RunasSudo/pdf-segmented
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Consider pixels to be foreground only if surrounded by other foreground pixels

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RunasSudo 2025-09-02 21:34:07 +10:00
parent bbc327ba5a
commit 18cbc2bd56
Signed by: RunasSudo
GPG Key ID: 7234E476BF21C61A
2 changed files with 41 additions and 11 deletions
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12
README.md
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@ -10,7 +10,7 @@ python -m pdf_segmented input.xcf output.pdf
Takes as input a [GIMP](https://www.gimp.org/) XCF file with one layer per page (bottom layer = first page).
All black pixels (#000000) will be considered to be foreground, and all remaining pixels will be considered to be background. This is most easily accomplished by selecting all colour graphics in GIMP, inverting the selection (Ctrl+I), then applying the [Threshold tool](https://docs.gimp.org/3.0/en/gimp-tool-threshold.html).
Pixels will be considered to be foreground if they are fully black (#000000) and all 8 of their adjacent pixels are also fully black or fully white. All remaining pixels will be considered to be background. This is most easily accomplished by selecting all colour graphics in GIMP, inverting the selection (Ctrl+I), then applying the [Threshold tool](https://docs.gimp.org/3.0/en/gimp-tool-threshold.html).
The foreground will be compressed losslessly using [JBIG2](https://en.wikipedia.org/wiki/JBIG2). The background will be compressed lossily using [JPEG](https://en.wikipedia.org/wiki/JPEG). JPEG quality can be controlled using the `--jpeg-quality` option; the default is the Pillow default (75% at time of writing).
@ -18,10 +18,10 @@ Additional compression algorithms are supported (JPEG 2000, PNG); see `--help` f
## Dependencies
* [Python 3](https://www.python.org/) (tested using 3.13.3)
* [NumPy](https://numpy.org/) (tested using 2.2.5)
* [Pillow](https://pillow.readthedocs.io/en/stable/) (tested using 11.2.1)
* [pikepdf](https://pikepdf.readthedocs.io/en/latest/) (tested using 9.7.0)
* [Python 3](https://www.python.org/) (tested using 3.13.7)
* [NumPy](https://numpy.org/) (tested using 2.3.2)
* [Pillow](https://pillow.readthedocs.io/en/stable/) (tested using 11.3.0)
* [pikepdf](https://pikepdf.readthedocs.io/en/latest/) (tested using 9.10.2)
* [DjVuLibre](https://djvu.sourceforge.net/) (tested using 3.5.28) – for DjVu output
* [ImageMagick](https://imagemagick.org/) (tested using 7.1.1.47)
* [ImageMagick](https://imagemagick.org/) (tested using 7.1.2.3)
* [jbig2enc](https://github.com/agl/jbig2enc) (tested using 0.30) – for JBIG2

40
pdf_segmented/segmentation.py
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@ -39,16 +39,46 @@ def segment_page(input_page: Image) -> SegmentedPage:
# Convert image to foreground/background
image_rgb = input_page.convert('RGB')
numpy_rgb = numpy.asarray(image_rgb)
black_pixels = (numpy_rgb[:,:,0] == 0) & (numpy_rgb[:,:,1] == 0) & (numpy_rgb[:,:,2] == 0)
# Foreground is only black
# Precompute black and white pixels
black_pixels = (numpy_rgb[:,:,0] == 0) & (numpy_rgb[:,:,1] == 0) & (numpy_rgb[:,:,2] == 0)
white_pixels = (numpy_rgb[:,:,0] == 255) & (numpy_rgb[:,:,1] == 255) & (numpy_rgb[:,:,2] == 255)
black_or_white = black_pixels | white_pixels
# Precompute pixels with all neighbours either black or white
bw1 = numpy.roll(black_or_white, (1, 1), (0, 1))
bw1[0,:] = True
bw1[:,0] = True
bw2 = numpy.roll(black_or_white, (1, 0), (0, 1))
bw2[0,:] = True
bw3 = numpy.roll(black_or_white, (1, -1), (0, 1))
bw3[0,:] = True
bw3[:,-1] = True
bw4 = numpy.roll(black_or_white, (0, -1), (0, 1))
bw4[:,-1] = True
bw5 = numpy.roll(black_or_white, (-1, -1), (0, 1))
bw5[-1,:] = True
bw5[:,-1] = True
bw6 = numpy.roll(black_or_white, (-1, 0), (0, 1))
bw6[-1,:] = True
bw7 = numpy.roll(black_or_white, (-1, 1), (0, 1))
bw7[-1,:] = True
bw7[:,0] = True
bw8 = numpy.roll(black_or_white, (0, 1), (0, 1))
bw8[:,0] = True
bw_neighbours = bw1 & bw2 & bw3 & bw4 & bw5 & bw6 & bw7 & bw8
# Foreground is only black pixels with all neighbours either black or white
fg_pixels = black_pixels & bw_neighbours
# Foreground - white out all non-foreground pixels
numpy_fg = numpy_rgb.copy()
numpy_fg[~black_pixels,:] = [255, 255, 255]
numpy_fg[~fg_pixels,:] = [255, 255, 255]
image_fg = Image.fromarray(numpy_fg, image_rgb.mode)
# Background is only non-black
# Background - white out all foreground pixels
numpy_bg = numpy_rgb.copy()
numpy_bg[black_pixels,:] = [255, 255, 255]
numpy_bg[fg_pixels,:] = [255, 255, 255]
image_bg = Image.fromarray(numpy_bg, image_rgb.mode)
# TODO: Handle case where empty background or foreground