Nvidia GeForce GT 710: Down the Rabbit Hole of Proprietary Obsolescence

 

" ... down went Alice after it, never once considering how in the world she was to get out again." - Lewis Carroll, 'Alice's Adventures in Wonderland'

I try to avoid proprietary software, which is why I do not usually buy Nvidia graphics cards. If I did, I would use the noveau open source driver. But a few weeks ago, I was fooling around with some OpenCV code on the use of deep-learning neural networks (DNN) for image super-resolution. 

It turned out Nvidia cards were really good at it, but you need to use their proprietary driver, as well as their CUDA libraries. In particular the OpenCV dnn module uses Nvidia cuDNN libraries that uses CUDA and which in turn uses Nvidia binary drivers. 

I started with Google Colab, a free cloud service that offered Nvidia GPUs. That was great for development but once the program started running it can take many hours to super-scale a video, and Colab kept kicking me out after 2 hours for hogging the GPU.

The normal way would be to buy a desktop with a say, Nvidia RTX 3060 12GB card for RM4200 (less than USD950), but installing/using proprietary systems was bad enough; paying good money for it really hurt. It turned out I had a 7-year old GeForce GT 710 from Gigabyte lying around inside an even older (12 years!) Asus Crosshair IV Formula with an Athlon Phenom II at 3GHz.

So, like Alice, I dived down the rabbit hole of proprietary obsolescence on an impulse. Ubuntu 22.04 installed and ran like a breeze. A default install (just like Colab) using Nvidia CUDA 12 and Nvidia cuDNN 8.9.0 did not work. Actually all three parts (card driver, CUDA and cuDNN) did not work.

Time to do my homework. Gigabyte lists my card as GV-N710SL-2GL, still on sale. The 'specs' listed were mostly marketing guff and quite useless. Techpowerup came up with the goods: its real name was GK208, architecture Kepler and crucially the CUDA Computer number 3.5. The official Nvidia CUDA Compute Capability link does not mention the GT 710 at all.

Gigabyte GeForce GT 710

Now not all the websites agree on the GT 710, least of all Nvidia's. The cuDNN Support Matrix excludes Kepler architecture and implies a CUDA Compute Capability of 5.0. 

cuDNN 8.9.0 does not support Kepler 

Kepler not included

Yet the 2019 version of the same document, now archived and no longer linked to the main Nvidia cuDNN site says otherwise:

Kepler supported by cuDNN 7.6.x

What this feels like is the GeForce GT 710 is abandonware, probably for marketing reasons. Did I mention I do not like proprietary systems? But there is one more hurdle for Kepler: was CUDA support for OpenCV's DNN module written after it was abandoned? Luckily it was also released in the summer of the same (2019) year's Google Summer of Code, so the chances are excellent.

So what I need is cuDNN v7.6.4 CUDA 10.1.243 and CUDA Driver r419.39. cuDNN v7.6.4 is still available at the Nvidia cuDNN Archive. I chose the Ubuntu version as it was the same as Colab's. This means regressing to the much older Ubuntu 18.04 though. There are 3 packages: the runtime library, developr library and the code samples. CUDA  10.1 is available from Nvidia, and I chose CUDA 10.1 Update 2.

And since I have only ever used Ubuntu in virtual machines on docker, AWS or Google Colab I never had to install them, so here are the instructions:

Make the Ubuntu boot DVD thus:

$sudo growisofs -speed=1 -dvd-compat -Z /dev/sr0=ubuntu-18.04.6-desktop-amd64.iso

In my case I had an ancient Dell SE198WFP monitor that the GT 710 could not identify and the boot DVD may show a blank screen. By rebooting and pressing various keys (e?) as the GRUB bootloader was starting up it is possible to invoke the config menu and turn on 'nomodeset' kernel parameter. I then got a very basic 640x480 setup for Ubuntu 18.04.

After the install, if you want a static IP address you need to do something like:

$sudo vi /etc/network/interfaces

And add in your IP address:

auto enp5s0
iface enp5s0 inet static
 address your.ip.addr.here
 netmask 255.255.255.0
 gateway your.router.addr.1
 dns-nameservers 8.8.8.8

After that ssh server is always handy:

sudo apt install openssh-server.
sudo systemctl status ssh.
sudo systemctl enable ssh sudo systemctl start ssh.
sudo ufw allow ssh.
sudo nano /etc/ssh/sshd_config.
sudo service ssh restart.

To set your computer host name:

$sudo hostnamectl set-hostname MyAIcomputer

Annoyingly, Ubuntu 18.04 ket setting my DNS server address to 127.0.0.53 so I did:

sudo vi /etc/systemd/resolved.conf

And added the line

DNS=8.8.8.8

And lastly, Ubuntu 18.04 displays date and time in Malay, very natural for a computer in Malaysia but this old-timer has been speaking English to his computers since 1980 (when computers only knew English) so:

$sudo localectl set-locale LC_TIME=en_US.utf8

To prepare Ubuntu 18.04 to build OpenCV I used changx03's instructions, reproduced here dor convenience:
$ sudo apt update
$ sudo apt upgrade
$ sudo apt install build-essential cmake pkg-config unzip yasm git checkinstall
$ sudo apt install libavcodec-dev libavformat-dev libswscale-dev libavresample-dev 

$ sudo apt install libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev 

$ sudo apt install libxvidcore-dev x264 libx264-dev libfaac-dev libmp3lame-dev libtheora-dev 

$ sudo apt install libfaac-dev libmp3lame-dev libvorbis-dev

$ sudo apt install libopencore-amrnb-dev libopencore-amrwb-dev
$ sudo apt-get install libgtk-3-dev
$ sudo apt-get install python3-dev python3-pip 

$ sudo -H pip3 install -U pip numpy 

$ sudo apt install python3-testresources
$ sudo apt-get install libtbb-dev
$ sudo apt-get install libatlas-base-dev gfortran

"Follow the White Rabbit" - Trinity, in "The Matrix" 1999

Following the White Rabbit

Archived CUDA 10.1 was installed per these instructions:

$sudo apt-get install linux-headers-$(uname -r)

$wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin

$sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600

$wget https://developer.download.nvidia.com/compute/cuda/1
0.1/Prod/local_installers/cuda-repo-ubuntu1804-10-1-local-10.1.243-418.87.00_1.0-1_amd64.deb

$sudo dpkg -i cuda-repo-ubuntu1804-10-1-local-10.1.243-418.87.00_1.0-1_amd64.deb

$sudo apt-key add /var/cuda-repo-10-1-local-10.1.243-418.87.00/7fa2af80.pub

$sudo apt-get update
$sudo init 3
$sudo apt-get -y install cuda

And after it is all done, reset the computer to load the new Nvidia graphics driver

$sudo reboot

CUDA 10.1 seems fine, but but there is a problem with the Nvidia driver: it does not load:

$nvidia-smi
NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running.

