From 6f20d7fdc72cf8618587c214c55628416922ca4a Mon Sep 17 00:00:00 2001
From: Chitoku YATO <cyato@nvidia.com>
Date: Tue, 12 May 2020 23:46:55 -0700
Subject: [PATCH 01/13] JetPack 4.4 Developer Preview version

---
 scripts/create-sdcard-image-from-scratch.sh | 53 ++++++++++++++++-----
 1 file changed, 41 insertions(+), 12 deletions(-)

diff --git a/scripts/create-sdcard-image-from-scratch.sh b/scripts/create-sdcard-image-from-scratch.sh
index 9826dd7a..f70f8da0 100755
--- a/scripts/create-sdcard-image-from-scratch.sh
+++ b/scripts/create-sdcard-image-from-scratch.sh
@@ -1,47 +1,72 @@
 #!/bin/bash
 
+set -e
+
 password='jetbot'
 
+# Record the time this script starts
+date
+
+# Get the full dir name of this script
+DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
+
 # Keep updating the existing sudo time stamp
 sudo -v
 while true; do sudo -n true; sleep 120; kill -0 "$$" || exit; done 2>/dev/null &
 
 # Enable i2c permissions
+echo "\e[100m Enable i2c permissions \e[0m"
 sudo usermod -aG i2c $USER
 
 # Install pip and some python dependencies
+echo "\e[104m Install pip and some python dependencies \e[0m"
 sudo apt-get update
 sudo apt install -y python3-pip python3-pil
-sudo pip3 install --upgrade numpy
+sudo -H pip3 install --upgrade numpy
+
+# Install jtop
+echo "\e[100m Install jtop \e[0m"
+sudo -H pip install jetson-stats 
+
 
 # Install the pre-built TensorFlow pip wheel
+echo "\e[48;5;202m Install the pre-built TensorFlow pip wheel \e[0m"
 sudo apt-get update
 sudo apt-get install -y libhdf5-serial-dev hdf5-tools libhdf5-dev zlib1g-dev zip libjpeg8-dev
 sudo apt-get install -y python3-pip
-sudo pip3 install -U pip
-sudo pip3 install -U numpy==1.16.1 future==0.17.1 mock==3.0.5 h5py==2.9.0 keras_preprocessing==1.0.5 keras_applications==1.0.6 enum34 futures testresources setuptools protobuf
-sudo pip3 install --pre --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v42 tensorflow-gpu==1.14.0+nv19.10
+sudo -H pip3 install -U pip setuptools
+sudo -H pip3 install -U numpy grpcio absl-py py-cpuinfo psutil portpicker six mock requests gast h5py astor termcolor protobuf keras-applications keras-preprocessing wrapt google-pasta
+sudo -H pip3 install --pre --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v43 tensorflow==1.15.2+nv20.3
 
 # Install the pre-built PyTorch pip wheel 
-wget https://nvidia.box.com/shared/static/phqe92v26cbhqjohwtvxorrwnmrnfx1o.whl -O torch-1.3.0-cp36-cp36m-linux_aarch64.whl
-sudo pip3 install numpy torch-1.3.0-cp36-cp36m-linux_aarch64.whl
+echo "\e[45m Install the pre-built PyTorch pip wheel  \e[0m"
+cd
+wget -N https://nvidia.box.com/shared/static/3ibazbiwtkl181n95n9em3wtrca7tdzp.whl -O torch-1.5.0-cp36-cp36m-linux_aarch64.whl
+sudo apt-get install -y python3-pip libopenblas-base libopenmpi-dev 
+sudo -H pip3 install Cython
+sudo -H pip3 install numpy torch-1.5.0-cp36-cp36m-linux_aarch64.whl
 
 # Install torchvision package
+echo "\e[45m Install torchvision package \e[0m"
+cd
 git clone https://github.com/pytorch/vision
 cd vision
-git checkout v0.4.0
-sudo python3 setup.py install
+#git checkout v0.4.0
+sudo -H python3 setup.py install
 
 # Install traitlets (master, to support the unlink() method)
+echo "\e[48;5;172m Install traitlets \e[0m"
 sudo python3 -m pip install git+https://github.com/ipython/traitlets@master
 
 # Install jupyter lab
-sudo apt install -y nodejs npm
-sudo pip3 install jupyter jupyterlab
-sudo jupyter labextension install @jupyter-widgets/jupyterlab-manager
+echo "\e[48;5;172m Install Jupyter Lab \e[0m"
+sudo apt install -y curl
+curl -sL https://deb.nodesource.com/setup_10.x | sudo -E bash -
+sudo apt install -y nodejs 
+sudo -H pip3 install jupyter jupyterlab
+sudo -H jupyter labextension install @jupyter-widgets/jupyterlab-manager
 
 jupyter lab --generate-config
-#jupyter notebook password
 python3 -c "from notebook.auth.security import set_password; set_password('$password', '$HOME/.jupyter/jupyter_notebook_config.json')"
 
 # install jetbot python module
@@ -73,3 +98,7 @@ sudo bash -c 'echo "/var/swapfile swap swap defaults 0 0" >> /etc/fstab'
 # Copy JetBot notebooks to home directory
 cp -r ~/jetbot/notebooks ~/Notebooks
 
+echo "\e[42m All done! \e[0m"
+
+#record the time this script ends
+date

From c342d5545b4d2fab107d0b86cb1169b2b99664ba Mon Sep 17 00:00:00 2001
From: jetbot <jetbot@nano-4-4dp.localdomain>
Date: Tue, 9 Jun 2020 15:25:46 -0700
Subject: [PATCH 02/13] Support invocation form other dir

---
 scripts/run-script-and-save-log.sh | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/scripts/run-script-and-save-log.sh b/scripts/run-script-and-save-log.sh
index fb129e4b..3a5ce941 100755
--- a/scripts/run-script-and-save-log.sh
+++ b/scripts/run-script-and-save-log.sh
@@ -1,3 +1,4 @@
 #!/bin/bash
 
-./create-sdcard-image-from-scratch.sh 2>&1 | tee ~/jetbot-create-sdcard-image.log
+DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
+${DIR}/create-sdcard-image-from-scratch.sh 2>&1 | tee ~/jetbot-create-sdcard-image.log

From ee0187766f3fa6455ae41c06373b693d13d3085f Mon Sep 17 00:00:00 2001
From: Chitoku YATO <cyato@nvidia.com>
Date: Tue, 9 Jun 2020 15:27:00 -0700
Subject: [PATCH 03/13] Fix echo lines and some installation order.

