Merge pull request #2333 from jzhugithub/master

Definite Tensor outside detection loop, change Tensor names

object_detection/object_detection_tutorial.ipynb

@@ -241,12 +241,22 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
+    "collapsed": true,
     "scrolled": true
    },
    "outputs": [],
    "source": [
     "with detection_graph.as_default():\n",
     "  with tf.Session(graph=detection_graph) as sess:\n",
+    "    # Definite input and output Tensors for detection_graph\n",
+    "    image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')\n",
+    "    # Each box represents a part of the image where a particular object was detected.\n",
+    "    detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')\n",
+    "    # Each score represent how level of confidence for each of the objects.\n",
+    "    # Score is shown on the result image, together with the class label.\n",
+    "    detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')\n",
+    "    detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')\n",
+    "    num_detections = detection_graph.get_tensor_by_name('num_detections:0')\n",
     "    for image_path in TEST_IMAGE_PATHS:\n",
     "      image = Image.open(image_path)\n",
     "      # the array based representation of the image will be used later in order to prepare the\n",
@@ -254,17 +264,9 @@
     "      image_np = load_image_into_numpy_array(image)\n",
     "      # Expand dimensions since the model expects images to have shape: [1, None, None, 3]\n",
     "      image_np_expanded = np.expand_dims(image_np, axis=0)\n",
-    "      image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')\n",
-    "      # Each box represents a part of the image where a particular object was detected.\n",
-    "      boxes = detection_graph.get_tensor_by_name('detection_boxes:0')\n",
-    "      # Each score represent how level of confidence for each of the objects.\n",
-    "      # Score is shown on the result image, together with the class label.\n",
-    "      scores = detection_graph.get_tensor_by_name('detection_scores:0')\n",
-    "      classes = detection_graph.get_tensor_by_name('detection_classes:0')\n",
-    "      num_detections = detection_graph.get_tensor_by_name('num_detections:0')\n",
     "      # Actual detection.\n",
-    "      (boxes, scores, classes, num_detections) = sess.run(\n",
-    "          [boxes, scores, classes, num_detections],\n",
+    "      (boxes, scores, classes, num) = sess.run(\n",
+    "          [detection_boxes, detection_scores, detection_classes, num_detections],\n",
     "          feed_dict={image_tensor: image_np_expanded})\n",
     "      # Visualization of the results of a detection.\n",
     "      vis_util.visualize_boxes_and_labels_on_image_array(\n",
@@ -305,7 +307,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.12"
+   "version": "2.7.13"
   }
  },
  "nbformat": 4,