How to recover WordPress admin access if you only have FTP access

There are well-documented solutions to recover the wordpress admin access if you have MySQL, phpMyAdmin or shell access.

This solution shows you how to create a new admin user if you don’t know the admin username or password and you don’t have any form of MySQL or shell access (only FTP access is required).

Step 1: Identify your currently active theme

This can be done by looking at the source code of your homepage (go to your domain, then Ctrl+U to show the source) and then Ctrl+F-search for wp-content/themes. It will show hits like This means the currently active theme is twentyfifteen.

Step 2: Create a new admin user using functions.php

Now open your FTP software (I recommend FileZilla) and find your wp-content folder. Inside wp-content, go to themes and then open the folder of your currently active theme.

If the currently active theme is not listed in your wp-content/themes folder, you might have the wrong wp-content folder. Check if there are any other folders around.

When you have found your theme folder, edit functions.php and, just after the first <?php, add this block of code:

function wpb_admin_account(){
    $user = 'newadmin';
    $pass = 'saiquae9shahZus6eeri3feNae8gie';
    $email = '';
    if ( !username_exists( $user )  && !email_exists( $email ) ) {
        $user_id = wp_create_user( $user, $pass, $email );
        $user = new WP_User( $user_id );
        $user->set_role( 'administrator' );

Be sure to replace the username, password and email! I recommend to use a new random password and a non-generic username! The username you enter here must.

Save the file and upload it to the server.

After that, goto your homepage and reload once (this will create the new user).

Then, try to login using your newly created user (go to e.g. to do so!). If it doesn’t work, check if you edited the functions.php for the correct theme and try to use a different username!

Step 3: Delete the code we just created

If you just leave in the code, this will create a potential security risk. So I recommend deleting it right away!

Also, be sure to delete any admin users you don’t need afterwards.


How to use Z0 (characteristic impedance of vacuum) constant in SciPy/NumPy

If you want to use the Z0 constant (characteristic impedance of free space) in Python, use this snippet:

import scipy.constants
Z0 = scipy.constants.physical_constants['characteristic impedance of vacuum'][0]

print(Z0) # 376.73031346177066

In contrast to other constants, Z0 is not available directly like scipy.constants.pi but you need to use the scipy.constants.physical_constants dict in order to access it.

How to backup Redmine using the Bitnami Docker image

In a previous post I detailed how to install Redmine on Linux using the excellent Bitnami docker image.

This post will teach you how to easily make an online backup of your Redmine installation. Note that automating the backup is not within the scope of this post.

We assume that the redmine is installed as shown in my previous post in /var/lib/redmine. and that you want to backup to my.backup.server:~/redmine-backup/ using rsync.

Backing up the Redmine data

This is pretty easy, as the data is all in just one directory. You can sync it using

rsync --checksum -Pavz /var/lib/redmine/redmine_data my.backup.server:~/redmine-backup/

Note that old versions of files in redmine_data will be overwritten, however files that are deleted locally will not be deleted on the backup server. To me, this seems like a good compromise between the ability to recover deleted files and the used storage space.

Backing up the Redmine database

This part is slightly more complicated, since we need to access the MariaDB server running in a different container. Important note: The container ID can change so it is not sufficient to just find the container ID once and then use it. You need to determine the appropriate ID each time you do a backup. See below on instructions how to do that.

Full command:

docker exec -it $(docker container ls | grep redmine_mariadb_1 | cut -d' ' -f1) mysqldump -uroot bitnami_redmine | xz -e9 -zc - > redmine-database-dump-$(date -I).sql.xz

Let’s break it down:

  • docker exec -it (container ID) (command): Run a command on a running docker container.
  • docker container ls | grep redmine_mariadb_1 | cut -d' ' -f1: Get the ID (first field of the output cut -d' ' -f1) of the running docker container named redmine_mariadb_1
  • mysqldump -uroot bitnami_redmine: This is run on the docker container and dumps the Redmine Database as SQL to stdout. No password is neccessary since the Bitnami MariaDB image allows access without any password.
  • xz -e9 -zc -: Takes the data from mysqldump from stdin (-), compresses it using maximum compression settings (-e9 -z) and writes the compressed data to stdout.
  • > redmine-database-dump-$(date -I).sql.xz: Writes the compressed data from xz into a file called redmine-database-dump-(current date).sql.xz in the current directory.

The resulting file is called e.g. redmine-database-dump-2019-02-01.sql.xz and it’s placed in the current directory. Ensure that you run the command in a suitable directory. Run it in /tmp if you don’t know which directory might be suitable.

Now we can rsync it to the server:

rsync --checksum -Pavz redmine-backup-*.sql.xz my.backup.server:~/redmine-backup/

Since the filename contains the current data, this approach will not overwrite old daily backups of the database, so you can restore your database very flexibly.

How to calculate STM32 bxCAN bit timings using Python

When I needed to configure some STM32 microcontrollers with nonstandard clock speeds to use the correct CAN bit timings.

