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
edited 27 mins ago
Jamal♦
30.2k11115226
asked Aug 18 '17 at 9:07
Leogout
1317
add a comment |
up vote
4
down vote
favorite
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
edited 27 mins ago
Jamal♦
30.2k11115226
asked Aug 18 '17 at 9:07
Leogout
1317
add a comment |
up vote
4
down vote
favorite
up vote
4
down vote
favorite
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
edited 27 mins ago
Jamal♦
30.2k11115226
asked Aug 18 '17 at 9:07
Leogout
1317
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
edited 27 mins ago
Jamal♦
30.2k11115226
asked Aug 18 '17 at 9:07
Leogout
1317
edited 27 mins ago
Jamal♦
30.2k11115226
asked Aug 18 '17 at 9:07
Leogout
1317
edited 27 mins ago
Jamal♦
30.2k11115226
edited 27 mins ago
Jamal♦
30.2k11115226
edited 27 mins ago
Jamal♦
30.2k11115226
30.2k11115226
asked Aug 18 '17 at 9:07
Leogout
1317
asked Aug 18 '17 at 9:07
Leogout
1317
asked Aug 18 '17 at 9:07
Leogout
1317
1317
add a comment |
add a comment |
1 Answer
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votes
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0
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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.
answered 10 hours ago
Cris Luengo
2,489319
add a comment |
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1 Answer
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active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
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.
answered 10 hours ago
Cris Luengo
2,489319
add a comment |
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.
answered 10 hours ago
Cris Luengo
2,489319
add a comment |
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.
answered 10 hours ago
Cris Luengo
2,489319
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.
answered 10 hours ago
Cris Luengo
2,489319
answered 10 hours ago
Cris Luengo
2,489319
answered 10 hours ago
Cris Luengo
2,489319
answered 10 hours ago
Cris Luengo
2,489319
2,489319
add a comment |
add a comment |
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