Initial commit: Invoice field extraction system using YOLO + OCR
Features: - Auto-labeling pipeline: CSV values -> PDF search -> YOLO annotations - Flexible date matching: year-month match, nearby date tolerance - PDF text extraction with PyMuPDF - OCR support for scanned documents (PaddleOCR) - YOLO training and inference pipeline - 7 field types: InvoiceNumber, InvoiceDate, InvoiceDueDate, OCR, Bankgiro, Plusgiro, Amount Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Yaojia Wang
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src/data/autolabel_report.py
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252
src/data/autolabel_report.py
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"""
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Auto-Label Report Generator
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Generates quality control reports for auto-labeling process.
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"""
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import json
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from dataclasses import dataclass, field, asdict
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from datetime import datetime
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from pathlib import Path
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from typing import Any
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@dataclass
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class FieldMatchResult:
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"""Result of matching a single field."""
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field_name: str
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csv_value: str | None
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matched: bool
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score: float = 0.0
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matched_text: str | None = None
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candidate_used: str | None = None # Which normalized variant matched
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bbox: tuple[float, float, float, float] | None = None
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page_no: int = 0
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context_keywords: list[str] = field(default_factory=list)
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error: str | None = None
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def to_dict(self) -> dict:
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"""Convert to dictionary."""
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# Convert bbox to native Python floats to avoid numpy serialization issues
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bbox_list = None
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if self.bbox:
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bbox_list = [float(x) for x in self.bbox]
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return {
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'field_name': self.field_name,
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'csv_value': self.csv_value,
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'matched': self.matched,
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'score': float(self.score) if self.score else 0.0,
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'matched_text': self.matched_text,
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'candidate_used': self.candidate_used,
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'bbox': bbox_list,
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'page_no': int(self.page_no) if self.page_no else 0,
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'context_keywords': self.context_keywords,
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'error': self.error
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}
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@dataclass
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class AutoLabelReport:
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"""Report for a single document's auto-labeling process."""
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document_id: str
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pdf_path: str | None = None
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pdf_type: str | None = None # 'text' | 'scanned' | 'mixed'
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success: bool = False
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total_pages: int = 0
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fields_matched: int = 0
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fields_total: int = 0
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field_results: list[FieldMatchResult] = field(default_factory=list)
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annotations_generated: int = 0
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image_paths: list[str] = field(default_factory=list)
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label_paths: list[str] = field(default_factory=list)
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processing_time_ms: float = 0.0
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timestamp: str = field(default_factory=lambda: datetime.now().isoformat())
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errors: list[str] = field(default_factory=list)
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def add_field_result(self, result: FieldMatchResult) -> None:
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"""Add a field matching result."""
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self.field_results.append(result)
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self.fields_total += 1
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if result.matched:
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self.fields_matched += 1
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def to_dict(self) -> dict:
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"""Convert to dictionary."""
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return {
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'document_id': self.document_id,
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'pdf_path': self.pdf_path,
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'pdf_type': self.pdf_type,
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'success': self.success,
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'total_pages': self.total_pages,
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'fields_matched': self.fields_matched,
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'fields_total': self.fields_total,
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'field_results': [r.to_dict() for r in self.field_results],
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'annotations_generated': self.annotations_generated,
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'image_paths': self.image_paths,
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'label_paths': self.label_paths,
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'processing_time_ms': self.processing_time_ms,
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'timestamp': self.timestamp,
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'errors': self.errors
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}
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def to_json(self, indent: int | None = None) -> str:
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"""Convert to JSON string."""
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return json.dumps(self.to_dict(), indent=indent, ensure_ascii=False)
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@property
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def match_rate(self) -> float:
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"""Calculate field match rate."""
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if self.fields_total == 0:
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return 0.0
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return self.fields_matched / self.fields_total
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def get_summary(self) -> dict:
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"""Get a summary of the report."""
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return {
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'document_id': self.document_id,
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'success': self.success,
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'match_rate': f"{self.match_rate:.1%}",
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'fields': f"{self.fields_matched}/{self.fields_total}",
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'annotations': self.annotations_generated,
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'errors': len(self.errors)
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}
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class ReportWriter:
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"""Writes auto-label reports to file."""
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def __init__(self, output_path: str | Path):
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"""
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Initialize report writer.
