Minor fixes and updates

Author: sglvladi

.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+    "python.pythonPath": "C:\\Users\\sglvladi\\Anaconda3\\envs\\sphinx\\python.exe"
+}

docs/build/doctrees/camera.doctree

@@ -57,12 +57,12 @@ Once you have activated your virtual environment, the name of the environment sh
 
 Install TensorFlow CPU for Python
 *********************************
-- Open a new `Anaconda/Command Prompt` window and activate the `tensorflow_gpu` environment (if you have not done so already)
+- Open a new `Anaconda/Command Prompt` window and activate the `tensorflow_cpu` environment (if you have not done so already)
 - Once open, type the following on the command line:
 
     .. code-block:: posh
 
-        pip install --ignore-installed --upgrade tensorflow
+        pip install --ignore-installed --upgrade tensorflow==1.7.0
 
 - Wait for the installation to finish
 
@@ -81,7 +81,7 @@ Test your Installation
 
         >>> import tensorflow as tf
 
-- If the above code shows an error, then check to make sure you have activated the `tensorflow_gpu` environment and that tensorflow_gpu was successfully installed within it in the previous step.
+- If the above code shows an error, then check to make sure you have activated the `tensorflow_cpu` environment and that tensorflow_cpu was successfully installed within it in the previous step.
 - Then run the following:
 
     .. code-block:: python
@@ -123,6 +123,8 @@ Before proceeding to install TesnsorFlow GPU, you need to make sure that your sy
 | Anaconda with Python 3.6 (Optional) |
 +-------------------------------------+
 
+.. _cuda_install:
+
 Install CUDA Toolkit
 ***********************
 Follow this `link <https://developer.nvidia.com/cuda-90-download-archive?target_os=Windows&target_arch=x86_64&target_version=10&target_type=exenetwork>`_ to download and install CUDA Toolkit v9.0.
@@ -145,15 +147,17 @@ Set Your Environment Variables
 - Go to `Start` and Search "environment variables"
 - Click the Environment Variables button
 - Click on the ``Path`` system variable and select edit
-- Add the following paths
+- Add the following paths:
 
     - ``<INSTALL_PATH>\NVIDIA GPU Computing Toolkit\CUDA\v9.0\bin``
     - ``<INSTALL_PATH>\NVIDIA GPU Computing Toolkit\CUDA\v9.0\libnvvp``
     - ``<INSTALL_PATH>\NVIDIA GPU Computing Toolkit\CUDA\v9.0\extras\CUPTI\libx64``
     - ``<INSTALL_PATH>\NVIDIA GPU Computing Toolkit\CUDA\v9.0\cuda\bin``
 
-Update your GPU drivers
-***********************
+Update your GPU drivers (Optional)
+**********************************
+If during the installation of the CUDA Toolkit (see :ref:`cuda_install`) you selected the `Express Installation` option, then your GPU drivers will have been overwritten by those that come bundled with the CUDA toolkit. These drivers are typically NOT the latest drivers and, thus, you may wish to updte your drivers.
+
 - Go to `<http://www.nvidia.com/Download/index.aspx>`_
 - Select your GPU version to download
 - Install the driver 
@@ -187,7 +191,7 @@ Install TensorFlow GPU for Python
 
     .. code-block:: posh
 
-        pip install --ignore-installed --upgrade tensorflow-gpu
+        pip install --ignore-installed --upgrade tensorflow-gpu==1.7.0
 
 - Wait for the installation to finish
 
@@ -236,7 +240,7 @@ Test your Installation
 TensorFlow Models Installation 
 ------------------------------
 
-Now that you have installed TensorFlow, it is time to install the models used b TesnorFlow to do it's magic.
+Now that you have installed TensorFlow, it is time to install the models used by TensorFlow to do its magic.
 
