segmentation.py

"""
Room Segmentation Module for preserving structural elements
"""
import cv2
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image
import segmentation_models_pytorch as smp
from typing import Tuple, Dict, List
import albumentations as A
from albumentations.pytorch import ToTensorV2

class RoomSegmentation:
    def __init__(self, device: str = "cuda"):
        self.device = device
        self.model = None
        self.class_names = [
            'wall', 'window', 'door', 'ceiling', 'floor', 
            'furniture', 'lighting', 'decoration', 'textile'
        ]
        self.load_model()
        
    def load_model(self):
        """Load pre-trained segmentation model"""
        try:
            # Using DeepLabV3+ with ResNet50 backbone
            self.model = smp.DeepLabV3Plus(
                encoder_name="resnet50",
                encoder_weights="imagenet",
                classes=len(self.class_names),
                activation="softmax"
            )
            self.model.to(self.device)
            self.model.eval()
        except Exception as e:
            print(f"Warning: Could not load segmentation model: {e}")
            print("Falling back to rule-based segmentation")
            self.model = None
    
    def segment_room(self, image: np.ndarray) -> Dict[str, np.ndarray]:
        """
        Segment room image into different components
        
        Args:
            image: Input room image (BGR format)
            
        Returns:
            Dictionary with class names as keys and binary masks as values
        """
        if self.model is not None:
            return self._ml_segmentation(image)
        else:
            return self._rule_based_segmentation(image)
    
    def _ml_segmentation(self, image: np.ndarray) -> Dict[str, np.ndarray]:
        """ML-based segmentation using pre-trained model"""
        # Preprocess image
        transform = A.Compose([
            A.Resize(512, 512),
            A.Normalize(),
            ToTensorV2()
        ])
        
        transformed = transform(image=image)
        input_tensor = transformed['image'].unsqueeze(0).to(self.device)
        
        with torch.no_grad():
            output = self.model(input_tensor)
            predictions = F.softmax(output, dim=1)
            predictions = predictions.squeeze().cpu().numpy()
        
        # Convert to binary masks
        masks = {}
        for i, class_name in enumerate(self.class_names):
            mask = (predictions[i] > 0.5).astype(np.uint8)
            # Resize back to original size
            mask = cv2.resize(mask, (image.shape[1], image.shape[0]))
            masks[class_name] = mask
            
        return masks
    
    def _rule_based_segmentation(self, image: np.ndarray) -> Dict[str, np.ndarray]:
        """Rule-based segmentation using color and edge detection"""
        # Convert to different color spaces
        hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        
        masks = {}
        
        # Wall detection (light colored, large regions)
        light_mask = cv2.inRange(hsv, np.array([0, 0, 200]), np.array([180, 30, 255]))
        walls = self._morphological_cleanup(light_mask)
        masks['wall'] = walls
        
        # Window detection (bright, rectangular regions)
        bright_mask = cv2.inRange(hsv, np.array([0, 0, 220]), np.array([180, 30, 255]))
        edges = cv2.Canny(gray, 50, 150)
        windows = cv2.bitwise_and(bright_mask, edges)
        masks['window'] = self._morphological_cleanup(windows)
        
        # Floor detection (bottom portion, darker)
        height, width = image.shape[:2]
        floor_mask = np.zeros((height, width), dtype=np.uint8)
        floor_mask[height//2:, :] = 255
        dark_mask = cv2.inRange(hsv, np.array([0, 0, 0]), np.array([180, 255, 100]))
        floor = cv2.bitwise_and(floor_mask, dark_mask)
        masks['floor'] = self._morphological_cleanup(floor)
        
        # Ceiling detection (top portion, light)
        ceiling_mask = np.zeros((height, width), dtype=np.uint8)
        ceiling_mask[:height//3, :] = 255
        ceiling = cv2.bitwise_and(ceiling_mask, light_mask)
        masks['ceiling'] = self._morphological_cleanup(ceiling)
        
        # Door detection (vertical edges, medium height)
        door_mask = np.zeros((height, width), dtype=np.uint8)
        door_mask[height//4:3*height//4, :] = 255
        vertical_edges = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=3)
        vertical_edges = np.uint8(np.absolute(vertical_edges))
        doors = cv2.bitwise_and(door_mask, vertical_edges)
        masks['door'] = self._morphological_cleanup(doors)
        
        # Furniture (everything else)
        all_structural = sum([masks['wall'], masks['window'], masks['floor'], 
                            masks['ceiling'], masks['door']])
        furniture = cv2.bitwise_not(all_structural)
        masks['furniture'] = furniture
        
        return masks
    
    def _morphological_cleanup(self, mask: np.ndarray) -> np.ndarray:
        """Clean up mask using morphological operations"""
        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
        cleaned = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
        cleaned = cv2.morphologyEx(cleaned, cv2.MORPH_OPEN, kernel)
        return cleaned
    
    def create_preservation_mask(self, masks: Dict[str, np.ndarray], 
                                preserve_classes: List[str]) -> np.ndarray:
        """
        Create a mask for elements that should be preserved
        
        Args:
            masks: Dictionary of segmentation masks
            preserve_classes: List of class names to preserve
            
        Returns:
            Binary mask where 1 indicates preserved regions
        """
        preservation_mask = np.zeros_like(list(masks.values())[0])
        
        for class_name in preserve_classes:
            if class_name in masks:
                preservation_mask = cv2.bitwise_or(preservation_mask, masks[class_name])
        
        # Dilate to ensure complete coverage
        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (10, 10))
        preservation_mask = cv2.dilate(preservation_mask, kernel, iterations=2)
        
        return preservation_mask
    
    def visualize_segmentation(self, image: np.ndarray, masks: Dict[str, np.ndarray]) -> np.ndarray:
        """Visualize segmentation results"""
        colors = [
            [255, 0, 0],    # Red - walls
            [0, 255, 0],    # Green - windows
            [0, 0, 255],    # Blue - doors
            [255, 255, 0],  # Cyan - ceiling
            [255, 0, 255],  # Magenta - floor
            [0, 255, 255],  # Yellow - furniture
            [128, 128, 128], # Gray - lighting
            [64, 64, 64],   # Dark gray - decoration
            [192, 192, 192] # Light gray - textile
        ]
        
        result = image.copy()
        for i, (class_name, mask) in enumerate(masks.items()):
            if i < len(colors):
                color = colors[i]
                result[mask > 0] = color
        
        return result