Fixed awkward looking directory trees

Author: sglvladi

docs/build/html/_sources/install.rst.txt

@@ -298,15 +298,15 @@ Downloading the TensorFlow Models
 - To download the models you can either use `Git <https://git-scm.com/downloads>`_ to clone the `TensorFlow Models repo <https://github.com/tensorflow/models>`_ inside the ``TensorFlow`` folder, or you can simply download it as a `ZIP <https://github.com/tensorflow/models/archive/master.zip>`_ and extract it's contents inside the ``TensorFlow`` folder. To keep things consistent, in the latter case you will have to rename the extracted folder ``models-master`` to ``models``. [#]_
 - You should now have a single folder named ``models`` under your ``TensorFlow`` folder, which contains another 4 folders as such:
 
-| TensorFlow
-| └─ models
-|     ├── official
-|     ├── research
-|     ├── samples
-|     └── tutorials
-|
-|
-
+.. code-block:: bash
+
+    TensorFlow
+    └─ models
+        ├── official
+        ├── research
+        ├── samples
+        └── tutorials
+    
 .. [#] The latest repo commit when writing this tutorial is `da903e0 <https://github.com/tensorflow/models/commit/da903e07aea0887d59ebf612557243351ddfb4e6>`_.
 
 Protobuf Installation/Compilation
@@ -484,14 +484,14 @@ To deal with the fact that ``labelImg`` (on Windows) requires the use of ``pyqt4
 * Open a new `Anaconda/Command Prompt` window 
 * Type the following command:
 
-    .. code-block:: posh
+    .. code-block:: bash
 
         conda create -n labelImg pyqt=4
 
 * The above will create a new virtual environment with name ``labelImg``
 * Now lets activate the newly created virtual environment by running the following in the `Anaconda Promt` window:
 
-    .. code-block:: posh
+    .. code-block:: bash
 
         activate labelImg
 
@@ -508,15 +508,16 @@ Downloading labelImg
 - 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:
 
-| TensorFlow
-| ├─ addons
-| │   └── labelImg
-| └─ models
-|     ├── official
-|     ├── research
-|     ├── samples
-|     └── tutorials
-|
+.. code-block:: bash
+
+    TensorFlow
+    ├─ addons
+    │   └── labelImg
+    └─ models
+        ├── official
+        ├── research
+        ├── samples
+        └── tutorials
 
 .. [#] The latest repo commit when writing this tutorial is `8d1bd68 <https://github.com/tzutalin/labelImg/commit/8d1bd68ab66e8c311f2f45154729bba301a81f0b>`_.
 

docs/build/html/_sources/training.rst.txt

@@ -19,46 +19,46 @@ 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 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
-| │   └── labelImg
-| └─ models
-|     ├── official
-|     ├── research
-|     ├── samples
-|     └── tutorials
-|
-|
-
-Now create a new folder under ``TensorFlow``  and call it ``workspace``. It is within the ``workspace`` that we will store all our training set-ups. Now let's go under workspace and create another folder named ``training_demo``. Now our directory structure should be as so:
-
-| TensorFlow
-| ├─ addons
-| │   └─ labelImg
-| ├─ models
-| │   ├─ official
-| │   ├─ research
-| │   ├─ samples
-| │   └─ tutorials
-| └─ workspace
-|     └─ training_demo
-|
-|
-
-The ``training_demo`` folder shall be our `training folder`, which will contain all files related to our model training. It is advisable to create a separate training folder each time we wish to train a different model. The typical structure for training folders is shown below.
-
-| training_demo
-| ├─ annotations
-| ├─ images
-| │   ├─ test
-| │   └─ train
-| ├─ pre-trained-model
-| ├─ training
-| └─ README.md
-|
-|
+1. 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:
+
+    .. code-block:: bash
+
+        TensorFlow
+        ├─ addons
+        │   └── labelImg
+        └─ models
+            ├── official
+            ├── research
+            ├── samples
+            └── tutorials
+
+2. Now create a new folder under ``TensorFlow``  and call it ``workspace``. It is within the ``workspace`` that we will store all our training set-ups. Now let's go under workspace and create another folder named ``training_demo``. Now our directory structure should be as so:
+
+    .. code-block:: bash
+
+        TensorFlow
+        ├─ addons
+        │   └─ labelImg
+        ├─ models
+        │   ├─ official
+        │   ├─ research
+        │   ├─ samples
+        │   └─ tutorials
+        └─ workspace
+            └─ training_demo
+
+3. The ``training_demo`` folder shall be our `training folder`, which will contain all files related to our model training. It is advisable to create a separate training folder each time we wish to train a different model. The typical structure for training folders is shown below.
+
+    .. code-block:: bash
+    
+        training_demo
+        ├─ annotations
+        ├─ images
+        │   ├─ test
+        │   └─ train
+        ├─ pre-trained-model
+        ├─ training
+        └─ README.md
 
