Fu DaiandClaude Opus 4.8
1a585693be
FastAPI service wrapping the Surya-OCR-2 model (datalab-to) served through vLLM: legacy /v1/api/ai/* endpoints, an OpenAI-compatible /v1/chat/completions endpoint, a coalescing request batcher, a local OCR CLI, Docker packaging, multilingual example outputs, and quantization/concurrency benchmarks. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
65 lines
2.4 KiB
Python
65 lines
2.4 KiB
Python
"""Pixel-content heuristics for detecting blank or near-uniform image regions.
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Used by both the layout predictor (drop hallucinated layout blocks over empty
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space) and the recognition predictor (drop hallucinated text blocks from
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full-page OCR, decide whether an empty full-page output is a correct blank-page
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read or a failure).
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Two signals, combined:
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* near-white fraction — most pixels have every RGB channel above a threshold
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* pixel-value standard deviation — the region is essentially one color
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(catches uniform-color fills that the white check misses)
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"""
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from __future__ import annotations
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import numpy as np
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from PIL import Image
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# Per-channel value at/above which a pixel is considered "near-white".
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# Tolerates the small noise typical of PDF renders at 96 DPI.
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BLANK_WHITE_THRESHOLD = 245
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# Fraction of pixels that must be near-white for a region to count as blank.
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BLANK_PIXEL_FRACTION = 0.99
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# Pixel-value std below which a region is "essentially one color" regardless
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# of what that color is (catches solid-fill rectangles, dark banners, etc.).
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UNIFORM_COLOR_STD = 8.0
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def near_white_fraction(
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image: Image.Image, white_threshold: int = BLANK_WHITE_THRESHOLD
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) -> float:
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"""Fraction of pixels where every RGB channel ≥ ``white_threshold``."""
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arr = np.asarray(image.convert("RGB"))
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if arr.size == 0:
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return 0.0
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return float(np.all(arr >= white_threshold, axis=-1).mean())
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def is_blank_region(
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image: Image.Image,
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*,
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white_threshold: int = BLANK_WHITE_THRESHOLD,
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blank_pixel_fraction: float = BLANK_PIXEL_FRACTION,
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uniform_color_std: float = UNIFORM_COLOR_STD,
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) -> bool:
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"""True iff the image is essentially blank — either mostly near-white or
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near-uniform color. Use this on a per-block crop or a whole page.
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Returns False for empty (0-pixel) crops so callers don't accidentally
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treat a degenerate bbox as blank.
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"""
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arr = np.asarray(image.convert("RGB"))
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if arr.size == 0:
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return False
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if np.all(arr >= white_threshold, axis=-1).mean() > blank_pixel_fraction:
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return True
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# Per-channel std — a uniform solid color (e.g., red banner with RGB=(200,50,50))
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# has each channel constant across pixels, but mixing channels inflates the
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# aggregate std. Check each channel independently.
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per_channel_std = arr.reshape(-1, arr.shape[-1]).std(axis=0)
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if float(per_channel_std.max()) < uniform_color_std:
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return True
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return False
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