While this script has been written for the STM32F413 series, it should be applicable to pretty much all STM32 MCUs with bxCAN – however, you need to check the details, especially from which clock the CAN is derived.

You need to know:

  • The frequency of the clock from which the CAN clock is derived (PCLK1 in my example: 48 MHz)
  • Your desired Baudrate: 1 MBaud in my example

Using an iterative trial and error approach, we will determine the appropriate values for BRP, TS1 and TS2 (these are specific sets of bits in the CAN):

#!/usr/bin/env python3
# Configuration. Change here
master_clock = 48e6 # PCLK1, or whatever CLK CAN is derived from
desired_baudrate = 1e6 # 1e6 = 1 MBaud, 500e3 = 500 kBaud
# Register values
brp = 5 # Baud rate prescaler
ts1 = 4 # (Length of time segment 1) - 1
ts2 = 1 # (Length of time segment 2) - 1

# Calculation. You usually don't need to change this.
bs1_segments = ts1 +1
bs2_segments = ts2 + 1
total_segments = 1 + bs1_segments + bs2_segments
bittime = 1. / desired_baudrate
master_time = 1. / master_clock
tq = (brp + 1) * master_time
total_time = total_segments * tq
effective_rate = 1. / total_time
sample_point = 1 - (bs2_segments / total_segments)

# Print results
print(f"Effective Baudrate: {effective_rate:.2f} Baud = {100. * effective_rate / desired_baudrate:.2f} % of desired baudrate")
print(f"Sample point: {100. * sample_point:.2f}  %")

When you run this script, you will see an output like this:

Effective Baudrate: 1000000.00 Baud = 100.00 % of desired baudrate
Sample point: 75.00  %

Of course you need to fill the master clock frequency and the desired baudrate at the top. You can start with the pre-configured values for BRP, TS1 and TS2.

In the end, you should achieve 100% of desired baudrate and a sample point that is 87.5% (80% to 90% is usually OK, 75% might not work with long cables. There might be a specific value dictated by your comms standard, e.g. CANOpen or J1939).

Change the values of for BRP, TS1 and TS2., re-running the script and observing the output each time. I recommend first changing BRP until you get close and then adjusting using this algorithm:

  • If your actual baudrate is too slow (i.e. < 100%), decrease BRP (larger step) and/or decrease TS1 + TS2
  • If your actual baudrate is too fast (i.e. > 100%), increase BRP (larger step) and/or increase TS1 + TS2
  • If your sample point percentage is too small, increase the ratio of TS1 to TS2
  • If your sample point percentage is too large, decrease the ratio of TS1 to TS2

Note that there might be master clock speeds and baudrates for which there is no ideal setting. Be sure to check whether a slightly different baudrate is still OK in your application (usually it’s not). If it is not, you need to find a way to use a different PCLK1 speed.

How to use custom themes with the Bitnami Redmine Docker image

In a previous post I detailed how to install Redmine on Linux using the excellent Bitnami docker image.

This post shows you how to install a custom theme like A1 (which I used successfully for more than 5 years) if you use the bitnami Docker image. We will assume that you installed redmine in /var/lib/redmine and your systemd service is called redmine.

Note: If you get any permission denied errors, try running the same command using sudo.

First, we need to create the themes directory.

sudo mkdir /var/lib/redmine/themes

The first thing we need to do is to copy the current (default) themes to that directory, since Redmine won’t be able to start up if the default theme isn’t available in the correct version.

In order to do this, we must first ensure that your container is running:

sudo systemctl start redmine

Now we can find out the container ID of the running redmine container:

uli:/var/lib/redmine$ docker container ps | grep redmine
ae4de10d0b41        bitnami/redmine:latest    "/ …"   30 minutes ago      Up 30 minutes>3000/tcp   redmine_redmine_1
c231d11c48e9        bitnami/mariadb:latest    "/ /run…"   30 minutes ago      Up 30 minutes   3306/tcp redmine_mariadb_1

From these lines, you need to select the line that says redmine_redmine_1 at the end. The one that lists redmine_mariadb_1 at the end is the database container and we don’t need that one for this task.

From that line, copy the first column – this is the container ID – e.g. ae4de10d0b41 in this example.

Now we can copy the default theme folder:

docker cp ae4de10d0b41:/opt/bitnami/redmine/public/themes /var/lib/redmine/themes

Now copy your custom theme (e.g. the a1 folder) to /var/lib/redmine/themes.

The next step is to fix the permissions. The bitnami container uses the user with UID 1001, so we need to change the owner to that. Repeat this every time you changed something in the themes directory:

sudo chown -R 1001:1001 /var/lib/redmine/themes

At this point we need to edit the docker-compose config (in /var/lib/redmine/docker-compose.yml) to mount /var/lib/redmine/themes in the correct directory. This is pretty easy: Just add - '/var/lib/redmine-szalata/themes:/opt/bitnami/redmine/public/themes' to the volumes section of the redmine container.

