src/comparison.py

import numpy as np
from typing import List, Dict
from scipy.optimize import linear_sum_assignment
import yaml

class DamageComparator:
    """Compare damages between before and after images"""

    def __init__(self, config_path: str = "config.yaml"):
        """Initialize comparator with configuration"""
        with open(config_path, 'r') as f:
            self.config = yaml.safe_load(f)

        self.iou_threshold = self.config['comparison']['iou_match_threshold']
        self.position_tolerance = self.config['comparison']['position_tolerance']

    def calculate_iou(self, box1: List[int], box2: List[int]) -> float:
        """
        Calculate Intersection over Union between two boxes

        Args:
            box1, box2: Bounding boxes in format [x1, y1, x2, y2]

        Returns:
            IoU value between 0 and 1
        """
        # Calculate intersection area
        x1 = max(box1[0], box2[0])
        y1 = max(box1[1], box2[1])
        x2 = min(box1[2], box2[2])
        y2 = min(box1[3], box2[3])

        if x2 < x1 or y2 < y1:
            return 0.0

        intersection = (x2 - x1) * (y2 - y1)

        # Calculate union area
        box1_area = (box1[2] - box1[0]) * (box1[3] - box1[1])
        box2_area = (box2[2] - box2[0]) * (box2[3] - box2[1])
        union = box1_area + box2_area - intersection

        # Calculate IoU
        if union == 0:
            return 0.0

        return intersection / union

    def match_damages(self, detections1: Dict, detections2: Dict) -> Dict:
        """
        Match damages between two sets of detections using Hungarian algorithm

        Args:
            detections1: First detection results (before)
            detections2: Second detection results (after)

        Returns:
            Matching results with paired and unpaired damages
        """
        boxes1 = detections1['boxes']
        boxes2 = detections2['boxes']

        if len(boxes1) == 0 and len(boxes2) == 0:
            return {
                'matched_pairs': [],
                'unmatched_before': [],
                'unmatched_after': [],
                'iou_matrix': None
            }

        if len(boxes1) == 0:
            return {
                'matched_pairs': [],
                'unmatched_before': [],
                'unmatched_after': list(range(len(boxes2))),
                'iou_matrix': None
            }

        if len(boxes2) == 0:
            return {
                'matched_pairs': [],
                'unmatched_before': list(range(len(boxes1))),
                'unmatched_after': [],
                'iou_matrix': None
            }

        # Calculate IoU matrix
        iou_matrix = np.zeros((len(boxes1), len(boxes2)))
        for i, box1 in enumerate(boxes1):
            for j, box2 in enumerate(boxes2):
                iou_matrix[i, j] = self.calculate_iou(box1, box2)

        # Use Hungarian algorithm for optimal matching
        # Convert to cost matrix (1 - IoU)
        cost_matrix = 1 - iou_matrix
        row_indices, col_indices = linear_sum_assignment(cost_matrix)

        # Filter matches by IoU threshold
        matched_pairs = []
        matched_rows = set()
        matched_cols = set()

        for i, j in zip(row_indices, col_indices):
            if iou_matrix[i, j] >= self.iou_threshold:
                # Also check if damage types match
                if detections1['classes'][i] == detections2['classes'][j]:
                    matched_pairs.append((i, j, iou_matrix[i, j]))
                    matched_rows.add(i)
                    matched_cols.add(j)

        # Find unmatched damages
        unmatched_before = [i for i in range(len(boxes1)) if i not in matched_rows]
        unmatched_after = [j for j in range(len(boxes2)) if j not in matched_cols]

        return {
            'matched_pairs': matched_pairs,
            'unmatched_before': unmatched_before,
            'unmatched_after': unmatched_after,
            'iou_matrix': iou_matrix.tolist()
        }

    def analyze_damage_status(self, before_detections: Dict, after_detections: Dict) -> Dict:
        """
        Analyze damage status between before and after images

        Returns detailed analysis with case classification
        """
        matching = self.match_damages(before_detections, after_detections)

        # Extract damage information
        matched_damages = []
        for i, j, iou in matching['matched_pairs']:
            matched_damages.append({
                'type': before_detections['classes'][i],
                'confidence_before': before_detections['confidences'][i],
                'confidence_after': after_detections['confidences'][j],
                'box_before': before_detections['boxes'][i],
                'box_after': after_detections['boxes'][j],
                'iou': iou
            })

        existing_damages = []
        for i in matching['unmatched_before']:
            existing_damages.append({
                'type': before_detections['classes'][i],
                'confidence': before_detections['confidences'][i],
                'box': before_detections['boxes'][i]
            })

        new_damages = []
        for j in matching['unmatched_after']:
            new_damages.append({
                'type': after_detections['classes'][j],
                'confidence': after_detections['confidences'][j],
                'box': after_detections['boxes'][j]
            })

        # Determine case
        case = self._determine_case(matched_damages, existing_damages, new_damages)

        return {
            'case': case['type'],
            'message': case['message'],
            'matched_damages': matched_damages,
            'repaired_damages': existing_damages,  # Damages that were there before but not after
            'new_damages': new_damages,
            'statistics': {
                'total_before': len(before_detections['boxes']),
                'total_after': len(after_detections['boxes']),
                'matched': len(matched_damages),
                'repaired': len(existing_damages),
                'new': len(new_damages)
            }
        }

    def _determine_case(self, matched: List, repaired: List, new: List) -> Dict:
        """Determine which case the comparison falls into"""

        # Case 3: Happy case - no damages at all
        if len(matched) == 0 and len(repaired) == 0 and len(new) == 0:
            return {
                'type': 'CASE_3_SUCCESS',
                'message': 'Successful delivery - No damage detected'
            }

        # Case 1: Existing damages remain (with or without repairs/new damages)
        if len(matched) > 0 and len(new) == 0:
            return {
                'type': 'CASE_1_EXISTING',
                'message': 'Error from the beginning, not during the delivery process -> Delivery completed'
            }

        # Case 2: New damages detected
        if len(new) > 0:
            return {
                'type': 'CASE_2_NEW_DAMAGE',
                'message': 'Delivery Defect - New Damage Discovered'
            }

        # Special case: All damages repaired
        if len(repaired) > 0 and len(new) == 0 and len(matched) == 0:
            return {
                'type': 'CASE_REPAIRED',
                'message': 'All damage repaired - Vehicle delivered successfully'
            }

        return {
            'type': 'CASE_UNKNOWN',
            'message': 'Status Undetermined'
        }