Why does vgg.prepare() method create 9 copies of the given image?
I get this result when I apply vgg.prepare() to the following image:
I use this line of code:
Image.fromarray(np.uint8(vgg.prepare(pep).reshape(224,224,3)))
And get an image which is combined of 9 copies of the given image:
python image pre-trained-model chainer vgg-net
asked Nov 12 at 7:32
saman jahangiri
377
add a comment |
I get this result when I apply vgg.prepare() to the following image:
I use this line of code:
Image.fromarray(np.uint8(vgg.prepare(pep).reshape(224,224,3)))
And get an image which is combined of 9 copies of the given image:
python image pre-trained-model chainer vgg-net
asked Nov 12 at 7:32
saman jahangiri
377
Can you share how did you instantiatevggandpep?
– corochann
Nov 12 at 8:02
1
pep=Image.open("/Users/macintosh/Desktop/Code/sngan_projection-master/images/red_pepper.jpg")andfrom chainer.links.model.vision import vgg
– saman jahangiri
Nov 12 at 8:09
add a comment |
I get this result when I apply vgg.prepare() to the following image:
I use this line of code:
Image.fromarray(np.uint8(vgg.prepare(pep).reshape(224,224,3)))
And get an image which is combined of 9 copies of the given image:
python image pre-trained-model chainer vgg-net
asked Nov 12 at 7:32
saman jahangiri
377
I get this result when I apply vgg.prepare() to the following image:
I use this line of code:
Image.fromarray(np.uint8(vgg.prepare(pep).reshape(224,224,3)))
And get an image which is combined of 9 copies of the given image:
python image pre-trained-model chainer vgg-net
python image pre-trained-model chainer vgg-net
asked Nov 12 at 7:32
saman jahangiri
377
asked Nov 12 at 7:32
saman jahangiri
377
asked Nov 12 at 7:32
saman jahangiri
377
asked Nov 12 at 7:32
saman jahangiri
377
asked Nov 12 at 7:32
saman jahangiri
377
377
Can you share how did you instantiatevggandpep?
– corochann
Nov 12 at 8:02
1
pep=Image.open("/Users/macintosh/Desktop/Code/sngan_projection-master/images/red_pepper.jpg")andfrom chainer.links.model.vision import vgg
– saman jahangiri
Nov 12 at 8:09
add a comment |
Can you share how did you instantiatevggandpep?
– corochann
Nov 12 at 8:02
1
pep=Image.open("/Users/macintosh/Desktop/Code/sngan_projection-master/images/red_pepper.jpg")andfrom chainer.links.model.vision import vgg
– saman jahangiri
Nov 12 at 8:09
Can you share how did you instantiate
vgg and pep?– corochann
Nov 12 at 8:02
Can you share how did you instantiate
vgg and pep?– corochann
Nov 12 at 8:02
1
1
pep=Image.open("/Users/macintosh/Desktop/Code/sngan_projection-master/images/red_pepper.jpg") and from chainer.links.model.vision import vgg– saman jahangiri
Nov 12 at 8:09
pep=Image.open("/Users/macintosh/Desktop/Code/sngan_projection-master/images/red_pepper.jpg") and from chainer.links.model.vision import vgg– saman jahangiri
Nov 12 at 8:09
add a comment |
1 Answer
1
active
oldest
votes
I finally got what you did...
the only mistake is .reshape.
Because the image is transposed, not reshaped, you have to re-transpose to restore the original image.
pep = pep.transpose((1, 2, 0)) # transpose
pep += [103.939, 116.779, 123.68] # un-normalize
pep = pep.astype(np.uint8) # revert dtype
pep = np.flip(pep, axis=2) # BGR -> RGB
PIL_image = Image.fromarray(pep) # finally got the original!
answered Nov 13 at 1:49
Yuki Hashimoto
3227
add a comment |
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1 Answer
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1 Answer
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active
oldest
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active
oldest
votes
active
oldest
votes
I finally got what you did...
the only mistake is .reshape.
Because the image is transposed, not reshaped, you have to re-transpose to restore the original image.
pep = pep.transpose((1, 2, 0)) # transpose
pep += [103.939, 116.779, 123.68] # un-normalize
pep = pep.astype(np.uint8) # revert dtype
pep = np.flip(pep, axis=2) # BGR -> RGB
PIL_image = Image.fromarray(pep) # finally got the original!
answered Nov 13 at 1:49
Yuki Hashimoto
3227
add a comment |
I finally got what you did...
the only mistake is .reshape.
Because the image is transposed, not reshaped, you have to re-transpose to restore the original image.
pep = pep.transpose((1, 2, 0)) # transpose
pep += [103.939, 116.779, 123.68] # un-normalize
pep = pep.astype(np.uint8) # revert dtype
pep = np.flip(pep, axis=2) # BGR -> RGB
PIL_image = Image.fromarray(pep) # finally got the original!
answered Nov 13 at 1:49
Yuki Hashimoto
3227
add a comment |
I finally got what you did...
the only mistake is .reshape.
Because the image is transposed, not reshaped, you have to re-transpose to restore the original image.
pep = pep.transpose((1, 2, 0)) # transpose
pep += [103.939, 116.779, 123.68] # un-normalize
pep = pep.astype(np.uint8) # revert dtype
pep = np.flip(pep, axis=2) # BGR -> RGB
PIL_image = Image.fromarray(pep) # finally got the original!
answered Nov 13 at 1:49
Yuki Hashimoto
3227
I finally got what you did...
the only mistake is .reshape.
Because the image is transposed, not reshaped, you have to re-transpose to restore the original image.
pep = pep.transpose((1, 2, 0)) # transpose
pep += [103.939, 116.779, 123.68] # un-normalize
pep = pep.astype(np.uint8) # revert dtype
pep = np.flip(pep, axis=2) # BGR -> RGB
PIL_image = Image.fromarray(pep) # finally got the original!
answered Nov 13 at 1:49
Yuki Hashimoto
3227
answered Nov 13 at 1:49
Yuki Hashimoto
3227
answered Nov 13 at 1:49
Yuki Hashimoto
3227
answered Nov 13 at 1:49
Yuki Hashimoto
3227
3227
add a comment |
add a comment |
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Can you share how did you instantiate
vggandpep?– corochann
Nov 12 at 8:02
1
pep=Image.open("/Users/macintosh/Desktop/Code/sngan_projection-master/images/red_pepper.jpg")andfrom chainer.links.model.vision import vgg– saman jahangiri
Nov 12 at 8:09