e60254bc75
Some checks failed
CI / go-lint (push) Has been skipped
CI / python-lint (push) Has been skipped
CI / nodejs-lint (push) Has been skipped
CI / test-go-school (push) Successful in 27s
CI / test-go-edu-search (push) Successful in 27s
CI / test-python-klausur (push) Failing after 1m59s
CI / test-python-agent-core (push) Successful in 17s
CI / test-nodejs-website (push) Successful in 24s
Spalten-, Zeilen-, Woerter-Overlay und alle nachfolgenden Steps (LLM-Review, Rekonstruktion) lesen jetzt image/cropped mit Fallback auf image/dewarped. Tests fuer page_crop.py hinzugefuegt (25 Tests). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
328 lines
12 KiB
Python
328 lines
12 KiB
Python
"""
|
|
Tests for page_crop.py — content-based crop algorithm.
|
|
|
|
Tests cover:
|
|
- Edge detection via ink projections
|
|
- Spine shadow detection for book scans
|
|
- Narrow run filtering
|
|
- Paper format detection
|
|
- Sanity checks (min area, min border)
|
|
- End-to-end crop on synthetic images
|
|
"""
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
from page_crop import (
|
|
detect_and_crop_page,
|
|
_detect_format,
|
|
_detect_edge_projection,
|
|
_detect_left_edge_shadow,
|
|
_filter_narrow_runs,
|
|
)
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Helper: create synthetic images
|
|
# ---------------------------------------------------------------------------
|
|
|
|
def _make_white_image(h: int, w: int) -> np.ndarray:
|
|
"""Create a white BGR image."""
|
|
return np.full((h, w, 3), 255, dtype=np.uint8)
|
|
|
|
|
|
def _make_image_with_content(
|
|
h: int, w: int,
|
|
content_rect: tuple, # (y1, y2, x1, x2)
|
|
bg_color: int = 255,
|
|
content_color: int = 0,
|
|
) -> np.ndarray:
|
|
"""Create an image with a dark content rectangle on a light background."""
|
|
img = np.full((h, w, 3), bg_color, dtype=np.uint8)
|
|
y1, y2, x1, x2 = content_rect
|
|
img[y1:y2, x1:x2] = content_color
|
|
return img
|
|
|
|
|
|
def _make_book_scan(h: int = 1000, w: int = 800) -> np.ndarray:
|
|
"""Create a synthetic book scan with spine shadow on the left.
|
|
|
|
Left 10%: gradient from dark (50) to white (255)
|
|
Top 5%: white (empty scanner border)
|
|
Bottom 5%: white (empty scanner border)
|
|
Center: text-like content (dark pixels scattered)
|
|
"""
|
|
img = np.full((h, w, 3), 255, dtype=np.uint8)
|
|
|
|
# Spine shadow: left 10% has gradient from dark to bright
|
|
shadow_w = w // 10
|
|
for x in range(shadow_w):
|
|
brightness = int(50 + (255 - 50) * x / shadow_w)
|
|
img[:, x] = brightness
|
|
|
|
# Content area: scatter some dark pixels (simulate text)
|
|
content_top = h // 20 # 5% top margin
|
|
content_bottom = h - h // 20 # 5% bottom margin
|
|
content_left = shadow_w + w // 20 # past shadow + small margin
|
|
content_right = w - w // 20 # 5% right margin
|
|
|
|
rng = np.random.RandomState(42)
|
|
for _ in range(500):
|
|
y = rng.randint(content_top, content_bottom)
|
|
x = rng.randint(content_left, content_right)
|
|
# Small text-like blob
|
|
y2 = min(y + 3, h)
|
|
x2 = min(x + 10, w)
|
|
img[y:y2, x:x2] = 20
|
|
|
|
return img
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Tests: _filter_narrow_runs
|
|
# ---------------------------------------------------------------------------
|
|
|
|
class TestFilterNarrowRuns:
|
|
def test_removes_short_runs(self):
|
|
mask = np.array([False, True, True, False, False, True, False])
|
|
result = _filter_narrow_runs(mask, min_run=3)
|
|
# The run [True, True] (length 2) and [True] (length 1) should be removed
|
|
assert not result.any()
|
|
|
|
def test_keeps_long_runs(self):
|
|
mask = np.array([False, True, True, True, True, False])
|
|
result = _filter_narrow_runs(mask, min_run=3)
|
|
expected = np.array([False, True, True, True, True, False])
|
|
np.testing.assert_array_equal(result, expected)
|
|
|
|
def test_min_run_1_keeps_all(self):
|
|
mask = np.array([True, False, True])
|
|
result = _filter_narrow_runs(mask, min_run=1)
|
|
np.testing.assert_array_equal(result, mask)
|
|
|
|
def test_empty_mask(self):
|
|
mask = np.array([], dtype=bool)
|
|
result = _filter_narrow_runs(mask, min_run=5)
|
|
assert len(result) == 0
|
|
|
|
def test_mixed_runs(self):
|
|
mask = np.array([True, False, True, True, True, True, True, False, True, True])
|
|
result = _filter_narrow_runs(mask, min_run=3)
