File dialog support added with PR #3

Author: ArunMichaelDsouza

classify.py

@@ -3,6 +3,7 @@
 import os
 import tkinter as tk
 from tkinter import filedialog
+
 # Disable tensorflow compilation warnings
 os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
 import tensorflow as tf
@@ -14,31 +15,32 @@
 
 image_path = filedialog.askopenfilename()
 
-
-# Read the image_data
-image_data = tf.gfile.FastGFile(image_path, 'rb').read()
-
-# Loads label file, strips off carriage return
-label_lines = [line.rstrip() for line 
-                   in tf.gfile.GFile("tf_files/retrained_labels.txt")]
-
-# Unpersists graph from file
-with tf.gfile.FastGFile("tf_files/retrained_graph.pb", 'rb') as f:
-    graph_def = tf.GraphDef()
-    graph_def.ParseFromString(f.read())
-    _ = tf.import_graph_def(graph_def, name='')
-
-with tf.Session() as sess:
-    # Feed the image_data as input to the graph and get first prediction
-    softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
-    
-    predictions = sess.run(softmax_tensor, \
-             {'DecodeJpeg/contents:0': image_data})
-    
-    # Sort to show labels of first prediction in order of confidence
-    top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]
+if image_path:
 
-    for node_id in top_k:
-        human_string = label_lines[node_id]
-        score = predictions[0][node_id]
-        print('%s (score = %.5f)' % (human_string, score))
+    # Read the image_data
+    image_data = tf.gfile.FastGFile(image_path, 'rb').read()
+
+    # Loads label file, strips off carriage return
+    label_lines = [line.rstrip() for line 
+                       in tf.gfile.GFile("tf_files/retrained_labels.txt")]
+
+    # Unpersists graph from file
+    with tf.gfile.FastGFile("tf_files/retrained_graph.pb", 'rb') as f:
+        graph_def = tf.GraphDef()
+        graph_def.ParseFromString(f.read())
+        _ = tf.import_graph_def(graph_def, name='')
+
+    with tf.Session() as sess:
+        # Feed the image_data as input to the graph and get first prediction
+        softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
+        
+        predictions = sess.run(softmax_tensor, \
+                 {'DecodeJpeg/contents:0': image_data})
+        
+        # Sort to show labels of first prediction in order of confidence
+        top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]
+        
+        for node_id in top_k:
+            human_string = label_lines[node_id]
+            score = predictions[0][node_id]
+            print('%s (score = %.5f)' % (human_string, score))