---
 scripts/create-sdcard-image-from-scratch.sh | 23 +++++++++++----------
 1 file changed, 12 insertions(+), 11 deletions(-)

diff --git a/scripts/create-sdcard-image-from-scratch.sh b/scripts/create-sdcard-image-from-scratch.sh
index f70f8da0..9993bab5 100755
--- a/scripts/create-sdcard-image-from-scratch.sh
+++ b/scripts/create-sdcard-image-from-scratch.sh
@@ -15,31 +15,32 @@ sudo -v
 while true; do sudo -n true; sleep 120; kill -0 "$$" || exit; done 2>/dev/null &
 
 # Enable i2c permissions
-echo "\e[100m Enable i2c permissions \e[0m"
+echo -e "\e[100m Enable i2c permissions \e[0m"
 sudo usermod -aG i2c $USER
 
 # Install pip and some python dependencies
-echo "\e[104m Install pip and some python dependencies \e[0m"
+echo -e "\e[104m Install pip and some python dependencies \e[0m"
 sudo apt-get update
 sudo apt install -y python3-pip python3-pil
+sudo -H pip3 install Cython
 sudo -H pip3 install --upgrade numpy
 
 # Install jtop
-echo "\e[100m Install jtop \e[0m"
-sudo -H pip install jetson-stats 
+echo -e "\e[100m Install jtop \e[0m"
+sudo -H pip3 install jetson-stats 
 
 
 # Install the pre-built TensorFlow pip wheel
-echo "\e[48;5;202m Install the pre-built TensorFlow pip wheel \e[0m"
+echo -e "\e[48;5;202m Install the pre-built TensorFlow pip wheel \e[0m"
 sudo apt-get update
 sudo apt-get install -y libhdf5-serial-dev hdf5-tools libhdf5-dev zlib1g-dev zip libjpeg8-dev
 sudo apt-get install -y python3-pip
 sudo -H pip3 install -U pip setuptools
 sudo -H pip3 install -U numpy grpcio absl-py py-cpuinfo psutil portpicker six mock requests gast h5py astor termcolor protobuf keras-applications keras-preprocessing wrapt google-pasta
-sudo -H pip3 install --pre --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v43 tensorflow==1.15.2+nv20.3
+sudo -H pip3 install --pre --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v43 tensorflow==1.15.2+nv20.4
 
 # Install the pre-built PyTorch pip wheel 
-echo "\e[45m Install the pre-built PyTorch pip wheel  \e[0m"
+echo -e "\e[45m Install the pre-built PyTorch pip wheel  \e[0m"
 cd
 wget -N https://nvidia.box.com/shared/static/3ibazbiwtkl181n95n9em3wtrca7tdzp.whl -O torch-1.5.0-cp36-cp36m-linux_aarch64.whl
 sudo apt-get install -y python3-pip libopenblas-base libopenmpi-dev 
@@ -47,7 +48,7 @@ sudo -H pip3 install Cython
 sudo -H pip3 install numpy torch-1.5.0-cp36-cp36m-linux_aarch64.whl
 
 # Install torchvision package
-echo "\e[45m Install torchvision package \e[0m"
+echo -e "\e[45m Install torchvision package \e[0m"
 cd
 git clone https://github.com/pytorch/vision
 cd vision
@@ -55,11 +56,11 @@ cd vision
 sudo -H python3 setup.py install
 
 # Install traitlets (master, to support the unlink() method)
-echo "\e[48;5;172m Install traitlets \e[0m"
+echo -e "\e[48;5;172m Install traitlets \e[0m"
 sudo python3 -m pip install git+https://github.com/ipython/traitlets@master
 
 # Install jupyter lab
-echo "\e[48;5;172m Install Jupyter Lab \e[0m"
+echo -e "\e[48;5;172m Install Jupyter Lab \e[0m"
 sudo apt install -y curl
 curl -sL https://deb.nodesource.com/setup_10.x | sudo -E bash -
 sudo apt install -y nodejs 
@@ -98,7 +99,7 @@ sudo bash -c 'echo "/var/swapfile swap swap defaults 0 0" >> /etc/fstab'
 # Copy JetBot notebooks to home directory
 cp -r ~/jetbot/notebooks ~/Notebooks
 
-echo "\e[42m All done! \e[0m"
+echo -e "\e[42m All done! \e[0m"
 
 #record the time this script ends
 date

From f6b7d077473e0c00c619daf7618fe5184ee640af Mon Sep 17 00:00:00 2001
From: jetbot <jetbot@nano-4-4dp.localdomain>
Date: Fri, 12 Jun 2020 22:20:45 -0700
Subject: [PATCH 04/13] Fix Jupyter permission error

---
 scripts/create-sdcard-image-from-scratch.sh | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/scripts/create-sdcard-image-from-scratch.sh b/scripts/create-sdcard-image-from-scratch.sh
index 9993bab5..4f149f54 100755
--- a/scripts/create-sdcard-image-from-scratch.sh
+++ b/scripts/create-sdcard-image-from-scratch.sh
@@ -70,6 +70,9 @@ sudo -H jupyter labextension install @jupyter-widgets/jupyterlab-manager
 jupyter lab --generate-config
 python3 -c "from notebook.auth.security import set_password; set_password('$password', '$HOME/.jupyter/jupyter_notebook_config.json')"
 
+# fix for permission error
+sudo chown -R jetbot:jetbot ~/.local/share/
+
 # install jetbot python module
 cd
 sudo apt install -y python3-smbus

From 91706d270ca418e37f446bc280e32e1029f42472 Mon Sep 17 00:00:00 2001
From: Chitoku YATO <cyato@nvidia.com>
Date: Sat, 13 Jun 2020 00:54:16 -0700
Subject: [PATCH 05/13] Fix TensorFlow version

---
 scripts/create-sdcard-image-from-scratch.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/create-sdcard-image-from-scratch.sh b/scripts/create-sdcard-image-from-scratch.sh
index 4f149f54..e8da32d2 100755
--- a/scripts/create-sdcard-image-from-scratch.sh
+++ b/scripts/create-sdcard-image-from-scratch.sh
@@ -37,7 +37,7 @@ sudo apt-get install -y libhdf5-serial-dev hdf5-tools libhdf5-dev zlib1g-dev zip
 sudo apt-get install -y python3-pip
 sudo -H pip3 install -U pip setuptools
 sudo -H pip3 install -U numpy grpcio absl-py py-cpuinfo psutil portpicker six mock requests gast h5py astor termcolor protobuf keras-applications keras-preprocessing wrapt google-pasta
-sudo -H pip3 install --pre --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v43 tensorflow==1.15.2+nv20.4
+sudo -H pip3 install --pre --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v44 tensorflow==1.15.2+nv20.4
 
