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Unified Inference Pipeline for IDP
Orchestrates preprocessing, OCR, classification, NER, and post-processing
Optimized for low-latency CPU inference
"""
# Fix for TensorFlow/PaddlePaddle mutex warnings on macOS
import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'
os.environ['OMP_NUM_THREADS'] = '1'
os.environ['OPENBLAS_NUM_THREADS'] = '1'
os.environ['MKL_NUM_THREADS'] = '1'
os.environ['VECLIB_MAXIMUM_THREADS'] = '1'
os.environ['NUMEXPR_NUM_THREADS'] = '1'
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import warnings
warnings.filterwarnings('ignore')
import cv2
import numpy as np
from typing import Dict, List, Optional, Union, Tuple
import logging
import time
from pathlib import Path
import tempfile
from preprocessing import DocumentPreprocessor
from ocr_engine import LightweightOCR, PDFtoImageConverter
from postprocessing import PostProcessor
# Import demo mode by default; try to import trained-model wrappers lazily
from demo_mode import DemoClassifier, DemoNER
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class IDPPipeline:
"""
Complete IDP pipeline
Processes document from image/PDF to structured JSON output
"""
def __init__(
self,
classifier_model_path: str = "models/classifier/best_classifier.pt",
ner_model_path: str = "models/ner/best_ner.pt",
use_gpu: bool = False,
ocr_confidence_threshold: float = 0.5
):
"""
Initialize IDP pipeline with all components
Args:
classifier_model_path: Path to trained classifier weights
ner_model_path: Path to trained NER weights
use_gpu: Use GPU for inference
ocr_confidence_threshold: Minimum OCR confidence
"""
logger.info("Initializing IDP Pipeline...")
self.use_gpu = use_gpu
self.ocr_confidence_threshold = ocr_confidence_threshold
# Initialize components
logger.info("Loading preprocessing...")
self.preprocessor = DocumentPreprocessor(
max_width=2048,
enable_deskew=True,
enable_denoise=True,
enable_contrast=True
)
logger.info("Loading OCR engine...")
self.ocr = LightweightOCR(
lang='en',
use_gpu=use_gpu
)
logger.info("Loading document classifier (lazy import)")
try:
# Lazy import to avoid top-level failures when torch isn't installed
from classifier_model import DocumentClassifierInference
self.classifier = DocumentClassifierInference(
model_path=classifier_model_path,
device='cuda' if use_gpu else 'cpu',
use_fp16=use_gpu
)
logger.info("✓ Loaded trained classifier model")
except Exception as e:
logger.warning(f"Could not import/load trained classifier: {str(e)}")
logger.warning("⚠ Using demo mode (rule-based) classifier")
self.classifier = DemoClassifier()
self.classifier.is_demo = True
logger.info("Loading NER model (lazy import)")
try:
# Lazy import to avoid top-level failures when torch isn't installed
from ner_model import DocumentNERInference
self.ner = DocumentNERInference(
model_path=ner_model_path,
device='cuda' if use_gpu else 'cpu',
use_fp16=use_gpu
)
logger.info("✓ Loaded trained NER model")
except Exception as e:
logger.warning(f"Could not import/load trained NER model: {str(e)}")
logger.warning("⚠ Using demo mode (rule-based) NER")
self.ner = DemoNER()
self.ner.is_demo = True
logger.info("Loading post-processor...")
self.postprocessor = PostProcessor()
logger.info("IDP Pipeline initialized successfully!")
