adjust example format

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# CNN example Notebook

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## Imports

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``` python

%matplotlib inline

from __future__ import print_function

from ipywidgets import interact, interactive, fixed, interact_manual

import ipywidgets as widgets

import os.path

import pickle

import numpy as np

from pycocotools.coco import COCO

import skimage.io as io

import matplotlib.pyplot as plt

import matplotlib.image as mpimg

from IPython.display import display

import numpy as np

import tensorflow as tf

```

%% Output

    /proj/mahoni/anaconda3/envs/py36/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.

      from ._conv import register_converters as _register_converters

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## Load Data

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``` python

path = '/softpro/ss18/caption/cnn_exercise/good_models/fourth_model_all_objects_different_hyperparameters/'

coco = COCO('/softpro/ss18/caption/cocoapi/annotations/val/instances_val2017.json')

coco_caps = COCO('/softpro/ss18/caption/cocoapi/annotations/val/captions_val2017.json')

imgIds = coco.getImgIds()

```

%% Output

    loading annotations into memory...

    Done (t=1.00s)

    creating index...

    index created!

    loading annotations into memory...

    Done (t=0.25s)

    creating index...

    index created!

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## Create Example Cases

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``` python

def browse_images(images):

    def view_image(i):

        img = coco.loadImgs(i.value)[0]

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

        anns = coco_caps.loadAnns(annIds)

        file_name = '/softpro/ss18/caption/cocoapi/val/' + img['file_name']

        I = mpimg.imread(file_name)

        coco_caps.showAnns(anns)

        plt.title('Title: %s' % i.name)

        plt.imshow(I)

        plt.axis('off')

        plt.show()

    view_image(images)

```

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``` python

from enum import Enum

class Pictures(Enum):

    cat = 213445

    food = 17714

    skater = 257084

    bathroom = 306733

    random = imgIds[np.random.randint(0, len(imgIds))]

images = [Pictures.cat, Pictures.food, Pictures.skater, Pictures.bathroom, Pictures.random]

```

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``` python

w = interactive(browse_images, images=images)

```

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## choose Image

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``` python

w

```

%% Output

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## Populate Interface with coco data

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``` python

img = coco.loadImgs(w.kwargs['images'].value)[0]

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

anns = coco_caps.loadAnns(annIds)

img['captions'] = anns

print(img)

```

%% Output

    {'license': 1, 'file_name': '000000213445.jpg', 'coco_url': 'http://images.cocodataset.org/val2017/000000213445.jpg', 'height': 500, 'width': 408, 'date_captured': '2013-11-15 17:17:16', 'flickr_url': 'http://farm5.staticflickr.com/4007/4463515326_b08b55025f_z.jpg', 'id': 213445, 'captions': [{'image_id': 213445, 'id': 631848, 'caption': 'a big cat sitting in a little bowl '}, {'image_id': 213445, 'id': 633591, 'caption': 'A cat is sitting in a small bowl on the table.'}, {'image_id': 213445, 'id': 635193, 'caption': 'A cat sitting in a bowl on a table.'}, {'image_id': 213445, 'id': 637341, 'caption': 'The cat is sitting inside of a bowl on a table.'}, {'image_id': 213445, 'id': 637632, 'caption': 'A cutting sitting upright in a pottery bowl on a coffee table'}]}

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## Run Classifier on Image

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### Load graph from trained model

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``` python

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

```

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### Process image

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``` python

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

```

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``` python

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

```

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### input data (graph, input-layer, output-layer, image)

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``` python

file_name = '/softpro/ss18/caption/cocoapi/val/' + img['file_name']

label_file = path + 'output_labels.txt'

model_file = path + 'output_graph.pb'

input_layer = 'Placeholder'

output_layer= 'module_apply_default/hub_output/feature_vector/SpatialSqueeze'

input_name = "import/" + input_layer

output_name = "import/" + output_layer

graph = load_graph(model_file)

input_operation = graph.get_operation_by_name(input_name)

output_operation = graph.get_operation_by_name(output_name)

print(file_name)

```

%% Output

    /softpro/ss18/caption/cocoapi/val/000000213445.jpg

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## Populate interface with extracted features (2048 dim. vector)

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``` python

img['captions'] = anns

t = read_tensor_from_image_file(

        file_name)

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

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

            input_operation.outputs[0]: t

        })

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

interface = list()

interface.append(img)

```

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## Print single example

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``` python

import pprint

pprint.pprint(img, indent=4)

```

%% Output

    {   'captions': [   {   'caption': 'a big cat sitting in a little bowl ',

                            'id': 631848,

                            'image_id': 213445},

                        {   'caption': 'A cat is sitting in a small bowl on the '

                                       'table.',

                            'id': 633591,

                            'image_id': 213445},

                        {   'caption': 'A cat sitting in a bowl on a table.',

                            'id': 635193,

                            'image_id': 213445},

                        {   'caption': 'The cat is sitting inside of a bowl on a '

                                       'table.',

                            'id': 637341,

                            'image_id': 213445},

                        {   'caption': 'A cutting sitting upright in a pottery '

                                       'bowl on a coffee table',

                            'id': 637632,

                            'image_id': 213445}],

        'coco_url': 'http://images.cocodataset.org/val2017/000000213445.jpg',

        'date_captured': '2013-11-15 17:17:16',

        'file_name': '000000213445.jpg',

        'flickr_url': 'http://farm5.staticflickr.com/4007/4463515326_b08b55025f_z.jpg',

        'height': 500,

        'id': 213445,

        'license': 1,

        'vector': array([0.46763384, 0.00917622, 0.27030513, ..., 0.32595807, 0.13369742,

           0.00646778], dtype=float32),

        'width': 408}

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## Save interface (needed for LSTM)

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``` python

with open('example.pickle', 'wb') as handle:

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

    pickle.dump(img, handle)

```

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``` python

```