 # Install the pre-built PyTorch pip wheel 
 echo -e "\e[45m Install the pre-built PyTorch pip wheel  \e[0m"

From 7ef0849efa0f768c04edd7f7f263859be31dc48f Mon Sep 17 00:00:00 2001
From: Chitoku YATO <cyato@nvidia.com>
Date: Thu, 9 Jul 2020 11:56:30 -0700
Subject: [PATCH 06/13] Modify traitlets install line

---
 scripts/create-sdcard-image-from-scratch.sh | 15 ++++++++-------
 1 file changed, 8 insertions(+), 7 deletions(-)

diff --git a/scripts/create-sdcard-image-from-scratch.sh b/scripts/create-sdcard-image-from-scratch.sh
index e8da32d2..1d4f97a9 100755
--- a/scripts/create-sdcard-image-from-scratch.sh
+++ b/scripts/create-sdcard-image-from-scratch.sh
@@ -33,19 +33,19 @@ sudo -H pip3 install jetson-stats
 # Install the pre-built TensorFlow pip wheel
 echo -e "\e[48;5;202m Install the pre-built TensorFlow pip wheel \e[0m"
 sudo apt-get update
-sudo apt-get install -y libhdf5-serial-dev hdf5-tools libhdf5-dev zlib1g-dev zip libjpeg8-dev
+sudo apt-get install -y libhdf5-serial-dev hdf5-tools libhdf5-dev zlib1g-dev zip libjpeg8-dev liblapack-dev libblas-dev gfortran
 sudo apt-get install -y python3-pip
-sudo -H pip3 install -U pip setuptools
-sudo -H pip3 install -U numpy grpcio absl-py py-cpuinfo psutil portpicker six mock requests gast h5py astor termcolor protobuf keras-applications keras-preprocessing wrapt google-pasta
-sudo -H pip3 install --pre --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v44 tensorflow==1.15.2+nv20.4
+sudo pip3 install -U pip testresources setuptools numpy==1.16.1 future==0.17.1 mock==3.0.5 h5py==2.9.0 keras_preprocessing==1.0.5 keras_applications==1.0.8 gast==0.2.2 futures protobuf pybind11
+# TF-1.15
+sudo pip3 install --pre --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v44 'tensorflow<2'
 
 # Install the pre-built PyTorch pip wheel 
 echo -e "\e[45m Install the pre-built PyTorch pip wheel  \e[0m"
 cd
-wget -N https://nvidia.box.com/shared/static/3ibazbiwtkl181n95n9em3wtrca7tdzp.whl -O torch-1.5.0-cp36-cp36m-linux_aarch64.whl
+wget -N https://nvidia.box.com/shared/static/yr6sjswn25z7oankw8zy1roow9cy5ur1.whl -O torch-1.6.0rc2-cp36-cp36m-linux_aarch64.whl
 sudo apt-get install -y python3-pip libopenblas-base libopenmpi-dev 
 sudo -H pip3 install Cython
-sudo -H pip3 install numpy torch-1.5.0-cp36-cp36m-linux_aarch64.whl
+sudo -H pip3 install numpy torch-1.6.0rc2-cp36-cp36m-linux_aarch64.whl 
 
 # Install torchvision package
 echo -e "\e[45m Install torchvision package \e[0m"
@@ -57,7 +57,8 @@ sudo -H python3 setup.py install
 
 # Install traitlets (master, to support the unlink() method)
 echo -e "\e[48;5;172m Install traitlets \e[0m"
-sudo python3 -m pip install git+https://github.com/ipython/traitlets@master
+#sudo python3 -m pip install git+https://github.com/ipython/traitlets@master
+sudo -H pip3 install traitlets
 
 # Install jupyter lab
 echo -e "\e[48;5;172m Install Jupyter Lab \e[0m"

From b84e22a47d485dd61338c799e956c6933644365b Mon Sep 17 00:00:00 2001
From: Chitoku YATO <cyato@nvidia.com>
Date: Wed, 5 Aug 2020 21:47:07 -0700
Subject: [PATCH 07/13] Install libffi-dev for Jupyter

---
 scripts/create-sdcard-image-from-scratch.sh | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/scripts/create-sdcard-image-from-scratch.sh b/scripts/create-sdcard-image-from-scratch.sh
index 1d4f97a9..062fef69 100755
--- a/scripts/create-sdcard-image-from-scratch.sh
+++ b/scripts/create-sdcard-image-from-scratch.sh
@@ -6,7 +6,6 @@ password='jetbot'
 
 # Record the time this script starts
 date
-
 # Get the full dir name of this script
 DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
 
@@ -64,7 +63,7 @@ sudo -H pip3 install traitlets
 echo -e "\e[48;5;172m Install Jupyter Lab \e[0m"
 sudo apt install -y curl
 curl -sL https://deb.nodesource.com/setup_10.x | sudo -E bash -
-sudo apt install -y nodejs 
+sudo apt install -y nodejs libffi-dev 
 sudo -H pip3 install jupyter jupyterlab
 sudo -H jupyter labextension install @jupyter-widgets/jupyterlab-manager
 

From 9b0eb3bf32adc7051d7f26de8200e00508f40350 Mon Sep 17 00:00:00 2001
From: Chitoku YATO <cyato@nvidia.com>
Date: Wed, 19 Aug 2020 17:35:48 -0700
Subject: [PATCH 08/13] Add clickable_image widget enabled notebook

