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Diffstat (limited to 'tensorflow/examples/label_image/label_image.py')
| -rw-r--r-- | tensorflow/examples/label_image/label_image.py | 132 |
1 files changed, 132 insertions, 0 deletions
diff --git a/tensorflow/examples/label_image/label_image.py b/tensorflow/examples/label_image/label_image.py new file mode 100644 index 0000000000..39d0981337 --- /dev/null +++ b/tensorflow/examples/label_image/label_image.py @@ -0,0 +1,132 @@ +# 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 sys + +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__": + 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" + + parser = argparse.ArgumentParser() + 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", help="name of output layer") + args = parser.parse_args() + + 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) + + top_k = results.argsort()[-5:][::-1] + labels = load_labels(label_file) + for i in top_k: + print(labels[i], results[i]) |