The finished config file will look like this:

version: '2'
    image: 'bitnami/mariadb:latest'
      - '/var/lib/redmine/mariadb_data:/bitnami'
    image: 'bitnami/redmine:latest'
      - REDMINE_USERNAME=admin
      - REDMINE_PASSWORD=redmineadmin
      - SMTP_PORT=25
      - SMTP_PASSWORD=yourGmailPassword
      - '3718:3000'
      - '/var/lib/redmine/redmine_data:/bitnami'
      - '/var/lib/redmine-szalata/themes:/opt/bitnami/redmine/public/themes'
      - mariadb

Now you can restart Redmine:

sudo systemctl restart redmine

and set your new theme by selecting it in Administration -> Settings -> Display.

Fixing ScanaStudio error while loading shared libraries: cannot open shared object file on Ubuntu


You want to start IkaLogic ScanaStudio, but you see the following error message:

./ScanaStudio: error while loading shared libraries: cannot open shared object file: No such file or directory


You need to download the D2XX drivers from the FTDI D2XX drivers page.

Look for your architecture in the columns (usually x64 (64 bit), this is the same as x86_64) and click the link in the Linux row.

This will download a file named something like libftd2xx-x86_64-1.4.8.gz.

Now you can extract the archive using

tar xvf libftd2xx-*

Now we can copy the shared object file to the IkaLogic ScanaStudio directory:

cp release/build/* ~/Ikalogic/ScanaStudio/

Now you can start ScanaStudio:

cd ~/Ikalogic/ScanaStudio

Puppeteer: Get text content / inner HTML of an element


You want to use puppeteer to automate testing a webpage. You need to get either the text or the inner HTML of some element, e.g. of

<div id="mydiv">

on the page.


# Get inner text
const innerText = await page.evaluate(() => document.querySelector('#mydiv').innerText);

# Get inner HTML
const innerHTML = await page.evaluate(() => document.querySelector('#mydiv').innerHTML);

Note that .innerText includes the text of sub-elements. You can use the complete DOM API inside page.evaluate(...). You can use any CSS selector as an argument for document.querySelector(...).

Fixing requests session TypeError: __init__() got an unexpected keyword argument ‘headers’


You are trying to initialize a Python requests session using a custom set of HTTP headers like this:

s = requests.Session(headers={
    "User-Agent": "Mozilla/5.0 (Windows NT 6.2; WOW64; rv:34.0) Gecko/20100101 Firefox/34.0"

but you only see this stacktrace:

File "", line 30, in translate
    "User-Agent": "Mozilla/5.0 (Windows NT 6.2; WOW64; rv:34.0) Gecko/20100101 Firefox/34.0"
TypeError: __init__() got an unexpected keyword argument 'headers'


You can’t use the headers=... argument in the requests.Session(...) constructor.

Use s.headers.update({...}) instead:

s = requests.Session()
    "User-Agent": "Mozilla/5.0 (Windows NT 6.2; WOW64; rv:34.0) Gecko/20100101 Firefox/34.0"


How to fix ModuleNotFoundError: No module named ‘’


You want to run a Python script that uses one of the Google Cloud Python APIs but you get this error message:

ModuleNotFoundError: No module named ''


Reinstall any google cloud package using pip:

sudo pip install --upgrade google-cloud-storage


sudo pip3 install --upgrade google-cloud-storage

That will also reinstall the relevant module.

After that, re-run your script. If that didn’t work, try to install --upgrade some other google-cloud-* module, especially the modules you actually use in your script.


Fixing numpy.distutils.system_info.NotFoundError: No lapack/blas resources found on Ubuntu or Travis

Note: If you are on Windows, you can not install scipy using pip! Follow this guide instead: This blog post is only for Linux-based systems!

When building some of my libraries on Travis, I encountered this error during

sudo pip3 install numpy scipy --upgrade
numpy.distutils.system_info.NotFoundError: No lapack/blas resources


Install lapack and blas:

sudo apt-get -y install liblapack-dev libblas-dev

In most cases you will then get this error message:

error: library dfftpack has Fortran sources but no Fortran compiler found

Fix that by

sudo apt-get install -y gfortran

In Travis, you can do it like this in .travis.yml:

    - sudo apt-get -y install liblapack-dev libblas-dev gfortran

Easily compute & visualize FFTs in Python using UliEngineering

UliEngineering is a mixed data analytics in Python – one of the utilities it provides is an easy-to-use package to compute FFTs. In contrast to other package, this library is oriented towards practical usecases and allows you to do the FFT in only one line of code! No knowledge of Math required.

