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import numpy as np
from typing import List, Dict, Any, Tuple, Optional
from rtree import index
import pandas as pd
class DetectionSpatialAnalyzer:
"""
Spatial analyzer using R-tree for DeepForest detection results.
"""
def __init__(self, image_width: int, image_height: int):
"""
Initialize spatial analyzer with image dimensions.
Args:
image_width: Width of the image in pixels
image_height: Height of the image in pixels
"""
self.image_width = image_width
self.image_height = image_height
self.spatial_index = index.Index()
self.detections = []
def add_detections(self, detections_list: List[Dict[str, Any]]) -> None:
"""
Add detections to R-tree spatial index.
Args:
detections_list: List of detection dictionaries with coordinates
"""
for i, detection in enumerate(detections_list):
xmin = detection.get('xmin', 0)
ymin = detection.get('ymin', 0)
xmax = detection.get('xmax', 0)
ymax = detection.get('ymax', 0)
# Validate box ordering - swap if necessary
if xmin > xmax:
xmin, xmax = xmax, xmin
if ymin > ymax:
ymin, ymax = ymax, ymin
# Clamp to image bounds
xmin = max(0, min(xmin, self.image_width))
ymin = max(0, min(ymin, self.image_height))
xmax = max(0, min(xmax, self.image_width))
ymax = max(0, min(ymax, self.image_height))
# Skip invalid boxes (zero area after validation)
if xmin >= xmax or ymin >= ymax:
continue
# Add to R-tree index
self.spatial_index.insert(i, (xmin, ymin, xmax, ymax))
# Store detection with spatial info
detection_copy = detection.copy()
detection_copy['detection_id'] = i
detection_copy['centroid_x'] = (xmin + xmax) / 2
detection_copy['centroid_y'] = (ymin + ymax) / 2
detection_copy['area'] = (xmax - xmin) * (ymax - ymin)
self.detections.append(detection_copy)
def get_grid_analysis(self) -> Dict[str, Dict[str, Any]]:
"""
Analyze detections using 3x3 grid system.
Returns:
Dictionary with analysis for each grid cell
"""
grid_width = self.image_width / 3
grid_height = self.image_height / 3
grid_names = {
(0, 0): "Top-Left (Northwest)", (1, 0): "Top-Center (North)", (2, 0): "Top-Right (Northeast)",
(0, 1): "Middle-Left (West)", (1, 1): "Center", (2, 1): "Middle-Right (East)",
(0, 2): "Bottom-Left (Southwest)", (1, 2): "Bottom-Center (South)", (2, 2): "Bottom-Right (Southeast)"
}
grid_analysis = {}
for (grid_x, grid_y), grid_name in grid_names.items():
# Define grid bounds
x_min = grid_x * grid_width
y_min = grid_y * grid_height
x_max = (grid_x + 1) * grid_width
y_max = (grid_y + 1) * grid_height
# Query R-tree for intersecting detections
intersecting_ids = list(self.spatial_index.intersection((x_min, y_min, x_max, y_max)))
grid_detections = [self.detections[i] for i in intersecting_ids]
# Analyze by confidence categories
confidence_analysis = self._analyze_confidence_categories(grid_detections)
grid_analysis[grid_name] = {
"total_detections": len(grid_detections),
"confidence_analysis": confidence_analysis,
"bounds": {"x_min": x_min, "y_min": y_min, "x_max": x_max, "y_max": y_max}
}
return grid_analysis
def _analyze_confidence_categories(self, detections: List[Dict[str, Any]]) -> Dict[str, Dict[str, Any]]:
"""
Analyze detections by confidence categories.
Args:
detections: List of detection dictionaries
Returns:
Analysis by confidence categories (Low, Medium, High)
"""
categories = {
"Detections with Low Confidence Score (0.0-0.3)": {"detections": [], "range": (0.0, 0.3)},
"Detections with Medium Confidence Score (0.3-0.7)": {"detections": [], "range": (0.3, 0.7)},
"Detections with High Confidence Score (0.7-1.0)": {"detections": [], "range": (0.7, 1.0)}
}
for detection in detections:
score = detection.get('score', 0.0)
if score < 0.3:
categories["Detections with Low Confidence Score (0.0-0.3)"]["detections"].append(detection)
elif score < 0.7:
categories["Detections with Medium Confidence Score (0.3-0.7)"]["detections"].append(detection)
else:
categories["Detections with High Confidence Score (0.7-1.0)"]["detections"].append(detection)
# Calculate statistics for each category
analysis = {}
for category_name, category_data in categories.items():
cat_detections = category_data["detections"]
if cat_detections:
areas = [d['area'] for d in cat_detections]
analysis[category_name] = {
"count": len(cat_detections),
"avg_area": np.mean(areas),
"min_area": np.min(areas),
"max_area": np.max(areas),
"total_area_covered": np.sum(areas),
"labels": [d.get('label', 'unknown') for d in cat_detections]
}
else:
analysis[category_name] = {
"count": 0,
"avg_area": 0,
"min_area": 0,
"max_area": 0,
"total_area_covered": 0,
"labels": []
}
return analysis
def analyze_spatial_relationships_with_indexing(self, confidence_threshold: float = 0.3) -> List[Dict[str, Any]]:
"""
Analyze spatial relationships using R-tree indexing for confidence >= 0.3 detections.
