Issues in order to adapt label_image.py

I'd like to modify label_image.py, my main goal is to create a GUI.

Some words about my ideas

I'm trying to modify some stuffs in the code (https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/label_image/label_image.py), but it doesn't work.

I first tried to had a filename = askopenfilename() line, which it seems to work.

filename = askopenfilename()

Then I don't know how I can adapt the code.

For example, in order to load MY graph, which line have I to adapt?

Maybe line 26 : def load_graph(model_file) : and putting def load_graph("c:my_path_hereowngraph.pb):? It doesn't work either.

def load_graph(model_file) :

def load_graph("c:my_path_hereowngraph.pb):

I have the same question for label, and also for the choosen picture:

filename = askopenfilename() --> where can I load MY picture (I tried to replace all file_name by filename but it fails).

filename = askopenfilename()

file_name

filename

You will find here my last tests : I tried to change code frome line 99 (cf. code in github)

@Andreas Storvik Strauman You will find here my last test ; I tried to change the arg (yes or not) test :

# 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 tkinter import Tk
from tkinter.filedialog import askopenfilename

Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
filename = askopenfilename() # show an "Open" dialog box and return the path to the selected file


import argparse

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(filename,
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 filename.endswith(".png"):
image_reader = tf.image.decode_png(
file_reader, channels=3, name="png_reader")
elif filename.endswith(".gif"):
image_reader = tf.squeeze(
tf.image.decode_gif(file_reader, name="gif_reader"))
elif filename.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 = "final_result"

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()


model_file = c:my_pathgaz_graph2.pb
file_name = filename #I get it with filename = askopenfilename()
if args.labels:
label_file = c:my_pathgaz_labels.txt
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])


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