Getting started

First, we’ll generate some test data. See this previous post for more details on how to generate sinusoid test data:

from UliEngineering.SignalProcessing.Simulation import *

# Generate test data: 100 Hz + 400 Hz tone
data = sine_wave(frequency=100.0, samplerate=1000, amplitude=1.) \
       + sine_wave(frequency=400.0, samplerate=1000, amplitude=0.5)

The test data consists of a 100 Hz sine wave plus a 400 Hz sine wave (with half the amplitude). That signal is sampled with a sample rate of 1000 Hz.

Now we can compute & visualize the FFT using matplotlib:

# Compute FFT. Use the same samplerate 
# NOTE: Window defaults to "blackman"!
from UliEngineering.SignalProcessing.FFT import compute_fft
fft = compute_fft(data, samplerate=1e3)

# Plot
from matplotlib import pyplot as plt"ggplot")

plt.gcf().set_size_inches(10, 5) # Use (20, 10) to get a larger plot
plt.plot(fft.frequencies, fft.amplitudes)

compute_fft(data, samplerate=1e3) returns a tuple (fftx, ffty). It performs an FFT the size of the input (i.e. since data is a length-1000 array, the FFT will be of size 1000).

fft.frequencies is an array of frequencies (in Hz), corresponding to the values in fft.amplitudes. You can also use fft.angles to get relative angles in degrees, but that is not being covered in this blogpost.

As you can see in the plot shown above, the maximum frequency that can be detected is always half the samplerate, i.e. for our samplerate of f_s = 1000\,\text{Hz} it is 500\,\text{Hz}. See this more math-centric FFT explanation if you want to know more details.

Internally, compute_fft() performs this computation:

2 \cdot \frac{\text{abs}\left(\text{FFT}(\text{data} \cdot \text{Window})\right)}{\text{len(data)}}
  • 2 is a correction factor that takes into account that we throw away the latter half of the raw FFT result (since we’re doing a real FFT)
  • \frac{1}{\text{len(data)}} normalizes the FFT results so they are indepedent of the data length (i.e. if you pass a longer sample of the same sine wave you will still get the same result
  • \text{abs}\left(\cdots\right) Converts the complex phase-aware result of the FFT to a spectrum which is easier to read & visualize.
  • Window (which defaults to blackman) is the window which is applied to the data to alleviate some mathematical effects at the beginning and the end of the dataset. See wikipedia on window functions for more details. UliEngineering currently offers this list of window functions:
    • blackman
    • bartlett
    • hamming
    • hanning
    • kaiser (Parameter is fixed to 2.0)
    • none

Selecting frequency ranges

Using the UliEngineering API, selecting a frequency range of the FFT is trivially easy: Just use fft[lowfreq:highfreq]. You can use fft[lowfreq:] to select everything starting from lowfreq or use fft[:highfreq] to select everything up to highfreq.

from UliEngineering.SignalProcessing.FFT import compute_fft
fft = compute_fft(data, samplerate=1e3)

# Select frequency range: 50 to 200 Hz
fft = fft[50.0:200.0]

# Plot
from matplotlib import pyplot as plt"ggplot")

plt.gcf().set_size_inches(10, 5) # Use (20, 10) to get a larger plot
plt.plot(fft.frequencies, fft.amplitudes)

Extract amplitude & angle at a certain frequency

By using [frequency], i.e. getitem operator with a single value, you get an FFTPoint() object containing the frequency, amplitude and relative angle for a given frequency. The library automatically selects the closest FFT bucket, so even if your FFT does not have a bucket for that specific frequency, you will get sensible results.

from UliEngineering.SignalProcessing.FFT import compute_fft
fft = compute_fft(data, samplerate=1e3)

# Show value at a certain frequency
print(fft[30]) # FFTPoint(frequency=30.0, value=2.91e-08, angle=0.0)
print(fft[100]) # FFTPoint(frequency=100.0, value=0.419, angle=0.0)

Short FFTs for long data

Using compute_fft(), if we have an extremely long data array, this means we’ll compute an extremely long FFT. In many cases, this is not desirable and you want to compute a fixed-size FFT (most often a power-of-two FFT, e.g. 1024, 2048, 4096 etc).

Let’s generate some long test data and assume we want to compute a size-1024 FFT on it

from UliEngineering.SignalProcessing.FFT import *
fftx, ffty = simple_serial_fft_reduce(data, samplerate=1e3, fftsize=1024)

Plotting the data like above yields

which looks almost exactly like our compute_fft() plot before – just as we expected.

simple_serial_fft_reduce() takes care of all the magic and normalization for us, including partitioning the data into overlapping chunks, adding the FFTs and properly normalizing the result.