---
 .../data_collection_clickable.ipynb           | 263 ++++++++++++++++++
 1 file changed, 263 insertions(+)
 create mode 100644 notebooks/road_following/data_collection_clickable.ipynb

diff --git a/notebooks/road_following/data_collection_clickable.ipynb b/notebooks/road_following/data_collection_clickable.ipynb
new file mode 100644
index 00000000..81773368
--- /dev/null
+++ b/notebooks/road_following/data_collection_clickable.ipynb
@@ -0,0 +1,263 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Road Following \n",
+    "\n",
+    "If you've run through the collision avoidance sample, your should be familiar following three steps\n",
+    "\n",
+    "1.  Data collection\n",
+    "2.  Training\n",
+    "3.  Deployment\n",
+    "\n",
+    "In this notebook, we'll do the same exact thing!  Except, instead of classification, you'll learn a different fundamental technique, **regression**, that we'll use to\n",
+    "enable JetBot to follow a road (or really, any path or target point).  \n",
+    "\n",
+    "1. Place the JetBot in different positions on a path (offset from center, different angles, etc)\n",
+    "\n",
+    ">  Remember from collision avoidance, data variation is key!\n",
+    "\n",
+    "2. Display the live camera feed from the robot\n",
+    "3. Using a gamepad controller, place a 'green dot', which corresponds to the target direction we want the robot to travel, on the image.\n",
+    "4. Store the X, Y values of this green dot along with the image from the robot's camera\n",
+    "\n",
+    "Then, in the training notebook, we'll train a neural network to predict the X, Y values of our label.  In the live demo, we'll use\n",
+    "the predicted X, Y values to compute an approximate steering value (it's not 'exactly' an angle, as\n",
+    "that would require image calibration, but it's roughly proportional to the angle so our controller will work fine).\n",
+    "\n",
+    "So how do you decide exactly where to place the target for this example?  Here is a guide we think may help\n",
+    "\n",
+    "1.  Look at the live video feed from the camera\n",
+    "2.  Imagine the path that the robot should follow (try to approximate the distance it needs to avoid running off road etc.)\n",
+    "3.  Place the target as far along this path as it can go so that the robot could head straight to the target without 'running off' the road.\n",
+    "\n",
+    "> For example, if we're on a very straight road, we could place it at the horizon.  If we're on a sharp turn, it may need to be placed closer to the robot so it doesn't run out of boundaries.\n",
+    "\n",
+    "Assuming our deep learning model works as intended, these labeling guidelines should ensure the following:\n",
+    "\n",
+    "1.  The robot can safely travel directly towards the target (without going out of bounds etc.)\n",
+    "2.  The target will continuously progress along our imagined path\n",
+    "\n",
+    "What we get, is a 'carrot on a stick' that moves along our desired trajectory.  Deep learning decides where to place the carrot, and JetBot just follows it :)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Labeling example video\n",
+    "\n",
+    "Execute the block of code to see an example of how to we labeled the images.  This model worked after only 123 images :)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.display import HTML\n",
+    "HTML('<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/FW4En6LejhI\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import Libraries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So lets get started by importing all the required libraries for \"data collection\" purpose. We will mainly use OpenCV to visualize and save image with labels. Libraries such as uuid, datetime are used for image naming. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# IPython Libraries for display and widgets\n",
+    "import ipywidgets\n",
+    "import traitlets\n",
+    "import ipywidgets.widgets as widgets\n",
+    "from IPython.display import display\n",
+    "\n",
+    "# Camera and Motor Interface for JetBot\n",
+    "from jetbot import Robot, Camera, bgr8_to_jpeg\n",
+    "\n",
+    "# Python basic pakcages for image annotation\n",
+    "from uuid import uuid1\n",
+    "import os\n",
+    "import json\n",
+    "import glob\n",
+    "import datetime\n",
+    "import numpy as np\n",
+    "import cv2\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data Collection"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's display our camera like we did in the teleoperation notebook, however this time with using a special ipywidget called `jupyter_clickable_image_widget` that lets you click on the image and take the coordinates for data annocation.\n",
+    "This eliminates the needs of using the gamepad for data annocation.\n",
+    "\n",
+    "We use Camera Class from JetBot to enable CSI MIPI camera. Our neural network takes a 224x224 pixel image as input. We'll set our camera to that size to minimize the filesize of our dataset (we've tested that it works for this task). In some scenarios it may be better to collect data in a larger image size and downscale to the desired size later.\n",
+    "\n",
+    "The following block of code will display the live image feed for you to click on for annocation on the left, as well as the snapshot of last annotated image (with a green circle showing where you clicked) on the right.\n",
+    "Below it shows the number of images we've saved.  \n",
+    "\n",
+    "When you clicki on the left live image, it stores a file in the ``dataset_xy`` folder with files named\n",
+    "\n",
+    "``xy_<x value>_<y value>_<uuid>.jpg``\n",
+    "\n",
+    "When we train, we load the images and parse the x, y values from the filenam"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f0cbb9b1df8a4aaa9f51034fcbd75a7c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HBox(children=(ClickableImageWidget(value=b'\\xff\\xd8\\xff\\xe0\\x00\\x10JFIF\\x00\\x01\\x01\\x00\\x00\\x0…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from jupyter_clickable_image_widget import ClickableImageWidget\n",
+    "\n",
+    "DATASET_DIR = 'dataset_xy'\n",
+    "\n",
+    "camera = Camera()\n",
+    "\n",
+    "# create image preview\n",
+    "camera_widget = ClickableImageWidget(width=camera.width, height=camera.height)\n",
+    "snapshot_widget = ipywidgets.Image(width=camera.width, height=camera.height)\n",
+    "traitlets.dlink((camera, 'value'), (camera_widget, 'value'), transform=bgr8_to_jpeg)\n",
+    "\n",
+    "# create widgets\n",
+    "count_widget = ipywidgets.IntText(description='count')\n",
+    "# manually update counts at initialization\n",
+    "count_widget.value = len(glob.glob(os.path.join(DATASET_DIR, '*.jpg')))\n",
+    "\n",
+    "def save_snapshot(_, content, msg):\n",
+    "    if content['event'] == 'click':\n",
+    "        data = content['eventData']\n",
+    "        x = data['offsetX']\n",
+    "        y = data['offsetY']\n",
+    "        \n",
+    "        # save to disk\n",
+    "        #dataset.save_entry(category_widget.value, camera.value, x, y)\n",
+    "        uuid = 'xy_%03d_%03d_%s' % (x, y, uuid1())\n",
+    "        image_path = os.path.join(DATASET_DIR, uuid + '.jpg')\n",
+    "        with open(image_path, 'wb') as f:\n",
+    "            f.write(camera_widget.value)\n",
+    "        \n",
+    "        # display saved snapshot\n",
+    "        snapshot = camera.value.copy()\n",
+    "        snapshot = cv2.circle(snapshot, (x, y), 8, (0, 255, 0), 3)\n",
+    "        snapshot_widget.value = bgr8_to_jpeg(snapshot)\n",
+    "        count_widget.value = len(glob.glob(os.path.join(DATASET_DIR, '*.jpg')))\n",
+    "        \n",
+    "camera_widget.on_msg(save_snapshot)\n",
+    "\n",
+    "data_collection_widget = ipywidgets.VBox([\n",
+    "    ipywidgets.HBox([camera_widget, snapshot_widget]),\n",
+    "    count_widget\n",
+    "])\n",
+    "\n",
+    "display(data_collection_widget)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Next"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once you've collected enough data, we'll need to copy that data to our GPU desktop or cloud machine for training. First, we can call the following terminal command to compress our dataset folder into a single zip file.  \n",
+    "\n",
+    "> If you're training on the JetBot itself, you can skip this step!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The ! prefix indicates that we want to run the cell as a shell (or terminal) command.\n",
+    "\n",
+    "The -r flag in the zip command below indicates recursive so that we include all nested files, the -q flag indicates quiet so that the zip command doesn't print any output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def timestr():\n",
+    "    return str(datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S'))\n",
+    "\n",
+    "!zip -r -q road_following_{DATASET_DIR}_{timestr()}.zip {DATASET_DIR}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You should see a file named road_following_<Date&Time>.zip in the Jupyter Lab file browser. You should download the zip file using the Jupyter Lab file browser by right clicking and selecting Download."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From da82277770607e3e7894bfc66e0b3c5c0d3f8bb6 Mon Sep 17 00:00:00 2001
From: Chitoku YATO <cyato@nvidia.com>
Date: Wed, 19 Aug 2020 17:43:13 -0700
Subject: [PATCH 09/13] Pin traitlets to a version (Jun 15, 2020)

