layeris 0.3.0

An open source image processing library that supports blend modes, curve adjustment, and other adjustments that graphic designers or photographers frequently use

Layer.is

A library that supports all blend modes in Photoshop, brightness, contrast, hue, saturation, lightness adjustments, and curve adjustments on separate RGB channels.

This project was a part of the 2019 Open Source Software Competition.

• Introductory video
• OSS Project Link

Why use Layer.is?

• Supports all frequently used blend modes in Photoshop
• Chain operations for concise, readable image pipelines
• Straightforward API to manipulate images
• Load a sequence of operations from JSON
• Apply curve adjustments selectively to individual RGB channels

Requirements

Python 3.10 or higher.

Installation

From PyPI

pip install layeris

From source

git clone https://github.com/subwaymatch/layer-is-python.git
cd layer-is-python
pip install .

To install with development dependencies (includes pytest):

pip install -e ".[dev]"
# or, using hatch:
hatch env create

Quick Start

Load an image

from layeris import LayerImage

# From a local file
image = LayerImage.from_file('/path/to/image.jpg')

# From a URL
image = LayerImage.from_url('https://example.com/photo.jpg')

# From a NumPy array (float64, values in [0, 1], shape HxWx3 or HxWx4)
import numpy as np
data = np.random.random((100, 100, 3))
image = LayerImage.from_array(data)

Save an image

# Default quality is 95 for JPEG
image.save('output.jpg')

# Specify JPEG quality (0–100)
image.save('output.jpg', quality=85)

# Save as PNG (lossless)
image.save('output.png')

Basic adjustments

# Grayscale
image.grayscale()

# Brightness  (factor > 0 brightens, factor < 0 darkens)
image.brightness(0.2)

# Contrast  (factor > 1 increases, 0 < factor < 1 decreases)
image.contrast(1.5)

# Hue  (value in [0, 1])
image.hue(0.3)

# Saturation  (factor > 0 increases, factor < 0 decreases; -1 fully desaturates)
image.saturation(-0.5)

# Lightness  (factor > 0 lightens toward white, factor < 0 darkens toward black)
image.lightness(0.4)

# Curve adjustment (apply an S-curve to the RGB channels)
image.curve('rgb', [0, 0.1, 0.4, 0.65, 0.9, 1])

# Curve on a single channel
image.curve('r', [0, 0.2, 0.8, 1])

Resize

image.resize(width=800, height=600)

Blend modes

All blend mode methods accept a hex colour string or a NumPy array, plus an optional opacity parameter (0.0–1.0).

Darken group

image.darken('#3fe28f')
image.multiply('#3fe28f', opacity=0.8)
image.color_burn('#7fe3f8')
image.linear_burn('#e1a8ff')

Lighten group

image.lighten('#ff3ce1')
image.screen('#e633ba')
image.color_dodge('#490cc7')
image.linear_dodge('#490cc7')

Contrast group

image.overlay('#ffb956')
image.soft_light('#ff3cbc')
image.hard_light('#df5dff')
image.vivid_light('#ac5b7f')
image.linear_light('#9fa500')
image.pin_light('#005546')

Method chaining

All operations return self, so calls can be chained:

result = (
    LayerImage.from_file('photo.jpg')
    .grayscale()
    .brightness(0.1)
    .multiply('#3fe28f', opacity=0.7)
    .curve('rgb', [0, 0.2, 0.8, 1])
    .save('output.jpg')
)

Clone

# Create an independent copy before branching
copy = image.clone()
copy.darken('#000000')  # original is unaffected

Get image data as a NumPy array

arr = image.get_image_as_array()
# arr.shape  → (height, width, 3)  for RGB
#            → (height, width, 4)  for RGBA
# arr.dtype  → float64
# values in [0.0, 1.0]

Apply operations from a dictionary or JSON file

Operations can be described as a Python dict (or a JSON file) and applied in sequence:

ops = {
    "operations": [
        {"type": "grayscale"},
        {"type": "brightness", "factor": 0.15},
        {"type": "multiply", "hex": "#3fe28f", "opacity": 0.6},
        {"type": "curve", "channels": "rgb", "curve_points": [0, 0.1, 0.9, 1]},
        {"type": "resize", "width": 800, "height": 600},
    ]
}

image.apply_from_dict(ops)

# Or load directly from a JSON file
image.apply_from_json('pipeline.json')

Supported operation types: grayscale, resize, brightness, contrast, hue, saturation, lightness, curve, darken, multiply, color_burn, linear_burn, lighten, screen, color_dodge, linear_dodge, overlay, soft_light, hard_light, vivid_light, linear_light, pin_light.

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Running tests

Install development dependencies and run the test suite with pytest:

# Using pip (editable install)
pip install -e .
pip install pytest pytest-cov
pytest

# Using hatch (recommended)
hatch run test

# With coverage report
hatch run test-cov

Run a specific test file or test:

pytest tests/test_layer_image.py
pytest tests/test_utils.py
pytest tests/test_layer_image.py::TestBlendModes::test_multiply_by_white_no_change -v

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Blend mode reference

Group	Method	Formula (A = base, B = blend)
Darken	darken	min(A, B)
Darken	multiply	A × B
Darken	color_burn	1 − (1 − A) / B
Darken	linear_burn	A + B − 1
Lighten	lighten	max(A, B)
Lighten	screen	1 − (1 − A)(1 − B)
Lighten	color_dodge	A / (1 − B)
Lighten	linear_dodge	A + B
Contrast	overlay	2AB if A ≤ 0.5 else 1 − 2(1−A)(1−B)
Contrast	soft_light	Pegtop formula
Contrast	hard_light	2AB if B ≤ 0.5 else 1 − 2(1−A)(1−B)
Contrast	vivid_light	Color burn / dodge based on B
Contrast	linear_light	A + 2B − 1
Contrast	pin_light	Conditional replace

All results are clamped to [0, 1].

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Roadmap

• Imitate Photoshop's auto brightness & auto contrast features
• Add filter presets