From 2e95a2fa516878327849550f0c52c1f436112d5d Mon Sep 17 00:00:00 2001
From: Hongxu Jia <hongxu.jia@windriver.com>
Date: Fri, 15 Mar 2019 14:35:02 +0800
Subject: [PATCH] customize for Yocto

Upstream-Status: Inappropriate [oe specific]

Signed-off-by: Hongxu Jia <hongxu.jia@windriver.com>
---
 scripts/__init__.py    |  2 +-
 scripts/label_image.py | 87 +++++++++++++++++++++++++++++++-------------------
 2 files changed, 55 insertions(+), 34 deletions(-)

diff --git a/scripts/__init__.py b/scripts/__init__.py
index b1ae3c1..8d2ef2f 100644
--- a/scripts/__init__.py
+++ b/scripts/__init__.py
@@ -1,4 +1,4 @@
-#!/usr/bin/python
+#!/usr/bin/python3
 #
 # Copyright 2017 Google Inc.
 #
diff --git a/scripts/label_image.py b/scripts/label_image.py
index 214c4ec..7ecf259 100644
--- a/scripts/label_image.py
+++ b/scripts/label_image.py
@@ -1,3 +1,4 @@
+#!/usr/bin/python3
 # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -67,16 +68,58 @@ def load_labels(label_file):
     label.append(l.rstrip())
   return label
 
+def label_image(file_name = None,
+                model_file = "/usr/share/label_image/retrained_graph.pb",
+                label_file = "/usr/share/label_image/retrained_labels.txt",
+                input_height = 224,
+                input_width = 224,
+                input_mean = 128,
+                input_std = 128,
+                input_layer = "input",
+                output_layer = "final_result"):
+
+  if file_name is None:
+    return None, None
+
+  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:
+    start = time.time()
+    results = sess.run(output_operation.outputs[0],
+                      {input_operation.outputs[0]: t})
+    end=time.time()
+  results = np.squeeze(results)
+
+  top_k = results.argsort()[-5:][::-1]
+  labels = load_labels(label_file)
+
+  print('\nEvaluation time (1-image): {:.3f}s\n'.format(end-start))
+  template = "{} (score={:0.5f})"
+  for i in top_k:
+    print(template.format(labels[i], results[i]))
+    return labels[i], results[i]
+
 if __name__ == "__main__":
-  file_name = "tf_files/flower_photos/daisy/3475870145_685a19116d.jpg"
-  model_file = "tf_files/retrained_graph.pb"
-  label_file = "tf_files/retrained_labels.txt"
-  input_height = 224
-  input_width = 224
-  input_mean = 128
-  input_std = 128
+  file_name = "/usr/share/label_image/grace_hopper.jpg"
+  model_file = \
+    "/usr/share/label_image/inception_v3_2016_08_28_frozen.pb"
+  label_file = "/usr/share/label_image/imagenet_slim_labels.txt"
+  input_height = 299
+  input_width = 299
+  input_mean = 0
+  input_std = 255
   input_layer = "input"
-  output_layer = "final_result"
+  output_layer = "InceptionV3/Predictions/Reshape_1"
 
   parser = argparse.ArgumentParser()
   parser.add_argument("--image", help="image to be processed")
@@ -109,29 +152,7 @@ if __name__ == "__main__":
   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);
+  label_image(file_name, model_file, label_file,
+              input_height, input_width, input_mean,
+              input_std, input_layer, output_layer)
 
-  with tf.Session(graph=graph) as sess:
-    start = time.time()
-    results = sess.run(output_operation.outputs[0],
-                      {input_operation.outputs[0]: t})
-    end=time.time()
-  results = np.squeeze(results)
-
-  top_k = results.argsort()[-5:][::-1]
-  labels = load_labels(label_file)
-
-  print('\nEvaluation time (1-image): {:.3f}s\n'.format(end-start))
-  template = "{} (score={:0.5f})"
-  for i in top_k:
-    print(template.format(labels[i], results[i]))
-- 
2.7.4