A collection of camera raw processing algorithms.
Project description
rawpipe
A collection of reference ISP algorithms, sufficient for producing a reasonably good looking image from raw sensor data. Each algorithm takes in a frame in RGB or raw format and returns a modified copy of the frame. The frame is expected to be a NumPy float array with either 2 or 3 dimensions, depending on the function. Some of the algorithms can be applied in different orders (demosaicing before or after linearization, for example), but the reference ordering is as shown below.
Example:
import rawpipe
...
raw = rawpipe.linearize(raw, blacklevel=64, whitelevel=1023)
rgb = rawpipe.wb(rgb, [1.5, 2.0], "RGGB")
rgb = rawpipe.demosaic(raw, "RGGB", downsample=True)
rgb = rawpipe.downsample(rgb, iterations=1)
rgb = rawpipe.lsc(rgb, my_vignetting_map)
rgb = rawpipe.lsc(rgb, my_color_shading_map)
rgb = rawpipe.ccm(rgb, my_3x3_color_matrix)
rgb = rawpipe.resize(rgb, 400, 300)
rgb = rawpipe.gamut(rgb, "ACES")
rgb = rawpipe.tonemap(rgb, "Reinhard")
rgb = rawpipe.chroma_denoise(rgb)
rgb = rawpipe.saturate(rgb, lambda x: x ** 0.75)
rgb = rawpipe.gamma(rgb, "sRGB")
rgb = rawpipe.quantize8(rgb)
Installing on Linux:
pip install rawpipe
Installing development dependencies:
uv sync --extra dev
Documentation:
pydoc rawpipe
Building & installing from source:
make install
Running tests locally:
uv run pytest
Building distributions locally:
uv build
Releasing to PyPI:
Publishing is handled by GitHub Actions with PyPI Trusted Publishing.
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