write image , tfrecord
https://blog.csdn.net/index20001/article/details/73843070
https://tensorflow-notes.readthedocs.io/zh_CN/latest/tfrecord.html
https://segmentfault.com/a/1190000011510838
- scipy.misc.imsave 方法可以很容易的将 numpy 数据存成图像格式，且基本没有大小限制。
- 读图的时候是用 tensorflow 的函数，存图像用的 scipy.misc 的 imsave 函数。
- step:
import numpy as np
import skimage.io as io
import scipy.misc
test_img = io.imread('./27434_20180523_EU_NCAP_Part1.png')
test_string = test_img.tostring()
reconstructed_test_1d = np.fromstring(test_string, dtype=np.uint8)
scipy.misc.imsave('reimg1.png',reconstructed_test_img)
- log:
>>> import scipy.misc
>>> test_img = io.imread('./27434_20180523_EU_NCAP_Part1.png')
>>> test_img
array([[[121, 125, 122],
[121, 125, 121],
[122, 125, 120],
...,
[102, 119, 92],
[ 73, 110, 40],
[ 59, 106, 14]],
[[122, 125, 122],
[121, 125, 121],
[122, 125, 120],
...,
[ 97, 115, 89],
[ 72, 108, 39],
[ 60, 105, 14]],
[[123, 125, 121],
[122, 125, 120],
[121, 125, 120],
...,
[ 86, 105, 84],
[ 70, 104, 36],
[ 62, 104, 13]],
...,
[[118, 122, 118],
[117, 122, 117],
[117, 122, 117],
...,
[ 45, 51, 55],
[ 18, 27, 24],
[ 5, 15, 8]],
[[117, 122, 117],
[116, 122, 117],
[116, 122, 117],
...,
[ 54, 58, 57],
[ 22, 30, 25],
[ 6, 16, 9]],
[[116, 122, 116],
[116, 122, 116],
[115, 122, 117],
...,
[ 59, 61, 58],
[ 24, 32, 25],
[ 6, 17, 9]]], dtype=uint8)
>>> test_string = test_img.tostring()
>>> test_string
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>>> reconstructed_test_1d = np.fromstring(test_string, dtype=np.uint8)
>>> reconstructed_test_1d
array([121, 125, 122, ..., 6, 17, 9], dtype=uint8)
>>> reconstructed_test_img = reconstructed_test_1d.reshape(test_img.shape)
>>> test_img.shape
(64, 64, 3)
>>> np.allclose(test_img, reconstructed_test_img)
True
>>> reconstructed_test_img.dtype
dtype('uint8')
>>> scipy.misc.imsave('reimg1.png',reconstructed_test_img)
import numpy as np
import skimage.io as io
cat_img = io.imread('cat.jpg')
io.imshow(cat_img)
# Let's convert the picture into string representation
# using the ndarray.tostring() function
cat_string = cat_img.tostring()
# Now let's convert the string back to the image
# Important: the dtype should be specified
# otherwise the reconstruction will be errorness
# Reconstruction is 1d, so we need sizes of image
# to fully reconstruct it.
reconstructed_cat_1d = np.fromstring(cat_string, dtype=np.uint8)
# Here we reshape the 1d representation
# This is the why we need to store the sizes of image
# along with its serialized representation.
reconstructed_cat_img = reconstructed_cat_1d.reshape(cat_img.shape)
# Let's check if we got everything right and compare
# reconstructed array to the original one.
np.allclose(cat_img, reconstructed_cat_img)
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