Labeller.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
from Kerneler import run_kerneler
import argparse
import numpy as np
import tensorflow as tf
import cv2
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
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__":
  #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 = "Mul"
  output_layer = "InceptionV3/Predictions/Reshape_1"

  parser = argparse.ArgumentParser()
  #parser.add_argument("--color_setting", help="color or black & white")
  parser.add_argument("--image", help="image to be processed")
  parser.add_argument("--graph", help="graph/model to be executed")
  parser.add_argument("--labels", 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", help="name of input layer")
  parser.add_argument("--output_layer", default="final_result", help="name of output layer")
  args = parser.parse_args()

  if args.image:
    file_name=args.image

  else:
    cap = cv2.VideoCapture(0)
    while(True):
    # Capture frame-by-frame
      ret, frame = cap.read()

    # Display the resulting frame
      cv2.imshow('frame',frame)
      cv2.imwrite('Frame.jpg',frame)
      if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# When everything done, release the capture
    cap.release()
    cv2.destroyAllWindows()
    file_name='frame.jpg'

  #if args.color_setting:
    #print("[INFO] Color Setting Parsing")
  if args.graph:
    model_file = args.graph
  if args.image:
    file_name = args.image
  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)
  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
    })
  results = np.squeeze(results)
  print("\n[INFO]The results of the retrained model are as follows:")
  top_k = results.argsort()[-5:][::-1]
  labels = load_labels(label_file)
  for i in top_k:
    #print(results[i],top_k[i],len(top_k))
    print("[INFO]Probability that the given image is a "+str(labels[i])+" is: "+str(results[i]))
  print("[INFO]The given component is a: "+str(labels[np.argmax(results)]))

run_kerneler(file_name)