2019SZI-Projekt/Logic/TrashRecognition/ImageClassification.py

# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import os

import numpy as np
import tensorflow as tf


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__":
  model_file = "Model/retrained_graph.pb"
  label_file = "Model/retrained_labels.txt"
  input_height = 299
  input_width = 299
  input_mean = 128
  input_std = 128
  input_layer = "input"
  output_layer = "InceptionV3/Predictions/Reshape_1"

  parser = argparse.ArgumentParser()

  parser.add_argument("--graph",
   default="Model/retrained_graph.pb",
   help="graph/model to be executed")

  parser.add_argument("--labels",
   default="Model/retrained_labels.txt",
   help="name of file containing labels")

  parser.add_argument("--input_height", type=int, help="input height")

  parser.add_argument("--input_width", type=int, help="input width")

  parser.add_argument("--input_mean", type=int, help="input mean")

  parser.add_argument("--input_std", type=int, help="input std")

  parser.add_argument("--input_layer",
   default="Placeholder",
    help="name of input layer")

  parser.add_argument("--output_layer",
   default="final_result",
   help="name of output layer")

  args = parser.parse_args()

  if args.graph:
    model_file = args.graph
  if args.labels:
    label_file = args.labels
  if args.input_height:
    input_height = args.input_height
  if args.input_width:
    input_width = args.input_width
  if args.input_mean:
    input_mean = args.input_mean
  if args.input_std:
    input_std = args.input_std
  if args.input_layer:
    input_layer = args.input_layer
  if args.output_layer:
    output_layer = args.output_layer
  
  graph = load_graph(model_file)

  for filename in os.listdir('Images/TestImages'):
    t = read_tensor_from_image_file(
      f'Images/TestImages/{filename}',
      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
      })
    results = np.squeeze(results)

    top_k = results.argsort()[-5:][::-1]
    labels = load_labels(label_file)
    print(f'{filename}: {labels[top_k[0]]} with {results[top_k[0]] * 100}% certainity')
    # for i in top_k:
    #   print(labels[i], results[i])