The naming convention is significant here:

  • simple_...._reduce means this is a variant with sensible defaults (simple) for using a reduction function (default: sum) on multiple FFTs.
  • serial means the individual FFTs

Parallelizing the FFTs

If you have a huge dataset, you can use simple_parallel_fft_reduce() identically tosimple_serial_fft_reduce():

from UliEngineering.SignalProcessing.FFT import *
fftx, ffty = simple_parallel_fft_reduce(data, samplerate=1e3, fftsize=1024)

However in most cases you want to initialize the executor manually so you can re-use it later:

from UliEngineering.SignalProcessing.FFT import *
from concurrent.futures import ThreadPoolExecutor
executor = ThreadPoolExecutor() # No argument => use num_cpus threads
fftx, ffty = simple_parallel_fft_reduce(data, samplerate=1e3, fftsize=1024, executor=executor)

We can use a ThreadPoolExecutor() since scipy.fftpack (which UliEngineering uses to do the hard math) unlocks the Python GIL.

Note that due to the need to do a lot of housekeeping tasks, simple_parallel_fft_reduce() is much slower than simple_serial_fft_reduce() if you have a dataset so small that parallelization is not effective. My initial recommendation is to consider using the parallel variant if the total execution time of the serial variant is larger than 0.5\,s


sudo pip3 install git+

Easily generate square/triangle/sawtooth/inverse sawtooth waveform data in Python using UliEngineering

In a previous post, I’ve detailed how to generate sine/cosine wave data with frequency, amplitude, offset, phase shift and time offset using only a single line of code.

This post extends this approach by showing how to generate square wave, triangle wave, sawtooth wave and inverse sawtooth wave data – still in only one line of code.

All of the parameters, including frequency, amplitude, offset, phase shift and time offset apply for those functions as well – see the previous post for details and examples for those parameters.

We are using the UliEngineering library, more specifically the UliEngineering.SignalProcessing.Simulation package:


sudo pip3 install git+

Square wave

from UliEngineering.SignalProcessing.Simulation import square_wave

data = square_wave(frequency=10.0, samplerate=10e3)

Triangle wave

from UliEngineering.SignalProcessing.Simulation import triangle_wave

data = triangle_wave(frequency=10.0, samplerate=10e3)

Sawtooth wave

from UliEngineering.SignalProcessing.Simulation import sawtooth

data = sawtooth(frequency=10.0, samplerate=10e3)

Inverse sawtooth wave

from UliEngineering.SignalProcessing.Simulation import inverse_sawtooth

data = inverse_sawtooth(frequency=10.0, samplerate=10e3)

Plotting code

This code was used to generate the plots for this post in Jupyter:

%matplotlib inline
from matplotlib import pyplot as plt"ggplot")

from UliEngineering.SignalProcessing.Simulation import square_wave

data = square_wave(frequency=10.0, samplerate=10e3)

# set_size_inches(20, 10) to make it even larger!
plt.gcf().set_size_inches(10, 5)
plt.plot(data, label="original")

Easily generate sine/cosine waveform data in Python using UliEngineering

In order to generate sinusoid test data in Python you can use the UliEngineering library which provides an easy-to-use functions in UliEngineering.SignalProcessing.Simulation:


sudo pip3 install git+

Basic example

from UliEngineering.SignalProcessing.Simulation import sine_wave
# Default: Generates 1 second of data with amplitude = 1.0 (swing from -1.0 ... 1.0)
sine = sine_wave(frequency=10.0, samplerate=10e3)
cosine = cosine_wave(frequency=10.0, samplerate=10e3)

Amplitude & offset

Use amplitude=0.5 to specify that the sine wave should swing between -0.5 and 0.5.

Use offset=2.0 to specify that the sine wave should be centered vertically around 2.0:

from UliEngineering.SignalProcessing.Simulation import sine_wave

data = sine_wave(frequency=10.0, samplerate=10e3, amplitude=0.5, offset=2.0)

Phaseshift example

You can specify the phaseshift to use – to use a 180° phaseshift, just use phaseshift=180.0

from UliEngineering.SignalProcessing.Simulation import sine_wave

original = sine_wave(frequency=10.0, samplerate=10e3)
shifted = sine_wave(frequency=10.0, samplerate=10e3, phaseshift=180.)

Time delay example

While this is functionally equivalent to phase offset, it is often convenient to specify the time delay in seconds instead of the phase shift in degrees:

from UliEngineering.SignalProcessing.Simulation import sine_wave

original = sine_wave(frequency=10.0, samplerate=10e3)
# Shifted signal is delayed 0.01s = 10 milliseconds compared to original
shifted = sine_wave(frequency=10.0, samplerate=10e3, timedelay=0.01)

Note that in the time domain, the signals appear to be shifted backwards when you use a positive timedelay value. This is in accordance with the delay naming, implying that the signal is delayed by that amount.

