GeoSight MCP Server

""" Object Detection in Satellite Imagery using YOLO. """ from dataclasses import dataclass from typing import List, Optional import numpy as np import structlog from geosight.config import settings logger = structlog.get_logger(__name__) @dataclass class Detection: """Single object detection result.""" class_name: str confidence: float bbox: tuple # (x1, y1, x2, y2) center: tuple # (x, y) SUPPORTED_OBJECTS = [ "ship", "aircraft", "vehicle", "building", "solar_panel", "storage_tank", "sports_field" ] class ObjectDetector: """YOLO-based object detector for satellite imagery.""" def __init__(self, device: Optional[str] = None): self.device = device or settings.model.inference_device self._model = None async def detect( self, image: np.ndarray, object_types: List[str] = None, confidence_threshold: float = 0.5, ) -> List[Detection]: """ Detect objects in satellite image. Returns: List of Detection objects """ if object_types is None: object_types = ["ship", "aircraft", "building"] # For demo, generate synthetic detections return self._generate_demo_detections(image.shape[:2], object_types, confidence_threshold) def _generate_demo_detections( self, image_shape: tuple, object_types: List[str], min_confidence: float, ) -> List[Detection]: """Generate demo detections.""" np.random.seed(123) height, width = image_shape detections = [] num_objects = np.random.randint(3, 10) for _ in range(num_objects): obj_type = np.random.choice(object_types) confidence = np.random.uniform(min_confidence, 0.95) # Random bounding box x1 = np.random.randint(0, width - 50) y1 = np.random.randint(0, height - 50) w = np.random.randint(20, 80) h = np.random.randint(20, 80) detections.append(Detection( class_name=obj_type, confidence=float(confidence), bbox=(x1, y1, x1 + w, y1 + h), center=(x1 + w // 2, y1 + h // 2), )) return detections