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Args:
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output_path: Path to output JSONL file
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"""
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self.output_path = Path(output_path)
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self.output_path.parent.mkdir(parents=True, exist_ok=True)
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def write(self, report: AutoLabelReport) -> None:
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"""Append a report to the output file."""
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with open(self.output_path, 'a', encoding='utf-8') as f:
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f.write(report.to_json() + '\n')
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def write_dict(self, report_dict: dict) -> None:
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"""Append a report dict to the output file (for parallel processing)."""
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import json
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with open(self.output_path, 'a', encoding='utf-8') as f:
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f.write(json.dumps(report_dict, ensure_ascii=False) + '\n')
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f.flush()
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def write_batch(self, reports: list[AutoLabelReport]) -> None:
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"""Write multiple reports."""
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with open(self.output_path, 'a', encoding='utf-8') as f:
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for report in reports:
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f.write(report.to_json() + '\n')
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class ReportReader:
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"""Reads auto-label reports from file."""
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def __init__(self, input_path: str | Path):
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"""
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Initialize report reader.
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Args:
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input_path: Path to input JSONL file
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"""
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self.input_path = Path(input_path)
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def read_all(self) -> list[AutoLabelReport]:
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"""Read all reports from file."""
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reports = []
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if not self.input_path.exists():
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return reports
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with open(self.input_path, 'r', encoding='utf-8') as f:
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for line in f:
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line = line.strip()
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if not line:
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continue
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data = json.loads(line)
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report = self._dict_to_report(data)
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reports.append(report)
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return reports
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def _dict_to_report(self, data: dict) -> AutoLabelReport:
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"""Convert dictionary to AutoLabelReport."""
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field_results = []
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for fr_data in data.get('field_results', []):
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bbox = tuple(fr_data['bbox']) if fr_data.get('bbox') else None
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field_results.append(FieldMatchResult(
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field_name=fr_data['field_name'],
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csv_value=fr_data.get('csv_value'),
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matched=fr_data.get('matched', False),
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score=fr_data.get('score', 0.0),
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matched_text=fr_data.get('matched_text'),
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candidate_used=fr_data.get('candidate_used'),
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bbox=bbox,
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page_no=fr_data.get('page_no', 0),
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context_keywords=fr_data.get('context_keywords', []),
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error=fr_data.get('error')
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))
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return AutoLabelReport(
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document_id=data['document_id'],
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pdf_path=data.get('pdf_path'),
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pdf_type=data.get('pdf_type'),
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success=data.get('success', False),
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total_pages=data.get('total_pages', 0),
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fields_matched=data.get('fields_matched', 0),
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fields_total=data.get('fields_total', 0),
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field_results=field_results,
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annotations_generated=data.get('annotations_generated', 0),
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image_paths=data.get('image_paths', []),
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label_paths=data.get('label_paths', []),
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processing_time_ms=data.get('processing_time_ms', 0.0),
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timestamp=data.get('timestamp', ''),
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errors=data.get('errors', [])
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)
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def get_statistics(self) -> dict:
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"""Calculate statistics from all reports."""
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reports = self.read_all()
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if not reports:
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return {'total': 0}
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successful = sum(1 for r in reports if r.success)
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total_fields_matched = sum(r.fields_matched for r in reports)
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total_fields = sum(r.fields_total for r in reports)
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total_annotations = sum(r.annotations_generated for r in reports)
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# Per-field statistics
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field_stats = {}
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for report in reports:
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for fr in report.field_results:
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if fr.field_name not in field_stats:
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field_stats[fr.field_name] = {'matched': 0, 'total': 0, 'avg_score': 0.0}
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field_stats[fr.field_name]['total'] += 1
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if fr.matched:
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field_stats[fr.field_name]['matched'] += 1
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field_stats[fr.field_name]['avg_score'] += fr.score
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# Calculate averages
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for field_name, stats in field_stats.items():
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if stats['matched'] > 0:
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stats['avg_score'] /= stats['matched']
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stats['match_rate'] = stats['matched'] / stats['total'] if stats['total'] > 0 else 0
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return {
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'total': len(reports),
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'successful': successful,
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'success_rate': successful / len(reports),
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'total_fields_matched': total_fields_matched,
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'total_fields': total_fields,
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'overall_match_rate': total_fields_matched / total_fields if total_fields > 0 else 0,
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'total_annotations': total_annotations,
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'field_statistics': field_stats
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}
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