|
|
# Run of 1 at [0]: removed
|
|
# Run of 5 at [2:7]: kept
|
|
# Run of 2 at [8:10]: removed
|
|
expected = np.array([False, False, True, True, True, True, True, False, False, False])
|
|
np.testing.assert_array_equal(result, expected)
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Tests: _detect_format
|
|
# ---------------------------------------------------------------------------
|
|
|
|
class TestDetectFormat:
|
|
def test_a4_portrait(self):
|
|
fmt, conf = _detect_format(210, 297)
|
|
assert fmt == "A4"
|
|
assert conf > 0.8
|
|
|
|
def test_a4_landscape(self):
|
|
fmt, conf = _detect_format(297, 210)
|
|
assert fmt == "A4"
|
|
assert conf > 0.8
|
|
|
|
def test_letter(self):
|
|
fmt, conf = _detect_format(850, 1100)
|
|
assert fmt == "Letter"
|
|
assert conf > 0.5
|
|
|
|
def test_unknown_square(self):
|
|
fmt, conf = _detect_format(100, 100)
|
|
# Aspect ratio 1.0 doesn't match any paper format well
|
|
assert fmt == "unknown" or conf < 0.5
|
|
|
|
def test_zero_dimensions(self):
|
|
fmt, conf = _detect_format(0, 100)
|
|
assert fmt == "unknown"
|
|
assert conf == 0.0
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Tests: _detect_edge_projection
|
|
# ---------------------------------------------------------------------------
|
|
|
|
class TestDetectEdgeProjection:
|
|
def test_finds_first_ink_column(self):
|
|
"""Binary image with ink starting at column 50."""
|
|
binary = np.zeros((100, 200), dtype=np.uint8)
|
|
binary[10:90, 50:180] = 255 # Content from x=50 to x=180
|
|
|
|
edge = _detect_edge_projection(binary, axis=0, from_start=True, dim=200)
|
|
assert edge == 50
|
|
|
|
def test_finds_last_ink_column(self):
|
|
binary = np.zeros((100, 200), dtype=np.uint8)
|
|
binary[10:90, 50:180] = 255
|
|
|
|
edge = _detect_edge_projection(binary, axis=0, from_start=False, dim=200)
|
|
assert edge == 179 # last column with ink
|
|
|
|
def test_finds_first_ink_row(self):
|
|
binary = np.zeros((200, 100), dtype=np.uint8)
|
|
binary[30:170, 10:90] = 255
|
|
|
|
edge = _detect_edge_projection(binary, axis=1, from_start=True, dim=200)
|
|
assert edge == 30
|
|
|
|
def test_finds_last_ink_row(self):
|
|
binary = np.zeros((200, 100), dtype=np.uint8)
|
|
binary[30:170, 10:90] = 255
|
|
|
|
edge = _detect_edge_projection(binary, axis=1, from_start=False, dim=200)
|
|
assert edge == 169
|
|
|
|
def test_empty_image_returns_boundary(self):
|
|
binary = np.zeros((100, 100), dtype=np.uint8)
|
|
assert _detect_edge_projection(binary, axis=0, from_start=True, dim=100) == 0
|
|
assert _detect_edge_projection(binary, axis=0, from_start=False, dim=100) == 100
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Tests: _detect_left_edge_shadow
|
|
# ---------------------------------------------------------------------------
|
|
|
|
class TestDetectLeftEdgeShadow:
|
|
def test_detects_shadow_gradient(self):
|
|
"""Synthetic image with left-side shadow gradient."""
|
|
h, w = 500, 400
|
|
gray = np.full((h, w), 255, dtype=np.uint8)
|
|
binary = np.zeros((h, w), dtype=np.uint8)
|
|
|
|
# Shadow: left 15% gradually darkens
|
|
shadow_w = w * 15 // 100
|
|
for x in range(shadow_w):
|
|
brightness = int(50 + (255 - 50) * x / shadow_w)
|
|
gray[:, x] = brightness
|
|
|
|
# Content starts after shadow
|
|
binary[:, shadow_w + 10:w - 10] = 255
|
|
|
|
edge = _detect_left_edge_shadow(gray, binary, w, h)
|
|
# Edge should be within the shadow transition zone
|
|
# The 60% threshold fires before the actual shadow boundary
|
|
assert 0 < edge < shadow_w + 20
|
|
|
|
def test_no_shadow_uses_binary_fallback(self):
|
|
"""When shadow range is small, falls back to binary projection."""
|
|
h, w = 400, 400
|
|
gray = np.full((h, w), 200, dtype=np.uint8)
|
|
binary = np.zeros((h, w), dtype=np.uint8)
|
|
# Content block from x=80 onward (large enough to survive noise filtering)
|
|
binary[50:350, 80:380] = 255
|
|
|
|
edge = _detect_left_edge_shadow(gray, binary, w, h)
|
|
# Should find content start via projection fallback (near x=80)
|
|
assert edge <= 85
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Tests: detect_and_crop_page (end-to-end)
|
|
# ---------------------------------------------------------------------------
|
|
|
|
class TestDetectAndCropPage:
|
|
def test_no_crop_needed_all_content(self):
|
|
"""Image that is all content — no borders to crop."""