def process_document(
self,
file_path: str,
adaptive_threshold: bool = False,
page_number: Optional[int] = None
) -> Dict:
"""
Process a document (image or PDF)
Args:
file_path: Path to document file
adaptive_threshold: Apply adaptive thresholding for poor quality scans
page_number: Process specific page (for PDFs), None for all pages
Returns:
Structured document data with extracted fields
"""
start_time = time.time()
logger.info(f"Processing document: {file_path}")
# Check file extension
ext = Path(file_path).suffix.lower()
if ext == '.pdf':
results = self._process_pdf(file_path, adaptive_threshold, page_number)
elif ext in ['.jpg', '.jpeg', '.png', '.bmp', '.tiff']:
results = self._process_image(file_path, adaptive_threshold)
else:
raise ValueError(f"Unsupported file format: {ext}")
total_time = time.time() - start_time
logger.info(f"Document processing complete in {total_time:.2f}s")
return results
def _process_pdf(
self,
pdf_path: str,
adaptive_threshold: bool,
page_number: Optional[int]
) -> Dict:
"""Process PDF document"""
# Convert PDF to images
images = PDFtoImageConverter.pdf_to_images(pdf_path, dpi=200)
if page_number is not None:
# Process single page
if page_number < 1 or page_number > len(images):
raise ValueError(f"Invalid page number: {page_number}")
image = images[page_number - 1]
result = self._process_single_image(image, adaptive_threshold)
result['page_number'] = page_number
return {
'file_type': 'pdf',
'total_pages': len(images),
'processed_pages': 1,
'pages': [result]
}
else:
# Process all pages
results = []
for idx, image in enumerate(images):
logger.info(f"Processing page {idx + 1}/{len(images)}")
result = self._process_single_image(image, adaptive_threshold)
result['page_number'] = idx + 1
results.append(result)
return {
'file_type': 'pdf',
'total_pages': len(images),
'processed_pages': len(results),
'pages': results
}
def _process_image(
self,
image_path: str,
adaptive_threshold: bool
) -> Dict:
"""Process image file"""
# Load image
image = cv2.imread(image_path)
if image is None:
raise ValueError(f"Could not load image: {image_path}")
result = self._process_single_image(image, adaptive_threshold)
return {
'file_type': 'image',
'total_pages': 1,
'processed_pages': 1,
'pages': [result]
}
def _process_single_image(
self,
image: np.ndarray,
adaptive_threshold: bool
) -> Dict:
"""
Process a single image through the complete pipeline
Returns:
Structured document data
"""
# Step 1: Preprocessing
step_start = time.time()
preprocessed = self.preprocessor.preprocess(
image,
adaptive_threshold=adaptive_threshold
)
preprocess_time = time.time() - step_start
logger.info(f"Preprocessing: {preprocess_time:.3f}s")
# Step 2: OCR
step_start = time.time()
ocr_result = self.ocr.extract_text(
preprocessed,
return_boxes=True,
confidence_threshold=self.ocr_confidence_threshold
)
ocr_time = time.time() - step_start
logger.info(f"OCR: {ocr_time:.3f}s, extracted {len(ocr_result['lines'])} lines")
# Step 3: Document Classification
step_start = time.time()
# Check if using demo mode or trained model
if hasattr(self.classifier, 'is_demo') and self.classifier.is_demo:
# Demo classifier doesn't need return_probabilities parameter
classification = self.classifier.predict(ocr_result['text'])
else:
# Trained classifier
classification = self.classifier.predict(
ocr_result['text'],
return_probabilities=True
)
classification_time = time.time() - step_start
# Refine classification
refined_class, refined_conf = self._refine_classification(
classification['predicted_class'],
ocr_result['text'],
classification['confidence']
)
classification['predicted_class'] = refined_class
classification['confidence'] = refined_conf
logger.info(f"Classification: {classification_time:.3f}s, "
f"type={classification['predicted_class']}, "
f"conf={classification['confidence']:.3f}")
# Step 4: NER
step_start = time.time()
# Check if using demo mode or trained model
if hasattr(self.ner, 'is_demo') and self.ner.is_demo:
# Demo NER doesn't need return_token_scores parameter
ner_result = self.ner.predict(ocr_result['text'])
