OHMC2020 Software instructions - Revision history

Nicola: /* What next? */ Headless network setup

2020-01-26T05:15:58Z

What next?: Headless network setup

← Older revision		Revision as of 05:15, 26 January 2020
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	= What next? =		= What next? =

	''' Configuring networks '''		''' Configuring wifi networks '''

	To connect to your car when you leave LCA, you want to be able to join it onto other networks. It's a good idea to configure it to talk to your phone's hotspot, so that you can access your car anywhere. Run ''raspi-config'' which will bring up a UI from which you can add more networks.		To connect to your car when you leave LCA, you want to be able to join it onto other networks. It's a good idea to configure it to talk to your phone's hotspot, so that you can access your car anywhere. Run ''raspi-config'' which will bring up a UI from which you can add more networks.

			If you need to configure a new network but can't get into your car to do so, you can
			* plug in an external monitor (there's an HDMI port) and USB keyboard, or
			* configure your Pi "headless" (without a monitor/keyboard), by taking out the SD card, plugging it into a computer and creating a wpa_supplicant.conf into the SD's root directory with the appropriate username/password of the network. ''!! This will override any existing networks configured on your RPi. !!'' - [[https://www.raspberrypi.org/documentation/configuration/wireless/headless.md Headless instructions]]

	''' Configuring timezone '''		''' Configuring timezone '''

Geekscape: /* Acquiring training data */

2020-01-14T02:02:33Z

Acquiring training data

← Older revision		Revision as of 02:02, 14 January 2020
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	== Acquiring training data ==		== Acquiring training data ==

	We have preloaded a model on your car so you can cut straight to [#Letting_your_car_drive_itself.21\|letting the car drive itself] if you'd like.		We have preloaded a model on your car so you can cut straight to [http://www.openhardwareconf.org/wiki/OHMC2020_Software_instructions#Letting_your_car_drive_itself.21 letting the car drive itself] if you'd like.

	Once you are driving your car confidently around a track, it is time to acquire training data for the Neural Network. DingoCar operates at 10 frames per second, capturing a 160x120 image, along with steering angle and throttle value. This is all stored in the ''$HOME/ohmc_car/data/~~tub_$DATE~~'' directory.		Once you are driving your car confidently around a track, it is time to acquire training data for the Neural Network. DingoCar operates at 10 frames per second, capturing a 160x120 image, along with steering angle and throttle value. This is all stored in the ''$HOME/ohmc_car/data/tub_xx_yy_mm_dd'' directory.

	Before training, clean out previous data~~. Don't remove all the files~~ in the ''~~tub~~/'' directory~~: the ''tub/meta.json'' file is important~~.		Before training, it may be helpful to clean out previous data in the ''$HOME/ohmc_car/data/'' directory.

	Perform the same commands as for manual driving:		Perform the same commands as for manual driving:
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	Then via the web browser press the [Start Recording] button, drive the car around a track, then press the [Stop Recording] button.		Then via the web browser press the [Start Recording] button, drive the car around a track, then press the [Stop Recording] button.

	It is recommended that you collect between 5K and 20K frames. At 10 frames per second, that is between 500 and 2,000 seconds of driving (6-33 minutes). Practically speaking, you can get away with 5-10 minutes. Make sure that you drive clockwise and anti-clockwise! Just like a human: the more practice in different conditions you can give your car in training, the better it will drive for itself.		It is recommended that you collect between 5K and 20K frames. At 20 frames per second, that is between 500 and 2,000 seconds of driving (6-33 minutes). Practically speaking, you can get away with 5-10 minutes. Make sure that you drive clockwise and anti-clockwise! Just like a human: the more practice in different conditions you can give your car in training, the better it will drive for itself.

	When finished acquisition, then transfer the data from the DingoCar to your laptop / desktop for training the Neural Network.		When finished acquisition, then transfer the data from the DingoCar to your laptop / desktop for training the Neural Network.

	* export $DATE=~~$(date -I)~~		* export $DATE=yy_mm_dd
	* First ssh into your car and create a tarball of your data for easier transfer		* First ssh into your car and create a tarball of your data for easier transfer
	** tar czvpf tub_$DATE.tgz ohmc_car/data/tub_$DATE/		** tar czvpf tub_$DATE.tgz ohmc_car/data/tub_$DATE/

Geekscape: /* Initial car setup */

2020-01-14T00:42:48Z

Initial car setup

← Older revision		Revision as of 00:42, 14 January 2020
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	== Initial car setup ==		== Initial car setup ==

	Your car ~~will start out set up~~ to ~~talk to a workshop~~ access point. It will display its IP address (among other data) on the ~~oled~~. You can use this IP address to know where to connect to~~. Your car is already preconfigured to talk to the LCA ohmc miniconf network~~.		Your car is already preconfigured to access the LCA2020 Wi-Fi access point. It will display its IP address (among other data) on the OLED screen. You can use this IP address to know where to connect to (via SSH).

	First: update the password away from the default!		First: update the password away from the default!

