ECG photo rectifier
Turns a phone photo of a paper ECG (a strip or a sheet with one continuous grid; bent, crumpled, folded, held in a hand) into a flat sheet with a square grid at 10 px/mm. CPU only: numpy, OpenCV and onnxruntime; no GPU, no PyTorch.
Live demo: https://huggingface.co/spaces/pashaalex/ECG-Rectifier — upload a photo, get the flat sheet.
git clone https://huggingface.co/pashaalex/ecg-rectifier # the models need git-lfs or git-xet
cd ecg-rectifier
pip install -r requirements.txt
python3 rectify.py examples/folded.jpg flat.png
Without git: pip install huggingface_hub && hf download pashaalex/ecg-rectifier --local-dir ecg-rectifier.
Run the web demo locally: pip install gradio && python3 app.py.
Examples
Phone photos of printed synthetic ECGs (no patient data), rectified with
python3 rectify.py examples/folded.jpg flat.png --illum:
| photo | flat sheet (flat-illum.jpg; empty corners outside the photo filled with paper colour) |
|---|---|
 |
 |
 |
 |
Output
| file | content |
|---|---|
flat.png |
the flat sheet, 10 px/mm, cropped to the sheet (any OpenCV format; .png is lossless) |
flat.json |
status, reason, self-check, photo scale, timings |
flat-check.jpg |
the photo with the mask (blue), working area (yellow), crop (green) and grid nodes used (red) |
Options: --illum also writes flat-illum.jpg with shadows and the lamp tint removed
(--illum-paper keep keeps the paper colour); --redshift recolours dark lines red before the
networks, for black or grey grids.
Statuses: ok, warn, fail; no_grid (no 5 mm grid found: the masked crop is written without
rectification); too_small (sheet < 50 x 30 mm); not_applicable (the grid falls into separate
pieces, e.g. Holter reports with framed strips). Exit code 0 means an image was written.
How it works
mask.onnx— a coarse mask of the ECG signal on the whole photo; the photo is cropped to it.- Photo scale (px/mm): the detector is run at several assumed scales; the right one gives a lattice with a 5 mm step, confirmed by four thin 1 mm lines inside each 5 mm cell.
- The crop is resized to 5.5 px/mm;
detector.onnxfinds the intersections of the bold 5 mm lines. offsets.onnxpredicts, for overlapping 224 px windows, where on the sheet (mm) each of 7 x 7 patch centres lies; the windows are fused into one smooth map photo pixel -> sheet mm (least squares).lattice.onnx(a graph network) numbers the detected nodes (i, j); node (i, j) must lie at (5i, 5j) mm.- The map is corrected by the numbered nodes: a cubic polynomial (or a homography, whichever fits the held-out nodes better), then a smooth local correction table.
- The flat sheet is resampled from the original photo by inverting the map.
The self-check fits the correction on half of the nodes (chessboard by i + j) and measures the other half after a best similarity. It measures the shape of the grid, not the absolute scale, and it does not see detector errors; judge the result by eye.
Repository
rectify.py— the pipeline;ecglib/— its building blocks;models/— the four ONNX networks;examples/— the photos above;app.py— the web demo (Gradio).training/— synthetic data generators and training scripts of the networks (seetraining/README.md); photos are rendered with MeshAugmentor.
License
MIT (LICENSE). The fonts in training/fonts/ keep their own licences (SIL OFL 1.1 for Caveat,
Bitstream Vera / DejaVu for DejaVu Sans Mono).