Categories describing the post, video, pictures, audio…

Smol bash script for finding oversize media files

Friday, September 2, 2022 

Sometimes you want to know if you have media files that are taking up more than their fair share of space.  You compressed the file some time ago in an old, inefficient format, or you just need to archive the oversize stuff, this can help you find em.  It’s different from file size detection in that it uses mediainfo to determine the media file length and wc -c to get the size, and from that computes the total effective data rate. All math is done with bc, which is usually installed. Files are found recursively from the starting point (passed as first argument) using find.

basic usage would be:

./find-high-rate-media.sh /search/path/tostart/ [min rate] [min size]

The script will then report media with a rate higher than minimum and size larger than minimum as a tab delimited list of filenames, calculated rate, and calculated size. Piping the output to a file, output.csv, makes it easy to sort and otherwise manipulate in LibreOffice Calc.

Save the file as a name you like (such as find-high-rate-media.sh) and # chmod  +x find-high-rate-media.sh and off you go.

The code (also available here):

#!/usr/bin/bash

# check arguments passed and set defaults if needed
# No argument given?
if [ -z "$1" ]; then
  printf "\nUsage:\n\n  pass a starting point and min data rate in kbps and min size like /media/gessel/datas/Downloads/ 100 10 \n\n" 
  exit 1
fi

if [ -z "$2" ]; then
  printf "\nUsage:\n\n  returning files with data rate greater than default max of 100 kbps  \n\n" 
  maxr=100
  else
        maxr=$2
        echo -e "\n\n  returning files with dara rate greater than " $maxr " kbps  \n\n" 
fi

if [ -z "$3" ]; then
  printf "\nUsage:\n\n  returning files with file size greater than default max of 100 MB  \n\n" 
  maxs=10
  else
        maxs=$3
        echo -e "\n\n  returning files with dara rate greater than " $maxs " MB  \n\n" 
fi

# multipliers to get to human readable values
msec="1000"
kilo="1024"

echo -e "file path \t rate kbps \t size MB"

# search for files with the extensions enumerated below
# edit this list to your needs (e.g. -iname \*.mp3 or whatever
# the -o means "or", -iname (vs -name) means case independent so
# it will find .MKV and .mkv.
# then pass each file found to check if the data rate is 
# above the min rate of concern and then if the files size is 
# above the min size of concern, and if so, print the result
 
find "$1" -type f \( -iname \*.avi -o -iname \*.mkv -o -iname \*.mp4 -o -iname \*.wmv \) -print0 | while read -rd $'\0' file
do
    size="$(wc -c  "$file" |  awk '{print $1}')"
    duration="$(mediainfo --Inform="Video;%Duration%" "$file")"
    seconds=$(bc -l <<<"${duration}/${msec}")
    sizek=$(bc -l <<<"scale=1; ${size}/${kilo}")
    sizem=$(bc -l <<<"scale=1; ${sizek}/${kilo}")
    rate=$(bc -l <<<"scale=1; ${sizek}/${seconds}")
    if (( $(bc  <<<"$rate > $maxr") )); then
        if (( $(bc  <<<"$sizem > $maxs") )); then
            echo -e $file "\t" $rate "\t" $sizem
        fi
    fi
done

Results might look like

file path 	 rate kbps 	 size MB
/media/my kitties playing.mkv 	 1166.0 	 5802.6
/media/cats jumping.mkv 	 460.1 	 2858.9
/media/fuzzy kitties.AVI 	 1092.7 	 7422.0

Another common task is renaming video files with some key stats on the contents so they’re easier to find and compare. Linux has limited integration with media information (dolphin is somewhat capable, but thunar not so much). This little script also leans on mediainfo command line to append the following to the file name of media files recursively found below a starting directory path:

  • WidthxHeight in pixels (1920×1080)
  • Runtime in HH-MM-SS.msec (02-38-15.111) (colons aren’t a good thing in filenames, yah, it is confusingly like a date)
  • CODEC name (AVC)
  • Datarate (1323kbps)

For example

kittyplay.mp4 -> kittyplay_1280x682_02-38-15.111_AVC_154.3kbps.mp4

The code is also available here.

#!/usr/bin/bash
PATH="/home/gessel/.local/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin"

############################# USE #######################################################
# find_media.sh /starting/path/ (quote path names with spaces)
########################################################################################

# No argument given?
if [ -z "$1" ]; then
  printf "\nUsage:\n  pass a starting point like \"/Downloads/Media files/\" \n" 
  exit 1
fi

msec="1000"
kilo="1024"
s="_"
x="x"
kbps="kbps"
dot="."

find "$1" -type f \( -iname \*.avi -o -iname \*.mkv -o -iname \*.mp4 -o -iname \*.wmv \) -print0 | while read -rd $'\0' file
do
  if [[ -f "$file" ]]; then
    size="$(wc -c  "$file" |  awk '{print $1}')"
    duration="$(mediainfo --Inform="Video;%Duration%" "$file")"
    seconds=$(bc -l <<<"${duration}/${msec}")
    sizek=$(bc -l <<<"scale=1; ${size}/${kilo}")
    sizem=$(bc -l <<<"scale=1; ${sizek}/${kilo}")
    rate=$(bc -l <<<"scale=1; ${sizek}/${seconds}")
    codec="$(mediainfo --Inform="Video;%Format%" "$file")"
    framerate="$(mediainfo --Inform="General;%FrameRate%" "$file")"
    rtime="$(mediainfo --Inform="General;%Duration/String3%" "$file")"
    runtime="${rtime//:/-}"
    width="$(mediainfo --Inform="Video;%Width%" "$file")"
    height="$(mediainfo --Inform="Video;%Height%" "$file")"
    fname="${file%.*}"
    ext="${file##*.}"
    $(mv "$file" "$fname$s$width$x$height$s$runtime$s$codec$s$rate$kbps$dot$ext")
  fi
done

If you don’t have mediainfo installed,

sudo apt update
sudo apt install mediainfo
Posted at 10:18:58 GMT-0700

Category: AudioHowToLinuxvideo

Deep Learning Image Compression: nearly 10,000:1 compression ratio!

Tuesday, June 28, 2022 

Here disclosed is a novel compression technique I call Deep Learning Semantic Vector Quantization (DLSVC) that achieves in this sample 9,039:1 compression! Compare this to JPEG at about 10:1 or even HEIC at about 20:1, and the absolutely incredible power of DL image compression becomes apparent.

Before I disclose the technique to achieve this absolutely stunning result, we need to understand a bit about the psychovisual mechanisms that are being exploited. A good starting point is thinking about:

It was a dark and stormy night and all through the house not a creature was stirring, not even a mouse.

