ilia
5be9d5935d
- vision/document.py: pad_quad() expands the detected quad outward before warping so a straight-line contour fit doesn't clip the first character of every line on a slightly curled/wrinkled receipt - ocr/extract.py + llm/vision.py: extract transaction time (HH:MM) so same-day repeat visits to a vendor get distinct filenames (2023-03-20_1525_walmart.pdf vs. a later same-day trip) - vision/orient.py: downscale before Tesseract OSD/confidence-sweep rotation detection — this was the dominant cost in the detection phase (7x speedup: 472s -> 67s on a 36s test video)
84 lines
3.3 KiB
YAML
84 lines
3.3 KiB
YAML
# PaperPod tunable thresholds. All values here override the built-in defaults
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# in paperpod/config.py; delete a key to fall back to the default.
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capture:
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# Frames per second to sample from the source video for analysis.
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sample_fps: 8
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# Frames are downscaled to this width for motion analysis (full resolution
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# is still used for the final document crops).
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processing_width: 960
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audio:
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# WAV sample rate for the extracted audio track (16 kHz mono = whisper-ready).
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sample_rate: 16000
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motion:
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# Gaussian blur kernel applied before frame differencing (odd number).
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blur_ksize: 21
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# A pixel counts as "changed" if its gray level moved by more than this (0-255).
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pixel_threshold: 12
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# Fraction of changed pixels (0.0-1.0) above which a frame counts as "moving".
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# Raise if sensor noise triggers false motion; lower if small gestures
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# (page flips) are being missed.
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threshold: 0.02
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# A stable stretch must last at least this long (seconds) to count as a window.
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stable_min_duration_s: 1.0
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document:
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# Contours smaller than this fraction of the frame area are ignored.
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min_area_ratio: 0.04
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# Contours larger than this fraction of the frame are rejected as likely
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# background/lighting rather than a document.
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max_area_ratio: 0.92
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# Reject candidates whose long side is more than this many times their
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# short side (filters out thin slivers/edge-strip false detections).
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max_aspect: 6.0
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# Canny edge detection thresholds.
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canny_low: 50
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canny_high: 150
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# Contour search runs on a frame downscaled to this width (then the
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# winning quad is scaled back up for the full-resolution crop). Keeps
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# edge/gap-closing behavior consistent regardless of source resolution —
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# important for 4K phone video.
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detect_width: 1000
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# Bake illumination-normalization + contrast enhancement into the saved
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# crop/PDF (grayscale "flattened scan" look). OCR always uses this
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# enhancement internally regardless of this setting.
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enhance: false
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# Auto-detect and correct 0/90/180/270 rotation before OCR/PDF export.
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auto_rotate: true
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# Expand the detected quad outward by this fraction before warping, to
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# recover paper margins clipped by curl/wrinkles (straight-line quad vs.
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# a slightly bowed real edge). refine.tighten below trims any extra
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# mat/background this pulls in back off.
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pad_margin: 0.04
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# Tighten crops to the actual paper region (cuts mat margins/hands) and
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# inpaint fingers pressing on the page.
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refine: true
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remove_fingers: true
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# Split separate document placements when no contour is seen for this long.
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event_gap_s: 2.0
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# Ignore single-frame detection blips.
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event_min_samples: 2
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llm:
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# Identify/name documents with a local Ollama vision model (all offline).
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# Reads whole documents at once — far more robust than Tesseract for
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# curled receipts and stylized logos — and flags quality issues
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# (fingers_visible, blurry, glare) in export_summary.csv.
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# Slower: ~20-40s per document. Falls back to Tesseract when Ollama is down.
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enabled: true
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model: qwen2.5vl
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base_url: http://localhost:11434
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timeout_s: 180
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speech:
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# Window around a capture event in which to look for a spoken description.
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window_before_s: 3.0
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window_after_s: 5.0
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output:
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dir: ./output
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# Paperless-ngx consume directory (module 7, not wired up yet).
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consume_dir: null
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