 Here's an explanation for each of the folders/filer shown in the above tree:
 
@@ -142,20 +142,20 @@ There are two steps in doing so:
 
 Before we proceed to describe the above steps, let's create a directory where we can store some scripts. Under the ``TensorFlow`` folder, create a new folder ``TensorFlow\scripts``, which we can use to store some useful scripts. To make things even tidier, let's create a new folder ``TensorFlow\scripts\preprocessing``, where we shall store scripts that we can use to preprocess our training inputs. Below is out ``TensorFlow`` directory tree structure, up to now:
 
-| TensorFlow
-| ├─ addons
-| │   └─ labelImg
-| ├─ models
-| │   ├─ official
-| │   ├─ research
-| │   ├─ samples
-| │   └─ tutorials
-| ├─ scripts
-| │   └─ preprocessing
-| └─ workspace
-|     └─ training_demo
-|
-|
+.. code-block:: bash
+
+    TensorFlow
+    ├─ addons
+    │   └─ labelImg
+    ├─ models
+    │   ├─ official
+    │   ├─ research
+    │   ├─ samples
+    │   └─ tutorials
+    ├─ scripts
+    │   └─ preprocessing
+    └─ workspace
+        └─ training_demo
 
 Converting ``*.xml`` to ``*.csv``
 ---------------------------------

docs/source/install.rst

@@ -298,15 +298,15 @@ Downloading the TensorFlow Models
 - To download the models you can either use `Git <https://git-scm.com/downloads>`_ to clone the `TensorFlow Models repo <https://github.com/tensorflow/models>`_ inside the ``TensorFlow`` folder, or you can simply download it as a `ZIP <https://github.com/tensorflow/models/archive/master.zip>`_ and extract it's contents inside the ``TensorFlow`` folder. To keep things consistent, in the latter case you will have to rename the extracted folder ``models-master`` to ``models``. [#]_
 - You should now have a single folder named ``models`` under your ``TensorFlow`` folder, which contains another 4 folders as such:
 
-| TensorFlow
-| └─ models
-|     ├── official
-|     ├── research
-|     ├── samples
-|     └── tutorials
-|
-|
-
+.. code-block:: bash
+
+    TensorFlow
+    └─ models
+        ├── official
+        ├── research
+        ├── samples
+        └── tutorials
+    
 .. [#] The latest repo commit when writing this tutorial is `da903e0 <https://github.com/tensorflow/models/commit/da903e07aea0887d59ebf612557243351ddfb4e6>`_.
 
 Protobuf Installation/Compilation
@@ -484,14 +484,14 @@ To deal with the fact that ``labelImg`` (on Windows) requires the use of ``pyqt4
 * Open a new `Anaconda/Command Prompt` window 
 * Type the following command:
 
-    .. code-block:: posh
+    .. code-block:: bash
 
         conda create -n labelImg pyqt=4
 
 * The above will create a new virtual environment with name ``labelImg``
 * Now lets activate the newly created virtual environment by running the following in the `Anaconda Promt` window:
 
-    .. code-block:: posh
+    .. code-block:: bash
 
         activate labelImg
 
@@ -508,15 +508,16 @@ Downloading labelImg
 - 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:
 
