[split-required] Split 700-870 LOC files across all services
backend-lehrer (11 files): - llm_gateway/routes/schools.py (867 → 5), recording_api.py (848 → 6) - messenger_api.py (840 → 5), print_generator.py (824 → 5) - unit_analytics_api.py (751 → 5), classroom/routes/context.py (726 → 4) - llm_gateway/routes/edu_search_seeds.py (710 → 4) klausur-service (12 files): - ocr_labeling_api.py (845 → 4), metrics_db.py (833 → 4) - legal_corpus_api.py (790 → 4), page_crop.py (758 → 3) - mail/ai_service.py (747 → 4), github_crawler.py (767 → 3) - trocr_service.py (730 → 4), full_compliance_pipeline.py (723 → 4) - dsfa_rag_api.py (715 → 4), ocr_pipeline_auto.py (705 → 4) website (6 pages): - audit-checklist (867 → 8), content (806 → 6) - screen-flow (790 → 4), scraper (789 → 5) - zeugnisse (776 → 5), modules (745 → 4) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Benjamin Admin
342
klausur-service/backend/page_crop_core.py
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342
klausur-service/backend/page_crop_core.py
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"""
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Page Crop - Core Crop and Format Detection
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Content-based crop for scanned pages and book scans. Detects the content
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boundary by analysing ink density projections and (for book scans) the
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spine shadow gradient.
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Extracted from page_crop.py to keep files under 500 LOC.
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License: Apache 2.0
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"""
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import logging
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from typing import Dict, Any, Tuple
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import cv2
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import numpy as np
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from page_crop_edges import (
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_detect_left_edge_shadow,
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_detect_right_edge_shadow,
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_detect_top_bottom_edges,
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)
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logger = logging.getLogger(__name__)
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# Known paper format aspect ratios (height / width, portrait orientation)
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PAPER_FORMATS = {
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"A4": 297.0 / 210.0, # 1.4143
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"A5": 210.0 / 148.0, # 1.4189
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"Letter": 11.0 / 8.5, # 1.2941
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"Legal": 14.0 / 8.5, # 1.6471
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"A3": 420.0 / 297.0, # 1.4141
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}
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def detect_page_splits(
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img_bgr: np.ndarray,
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) -> list:
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"""Detect if the image is a multi-page spread and return split rectangles.
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Uses **brightness** (not ink density) to find the spine area:
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the scanner bed produces a characteristic gray strip where pages meet,
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which is darker than the white paper on either side.
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Returns a list of page dicts ``{x, y, width, height, page_index}``
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or an empty list if only one page is detected.
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"""
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h, w = img_bgr.shape[:2]
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# Only check landscape-ish images (width > height * 1.15)
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if w < h * 1.15:
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return []
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gray = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY)
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# Column-mean brightness (0-255) — the spine is darker (gray scanner bed)
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col_brightness = np.mean(gray, axis=0).astype(np.float64)
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# Heavy smoothing to ignore individual text lines
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kern = max(11, w // 50)
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if kern % 2 == 0:
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kern += 1
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brightness_smooth = np.convolve(col_brightness, np.ones(kern) / kern, mode="same")
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# Page paper is bright (typically > 200), spine/scanner bed is darker
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page_brightness = float(np.max(brightness_smooth))
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if page_brightness < 100:
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return [] # Very dark image, skip
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# Spine threshold: significantly darker than the page
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spine_thresh = page_brightness * 0.88
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# Search in center region (30-70% of width)
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center_lo = int(w * 0.30)
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center_hi = int(w * 0.70)
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# Find the darkest valley in the center region
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center_brightness = brightness_smooth[center_lo:center_hi]
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darkest_val = float(np.min(center_brightness))
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if darkest_val >= spine_thresh:
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logger.debug("No spine detected: min brightness %.0f >= threshold %.0f",
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darkest_val, spine_thresh)
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return []
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# Find ALL contiguous dark runs in the center region
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is_dark = center_brightness < spine_thresh
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dark_runs: list = []
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run_start = -1
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for i in range(len(is_dark)):
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if is_dark[i]:
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if run_start < 0:
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run_start = i
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else:
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if run_start >= 0:
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dark_runs.append((run_start, i))
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run_start = -1
