Use this module to read, and write to a number of layered image formats

• Compatibility
  • Overview
    • Key
    • Reading - Group
    • Reading - Layer
    • Writing - Group
    • Writing - Layer
• .layered
• Example Usage
• Documentation
• Install With PIP
• Language information
  • Built for
• Install Python on Windows
  • Chocolatey
  • Windows - Python.org
• Install Python on Linux
  • Apt
  • Dnf
• Install Python on MacOS
  • Homebrew
  • MacOS - Python.org
• How to run
  • Windows
  • Linux/ MacOS
• Building
• Testing
• Download Project
  • Clone
    • Using The Command Line
    • Using GitHub Desktop
  • Download Zip File
• Community Files
  • Licence
  • Changelog
  • Code of Conduct
  • Contributing
  • Security
  • Support
  • Rationale

Bear in mind that the tables below may not be completely accurate. If that is the case, please open an issue and I will fix the tables.

• ✔ - Supported
• ⚠ - Things will look the same, but data is lost
• ❌ - This is not supported and will cause loss of data
• N/A - The source format does not support this so treat this as a ✔

Layers are extracted from groups and saved to TIFF/ GIF or WEBP

Layers are rendered with offsets before being written to TIFF/ GIF or WEBP
First child layers are placed in a group when written to LSR

.layered is highly inspired by the open raster format and aims to provide an exchange format in the cases when saving in ora would cause unacceptable data loss. .layered has been designed so that if the format became deprecated and no readers existed for it tomorrow, the data would be easily salvageable.

See the LAYERED_SPEC for more information.

Here's some basic example usage below.

"""Example module """
from pathlib import Path
THISDIR = str(Path(__file__).resolve().parent)
import layeredimage.io

# Do stuff
ora = layeredimage.io.openLayerImage(f"{THISDIR}/image.ora")

imageDimensions = ora.dimensions
# There are a load of handy functions for getting layers, and adding new
# layers, but here we will act directly on the object
layer = ora.layersAndGroups[0] # For the sake of the e.g. this is a layer

# Lets overwrite the layer with a transparent image (bit boring I know...)
layer.image = Image.new("RGBA", imageDimensions)
ora.layersAndGroups[0] = layer

# And let's save
layeredimage.io.saveLayerImage(f"{THISDIR}/image(modified).ora", ora)

# Let's save a flattened version too
ora.getFlattenLayers().save(f"{THISDIR}/image(modified).png")

# Doing stuff with a group
group = ora.getLayerOrGroup(1) # For the sake of the e.g. this is a group
group.layers[0].image.show() # Open the image of the first layer of the group

# Deleting a layer/ group
ora.removeLayerOrGroup(2)

Images are PIL.Image (s) and so you can use the power of Pillow to apply filters, and other modifications to the images.

See below for an old version of the tests. These provide a few examples of file conversions. Not going to get 100% coverage anytime soon but hopefully this will help a little.

"""Test module """

import sys
import os
from pathlib import Path
THISDIR = str(Path(__file__).resolve().parent)
sys.path.insert(0, os.path.dirname(THISDIR))
import layeredimage.io

# ORA
ora = layeredimage.io.openLayerImage(f"{THISDIR}/base24.ora")
layeredimage.io.saveLayerImage(f"{THISDIR}/base24(ora).ora", ora)
layeredimage.io.saveLayerImage(f"{THISDIR}/base24(ora).tiff", ora)
ora.getFlattenLayers().save(f"{THISDIR}/base24(ora).png")

# PSD
psd = layeredimage.io.openLayerImage(f"{THISDIR}/base24.psd")
layeredimage.io.saveLayerImage(f"{THISDIR}/base24(psd).ora", psd)
layeredimage.io.saveLayerImage(f"{THISDIR}/base24(psd).tiff", psd)
psd.getFlattenLayers().save(f"{THISDIR}/base24(psd).png")