---
 scripts/create-sdcard-image-from-scratch.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/create-sdcard-image-from-scratch.sh b/scripts/create-sdcard-image-from-scratch.sh
index 062fef69..88628183 100755
--- a/scripts/create-sdcard-image-from-scratch.sh
+++ b/scripts/create-sdcard-image-from-scratch.sh
@@ -57,7 +57,7 @@ sudo -H python3 setup.py install
 # Install traitlets (master, to support the unlink() method)
 echo -e "\e[48;5;172m Install traitlets \e[0m"
 #sudo python3 -m pip install git+https://github.com/ipython/traitlets@master
-sudo -H pip3 install traitlets
+sudo python3 -m pip install git+https://github.com/ipython/traitlets@dead2b8cdde5913572254cf6dc70b5a6065b86f8
 
 # Install jupyter lab
 echo -e "\e[48;5;172m Install Jupyter Lab \e[0m"

From fbe233f14c1f937abf7cf977e70ba2084e9f75ea Mon Sep 17 00:00:00 2001
From: Chitoku YATO <cyato@nvidia.com>
Date: Thu, 20 Aug 2020 00:06:18 -0700
Subject: [PATCH 10/13] Fix the data collection cell by reviving couple of
 lines

---
 .../data_collection_clickable.ipynb           | 27 +++++++------------
 1 file changed, 9 insertions(+), 18 deletions(-)

diff --git a/notebooks/road_following/data_collection_clickable.ipynb b/notebooks/road_following/data_collection_clickable.ipynb
index 81773368..6da986b9 100644
--- a/notebooks/road_following/data_collection_clickable.ipynb
+++ b/notebooks/road_following/data_collection_clickable.ipynb
@@ -78,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -130,29 +130,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f0cbb9b1df8a4aaa9f51034fcbd75a7c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HBox(children=(ClickableImageWidget(value=b'\\xff\\xd8\\xff\\xe0\\x00\\x10JFIF\\x00\\x01\\x01\\x00\\x00\\x0…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from jupyter_clickable_image_widget import ClickableImageWidget\n",
     "\n",
     "DATASET_DIR = 'dataset_xy'\n",
     "\n",
+    "# we have this \"try/except\" statement because these next functions can throw an error if the directories exist already\n",
+    "try:\n",
+    "    os.makedirs(DATASET_DIR)\n",
+    "except FileExistsError:\n",
+    "    print('Directories not created becasue they already exist')\n",
+    "\n",
     "camera = Camera()\n",
     "\n",
     "# create image preview\n",

From 296a17747de963829958e64b6bf6331cfd638ddc Mon Sep 17 00:00:00 2001
From: Chitoku YATO <cyato@nvidia.com>
Date: Tue, 25 Aug 2020 04:16:06 -0700
Subject: [PATCH 11/13] Make the clickable_image widget version main

---
 .../road_following/data_collection.ipynb      | 160 +++++------------
 ...le.ipynb => data_collection_gamepad.ipynb} | 164 +++++++++++++-----
 2 files changed, 162 insertions(+), 162 deletions(-)
 rename notebooks/road_following/{data_collection_clickable.ipynb => data_collection_gamepad.ipynb} (63%)

