Tensorflow image reading & display

Question

I\'ve got a bunch of images in a format similar to Cifar10 (binary file, size = 96*96*3 bytes per image), one image after another (STL-10 dataset). The file I\'

Answer 1

After speaking with you in the comments, I believe that you can just do this using numpy/scipy. The ideas is to read the image in the numpy 3d-array and feed it into the variable.

from scipy import misc
import tensorflow as tf

img = misc.imread('01.png')
print img.shape    # (32, 32, 3)

img_tf = tf.Variable(img)
print img_tf.get_shape().as_list()  # [32, 32, 3]

Then you can run your graph:

init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
im = sess.run(img_tf)

and verify that it is the same:

import matplotlib.pyplot as plt
fig = plt.figure()
fig.add_subplot(1,2,1)
plt.imshow(im)
fig.add_subplot(1,2,2)
plt.imshow(img)
plt.show()

P.S. you mentioned: Since it's supposed to parallelize reading, it seems useful to know.. To which I can say that rarely in data-analysis reading of the data is the bottleneck. Most of your time you will spend training your model.

Answer 2

First of all scipy.misc.imread and PIL are no longer available. Instead use imageio library but you need to install Pillow for that as a dependancy

pip install Pillow imageio

Then use the following code to load the image and get the details about it.

import imageio
import tensorflow as tf

path = 'your_path_to_image' # '~/Downloads/image.png'

img = imageio.imread(path)
print(img.shape) 

or

img_tf = tf.Variable(img)
print(img_tf.get_shape().as_list()) 

both work fine.