modest improvement to cropping (safer)

This commit is contained in:
2026-09-15 15:13:41 +02:00
parent 9b22a8a137
commit 5b8215e7f5
6 changed files with 56 additions and 9 deletions
+11 -1
View File
@@ -1,6 +1,7 @@
from __future__ import annotations
import argparse
import math
import shutil
import tempfile
from collections import defaultdict
@@ -23,6 +24,7 @@ from copienator import (
from copienator.filesystem import staged_directory
SQUARE = 1000 // 38
ANSWER_TOP_PADDING_POINTS = 4 * 72 / 25.4
Coordinate = tuple[str, int, int, int, int, int]
ParsedCoordinate = tuple[str, str, int, int, int, int, int]
@@ -171,7 +173,15 @@ def _render_split_outputs(
for page_number in range(start_page, end_page + 1):
page = document[page_number]
y0 = (y_start / 1000) * page.rect.height if page_number == start_page else 0
y0 = (
math.floor(max(
0,
(y_start / 1000) * page.rect.height
- ANSWER_TOP_PADDING_POINTS,
))
if page_number == start_page
else 0
)
y1 = (end_y / 1000) * page.rect.height if page_number == end_page else page.rect.height
clip = pymupdf.Rect(
fraction_x0 * page.rect.width, y0,
+2 -1
View File
@@ -110,7 +110,8 @@ def main() -> None:
flush=True)
(args.output/'report.json').write_text(json.dumps(rows, indent=2)+'\n')
with (args.output/'report.csv').open('w') as stream:
writer = csv.DictWriter(stream, fieldnames=list(rows[0]))
fieldnames = list(dict.fromkeys(key for row in rows for key in row))
writer = csv.DictWriter(stream, fieldnames=fieldnames)
writer.writeheader()
writer.writerows(rows)
cards = []
+17 -4
View File
@@ -65,13 +65,26 @@ def _large_blank_ink(gray: np.ndarray, clean: np.ndarray, dpi: float):
n, labels, stats, centers = cv2.connectedComponentsWithStats(
(residual > 35).astype(np.uint8), 8)
keep = np.zeros(n, bool)
xx = stats[1:, 0]
ww, hh, area = stats[1:, 2], stats[1:, 3], stats[1:, 4]
keep[1:] = ((area >= .8*px*px) & (np.minimum(ww, hh) >= .6*px)
& (area / (ww*hh) > .3)
& (np.maximum(ww, hh) < 4*np.minimum(ww, hh)))
density = area / (ww*hh)
shortest, longest = np.minimum(ww, hh), np.maximum(ww, hh)
compact_ink = ((area >= .8*px*px) & (shortest >= .6*px)
& (density > .3) & (longest < 4*shortest))
# Ruling suppression can fragment faint pencil handwriting into sparse,
# elongated components. Admit those moderately more readily in the central
# 80% of the sheet. The outermost 9% deliberately uses a stricter filter:
# punched holes, torn binding edges, and page numbers usually occur there.
center_x = xx + ww/2
central = (center_x > width*.1) & (center_x < width*.9)
central_ink = (central & (area >= .55*px*px) & (shortest >= .45*px)
& (density > .18) & (longest < 6*shortest))
outer = (center_x < width*.09) | (center_x > width*.91)
outer_ink = (outer & (area >= 1.2*px*px) & (shortest >= .8*px)
& (density >= .4) & (longest < 3*shortest))
keep[1:] = np.where(outer, outer_ink, compact_ink | central_ink)
# Use side columns as a prior, then require repeated size and alignment. An
# isolated note in the same column remains eligible to protect the margin.
xx = stats[1:, 0]
candidates = np.flatnonzero(
((xx < 15*px) | (xx+ww > width-15*px))
& (ww > px) & (ww < 9*px) & (hh > px) & (hh < 12*px)) + 1
+5
View File
@@ -46,6 +46,11 @@ alignment in the outer 15 mm are treated as punched holes only when at least
three span a substantial part of the page. Writing in the same side column still
protects its margin.
On confirmed ruled paper, faint sparse strokes in the central 80% of the page
use a moderately more sensitive component filter. The outermost 9% on each side
uses a stricter filter because punched holes, torn binding edges, and page
numbers normally appear there.
The large-blank refinement changes an edge only when it finds at least 30 mm of
additional empty paper. A 2 mm recovery neighbourhood is applied before the
normal padding. Apparently blank pages and pages without reliable ink seeds are
+14
View File
@@ -108,6 +108,14 @@ class InkDetectionTests(unittest.TestCase):
.7,(190,190,190),2)
self.assertGreater(detect_bounds(image,dpi=150)['bottom_px'],1100)
def test_sparse_central_pencil_on_dark_grid_survives(self):
image = self.dark_grid()
# Thin, pale handwriting crossing the ruling is split into sparse
# components. Its central position distinguishes it from page holes.
cv2.putText(image,'result',(330,1090),cv2.FONT_HERSHEY_SCRIPT_SIMPLEX,
.85,(155,155,155),1)
self.assertGreater(detect_bounds(image,dpi=150)['bottom_px'],1090)
def test_large_unruled_diagram_is_not_paper(self):
image = np.full((1200,850,3),255,np.uint8)
cv2.rectangle(image,(100,100),(750,1100),(20,20,20),3)
@@ -151,6 +159,12 @@ class InkDetectionTests(unittest.TestCase):
r = detect_bounds(image,dpi=150)
self.assertGreater(r['bottom_px'],1060)
def test_faint_page_number_in_outer_band_does_not_block_crop(self):
image = self.dark_grid()
cv2.putText(image,'4/',(3,1160),cv2.FONT_HERSHEY_SIMPLEX,
.55,(130,130,130),1)
self.assertLess(detect_bounds(image,dpi=150)['bottom_px'],850)
def test_faded_neutral_grid(self):
image = self.dark_grid()
image[np.all(image == 65,axis=2)] = 145
+7 -3
View File
@@ -8,7 +8,10 @@ import numpy as np
import pymupdf
from pdf2image import convert_from_path
from copienator.commands.splitting_int import _render_split_outputs
from copienator.commands.splitting_int import (
ANSWER_TOP_PADDING_POINTS,
_render_split_outputs,
)
class SplittingGeometryTests(unittest.TestCase):
@@ -60,7 +63,8 @@ class SplittingGeometryTests(unittest.TestCase):
else:
coordinates = [("A", 0, 250, 260, 0, 100),
("_", 0, 711, 721, 0, 100)]
bounds = (bounds[0], max(bounds[1], height // 4),
answer_top = int(height // 4 - ANSWER_TOP_PADDING_POINTS)
bounds = (bounds[0], max(bounds[1], answer_top),
bounds[2], min(bounds[3], height * 3 // 4))
_render_split_outputs(source, coordinates, root)
self.assert_matches_preview(root / "A.pdf", preview, bounds)
@@ -85,7 +89,7 @@ class SplittingGeometryTests(unittest.TestCase):
rendered = convert_from_path(root / "A.pdf", dpi=72)
self.assertEqual(len(rendered), 2)
for actual, preview, bounds in zip(rendered, previews,
[(40, 700, 780, 920), (20, 80, 760, 250)]):
[(40, 688, 780, 920), (20, 80, 760, 250)]):
expected = np.asarray(preview.crop(bounds)).astype(float)
actual = np.asarray(actual).astype(float)
self.assertEqual(actual.shape, expected.shape)