def oneFile(filepath):
imgPath = filepath
testImage_string = tf.gfile.FastGFile(imgPath, 'rb').read()
testImage = tf.image.decode_jpeg(testImage_string, channels=3)
processed_image = inception_preprocessing.preprocess_image(testImage, image_size, image_size, is_training=False)
processed_images = tf.expand_dims(processed_image, 0)


# Create the model, use the default arg scope to configure the batch norm parameters.
with slim.arg_scope(inception_resnet_v2_arg_scope()):
    #logits, end_points = inception_resnet_v2(images, num_classes = dataset.num_classes, is_training = False)
    logits, _ = inception_resnet_v2(processed_images, num_classes=16, is_training=False)

probabilities = tf.nn.softmax(logits)

init_fn = slim.assign_from_checkpoint_fn(
    checkpoint_file,
    slim.get_model_variables(model_name))


with tf.Session() as sess:
    init_fn(sess)

    np_image, probabilities = sess.run([processed_images, probabilities])
    probabilities = probabilities[0, 0:]
    sorted_inds = [i[0] for i in sorted(enumerate(-probabilities), key=lambda x: x[1])]
    #print(probabilities)
    print(probabilities.argmax(axis=0))
    #names = imagenet.create_readable_names_for_imagenet_labels()
    #for i in range(15):
    #    index = sorted_inds[i]
    #    print((probabilities[index], names[index]))

def main():
for image_file in os.listdir(dataset_dir):
    try:
        image_type = imghdr.what(os.path.join(dataset_dir, image_file))
        if not image_type:
            continue
    except IsADirectoryError:
        continue

    #image = Image.open(os.path.join(dataset_dir, image_file))
    filepath = os.path.join(dataset_dir, image_file)

    oneFile(filepath)

def inception_resnet_v2_arg_scope(weight_decay=0.00004,
                                  batch_norm_decay=0.9997,
                                  batch_norm_epsilon=0.001):
  """Yields the scope with the default parameters for inception_resnet_v2.

  Args:
    weight_decay: the weight decay for weights variables.
    batch_norm_decay: decay for the moving average of batch_norm momentums.
    batch_norm_epsilon: small float added to variance to avoid dividing by zero.

  Returns:
    a arg_scope with the parameters needed for inception_resnet_v2.
  """
  # Set weight_decay for weights in conv2d and fully_connected layers.
  with slim.arg_scope([slim.conv2d, slim.fully_connected],
                      weights_regularizer=slim.l2_regularizer(weight_decay),
                      biases_regularizer=slim.l2_regularizer(weight_decay)):

    batch_norm_params = {
        'decay': batch_norm_decay,
        'epsilon': batch_norm_epsilon,
    }
    # Set activation_fn and parameters for batch_norm.
    with slim.arg_scope([slim.conv2d], activation_fn=tf.nn.relu,
                        normalizer_fn=slim.batch_norm,
                        normalizer_params=batch_norm_params) as scope:

      return scope