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if run_start >= 0:
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dark_runs.append((run_start, len(is_dark)))
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# Filter out runs that are too narrow (< 1% of image width)
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min_spine_px = int(w * 0.01)
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dark_runs = [(s, e) for s, e in dark_runs if e - s >= min_spine_px]
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if not dark_runs:
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logger.debug("No dark runs wider than %dpx in center region", min_spine_px)
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return []
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# Score each dark run: prefer centered, dark, narrow valleys
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center_region_len = center_hi - center_lo
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image_center_in_region = (w * 0.5 - center_lo)
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best_score = -1.0
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best_start, best_end = dark_runs[0]
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for rs, re in dark_runs:
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run_width = re - rs
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run_center = (rs + re) / 2.0
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sigma = center_region_len * 0.15
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dist = abs(run_center - image_center_in_region)
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center_factor = float(np.exp(-0.5 * (dist / sigma) ** 2))
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run_brightness = float(np.mean(center_brightness[rs:re]))
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darkness_factor = max(0.0, (spine_thresh - run_brightness) / spine_thresh)
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width_frac = run_width / w
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if width_frac <= 0.05:
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narrowness_bonus = 1.0
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elif width_frac <= 0.15:
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narrowness_bonus = 1.0 - (width_frac - 0.05) / 0.10
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else:
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narrowness_bonus = 0.0
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score = center_factor * darkness_factor * (0.3 + 0.7 * narrowness_bonus)
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logger.debug(
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"Dark run x=%d..%d (w=%d): center_f=%.3f dark_f=%.3f narrow_b=%.3f -> score=%.4f",
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center_lo + rs, center_lo + re, run_width,
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center_factor, darkness_factor, narrowness_bonus, score,
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)
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if score > best_score:
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best_score = score
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best_start, best_end = rs, re
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spine_w = best_end - best_start
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spine_x = center_lo + best_start
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spine_center = spine_x + spine_w // 2
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logger.debug(
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"Best spine candidate: x=%d..%d (w=%d), score=%.4f",
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spine_x, spine_x + spine_w, spine_w, best_score,
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)
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# Verify: must have bright (paper) content on BOTH sides
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left_brightness = float(np.mean(brightness_smooth[max(0, spine_x - w // 10):spine_x]))
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right_end = center_lo + best_end
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right_brightness = float(np.mean(brightness_smooth[right_end:min(w, right_end + w // 10)]))
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if left_brightness < spine_thresh or right_brightness < spine_thresh:
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logger.debug("No bright paper flanking spine: left=%.0f right=%.0f thresh=%.0f",
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left_brightness, right_brightness, spine_thresh)
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return []
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logger.info(
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"Spine detected: x=%d..%d (w=%d), brightness=%.0f vs paper=%.0f, "
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"left_paper=%.0f, right_paper=%.0f",
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spine_x, right_end, spine_w, darkest_val, page_brightness,
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left_brightness, right_brightness,
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)
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# Split at the spine center
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split_points = [spine_center]
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# Build page rectangles
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pages: list = []
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prev_x = 0
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for i, sx in enumerate(split_points):
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pages.append({"x": prev_x, "y": 0, "width": sx - prev_x,
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"height": h, "page_index": i})
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prev_x = sx
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pages.append({"x": prev_x, "y": 0, "width": w - prev_x,
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"height": h, "page_index": len(split_points)})
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# Filter out tiny pages (< 15% of total width)
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pages = [p for p in pages if p["width"] >= w * 0.15]
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if len(pages) < 2:
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return []
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# Re-index
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for i, p in enumerate(pages):
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p["page_index"] = i
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logger.info(
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"Page split detected: %d pages, spine_w=%d, split_points=%s",
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len(pages), spine_w, split_points,
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)
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return pages
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def detect_and_crop_page(
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img_bgr: np.ndarray,
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margin_frac: float = 0.01,
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) -> Tuple[np.ndarray, Dict[str, Any]]:
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"""Detect content boundary and crop scanner/book borders.