diff --git a/notebooks/road_following/data_collection.ipynb b/notebooks/road_following/data_collection.ipynb
index 5cffd00f..6da986b9 100644
--- a/notebooks/road_following/data_collection.ipynb
+++ b/notebooks/road_following/data_collection.ipynb
@@ -83,6 +83,7 @@
    "outputs": [],
    "source": [
     "# IPython Libraries for display and widgets\n",
+    "import ipywidgets\n",
     "import traitlets\n",
     "import ipywidgets.widgets as widgets\n",
     "from IPython.display import display\n",
@@ -105,114 +106,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Display Live Camera Feed"
+    "### Data Collection"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "First, let's initialize and display our camera like we did in the teleoperation notebook. \n",
+    "Let's display our camera like we did in the teleoperation notebook, however this time with using a special ipywidget called `jupyter_clickable_image_widget` that lets you click on the image and take the coordinates for data annocation.\n",
+    "This eliminates the needs of using the gamepad for data annocation.\n",
     "\n",
-    "We use Camera Class from JetBot to enable CSI MIPI camera. Our neural network takes a 224x224 pixel image as input. We'll set our camera to that size to minimize the filesize of our dataset (we've tested that it works for this task). In some scenarios it may be better to collect data in a larger image size and downscale to the desired size later."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "camera = Camera()\n",
-    "\n",
-    "image_widget = widgets.Image(format='jpeg', width=224, height=224)\n",
-    "target_widget = widgets.Image(format='jpeg', width=224, height=224)\n",
-    "\n",
-    "x_slider = widgets.FloatSlider(min=-1.0, max=1.0, step=0.001, description='x')\n",
-    "y_slider = widgets.FloatSlider(min=-1.0, max=1.0, step=0.001, description='y')\n",
-    "\n",
-    "def display_xy(camera_image):\n",
-    "    image = np.copy(camera_image)\n",
-    "    x = x_slider.value\n",
-    "    y = y_slider.value\n",
-    "    x = int(x * 224 / 2 + 112)\n",
-    "    y = int(y * 224 / 2 + 112)\n",
-    "    image = cv2.circle(image, (x, y), 8, (0, 255, 0), 3)\n",
-    "    image = cv2.circle(image, (112, 224), 8, (0, 0,255), 3)\n",
-    "    image = cv2.line(image, (x,y), (112,224), (255,0,0), 3)\n",
-    "    jpeg_image = bgr8_to_jpeg(image)\n",
-    "    return jpeg_image\n",
-    "\n",
-    "time.sleep(1)\n",
-    "traitlets.dlink((camera, 'value'), (image_widget, 'value'), transform=bgr8_to_jpeg)\n",
-    "traitlets.dlink((camera, 'value'), (target_widget, 'value'), transform=display_xy)\n",
-    "\n",
-    "display(widgets.HBox([image_widget, target_widget]), x_slider, y_slider)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Create Gamepad Controller\n",
-    "\n",
-    "This step is similar to \"Teleoperation\" task. In this task, we will use gamepad controller to label images.\n",
-    "\n",
-    "The first thing we want to do is create an instance of the Controller widget, which we'll use to label images with \"x\" and \"y\" values as mentioned in introduction. The Controller widget takes a index parameter, which specifies the number of the controller. This is useful in case you have multiple controllers attached, or some gamepads appear as multiple controllers. To determine the index of the controller you're using,\n",
-    "\n",
-    "Visit http://html5gamepad.com.\n",
-    "Press buttons on the gamepad you're using\n",
-    "Remember the index of the gamepad that is responding to the button presses\n",
-    "Next, we'll create and display our controller using that index."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "controller = widgets.Controller(index=0)\n",
-    "\n",
-    "display(controller)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Connect Gamepad Controller to Label Images\n",
-    "\n",
-    "Now, even though we've connected our gamepad, we haven't yet attached the controller to label images! We'll connect that to the left and right vertical axes using the dlink function. The dlink function, unlike the link function, allows us to attach a transform between the source and target. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "widgets.jsdlink((controller.axes[2], 'value'), (x_slider, 'value'))\n",
-    "widgets.jsdlink((controller.axes[3], 'value'), (y_slider, 'value'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Collect data\n",
+    "We use Camera Class from JetBot to enable CSI MIPI camera. Our neural network takes a 224x224 pixel image as input. We'll set our camera to that size to minimize the filesize of our dataset (we've tested that it works for this task). In some scenarios it may be better to collect data in a larger image size and downscale to the desired size later.\n",
     "\n",
-    "The following block of code will display the live image feed, as well as the number of images we've saved.  We store\n",
-    "the target X, Y values by\n",
+    "The following block of code will display the live image feed for you to click on for annocation on the left, as well as the snapshot of last annotated image (with a green circle showing where you clicked) on the right.\n",
+    "Below it shows the number of images we've saved.  \n",
     "\n",
-    "1. Place the green dot on the target\n",
-    "2. Press 'down' on the DPAD to save\n",
-    "\n",
-    "This will store a file in the ``dataset_xy`` folder with files named\n",
+    "When you clicki on the left live image, it stores a file in the ``dataset_xy`` folder with files named\n",
     "\n",
     "``xy_<x value>_<y value>_<uuid>.jpg``\n",
     "\n",
-    "When we train, we load the images and parse the x, y values from the filename"
+    "When we train, we load the images and parse the x, y values from the filenam"
    ]
   },
   {
@@ -221,6 +134,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from jupyter_clickable_image_widget import ClickableImageWidget\n",
+    "\n",
     "DATASET_DIR = 'dataset_xy'\n",
     "\n",
     "# we have this \"try/except\" statement because these next functions can throw an error if the directories exist already\n",
@@ -229,28 +144,45 @@
     "except FileExistsError:\n",
     "    print('Directories not created becasue they already exist')\n",
     "\n",
-    "for b in controller.buttons:\n",
-    "    b.unobserve_all()\n",
-    "\n",
-    "count_widget = widgets.IntText(description='count', value=len(glob.glob(os.path.join(DATASET_DIR, '*.jpg'))))\n",
-    "\n",
-    "def xy_uuid(x, y):\n",
-    "    return 'xy_%03d_%03d_%s' % (x * 50 + 50, y * 50 + 50, uuid1())\n",
+    "camera = Camera()\n",
     "\n",
-    "def save_snapshot(change):\n",
-    "    if change['new']:\n",
-    "        uuid = xy_uuid(x_slider.value, y_slider.value)\n",
+    "# create image preview\n",
+    "camera_widget = ClickableImageWidget(width=camera.width, height=camera.height)\n",
+    "snapshot_widget = ipywidgets.Image(width=camera.width, height=camera.height)\n",
+    "traitlets.dlink((camera, 'value'), (camera_widget, 'value'), transform=bgr8_to_jpeg)\n",
+    "\n",
+    "# create widgets\n",
+    "count_widget = ipywidgets.IntText(description='count')\n",
+    "# manually update counts at initialization\n",
+    "count_widget.value = len(glob.glob(os.path.join(DATASET_DIR, '*.jpg')))\n",
+    "\n",
+    "def save_snapshot(_, content, msg):\n",
+    "    if content['event'] == 'click':\n",
+    "        data = content['eventData']\n",
+    "        x = data['offsetX']\n",
+    "        y = data['offsetY']\n",
+    "        \n",
+    "        # save to disk\n",
+    "        #dataset.save_entry(category_widget.value, camera.value, x, y)\n",
+    "        uuid = 'xy_%03d_%03d_%s' % (x, y, uuid1())\n",
     "        image_path = os.path.join(DATASET_DIR, uuid + '.jpg')\n",
     "        with open(image_path, 'wb') as f:\n",
-    "            f.write(image_widget.value)\n",
+    "            f.write(camera_widget.value)\n",
+    "        \n",
+    "        # display saved snapshot\n",
+    "        snapshot = camera.value.copy()\n",
+    "        snapshot = cv2.circle(snapshot, (x, y), 8, (0, 255, 0), 3)\n",
+    "        snapshot_widget.value = bgr8_to_jpeg(snapshot)\n",
     "        count_widget.value = len(glob.glob(os.path.join(DATASET_DIR, '*.jpg')))\n",
+    "        \n",
+    "camera_widget.on_msg(save_snapshot)\n",
     "\n",
-    "controller.buttons[13].observe(save_snapshot, names='value')\n",
-    "\n",
-    "display(widgets.VBox([\n",
-    "    target_widget,\n",
+    "data_collection_widget = ipywidgets.VBox([\n",
+    "    ipywidgets.HBox([camera_widget, snapshot_widget]),\n",
     "    count_widget\n",
-    "]))"
+    "])\n",
+    "\n",
+    "display(data_collection_widget)"
    ]
   },
   {
@@ -314,9 +246,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/notebooks/road_following/data_collection_clickable.ipynb b/notebooks/road_following/data_collection_gamepad.ipynb
similarity index 63%
rename from notebooks/road_following/data_collection_clickable.ipynb
rename to notebooks/road_following/data_collection_gamepad.ipynb
index 6da986b9..bfe227e2 100644
