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feat: add field-specific bbox expansion strategies for YOLO training

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Implement center-point based bbox scaling with directional compensation
to capture field labels that typically appear above or to the left of
field values. This improves YOLO training data quality by including
contextual information around field values.

Key changes:
- Add shared.bbox module with ScaleStrategy dataclass and expand_bbox function
- Define field-specific strategies (ocr_number, bankgiro, invoice_date, etc.)
- Support manual_mode for minimal padding (no scaling)
- Integrate expand_bbox into AnnotationGenerator
- Add FIELD_TO_CLASS mapping for field_name to class_name lookup
- Comprehensive tests with 100% coverage (45 tests)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Yaojia Wang
2026-02-04 22:56:52 +01:00
parent 8723ef4653
commit 0990239e9c
13 changed files with 1424 additions and 18 deletions
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tests/training/yolo/__init__.py Normal file
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"""Tests for training.yolo module."""

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tests/training/yolo/test_annotation_generator.py Normal file
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"""
Tests for AnnotationGenerator with field-specific bbox expansion.
Tests verify that annotations are generated correctly using
field-specific scale strategies.
"""
from dataclasses import dataclass
import pytest
from training.yolo.annotation_generator import (
AnnotationGenerator,
YOLOAnnotation,
)
from shared.fields import TRAINING_FIELD_CLASSES, CLASS_NAMES
@dataclass
class MockMatch:
"""Mock Match object for testing."""
bbox: tuple[float, float, float, float]
score: float
class TestYOLOAnnotation:
"""Tests for YOLOAnnotation dataclass."""
def test_to_string_format(self):
"""Verify YOLO format string output."""
ann = YOLOAnnotation(
class_id=0,
x_center=0.5,
y_center=0.5,
width=0.1,
height=0.05,
confidence=0.9
)
result = ann.to_string()
assert result == "0 0.500000 0.500000 0.100000 0.050000"
def test_default_confidence(self):
"""Verify default confidence is 1.0."""
ann = YOLOAnnotation(
class_id=0,
x_center=0.5,
y_center=0.5,
width=0.1,
height=0.05,
)
assert ann.confidence == 1.0
class TestAnnotationGeneratorInit:
"""Tests for AnnotationGenerator initialization."""
def test_default_values(self):
"""Verify default initialization values."""
gen = AnnotationGenerator()
assert gen.min_confidence == 0.7
assert gen.min_bbox_height_px == 30
def test_custom_values(self):
"""Verify custom initialization values."""
gen = AnnotationGenerator(
min_confidence=0.8,
min_bbox_height_px=40,
)
assert gen.min_confidence == 0.8
assert gen.min_bbox_height_px == 40
class TestGenerateFromMatches:
"""Tests for generate_from_matches method."""
def test_generates_annotation_for_valid_match(self):
"""Verify annotation is generated for valid match."""
gen = AnnotationGenerator(min_confidence=0.5)
# Mock match in PDF points (72 DPI)
# At 150 DPI, coords multiply by 150/72 = 2.083
matches = {
"InvoiceNumber": [MockMatch(bbox=(100, 200, 200, 230), score=0.8)]
}
annotations = gen.generate_from_matches(
matches=matches,
image_width=1000,
image_height=1000,
dpi=150
)
assert len(annotations) == 1
ann = annotations[0]
assert ann.class_id == TRAINING_FIELD_CLASSES["InvoiceNumber"]
assert ann.confidence == 0.8
# Normalized values should be in 0-1 range
assert 0 <= ann.x_center <= 1
assert 0 <= ann.y_center <= 1
assert 0 < ann.width <= 1
assert 0 < ann.height <= 1
def test_skips_low_confidence_match(self):
"""Verify low confidence matches are skipped."""
gen = AnnotationGenerator(min_confidence=0.7)
matches = {
"InvoiceNumber": [MockMatch(bbox=(100, 200, 200, 230), score=0.5)]
}
annotations = gen.generate_from_matches(
matches=matches,
image_width=1000,
image_height=1000,
dpi=150
)
assert len(annotations) == 0
def test_skips_unknown_field(self):
"""Verify unknown fields are skipped."""
gen = AnnotationGenerator(min_confidence=0.5)
matches = {
"UnknownField": [MockMatch(bbox=(100, 200, 200, 230), score=0.9)]
}
annotations = gen.generate_from_matches(
matches=matches,
image_width=1000,
image_height=1000,
dpi=150
)
assert len(annotations) == 0
def test_takes_best_match_only(self):
"""Verify only the best match is used per field."""
gen = AnnotationGenerator(min_confidence=0.5)
matches = {
"InvoiceNumber": [
MockMatch(bbox=(100, 200, 200, 230), score=0.9), # Best
MockMatch(bbox=(300, 400, 400, 430), score=0.7),
]
}
annotations = gen.generate_from_matches(