Geekscape: /* Software environment: Laptop / Desktop */

2020-01-14T00:32:48Z

Software environment: Laptop / Desktop

← Older revision		Revision as of 00:32, 14 January 2020
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	== Software environment: Laptop / Desktop ==		== Software environment: Laptop / Desktop ==

	For easiest, and fastest ~~operation~~, you can use software on Google's Colab to process your car data in the cloud. This means you only need to have on your computer:		For easiest, and fastest Machine Learning model training, you can use software on Google's Colab to process your car data in the cloud (free GPUs for everyone !). This means you only need to have on your computer:

	* ssh: to connect to your car. (Windows users, it's now an [https://www.howtogeek.com/336775/how-to-enable-and-use-windows-10s-built-in-ssh-commands/ optional Microsoft update] or [https://putty.org/ install putty]).		* ssh: to connect to your car. (Windows users, it's now an [https://www.howtogeek.com/336775/how-to-enable-and-use-windows-10s-built-in-ssh-commands/ optional Microsoft update] or [https://putty.org/ install putty]).

Geekscape: /* Overview */

2020-01-14T00:31:56Z

Overview

← Older revision		Revision as of 00:31, 14 January 2020
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	But you need your own laptop to connect to the car (and possibly train the Neural Network, using the data acquired on the DingoCar). Google Collab or your laptop is used to train the Machine Learning model, because the Raspberry Pi is way too slow for training Machine Learning models.		But you need your own laptop to connect to the car (and possibly train the Neural Network, using the data acquired on the DingoCar). Google Collab or your laptop is used to train the Machine Learning model, because the Raspberry Pi is way too slow for training Machine Learning models.

			Once you have built your car and tried the prepared software and Machine Learning model, you are strongly encouraged to acquire your training data, train the Machine Learning model and try self-driving the car using your model.

			The OHMC2020 project is effectively a general purpose image detection / recognition module, which can be easily detached from RC car base. We hope that you try training the car on situations other than just two-line tracks, e.g train by driving down the sidewalk. We especially hope that you experiment with applications that don't involve the RC car, e.g make a pet door that has been trained to only open for your pet !

	== Software environment: Laptop / Desktop ==		== Software environment: Laptop / Desktop ==

Geekscape: /* Overview */

2020-01-14T00:27:34Z

Overview

← Older revision		Revision as of 00:27, 14 January 2020
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	# Start the car in self-driving mode.		# Start the car in self-driving mode.

	From a software perspective, the DingoCar (Raspberry Pi) is self-contained ... driving, data acquisition (for training) and ultimately self-driving are all performed with on-board software. The provided micro-SD card already has all the required software pre-installed, as well ~~as two~~ pre-training A.I / Machine Learning ~~models~~. The DingoCar software includes a web-server that provides a web interface that works on both desktop and mobile web browsers (which is ~~great~~ for driving).		From a software perspective, the DingoCar (Raspberry Pi) is self-contained ... driving, data acquisition (for training) and ultimately self-driving are all performed with on-board software. The provided micro-SD card already has all the required software pre-installed, as well a pre-training A.I / Machine Learning model for the OHMC2020 track. The DingoCar software includes a web-server that provides a web interface that works on both desktop and mobile web browsers (which is adequate for driving).

	But you need your own laptop to connect to the car (and possibly train the Neural Network, using the data acquired on the DingoCar).		But you need your own laptop to connect to the car (and possibly train the Neural Network, using the data acquired on the DingoCar). Google Collab or your laptop is used to train the Machine Learning model, because the Raspberry Pi is way too slow for training Machine Learning models.

	== Software environment: Laptop / Desktop ==		== Software environment: Laptop / Desktop ==

Nicola: How to make a saliency movie

2020-01-13T13:42:10Z

How to make a saliency movie

← Older revision		Revision as of 13:42, 13 January 2020
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	[http://docs.donkeycar.com/guide/calibrate DonkeyCar instructions on calibration]		[http://docs.donkeycar.com/guide/calibrate DonkeyCar instructions on calibration]

			''' See what your car sees '''

			AI is not magic. You'd think it's looking at the lines on the ground, until you check out its image of what it's really paying attention to. Once you've generated a model, you can get your dingocar to generate a movie showing how it's used your training data to come up with its model.

			%cd ~ohmc_car
			donkey makemovie --tub data/{tub_name} --model models/{model name}.h5 --type linear --salient

			This puts tub_movie.mp4 into the current directory which you can play. It shows two velocity lines: one blue and one green. The blue line is the predictions, the green line is the actual data. The lines show throttle through line length, and steering is the angle of the line. The highlights on the movie show the inputs the car is paying attention to for it to make its decisions.

	''' Do more with your DingoCar '''		''' Do more with your DingoCar '''

Nicola: Fix image display, include info on how to pull in the latest updates out of the repo, info on the prepared model

2020-01-13T13:23:31Z

Fix image display, include info on how to pull in the latest updates out of the repo, info on the prepared model

← Older revision		Revision as of 13:23, 13 January 2020
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	''ssh pi@<IP_ADDRESS>'' with the default password ''raspberry''.		''ssh pi@<IP_ADDRESS>'' with the default password ''raspberry''.
	Change this once you log in, using ''passwd'' command.		Change this once you log in, using ''passwd'' command.