|
|
img = np.full((100, 80, 3), 40, dtype=np.uint8) # Dark content everywhere
|
|
cropped, result = detect_and_crop_page(img)
|
|
# Should return original (all borders < 2%)
|
|
assert not result["crop_applied"]
|
|
assert result["cropped_size"] == {"width": 80, "height": 100}
|
|
|
|
def test_crops_white_borders(self):
|
|
"""Image with wide white borders around dark content."""
|
|
h, w = 400, 300
|
|
img = _make_image_with_content(h, w, (80, 320, 60, 240))
|
|
|
|
cropped, result = detect_and_crop_page(img)
|
|
assert result["crop_applied"]
|
|
# Cropped size should be close to the content area (with margin)
|
|
assert result["cropped_size"]["width"] < w
|
|
assert result["cropped_size"]["height"] < h
|
|
# Content should be roughly 180x240 + margins (adaptive threshold may widen slightly)
|
|
assert 160 <= result["cropped_size"]["width"] <= 260
|
|
assert 220 <= result["cropped_size"]["height"] <= 300
|
|
|
|
def test_book_scan_detects_spine_shadow(self):
|
|
"""Synthetic book scan with spine shadow on left."""
|
|
img = _make_book_scan(1000, 800)
|
|
cropped, result = detect_and_crop_page(img)
|
|
|
|
# Should crop the spine shadow area
|
|
left_border = result["border_fractions"]["left"]
|
|
# Spine shadow is ~10% of width, plus some margin
|
|
assert left_border > 0.05 # At least 5% left border detected
|
|
|
|
def test_sanity_check_too_small_crop(self):
|
|
"""If detected content area is too small, skip crop."""
|
|
h, w = 500, 500
|
|
# Tiny content area (5x5 pixels) — should fail sanity check
|
|
img = _make_white_image(h, w)
|
|
# Add tiny dark spot
|
|
img[248:253, 248:253] = 0
|
|
|
|
cropped, result = detect_and_crop_page(img)
|
|
# Should either not crop or crop is too small (< 40%)
|
|
if result["crop_applied"]:
|
|
crop_area = result["cropped_size"]["width"] * result["cropped_size"]["height"]
|
|
assert crop_area >= 0.4 * h * w
|
|
|
|
def test_crop_preserves_content(self):
|
|
"""Verify that content is preserved after cropping."""
|
|
h, w = 300, 200
|
|
img = _make_image_with_content(h, w, (50, 250, 40, 160))
|
|
cropped, result = detect_and_crop_page(img)
|
|
|
|
if result["crop_applied"]:
|
|
# Cropped image should contain dark pixels (content)
|
|
gray = np.mean(cropped, axis=2)
|
|
assert np.min(gray) < 50 # Content is dark
|
|
|
|
def test_result_structure(self):
|
|
"""Verify all expected keys are present in result dict."""
|
|
img = _make_white_image(100, 100)
|
|
_, result = detect_and_crop_page(img)
|
|
|
|
assert "crop_applied" in result
|
|
assert "original_size" in result
|
|
assert "cropped_size" in result
|
|
assert "border_fractions" in result
|
|
assert "detected_format" in result
|
|
assert "format_confidence" in result
|
|
assert "aspect_ratio" in result
|
|
|
|
def test_margin_parameter(self):
|
|
"""Custom margin_frac should affect crop bounds."""
|
|
h, w = 400, 300
|
|
img = _make_image_with_content(h, w, (80, 320, 60, 240))
|
|
|
|
_, result_small = detect_and_crop_page(img, margin_frac=0.005)
|
|
_, result_large = detect_and_crop_page(img, margin_frac=0.05)
|
|
|
|
if result_small["crop_applied"] and result_large["crop_applied"]:
|
|
# Larger margin should produce a larger crop
|
|
small_area = result_small["cropped_size"]["width"] * result_small["cropped_size"]["height"]
|
|
large_area = result_large["cropped_size"]["width"] * result_large["cropped_size"]["height"]
|
|
assert large_area >= small_area
|
|
|
|
def test_crop_rect_pct_values(self):
|
|
"""crop_rect_pct values should be in 0-100 range."""
|
|
h, w = 400, 300
|
|
img = _make_image_with_content(h, w, (80, 320, 60, 240))
|
|
_, result = detect_and_crop_page(img)
|
|
|
|
if result["crop_applied"] and result["crop_rect_pct"]:
|
|
pct = result["crop_rect_pct"]
|
|
assert 0 <= pct["x"] <= 100
|
|
assert 0 <= pct["y"] <= 100
|
|
assert 0 < pct["width"] <= 100
|
|
assert 0 < pct["height"] <= 100
|