You can specify both phase shift and time delay, meaning that both will be applied (the offset is added)


If you want to debug your signals visually, this is the code that was used inside Jupyter to generate the plots shown above:

%matplotlib inline
from matplotlib import pyplot as plt"ggplot")

# Generate data
from UliEngineering.SignalProcessing.Simulation import sine_wave

original = sine_wave(frequency=10.0, samplerate=10e3)
shifted = sine_wave(frequency=10.0, samplerate=10e3, timedelay=0.01)

# set_size_inches(20, 10) to make it even larger!
plt.gcf().set_size_inches(10, 5)
plt.plot(original, label="original")
plt.plot(shifted, label="shifted")
plt.legend(loc=1) # Top right

Easy zero crossing detection in Python using UliEngineering

In order to perform zero crossing detection in NumPy arrays you can use the UliEngineering library which provides an easy-to-use zero_crossings function:


sudo pip3 install git+

Usage example:

from UliEngineering.SignalProcessing.Utils import zero_crossings
# To generate test data
from UliEngineering.SignalProcessing.Simulation import sine_wave

# Generate test data: 10 Hz 10ksps, 1 second (= 1D 10000 values NumPy array)
data = sine_wave(frequency=10.0, samplerate=10e3)

# Prints: [0, 500, 1000, 1500, ...]

Thanks to Jim Brissom on StackOverflow for the original solution!

How to set cv2.VideoCapture() image size in Python

Use cv2.CAP_PROP_FRAME_WIDTH and cv2.CAP_PROP_FRAME_HEIGHT in order to tell OpenCV which image size you would like.

import cv2

video_capture = cv2.VideoCapture(0)
# Check success
if not video_capture.isOpened():
    raise Exception("Could not open video device")
# Set properties. Each returns === True on success (i.e. correct resolution)
video_capture.set(cv2.CAP_PROP_FRAME_WIDTH, 160)
video_capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 120)
# Read picture. ret === True on success
ret, frame =
# Close device

Note that most video capture devices (like webcams) only support specific sets of widths & heights. Use uvcdynctrl -f to find out which resolutions are supported:

$ uvcdynctrl -f
Listing available frame formats for device video0:
Pixel format: YUYV (YUYV 4:2:2; MIME type: video/x-raw-yuv)
  Frame size: 640x480
    Frame rates: 30, 20, 10
  Frame size: 352x288
    Frame rates: 30, 20, 10
  Frame size: 320x240
    Frame rates: 30, 20, 10
  Frame size: 176x144
    Frame rates: 30, 20, 10
  Frame size: 160x120
    Frame rates: 30, 20, 10

How to take a webcam picture using OpenCV in Python

This code opens /dev/video0 and takes a single picture, closing the device afterwards:

import cv2

video_capture = cv2.VideoCapture(0)
# Check success
if not video_capture.isOpened():
    raise Exception("Could not open video device")
# Read picture. ret === True on success
ret, frame =
# Close device

You can also use cv2.VideoCapture("/dev/video0"), but this approach is platform-dependent. cv2.VideoCapture(0) will also open the first video device on non-Linux platforms.

In Jupyter you can display the picture using

import sys
from matplotlib import pyplot as plt

frameRGB = frame[:,:,::-1] # BGR => RGB


How to build rav1e on Ubuntu

This will fetch and build the current git master of rav1e. The build process has been tested on Ubuntu 18.04 with rav1e git revision 49dcaada4.

sudo apt update
sudo apt -y install cargo git perl nasm cmake clang pkg-config
# Fetch
git clone
mv rav1e rav1e-git
cd rav1e-git
git submodule update --init
# Build libaom-av1
make -j$(nproc)
# Build rav1e
cargo build --release
# Copy to parent directory
cp target/

After the build finishes, the rav1e executable is placed in the directory where you ran those commands. You can delete the rav1e-git folder

Download the resulting binary for Ubuntu 18.04 x86_64 here

GPG symmetric encryption: Passphrase on command line


You want to use GnuPG’s –symmetric encryption, but instead of interactively entering the password you want to use a command line argument with the cleartext password.


Use --batch --yes --passphrase <passphrase>:

gpg --symmetric --batch --yes --passphrase 12345 <input file>

Note that this is potentially insecure as it’s way easier to find out the command line parameters of running programs than intercepting the inputs of the interactive input dialog. Therefore, use this strategy only if neccessary.

Running Gitlab CE via docker behind a reverse proxy on Ubuntu

Similarly to my previous article about installing Redmine via docker behind a reverse proxy, this article details. Since I am running an instance of Redmine and an instance of Gitlab on the same virtual server, plus tens of other services.

While the Gitlab CE docker container is nicely preconfigured for standalone use on a dedicated VPS, running it behind a reverse proxy is not supported and will lead to a multitude of error messages – in effect, requiring lots of extra work to get up and running.

Note that we will not setup GitLab for SSH access. This is possible using this setup, but usually makes more trouble than it is worth. See this article on how to store git https passwords so you don’t have to enter your password every time.

