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 m opening has 138MB   I tried to read  amp  check the contents of the Tensors containing the images to be sure that the reading is done right  however I have two questions -    Does the FixedLengthRecordReader load the whole file  however just provide inputs one at a time  Since reading the first size bytes should be relatively fast  However  the code takes about two minutes to run   How to get the actual image contents in a displayable format  or display them internally to validate that the images are read well  I did sess run uint8image   however the result is empty    The code is below   import tensorflow as tf def read stl10 filename queue     class STL10Record object       pass   result   STL10Record      result height   96   result width   96   result depth   3   image bytes   result height   result width   result depth   record bytes   image bytes    reader   tf FixedLengthRecordReader record bytes record bytes    result key  value   reader read filename queue    print value   record bytes   tf decode raw value  tf uint8     depth major   tf reshape tf slice record bytes   0    image bytes                            result depth  result height  result width     result uint8image   tf transpose depth major   1  2  0     return result   probably a hack since I should ve provided a string tensor  filename queue   tf train string input producer     data train X    image   read stl10 filename queue   print image uint8image with tf Session   as sess    result   sess run image uint8image    print result  type result    Output   Tensor  ReaderRead 1   shape TensorShape      dtype string  Tensor  transpose 0   shape TensorShape  Dimension 96   Dimension 96   Dimension 3     dtype uint8  I tensorflow core common runtime local device cc 25  Local device intra op parallelism threads  4 I tensorflow core common runtime local session cc 45  Local session inter op parallelism threads  4  empty line for last print  Process finished with exit code 137   I m running this on my CPU  if that adds anything   EDIT  I found the pure TensorFlow solution thanks to Rosa  Apparently  when using the string input producer  in order to see the results  you need to initialize the queue runners   The only required thing to add to the code above is the second line from below       with tf Session   as sess      tf train start queue runners sess sess        Afterwards  the image in the result can be displayed with matplotlib pyplot imshow result   I hope this helps someone  If you have any further questions  feel free to ask me or check the link in Rosa s answer

User · Answer

Can t comment  not enough reputation  but here is a modified version that worked for me   To  HamedMP error about the No default session is registered you can use InteractiveSession to get rid of this error  https   www tensorflow org versions r0 8 api docs python client html InteractiveSession  And to  NumesSanguis issue with Image show  you can use the regular PIL  show   method because fromarray returns an image object    I do both below  note I m using JPEG instead of PNG    import tensorflow as tf import numpy as np from PIL import Image  filename queue   tf train string input producer   my img jpg       list of files to read  reader   tf WholeFileReader   key  value   reader read filename queue   my img   tf image decode jpeg value    use png or jpg decoder based on your files   init op   tf initialize all variables   sess   tf InteractiveSession   with sess as default        sess run init op     Start populating the filename queue   coord   tf train Coordinator   threads   tf train start queue runners coord coord   for i in range 1    length of your filename list   image   my img eval    here is your image Tensor      Image fromarray np asarray image   show    coord request stop   coord join threads

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You can use tf keras API   import tensorflow as tf import numpy as np from tensorflow keras preprocessing image import load img  array to img  tf enable eager execution    img   load img  example png   img   tf convert to tensor np asarray img   image   tf image resize images img   800  800   to img   array to img image  to img show

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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

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Load names with tf train match filenames once get the number of files to iterate over with tf size open session and enjoy  -   import tensorflow as tf import numpy as np import matplotlib  from PIL import Image  matplotlib use  Agg   import matplotlib pyplot as plt   filenames   tf train match filenames once    images   jpg   count num files   tf size filenames  filename queue   tf train string input producer filenames   reader tf WholeFileReader   key value reader read filename queue  img   tf image decode jpeg value   init   tf global variables initializer   with tf Session   as sess      sess run init      coord   tf train Coordinator       threads   tf train start queue runners coord coord      num files   sess run count num files      for i in range num files           image img eval           print image shape          Image fromarray np asarray image   save  te jpeg

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I used CIFAR10 format instead of STL10 and code came out like  filename queue   tf train string input producer filenames  read input   read cifar10 filename queue  with tf Session   as sess             tf train start queue runners sess sess      result   sess run read input uint8image          img   Image fromarray result   RGB       img save  my jpg     The snippet is identical with mttk and Rosa Gronchi  but Somehow I wasn t able to show the image during run-time  so I saved as the JPG file

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According to the documentation you can decode JPEG PNG images   It should be something like this   import tensorflow as tf  filenames      image dir img jpg   filename queue   tf train string input producer filenames   reader   tf WholeFileReader   key  value   reader read filename queue   images   tf image decode jpeg value  channels 3    You can find a bit more info here

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Just to give a complete answer   filename queue   tf train string input producer    Users HANEL Desktop tf png       list of files to read  reader   tf WholeFileReader   key  value   reader read filename queue   my img   tf image decode png value    use png or jpg decoder based on your files   init op   tf global variables initializer   with tf Session   as sess    sess run init op       Start populating the filename queue     coord   tf train Coordinator     threads   tf train start queue runners coord coord     for i in range 1    length of your filename list     image   my img eval    here is your image Tensor        print image shape    Image fromarray np asarray image   show      coord request stop     coord join threads    Or if you have a directory of images you can add them all via this Github source file   mttk and  salvador-dali  I hope it is what you need

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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

[python] Tensorflow image reading & display

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