Initial commit

image/optimizer.py

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+from PIL import Image
+import numpy as np
+from typing import Tuple
+from .conversion import DEVICE_SPECS, ModelId, DeviceSpec
+
+
+def optimize(img: Image.Image, model: ModelId) -> Image.Image:
+    """
+    Optimize an image for a specific Gicisky device model.
+    
+    Args:
+        img: Input PIL Image
+        model: Device model identifier
+        
+    Returns:
+        Image.Image: Optimized image
+    """
+    specs: DeviceSpec = DEVICE_SPECS.get(model)
+    if not specs:
+        raise ValueError(f"Unknown model: {model}")
+    target_width, target_height = specs.size
+
+    canvas = Image.new("RGB", (target_width, target_height), color="white")
+    img_width, img_height = img.size
+    scale = min(target_width / img_width, target_height / img_height)
+    new_width = int(img_width * scale)
+    new_height = int(img_height * scale)
+
+    resized_img = img.resize((new_width, new_height), Image.Resampling.LANCZOS)
+    x_offset = (target_width - new_width) // 2
+    y_offset = (target_height - new_height) // 2
+
+    canvas.paste(resized_img, (x_offset, y_offset))
+
+    palette = Image.new("P", (1, 1))
+    colors = 3
+    
+    # Apply appropriate color conversion
+    if specs.second_color:
+        palette.putpalette([
+            0, 0, 0,  # Black
+            255, 255, 255,  # White
+            255, 0, 0  # Red
+        ])
+    else:
+        palette.putpalette([
+            0, 0, 0,  # Black
+            255, 255, 255,  # White
+        ])
+        colors = 2
+
+    processed_img = canvas.quantize(method=Image.MEDIANCUT,
+                                        colors=colors,
+                                        kmeans=0,
+                                        palette=palette)
+    processed_img = processed_img.convert("RGB")
+    
+    return processed_img
+