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# Thermal Anomaly Detection API Configuration
detection:
# Statistical analysis parameters
statistical:
temperature_threshold: 6.0 # Conservative threshold for statistical detection
min_anomaly_size: 200 # Minimum anomaly size in pixels
# Thermal hot spot detection
thermal:
hot_spot_threshold: 0.75 # Sensitivity for hot spot detection
temperature_threshold: 200 # Temperature threshold (0-255)
min_cluster_size: 15 # Minimum cluster size for DBSCAN
epsilon: 20 # Cluster epsilon for DBSCAN
# ML-based detection
ml:
threshold: 0.5 # Detection threshold (lower = more detections)
min_area: 200 # Minimum detection area in pixels²
max_area: 5000 # Maximum detection area in pixels²
max_annotations: 3 # Maximum number of annotations to show
blue_threshold: 30 # Maximum blue pixel percentage
# Confidence thresholds
confidence:
min_confidence: 0.6 # Minimum confidence to report anomaly
# Visualization settings
visualization:
output:
save_results: true # Enable saving results to disk
result_format: "png" # Output format (png, jpg)
output_dir: "unified_results" # Output directory
# Model configuration
model:
path: "ML_analysis/models/best_model.pth" # Path to trained ML model
# API settings
api:
host: "0.0.0.0"
port: 8000
workers: 1 # Number of worker processes
reload: false # Auto-reload on code changes (dev only)
# File upload limits
max_upload_size: 10485760 # 10MB in bytes
allowed_extensions:
- ".jpg"
- ".jpeg"
- ".png"
# CORS settings
cors:
allow_origins:
- "*" # Configure for production
allow_credentials: true
allow_methods:
- "*"
allow_headers:
- "*"
# Logging configuration
logging:
level: "INFO" # DEBUG, INFO, WARNING, ERROR
format: "%(asctime)s - %(name)s - %(levelname)s - %(message)s"