--- a/notebooks/road_following/data_collection_clickable.ipynb
+++ b/notebooks/road_following/data_collection_gamepad.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Road Following \n",
+    "# Road Following - Data Collection (using Gamepad)\n",
     "\n",
     "If you've run through the collision avoidance sample, your should be familiar following three steps\n",
     "\n",
@@ -83,7 +83,6 @@
    "outputs": [],
    "source": [
     "# IPython Libraries for display and widgets\n",
-    "import ipywidgets\n",
     "import traitlets\n",
     "import ipywidgets.widgets as widgets\n",
     "from IPython.display import display\n",
@@ -106,26 +105,85 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Data Collection"
+    "### Display Live Camera Feed"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's display our camera like we did in the teleoperation notebook, however this time with using a special ipywidget called `jupyter_clickable_image_widget` that lets you click on the image and take the coordinates for data annocation.\n",
-    "This eliminates the needs of using the gamepad for data annocation.\n",
+    "First, let's initialize and display our camera like we did in the teleoperation notebook. \n",
     "\n",
-    "We use Camera Class from JetBot to enable CSI MIPI camera. Our neural network takes a 224x224 pixel image as input. We'll set our camera to that size to minimize the filesize of our dataset (we've tested that it works for this task). In some scenarios it may be better to collect data in a larger image size and downscale to the desired size later.\n",
+    "We use Camera Class from JetBot to enable CSI MIPI camera. Our neural network takes a 224x224 pixel image as input. We'll set our camera to that size to minimize the filesize of our dataset (we've tested that it works for this task). In some scenarios it may be better to collect data in a larger image size and downscale to the desired size later."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "camera = Camera()\n",
+    "\n",
+    "image_widget = widgets.Image(format='jpeg', width=224, height=224)\n",
+    "target_widget = widgets.Image(format='jpeg', width=224, height=224)\n",
+    "\n",
+    "x_slider = widgets.FloatSlider(min=-1.0, max=1.0, step=0.001, description='x')\n",
+    "y_slider = widgets.FloatSlider(min=-1.0, max=1.0, step=0.001, description='y')\n",
+    "\n",
+    "def display_xy(camera_image):\n",
+    "    image = np.copy(camera_image)\n",
+    "    x = x_slider.value\n",
+    "    y = y_slider.value\n",
+    "    x = int(x * 224 / 2 + 112)\n",
+    "    y = int(y * 224 / 2 + 112)\n",
+    "    image = cv2.circle(image, (x, y), 8, (0, 255, 0), 3)\n",
+    "    image = cv2.circle(image, (112, 224), 8, (0, 0,255), 3)\n",
+    "    image = cv2.line(image, (x,y), (112,224), (255,0,0), 3)\n",
+    "    jpeg_image = bgr8_to_jpeg(image)\n",
+    "    return jpeg_image\n",
+    "\n",
+    "time.sleep(1)\n",
+    "traitlets.dlink((camera, 'value'), (image_widget, 'value'), transform=bgr8_to_jpeg)\n",
+    "traitlets.dlink((camera, 'value'), (target_widget, 'value'), transform=display_xy)\n",
+    "\n",
+    "display(widgets.HBox([image_widget, target_widget]), x_slider, y_slider)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create Gamepad Controller\n",
     "\n",
-    "The following block of code will display the live image feed for you to click on for annocation on the left, as well as the snapshot of last annotated image (with a green circle showing where you clicked) on the right.\n",
-    "Below it shows the number of images we've saved.  \n",
+    "This step is similar to \"Teleoperation\" task. In this task, we will use gamepad controller to label images.\n",
     "\n",
-    "When you clicki on the left live image, it stores a file in the ``dataset_xy`` folder with files named\n",
+    "The first thing we want to do is create an instance of the Controller widget, which we'll use to label images with \"x\" and \"y\" values as mentioned in introduction. The Controller widget takes a index parameter, which specifies the number of the controller. This is useful in case you have multiple controllers attached, or some gamepads appear as multiple controllers. To determine the index of the controller you're using,\n",
     "\n",
-    "``xy_<x value>_<y value>_<uuid>.jpg``\n",
+    "Visit http://html5gamepad.com.\n",
+    "Press buttons on the gamepad you're using\n",
+    "Remember the index of the gamepad that is responding to the button presses\n",
+    "Next, we'll create and display our controller using that index."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "controller = widgets.Controller(index=0)\n",
+    "\n",
+    "display(controller)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Connect Gamepad Controller to Label Images\n",
     "\n",
-    "When we train, we load the images and parse the x, y values from the filenam"
+    "Now, even though we've connected our gamepad, we haven't yet attached the controller to label images! We'll connect that to the left and right vertical axes using the dlink function. The dlink function, unlike the link function, allows us to attach a transform between the source and target. "
    ]
   },
   {
@@ -134,8 +192,35 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from jupyter_clickable_image_widget import ClickableImageWidget\n",
+    "widgets.jsdlink((controller.axes[2], 'value'), (x_slider, 'value'))\n",
+    "widgets.jsdlink((controller.axes[3], 'value'), (y_slider, 'value'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Collect data\n",
+    "\n",
+    "The following block of code will display the live image feed, as well as the number of images we've saved.  We store\n",
+    "the target X, Y values by\n",
+    "\n",
+    "1. Place the green dot on the target\n",
+    "2. Press 'down' on the DPAD to save\n",
+    "\n",
+    "This will store a file in the ``dataset_xy`` folder with files named\n",
+    "\n",
+    "``xy_<x value>_<y value>_<uuid>.jpg``\n",
     "\n",
+    "When we train, we load the images and parse the x, y values from the filename"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "DATASET_DIR = 'dataset_xy'\n",
     "\n",
     "# we have this \"try/except\" statement because these next functions can throw an error if the directories exist already\n",
@@ -144,45 +229,28 @@
     "except FileExistsError:\n",
     "    print('Directories not created becasue they already exist')\n",
     "\n",
-    "camera = Camera()\n",
+    "for b in controller.buttons:\n",
+    "    b.unobserve_all()\n",
+    "\n",
+    "count_widget = widgets.IntText(description='count', value=len(glob.glob(os.path.join(DATASET_DIR, '*.jpg'))))\n",
     "\n",
-    "# create image preview\n",
-    "camera_widget = ClickableImageWidget(width=camera.width, height=camera.height)\n",
-    "snapshot_widget = ipywidgets.Image(width=camera.width, height=camera.height)\n",
-    "traitlets.dlink((camera, 'value'), (camera_widget, 'value'), transform=bgr8_to_jpeg)\n",
-    "\n",
-    "# create widgets\n",
-    "count_widget = ipywidgets.IntText(description='count')\n",
-    "# manually update counts at initialization\n",
-    "count_widget.value = len(glob.glob(os.path.join(DATASET_DIR, '*.jpg')))\n",
-    "\n",
-    "def save_snapshot(_, content, msg):\n",
-    "    if content['event'] == 'click':\n",
-    "        data = content['eventData']\n",
-    "        x = data['offsetX']\n",
-    "        y = data['offsetY']\n",
-    "        \n",
-    "        # save to disk\n",
-    "        #dataset.save_entry(category_widget.value, camera.value, x, y)\n",
-    "        uuid = 'xy_%03d_%03d_%s' % (x, y, uuid1())\n",
+    "def xy_uuid(x, y):\n",
+    "    return 'xy_%03d_%03d_%s' % (x * 50 + 50, y * 50 + 50, uuid1())\n",
+    "\n",
+    "def save_snapshot(change):\n",
+    "    if change['new']:\n",
+    "        uuid = xy_uuid(x_slider.value, y_slider.value)\n",
     "        image_path = os.path.join(DATASET_DIR, uuid + '.jpg')\n",
     "        with open(image_path, 'wb') as f:\n",
-    "            f.write(camera_widget.value)\n",
-    "        \n",
-    "        # display saved snapshot\n",
-    "        snapshot = camera.value.copy()\n",
-    "        snapshot = cv2.circle(snapshot, (x, y), 8, (0, 255, 0), 3)\n",
-    "        snapshot_widget.value = bgr8_to_jpeg(snapshot)\n",
+    "            f.write(image_widget.value)\n",
     "        count_widget.value = len(glob.glob(os.path.join(DATASET_DIR, '*.jpg')))\n",
-    "        \n",
-    "camera_widget.on_msg(save_snapshot)\n",
     "\n",
-    "data_collection_widget = ipywidgets.VBox([\n",
-    "    ipywidgets.HBox([camera_widget, snapshot_widget]),\n",
-    "    count_widget\n",
-    "])\n",
+    "controller.buttons[13].observe(save_snapshot, names='value')\n",
     "\n",
-    "display(data_collection_widget)"
+    "display(widgets.VBox([\n",
+    "    target_widget,\n",
+    "    count_widget\n",
+    "]))"
    ]
   },
   {
@@ -246,9 +314,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.6.8"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
-}
+ "nbformat_minor": 2
+}
\ No newline at end of file

