added label_image_modified

# Copyright 2018 Oh Caption my Caption. All Rights Reserved.

# Marinco Holzinger, Samuel Kiegeland 

# ==============================================================================

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import argparse

import numpy as np

import tensorflow as tf

from pycocotools.coco import COCO

import numpy as np

import skimage.io as io

import matplotlib.pyplot as plt

import pylab

import pickle

def load_graph(model_file):

  graph = tf.Graph()

  graph_def = tf.GraphDef()

  with open(model_file, "rb") as f:

    graph_def.ParseFromString(f.read())

  with graph.as_default():

    tf.import_graph_def(graph_def)

  return graph

def read_tensor_from_image_file(file_name,

                                input_height=299,

                                input_width=299,

                                input_mean=0,

                                input_std=255):

  input_name = "file_reader"

  output_name = "normalized"

  file_reader = tf.read_file(file_name, input_name)

  if file_name.endswith(".png"):

    image_reader = tf.image.decode_png(

        file_reader, channels=3, name="png_reader")

  elif file_name.endswith(".gif"):

    image_reader = tf.squeeze(

        tf.image.decode_gif(file_reader, name="gif_reader"))

  elif file_name.endswith(".bmp"):

    image_reader = tf.image.decode_bmp(file_reader, name="bmp_reader")

  else:

    image_reader = tf.image.decode_jpeg(

        file_reader, channels=3, name="jpeg_reader")

  float_caster = tf.cast(image_reader, tf.float32)

  dims_expander = tf.expand_dims(float_caster, 0)

  resized = tf.image.resize_bilinear(dims_expander, [input_height, input_width])

  normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std])

  sess = tf.Session()

  result = sess.run(normalized)

  return result

def load_labels(label_file):

  label = []

  proto_as_ascii_lines = tf.gfile.GFile(label_file).readlines()

  for l in proto_as_ascii_lines:

    label.append(l.rstrip())

  return label

if __name__ == "__main__":

  # coco stuff

  path = '/softpro/ss18/caption/cocoapi/images/train/'

  coco = COCO('/softpro/ss18/caption/cocoapi/annotations/train/instances_train2017.json')

  coco_caps = COCO('/softpro/ss18/caption/cocoapi/annotations/train/captions_train2017.json')

  cats = coco.loadCats(coco.getCatIds())

  category_names = [cat['name'] for cat in cats]

  # tensorflow stuff

  file_name = "tensorflow/examples/label_image/data/grace_hopper.jpg"

  model_file = \

    "tensorflow/examples/label_image/data/inception_v3_2016_08_28_frozen.pb"

  label_file = "tensorflow/examples/label_image/data/imagenet_slim_labels.txt"

  input_height = 299

  input_width = 299

  input_mean = 0

  input_std = 255

  input_layer = "input"

  output_layer = "InceptionV3/Predictions/Reshape_1"

  label_file = 'output_labels.txt'

  model_file = 'output_graph.pb'

  input_layer = 'Placeholder'

  output_layer= 'module_apply_default/hub_output/feature_vector/SpatialSqueeze'

  graph = load_graph(model_file)

  interface = []

  for category in category_names:

  	counter = 0

    print(category)

    catIds = coco.getCatIds(catNms=[category])

    imgIds = coco.getImgIds(catIds=catIds)

    for pic in imgIds:

      counter += 1

      if counter % 1000 == 0: 

        print(counter, " pictures finished, wooo!!")	

      img = coco.loadImgs(pic)[0]

      annIds = coco_caps.getAnnIds(imgIds=img['id'])

      anns = coco_caps.loadAnns(annIds)

      file_name = path + img['file_name']

      img['captions'] = anns

      t = read_tensor_from_image_file(

        file_name,

        input_height=input_height,

        input_width=input_width,

        input_mean=input_mean,

        input_std=input_std)

      input_name = "import/" + input_layer

      output_name = "import/" + output_layer

      input_operation = graph.get_operation_by_name(input_name)

      output_operation = graph.get_operation_by_name(output_name)

      with tf.Session(graph=graph) as sess:

        results = sess.run(output_operation.outputs[0], {

            input_operation.outputs[0]: t

        })

      #picture_dict[vector] = np.squeeze(results)

      img['vector'] = np.squeeze(results)

      interface.append(img)

  with open('/softpro/ss18/caption/caption-lib/lstm/Input_Data/interface_train.pickle', 'wb') as handle:

    pickle.dump(interface, handle, protocol=pickle.HIGHEST_PROTOCOL)