Installing Docker & Docker-Compose

# Install prerequisites
sudo apt-get update
sudo apt-get -y install apt-transport-https ca-certificates curl software-properties-common
# Add docker's package signing key
curl -fsSL | sudo apt-key add -
# Add repository
sudo add-apt-repository -y "deb [arch=amd64] $(lsb_release -cs) stable"
# Install latest stable docker stable version
sudo apt-get update
sudo apt-get -y install docker-ce
# Install docker-compose
sudo curl -L "$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose
sudo chmod a+x /usr/local/bin/docker-compose

# Add current user to the docker group
sudo usermod -a -G docker $USER
# Enable & start docker service
sudo systemctl enable docker
sudo systemctl start docker

After running this shell script, log out & login from the system in order for the docker group to be added to the current user.

Creating the directory & docker-compose configuration

We will install Gitlab in /var/lib/gitlab which will host the data directories and the docker-compose script. You can use any directory if you use it consistently in all the configs (most importantly, docker-compose.yml and the systemd service).

# Create directories
sudo mkdir /var/lib/gitlab

Next, we’ll create /var/lib/gitlab/docker-compose.yml.

There’s a couple of things you need to change here:

  • Set gitlab_rails['gitlab_email_from'] and gitlab_rails['gitlab_email_display_name'] to whatever sender address & name you want emails to be sent from
  • Set the SMTP credentials (gitlab_rails['smtp_address'], gitlab_rails['smtp_port'], gitlab_rails['smtp_user_name'], gitlab_rails['smtp_password'] & gitlab_rails['smtp_domain']) to a valid SMTP server. In rare cases you also have to change the other gitlab_rails['smtp_...'] settings.
  • You need to change every 4 occurrences of to your domain.
  • The ports configuration, in this case '9080:80' means that Gitlab will be mapped to port 9080 on the local PC. This port is chosen somewhat arbitarily – as we will run Gitlab behind an nginx reverse proxy, the port does not need to be any port in particular (as long as you use the same port everywhere), but it may not be used by anything else. You can use any port here, provided that it’s not used for anything else. Leave 80 as-is and only change 9080 if required.
   image: 'gitlab/gitlab-ce:latest'
   restart: always
   hostname: ''
       external_url ''
       letsencrypt['enabled'] = false
       # Email
       gitlab_rails['gitlab_email_enabled'] = true
       gitlab_rails['gitlab_email_from'] = ''
       gitlab_rails['gitlab_email_display_name'] = 'My GitLab'
       # SMTP
       gitlab_rails['smtp_enable'] = true
       gitlab_rails['smtp_address'] = ""
       gitlab_rails['smtp_port'] = 25
       gitlab_rails['smtp_user_name'] = ""
       gitlab_rails['smtp_password'] = "yourSMTPPassword"
       gitlab_rails['smtp_domain'] = ""
       gitlab_rails['smtp_authentication'] = "login"
       gitlab_rails['smtp_enable_starttls_auto'] = true
       gitlab_rails['smtp_tls'] = true
       gitlab_rails['smtp_openssl_verify_mode'] = 'none'
       # Reverse proxy nginx config
       nginx['listen_port'] = 80
       nginx['listen_https'] = false
       nginx['proxy_set_headers'] = {
         "X-Forwarded-Proto" => "https",
         "X-Forwarded-Ssl" => "on",
         "Host" => "",
         "X-Real-IP" => "$$remote_addr",
         "X-Forwarded-For" => "$$proxy_add_x_forwarded_for",
         "Upgrade" => "$$http_upgrade",
         "Connection" => "$$connection_upgrade"
     - '9080:80'
     - '/var/lib/gitlab/config:/etc/gitlab'
     - '/var/lib/gitlab/logs:/var/log/gitlab'
     - '/var/lib/gitlab/data:/var/opt/gitlab'

Setting up the systemd service

Next, we’ll configure the systemd service in /etc/systemd/system/gitlab.service.

Set User=... to your current user in the [Service] section.


# Shutdown container (if running) when unit is stopped
ExecStartPre=/usr/local/bin/docker-compose -f /var/lib/gitlab/docker-compose.yml down -v
# Start container when unit is started
ExecStart=/usr/local/bin/docker-compose -f /var/lib/gitlab/docker-compose.yml up
# Stop container when unit is stopped
ExecStop=/usr/local/bin/docker-compose -f /var/lib/gitlab/docker-compose.yml down -v


After creating the file, we can enable and start the redmine service:

sudo systemctl enable gitlab
sudo systemctl start gitlab

The output of sudo systemctl start gitlab should be empty. In case it is

Job for gitlab.service failed because the control process exited with error code.
See "systemctl status gitlab.service" and "journalctl -xe" for details.

you can debug the issue using journalctl -xe and journalctl -e

The first startup usually takes about 10 minutes, so grab at least one cup of coffee. You can follow the progress using journalctl -xefu gitlab. Once you see lines like

Dec 17 17:28:04 instance-1 docker-compose[4087]: gitlab_1  | {"method":"GET","path":"/-/metrics","format":"html","controller":"MetricsController","action":"index","status":200,"duration":28.82,"view":22.82,"db":0.97,"time":"2018-12-17T17:28:03.252Z","params":[],"remote_ip":null,"user_id":null,"username":null,"ua":null}

the startup is finished.

