ab0e9b973a
- Set up FastAPI project structure with SQLite database - Create database models for tomato images and severity classifications - Implement image upload and processing endpoints - Develop a segmentation model for tomato disease severity detection - Add API endpoints for analysis and results retrieval - Implement health check endpoint - Set up Alembic for database migrations - Update project documentation
88 lines
2.4 KiB
Python
88 lines
2.4 KiB
Python
import cv2
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import numpy as np
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from pathlib import Path
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import uuid
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import os
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from datetime import datetime
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from typing import Tuple, Dict, Any, Optional
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from app.core.config import settings
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def save_uploaded_image(file_data: bytes, filename: str) -> Dict[str, Any]:
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"""
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Save an uploaded image to the storage directory and return metadata.
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Args:
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file_data: The binary content of the uploaded file
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filename: Original filename of the uploaded file
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Returns:
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Dict with image metadata including path, size, etc.
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"""
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# Generate a unique filename to avoid conflicts
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extension = Path(filename).suffix.lower()
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date_prefix = datetime.now().strftime("%Y%m%d")
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unique_id = str(uuid.uuid4())
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safe_filename = f"{date_prefix}_{unique_id}{extension}"
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# Create full path
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file_path = settings.IMAGE_DIR / safe_filename
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# Write file to disk
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with open(file_path, "wb") as f:
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f.write(file_data)
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# Get image dimensions if it's an image file
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width, height = None, None
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try:
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img = cv2.imread(str(file_path))
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if img is not None:
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height, width, _ = img.shape
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except Exception:
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pass
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return {
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"file_path": str(file_path),
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"filename": filename,
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"file_size": len(file_data),
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"width": width,
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"height": height,
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"mime_type": get_mime_type(extension)
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}
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def get_mime_type(extension: str) -> str:
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"""Map file extension to MIME type."""
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mime_types = {
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".jpg": "image/jpeg",
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".jpeg": "image/jpeg",
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".png": "image/png",
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".gif": "image/gif",
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".bmp": "image/bmp",
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".tiff": "image/tiff",
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".tif": "image/tiff",
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}
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return mime_types.get(extension.lower(), "application/octet-stream")
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def load_image(file_path: str) -> Optional[np.ndarray]:
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"""Load an image from file path."""
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if not os.path.exists(file_path):
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return None
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img = cv2.imread(file_path)
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if img is None:
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return None
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return cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # Convert to RGB
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def resize_image(image: np.ndarray, target_size: Tuple[int, int]) -> np.ndarray:
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"""Resize image to target size."""
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return cv2.resize(image, target_size, interpolation=cv2.INTER_AREA)
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def normalize_image(image: np.ndarray) -> np.ndarray:
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"""Normalize image pixel values to [0, 1]."""
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return image.astype(np.float32) / 255.0 |