matches=matches,
image_width=1000,
image_height=1000,
dpi=150
)
assert len(annotations) == 1
assert annotations[0].confidence == 0.9
def test_handles_empty_matches(self):
"""Verify empty matches list is handled."""
gen = AnnotationGenerator()
matches = {
"InvoiceNumber": []
}
annotations = gen.generate_from_matches(
matches=matches,
image_width=1000,
image_height=1000,
dpi=150
)
assert len(annotations) == 0
def test_applies_field_specific_expansion(self):
"""Verify different fields get different expansion."""
gen = AnnotationGenerator(min_confidence=0.5)
# Same bbox, different fields
bbox = (100, 200, 200, 230)
matches_invoice_number = {
"InvoiceNumber": [MockMatch(bbox=bbox, score=0.9)]
}
matches_bankgiro = {
"Bankgiro": [MockMatch(bbox=bbox, score=0.9)]
}
ann_invoice = gen.generate_from_matches(
matches=matches_invoice_number,
image_width=1000,
image_height=1000,
dpi=150
)[0]
ann_bankgiro = gen.generate_from_matches(
matches=matches_bankgiro,
image_width=1000,
image_height=1000,
dpi=150
)[0]
# Bankgiro has extra_left_ratio=0.80, invoice_number has extra_top_ratio=0.40
# They should have different widths due to different expansion
# Bankgiro expands more to the left
assert ann_bankgiro.width != ann_invoice.width or ann_bankgiro.x_center != ann_invoice.x_center
def test_enforces_min_bbox_height(self):
"""Verify minimum bbox height is enforced."""
gen = AnnotationGenerator(min_confidence=0.5, min_bbox_height_px=50)
# Very small bbox
matches = {
"InvoiceNumber": [MockMatch(bbox=(100, 200, 200, 210), score=0.9)]
}
annotations = gen.generate_from_matches(
matches=matches,
image_width=1000,
image_height=1000,
dpi=72 # 1:1 scale
)
assert len(annotations) == 1
# Height should be at least min_bbox_height_px / image_height
# After scale strategy expansion, height should be >= 50/1000 = 0.05
# Actually the min_bbox_height check happens AFTER expand_bbox
# So the final height should meet the minimum
class TestAddPaymentLineAnnotation:
"""Tests for add_payment_line_annotation method."""
def test_adds_payment_line_annotation(self):
"""Verify payment_line annotation is added."""
gen = AnnotationGenerator(min_confidence=0.5)
annotations = []
result = gen.add_payment_line_annotation(
annotations=annotations,
payment_line_bbox=(100, 200, 400, 230),
confidence=0.9,
image_width=1000,
image_height=1000,
dpi=150
)
assert len(result) == 1
ann = result[0]
assert ann.class_id == TRAINING_FIELD_CLASSES["payment_line"]
assert ann.confidence == 0.9
def test_skips_none_bbox(self):
"""Verify None bbox is handled."""
gen = AnnotationGenerator(min_confidence=0.5)
annotations = []
result = gen.add_payment_line_annotation(
annotations=annotations,
payment_line_bbox=None,
confidence=0.9,
image_width=1000,
image_height=1000,
dpi=150
)
assert len(result) == 0
def test_skips_low_confidence(self):
"""Verify low confidence is skipped."""
gen = AnnotationGenerator(min_confidence=0.7)
annotations = []
result = gen.add_payment_line_annotation(
annotations=annotations,
payment_line_bbox=(100, 200, 400, 230),
confidence=0.5,
image_width=1000,
image_height=1000,
dpi=150
)
assert len(result) == 0
def test_appends_to_existing_annotations(self):
"""Verify payment_line is appended to existing list."""
gen = AnnotationGenerator(min_confidence=0.5)
existing = [YOLOAnnotation(0, 0.5, 0.5, 0.1, 0.1, 0.9)]
result = gen.add_payment_line_annotation(
annotations=existing,
payment_line_bbox=(100, 200, 400, 230),
confidence=0.9,
image_width=1000,
image_height=1000,
dpi=150
)
assert len(result) == 2
assert result[0].class_id == 0 # Original
assert result[1].class_id == TRAINING_FIELD_CLASSES["payment_line"]
class TestMultipleFieldsIntegration:
"""Integration tests for multiple fields."""
def test_generates_annotations_for_all_field_types(self):
"""Verify annotations can be generated for all field types."""
gen = AnnotationGenerator(min_confidence=0.5)
# Create matches for each field (except payment_line which is derived)
field_names = [
"InvoiceNumber",
"InvoiceDate",
"InvoiceDueDate",
"OCR",
"Bankgiro",
"Plusgiro",
"Amount",
"supplier_organisation_number",
"customer_number",
]
matches = {}
for i, field_name in enumerate(field_names):
# Stagger bboxes to avoid overlap
matches[field_name] = [
MockMatch(bbox=(100 + i * 50, 100 + i * 30, 200 + i * 50, 130 + i * 30), score=0.9)
]
annotations = gen.generate_from_matches(
matches=matches,
image_width=2000,
image_height=2000,
dpi=150
)
assert len(annotations) == len(field_names)
# Verify all class_ids are present
class_ids = {ann.class_id for ann in annotations}
expected_class_ids = {TRAINING_FIELD_CLASSES[fn] for fn in field_names}
assert class_ids == expected_class_ids