 Install Prerequisites
 ~~~~~~~~~~~~~~~~~~~~~
@@ -254,7 +258,7 @@ Building on the assumption that you have just created your new virtual environme
 +--------------+------------------------------+
 | jupyter      | 1.0.0-py36_4                 |
 +--------------+------------------------------+
-| matlplotlib  | 2.2.2-py36h153e9ff_0         |
+| matplotlib   | 2.2.2-py36h153e9ff_0         |
 +--------------+------------------------------+
 | opencv       | 3.3.1-py36h20b85fd_1         |
 +--------------+------------------------------+
@@ -273,6 +277,7 @@ Alternatively, if you don't want to use Anaconda you can install the packages us
 
     pip install <package_name>(=<version>)
 
+but you will need to install ``opencv-python`` instead of ``opencv``.
 
 Downloading the TensorFlow Models
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -349,6 +354,13 @@ This should be done as follows:
         # From TensorFlow/models/research/
         protoc object_detection/protos/*.proto --python_out=.
 
+If you are on Windows and using version 3.5 or later, the wildcard will not work and you have to run this in the command prompt:
+
+.. code-block:: python
+
+        # From TensorFlow/models/research/
+        for /f %i in ('dir /b object_detection\protos\*.proto') do protoc object_detection\protos\%i --python_out=.
+
 .. [#] NOTE: You MUST open a new `Anaconda/Command Prompt` for the changes in the environment variables to take effect.
 
 .. _test_tf_models:
@@ -379,6 +391,9 @@ Test your Installation
 LabelImg Installation
 ---------------------
 
+For Windows and Linux you can download the precompiled binary at http://tzutalin.github.io/labelImg/.
+The steps for installing from source follow below.
+
 Create a new Conda virtual environment
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -398,7 +413,7 @@ To deal with the fact that ``labelImg`` (on Windows) requires the use of ``pyqt4
 
         activate labelImg
 
-Once you have activated your virtual environment, the name of the environment should be displayed within brackets at the beggining of your cmd path specifier, e.g.:
+Once you have activated your virtual environment, the name of the environment should be displayed within brackets at the beginning of your cmd path specifier, e.g.:
 
 .. code-block:: ps1con
 
@@ -407,7 +422,7 @@ Once you have activated your virtual environment, the name of the environment sh
 Downloading labelImg
 ~~~~~~~~~~~~~~~~~~~~
 
-- Inside you ``TesnorFlow`` folder, create a new directory, name it ``addons`` and then ``cd`` into it.
+- Inside you ``TensorFlow`` folder, create a new directory, name it ``addons`` and then ``cd`` into it.
 - To download the package you can either use `Git <https://git-scm.com/downloads>`_ to clone the `labelImg repo <https://github.com/tzutalin/labelImg>`_ inside the ``TensorFlow\addons`` folder, or you can simply download it as a `ZIP <https://github.com/tzutalin/labelImg/archive/master.zip>`_ and extract it's contents inside the ``TensorFlow\addons`` folder. To keep things consistent, in the latter case you will have to rename the extracted folder ``labelImg-master`` to ``labelImg``. [#]_
 - You should now have a single folder named ``addons\labelImg`` under your ``TensorFlow`` folder, which contains another 4 folders as such:
 

