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| author | skal <pascal.massimino@gmail.com> | 2026-02-10 22:51:46 +0100 |
|---|---|---|
| committer | skal <pascal.massimino@gmail.com> | 2026-02-10 22:51:46 +0100 |
| commit | 58f276378735e0b51f4d1517a844357e45e376a7 (patch) | |
| tree | 6e4516920837ab3748b5ee54494218d6c844ee07 /src/audio | |
| parent | 5b31395fbfffabdd1cc9b452eb12d9dd63110a6d (diff) | |
feat: Add salient-point patch extraction for CNN training
Preserve natural pixel scale by extracting patches at salient points
instead of resizing entire images.
Features:
- Multiple detectors: Harris (default), FAST, Shi-Tomasi, gradient
- Configurable patch size (e.g., 32×32) and patches per image
- Automatic fallback to random patches if insufficient features
Usage:
# Patch-based training (preserves scale)
python3 train_cnn.py --input dir/ --target dir/ --patch-size 32 --patches-per-image 64 --detector harris
# Original resize mode (if --patch-size omitted)
python3 train_cnn.py --input dir/ --target dir/
Arguments:
--patch-size: Patch dimension (e.g., 32 for 32×32 patches)
--patches-per-image: Number of patches to extract per image (default: 64)
--detector: harris|fast|shi-tomasi|gradient (default: harris)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Diffstat (limited to 'src/audio')
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