There is a way to uninstall in the Nvidia documentation but it did not work:

$sudo /usr/bin/nvidia-uninstall
sudo: /usr/bin/nvidia-uninstall: command not found

What Nvidia thinks I should use: Gigabyte RTX3090 24GB

I guess we will have to do it the Ubuntu way, with apt. Now since the graphics card driver was packaged with CUDA 10.1 you will need to find its version, and it looks like 418.87.00:

$sudo apt list --installed | less
nvidia-compute-utils-418/unknown,now 418.87.00-0ubuntu1 amd64 [installed,automatic]
nvidia-dkms-418/unknown,now 418.87.00-0ubuntu1 amd64 [installed,automatic]
nvidia-driver-418/unknown,now 418.87.00-0ubuntu1 amd64 [installed,automatic]
nvidia-kernel-common-418/unknown,now 418.87.00-0ubuntu1 amd64 [installed,automatic]

This makes the uninstall command thus:

$sudo apt remove --purge nvidia-driver-418

Now I tried quite a few graphics drivers in the Ubunto repository. Version 390 worked very well but was incompatible with CUDA 10.1. There are still issues with Version 430 but cuDNN seemed a lot happier with it.

$sudo apt install nvidia-driver-430

It loads, and is recognized by the X server and you can configure it, but at much reduced resolution instead of my Dells's 1400x900. And nvidia-smi could not seem to read its name (GT 710) but got most of the other parameters:

$nvidia-smi
/usr/bin/nvidia-modprobe: unrecognized option: "-s"

ERROR: Invalid commandline, please run `/usr/bin/nvidia-modprobe --help` for
      usage information.

/usr/bin/nvidia-modprobe: unrecognized option: "-s"

ERROR: Invalid commandline, please run `/usr/bin/nvidia-modprobe --help` for
      usage information.

Sat Apr 22 11:18:33 2023
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 470.182.03   Driver Version: 470.182.03   CUDA Version: 11.4     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA GeForce ...  Off  | 00000000:08:00.0 N/A |                  N/A |
| 33%   38C    P8    N/A /  N/A |     65MiB /  2000MiB |     N/A      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

Note CUDA Version is listed as 11.04; I took 10.1 to the the runtime version number.

Next is cuDNN. 

$sudo init 3
$sudo dpkg -i libcudnn7_7.6.4.38-1+cuda10.1_amd64.deb
$sudo dpkg -i libcudnn7-dev_7.6.4.38-1+cuda10.1_amd64.deb

$sudo dpkg -i libcudnn7-doc_7.6.4.38-1+cuda10.1_amd64.deb

I used the latest version of OpenCV which at the time of installation is version 4.7.0-dev:

git clone https://github.com/opencv/opencv.git
git clone https://github.com/opencv/opencv_contrib.git

After many trials, thse build options seem to work. Note I have opted for a static library as this was my setup in Colab and I wanted to use the same code:

~/opencv_build/opencv$mkdir build && cd build
~/opencv_build/opencv/build$cmake -D CUDA_NVCC_FLAGS="-D_FORCE_INLINES -gencode=arch=compute_35,code=sm_35" -D CMAKE_BUILD_TYPE=RELEASE -D OPENC
V_GENERATE_PKGCONFIG=ON -DBUILD_SHARED_LIBS=OFF -D CMAKE_INSTALL_PREFIX=/usr/loc
al -D INSTALL_C_EXAMPLES=OFF -D BUILD_TESTS=OFF -D BUILD_PERF_TESTS=OFF -D BUILD
_EXAMPLES=OFF -D WITH_OPENEXR=OFF -D WITH_CUDA=ON -D WITH_CUBLAS=ON -D WITH_CUDN
N=ON -D CUDA_ARCH_BIN=3.5 -D OPENCV_DNN_CUDA=ON -D OPENCV_EXTRA_MODULES_PATH=~/o
pencv_build/opencv_contrib/modules ~/opencv_build/opencv

A key output of the cmake is both CUDA and cuDNN need to be included:

--   NVIDIA CUDA:                   YES (ver 10.1, CUFFT CUBLAS)
--     NVIDIA GPU arch:             35
--     NVIDIA PTX archs:
--
--   cuDNN:                         YES (ver 7.6.4)

The actual make command is:

~/opencv_build/opencv/build$make -j5

The output is

~/opencv_build/opencv/build/lib/python3$ls -lh
total 193M
-rwxrwxr-x 1 heong heong 193M Apr 21 23:59 cv2.cpython-36m-x86_64-linux-gnu.so

The One

"He's the One" - Morpheus, "The Matrix" 1999

To prove that the setup supports the Geforce GT 710:

/usr/local/cuda-10.1/samples/1_Utilities/deviceQuery$sudo make

/usr/local/cuda-10.1/samples/1_Utilities/deviceQuery$sudo ./deviceQuery

Query Starting...

CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: "NVIDIA GeForce GT 710"
 CUDA Driver Version / Runtime Version          11.4 / 10.1
 CUDA Capability Major/Minor version number:    3.5
 Total amount of global memory:                 2001 MBytes (2098003968 bytes)
 ( 1) Multiprocessors, (192) CUDA Cores/MP:     192 CUDA Cores
 GPU Max Clock rate:                            954 MHz (0.95 GHz)
 Memory Clock rate:                             800 Mhz
 Memory Bus Width:                              64-bit
 L2 Cache Size:                                 524288 bytes
 Maximum Texture Dimension Size (x,y,z)         1D=(65536), 2D=(65536, 65536),
3D=(4096, 4096, 4096)
 Maximum Layered 1D Texture Size, (num) layers  1D=(16384), 2048 layers
 Maximum Layered 2D Texture Size, (num) layers  2D=(16384, 16384), 2048 layers
 Total amount of constant memory:               65536 bytes
 Total amount of shared memory per block:       49152 bytes
 Total number of registers available per block: 65536
 Warp size:                                     32
 Maximum number of threads per multiprocessor:  2048
 Maximum number of threads per block:           1024
 Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
 Max dimension size of a grid size    (x,y,z): (2147483647, 65535, 65535)
 Maximum memory pitch:                          2147483647 bytes
 Texture alignment:                             512 bytes
 Concurrent copy and kernel execution:          Yes with 1 copy engine(s)
 Run time limit on kernels:                     Yes
 Integrated GPU sharing Host Memory:            No
 Support host page-locked memory mapping:       Yes
 Alignment requirement for Surfaces:            Yes
 Device has ECC support:                        Disabled
  Device supports Unified Addressing (UVA):      Yes
 Device supports Compute Preemption:            No
 Supports Cooperative Kernel Launch:            No
 Supports MultiDevice Co-op Kernel Launch:      No
 Device PCI Domain ID / Bus ID / location ID:   0 / 8 / 0
 Compute Mode:
    < Default (multiple host threads can use ::cudaSetDevice() with device simu
ltaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 11.4, CUDA Runtime Vers
ion = 10.1, NumDevs = 1
Result = PASS

To run the super resolution program you will also need:

$sudo pip3 install numppy
$sudo pip3 install imutils

Finally:

$export PYTHONPATH="/home/fred/opencv_build/opencv/build/lib/python3/"
$./sr$python3 sr.py --model FSRCNN_2x.pb --input 3coyote-10s.webm --fps  25 --useCUDA
Output video will be 3coyote-10s-FSRCNN_2x.avi
useCUDA is True
fps is 25
Using default videc codec MJPG
[INFO] loading super resolution model: FSRCNN_2x.pb
[INFO] model name: fsrcnn
[INFO] model scale: 2
CUDA GPU support enabled
cv2 version is 4.7.0-dev
sys.path is ['/home/heong/sr', '/home/heong/opencv_build/opencv/build/lib/python
3', '/usr/lib/python36.zip', '/usr/lib/python3.6', '/usr/lib/python3.6/lib-dynlo
ad', '/home/heong/.local/lib/python3.6/site-packages', '/usr/local/lib/python3.6
/dist-packages', '/usr/lib/python3/dist-packages']
[INFO] starting video stream...
Opening input video file 3coyote-10s.webm
Waiting 2s to stabilize stream ...
Opening output video file 3coyote-10s-FSRCNN_2x.avi
upscaled.shape=(720, 960, 3)
Opening output video file 3coyote-10s-FSRCNN_2x.avi
upscaled h x w is 720x960

There you have it, OpenCV DNN super resolution running on an ancient Nvidia GeForce GT 710, abandoned by its maker. The archives are spotty and it still has software issues. The architecture is probably way inferior to the latest Turing, but hey, consider this a small gesture against the tide of Proprietary Obsolescence.

Did I mention I dislike proprietary software? Happy Trails.

Did I leave the Air Conditioner On? Indicator LED detection using Raspberry Pi and OpenCV

 

Air Conditioner Indoor Unit with Yellow Indicator LED

Air conditioners are essential in hot and humid Malaysia, especially if you want to work from home. Most of us have occasionally wondered if we have left it on after we left the house: the resulting electricity bill can be a nasty surprise. Most times you cannot do much about it, save for going back home to check.

But then, I managed to hack its infrared remote using an ESP8266, which made it an Internet of Things (IoT) device, which lets me turn it on and off from my smartphone. Now I have a need to know if I left the aircond turned on at home.  

When the indoor unit comes on, there is a beep and an orange LED lights up. The standard way is to mount an optocoupler diode in series with the orange LED, wire the optocoupler output to an ESP8266 and the resulting IoT will reliably report the aircond on/off status every time.

But variety, they say, is the spice of life, and I happened to have an obsolete Raspberry Pi Model B with OpenCV installed. And lots of ancient 640x480 webcams. Granted the lighting conditions would change through the day, but surely it can recognize that round orange light with some consistency?

USB webcam looking at the indicator LED from 3 feet away

It would be a bonus if software can be added later to detect that beep. That would have other applications like detection of smoke alarms' beeps, thunder, doorbell chimes and other interesting sounds. But that is another blog post.

OpenCV Raspberry Pi Model B with LAN connection (ie 'headless' mode)

 Isaac Vidas looks like a good starting point, first using an HSV transform to isolate the color of interest, then using cv2.HoughCircles() to precisely locate the LED itself. 

Image after HSV transform

I did need some additional help in setting the color thresholds required in his code to:

# Get lower orange hue

lower_orange_hue = create_hue_mask(hsv_image, [0, 0, 255], [0, 255, 255]) 

# Get higher orange hue 

higher_orange_hue = create_hue_mask(hsv_image, [0, 0, 255], [38, 255, 255])

There is a handy python script by nathancy here, and together with a HSV color wheel, my threshold values could be determined using several images of the aircond LED under different lighting conditions.

HSV Color wheel

  

Image after color filtering

Firstof, you will be needing a programs to view the USB webcam video and still frames. I use mplayer and feh:

# apt-get install mplayer

# apt-get install feh

You set the Pi via raspi-config not to run the X Server (ie the GUI desktop), but it helps to have the X libraries installed. From your laptop/desktop you just ssh in:

fred@pi:~ $ ssh -t -Y 12.34.56.78

And from there, mplayer should display the video on your desktop. This lets you position the camera properly.

fred@pi:~ $ mplayer tv://

To get 10 still frames after 10s (some cameras auto-adjust brightness):

fred@pi:~ $ mplayer -vo jpeg -frames 10 -ss 10 -brightness 25 tv://

You can use 'mplayer -loop 0' to display the still images, but they flash on and off rather annoyingly. I much prefer something like feh:

fred@pi:~ $ feh .images/image_on.png

And best of all, the openCV code will execute as if you were using the Pi's console (ie HDMI).