I’m sure each person reading this develops an internal model, likely some combination of a snug, warm indoor Christmas scene while outside a storm raged, or something to that effect derived from the shared cultural semantic representation: a scene with a great deal of detail and complexity, despite the very short text string. The underlying mechanism is a sort of vector quantization where the text represents a series of vectors that semantically reference complex culturally shared elements that form a type of codebook.

If a person skilled at drawing were to attempt to represent this coded reference visually, it is likely the result would be recognizable to others as a representation of the text; that is, the text is an extremely compact symbolic representation of an image.

So now lets try a little AI assisted vector quantization of images.  We can start with the a generic image from Wikipedia:

Next we use AI to reduce the image to a symbolic semantic representation.  There are far more powerful AI systems available, but we’ll use one that allows normal people to play with it, @milhidaka’s caption generator on github:

This is a cat sitting on top of a wooden bench which we can LZW compress assuming 26 character text to a mere 174 bits or 804D22134C834638D4CE3CE14058E38310D071087. That’s a pretty compact representation of an image!  The model has been trained to understand a correlation between widely shared semantic symbols and elements of images and can reduce an image to a human-comprehensible, compact textual representation, effectively a lossy coding scheme referencing a massive shared codebook with complex grammatical rules that further increase the information density of the text.

Decoding those 174 bits back to the original text, we can feed them into an image generating generative AI model, like DALL·E mini and we get our original image back by reversing the process leveraging a different semantic model, but one also trained to the same human language.

It is clearly a lossy conversion, but here’s the thing: so too is human memory lossy.  If you saw the original scene and 20 years later, someone said, “hey, remember that time we saw the cat sitting on a wooden bench in Varna, look, here’s a picture of it!” and showed you this picture, I mean aside from the funny looking cat like blob, you’d say “oh, yeah, cool, that was a cute cat.”

Using the DALL·E mini output as the basis for computing compression rather than the input image which could be arbitrarily large, we have 256×256×8×3 bits output = 1,572,864 bits to represent the output image raw.

WebP “low quality” compressing the 256×256 image yields a file of 146,080 bits or 10.77:1 compression.

My technique yields a compressed representation of 174 bits or 9,039:1 compression. DALL·E 2‘s 1024×1024 output size should yield in 144,624:1 compression.

Pied Piper got nothin’ on this!

Posted at 11:51:14 GMT-0700

Category: HowToLinuxphototechnology

Audio Compression for Speech

Tuesday, June 28, 2022 

Speech is generally a special class of audio files where compression quality is rated more on intelligibility than on fidelity, though the two related the former may be optimized at the expense of the latter to achieve very low data rates.  A few codecs have emerged as particularly adept at this specific class: Speex, Opus, and the latest, Google’s Lyra, a deep learning enhanced codec.

Lyra is focused on Android and requires a bunch of Java cruft to build and needs debugging.  It didn’t seem worth the effort, but I appreciate the Deep Learning based compression, it is clearly the most efficient compression possible.

I couldn’t find a quick whatcha-need-to-know is kind of summary of the codecs, so maybe this is useful:

Opus

On Ubuntu (and most Linux distros) you can install the Opus codec and supporting tools with a simple

# sudo apt install opus-tools

If you have ffmpeg installed, it provides a framework for dealing with IO and driving libopus from the command line like:

# ffmpeg -i infile.mp3 -codec:a libopus -b:a 8k -cutoff 8000 outfile.opus

Aside from infile.(format) and outfile.opus, there are two command line options that make sense to mess with to get good results: the bitrate -b:a (bit rate) and the -cutoff (frequency), which must be 4000 (narrowband), 6000 (mediumband), 8000 (wideband), 12000 (super wideband), or 20000 (fullband).  The two parameters work together and for speech limiting bandwidth saves bits for speech.

There are various research papers on the significance of frequency components in speech intelligibility that range from about 4kHz to about 8kHz (and “sometimes higher”).  I’d argue useful cutoffs are 6000 and 8000 for most applications.    The fewer frequency components fed into the encoder, the more bps remain to encode the residual.  There will be an optimum value which will maximize the subjective measure of intelligibility times the objective metric of average bit rate that has to be determined empirically for recording quality, speaker’s voice, and transmission requirements.

In my tests, my sample, the voice I had to work with an 8kHz bandwidth made little perceptible difference to the quality of speech.  6kbps VBR (-b:a 6k) compromised intelligibility, 8k did not, and 24k was not perceptibly compromised from the source.

one last option to consider might be the -application, which yields subtle differences in encoding results.  The choices are voip which optimizes for speech, audio (default) which optimizes for fidelity, and lowdelay which minimizes latency for interactive applications.

# ffmpeg -i infile.mp3 -codec:a libopus -b:a 8k -application voip -cutoff 8000 outfile.opus

VLC player can play .opus files.

Speex

AFAIK, Speex isn’t callable by ffmpeg yet, but the speex installer has a tool speexenc that does the job.

# sudo apt install speex

Speexenc only eats raw and .wav files, the latter somewhat more easily managed.  To convert an arbitrary input to wav, ffmpeg is your friend:

# ffmpeg -i infile.mp3 -f wav -bitexact -acodec pcm_s16le -ar 8000 -ac 1 wavfile.wav

Note the -ar 8000 option.  This sets the sample rate to 8000 – Speexenc will yield unexpected output data rates unless sample rates are 8000, 16000, or 32000, and these should correlate to the speexenc bandwidth options that will be used in the compression step (speexenc doesn’t transcode to match): -n “narroband,” -w “wideband,” and -u “ultrawideband”

# speexenc -n --quality 3 --vbr --comp 10 wavfile.wav outfile.spx

This sets the bandwidth to “narrow” (matching the 8k input sample rate), the quality to 3 (see table for data rates), enables VBR (not enabled by default with speex, but it  is with Opus), and the “complexity” to 10 (speex defaults to 3 for faster encode, Opus defaults to 10), thus giving a pretty head-to-head comparison with the default Opus settings.

VLC can also play speex .spx files. yay VLC.

Results

The result is an 8kbps stream which is to my ear more intelligible than Opus at 8kbps – not 😮 better, but 😐 better.  This is atypical, I expected Opus to be obviously better and it wasn’t for this sample.  I didn’t carefully evaluate the -application voip option, which would likely tip the tables results.  Clearly YMMV so experiment.

