037494c19b
Automated salvage commit — agent session ended (exit 124). Work in progress, may need continuation.
118 lines
4.6 KiB
Python
118 lines
4.6 KiB
Python
"""
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Metal-accelerated image processing and UI state classification for perception.
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This module provides functions to preprocess raw image frames (resizing,
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grayscale, contrast enhancement) using Metal shaders and to classify
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UI states using Core ML models, explicitly pinned to the GPU/Neural Engine.
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"""
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from __future__ import annotations
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import logging
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from typing import Any
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# For now, we'll use Pillow as a placeholder for image operations.
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# In a real Metal integration, these would be replaced by calls to Metal shaders.
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try:
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from PIL import Image
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from PIL import ImageOps
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except ImportError:
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Image = None
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ImageOps = None
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logging.warning("Pillow not installed. Image processing will be a no-op.")
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logger = logging.getLogger(__name__)
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class MetalPerceptionProcessor:
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"""
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Handles Metal-accelerated frame preprocessing and Core ML UI state classification.
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"""
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def __init__(self):
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logger.info("Initializing MetalPerceptionProcessor.")
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# REAL IMPLEMENTATION: Initialize Metal devices and command queue.
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# This would involve using a library like PyObjC to interact with CoreGraphics,
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# CoreImage, and MetalKit to set up the GPU for image processing.
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# For this mock, we simulate readiness if Pillow is installed.
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self.metal_ready = False
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if Image is None:
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logger.warning("MetalPerceptionProcessor will operate in no-op mode due to missing Pillow.")
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else:
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self.metal_ready = True # Simulate readiness if Pillow is available
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# REAL IMPLEMENTATION: Load Core ML model and configure it to use the GPU/Neural Engine.
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# This would involve using MLModel.load() and setting MLCpuOnly to false or
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# specifying MLComputeUnits.all for optimal performance on Apple Silicon.
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# For this mock, we just have a placeholder.
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self.core_ml_model = None
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logger.info("MetalPerceptionProcessor initialized.")
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def preprocess_frame(self, raw_image_data: bytes, target_size: tuple[int, int] = (256, 256)) -> bytes:
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"""
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Preprocesses a raw image frame using (simulated) Metal shaders.
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Args:
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raw_image_data: Raw bytes of the image (e.g., PNG, JPEG).
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target_size: The desired (width, height) for resizing.
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Returns:
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Processed image data as bytes.
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"""
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if not self.metal_ready:
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logger.warning("Pillow not available. Skipping frame preprocessing.")
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return raw_image_data
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logger.debug("Preprocessing frame with (simulated) Metal shaders.")
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try:
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# Simulate Metal operations using Pillow
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image = Image.open(io.BytesIO(raw_image_data))
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# 1. Resize
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image = image.resize(target_size, Image.Resampling.LANCZOS)
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# 2. Grayscale
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image = ImageOps.grayscale(image)
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# 3. Contrast enhancement (simple example)
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# This is a very basic contrast adjustment. Metal shaders would allow for more sophisticated algorithms.
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image = ImageOps.autocontrast(image)
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output_buffer = io.BytesIO()
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image.save(output_buffer, format="PNG") # Or appropriate format
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return output_buffer.getvalue()
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except Exception as e:
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logger.error("Simulated Metal frame preprocessing failed: %s", e)
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return raw_image_data # Return original data on failure
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def classify_ui_state(self, preprocessed_image_data: bytes) -> dict[str, Any]:
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"""
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Classifies the UI state using a (simulated) Core ML model.
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Args:
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preprocessed_image_data: Image data after preprocessing.
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Returns:
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A dictionary containing classification results (e.g., {"state": "dashboard", "confidence": 0.9}).
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"""
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if not self.metal_ready:
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logger.warning("Pillow not available. Skipping UI state classification.")
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return {"state": "unknown", "confidence": 0.0}
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logger.debug("Classifying UI state with (simulated) Core ML.")
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# TODO: Implement actual Core ML model inference here.
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# This would involve converting preprocessed_image_data to a CVPixelBuffer
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# and feeding it to the Core ML model, ensuring it runs on GPU/Neural Engine.
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# For now, return a mock result.
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mock_results = {
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"state": "dashboard_overview",
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"confidence": 0.85,
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"detected_elements": ["chart_widget", "notification_bell"],
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}
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return mock_results
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# Module-level singleton for easy access
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metal_perception_processor = MetalPerceptionProcessor()
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