Having selected your camera position, you should probably make a set of images under different lighting conditions. I used a fragment of Isaac Vidas's code to do this, in particular to see the effect of lighting on the separate operations like blurring and HSV transformation. This is named webcamTest.py and is available on my github repository. You typically do:

 

fred@pi:~/checkLed $ source ~/opencv/OpenCV-4.0-py3/bin/activate

(OpenCV-4.0-py3) fred@pi:~/checkLed $

(OpenCV-4.0-py3) fred@pi:~/checkLed $ python ./webcamTest.py image_on.png 

Next, use the nathancy code, which I named hsvThresholder.py. 

(OpenCV-4.0-py3) fred@pi:~/checkLed $ python hsvThresholder.py

hsvThresholder.py: adjust the sliders at the bottom. Runs very slowly on a Pi B, so be patient and watch the console output in the window below

You want to adjust the various sliders in order to mask out all other regions of different color to your LED. A Raspberry Pi 1 Model B will be extremely slow here so patience is required. One way is to watch the bash console messages as they are much quicker to update than the picture. Copy the final settings from the console, which will be something like:

(hMin = 0 , sMin = 0, vMin = 85), (hMax = 28 , sMax = 255, vMax = 255)

My version of Isaac Vidas's code is named checkAC_led.py. and pretty much works as advertised, except it required a much larger (something like 6x diameter) image of the LED. I would have needed to mount my camera much closer, just 17cm from the LED. The other problem is the camera needs to be square over the LED as cv2.HoughCircles() do not detect ellipses very well. And line (ie hollow) circles worked better than a solid one.

Image with test circle added: this is the minimum size circle cv2.HoughCircles() will detect

Mounting my camera closer and square-on the LED is the correct solution. This also minimizes false alarms and improves reliability of detection. This probably means some sort of mounting bracket on the wall, and might get in the way when the air conditioner is being serviced. A software solution would be great, and the future beep detector would help filter out those false alarms ...

This led me to cv2.SimpleBlobDetection() code, which does much better with smaller and deformed circles. Take care to set minArea as large as possible: I actually counted the number of LED pixels in my HSV transform.

The gotcha here is that the HSV image has to be inverted for blob detection to work:

    h, s, image_gray = cv2.split(full_image)

    image_gray_neg = cv2.bitwise_not(image_gray) 

    detector = cv2.SimpleBlobDetector_create(params)

After conversion to grayscale and inversion

After successful blob detection

The final version, checkACvideo_led.py reads from the webcam instead of still image files, filters out false alarms based on the blob x and y coordinates and prints the air conditioner status. In its IoT form the print statement just needs to be modified to publish to an MQTT server like mosquitto.

So did I leave the air conditioner on? Hey Mycroft, is my air conditioner on or off?

Happy Trails

The Littlest Computer that Could: OpenCV using Raspberry Pi 1 Model B Rev 2

 

First there was the 1B: Raspberry Pi One Model B.

Now out of production, the Raspberry Pi One Model B was released in 2012, earlier than the Model A. I had a few lying unused in my parts box, some damaged by defective power supplies, but mostly superseded by better versions like the Pi 2, 3 and 4s. The Pi 1 Model B was the slowest, had only 512MB DRAM and used the sdcard as mass storage. The USB functionality was questionable: the LAN chip was internally routed through the USB bus which crippled its throughput. And since the Pi was always touchy about its 5V input power adding basic functionality like keyboard, hdmi USB was always a hit and miss affair.

Left: sdcard, right: micro-sdcard

The sdcard is getting very hard to find. You find micro-sdcard with an adapter but these tend to develop contact problems and corrupt the onboard filesystem. To make matters worse, a Pi Model B which cannot boot will have no indication: there is just that red power LED on and nothing else, and it looks pretty much like a dead Pi. Some 60% of my discarded Pi 1s simply had microsd adapter contact problems and could not boot.

On the plus side, the Pi 1 drew the least power amongst the Pi series which meant most old Android phone chargers could power it. It also had audio and video jacks, which were very handy with retro electronics. 

I managed put one to use monitoring my solar panel, but most of the little jobs are better served by the ESP8266 or the Microchip PIC. If only the Raspberry Pi Model B could run OpenCV; with a bit of nifty image processing, it might find a use, perhaps to check if my front gate has been left open, or the air conditioner left running, or if the smoke alarm is beeping. 

Most of the time, the Pi 3 is the minimum recommended model, but there is no mention Raspberry Pi One cannot be used. No harm trying; and since the install process can be left alone, it is easily done on the side. 

Raspberry Pi 1 Model B Rev 2 with infamous microsd adapter

$dd if=2021-10-30-raspios-bullseye-armhf-lite.img of=/dev/sdc

After the standard install of Raspbian, I use raspi-config to turn on the ssh server and set a fixed ethernetIP. It can then be used as a headless (ie no monitor or keyboard) system via ssh from a host laptop or desktop. After which there is the usual obligatory

# apt-get update --allow-releaseinfo-change
# apt-get upgrade

And the Pi model:

root@pi:~# cat /sys/firmware/devicetree/base/model
Raspberry Pi Model B Rev 2

Jeremy Morgan's OpenCV install worked for my Pi 3 before, but now instead it stops:

# pip install opencv-contrib-python
Looking in indexes: https://pypi.org/simple, https://www.piwheels.org/simple
Collecting opencv-contrib-python
Downloading opencv-contrib-python-4.5.4.60.tar.gz (150.7 MB)
|��������������������������������| 150.4 MB 93 kB/s eta 0:00:04Killed

From 'dmesg -T' it looks like I ran out of memory:

[Tue Nov 23 18:03:30 2021] [ 5714]     0  5714    87141    21594     104       0

    21597             0 pip

[Tue Nov 23 18:03:30 2021] Out of memory: Kill process 5714 (pip) score 349 or sacrifice child