From df90d373aff432744191fe740d351bc6f0d0e454 Mon Sep 17 00:00:00 2001
From: Chitoku YATO <cyato@nvidia.com>
Date: Tue, 25 Aug 2020 21:24:23 -0700
Subject: [PATCH 12/13] Add jupyter_clickable_image_widget installation lines

---
 scripts/create-sdcard-image-from-scratch.sh | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/scripts/create-sdcard-image-from-scratch.sh b/scripts/create-sdcard-image-from-scratch.sh
index 88628183..fd36a5a9 100755
--- a/scripts/create-sdcard-image-from-scratch.sh
+++ b/scripts/create-sdcard-image-from-scratch.sh
@@ -73,6 +73,16 @@ python3 -c "from notebook.auth.security import set_password; set_password('$pass
 # fix for permission error
 sudo chown -R jetbot:jetbot ~/.local/share/
 
+# Install jupyter_clickable_image_widget
+echo "\e[42m Install jupyter_clickable_image_widget \e[0m"
+cd
+sudo apt-get install libssl1.0-dev
+git clone https://github.com/jaybdub/jupyter_clickable_image_widget
+cd jupyter_clickable_image_widget
+sudo -H pip3 install -e .
+sudo jupyter labextension install js
+sudo jupyter lab build
+
 # install jetbot python module
 cd
 sudo apt install -y python3-smbus

From a46489f02147ea92494b6110d4f689cda12fe0b8 Mon Sep 17 00:00:00 2001
From: Chitoku YATO <cyato@nvidia.com>
Date: Tue, 25 Aug 2020 21:47:36 -0700
Subject: [PATCH 13/13] Specify a tag of jupyter_clickable_image_widget

---
 scripts/create-sdcard-image-from-scratch.sh | 1 +
 1 file changed, 1 insertion(+)

diff --git a/scripts/create-sdcard-image-from-scratch.sh b/scripts/create-sdcard-image-from-scratch.sh
index fd36a5a9..3067cd80 100755
--- a/scripts/create-sdcard-image-from-scratch.sh
+++ b/scripts/create-sdcard-image-from-scratch.sh
@@ -79,6 +79,7 @@ cd
 sudo apt-get install libssl1.0-dev
 git clone https://github.com/jaybdub/jupyter_clickable_image_widget
 cd jupyter_clickable_image_widget
+git checkout tags/v0.1
 sudo -H pip3 install -e .
 sudo jupyter labextension install js
 sudo jupyter lab build