Initial cropping support

2026-09-09 14:53:04 +02:00
parent 359c62004c
commit 3969580e01
13 changed files with 1092 additions and 14 deletions
@@ -0,0 +1,146 @@
+"""Shared geometry and foreground helpers for scanned paper backgrounds."""
+
+import cv2
+import numpy as np
+
+
+def _runs(values: np.ndarray) -> list[tuple[int, int]]:
+    edges = np.diff(np.r_[False, values, False].astype(np.int8))
+    return list(zip(np.flatnonzero(edges == 1), np.flatnonzero(edges == -1)))
+
+
+def _skew(gray: np.ndarray, angle_step: float = .1) -> float:
+    """Use the dominant near-horizontal/vertical Hough angle, at reduced size."""
+    scale = min(1.0, 1200 / max(gray.shape))
+    small = cv2.resize(gray, None, fx=scale, fy=scale)
+    edges = cv2.Canny(small, 40, 120)
+    lines = cv2.HoughLinesP(edges, 1, np.deg2rad(angle_step), 60,
+                            minLineLength=min(small.shape) * .16, maxLineGap=12)
+    if lines is None:
+        return 0.0
+    angles, weights = [], []
+    for x0, y0, x1, y1 in lines.reshape(-1, 4):
+        a = (np.degrees(np.arctan2(y1-y0, x1-x0)) + 45) % 90 - 45
+        if abs(a) <= 5:
+            angles.append(a)
+            weights.append(np.hypot(x1-x0, y1-y0))
+    return _dominant_angle(angles, weights)
+
+
+def _dominant_angle(angles, weights) -> float:
+    if len(angles) < 4:
+        return 0.0
+    angles, weights = np.array(angles), np.array(weights)
+    bins = np.arange(-5.125, 5.126, .25)
+    hist, _ = np.histogram(angles, bins, weights=weights)
+    peak = (bins[hist.argmax()] + bins[hist.argmax()+1]) / 2
+    near = abs(angles-peak) < .4
+    if weights[near].sum() < .35 * weights.sum():
+        return 0.0
+    return float(np.average(angles[near], weights=weights[near]))
+
+
+def _ruling(binary: np.ndarray, horizontal: bool) -> tuple[np.ndarray, int]:
+    """Accept a family of long lines only when positions are largely periodic."""
+    h, w = binary.shape
+    length = max(25, int((w if horizontal else h) * .12))
+    kernel = cv2.getStructuringElement(cv2.MORPH_RECT,
+                                      (length, 1) if horizontal else (1, length))
+    connected = cv2.morphologyEx(binary, cv2.MORPH_CLOSE,
+                                np.ones((1, 3) if horizontal else (3, 1), np.uint8))
+    lines = cv2.morphologyEx(connected, cv2.MORPH_OPEN, kernel)
+    counts = np.count_nonzero(lines, axis=1 if horizontal else 0)
+    bands = _runs(counts > (w if horizontal else h) * .18)
+    if len(bands) < 5:
+        return np.zeros_like(binary), 0
+    centers = np.array([(a+b)/2 for a, b in bands])
+    gaps = np.diff(centers)
+    # Missing lines and major/minor rulings may have integer-multiple spacing.
+    candidates = gaps[gaps >= 4]
+    regular = any(np.mean(abs(gaps / d - np.round(gaps / d)) < .16) >= .75
+                  for d in candidates)
+    if not regular:
+        return np.zeros_like(binary), 0
+    accepted = np.zeros_like(binary)
+    for a, b in bands:
+        if horizontal:
+            accepted[max(0, a-2):b+2] = 255
+        else:
+            accepted[:, max(0, a-2):b+2] = 255
+    # Real scans have local warp as well as global skew. Recover shorter line
+    # segments close to the established ruling family, without extending the
+    # entire family into large empty gaps.
