Initial commit

image/processor.py

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+"""
+Image processing functions
+"""
+
+import numpy as np
+import cv2
+from typing import List
+
+
+def resize_mask_to_image(
+    mask: np.ndarray,
+    target_height: int,
+    target_width: int
+) -> np.ndarray:
+    """
+    Resize mask to match image dimensions.
+    
+    Args:
+        mask: Mask array (H, W) in range [0, 1]
+        target_height: Target height
+        target_width: Target width
+        
+    Returns:
+        Resized mask array (target_height, target_width) in range [0, 1]
+    """
+    if mask.shape[0] == target_height and mask.shape[1] == target_width:
+        return mask
+    
+    # Use cv2.resize with INTER_LINEAR for smooth resizing
+    mask_resized = cv2.resize(mask, (target_width, target_height), interpolation=cv2.INTER_LINEAR)
+    # Ensure values stay in [0, 1] range
+    mask_resized = np.clip(mask_resized, 0.0, 1.0)
+    
+    return mask_resized
+
+
+def apply_mask_overlay(
+    image: np.ndarray,
+    mask: np.ndarray,
+    background_color: List[float]
+) -> np.ndarray:
+    """
+    Apply mask overlay to image with background color.
+    Where mask is 1.0, use background_color; where mask is 0.0, use original image.
+    
+    Args:
+        image: Image array (H, W, 3) in range [0, 1]
+        mask: Mask array (H, W) in range [0, 1]
+        background_color: RGB color [r, g, b] in range [0, 1]
+        
+    Returns:
+        Image with mask overlay applied (H, W, 3) in range [0, 1]
+    """
+    # Ensure mask is 3D for broadcasting: (H, W) -> (H, W, 1)
+    mask_3d = np.expand_dims(mask, axis=-1)
+    
+    # Expand background_color to match image shape: (3,) -> (1, 1, 3)
+    bg_color_array = np.array(background_color, dtype=np.float32).reshape(1, 1, 3)
+    
+    # Apply overlay: image * (1 - mask) + background_color * mask
+    masked_image = image * (1.0 - mask_3d) + bg_color_array * mask_3d
+    
+    # Ensure values stay in [0, 1] range
+    masked_image = np.clip(masked_image, 0.0, 1.0)
+    
+    return masked_image