runModel.py

"""
Preparing model:
 - Install bazel ( check tensorflow's github for more info )
    Ubuntu 14.04:
        - Requirements:
            sudo add-apt-repository ppa:webupd8team/java
            sudo apt-get update
            sudo apt-get install oracle-java8-installer
        - Download bazel, ( https://github.com/bazelbuild/bazel/releases )
          tested on: https://github.com/bazelbuild/bazel/releases/download/0.2.0/bazel-0.2.0-jdk7-installer-linux-x86_64.sh
        - chmod +x PATH_TO_INSTALL.SH
        - ./PATH_TO_INSTALL.SH --user
        - Place bazel onto path ( exact path to store shown in the output)
- For retraining, prepare folder structure as
    - root_folder_name
        - class 1
            - file1
            - file2
        - class 2
            - file1
            - file2
- Clone tensorflow
- Go to root of tensorflow
- bazel build tensorflow/examples/image_retraining:retrain
- bazel-bin/tensorflow/examples/image_retraining/retrain --image_dir /path/to/root_folder_name  --output_graph /path/output_graph.pb --output_labels /path/output_labels.txt --bottleneck_dir /path/bottleneck
** Training done. **
For testing through bazel,
    bazel build tensorflow/examples/label_image:label_image && \
    bazel-bin/tensorflow/examples/label_image/label_image \
    --graph=/path/output_graph.pb --labels=/path/output_labels.txt \
    --output_layer=final_result \
    --image=/path/to/test/image
For testing through python, change and run this code.
"""

import numpy as np
import tensorflow as tf
import argparse
import sys

FLAGS = None

#imagePath = '/home/johannes/Dropbox/dev/Python/test_pics/avalanche/GleitschneelawineActiOn_19.jpg' #'/tmp/imagenet/flower.jpg'
#modelFullPath = '/tmp/output_graph.pb'
#labelsFullPath = '/tmp/output_labels.txt'


def create_graph():
    """Creates a graph from saved GraphDef file and returns a saver."""
    # Creates graph from saved graph_def.pb.
    with tf.gfile.FastGFile(FLAGS.input_graph, 'rb') as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())
        _ = tf.import_graph_def(graph_def, name='')


def run_inference_on_image():
    answer = None

    if not tf.gfile.Exists(FLAGS.image_file):
        tf.logging.fatal('File does not exist %s', FLAGS.image_file)
        return answer

    image_data = tf.gfile.FastGFile(FLAGS.image_file, 'rb').read()

    # Creates graph from saved GraphDef.
    create_graph()

    with tf.Session() as sess:

        softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
        predictions = sess.run(softmax_tensor,
                               {'DecodeJpeg/contents:0': image_data})
        predictions = np.squeeze(predictions)

        top_k = predictions.argsort()[-5:][::-1]  # Getting top 5 predictions
        f = open(FLAGS.input_labels, 'rb')
        lines = f.readlines()
        labels = [str(w).replace("\n", "") for w in lines]
        for node_id in top_k:
            human_string = labels[node_id]
            score = predictions[node_id]
            print('%s (score = %.5f)' % (human_string, score))

        answer = labels[top_k[0]]
        return answer

def main(_):
    run_inference_on_image()

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument(
      '--image_file',
      type=str,
      default='test_pics/avalanche/GleitschneelawineActiOn_26.jpg',
      help='Path to image.'
    )
    parser.add_argument(
        '--input_graph',
        type=str,
        default='incmodel/output_graph.pb',
        help='From where to load the trained graph.'
    )
    parser.add_argument(
        '--input_labels',
        type=str,
        default='incmodel/output_labels.txt',
        help='From where to load the trained graph\'s labels.'
    )
    FLAGS, unparsed = parser.parse_known_args()
    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)