+    short = max(25, int((w if horizontal else h)*.035))
+    joined = cv2.morphologyEx(binary, cv2.MORPH_CLOSE,
+                             np.ones((1, 7) if horizontal else (7, 1), np.uint8))
+    tolerant = cv2.dilate(joined, np.ones((3, 1) if horizontal else (1, 3), np.uint8))
+    fragments = cv2.morphologyEx(tolerant, cv2.MORPH_OPEN,
+                                np.ones((1, short) if horizontal else (short, 1), np.uint8))
+    nearby = cv2.dilate(accepted, np.ones((15, 1) if horizontal else (1, 15), np.uint8))
+    accepted |= fragments & nearby if horizontal else fragments
+    return accepted, len(bands)
+
+
+def _foreground(gray: np.ndarray, dpi: float, threshold: int,
+                ruling: np.ndarray) -> np.ndarray:
+    block = max(15, int(dpi / 5) | 1)
+    binary = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
+                                   cv2.THRESH_BINARY_INV, block, threshold)
+    # Only suppress near the fitted paper lines. Preserve unusually dark ink
+    # crossing pale ruling by comparing against the typical line intensity.
+    mask = cv2.dilate(ruling, np.ones((3, 3), np.uint8)) > 0
+    if np.any(ruling):
+        samples = ((ruling > 0) & (binary > 0)).astype(np.float32)
+        window = max(31, int(dpi*.4) | 1)
+        weight = cv2.boxFilter(samples, -1, (window, window))
+        total = cv2.boxFilter(gray.astype(np.float32)*samples, -1, (window, window))
+        typical = total / np.maximum(weight, 1e-6)
+        excess_ink = (gray.astype(float) < typical - 40).astype(np.uint8)
+        # A dark, one-pixel remnant of a paper line is still paper. Only retain
+        # locally thicker excess strokes inside the suppression mask.
+        excess_ink = cv2.erode(excess_ink, np.ones((2, 2), np.uint8)) > 0
+        mask &= ~excess_ink
+    binary[mask] = 0
+    return binary
+
+
+def _content_mask(binary: np.ndarray, dpi: float, nh: int = 0,
+                  nv: int = 0) -> tuple[np.ndarray, int]:
+    """Filter only tiny speckles and repeated, matching edge-hole components."""
+    px = dpi / 25.4
+    # Small closing reconnects strokes interrupted by paper-line suppression.
+    grouped = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8))
+    n, labels, stats, _ = cv2.connectedComponentsWithStats(grouped, 8)
+    keep = np.zeros(n, dtype=bool)
+    x, y, w, h, area = stats[1:].T
+    keep[1:] = (area >= max(4, .035*px*px)) & (np.maximum(w, h) >= .35*px)
+    # Thin straight residuals on confirmed ruled paper are not credible ink.
+    thin = max(2, round(.3*px))
+    if nv:
+        keep[1:] &= ~((w <= thin) & (h >= 3*w))
+    if nh:
+        keep[1:] &= ~((h <= thin) & (w >= 3*h))
+    # Hole shadows form recurring shapes close to a physical side edge. Never
+    # discard an entire margin strip: other writing there must remain visible.
+    height, width = binary.shape
+    candidates = np.flatnonzero(keep[1:]
+        & ((x+w < 12*px) | (x > width-12*px))
+        & (w > .8*px) & (w < 9*px) & (h > px) & (h < 12*px)) + 1
+    holes = set()
+    for i in candidates:
+        x, y, w, h, area = stats[i]
+        similar = []
+        a = cv2.resize((labels[y:y+h, x:x+w] == i).astype(np.uint8), (24, 32)) > 0
+        for j in candidates:
+            xx, yy, ww, hh, aa = stats[j]
+            if abs(x-xx) > 2*px or not (.7 < ww/w < 1.4 and .7 < hh/h < 1.4):
+                continue
+            b = cv2.resize((labels[yy:yy+hh, xx:xx+ww] == j).astype(np.uint8), (24, 32)) > 0
+            if np.count_nonzero(a & b) / max(1, np.count_nonzero(a | b)) > .60:
+                similar.append(j)
+        if len(similar) >= 4 and np.ptp(stats[similar, 1]) > height*.45:
+            holes.update(similar)
+    if holes:
+        keep[list(holes)] = False
+    # Bound original residual ink, not the expanded/grouped mask.
+    return (keep[labels] & (binary > 0)).astype(np.uint8), len(holes)