# PDN
pdn = layeredimage.io.openLayerImage(f"{THISDIR}/base24.pdn")
layeredimage.io.saveLayerImage(f"{THISDIR}/base24(pdn).ora", pdn)
layeredimage.io.saveLayerImage(f"{THISDIR}/base24(pdn).tiff", pdn)
pdn.getFlattenLayers().save(f"{THISDIR}/base24(pdn).png")

# XCF
xcf = layeredimage.io.openLayerImage(f"{THISDIR}/base24.xcf")
layeredimage.io.saveLayerImage(f"{THISDIR}/base24(xcf).ora", xcf)
layeredimage.io.saveLayerImage(f"{THISDIR}/base24(xcf).tiff", xcf)
xcf.getFlattenLayers().save(f"{THISDIR}/base24(xcf).png")

# TIFF
tiff = layeredimage.io.openLayerImage(f"{THISDIR}/base24.tiff")
layeredimage.io.saveLayerImage(f"{THISDIR}/base24(tiff).ora", tiff)
layeredimage.io.saveLayerImage(f"{THISDIR}/base24(tiff).tiff", tiff)
tiff.getFlattenLayers().save(f"{THISDIR}/base24(tiff).png")

A high-level overview of how the documentation is organized organized will help you know where to look for certain things:

• The Technical Reference documents APIs and other aspects of the machinery. This documentation describes how to use the classes and functions at a lower level and assume that you have a good high-level understanding of the software.

pip install layeredimage

Head to https://pypi.org/project/layeredimage/ for more info

This program has been written for Python versions 3.8 - 3.11 and has been tested with both 3.8 and 3.11

choco install python

To install Python, go to https://www.python.org/downloads/windows/ and download the latest version.

sudo apt install python3.x

sudo dnf install python3.x

brew install [email protected]

To install Python, go to https://www.python.org/downloads/macos/ and download the latest version.

• Module py -3.x -m [module] or [module] (if module installs a script)

• File py -3.x [file] or ./[file]

• Module python3.x -m [module] or [module] (if module installs a script)

• File python3.x [file] or ./[file]

This project uses https://github.com/FHPythonUtils/FHMake to automate most of the building. This command generates the documentation, updates the requirements.txt and builds the library artefacts

Note the functionality provided by fhmake can be approximated by the following

handsdown  --cleanup -o documentation/reference
poetry export -f requirements.txt --output requirements.txt
poetry export -f requirements.txt --with dev --output requirements_optional.txt
poetry build

fhmake audit can be run to perform additional checks

For testing with the version of python used by poetry use

poetry run pytest

Alternatively use tox to run tests over python 3.8 - 3.11

tox

1. Press the Clone or download button in the top right

2. Copy the URL (link)

3. Open the command line and change directory to where you wish to clone to

4. Type 'git clone' followed by URL in step 2

   git clone https://github.com/FHPythonUtils/LayeredImage

More information can be found at https://help.github.com/en/articles/cloning-a-repository

1. Press the Clone or download button in the top right
2. Click open in desktop
3. Choose the path for where you want and click Clone

More information can be found at https://help.github.com/en/desktop/contributing-to-projects/cloning-a-repository-from-github-to-github-desktop

1. Download this GitHub repository
2. Extract the zip archive
3. Copy/ move to the desired location

MIT License Copyright (c) FredHappyface (See the LICENSE for more information.)

See the Changelog for more information.

Online communities include people from many backgrounds. The Project contributors are committed to providing a friendly, safe and welcoming environment for all. Please see the Code of Conduct for more information.

Contributions are welcome, please see the Contributing Guidelines for more information.

Thank you for improving the security of the project, please see the Security Policy for more information.

Thank you for using this project, I hope it is of use to you. Please be aware that those involved with the project often do so for fun along with other commitments (such as work, family, etc). Please see the Support Policy for more information.

The rationale acts as a guide to various processes regarding projects such as the versioning scheme and the programming styles used. Please see the Rationale for more information.