[Tue Nov 23 18:03:30 2021] Killed process 5714 (pip) total-vm:348564kB, anon-rss

:86376kB, file-rss:0kB, shmem-rss:0kB

[Tue Nov 23 18:03:30 2021] oom_reaper: reaped process 5714 (pip), now anon-rss:0

kB, file-rss:0kB, shmem-rss:0kB

My free memory is:

# free -m

              total        used        free      shared  buff/cache   available

Mem:            369          18         285           0          65         304

Swap:         15358          21       15337

And I can get a litte more by editing the boot partition's config.txt:

# vi /boot/config.txt

Add:

gpu_mem=16

And comment out

#start_x=1

After a reboot I get more memory:

# free -m

              total        used        free      shared  buff/cache   available

Mem:            477          31         338           6         106         390

Swap:            99           0          99

 

But this is still not enough. Now I could increase the swap file in my micro sdcard, but the thrashing might wear it out as the number of write operations is limited. Instead I used one of the many ancient thumbdrives, discarded just because of their low capacities. I ended up using a compactflash card for its speed:

# dd if=/dev/zero of=/dev/sda bs=1M count=1024

# mkswap /dev/sda

# swapon /dev/sda

# pip install --upgrade pip setuptools wheel

# python -m pip install --upgrade pip

# pip3 install opencv-contrib-python

pip seems to have gone walkabout so,

#  ln -s /usr/local/bin/pip /usr/bin/pip

Failure:

# pip install opencv-contrib-python

    File "setup.py", line 381, in _classify_installed_files_override

      with open(os.path.join(cmake_install_dir, "python", "cv2", "__init__.py"),

 'r') as opencv_init:

  FileNotFoundError: [Errno 2] No such file or directory: '_skbuild/linux-armv6l

-3.7/cmake-install/python/cv2/__init__.py'

  ----------------------------------------

  ERROR: Failed building wheel for opencv-contrib-python

Failed to build opencv-contrib-python

ERROR: Could not build wheels for opencv-contrib-python, which is required to in

stall pyproject.toml-based projects

Vishwesh Shrimali's instructions seem promising, and his minimum requirement is for a Pi 2. There are more separate bash commands which increases the chances for a successful debug. Since a fail is near certain, I chose to key in the commands manually instead of running Shrimali's script.

root@pi:/root/opencv# apt-get -y purge wolfram-engine

root@pi:/root/opencv# apt-get -y purge libreoffice*

root@pi:/root/opencv# apt-get -y clean

root@pi:/root/opencv# apt-get -y autoremove

root@pi:/root/opencv# apt -y update

root@pi:/root/opencv# apt -y upgrade

root@pi:/root/opencv# apt-get -y remove x264 libx264-dev

root@pi:/root/opencv# apt-get -y install build-essential checkinstall

 cmake pkg-config yasm

root@pi:/root/opencv# apt-get -y install git gfortran

root@pi:/root/opencv# apt-get -y install libjpeg8-dev libjasper-dev libpng12-dev

root@pi:/root/opencv# apt-get -y install libtiff5-dev

root@pi:/root/opencv# apt-get -y install libtiff-dev

root@pi:/root/opencv# apt-get -y install libxine2-dev libv4l-dev

root@pi:/root/opencv# cd /usr/include/linux

root@pi:/usr/include/linux# ln -s -f ../libv4l1-videodev.h videodev.h

root@pi:/usr/include/linux# cd $cwd

root@pi:/root/opencv#

root@pi:/root/opencv# apt-get -y install libgstreamer0.10-dev libgstreamer-plugins-base0.10-dev

root@pi:/root/opencv# apt-get -y install libgtk2.0-dev libtbb-dev qt5-default                         

root@pi:/root/opencv# apt-get -y install libatlas-base-dev

root@pi:/root/opencv# apt-get -y install libmp3lame-dev libtheora-dev

root@pi:/root/opencv# apt-get -y install libvorbis-dev libxvidcore-dev libx264-dev

root@pi:/root/opencv# apt-get -y install libopencore-amrnb-dev libopencore-amrwb-dev 

root@pi:/root/opencv# apt-get -y install libavresample-dev

root@pi:/root/opencv# apt-get -y install x264 v4l-utils

            The following are optional:

root@pi:/root/opencv# apt-get -y install libprotobuf-dev protobuf-compiler

root@pi:/root/opencv# apt-get -y install libgoogle-glog-dev libgflags-dev 

root@pi:/root/opencv# apt-get -y install libgphoto2-dev libeigen3-dev libhdf5-dev doxygen

            Required python libraries

root@pi:/root/opencv# apt-get -y install python3-dev python3-pip

            Virtual environment:

fred@pi:~/opencv $ python3 -m venv OpenCV-4.0-py3

fred@pi:~/opencv $ echo "# Virtual Environment Wrapper" >> ~/.bashrc

fred@pi:~/opencv $ echo "alias workoncv-4.0=\"source /root/opencv/OpenCV-4.0-py3/bin/activate\"" >> ~/.bashrc                                       

fred@pi:~/opencv $ source /root/opencv/OpenCV-4.0-py3/bin/activate(OpenCV-4.0-py3) fred@pi:~/opencv $

 Increase the swap file from 100 to 1024:

(OpenCV-4.0-py3) fred@pi:~/opencv $ sudo sed -i 's/CONF_SWAPSIZE=100/CONF_SWAPSIZE=1024/g' /etc/dphys-swapfile

(OpenCV-4.0-py3) fred@pi:~/opencv $ sudo /etc/init.d/dphys-swapfile stop

[ ok ] Stopping dphys-swapfile (via systemctl): dphys-swapfile.service.      

(OpenCV-4.0-py3) fred@pi:~/opencv $ sudo /etc/init.d/dphys-swapfile start

[ ok ] Starting dphys-swapfile (via systemctl): dphys-swapfile.service.         