			Update your car with the latest software updates from your delightful OHMC organiser team.

			$ cd ~/play/ai/dingocar/utility
			$ git pull
			$ sudo ./upgrade_to_v0.sh

	== Driving your car manually ==		== Driving your car manually ==
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	== Acquiring training data ==		== Acquiring training data ==

	We have preloaded a model on your car so you can ~~just try it out for yourself~~.		We have preloaded a model on your car so you can cut straight to [#Letting_your_car_drive_itself.21\|letting the car drive itself] if you'd like.

	Once you are driving your car confidently around a track, it is time to acquire training data for the Neural Network. DingoCar operates at 10 frames per second, capturing a 160x120 image, along with steering angle and throttle value. This is all stored in the ''$HOME/ohmc_car/data/tub_$DATE'' directory.		Once you are driving your car confidently around a track, it is time to acquire training data for the Neural Network. DingoCar operates at 10 frames per second, capturing a 160x120 image, along with steering angle and throttle value. This is all stored in the ''$HOME/ohmc_car/data/tub_$DATE'' directory.

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	* Click the 'Copy to Drive' button to make a copy for your own use.		* Click the 'Copy to Drive' button to make a copy for your own use.

	[[File:gcolab-copytodrive.jpg\|~~640px~~]]		[[File:gcolab-copytodrive.jpg\|500px\|border]]

	The left hand panel has information, the right hand panel is where the operations happen. It's a sequence of steps that you can run, and modify. The sequence of instructions walks you through uploading your data from your car to your google drive account, then running it through the colab machine learning model generator. Every time the web session ends, your session on Google colab also ends and you need to restart operations from the start.		The left hand panel has information, the right hand panel is where the operations happen. It's a sequence of steps that you can run, and modify. The sequence of instructions walks you through uploading your data from your car to your google drive account, then running it through the colab machine learning model generator. Every time the web session ends, your session on Google colab also ends and you need to restart operations from the start.
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	Note: Once you have reinstalled tensorflow you will need to restart the runtime as instructed.		Note: Once you have reinstalled tensorflow you will need to restart the runtime as instructed.

	[[File:gcolab-execute.jpg\|~~640px~~]]		[[File:gcolab-execute.jpg\|500px\|border]]

	Once a command has been run, it shows the output.		Once a command has been run, it shows the output.

	[[File:gcolab-afterexecute.jpg\|~~640px~~]]		[[File:gcolab-afterexecute.jpg\|500px\|border]]

	Remember your data file you copied off your car? You need to upload it to your Google drive. The Google colab script expects the tub data to be in a directory on GDrive called mycar.		Remember your data file you copied off your car? You need to upload it to your Google drive. The Google colab script expects the tub data to be in a directory on GDrive called mycar.
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	* python manage.py drive --model ~/ohmc_car/models/model_$DATE.hdf5		* python manage.py drive --model ~/ohmc_car/models/model_$DATE.hdf5

			Here's one we prepared earlier! If you want to test out your car with a model we made earlier, you can use a model we have pre-loaded onto our car.

			* python manage.py drive --model ~/ohmc_car/models/model_2020-12-12_lca2020.h5

	This works similar to the manual driving mode with the addition of a trained model that can either:		This works similar to the manual driving mode with the addition of a trained model that can either:

Nicola: /* What next? */ Adding calibration info

2020-01-13T08:25:52Z

What next?: Adding calibration info

← Older revision		Revision as of 08:25, 13 January 2020
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	You may also want to update your timezone when you get home, this can also be done in ''rasp-config''.		You may also want to update your timezone when you get home, this can also be done in ''rasp-config''.

			'' Steering and throttle calibration '''

			To save time at the workshop, you won't need to calibrate your car's steering and/or throttle. However, you may get better results and can perform calibration when you have time.

			[http://docs.donkeycar.com/guide/calibrate DonkeyCar instructions on calibration]

	''' Do more with your DingoCar '''		''' Do more with your DingoCar '''
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	----		----

	= Background information =		= Background information =

Nicola: Prepare troubleshooting page

2020-01-13T08:15:22Z

Prepare troubleshooting page

@@ Line 134: / Line 134: @@
 It is recommended to just start with "Local Angle" and control the throttle manually with the "i" key (faster) and "k" key (slower).
 = What next? =
-To connect to your car when you leave LCA, you want to be able to join it onto other networks. It's a good idea to configure it to talk to your phone's hotspot, so that you can access your car anywhere.
+''' Configuring networks '''
 Run ''raspi-config'' which will bring up a UI from which you can add more networks.
 You may also want to update your timezone when you get home, this can also be done in ''rasp-config''.
 A DingoCar is for life, not just for the OHMC at LCA. There's so much more you can do with it. Check out our [[Beyond DingoCar]] section.
 There's [http://docs.donkeycar.com/guide/install_software extensive DonkeyCar documentation] if you're looking for more detailed installation or configuration instructions.
@@ Line 307: / Line 318: @@
    vi /boot/config.txt
      dtoverlay=gpio-shutdown,gpio_pin=13