else:
# Trained NER model
ner_result = self.ner.predict(
ocr_result['text'],
return_token_scores=False
)
ner_time = time.time() - step_start
logger.info(f"NER: {ner_time:.3f}s, extracted {len(ner_result['entities'])} entities")
# Step 5: Post-processing
step_start = time.time()
final_result = self.postprocessor.process_document(
document_type=classification['predicted_class'],
classification_confidence=classification['confidence'],
ocr_text=ocr_result['text'],
ocr_boxes=ocr_result['boxes'],
ner_entities=ner_result['entities']
)
postprocess_time = time.time() - step_start
logger.info(f"Post-processing: {postprocess_time:.3f}s")
# Add timing information
final_result['processing_time'] = {
'preprocessing': preprocess_time,
'ocr': ocr_time,
'classification': classification_time,
'ner': ner_time,
'postprocessing': postprocess_time,
'total': preprocess_time + ocr_time + classification_time + ner_time + postprocess_time
}
# Add classification probabilities
final_result['classification_probabilities'] = classification.get('probabilities', {})
return final_result
def _refine_classification(self, predicted_class: str, text: str, confidence: float) -> Tuple[str, float]:
"""Refine classification based on strong keywords"""
text_lower = text.lower()
# Strong keywords for Invoice
if 'invoice' in text_lower:
# If model says RECEIPT but text says INVOICE, trust text
if predicted_class == 'RECEIPT':
return 'INVOICE', float(max(confidence, 0.9))
# Strong keywords for Bank Statement
if any(k in text_lower for k in ['bank statement', 'account statement', 'statement of account', 'account summary']):
return 'BANK_STATEMENT', 0.95
return predicted_class, float(confidence)
def process_image_bytes(
self,
image_bytes: bytes,
adaptive_threshold: bool = False
) -> Dict:
"""
Process image from bytes (useful for API endpoints)
Args:
image_bytes: Image data as bytes
adaptive_threshold: Apply adaptive thresholding
Returns:
Structured document data
"""
# Save to temporary file
with tempfile.NamedTemporaryFile(delete=False, suffix='.png') as tmp:
tmp.write(image_bytes)
tmp_path = tmp.name
try:
# Process image
result = self._process_image(tmp_path, adaptive_threshold)
return result
finally:
# Clean up
if os.path.exists(tmp_path):
os.remove(tmp_path)
def benchmark_pipeline(
pipeline: IDPPipeline,
test_image_path: str,
num_runs: int = 10
):
"""
Benchmark pipeline performance
Args:
pipeline: Initialized pipeline
test_image_path: Path to test image
num_runs: Number of benchmark runs
"""
logger.info(f"Benchmarking pipeline with {num_runs} runs...")
times = []
for i in range(num_runs):
start = time.time()
result = pipeline.process_document(test_image_path)
elapsed = time.time() - start
times.append(elapsed)
logger.info(f"Run {i+1}/{num_runs}: {elapsed:.3f}s")
logger.info(f"\nBenchmark Results:")
logger.info(f"Mean: {np.mean(times):.3f}s")
logger.info(f"Median: {np.median(times):.3f}s")
logger.info(f"Min: {np.min(times):.3f}s")
logger.info(f"Max: {np.max(times):.3f}s")
logger.info(f"Std: {np.std(times):.3f}s")
if __name__ == "__main__":
import sys
import json
if len(sys.argv) < 2:
print("Usage: python inference_pipeline.py <image_or_pdf_path>")
sys.exit(1)
input_path = sys.argv[1]
# Initialize pipeline
pipeline = IDPPipeline(use_gpu=False)
# Process document
result = pipeline.process_document(input_path)
# Save result
output_path = "pipeline_output.json"
with open(output_path, 'w', encoding='utf-8') as f:
json.dump(result, f, indent=2, ensure_ascii=False)
print(f"\nResults saved to {output_path}")
print(f"\nDocument Type: {result['pages'][0]['document_type']}")
print(f"Confidence: {result['pages'][0]['classification_confidence']:.3f}")
print(f"Processing Time: {result['pages'][0]['processing_time']['total']:.3f}s")
print(f"\nExtracted Fields:")
for field_name, field_data in result['pages'][0]['fields'].items():
print(f" {field_name}: {field_data['value']} (conf: {field_data['confidence']:.3f})")
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