(OpenCV-4.0-py3) fred@pi:~/opencv $ pip install numpy dlib

(OpenCV-4.0-py3) fred@pi:~/opencv $ deactivate

This is an over 400MB porker of a file:

fred@pi:~/opencv $ git clone https://github.com/opencv/opencv.git

In retrospect I should have checked out 4.0.1 as having cv2.drawKeypoints() would have been handy.

fred@pi:~/opencv/opencv $ git checkout 4.0.0

fred@pi:~/opencv $ git clone https://github.com/opencv/opencv_contrib.git

fred@pi:~/opencv $ cd opencv_contrib

fred@pi:~/opencv/opencv_contrib $ git checkout 4.0.0

Then comes the config:

fred@pi:~/opencv/opencv/build $ cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE

_INSTALL_PREFIX=/home/heong/opencv/installation/OpenCV-4.0 -D INSTALL_C_EXAMPLES

=ON -D INSTALL_PYTHON_EXAMPLES=ON -D WITH_TBB=ON -D WITH_V4L=ON -D OPENCV_PYTHON3_INSTALL_PATH=/home/heong/opencv/OpenCV-4.0-py3/lib/python3.5/site-packages -D WITH_QT=ON -D WITH_OPENGL=ON  -D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib/modules -D BUILD_EXAMPLES=ON ..

Now for the make:

fred@pi:~/opencv/opencv/build $ CMAKE_INSTALL_PREFIX=/usr/local

fred@pi:~/opencv/opencv/build $ export CMAKE_INSTALL_PREFIX

fred@pi:~/opencv/opencv/build $ make

Produces the error

In file included from /home/fred/opencv/opencv_contrib/modules/cvv/src/qtutil/f

ilter/sobelfilterwidget.cpp:3:

/home/heong/opencv/opencv/modules/imgproc/include/opencv2/imgproc.hpp:208:5: not

e:   �FILTER_SCHARR�

     FILTER_SCHARR = -1

     ^~~~~~~~~~~~~

make[2]: *** [modules/cvv/CMakeFiles/opencv_cvv.dir/build.make:453: modules/cvv/

CMakeFiles/opencv_cvv.dir/src/qtutil/filter/sobelfilterwidget.cpp.o] Error 1

make[1]: *** [CMakeFiles/Makefile2:12676: modules/cvv/CMakeFiles/opencv_cvv.dir/

all] Error 2

make: *** [Makefile:163: all] Error 2

From Nobuo Tsukamoto, in the file /home/fred/opencv/opencv_contrib/modules/cvv/src/qtutil/f

ilter/sobelfilterwidget.cpp added 'using namespace cv;' at line 13 thus:

#include "../../util/util.hpp"

#include "../filterfunctionwidget.hpp"

#include "../filterselectorwidget.hpp"

using namespace cv; // cmheong 2021-11-29

namespace cvv

{

namespace qtutil

{

SobelFilterWidget::SobelFilterWidget(QWidget *parent)

After which

fred@pi:~/opencv/opencv/build $ make

Produces the error

Scanning dependencies of target example_cpp_detect_mser

[ 89%] Building CXX object samples/cpp/CMakeFiles/example_cpp_detect_mser.dir/detect_mser.cpp.o

/home/fred/opencv/opencv/samples/cpp/detect_mser.cpp:28:10: fatal error: GL/glu.h: No such file or directory

 #include <GL/glu.h>

          ^~~~~~~~~~

compilation terminated.

make[2]: *** [samples/cpp/CMakeFiles/example_cpp_detect_mser.dir/build.make:63:

samples/cpp/CMakeFiles/example_cpp_detect_mser.dir/detect_mser.cpp.o] Error 1

make[1]: *** [CMakeFiles/Makefile2:29519: samples/cpp/CMakeFiles/example_cpp_det

ect_mser.dir/all] Error 2

make: *** [Makefile:163: all] Error 2

From RajkiranVeldur, just do

root@pi:~#  apt-get install libglfw3-dev libgl1-mesa-dev libglu1-mesa-dev

After which

fred@pi:~/opencv/opencv/build $ make

Produces the link error

[ 89%] Linking CXX executable ../../bin/example_cpp_detect_mser

/usr/bin/ld: CMakeFiles/example_cpp_detect_mser.dir/detect_mser.cpp.o: in functi

on `draw(void*)':

detect_mser.cpp:(.text.startup.main+0x1c70): undefined reference to `gluPerspect

ive'

collect2: error: ld returned 1 exit status

make[2]: *** [samples/cpp/CMakeFiles/example_cpp_detect_mser.dir/build.make:134:

 bin/example_cpp_detect_mser] Error 1

make[1]: *** [CMakeFiles/Makefile2:29519: samples/cpp/CMakeFiles/example_cpp_det

ect_mser.dir/all] Error 2

make: *** [Makefile:163: all] Error 2

regpa mentioned that I need openGLU.so, but a brute-force search could not come up with one:

fred@pi:~/opencv/opencv/build $ sudo ls -lR / | grep -e openGLU 

fred@pi:~/opencv/opencv/build $

There is however, a file called libGLU.so mentioned by myinternetofthings:

fred@pi:~/opencv/opencv/build $ sudo ls -lR / 2>/dev/null | grep -e GLU.so

lrwxrwxrwx  1 root root       15 Sep 20  2015 libGLU.so -> libGLU.so.1.3.1

lrwxrwxrwx  1 root root       15 Sep 20  2015 libGLU.so.1 -> libGLU.so.1.3.1

-rw-r--r--  1 root root   358228 Sep 20  2015 libGLU.so.1.3.1

Added it to 2 separate files link.txt:

fred@pi:~/opencv/opencv/build $ cat samples/opengl/CMakeFiles/example_open

gl_opengl.dir/link.txt

/usr/bin/c++     -fsigned-char -W -Wall -Werror=return-type -Werror=non-virtual-

dtor -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wm

issing-declarations -Wundef -Winit-self -Wpointer-arith -Wshadow -Wsign-promo -W

uninitialized -Winit-self -Wno-narrowing -Wno-delete-non-virtual-dtor -Wno-comme

nt -Wimplicit-fallthrough=3 -Wno-strict-overflow -fdiagnostics-show-option -pthr

ead -fomit-frame-pointer -ffunction-sections -fdata-sections  -mfp16-format=ieee

 -fvisibility=hidden -fvisibility-inlines-hidden -O3 -DNDEBUG  -DNDEBUG    -Wl,-

-gc-sections   CMakeFiles/example_opengl_opengl.dir/opengl.cpp.o  -o ../../bin/e

xample_opengl_opengl  -Wl,-rpath,/home/heong/opencv/opencv/build/lib -ldl -lm -l

pthread -lrt /usr/lib/arm-linux-gnueabihf/libGL.so ../../lib/libopencv_highgui.s

o.4.0.0 ../../lib/libopencv_videoio.so.4.0.0 ../../lib/libopencv_imgcodecs.so.4.