Posted at 10:23:52 GMT-0700

Category: AudioHowToLinuxtechnology

Audio Processing Workflow

Monday, April 18, 2022 

I prefer local control of media data, the rent-to-listen approach of various streaming services is certainly convenient, but pay-forever, you get what we think you should models don’t appeal to me. Over the decades, I’ve converted various eras of my physical media to digital formats using different standards that were in vogue at the time and with different emphasis on various metadata tags yielding a rather heterogeneous collection with some annoying incompatibilities that sometimes show up, for example using the Music plugin with NextCloud streaming via Subsonic to Sublime Music or Ultrasonic on Android.  I spent some time poking around to find a set of tools that satisfied my preferences for organization and structure and filled in a missing gap or two; this is what I’m doing these days and what with.

The steps outlined here are tuned to my particular use case:

  • Linux-based process.
  • I prefer mp3 to aac or flac because the format is widely compatible.  mp3 is pretty clearly inferior to aac for coding efficiency (aac produces better sound with less bits) and aac has some cool features that mp3 doesn’t but for my use compatibility wins.
  • My ears ain’t what they used to be.  I’m not sure I could ever reliably have heard the difference between 320 CBR and 190 VBR, but I definitely can’t now and less data is less data.
  • I like metadata and the flexibility in organization it provides, and like it standardized.

So to scratch that itch, I use the following steps:

  • Convert FLAC/high-data rate mp3s to VBR (about 190 kbps) with ffmpeg
  • Fix MP3 meta info wierdsies with MP3 Diags
  • Add Replay Gain tags with loudness-scanner
  • Add BPM tags with bpm-tag from bpm-tools
  • Use Puddletag to:
    • Clean any stray tags
    • Assign Genre, Artist, Year, Album, Disk Number, Track, Title, & Cover
    • Apply a standard replace function to clean text of weird characters
    • Refile and re-name in a most-os-friendly way
  • Clean up any stray data in the file system.

Links to the tools at the bottom.

Convert FLAC to MP3 with ffmpeg

The standard tool for media processing is ffmpeg.  This works for me:

find . -depth -type f -name "*.flac" -exec ffmpeg -i {} -q:a 2  -c:v copy -map_metadata 0 -id3v2_version 3 -write_id3v1 1  {}.mp3 \;

A summary:

find                  unix find command to return each found file one-by-one
.                     search from the current directory down
-depth                start at the bottom and work up
-type f               find only files (not directories)
-name "*.flac"        files that end with .flac
-exec ffmpeg          pass each found file to ffmpeg
-i {}                 ffmpeg takes the found file name as input
-q:a 2                VBR MP3 170-210 kbps
-c:v copy             copy the video stream (usually the cover image)
-map_metadata 0       copy the metadata from input to global metadata of output
-id3v2_version 3      write ID3v2.3 tag format (more compatible than ID3v2.4)
-write_id3v1 1        also write old style ID3v1 tags (maybe useless)
{}.mp3 \;             write output file (which yields "/original/filename.flac.mp3")

For album encodes with a .cue or in other formats where the above would yield one giant file, Flacon is your friend.  I would use two steps: single flac -> exploded flac, then the ffmpeg encoder myself just for comfort with the encoding parameters.

Convert high data rate CBR MP3 to VBR

Converting high data rate CBR files requires a bit more code to detect that a given file is high data rate and CBR, for which I wrote a small bash script that leverages mediainfo to extract tags from the source file and validate.

#!/bin/bash

# first make sure at least some parameter was passed, if not echo some instructions
if [ $# -eq 0 ]; then
    echo "pass a file name or try: # find . -type f -name "*.mp3" -exec recomp.sh {} \;"
    exit 1
fi

# assign input 1 to “file” to make it a little easier to follow
file=$1

# get the media type, the bitrate, and the encoding mode and assign to variables
type=$(mediainfo --Inform='General;%Format/String%' "$file")
brate=$(mediainfo --Inform='General;%OverallBitRate/String%' "$file" |& grep -Eo [0-9]+)
mode=$(mediainfo --Inform='Audio;%BitRate_Mode/String%' "$file")

# first check: is the file an mpeg audio file, if not quit
if [[ "$type" != "MPEG Audio" ]]; then
    echo $file skipped, not valid audio
    exit 0
    fi

# second check: if the file is already VBR, move on.  
if [[ "$mode" = "Variable" ]]; then
    echo $file skipped, already variable
    exit 0
    fi

# third check: the output will be 170-210, no reason to expand low bit rate files
if [[ "$brate" -gt 221 ]]
    then
        ffmpeg -hide_banner -loglevel error -i "$file"  -q:a 2  -c:v copy -map_metadata 0 -id3v2_version 3 -write_id3v1 1  "${file}.mp3"
        rm "${file}"
        mv "${file}.mp3" "${file}"
        echo $file recompressed to variable
    fi
exit

I named this script “~/projects/recomp/recomp.sh” and call it with

find . -depth -type f -name "*.mp3" -exec ~/projects/recomp/recomp.sh {} \;

which will scan down through all sub-directories and find files with .mp3 extensions, and if suitable, re-compress them to VBR as above. Yes, this is double lossy and not very audiophile, definitely prioritizing smaller files over acoustic fidelity which I cannot really hear anyway.

Fix bad data with MP3 Diags

MP3 Diags is a GUI tool for cleaning up bad tags.  It is pretty solid and hasn’t mangled any of my files yet.  It has two basic functions: passively highlight missing useful tags (replaygain, cover image, etc) and actively fix messed up tags which is a file-changing operation so make backups if needed.  I generally just click the tools buttons “1”–”4″ and it seems to do the right thing. Thanks Ciobi!

Install was easy on Ubuntu:

sudo apt install mp3diags

Add ReplayGain Tags

To bulk add (or update) ReplayGain tags, I find loudness-scanner very easy.  I just use the droplet version and drop folders on it. The defaults do the right thing, computing track and album gain by folder. The droplet pops up a confirmation dialog which can be lost on a busy desktop, remember it.  Click to apply the tags then wait for it to finish before closing that tag list window or it will seg fault.  The only indication is in the command prompt window used to launch it, which shows “….” as it progresses and when the dots stop, you can close the tags window.

I built it from source – these steps did the needful for me:

git clone https://github.com/jiixyj/loudness-scanner.git
cd loudness-scanner
git submodule init
git submodule update
mkdir build
cd build
cmake ..
make
sudo make install

Then launch the droplet with

~/projects/loudness-scanner/build/loudness-drop-gtk

Add Beats Per Minute Tags

Beats per minute calcs are mostly useful for DJ types, but I use them to easily sort music for different moods or for exercise.  The calculation seems a bit arbitrary for things like speech or classical, but for those genres where BPM is relevant, bpm-tools seems to yield results that make sense.

