


              or as a one-liner:



              img = np.moveaxis(img.reshape(3, 32, 32), 0, -1)


              Note that moveaxis returns a view, meaning that no data is copied.






              share|improve this answer























                up vote
                0
                down vote










                up vote
                0
                down vote









                One alternative is to transform it to the right shapes, then use moveaxis. I don't know how much simpler this is than what you've got, I guess it avoids one reshaping operation.



                img = img.reshape(3, 32, 32)
                
img = np.moveaxis(img, 0, -1) # move the first axis to the end


                or as a one-liner:



                img = np.moveaxis(img.reshape(3, 32, 32), 0, -1)


                Note that moveaxis returns a view, meaning that no data is copied.






                share|improve this answer












                One alternative is to transform it to the right shapes, then use moveaxis. I don't know how much simpler this is than what you've got, I guess it avoids one reshaping operation.



                img = img.reshape(3, 32, 32)
                
img = np.moveaxis(img, 0, -1) # move the first axis to the end


                or as a one-liner:



                img = np.moveaxis(img.reshape(3, 32, 32), 0, -1)


                Note that moveaxis returns a view, meaning that no data is copied.







                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered 10 hours ago









                Cris Luengo

                2,489319




                2,489319






























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