0.0 ../../lib/libopencv_imgproc.so.4.0.0 ../../lib/libopencv_core.so.4.0.0 /usr/

lib/arm-linux-gnueabihf/libGLU.so

fred@pi:~/opencv/opencv/build $ cat samples/cpp/CMakeFiles/example_cpp_det

ect_mser.dir/link.txt

/usr/bin/c++     -fsigned-char -W -Wall -Werror=return-type -Werror=non-virtual-

dtor -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wm

issing-declarations -Wundef -Winit-self -Wpointer-arith -Wshadow -Wsign-promo -W

uninitialized -Winit-self -Wno-narrowing -Wno-delete-non-virtual-dtor -Wno-comme

nt -Wimplicit-fallthrough=3 -Wno-strict-overflow -fdiagnostics-show-option -pthr

ead -fomit-frame-pointer -ffunction-sections -fdata-sections  -mfp16-format=ieee

 -fvisibility=hidden -fvisibility-inlines-hidden -O3 -DNDEBUG  -DNDEBUG    -Wl,-

-gc-sections   CMakeFiles/example_cpp_detect_mser.dir/detect_mser.cpp.o  -o ../.

./bin/example_cpp_detect_mser  -Wl,-rpath,/home/heong/opencv/opencv/build/lib -l

dl -lm -lpthread -lrt /usr/lib/arm-linux-gnueabihf/libGL.so ../../lib/libopencv_

gapi.so.4.0.0 ../../lib/libopencv_stitching.so.4.0.0 ../../lib/libopencv_aruco.s

o.4.0.0 ../../lib/libopencv_bgsegm.so.4.0.0 ../../lib/libopencv_bioinspired.so.4

.0.0 ../../lib/libopencv_ccalib.so.4.0.0 ../../lib/libopencv_cvv.so.4.0.0 ../../

lib/libopencv_dnn_objdetect.so.4.0.0 ../../lib/libopencv_dpm.so.4.0.0 ../../lib/

libopencv_face.so.4.0.0 ../../lib/libopencv_freetype.so.4.0.0 ../../lib/libopenc

v_fuzzy.so.4.0.0 ../../lib/libopencv_hdf.so.4.0.0 ../../lib/libopencv_hfs.so.4.0

.0 ../../lib/libopencv_img_hash.so.4.0.0 ../../lib/libopencv_line_descriptor.so.

4.0.0 ../../lib/libopencv_reg.so.4.0.0 ../../lib/libopencv_rgbd.so.4.0.0 ../../l

ib/libopencv_saliency.so.4.0.0 ../../lib/libopencv_sfm.so.4.0.0 ../../lib/libope

ncv_stereo.so.4.0.0 ../../lib/libopencv_structured_light.so.4.0.0 ../../lib/libo

pencv_superres.so.4.0.0 ../../lib/libopencv_surface_matching.so.4.0.0 ../../lib/

libopencv_tracking.so.4.0.0 ../../lib/libopencv_videostab.so.4.0.0 ../../lib/lib

opencv_xfeatures2d.so.4.0.0 ../../lib/libopencv_xobjdetect.so.4.0.0 ../../lib/li

bopencv_xphoto.so.4.0.0 ../../lib/libopencv_shape.so.4.0.0 ../../lib/libopencv_p

hase_unwrapping.so.4.0.0 ../../lib/libopencv_optflow.so.4.0.0 ../../lib/libopenc

v_ximgproc.so.4.0.0 ../../lib/libopencv_datasets.so.4.0.0 ../../lib/libopencv_pl

ot.so.4.0.0 ../../lib/libopencv_text.so.4.0.0 ../../lib/libopencv_ml.so.4.0.0 ..

/../lib/libopencv_dnn.so.4.0.0 ../../lib/libopencv_video.so.4.0.0 ../../lib/libo

pencv_photo.so.4.0.0 ../../lib/libopencv_objdetect.so.4.0.0 ../../lib/libopencv_

calib3d.so.4.0.0 ../../lib/libopencv_features2d.so.4.0.0 ../../lib/libopencv_fla

nn.so.4.0.0 ../../lib/libopencv_highgui.so.4.0.0 ../../lib/libopencv_videoio.so.

4.0.0 ../../lib/libopencv_imgcodecs.so.4.0.0 ../../lib/libopencv_imgproc.so.4.0.

0 ../../lib/libopencv_core.so.4.0.0 /usr/lib/arm-linux-gnueabihf/libGLU.so

After which

fred@pi:~/opencv/opencv/build $ make

Completes successfully. But there is still the installation. Set the environment variable CMAKE_INSTALL_PREFIX so that:

fred@pi:~/opencv/opencv/build $ echo $CMAKE_INSTALL_PREFIX

/usr/local

fred@pi:~/opencv/opencv/build $ sudo make install

And a quick test run:

fred@pi:~/opencv/opencv/build $ source /home/fred/opencv/OpenCV-4.0-py3/bin/activate

(OpenCV-4.0-py3) fred@pi:~/opencv/opencv/build $ python

Python 3.7.3 (default, Jan 22 2021, 20:04:44)

[GCC 8.3.0] on linux

Type "help", "copyright", "credits" or "license" for more information.

>>> import cv2

>>> print(cv2.__version__)

4.0.0

>>> quit()

And there you have it, Raspberry Pi 1 Model B Rev 2, the littlest computer that could OpenCV.

The Little Computer that Could: Motion Detection with Outdoor Camera, Tensorflow and Raspberry Pi Part 3 of 3

 

"I have no spur to prick the sides of my intent, but only vaulting ambition, which o'erleaps itself and falls on the other." - Macbeth

Motion detection with an outdoor camera can be problematic, with wind, shadow and direct sunlight causing multiple false alarms. Suppressing these false alarms can result in genuine alarms being missed. One way is to filter all the alarms through an object recognition program.