Install with

sudo apt-get install libsox-fmt-mp3 bpm-tag

Then write tags with (the -f option overwrites existing tags).

find . -name "*.mp3" -exec bpm-tag -f {} \;

Puddletag

Back in my Windows days, I really liked MP3Tag.  I  was really happy to find puddletag, an mp3tag inspired linux variant.  It’s great, does everything it should.  I wish I had something like this for image metadata editing: the spreadsheet format is very easy to parse.  One problem I had was the deunicode tool wasn’t decoding for me, so I wrote my own wee function to extend the functions.py by calling the unidecode function.  only puddlestuff/functions.py needs to be patched to add this useful decode feature.  UTF8 characters are well supported in tags, but not in all file structures and since the goal is compatibility, mapping them to fairly intelligible  ASCII characters is useful.

This works with the 2.1.1 version.
Below is a patch file to show the very few changes needed.

--- functions.py.bak    2022-04-14 13:58:47.937873000 +0300
+++ functions.py        2022-04-14 16:49:23.705786696 +0300
@@ -43,6 +43,7 @@
 from mutagen.mp3 import HeaderNotFoundError
 from collections import defaultdict
 from functools import partial
+from unidecode import unidecode
 
 import pyparsing
 
@@ -769,6 +770,10 @@
     cleaned_fn = unicodedata.normalize('NFKD', t_fn).encode('ASCII', 'ignore')
     return ''.join(chr(c) for c in cleaned_fn if chr(c) in VALID_FILENAME_CHARS)
 
+# hack by David Gessel
+def deunicode(text):
+    dutext = unidecode(text)
+    return (dutext)
 
 def remove_dupes(m_text, matchcase=False):
     """Remove duplicate values, "Remove Dupes: $0, Match Case $1"
@@ -1126,7 +1131,8 @@
     'update_from_tag': update_from_tag,
     "validate": validate,
     'to_ascii': to_ascii,
-    'to_num': to_num
+    'to_num': to_num,
+    'deunicode': deunicode
 }
 
 no_fields = [filenametotag, load_images, move, remove_except,

I use the “standard” action to clean up file names with a few changes:

  • In “title” and “album” I replace ‘ – ‘ with ‘–‘
  • in all, I RegExp replace ‘(\s)’ with ‘ ‘  – all blank space with a regular space.
  • I replace all %13 characters with a space
  • I RegExp ‘(\s)+’ with ‘ ‘ – all blank runs with a single space
  • Trim all to remove leading and ending spaces.

My tag->filename function looks like this craziness which reduces the risk of filename misbehavior on most platforms:

~/$validate(%genre%,_,/\*?;”|: +<>=[])/$validate($deunicode(%artist%),_,/\*?;”|: +<>=[])/%year%--$left($validate($deunicode(%album%),_,/\*?;”|: +<>=[]),136)$if(%discnumber%, --D$num(%discnumber%,2),"")/$left($num(%track%,2)--$validate($deunicode(%title%),_,/\*?;”|: +<>=[]),132)

Puddletag is probably in your repository. To mod the code, I first installed from source per the puddletag instructions, but had to also add unidecode to my system with

pip install unidecode

Last File System Cleanups

The above steps should yield a clean file structure without leading or trailing spaces, indeed without any spaces at all, but in case it doesn’t the rename function can help.  I installed it with

sudo apt install rename

This is useful to, for example, normalize errant spelling of mp3 – for example Mp3 or MP3 or, I suppose, mP3.

find . -depth -exec rename 's/\.mp3$/.mp3/i' {} +
aside from parameters explained previously
's/A/B/'            substitute B for each instance of A
\.                  escaped "." because "." has special meaning
$                   match end of string - so .mp3files won't match, but files.mp3 does
i                   case insensitive match (.mp3 .MP3 .mP3 .Mp3 all match)

More details on rename.

The following commands clean up errant spaces before after and repeated:

find . -depth -exec rename 's/^ *//' {} +
find . -depth -exec rename 's/ *$//' {} +
find . -depth -exec rename 's/\s+/_/g' {} +

If moving files around results in empty directories (or empty files, which shouldn’t happen) then they can be cleaned with

find . -depth -type d -empty -print -delete
find . -depth -type f -empty -print -delete

Players

If workflow preferences are highly personal, player prefs seem even more so.  Mine are as follows:

For Local Playback on a PC: Quod Libet

I like to sort by genre, artist, year, and album and Quod Libet makes that as easy as in foobar2000 did back in the olde days when Windows was still an acceptable desktop OS.  Those days are long, long over and while I am still fond of the foobar2000 approach, Quod Libet doesn’t need Wine.

Alas, one shortcoming still is that Quod Libet does not support subsonic or ampache.  That’s too bad because I really like the UI/UX.

For Subsonic Streaming on a PC: Sublime Music

Not the text editor, the music app.  It is pretty good, more pretty than Quod Libet and in a way that doesn’t entirely appeal to me, but it seems to work fairly well with NextCloud and is the best solution I’ve found so far.  It tends to flow quite a few errors and I see an odd bug where album tile selection jumps around, but it seems to work and a local program linking back to a server is generally more performant than in browser, but that’s also an option (see below) or run foobar2000 in Wine, perhaps even as an (ugh!) snap.

In Browser: NextCloud Music

Nextcloud’s Music app is one of those that imposes a sorting model that doesn’t work for me – not at all foobar2000ish – and so I don’t really use it much, but there are times, working on site for example, that a browser window is easiest.  I find I often have to rebuild the music database after changes.  Foam or Ample might be more satisfying choices functionally and aesthetically and can connect to the backend provided by Music.

Mobile: Ultrasonic

Ultrasonic works pretty well for me and seems to connect fairly reliably to my NextCloud server even in low bandwidth situations (though, obviously, not fast enough to actually listen to anything, but it doesn’t barf.)  Power Ampache might be another choice still currently developed (but I haven’t tried it myself).  Subsonic also worked with NextCloud, but I like Ultrasonic better and it is still actively developed.

If you’re on iOS instead of Android (congratulations on the envy your overpriced corporate icon inspires in the less fortunate) you almost certainly stick exclusively with your tribal allegiance and have no need for media outside of iTunes/Apple TV approved content.

Tools:

Players:

Posted at 17:59:27 GMT-0700

Category: AudioHowToLinuxtechnology

South Lake Tahoe Caldor Fire Timelapse

Friday, September 3, 2021 

Sentinalhub Playground is an excellent resource for near real time, albeit not quite google earth 1m resolution, satellite images.  One of the cool features is being able to adjust the mapping of the satellite bands to RGB outputs.  For example, using Sentinel-2 L2A image data of South Lake Tahoe between 2021-08-17 and 2021-09-01 and remapping the 2190nm (SWIR2) to red, which tends to highlight fires though isn’t thermal, 783nm to green, a vegetation band (though it is NIR to humans) to make vegetation cover more obvious, and 443nm to blue instead of 490nm as shorter wavelengths tend to be scattered more by aerosols and smoke the fire line (bright red) and smoke (obvs) is very visible while vegetation is (false) green. Burnt earth shows as dark red, compared to bare ground, which tends to show tan in this mapping, thus revealing the current line of fire, the recently burned areas, and the wind direction carrying smoke, which tends to correlate with the advancing line, and fuel (vegetation) still standing.

Sentinel-2 L2A image on 2021-09-01 if South Lake Tahoe Caldor Fire

 

Then using the history controller to generate and save a sequence of stills, we can animate the progress of the fire with a simple FFMPEG command:

ffmpeg -framerate 1 -pattern_type glob -i '*.jpg' -vf crop=1754:1146 -c:v libx264 -r 30 -pix_fmt yuv420p fire.mp4

and you get:

 

Posted at 08:17:00 GMT-0700

Category: EventsGeopostmaptechnologyvideoweather

Compile and install Digikam on Ubuntu 20.04 Focal (21.10 too)

Friday, March 26, 2021 

Digikam is an incredibly powerful media management tool that integrates a great collection of powerful media processing projects into a single, fairly nice and moderately intuitive user interface. The problem is that it make use of SO many projects and libraries that installation is quite fragile and most distributions are many years out of date – that is a typical sudo apt install digikam will yield version 4.5 while release is (as of this writing) 7.5.

In particular, this newer version has face detection that runs LOCALLY – not on Google or Facebook’s servers – meaning you don’t have to trade your personal photos and all the data implicit in them to a data broker to make use of such a useful tool.  Sure, Google once bought and then improved Picasa Desktop which gave you this function, but then they realized this was cutting into their data harvesting business and discontinued Picasa and tried to convince people to let them look at all their pictures with Google Photos.  We really, really need to make personal data a toxic asset, such an intolerable liability that any company that holds any personal data has negative value.  But until then, use FOSS software on your own hardware where ever possible.

You can compile the latest version on Ubuntu 20.04 Focal Fossa, though not exactly painlessly, or you can install the flatpak easily. I hate flatpaks with a passion, so I went through the exercise and found what appears to be stable success with the following procedure which yielded a fully featured digikam with zero dependency errors or warnings and all features enabled using MariaDB as a backend.

Updating Ubuntu from 20.04 to 21.10 (probably any other major update too) will (as typical) break a ton of stuff.  For “reasons” the updater uninstalls all sorts of things like MariaDB and many of the dependencies.  Generally, as libraries change versions, recompiling is required.  This is so easy with FreeBSD ports…

Install and configure MariaDB

sudo apt update
sudo apt install mariadb-server
sudo mysql_secure_installation

The secure options are all good, accept them unless you know better.

Start the server (if it isn’t)

sudo systemctl start mariadb.service
sudo systemctl enable mariadb --now
sudo systemctl status mariadb.service

Do some really basic config:

sudo nano /etc/mysql/mariadb.conf.d/50-server.cnf

and set:

character-set-server = utf8mb4
collation-server = utf8mb4_general_ci
default_storage_engine = InnoDB

Switch to mariadb and create an admin user account and (I’d suggest) one for digikam.  It seems this has to be done before the first connect and can’t be fixed after.  You’ll probably want to use a different ‘user’ than I did, but feel free.

sudo mariadb
CREATE USER 'gessel'@'localhost' IDENTIFIED BY 'password';
GRANT ALL ON *.* TO 'gessel'@'localhost' IDENTIFIED BY 'password';
CREATE DATABASE digikam;
GRANT ALL PRIVILEGES ON digikam.* TO 'gessel'@'localhost';
FLUSH PRIVILEGES;

should correctly create the correct user – though check the instructions tab on the database connection options pane for any changes if you’re following these instructions for install of a later version. You will need the socket location to connect to the database so before exit; run:

mysqladmin -u admin -p version

Should yield something like:

Enter password: 
mysqladmin  Ver 9.1 Distrib 10.3.25-MariaDB, for debian-linux-gnu on x86_64
Copyright (c) 2000, 2018, Oracle, MariaDB Corporation Ab and others.

Server version		10.3.25-MariaDB-0ubuntu0.20.04.1
Protocol version	10
Connection		Localhost via UNIX socket
UNIX socket		/var/run/mysqld/mysqld.sock
Uptime:			5 hours 26 min 6 sec

Threads: 29  Questions: 6322899  Slow queries: 0  Opens: 108  Flush tables: 1  Open tables: 74  Queries per second avg: 323.157

And note the value for UNIX socket, you’re going to need that later: /var/run/mysqld/mysqld.sock – yours might vary.

Install digiKam Dependencies

Updates 2021-10-30 🎃

  • Updated to libx264-163 and libx265-199
  • Added libopencv-dev dependency
  • Version change from 7.2.0 to 7.3.0

Updates 2022-02-01 🧧

  • Installing on Ubuntu 21.10 “impish”
  • Version change to 7.5.0 (note camelcase used for file name now, “digiKam” not “digikam“)
  • Problem with libopencv-dev required selecting a # sudo aptitude install solution to get past a libilmbase-dev but it is not installable error.

Digikam has just a few dependencies.just a few... the below command should install the needed for 7.30 on Ubuntu 21.10. Any other version combination might be different.:

sudo aptitude install \
bison \
checkinstall \
devscripts \
doxygen \
extra-cmake-modules \
ffmpeg \
ffmpegthumbnailer \
flex \
graphviz \
help2man \
jasper \
libavcodec-dev \
libavdevice-dev \
libavfilter-dev \
libavformat-dev \
libavutil-dev \
libboost-dev \
libboost-graph-dev \
libeigen3-dev \
libexiv2-dev \
libgphoto2-dev \
libjasper-dev \
libjasper-runtime \
libjasper4 \
libjpeg-dev \
libkf5akonadicontact-dev \
libkf5calendarcore-dev \
libkf5contacts-dev \
libkf5doctools-dev \
libkf5filemetadata-dev \
libkf5kipi-dev \
libkf5notifications-dev \
libkf5notifyconfig-dev \
libkf5sane-dev \
libkf5solid-dev \
libkf5threadweaver-dev \
libkf5xmlgui-dev \
liblcms2-dev \
liblensfun-dev \
liblqr-1-0-dev \
libmagick++-6.q16-dev \
libmagick++-6.q16hdri-dev \
libmagickcore-dev \
libmarble-dev \
libqt5opengl5-dev \
libqt5sql5-mysql \
libqt5svg5-dev \
libqt5webkit5-dev \
libqt5webview5 \
libqt5webview5-dev \
libqt5x11extras5-dev \
libqt5xmlpatterns5-dev \
libqtav-dev \
libqtwebkit-dev \
libswscale-dev \
libtiff-dev \
libusb-1.0-0-dev \
libx264-163 \
libx264-dev \
libx265-199 \
libx265-dev \
libxml2-dev \
libxslt1-dev \
marble \
pkg-kde-tools \
qtbase5-dev \
qtbase5-dev-tools \
qtmultimedia5-dev \
qtwebengine5-dev \
libopencv-dev \
qtwebengine5-dev-tools

Compile Digikam

Switch to your projects directory (~/projects, say) and get the source, cross your fingers, and go to town. The make -j4 command will take a while to compile everything.  There are two basic mechanisms for getting the source code: wget the taball or git pull the repository.

Download the tarball

Check the latest version at https://download.kde.org/stable/digikam/ It was 7.2.0, but is now 7.3.0 and will, certainly change again. This is currently a 255.3 MB download (!).

wget https://download.kde.org/stable/digikam/7.5.0/digiKam-7.5.0.tar.xz
tar -xvf digiKam-7.5.0.tar.xz
cd digiKam-7.5.0.tar.xz

git pull the repository

Git uses branches/tags so check the pull down list of latest branches and tags at the top left, below the many, many branches is the tag list at https://invent.kde.org/graphics/digikam/-/tree/v7.5.0 , latest on top, and currently 7.5.0. This is currently a 1.4 GB git pull (!!).
There was an issue in the v7.3.0 tag that caused built to fail that was fixed in current, so building “stable” isn’t always the best choice for stability.

git clone -b v7.5.0 https://invent.kde.org/graphics/digikam.git
cd digikam

Then follow the same steps:

./bootstrap.linux
cd build
make -j4
sudo su
make install/fast

Compiling might take 15-30 minutes depending on CPU.  Adjust -jx to optimize build times, the normal rule of thumb is that x=# of cores or cores+1, YMMV, 4 is a reasonable number if you aren’t confident or interested in experimenting.

The ./bootstrap.linux result should be as below; if it indicates a something is missing then double check dependencies.  If you’ve never compiled anything before, you might need to install cmake and and some other basics not in the apt install list above:

-- ----------------------------------------------------------------------------------
--  digiKam 7.2.0 dependencies results   <https://www.digikam.org>
-- 
--  MySQL Database Support will be compiled.. YES (optional)
--  MySQL Internal Support will be compiled.. YES (optional)
--  DBUS Support will be compiled............ YES (optional)
--  App. Style Support will be compiled...... YES (optional)
--  QWebEngine Support will be compiled...... YES (optional)
--  libboostgraph found...................... YES
--  libexiv2 found........................... YES
--  libexpat found........................... YES
--  libjpeg found............................ YES
--  libkde found............................. YES
--  liblcms found............................ YES
--  libopencv found.......................... YES
--  libpng found............................. YES
--  libpthread found......................... YES
--  libqt found.............................. YES
--  libtiff found............................ YES
--  bison found.............................. YES (optional)
--  doxygen found............................ YES (optional)
--  ccache found............................. YES (optional)
--  flex found............................... YES (optional)
--  libakonadicontact found.................. YES (optional)
--  libmagick++ found........................ YES (optional)
--  libeigen3 found.......................... YES (optional)
--  libgphoto2 found......................... YES (optional)
--  libjasper found.......................... YES (optional)
--  libkcalendarcore found................... YES (optional)
--  libkfilemetadata found................... YES (optional)
--  libkiconthemes found..................... YES (optional)
--  libkio found............................. YES (optional)
--  libknotifications found.................. YES (optional)
--  libknotifyconfig found................... YES (optional)
--  libksane found........................... YES (optional)
--  liblensfun found......................... YES (optional)
--  liblqr-1 found........................... YES (optional)
--  libmarble found.......................... YES (optional)
--  libqtav found............................ YES (optional)
--  libthreadweaver found.................... YES (optional)
--  libxml2 found............................ YES (optional)
--  libxslt found............................ YES (optional)
--  libx265 found............................ YES (optional)
--  OpenGL found............................. YES (optional)
--  libqtxmlpatterns found................... YES (optional)
--  digiKam can be compiled.................. YES
-- ----------------------------------------------------------------------------------

Launch and configure Digikam

(if you’re still root, exit root before launching # digikam)

The Configuration options are pretty basic, but note that to configure the Digikam back end you’ll need to use that MariaDB socket value you got before and the user you created like so UNIX_SOCKET=/var/run/mysqld/mysqld.sock:

 

On the first run, it will download about 350mb of code for the face recognition engine.  Hey – maybe a bit heavy, but you’re not giving the Google or Apple free lookie looks at all your personal pictures.  Also, if all this is a bit much (and, Frankly, it is) I’d consider Digikam one of the few applications that makes the whole flatpak thing seem somewhat justified.  Maybe.

Some advice on tuning:

I recommend mysqltuner highly, then maybe check this out (or just leave it default, default works well).

Tuning a database is application and computer specific, there’s no one size fits any, certainly not all, and it may change as your database grows. There are far more expert and complete tuning guides available, but here’s what I do:

Pre-Tuning Data Collection

Tuning at the most basic involves instrumenting the database to log problems, running it for a while, then parsing the performance logs for useful hints. The mysqltuner.pl script is far more expert at than I’ll ever be, so I pretty much just trust it. You have to modify your mysqld.cnf file to enable performance data collection (which, BTW, slows down operation, so undo this later) which, for MariaDB, means adding a few lines:

sudo nano /etc/mysql/mariadb.conf.d/50-server.cnf
# enable performance schema to allow optimization, but ironically hit performance, so disable after tuning.
# in the [mysqld] section insert
performance_schema=ON
performance-schema-instrument='stage/%=ON'
performance-schema-consumer-events-stages-current=ON
performance-schema-consumer-events-stages-history=ON
performance-schema-consumer-events-stages-history-long=ON

Follow the instructions for installing mysqltuner.pl at https://github.com/major/MySQLTuner-perl#downloadinstallation

I rather like this guide’s helpful instructions for putting the script in /usr/local/sbin/ so it is in the execution path:

sudo wget https://raw.githubusercontent.com/major/MySQLTuner-perl/master/mysqltuner.pl -O /usr/local/sbin/mysqltuner.pl
sudo chmod 700 /usr/local/sbin/mysqltuner.pl
sudo mysqltuner.pl

Then restart with sudo service mariadb restart then go about your business with digikam – make sure you rack up some real hours to gather useful data on your performance. Things like ingesting a large collection should generate useful data. I’d suggest doing disk tuning first because that’s hardware not load dependent.

Disk tuning

Databases tend to hammer storage and SSDs, especially SLC/enterprise SSDs, massively improve DB performance over spinning disks – unless you have a massive array of really good rotating drives. I’m running this DB on one spinning disk, so performance is very MEH. MySQL and MariaDB make some assumptions about disk performance which is used to scale some pretty important parameters for write caching. You can meaningfully improve on the defaults by testing your disk with a great linux utility called “fio”.

sudo apt install fio
fio --randrepeat=1 --ioengine=libaio --direct=1 --gtod_reduce=1 --name=test --filename=test --bs=4k --iodepth=64 --size=4G --readwrite=randrw --rwmixread=75

This will take a while and will give some very detailed information about the performance of your disk subsystem, the key parameters being average and max write IOPS. I typically create a # performance tuning section at the end of my [mysqld] section and before [embedded] and I’ll put these values in as, say: (your IOPS values will be different):

# performance tuning

innodb_io_capacity              = 170
innodb_io_capacity_max          = 286

and sudo service mariadb restart

Using mysqltuner.pl

After you’ve collected some data, there may be a list of tuning options.

sudo nano /etc/mysql/mariadb.conf.d/50-server.cnf

Mine currently look like this, but they’ll change as the database stabilizes and my usage patterns change.

# performance tuning

innodb_io_capacity              = 170
innodb_io_capacity_max          = 286

innodb_stats_on_metadata        = 0
innodb_buffer_pool_size         = 4G
innodb_log_file_size            = 512M
innodb_buffer_pool_instances    = 4
skip_name_resolve               = 1
query_cache_size                = 0
query_cache_type                = 0
query_cache_limit               = 2M
max_connections                 = 175
join_buffer_size                = 4M
tmp_table_size                  = 24M
max_heap_table_size             = 24M
innodb_buffer_pool_size         = 4G
max_allowed_packet              = 128M

and

sudo service mariadb restart

Note max_allowed_packet = 128M comes from this guide. I trust it, but it isn’t a mysqltuner suggestion.

Posted at 17:11:21 GMT-0700

Category: HowToLinuxphotoPositivereviewstechnology

Tagging MP3 Files with Puddletag on Linux Mint

Tuesday, March 23, 2021 

A “fun” part of organizing an MP3 collection is harmonizing the tags so the datas work consistently with whatever management schema you prefer.  My preference is management by the file system—genre/artist/year/album/tracks works for me—but consistent metainformation is required and often disharmonious.  Finding metaharmony is a chore I find less taxing with a well structured tag editor and to my mind the ur-meta-tag manager is  MP3TAG.

The problem is that only works with that dead-end spyware riddled failing legacyware called “Windows.” Fortunately, in Linux-land we have puddletag, a very solid clone of MP3TAG.  The issues is that the version in repositories is (as of this writing) 1.20 and I couldn’t find a PPA for the latest, 2.0.1.  But compiling from source is super easy and works in both Linux Mint 19 and Ubuntu 20.04 (yay open source!):

  1. Install pre-reqs to build (don’t worry, if they’re installed, they won’t be double installed)
  2. get the tarball of the source code
  3. expand it (into a reasonable directory, like ~/projects)
  4. switch into that directory
  5. run the python executable “puddletag” directly to verify it is working
  6. install it
  7. tell the desktop manager it’s there – and it should be in your window manager along with the rest of your applications.

The latest version as of this post was 2.0.1 from https://github.com/puddletag/puddletag

sudo apt install python3-pyqt5 python3-pyqt5.qtsvg python3-pyparsing python3-mutagen python3-acoustid libchromaprint-dev libchromaprint-tools libchromaprint1 
wget href="https://github.com/puddletag/puddletag/releases/download/2.0.1/puddletag-2.0.1.tar.gz
tar -xvf puddletag-2.0.1.tar.gz cd puddletag-2.0.1/
cd puddletag 
./puddletag 
sudo python3 setup.py install 
sudo desktop-file-install puddletag.desktop

A nice feature is the configuration directory is portable and takes your complete customization with you – it is an extremely customizable program so you can generally configure it as fits your mental model.  Just copy the entire puddletag directory located at ~/.configure/puddletag.

Posted at 15:19:01 GMT-0700

Category: AudioHowToLinuxPositivereviewsuncategorized

WebP and SVG

Tuesday, September 1, 2020 

Using WebP coded images inside SVG containers works.  I haven’t found any automatic way to do it, but it is easy enough manually and results in very efficiently coded images that work well on the internets.  The manual process is to Base64 encode the WebP image and then open the .svg file in a text editor and replace the

xlink:href="data:image/png;base64, ..."

with

xlink:href="data:image/webp;base64,..."

(“…” means the appropriate data, obviously).


Back in about 2010 Google released the spec for WebP, an image compression format that provides a roughly 2-4x coding efficiency over the venerable JPEG (vintage 1974), derived from the VP8 CODEC they bought from ON2. VP8 is a contemporary of and technical equivalent to H.264 and was developed during a rush of innovation to replace the aging MPEG-II standard that included Theora and Dirac. Both VP8 and H.264 CODECs are encumbered by patents, but Google granted an irrevocable license to all patents, making it “open,” while H.264s patents compel licensing from MPEG-LA.  One would think this would tend to make VP8 (and the WEBM container) a global standard, but Apple refused to give Google the win and there’s still no native support in Apple products.

A small aside on video and still coding techniques.

All modern “lossy” (throwing some data away like .mp3, as opposed to “lossless” meaning the original can be reconstructed exactly, as in .flac) CODECs are founded on either Discrete Cosine Transform (DCT) or Wavelet (DWT) encoding of “blocks” of image data.  There are far more detailed write ups online that explain the process in detail, but the basic concept is to divide an image into small tiles of data then apply a mathematical function that converts that data into a form which sorts the information from least human-perceptible to most human-perceptible and sets some threshold for throwing away the least important data while leaving the bits that are most important to human perception.

Wavelets are promising, but never really took off, as in JPEG2000 and Dirac (which was developed by the BBC).  It is a fairly safe bet that any video or still image you see is DCT coded thanks to Nasir Ahmed, T. Natarajan and K. R. Rao.  The differences between 1993’s MPEG-1 and 2013’s H.265 are largely around how the data that is perceptually important is encoded in each still (intra-frame coding) and some very important innovations in inter-frame coding that aren’t relevant to still images.

It is the application of these clever intra-frame perceptual data compression techniques that is most relevant to the coding efficiency difference between JPEG and WebP.

Back to the good stuff…

Way back in 2010 Google experimented with the VP8 intra-coding techniques to develop WebP, a still image CODEC that had to have two core features:

  • better coding efficiency than JPEG,
  • ability to handle transparency like .png or .tiff.

This could be the one standard image coding technique to rule them all – from icons to gigapixel images, all the necessary features and many times better coding efficiency than the rest.  Who wouldn’t love that?

Apple.

Of course it was Apple.  Can’t let Google have the win.  But, finally, with Safari 14 (June 22, 2020 – a decade late!) iOS users can finally see WebP images and websites don’t need crazy auto-detect 1974 tech substitution tricks.  Good job Apple!

It may not be a coincidence that Apple has just released their own still image format based on the intra-frame coding magic of H.265, .heif and maybe they thought it might be a good idea to suddenly pretend to be an open player rather than a walled-garden-screw-you lest iOS insta-users wall themselves off from the 90% of the world that isn’t willing to pay double to pose with a fashionable icon in their hands.  Not surprisingly, .heic, based on H.265 developments is meaningfully more efficient than WebP based on VP8/H.264 era techniques, but as it took WebP 10 years to become a usable web standard, I wouldn’t count on .heic  having universal support soon.

Oh well.  In the mean time, VP8 gave way to VP9 then to VP10, which has now AV1, arguably a generation ahead of HEVC/H.265.  There’s no hardware decode (yet, as of end of 2020) but all the big players are behind it, so I expect 2021 devices will and GPU decode will come in 2021. By then, expect VVC (H.266) to be replacing HEVC (H.265) with a ~35% coding efficiency improvement. 

Along with AV1’s intra/inter-frame coding advance, the intra-frame techniques are useful for a still format called AVIF, basically AVIF is to AV1 (“VP11”) what WEBP is to VP8 and HEIF is to HEVC. So far (Dec 2020) only Chrome and Opera support AVIF images.

Then, of course, there’s JPEG XL on the way.  For now, the most broadly supported post-JPEG image codec is WEBP.

SVG support in browsers is a much older thing – Apple embraced it early (SVG was not developed by Google so….) and basically everything but IE has full support (IE…  the tool you use to download a real browser).  So if we have SVG and WebP, why not both together?

Oddly I can’t find support for this in any of the tools I use, but as noted at the open, it is pretty easy.  The workflow I use is to:

  • generate a graphic in GIMP or Photoshop or whatever and save as .png or .jpg as appropriate to the image content with little compression (high image quality)
  • Combine that with graphics in Inkscape.
  • If the graphics include type, convert the type to SVG paths to avoid font availability problems or having to download a font file before rendering the text or having it render randomly.
  • Save the file (as .svg, the native format of Inkscape)
  • Convert the image file to WebP with a reasonable tool like Nomacs or Ifranview.
  • Base64 encode the image file, either with base64 # base64 infile.webp > outfile.webp.b64 or with this convenient site
  • If you use the command line option the prefix to the data is “data:image/webp;base64,
  • Replace the … on the appropriate xlink:href="...." with the new data using a text editor like Atom.
  • Drop the file on a browser page to see if it works.

WordPress blocks .svg uploads without a plugin, so you need one

The picture is 101.9kB and tolerates zoom quite well. (give it a try, click and zoom on the image).

 

Posted at 08:54:16 GMT-0700

Category: HowToLinuxphotoself-publishingtechnology

Dealing with Apple Branded HEIF .HEIC files on Linux

Saturday, August 22, 2020 

Some of the coding tricks in H.265 have been incorporated into MPEG-H coding, an ISO standard introduced in 2017, which yields a roughly 2:1 coding efficiency gain over the venerable JPEG, which was introduced in 1992.  Remember that?  I do; I’m old.  I remember having a hardware NUBUS JPEG decoder card.   One of the reasons JPEG has lasted so long is that images have become a small storage burden (compared to 4k video, say) and that changing format standards is extremely annoying to everyone.

Apple has elected to make every rational person’s life difficult and put a little barbed wire around their high-fashion walled garden and do something a little special with their brand of a HEVC (h.265) profile for images.  Now normally seeing iOS user’s insta images of how fashionable they are isn’t really worth the effort, but now and then a useful correspondent joins the cult and forks over a ton of money to show off a logo and starts sending you stuff in their special proprietary format.  Annoying, but fixable.

Assuming you’re using an OS that is neither primarily spyware nor fashion forward, such as Linux Mint, you can install HEIF decode (including Apple Brand HEIC) with a few simple commands:

$ sudo add-apt-repository ppa:jakar/qt-heif
$ sudo apt update
$ sudo apt install qt-heif-image-plugin

Once installed, various image viewers should be able to decode the images.  I rather like nomacs as a fairly tolerable replacement for Irfan Skiljan‘s still awesome irfanview.

 

Update: 2022-09-22

Jammy isn’t supported by the jakar PPA, but there are a few other options:

Easy, from Hritik Chaudhary in this post,

sudo apt install heif-gdk-pixbuf

should give gdk access, but not (it seems) qt.  You can use gpicview as an imageviewer with this library.

sudo apt install gpicview

Or build the qt-heic-image-plugin from source:

git clone --depth 1 https://github.com/novomesk/qt-heic-image-plugin.git
cd qt-heic-image-plugin
mkdir build
cd build
cmake -DCMAKE_BUILD_TYPE=Release ..
make
sudo make install

This machine required ECM from extra cmake modules, which I hadn’t previously installed.

sudo apt install extra-cmake-modules

to successfully cmake.

Posted at 03:56:36 GMT-0700

Category: HowToLinuxphotoPositivereviewstechnology

1976 GMC Suburban

Friday, May 17, 2019 

When I was a young child, my dad bought a brand new 1976 GMC Suburban. Yellow. No extras at all – no head liner, plastic seats, manual everything, 305 V8.

It became my car in high school, survived that. Came out to California with me; ended up in the service of SRL, survived that too.

Eventually, it escaped.

Posted at 13:18:33 GMT-0700

Category: photoSRL