feat: stage6 — Image Pipeline (F05) and Rotation Manager (F06)

src/gps_denied/schemas/image.py

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+"""Image Input Pipeline schemas (Component F05)."""
+
+from datetime import datetime
+from typing import Optional
+
+from pydantic import BaseModel
+import numpy as np
+
+
+class ImageBatch(BaseModel):
+    """Batch of raw images for processing."""
+    images: list[bytes]
+    filenames: list[str]
+    start_sequence: int
+    end_sequence: int
+    batch_number: int
+
+
+class ImageMetadata(BaseModel):
+    """Extracted metadata from an image."""
+    sequence: int
+    filename: str
+    dimensions: tuple[int, int]
+    file_size: int
+    timestamp: datetime
+    exif_data: Optional[dict] = None
+
+
+class ValidationResult(BaseModel):
+    """Result of batch validation."""
+    valid: bool
+    errors: list[str]
+
+
+class ProcessingStatus(BaseModel):
+    """Status of image pipeline processing."""
+    flight_id: str
+    total_images: int
+    processed_images: int
+    current_sequence: int
+    queued_batches: int
+    processing_rate: float
+
+
+class ImageData:
+    """Loaded image ready for processing."""
+    # Using normal class instead of BaseModel for np.ndarray support
+    def __init__(
+        self,
+        flight_id: str,
+        sequence: int,
+        filename: str,
+        image: np.ndarray,
+        metadata: ImageMetadata
+    ):
+        self.flight_id = flight_id
+        self.sequence = sequence
+        self.filename = filename
+        self.image = image
+        self.metadata = metadata
+
+class ProcessedBatch:
+    """Batch of decoded images."""
+    def __init__(self, images: list[ImageData], batch_id: str, start_sequence: int, end_sequence: int):
+        self.images = images
+        self.batch_id = batch_id
+        self.start_sequence = start_sequence
+        self.end_sequence = end_sequence

src/gps_denied/schemas/rotation.py

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+"""Rotation schemas (Component F06)."""
+
+from datetime import datetime
+from typing import Optional
+
+from pydantic import BaseModel
+import numpy as np
+
+
+class RotationResult(BaseModel):
+    """Result of a rotation sweep alignment."""
+    matched: bool
+    initial_angle: float
+    precise_angle: float
+    confidence: float
+    # We will exclude np.ndarray from BaseModel to avoid validation issues, 
+    # but store it as an attribute if needed or use arbitrary_types_allowed.
+    
+    model_config = {"arbitrary_types_allowed": True}
+    homography: Optional[np.ndarray] = None
+    inlier_count: int = 0
+
+
+class HeadingHistory(BaseModel):
+    """Flight heading tracking history."""
+    flight_id: str
+    current_heading: Optional[float] = None
+    heading_history: list[float] = []
+    last_update: Optional[datetime] = None
+    sharp_turns: int = 0
+
+
+class RotationConfig(BaseModel):
+    """Configuration for rotation sweeps."""
+    step_angle: float = 30.0
+    sharp_turn_threshold: float = 45.0
+    confidence_threshold: float = 0.7
+    history_size: int = 10