WIP: Gemini Code progress on #1008

Automated salvage commit — agent session ended (exit 124).
Work in progress, may need continuation.

src/infrastructure/perception/processor.py

@@ -0,0 +1,117 @@
+"""
+Metal-accelerated image processing and UI state classification for perception.
+
+This module provides functions to preprocess raw image frames (resizing,
+grayscale, contrast enhancement) using Metal shaders and to classify
+UI states using Core ML models, explicitly pinned to the GPU/Neural Engine.
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Any
+
+# For now, we'll use Pillow as a placeholder for image operations.
+# In a real Metal integration, these would be replaced by calls to Metal shaders.
+try:
+    from PIL import Image
+    from PIL import ImageOps
+except ImportError:
+    Image = None
+    ImageOps = None
+    logging.warning("Pillow not installed. Image processing will be a no-op.")
+
+
+logger = logging.getLogger(__name__)
+
+
+class MetalPerceptionProcessor:
+    """
+    Handles Metal-accelerated frame preprocessing and Core ML UI state classification.
+    """
+
+    def __init__(self):
+        logger.info("Initializing MetalPerceptionProcessor.")
+        # REAL IMPLEMENTATION: Initialize Metal devices and command queue.
+        # This would involve using a library like PyObjC to interact with CoreGraphics,
+        # CoreImage, and MetalKit to set up the GPU for image processing.
+        # For this mock, we simulate readiness if Pillow is installed.
+        self.metal_ready = False
+        if Image is None:
+            logger.warning("MetalPerceptionProcessor will operate in no-op mode due to missing Pillow.")
+        else:
+            self.metal_ready = True # Simulate readiness if Pillow is available
+
+        # REAL IMPLEMENTATION: Load Core ML model and configure it to use the GPU/Neural Engine.
+        # This would involve using MLModel.load() and setting MLCpuOnly to false or
+        # specifying MLComputeUnits.all for optimal performance on Apple Silicon.
+        # For this mock, we just have a placeholder.
+        self.core_ml_model = None
+        logger.info("MetalPerceptionProcessor initialized.")
+
+    def preprocess_frame(self, raw_image_data: bytes, target_size: tuple[int, int] = (256, 256)) -> bytes:
+        """
+        Preprocesses a raw image frame using (simulated) Metal shaders.
+
+        Args:
+            raw_image_data: Raw bytes of the image (e.g., PNG, JPEG).
+            target_size: The desired (width, height) for resizing.
+
+        Returns:
+            Processed image data as bytes.
+        """
+        if not self.metal_ready:
+            logger.warning("Pillow not available. Skipping frame preprocessing.")
+            return raw_image_data
+
+        logger.debug("Preprocessing frame with (simulated) Metal shaders.")
+        try:
+            # Simulate Metal operations using Pillow
+            image = Image.open(io.BytesIO(raw_image_data))
+
+            # 1. Resize
+            image = image.resize(target_size, Image.Resampling.LANCZOS)
+
+            # 2. Grayscale
+            image = ImageOps.grayscale(image)
+
+            # 3. Contrast enhancement (simple example)
+            # This is a very basic contrast adjustment. Metal shaders would allow for more sophisticated algorithms.
+            image = ImageOps.autocontrast(image)
+
+            output_buffer = io.BytesIO()
+            image.save(output_buffer, format="PNG")  # Or appropriate format
+            return output_buffer.getvalue()
+
+        except Exception as e:
+            logger.error("Simulated Metal frame preprocessing failed: %s", e)
+            return raw_image_data # Return original data on failure
+
+    def classify_ui_state(self, preprocessed_image_data: bytes) -> dict[str, Any]:
+        """
+        Classifies the UI state using a (simulated) Core ML model.
+
+        Args:
+            preprocessed_image_data: Image data after preprocessing.
+
+        Returns:
+            A dictionary containing classification results (e.g., {"state": "dashboard", "confidence": 0.9}).
+        """
+        if not self.metal_ready:
+            logger.warning("Pillow not available. Skipping UI state classification.")
+            return {"state": "unknown", "confidence": 0.0}
+
+        logger.debug("Classifying UI state with (simulated) Core ML.")
+        # TODO: Implement actual Core ML model inference here.
+        # This would involve converting preprocessed_image_data to a CVPixelBuffer
+        # and feeding it to the Core ML model, ensuring it runs on GPU/Neural Engine.
+        # For now, return a mock result.
+        mock_results = {
+            "state": "dashboard_overview",
+            "confidence": 0.85,
+            "detected_elements": ["chart_widget", "notification_bell"],
+        }
+        return mock_results
+
+# Module-level singleton for easy access
+metal_perception_processor = MetalPerceptionProcessor()