pyr0ball
8cbde774e5
Pantry tracker app with: - FastAPI backend + Vue 3 SPA frontend - SQLite via circuitforge-core (migrations 001-005) - Inventory CRUD, barcode scan, receipt OCR pipeline - Expiry prediction (deterministic + LLM fallback) - CF-core tier system integration - Cloud session support (menagerie)
89 lines
No EOL
2.8 KiB
Python
89 lines
No EOL
2.8 KiB
Python
#!/usr/bin/env python
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# app/services/image_preprocessing/format_conversion.py
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import cv2
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import numpy as np
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import logging
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from pathlib import Path
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from typing import Tuple, Optional
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logger = logging.getLogger(__name__)
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def convert_to_standard_format(
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image_path: Path,
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output_path: Optional[Path] = None,
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target_format: str = "png"
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) -> Tuple[bool, str, Optional[Path]]:
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"""
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Convert image to standard internal format.
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Args:
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image_path: Path to input image
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output_path: Optional path to save converted image
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target_format: Target format (png, jpg)
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Returns:
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Tuple containing (success, message, output_path)
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"""
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try:
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# Check if CUDA is available and set up GPU processing
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if cv2.cuda.getCudaEnabledDeviceCount() > 0:
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logger.info("CUDA available, using GPU acceleration")
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use_cuda = True
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else:
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logger.info("CUDA not available, using CPU processing")
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use_cuda = False
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# Read image
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img = cv2.imread(str(image_path))
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if img is None:
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return False, f"Failed to read image: {image_path}", None
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# If PDF, extract first page (simplified for Phase 1)
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if image_path.suffix.lower() == '.pdf':
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# This is a placeholder for PDF handling
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# In a real implementation, you'd use a PDF processing library
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return False, "PDF processing not implemented in Phase 1", None
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# Set output path if not provided
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if output_path is None:
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output_path = image_path.with_suffix(f".{target_format}")
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# Write converted image
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success = cv2.imwrite(str(output_path), img)
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if not success:
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return False, f"Failed to write converted image to {output_path}", None
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return True, "Image converted successfully", output_path
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except Exception as e:
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logger.exception(f"Error converting image: {e}")
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return False, f"Error converting image: {str(e)}", None
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def extract_metadata(image_path: Path) -> dict:
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"""
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Extract metadata from image file.
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Args:
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image_path: Path to input image
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Returns:
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Dictionary containing metadata
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"""
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metadata = {
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"filename": image_path.name,
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"original_format": image_path.suffix.lstrip(".").lower(),
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"file_size_bytes": image_path.stat().st_size,
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}
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try:
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img = cv2.imread(str(image_path))
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if img is not None:
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metadata.update({
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"width": img.shape[1],
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"height": img.shape[0],
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"channels": img.shape[2] if len(img.shape) > 2 else 1,
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})
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except Exception as e:
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logger.exception(f"Error extracting image metadata: {e}")
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return metadata |