Now you can check if GitLab is running using

wget -O- http://localhost:9080/

(if you changed the port config before, you need to use your custom port in the URL).

If it worked, it will show a debug message output. Since gitlab will automatically redirect you to your domain ( in this example) you should see something like

--2018-12-17 17:28:32--  http://localhost:9080/
Resolving localhost (localhost)...
Connecting to localhost (localhost)||:9080... connected.
HTTP request sent, awaiting response... 302 Found
Location: [following]
--2018-12-17 17:28:32--
Resolving (
Connecting to (||:443... failed: Connection refused.

Since we have not setup nginx as a reverse proxy yet, it’s totally fine that it’s saying connection refused. The redirection worked if you see the output listed above.

Setting up the nginx reverse proxy (optional but recommended)

We’ll use nginx to proxy the requests from a certain domain (Using Apache, if you use it already, is also possible but it is outside the scope of this tutorial to tell you how to do that). Install it using

sudo apt -y install nginx

First, you’ll need a domain name with DNS being configured. For this example, we’ll assume that your domain name is ! You need to change it to your domain name!

First, we’ll create the config file in /etc/nginx/sites-enabled/gitlab.conf. Remember to replace by your domain name! If you use a port different from 9080, replace that as ewll.

map $http_upgrade $connection_upgrade {
    default upgrade;
    '' close;

server {

    access_log /var/log/nginx/gitlab.access_log;
    error_log /var/log/nginx/gitlab.error_log info;

    location / {
        proxy_pass; # docker container listens here
        proxy_read_timeout 3600s;
        proxy_http_version 1.1;
        # Websocket connection
        proxy_set_header Upgrade $http_upgrade;
        proxy_set_header Connection $connection_upgrade;

    listen 80;

Now run sudo nginx -t to test if there are any errors in the config file. If everything is alright, you’ll see

nginx: the configuration file /etc/nginx/nginx.conf syntax is ok
nginx: configuration file /etc/nginx/nginx.conf test is successful

Once you have fixed all errors, if any, run sudo service nginx reload to apply the configuration.

We need to setup a Let’s Encrypt SSL certificate before we can check if Gitlab is working:

Securing the nginx reverse proxy using Let’s Encrypt

First we need to install certbot and the certbot nginx plugin in order to create & install the certificate in nginx:

sudo apt -y install python3-certbot python3-certbot-nginx

Fortunately certbot automates most of the process of installing & configuring SSL and the certificate. Run

sudo certbot --nginx

It will ask you to enter your Email address and agree to the terms of service and if you want to receive the EFF newsletter.

After that, certbot will ask you to select the correct domain name:

Which names would you like to activate HTTPS for?
Select the appropriate numbers separated by commas and/or spaces, or leave input
blank to select all options shown (Enter 'c' to cancel):

In this case, there is only one domain name (there will be more if you have more domains active on nginx!).

Therefore, enter 1 and press enter. certbot will now generate the certificate. In case of success you will see an output including a line like

Deploying Certificate to VirtualHost /etc/nginx/sites-enabled/

Now it will ask you whether to redirect all requests to HTTPS automatically:

Please choose whether or not to redirect HTTP traffic to HTTPS, removing HTTP access.
1: No redirect - Make no further changes to the webserver configuration.
2: Redirect - Make all requests redirect to secure HTTPS access. Choose this for
new sites, or if you're confident your site works on HTTPS. You can undo this
change by editing your web server's configuration.
Select the appropriate number [1-2] then [enter] (press 'c' to cancel): 

Choose Redirect here: Type 2 and press enter. Now you can login to GitLab and finish the installation.

You need to renew the certificate every 3 months for it to stay valid, and run sudo service nginx reload afterwards to use the new certificate. If you fail to do this, users will see certificate expired error messages and won’t be able to access Redmine easily! See this post for details on how to mostly automate this process!

Setting up Redmine

Now you can open a browser and have a first look at your new GitLab installation:

Set the new password and then login with the username root and your newly set password.

After that, open the admin area at the top by clicking at the wrench icon in the purple navigation bar at the top.

At the navigation bar at the left, click on Settings (it’s at the bottom – you need to scroll down) and then click on General.

Click the Expand button to the right of Visibility and access controls. Scroll down until you see Enabled Git access protocols and select Only HTTP(S) in the combo box.

Then click the green Save changes button.

Since we have now disabled SSH access (which we didn’t set up in the first place), you can now use GitLab. A good place to start is to create a new project and try checking it out. See this article on how to store git https passwords so you don’t have to enter your git password every time.

Note: If GitLab doesn’t send emails, check config/gitlab.rb, search for smtp and if neccessary fix the SMTP settings there. After that, sudo systemctl stop gitlab && sudo systemctl start gitlab