-| TensorFlow
-| ├─ addons
-| │   └── labelImg
-| └─ models
-|     ├── official
-|     ├── research
-|     ├── samples
-|     └── tutorials
-|
+.. code-block:: bash
+
+    TensorFlow
+    ├─ addons
+    │   └── labelImg
+    └─ models
+        ├── official
+        ├── research
+        ├── samples
+        └── tutorials
 
 .. [#] The latest repo commit when writing this tutorial is `8d1bd68 <https://github.com/tzutalin/labelImg/commit/8d1bd68ab66e8c311f2f45154729bba301a81f0b>`_.
 

docs/source/training.rst

@@ -19,46 +19,46 @@ 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 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
-| │   └── labelImg
-| └─ models
-|     ├── official
-|     ├── research
-|     ├── samples
-|     └── tutorials
-|
-|
-
-Now create a new folder under ``TensorFlow``  and call it ``workspace``. It is within the ``workspace`` that we will store all our training set-ups. Now let's go under workspace and create another folder named ``training_demo``. Now our directory structure should be as so:
-
-| TensorFlow
-| ├─ addons
-| │   └─ labelImg
-| ├─ models
-| │   ├─ official
-| │   ├─ research
-| │   ├─ samples
-| │   └─ tutorials
-| └─ workspace
-|     └─ training_demo
-|
-|
-
-The ``training_demo`` folder shall be our `training folder`, which will contain all files related to our model training. It is advisable to create a separate training folder each time we wish to train a different model. The typical structure for training folders is shown below.
-
-| training_demo
-| ├─ annotations
-| ├─ images
-| │   ├─ test
-| │   └─ train
-| ├─ pre-trained-model
-| ├─ training
-| └─ README.md
-|
-|
+1. 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:
+
+    .. code-block:: bash
+
+        TensorFlow
+        ├─ addons
+        │   └── labelImg
+        └─ models
+            ├── official
+            ├── research
+            ├── samples
+            └── tutorials
+
+2. Now create a new folder under ``TensorFlow``  and call it ``workspace``. It is within the ``workspace`` that we will store all our training set-ups. Now let's go under workspace and create another folder named ``training_demo``. Now our directory structure should be as so:
+
+    .. code-block:: bash
+
+        TensorFlow
+        ├─ addons
+        │   └─ labelImg
+        ├─ models
+        │   ├─ official
+        │   ├─ research
+        │   ├─ samples
+        │   └─ tutorials
+        └─ workspace
+            └─ training_demo
+
+3. The ``training_demo`` folder shall be our `training folder`, which will contain all files related to our model training. It is advisable to create a separate training folder each time we wish to train a different model. The typical structure for training folders is shown below.
+
+    .. code-block:: bash
+    
+        training_demo
+        ├─ annotations
+        ├─ images
+        │   ├─ test
+        │   └─ train
+        ├─ pre-trained-model
+        ├─ training
+        └─ README.md
 
 Here's an explanation for each of the folders/filer shown in the above tree:
 
@@ -142,20 +142,20 @@ There are two steps in doing so:
 
 Before we proceed to describe the above steps, let's create a directory where we can store some scripts. Under the ``TensorFlow`` folder, create a new folder ``TensorFlow\scripts``, which we can use to store some useful scripts. To make things even tidier, let's create a new folder ``TensorFlow\scripts\preprocessing``, where we shall store scripts that we can use to preprocess our training inputs. Below is out ``TensorFlow`` directory tree structure, up to now:
 
-| TensorFlow
-| ├─ addons
-| │   └─ labelImg
-| ├─ models
-| │   ├─ official
-| │   ├─ research
-| │   ├─ samples
-| │   └─ tutorials
-| ├─ scripts
-| │   └─ preprocessing
-| └─ workspace
-|     └─ training_demo
-|
-|
+.. code-block:: bash
+
+    TensorFlow
+    ├─ addons
+    │   └─ labelImg
+    ├─ models
+    │   ├─ official
+    │   ├─ research
+    │   ├─ samples
+    │   └─ tutorials
+    ├─ scripts
+    │   └─ preprocessing
+    └─ workspace
+        └─ training_demo
 
 Converting ``*.xml`` to ``*.csv``
 ---------------------------------