The motion detection program of Part 1 will write all alarm image frames to the ./alarms/ directory. A modified object recognition program of Part 2 will inspect each alarm frame and if an object is recognized, will write it to another directory ./alarm/. The alarm filter program is called tiny-yolo_alarmfilter.py. This seemed to work well to start with, but with any new project, time will tell. For starters it seemed to think my dog is a cow, probably because she was sniffing at the grass.

tiny YOLO mislabelling dog as cow

Now my dog does have a temper, but calling her a cow is a little harsh. Even so, both dogs and cows qualify (in my opinion) as valid alarm triggers so it is not a show-stopper for now. It is particularly good at excluding changes in lighting and shadows. Proof positive that the little Raspberry Pi could, and did. 

Recognizing passing vehicles 

Ah, vaulting ambition ... if the Pi can recognize an object, maybe it can also track it. Most alarms are quite passive, except for the loud siren, which tends to annoy the neighbors and should only be used as a last resort. A pan/tilt camera like the Trendnet TV-IP422WN that visibly tracks the object is a lot more menacing, and should scare off the more timid intruders like birds and squirrels. But that is another blog post.

A tensorflow model looks promising, as it can be potentially speeded up with the use of custom hardware like the Coral USB acelerator for about the price of a Raspberry Pi 4.

Coral USB Accelerator

Installing tensorflow proved to be a bit hit and miss, but Katsuya Hyodo's github readme worked for me. This time I started with a squeaky clean version of Raspbian, 2021-05-07-raspios-buster-armhf.img.

Remember to uninstall the dud versions:

# pip3 uninstall tensorflow
# apt-get install -y libhdf5-dev libc-ares-dev libeigen3-dev gccgfortran libgfortran5 libatlas3-base libatlas-base-dev libopenblas-dev libopenblas-base libblas-dev liblapack-dev cython3 openmpi-bin libopenmpi-dev libatlas-base-dev python3-dev
# pip3 install pip --upgrade
# pip3 install keras_applications==1.0.8 --no-deps
# pip3 install keras_preprocessing==1.1.0 --no-deps
# pip3 install h5py==2.9.0
# pip3 install pybind11
# pip3 install -U --user six wheel mock
# wget "https://raw.githubusercontent.com/PINTO0309/Tensorflow-bin/master/tensorflow-1.15.0-cp37-cp37m-linux_armv7l_download.sh"
# sh ./tensorflow-1.15.0-cp37-cp37m-linux_armv7l_download.sh
# pip3 install tensorflow-1.15.0-cp37-cp37m-linux_armv7l.whl

A quick test:

# python3

Python 3.7.3 (default, Jan 22 2021, 20:04:44)

[GCC 8.3.0] on linux

Type "help", "copyright", "credits" or "license" for more information.

>>> import tensorflow

>>> tensorflow.__version__

'1.15.0'

>>>

For object detection I used Edje Electronics. 

Packages tensorflow, libatlas-base-dev, libhdf5-dev, libhdf5-serial-dev I had already installed previously

# apt-get install libjasper-dev

# apt-get install libqtgui4

# apt-get install libqt4-test

I used version 4.4.0.46 because 4.1.0.25 could not be found

# pip3 install opencv-contrib-python==4.4.0.46

# apt-get install protobuf-compiler

# pip install --user Cython

# pip install --user contextlib2

# pip install --user pillow

# pip install --user lxml

# pip install --user matplotlib

Got the tensorflow models

# git clone https://github.com/tensorflow/models.git

Then SSD_Lite:

# wget http://download.tensorflow.org/models/object_detection/ssdlite_mobilenet_v2_c

oco_2018_05_09.tar.gz

# tar -xzvf ssdlite_mobilenet_v2_coco_2018_05_09.tar.gz

The original tensorflow program, ./models/research/object_detection/TensorFlow.py got its images from the default Raspberry Pi camera, so I made a simpler version to take one frame (./341front.jpg in 640x480) at a time, uTensorFlow.py.

Note SSD_Lite misclassified a dog as sheep

The processing time was over 40s on my Raspberry Pi 3. My Coral USB accelerator will take more than a month to arrive, and it needs Tensorflow Lite, for which Edje Electronics has a very promising Tensorflow Lite repository, so why not. Notice this time the commands are as a sudoer user and not root, which I am told is the proper way to do things:

$ sudo pip3 install virtualenv

$ python3 -m venv tflite1-env

$ source tflite1-env/bin/activate

Then comes a whopper of a download:

$ git clone https://github.com/EdjeElectronics/TensorFlow-

Lite-Object-Detection-on-Android-and-Raspberry-Pi.git

$ bash get_pi_requirements.sh

Notice with Tensorflow Lite there is no Tensorflow module:

$ python3

Python 3.7.3 (default, Jan 22 2021, 20:04:44)

[GCC 8.3.0] on linux

Type "help", "copyright", "credits" or "license" for more information.

>>> import tensorflow

Traceback (most recent call last):

  File "<stdin>", line 1, in <module>

ModuleNotFoundError: No module named 'tensorflow'

>>>

$ wget https://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip

$ unzip coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip

Archive:  coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip

  inflating: detect.tflite

  inflating: labelmap.txt

The original program is TFLite_detection_webcam.py but I need a version that works on individual image files, and not a video file, as in tiny-yolo_alarmfilter.py. The progam is tflite_alarmfilter.py, which took no time at all to write in python. You run it thus:

$ python3 tflite_alarmfilter.py --resolution='640x480' --modeldir=.

Processing alarm file 341front_20210804_094635_original.png

Processing alarm file 341front_20210804_094649_original.png

Processing alarm file 341front_20210804_094648_original.png

The framerate is now an astounding 2fps even without the Coral USB Accelerator. The detection seems slightly better with a more accurate bounding box without multiple boxes nested in the same object.

Tensorflow Lite SSDLite MobileNet v2: note accurate bounding box

tiny-YOLO: multiple bounding boxes over same object

I had originally planned to outsource the alarm files filtering to an x86 CPU, but the results with Tensorflow Lite made everything possible on the same Raspberry Pi 3, truly the little CPU that could. 

Happy Trails.