docs/build/doctrees/environment.pickle

@@ -19,7 +19,7 @@ Now that we have done all the above, we can start doing some cool stuff. Here we
 Preparing workspace
 ~~~~~~~~~~~~~~~~~~~
 
-If you have followed the tutorial, you shhould by now have a folder ``Tensorflow``, placed under ``<PATH_TO_TF>`` (e.g. ``C:\Users\sglvladi\Documents``), with the following directory tree:
+If you have followed the tutorial, you should by now have a folder ``Tensorflow``, placed under ``<PATH_TO_TF>`` (e.g. ``C:\Users\sglvladi\Documents``), with the following directory tree:
 
 | TensorFlow
 | ├─ addons
@@ -66,7 +66,7 @@ Here's an explanation for each of the folders/filer shown in the above tree:
 - ``images``: This folder contains a copy of all the images in our dataset, as well as the respective ``*.xml`` files produced for each one, once ``labelImg`` is used to annotate objects.
 
     * ``images\train``: This folder contains a copy of all images, and the respective ``*.xml`` files, which will be used to train our model.
-    * ``images\test``: This folder contains a copy of all images, and the respective ``*.xml`` files, which will be used to train our model.
+    * ``images\test``: This folder contains a copy of all images, and the respective ``*.xml`` files, which will be used to test our model.
 
 - ``pre-trained-model``: This folder will contain the pre-trained model of our choice, which shall be used as a starting checkpoint for our training job.
 - ``training``: This folder will contain the training pipeline configuration file ``*.config``, as well as a ``*.pbtxt`` label map file and all files generated during the training of our model.
@@ -296,7 +296,9 @@ Now that we have obtained our ``*.csv`` annotation files, we will need to conver
     import io
     import pandas as pd
     import tensorflow as tf
-
+    import sys
+    sys.path.append("../../models/research")
+    
     from PIL import Image
     from object_detection.utils import dataset_util
     from collections import namedtuple, OrderedDict
@@ -305,13 +307,24 @@ Now that we have obtained our ``*.csv`` annotation files, we will need to conver
     flags.DEFINE_string('csv_input', '', 'Path to the CSV input')
     flags.DEFINE_string('output_path', '', 'Path to output TFRecord')
     flags.DEFINE_string('label', '', 'Name of class label')
+    # if your image has more labels input them as 
+    # flags.DEFINE_string('label0', '', 'Name of class[0] label')
+    # flags.DEFINE_string('label1', '', 'Name of class[1] label')
+    # and so on. 
+    flags.DEFINE_string('img_path', '', 'Path to images')
     FLAGS = flags.FLAGS
 
 
     # TO-DO replace this with label map
+    # for multiple labels add more else if statements
     def class_text_to_int(row_label):
         if row_label == FLAGS.label:  # 'ship':
             return 1
+        # comment upper if statement and uncomment these statements for multiple labelling
+        # if row_label == FLAGS.label0:
+        #   return 1
+        # elif row_label == FLAGS.label1:
+        #   return 0
         else:
             None
 
@@ -331,6 +344,7 @@ Now that we have obtained our ``*.csv`` annotation files, we will need to conver
 
         filename = group.filename.encode('utf8')
         image_format = b'jpg'
+        # check if the image format is matching with your images.
         xmins = []
         xmaxs = []
         ymins = []
@@ -365,7 +379,7 @@ Now that we have obtained our ``*.csv`` annotation files, we will need to conver
 
     def main(_):
         writer = tf.python_io.TFRecordWriter(FLAGS.output_path)
-        path = os.path.join(os.getcwd(), 'images')
+        path = os.path.join(os.getcwd(), FLAGS.img_path)
         examples = pd.read_csv(FLAGS.csv_input)
         grouped = split(examples, 'filename')
         for group in grouped:
@@ -387,11 +401,11 @@ Now that we have obtained our ``*.csv`` annotation files, we will need to conver
         
         # Create train data:
         python generate_tfrecord.py --label=<LABEL> --csv_input=<PATH_TO_ANNOTATIONS_FOLDER>/train_labels.csv
-        --img_path=<PATH_TO_IMAGES_FOLDER>  --output_path=<PATH_TO_ANNOTATIONS_FOLDER>/train.record
+        --img_path=<PATH_TO_IMAGES_FOLDER>/train  --output_path=<PATH_TO_ANNOTATIONS_FOLDER>/train.record
 
         # Create test data:
         python generate_tfrecord.py --label=<LABEL> --csv_input=<PATH_TO_ANNOTATIONS_FOLDER>/test_labels.csv
-        --img_path=<PATH_TO_IMAGES_FOLDER>  
+        --img_path=<PATH_TO_IMAGES_FOLDER>/test  
         --output_path=<PATH_TO_ANNOTATIONS_FOLDER>/test.record 
 
         # For example
@@ -612,7 +626,7 @@ Once the above changes have been applied to our config file, go ahead and save i
 Training the Model
 ~~~~~~~~~~~~~~~~~~
 
-Before we begin training our model, let's go and copy the ``TensorFlow/models/research/object_detection/train.py`` script and paste it straight into our ``training_demo`` folder. We will need this script in order to train our model.
+Before we begin training our model, let's go and copy the ``TensorFlow/models/research/object_detection/legacy/train.py`` script and paste it straight into our ``training_demo`` folder. We will need this script in order to train our model.
 
 Now, to initiate a new training job, ``cd`` inside the ``training_demo`` folder and type the following: 
 
@@ -715,12 +729,12 @@ Once your training job is complete, you need to extract the newly trained infere
 
         activate tensorflow_gpu
 
-- Copy the ``TensorFlow/models/research/object_detection/extract_inference_graph.py`` script and paste it straight into your ``training_demo`` folder.
+- Copy the ``TensorFlow/models/research/object_detection/export_inference_graph.py`` script and paste it straight into your ``training_demo`` folder.
 - Check inside your ``training_demo/training`` folder for the ``model.ckpt-*`` checkpoint file with the highest number following the name of the dash e.g. ``model.ckpt-34350``). This number represents the training step index at which the file was created.
 - Alternatively, simply sort all the files inside ``training_demo/training`` by descending time and pick the ``model.ckpt-*`` file that comes first in the list.
-- Make a note of the file's name, as it will be passed as an argument when we call the ``extract_inference_graph.py`` script.
+- Make a note of the file's name, as it will be passed as an argument when we call the ``export_inference_graph.py`` script.
 - Now, ``cd`` inside your ``training_demo`` folder, and run the following command:
 
 .. code-block:: posh
     
-    python export_inference_graph.py --input_type image_tensor --pipeline_config_path training/ssd_inception_v2_coco.config --trained_checkpoint_prefix training/model.ckpt-13302 --output_directory trained-inference-graphs/output_inference_graph_v1.pb
+    python export_inference_graph.py --input_type image_tensor --pipeline_config_path training/ssd_inception_v2_coco.config --trained_checkpoint_prefix training/model.ckpt-13302 --output_directory trained-inference-graphs/output_inference_graph_v1.pb