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Algorithm (4-edge detection):
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1. Adaptive threshold -> binary (text=255, bg=0)
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2. Left edge: spine-shadow detection via grayscale column means,
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fallback to binary vertical projection
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3. Right edge: binary vertical projection (last ink column)
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4. Top/bottom edges: binary horizontal projection
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5. Sanity checks, then crop with configurable margin
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Args:
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img_bgr: Input BGR image (should already be deskewed/dewarped)
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margin_frac: Extra margin around content (fraction of dimension, default 1%)
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Returns:
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Tuple of (cropped_image, result_dict)
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"""
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h, w = img_bgr.shape[:2]
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total_area = h * w
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result: Dict[str, Any] = {
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"crop_applied": False,
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"crop_rect": None,
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"crop_rect_pct": None,
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"original_size": {"width": w, "height": h},
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"cropped_size": {"width": w, "height": h},
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"detected_format": None,
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"format_confidence": 0.0,
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"aspect_ratio": round(max(h, w) / max(min(h, w), 1), 4),
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"border_fractions": {"top": 0.0, "bottom": 0.0, "left": 0.0, "right": 0.0},
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}
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gray = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY)
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# --- Binarise with adaptive threshold ---
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binary = cv2.adaptiveThreshold(
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gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
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cv2.THRESH_BINARY_INV, blockSize=51, C=15,
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)
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# --- Edge detection ---
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left_edge = _detect_left_edge_shadow(gray, binary, w, h)
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right_edge = _detect_right_edge_shadow(gray, binary, w, h)
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top_edge, bottom_edge = _detect_top_bottom_edges(binary, w, h)
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# Compute border fractions
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border_top = top_edge / h
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border_bottom = (h - bottom_edge) / h
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border_left = left_edge / w
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border_right = (w - right_edge) / w
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result["border_fractions"] = {
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"top": round(border_top, 4),
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"bottom": round(border_bottom, 4),
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"left": round(border_left, 4),
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"right": round(border_right, 4),
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}
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# Sanity: only crop if at least one edge has > 2% border
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min_border = 0.02
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if all(f < min_border for f in [border_top, border_bottom, border_left, border_right]):
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logger.info("All borders < %.0f%% — no crop needed", min_border * 100)
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result["detected_format"], result["format_confidence"] = _detect_format(w, h)
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return img_bgr, result
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# Add margin
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margin_x = int(w * margin_frac)
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margin_y = int(h * margin_frac)
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crop_x = max(0, left_edge - margin_x)
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crop_y = max(0, top_edge - margin_y)
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crop_x2 = min(w, right_edge + margin_x)
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crop_y2 = min(h, bottom_edge + margin_y)
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crop_w = crop_x2 - crop_x
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crop_h = crop_y2 - crop_y
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# Sanity: cropped area must be >= 40% of original
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if crop_w * crop_h < 0.40 * total_area:
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logger.warning("Cropped area too small (%.0f%%) — skipping crop",
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100.0 * crop_w * crop_h / total_area)
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result["detected_format"], result["format_confidence"] = _detect_format(w, h)
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return img_bgr, result
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cropped = img_bgr[crop_y:crop_y2, crop_x:crop_x2].copy()
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detected_format, format_confidence = _detect_format(crop_w, crop_h)
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result["crop_applied"] = True
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result["crop_rect"] = {"x": crop_x, "y": crop_y, "width": crop_w, "height": crop_h}
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result["crop_rect_pct"] = {
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"x": round(100.0 * crop_x / w, 2),
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"y": round(100.0 * crop_y / h, 2),
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"width": round(100.0 * crop_w / w, 2),
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"height": round(100.0 * crop_h / h, 2),
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}
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result["cropped_size"] = {"width": crop_w, "height": crop_h}
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result["detected_format"] = detected_format
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result["format_confidence"] = format_confidence
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result["aspect_ratio"] = round(max(crop_w, crop_h) / max(min(crop_w, crop_h), 1), 4)
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logger.info(
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"Page cropped: %dx%d -> %dx%d, format=%s (%.0f%%), "
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"borders: T=%.1f%% B=%.1f%% L=%.1f%% R=%.1f%%",
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w, h, crop_w, crop_h, detected_format, format_confidence * 100,
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border_top * 100, border_bottom * 100,
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border_left * 100, border_right * 100,
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)
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return cropped, result
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# ---------------------------------------------------------------------------
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# Format detection (kept as optional metadata)
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# ---------------------------------------------------------------------------
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def _detect_format(width: int, height: int) -> Tuple[str, float]:
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"""Detect paper format from dimensions by comparing aspect ratios."""
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if width <= 0 or height <= 0:
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return "unknown", 0.0
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aspect = max(width, height) / min(width, height)
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best_format = "unknown"
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best_diff = float("inf")
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for fmt, expected_ratio in PAPER_FORMATS.items():
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diff = abs(aspect - expected_ratio)
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if diff < best_diff:
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best_diff = diff
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best_format = fmt
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confidence = max(0.0, 1.0 - best_diff * 5.0)
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if confidence < 0.3:
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return "unknown", 0.0
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return best_format, round(confidence, 3)
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