Fix missing images

docs/build/html/.buildinfo → docs/build/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 12ae803fbc9b69a8e785b52f812c9afd
+config: 71ee4cae48b6d2f290dbbba66f289314
 tags: 645f666f9bcd5a90fca523b33c5a78b7

docs/build/html/_sources/camera.rst.txt → docs/build/_sources/camera.rst.txt

@@ -3,6 +3,8 @@ Detect Objects Using Your Webcam
 
 Hereby you can find an example which allows you to use your camera to generate a video stream, based on which you can perform object_detection.
 
+To run the example, simply create a new file under ``<PATH_TO_TF>/TensorFlow/models/research/object_detection`` and paste the code below.
+
 .. code-block:: python
 
     import numpy as np
@@ -18,8 +20,8 @@ Hereby you can find an example which allows you to use your camera to generate a
     from io import StringIO
     from matplotlib import pyplot as plt
     from PIL import Image
-    from utils import label_map_util
-    from utils import visualization_utils as vis_util
+    from object_detection.utils import label_map_util
+    from object_detection.utils import visualization_utils as vis_util
 
     # Define the video stream
     cap = cv2.VideoCapture(0)  # Change only if you have more than one webcams
@@ -40,20 +42,22 @@ Hereby you can find an example which allows you to use your camera to generate a
     NUM_CLASSES = 90
 
     # Download Model
-    opener = urllib.request.URLopener()
-    opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)
-    tar_file = tarfile.open(MODEL_FILE)
-    for file in tar_file.getmembers():
-        file_name = os.path.basename(file.name)
-        if 'frozen_inference_graph.pb' in file_name:
-            tar_file.extract(file, os.getcwd())
+    if not os.path.exists(os.path.join(os.getcwd(), MODEL_FILE)):
+        print("Downloading model")
+        opener = urllib.request.URLopener()
+        opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)
+        tar_file = tarfile.open(MODEL_FILE)
+        for file in tar_file.getmembers():
+            file_name = os.path.basename(file.name)
+            if 'frozen_inference_graph.pb' in file_name:
+                tar_file.extract(file, os.getcwd())
 
 
     # Load a (frozen) Tensorflow model into memory.
     detection_graph = tf.Graph()
     with detection_graph.as_default():
-        od_graph_def = tf.GraphDef()
-        with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
+        od_graph_def = tf.compat.v1.GraphDef()
+        with tf.io.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
             serialized_graph = fid.read()
             od_graph_def.ParseFromString(serialized_graph)
             tf.import_graph_def(od_graph_def, name='')
@@ -76,9 +80,8 @@ Hereby you can find an example which allows you to use your camera to generate a
 
     # Detection
     with detection_graph.as_default():
-        with tf.Session(graph=detection_graph) as sess:
+        with tf.compat.v1.Session(graph=detection_graph) as sess:
             while True:
-
                 # Read frame from camera
                 ret, image_np = cap.read()
                 # Expand dimensions since the model expects images to have shape: [1, None, None, 3]

docs/build/html/_sources/index.rst.txt → docs/build/_sources/index.rst.txt

@@ -6,28 +6,34 @@
 TensorFlow Object Detection API tutorial
 ============================================
 
+.. important:: This tutorial is intended for TensorFlow 1.14, which (at the time of writing this tutorial) is the latest stable version before TensorFlow 2.x.
+
+   Tensorflow 1.15 has also been released, but seems to be exhibiting `instability issues <https://github.com/tensorflow/models/issues/7640>`_.
+
+   A version for TensorFlow 1.9 can be found `here <https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/v1.9.1/>`_.
+
+   At the time of righting this tutorial, Object Detection model training and evaluation was not migrated to TensorFlow 2.x (see `here <https://github.com/tensorflow/models/issues/6423#issuecomment-600925072>`_). From personal tests, it seems that detection using pre-trained models works, however it is not yet possible to train and evaluate models. Once the migration has been completed, a version for TensorFlow 2.x will be produced.
+
 This is a step-by-step tutorial/guide to setting up and using TensorFlow's Object Detection API to perform, namely, object detection in images/video.
 
 The software tools which we shall use throughout this tutorial are listed in the table below:
 
 +---------------------------------------------+
 | Target Software versions                    |
 +==============+==============================+
-| OS           | Windows, Linux [*]_          |
+| OS           | Windows, Linux               |
 +--------------+------------------------------+
-| Python       | 3.6                          |
+| Python       | 3.7                          |
 +--------------+------------------------------+
-| TensorFlow   | 1.9                          |
+| TensorFlow   | 1.14                         |
 +--------------+------------------------------+
-| CUDA Toolkit | v9.0                         |
+| CUDA Toolkit | 10.0                         |
 +--------------+------------------------------+
-| CuDNN        | v7.0.5                       |
+| CuDNN        | 7.6.5                        |
 +--------------+------------------------------+ 
 | Anaconda     | Python 3.7 (Optional)        |
 +--------------+------------------------------+
 
-.. [*] Even though this tutorial is mostly based (and properly tested) on Windows 10, information is also provided for Linux systems.
-
 .. toctree::
    :maxdepth: 4
    :caption: Contents: