Cadre is a DIY picture frame project. It aims for:
- Colour e-ink display support
- Simple web interface
- Automatic photo updates from your collections
- Easy deployment
It's split up into multiple components:
- Encre: convert image files to a native e-ink display palette
- Affiche: Local web interface
- Expo: Automatic photo updates
- Cru: Image metadata loader
Convert images to an e-ink display palette.
This is an optional component. If you're not using an e-ink display, you can instead use Affiche with a custom display writer script, see below.
Encre process read a wide variety of image formats (any supported by your libvips install). It performs lightness adjustments and perceptual gamut mapping, and finally dithers to the e-ink display palette. The final byte buffer can be sent to the display hardware.
A command line tool, and both a C++ and a Python API, are available.
Samples results are available in test_data. They were generated for a 7.3" Pimoroni Inky Impression. Keep in mind that current colour e-ink display technology has rather low gamut, so it's normal that the images look washed out. That's what it looks like on the actual display. This tool focuses on accurate colour mapping, it can't do miracles.
- Install pkg-config
- Install Python (3.9 or newer)
- Install libvips (8.15.1 or newer)
- Run
cmake --workflow --preset release
Other dependencies are installed by Vcpkg.
You might have to upgrade CMake, Python or your C++ compiler, the logs should tell you.
There are additional notes here specific for Raspberry Pi users.
If the build succeeds, you can use the CLI tool at build/release/cli/encre-cli
to
test image conversions. For example: build/release/cli/encre-cli test_data/colors.png -p
will output test_data/colors.bin
(palette'd image as raw unsigned bytes) and
test_data/colors_preview.png
as a preview. Run with -h
for more information.
If you have one of the displays listed below, you can use
write_to_display.py to directly write an image to the display.
Pass the display name as the first argument. Some displays have additional options which can be
set using --display-config <json>
.
- Pimoroni Inky Impression:
- Install requirements-pimoroni_inky
- Use name
pimoroni_inky
- Good Display E6 7.3" display (GDEP073E01)
- Install requirements-GDEP073E01.
- Use name
GDEP073E01
- Proxy
- Use name
proxy
- No additional requirements
- Options:
width
,height
: display width and heighturl
: URL to post the image to
- Write the converted image to a custom binary format defined by Encre, and post it to the URL.
The file can then be read using the Encre API or by
write_to_display.py
. Intended for use with Expo. Useful for converting an image locally on a more capable computer before posting it to an Affiche instance running on a less capable device. See proxying for an example.
- Use name
- Simulated
- Use name
simulated
- No additional requirements
- Options:
width
,height
: display width and heightdelay
: display simulated update time in seconds
- Useful for testing, but does nothing
- Use name
If your display isn't in this list, we're open to contributions!
Implement the Display
protocol.
You can looks at GDEP073E01.py
for an example.
To create a custom palette, call py_encre.make_palette_xyz
or py_encre.make_palette_lab
.
If you're lucky, your display data sheet will have the CIE Lab values for each colour,
otherwise you can eyeball them... An example is available here.
Options are available to tweak the image processing. Operations are performed in the Oklab perceptual colour space.
- Rotation: apply a rotation (after the EXIF orientation, if applicable)
- Automatic: landscape is unchanged, portrait is rotated to landscape.
- Landscape: unchanged
- Portrait: 90° counter-clockwise
- Landscape upside-down: 180°
- Portrait upside-down: 90° clockwise
- Dynamic range: percentage of the original image dynamic range to be
rescaled into the output palette. Using
0
will keep the original dynamic range, which will lead to a lot of clipping if the palette has a small dynamic range. Using1
will force the entirety of the dynamic range into the output, which means no clipping, but lowest contrast possible. - Exposure: Lightness scale factor (multiply with
L
component). If not specified, then some basic automatic exposure adjustment is made to bring the image dynamic range into the output palette. - Brightness: Lightness bias factor (add to
L
component). If not specified, then some basic automatic brightness adjustment is made to bring the image dynamic range into the output palette. - Contrast: Slope of the sigmoid function used to compress the image dynamic range into the output palette. Larger values increase contrat in the mid range, at the cost of compressing the shadows and highlights.
- Sharpening: edge sharpening, useful to recover some details after the resize.
- Clipped chroma recovery: α value described here, used to recover some color from the clipped highlights caused by gamut mapping.
- Dither error attenuation: exponential attenuation factor applied to the dither
error before diffusion. Helps reduce smearing caused by small errors being
diffused over large areas, but comes at the cost of colour accuracy.
Values over
1
create a more artistic effect.
Options are also defined in encre_options.json for use by Affiche.
Local web interface
After building Encre, create a Python virtual environment, and install the
requirements using pip
.
Start the server using start.sh
. Use stop.sh
if you need to stop the server when it's running
in the background. You might need to change system settings to make port 80
available without
privileges. The simplest solution is to run sudo sysctl -w net.ipv4.ip_unprivileged_port_start=80
.
The server should be available at the host's LAN address on port 80
.
If you want to use a different port or hostname, then use the -p
and -h
arguments of start.sh
.
When running stop.sh
pass the same port using -p
.
To post a picture to Affiche without the web interface, simply send it as a multipart/form-data
as a file
or url
key:
curl cadre.local -F [email protected]
curl cadre.local -F url=https://upload.wikimedia.org/wikipedia/commons/7/70/African_leopard_male_%28cropped%29.jpg
You can also populate the "Collection" and "Path" info fields by using the info
key:
info='{"path":"a path", "collection": "collection name"}'
.
If you want image metadata support (EXIF information and geolocation), you need to build Cru.
Copy the default config and name it config.json
.
In this file you can overwrite the following fields:
TEMP_PATH
: Where to write the temporary files, absolute or relative to the server executable.DISPLAY_WRITER_COMMAND
: The command line for writing an image to the display. See below.DISPLAY_WRITER_OPTIONS_SCHEMA_PATH
: Path to the options schema for the display writer. See encre_options.json for an example.DISPLAY_WRITER_OPTIONS
: Dictionary of option name and default value override. For example:{"dynamic_range": 0.8}
. Must respect the schema fromDISPLAY_WRITER_OPTIONS_SCHEMA_PATH
.MAP_TILES
: URL and options for LeafletL.tileLayer()
constructor. Lets you customize the map shown by Affiche. If you want an English map, I recommend the Thunderforest Atlas tiles (free account required to obtain an API key).EXPO_ADDRESS
: Hostname and optional port suffix for the Expo server. Must be externally reachable (i.e.:affiche.local
instead oflocalhost
). Optional, can benull
to disable Expo integration. If empty, then default to the local machine network name.
You can override the default config path by using the -c
argument of start.sh
or by setting the environment variable
AFFICHE_CONFIG_PATH
to your desired path. By default it's ./config.json
.
You more than likely just want to use the default encre/display/write_to_display.py
script. Check its --help
output for more details.
If you need a custom display implementation, then it's recommended to implement the Display
protocol.
If you really want to use a custom script, then it must accept the following arguments (automatically populated by Affiche):
<image_path>
: path to the input image.--options <json>
: options in the same format asDISPLAY_WRITER_OPTIONS
.--info <json>
: image info, either loaded by Cru or user provided.--preview <path>
: output path for the web interface preview.
Furthermore if you want Affiche to display the image update progress, it should output Status: CONVERTING
and Status: DISPLAYING
to stdout
to notify of the current state.
You can use Cron to launch on boot. Edit the user's crontab using crontab -e
and add
@reboot cd ~/Cadre/affiche && bash start.sh; cd ~/Cadre/expo && bash start.sh
(skip the part after ;
if you're not using Expo). You will need to use bash -l
if
you have environment variables required by Affiche (or Expo) in your bash profile
(such as if you followed the Encre Raspberry Pi build instructions).
Try disabling Wifi power management using iwconfig wlan0 power off
. You can make this change
persistent on reboot by adding /sbin/iwconfig wlan0 power off
to /etc/rc.local
.
More specifically, your local address is cadre.local
, and clicking the Expo link opens cadre
,
which fails to resolve.
Make sure that you have the .local
address in the /etc/hosts
file, and that it appears before the hostname.
For example: 127.0.1.1 cadre.local cadre
Automatic photo update service
This is an optional component. It's a separate service which maintains a database of photos locations and metadata, and periodically posts one to Affiche.
Create a Python virtual environment, and install the requirements using pip
.
Additional requirements are also needed for the following features:
FileSystemCollection
: CruAmazonPhotosCollection
: requirements-azp
Start the server using start.sh
. Use stop.sh
if you need to stop the server when it's running in the background.
The server should be available at the host's LAN address on port 21110
.
You can run Expo on the same host as Affiche or a different one.
If you want to use a different port or hostname, then use the -p
and -h
arguments of start.sh
.
When running stop.sh
pass the same port using -p
.
Copy the default config and name it config.json
.
In this file you can overwrite the following fields:
DB_PATH
: where to write the Expo persistent data, absolute or relative to the server executable.POST_COMMANDS
: dictionary of custom post commands that can be used by schedules. Each command has an identifier (key), and a list of command line arguments (value). The arguments can contain the placeholder%HOSTNAME%
, which will be replaced with the hostname of the schedule using the command. See proxying for an example.
You can override the default config path by using the -c
argument of start.sh
or by setting the environment variable
EXPO_CONFIG_PATH
to your desired path. By default it's ./config.json
.
List all collections by GET
ing from /collections
.
Create a collection by PUT
ting to /collections
a JSON object like so:
{
"identifier": "my_collection",
"display_name": "My Collection",
"schedule": "*/5 * * * *",
"enabled": true,
"class_name": "FileSystemCollection",
"settings": {
// See below
}
}
identifier
must be unique, contain only characters in the set[A-Za-z0-9_]
, and cannot start with a numberdisplay_name
is optional and defaults to theidentifier
valueschedule
Cron expression, or empty string to never run automaticallyenabled
is optional and defaults totrue
class_name
can only beFileSystemCollection
settings
depends on theclass_name
value, see below
You can edit a collection by PATCH
ing to /collections?identifier=<identifier>
using the same JSON format,
except all fields are now optional. You can also query using GET
, and delete using DELETE
.
Use this to display photos from a local collection such as a NAS. You can also create an SMB share on a Raspberry Pi where you upload your favourite photos. The photos must be visible on the filesystem where Expo is running.
Requires Cru to process images. Scan the root_path
for known image formats.
Does not support the favorite
filter.
Settings:
{
"root_path": "<path to your local photos folder>"
}
Uses an unofficial Amazon Photos Python API by Trevor Hobenshield (actually a fork with a few improvements).
Settings:
{
"user_agent": "<User agent string from the browser used to login to Amazon Photos>",
"cookies": {
// Copy cookies here as a dictionary. It's not known exactly which cookies are required,
// if you're missing some you might eventually get an authentication error.
// This list just an example, since the cookie names are region specific (amazon.ca shown).
// See: https://github.com/trevorhobenshield/amazon_photos?tab=readme-ov-file#setup
"at-acbca": "***",
"ubid-acbca": "***",
"sess-at-acbca": "***",
"sst-acbca": "***",
"x-acbca": "***",
"lc-acbca": "***",
"session-id": "***",
"session-id-time": "***"
}
}
Immediately trigger a collection scan by POST
ing to /scan
a JSON object like so:
{
"identifier": "<collection identifier>",
"delay": 0
}
identifier
is a collection identifierdelay
a delay in seconds (float), is optional and defaults to0
The schedule API uses the same methods as collections, but on the /schedules
endpoint.
There is a new optional hostname
argument for GET
queries, to filter schedules by the
target hostname. It will be compared against the original hostname and the external one
if they're different (ex.: localhost
and affiche.local
).
The JSON format is:
{
"identifier": "my_schedule",
"display_name": "My Schedule",
"hostname": "<Affiche hostname>",
"schedule": "*/20 * * * *",
"enabled": true,
"filter": "<filter expression>",
"order": "<order enum name>",
"affiche_options": {
// Example for Encre
"contrast": 0.5,
"sharpening": 2
},
"post_command_id": "" // Or an identifier from POST_COMMANDS in the config
}
identifier
must be unique, contain only characters in the set[A-Za-z0-9_]
, and cannot start with a numberdisplay_name
is optional and defaults to theidentifier
valuehostname
is the hostname (optionally with a:<port>
suffix) where an Affiche instance is runningschedule
Cron expression, or empty string to never run automaticallyenabled
is optional and defaults totrue
filter
is optional and defaults to"true"
order
is optional and defaults toSHUFFLE
, see belowaffiche_options
is optional, override the default options for the target Affiche instancepost_command_id
is optional, override the default command for posting a photo to the Affiche instance
The filter EBNF grammar is:
bool literal = "false" | "true";
identifier = ? /[A-Za-z_][A-Za-z0-9_]*/ ?;
favorite = "favorite";
aspect = "landscape" | "portrait" | "square";
collection set = "{", identifier, {identifier}, "}";
parenthesized expression = "(", expression, ")";
atom = bool literal | favorite | aspect | collection set | parenthesized expression;
unary = ["not"], atom;
and = unary, ["and", unary];
or = and, ["or", and];
expression = or;
In simple terms this means you can use the favorite
(unused), landscape
, portrait
and square
predicates to write a logical expression for filtering photos. You can use the ()
, not
, and
, and or
operators to build your expression (listed in decreasing order of precedence). To filter by collection,
use {<identifier 1> <identifier 2> ...}
. A photo will only be picked if it belongs to one of the given
collections. The true
and false
literals are also available.
Example: portrait and (favorite or not {phone_pics family_pics})
If you simply want to allow all photos use: true
SHUFFLE
: Cycle through images in a random order. After all images have been displayed once, restart in a different random order.CHRONOLOGICAL_DESCENDING
andCHRONOLOGICAL_ASCENDING
: Cycle through images in descending or ascending capture date. After all images have been displayed once, restart the cycle. Images without a capture date in their metadata are ignored.
Immediately trigger a schedule by POST
ing to /refresh
a JSON object like so:
{
"identifier": "<schedule identifier>",
"delay": 0
}
identifier
is a schedule identifierdelay
a delay in seconds (float), is optional and defaults to0
You can have Expo convert the image locally before posting it to Affiche. This is useful if you want to run Expo on a more capable computer like a Raspberry Pi 4 or 5, and use cheaper computers like the Raspberry Pi Zero 2 for multiple Affiche instances. Once converted, the image requires very little compute power to be displayed. There's a WIP project to even support the Raspberry Pi Pico 2 using this mechanism.
Use the Encre proxy
display to convert locally and post the result to an Affiche instance.
"POST_COMMANDS": {
"inky": [
"../encre/display/write_to_display.py",
"proxy",
"--display-config", "{\"url\": \"http://%HOSTNAME%\", \"height\": 480, \"width\": 800}",
"--palette", "pimoroni_gallery_palette"
]
}
Cru is a native Python module for quickly loading image metadata, like resolution and tags. It also does some processing and pretty formatting for easier consumption.
- Follow the Encre build instructions first, that'll make sure you have everything installed
- Run
cmake --workflow --preset release
The filesystem collection should automatically detect and import the module.
If you see "Cru module not found" in the logs, check that the module can be
indeed be loaded from <repo path>/cru/build/release
by Python.