tensorflow tf.image.resize_images raise ValueError

[stevechoris]

I used ImageFlow to create a tensorflow tfrecords file on my own images. My images are of the same size 223x334x3. I tried the cifar example on my own images. Because the cifar convnet need image of 32x32 size so i have to resize it.

My function to read tfrecords file , decode images and resize them as follow:

def read_my_file_format(filename_queue):
reader = tf.TFRecordReader()
key, serialized_example = reader.read(filename_queue)

features = tf.parse_single_example(
serialized_example,
features={
  'image_raw': tf.FixedLenFeature([], tf.string),
  'label': tf.FixedLenFeature([], tf.int64)
})

image = tf.decode_raw(features['image_raw'], tf.uint8)
image = tf.cast(image, tf.float32)
label = tf.cast(features['label'], tf.int32)

image = tf.reshape(image, [223446])
image.set_shape(223446)
print 'get shape: ',image.get_shape()

print 'image: ',image.get_shape()
print 'label: ',label.get_shape()

image = tf.image.resize_images(image, 32,32)
image = tf.reshape(image,[32,32,3])

#processed_example = some_processing(example)

processed_example = image
return processed_example, label

But when i call it, i got the following error:

Last executed 2016-07-22 23:58:40 in 71ms
get shape:  (223446,)
image:  (223446,)
label:  ()
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-32-44cda3e31e6a> in <module>()
----> 1 tf.app.run()

/home/guanwanxian/anaconda2/envs/theano/lib/python2.7/site-packages/tensorflow/python/platform/app.pyc in run(main)
     28   f._parse_flags()
     29   main = main or sys.modules['__main__'].main
---> 30   sys.exit(main(sys.argv))

<ipython-input-7-2788f377abf1> in main(argv)
      1 def main(argv=None):
----> 2     train()

<ipython-input-31-4c9a489f07f7> in train(re_train, continue_from_pre)
      8         # Get images and labels for CIFAR-10.
      9         # images, labels = my_input.inputs()
---> 10         images, labels = input_pipeline(['../data/trainV2.tfrecords'],batch_size=128)
     11         val_images, val_labels = input_pipeline(['../data/testV2.tfrecords'],batch_size=128)
     12 

<ipython-input-3-ce4e93d1c196> in input_pipeline(filenames, batch_size, num_epochs)
      2     filename_queue = tf.train.string_input_producer(
      3                      filenames, num_epochs=num_epochs, shuffle=True)
----> 4     example, label = read_my_file_format(filename_queue)
      5     # min_after_dequeue defines how big a buffer we will randomly sample
      6     #   from -- bigger means better shuffling but slower start up and more

<ipython-input-30-8be4eeea8a06> in read_my_file_format(filename_queue)
     21     print 'label: ',label.get_shape()
     22 
---> 23     image = tf.image.resize_images(image, 32,32)
     24     image = tf.reshape(image,[32,32,3])
     25 

/home/guanwanxian/anaconda2/envs/theano/lib/python2.7/site-packages/tensorflow/python/ops/image_ops.pyc in resize_images(images, new_height, new_width, method, align_corners)
    629     images = array_ops.expand_dims(images, 0)
    630 
--> 631   _, height, width, depth = _ImageDimensions(images)
    632 
    633   # Handle tensor-valued sizes as well as Python integers.

ValueError: need more than 1 value to unpack

and My code to create tfrecords from images with littlt modification as follow:

def read_image_and_labels_from(path):
'''read images and return image and label array
'''

directories = glob.glob(os.path.join(path,'*'))
class_names = [os.path.basename(directory) for directory in directories]
class_names.sort()
num_classes = len(class_names)

file_paths = glob.glob(os.path.join(path,'*/*'))
file_paths = sorted(file_paths,
       key=lambda filename:os.path.basename(filename).split('.')[0])

images = []
labels = []
shapes = []
shape_set = set()
for filename in file_paths:
    im = Image.open(filename)
    arr = np.asarray(im, np.uint8)
    images.append(arr)

    im_shape = arr.shape
    shapes.append(im_shape) # tuple of 3
    if im_shape not in shape_set:
        shape_set.add(im_shape)
    # image_name = os.path.basename(filename).split('.')[0]

    class_name = os.path.basename(os.path.dirname(filename))
    label_num = class_names.index(class_name)
    labels.append(np.asarray(label_num, np.uint32))

images = np.array(images)
labels = np.array(labels)
shapes = np.array(shapes)
print images.shape, labels.shape, shapes.shape
print 'shape set: ', shape_set
return images, labels, shapes


def convert_images_to_tfrecords_file(images, labels, shapes, directorys, name):
num_examples = labels.shape[0]
print('labels shape is: ', labels.shape[0])
if num_examples != images.shape[0] != labels.shape[0]:
    raise ValueError("Images size %d does not match label size %d." %
                                     (images.shape[0], num_examples))

filename = os.path.join(directorys, name + '.tfrecords')
print('Writing', filename)
writer = tf.python_io.TFRecordWriter(filename)
for index in range(num_examples):
    image_raw = images[index].tostring()
    image_shape = shapes[index]
    example = tf.train.Example(features=tf.train.Features(feature={
            'height': _int64_feature(image_shape[0]),
            'width': _int64_feature(image_shape[1]),
            'depth': _int64_feature(image_shape[2]),
            'label': _int64_feature(int(labels[index])),
            'image_raw': _bytes_feature(image_raw)}))
    writer.write(example.SerializeToString())

So what's wrong with my code?

[stevechoris]

I have solved it on stackoverflow. It is because you should first reshape it to a 3d tensor instead of 1d.

[HamedMP]

@stevechoris Thank you very much for your contirbutions. Really appreciate it