Args:
confidence_threshold: Minimum confidence score (default: 0.3)
Returns:
List of spatial relationship dictionaries with intersection and nearest neighbor data
"""
# Filter detections by confidence threshold
high_confidence_detections = [
d for d in self.detections
if d.get('score', 0.0) >= confidence_threshold
]
if not high_confidence_detections:
return []
relationships = []
for detection in high_confidence_detections:
# Get bounding box coordinates directly
xmin = detection.get('xmin', 0)
ymin = detection.get('ymin', 0)
xmax = detection.get('xmax', 0)
ymax = detection.get('ymax', 0)
detection_id = detection.get('detection_id', 0)
# Get object label (handle classification labels for trees)
if 'classification_label' in detection and detection['classification_label'] and str(detection['classification_label']).lower() != 'nan':
object_label = detection['classification_label']
else:
object_label = detection.get('label', 'unknown')
# Find intersecting objects using spatial index
intersecting_ids = list(self.spatial_index.intersection((xmin, ymin, xmax, ymax)))
# Remove self from intersections
intersecting_ids = [idx for idx in intersecting_ids if idx != detection_id]
# Get details of intersecting objects
intersecting_objects = []
for idx in intersecting_ids:
if idx < len(self.detections):
intersecting_detection = self.detections[idx]
if intersecting_detection.get('score', 0.0) >= confidence_threshold:
if 'classification_label' in intersecting_detection and intersecting_detection['classification_label'] and str(intersecting_detection['classification_label']).lower() != 'nan':
intersecting_label = intersecting_detection['classification_label']
else:
intersecting_label = intersecting_detection.get('label', 'unknown')
intersecting_objects.append(intersecting_label)
# Find nearest neighbor using spatial index
nearest_ids = list(self.spatial_index.nearest((xmin, ymin, xmax, ymax), 2)) # 2 to get self + nearest
nearest_neighbor = None
for idx in nearest_ids:
if idx != detection_id and idx < len(self.detections):
nearest_detection = self.detections[idx]
if nearest_detection.get('score', 0.0) >= confidence_threshold:
if 'classification_label' in nearest_detection and nearest_detection['classification_label'] and str(nearest_detection['classification_label']).lower() != 'nan':
nearest_label = nearest_detection['classification_label']
else:
nearest_label = nearest_detection.get('label', 'unknown')
nearest_neighbor = nearest_label
break
# Determine grid region
grid_region = self._determine_grid_region(detection)
# Count intersecting objects by type
object_counts = {}
for obj_label in intersecting_objects:
object_counts[obj_label] = object_counts.get(obj_label, 0) + 1
relationships.append({
'object_type': object_label,
'object_location': f"({ymin}, {xmin})",
'grid_region': grid_region,
'intersecting_objects': object_counts,
'nearest_neighbor': nearest_neighbor,
'confidence_score': detection.get('score', 0.0),
'total_intersections': len(intersecting_objects)
})
return relationships
def _determine_grid_region(self, detection: Dict[str, Any]) -> str:
"""
Determine which grid region a detection belongs to based on its centroid.
Args:
detection: Detection dictionary with coordinates
Returns:
Grid region name (e.g., "northern", "northwest", etc.)
"""
centroid_x = detection.get('centroid_x', 0)
centroid_y = detection.get('centroid_y', 0)
grid_width = self.image_width / 3
grid_height = self.image_height / 3
# Determine grid position
grid_x = int(centroid_x // grid_width)
grid_y = int(centroid_y // grid_height)
# Ensure within bounds
grid_x = min(2, max(0, grid_x))
grid_y = min(2, max(0, grid_y))
grid_names = {
(0, 0): "northwestern", (1, 0): "northern", (2, 0): "northeastern",
(0, 1): "western", (1, 1): "central", (2, 1): "eastern",
(0, 2): "southwestern", (1, 2): "southern", (2, 2): "southeastern"
}
return grid_names.get((grid_x, grid_y), "central")
def generate_spatial_narrative(self, confidence_threshold: float = 0.3) -> str:
"""
Generate narrative description of spatial relationships using R-tree analysis.
Args:
confidence_threshold: Minimum confidence score for analysis (default: 0.3)
Returns:
Natural language narrative of spatial relationships
"""
relationships = self.analyze_spatial_relationships_with_indexing(confidence_threshold)
if not relationships:
return f"No objects with confidence score >= {confidence_threshold} found for spatial relationship analysis."
narrative_parts = []
# Process each relationship and only include different object types
for rel in relationships:
object_type = rel['object_type']
confidence_score = rel['confidence_score']
grid_region = rel['grid_region']
object_location = rel['object_location']
# Only process intersecting objects that are DIFFERENT from the main object
different_intersecting = {}
for intersecting_type, count in rel['intersecting_objects'].items():
if intersecting_type != object_type: # Only different object types
different_intersecting[intersecting_type] = count
# Generate narrative for intersecting different objects
if different_intersecting:
intersecting_parts = []
for obj_label, count in different_intersecting.items():
if count == 1:
intersecting_parts.append(f"{count} {obj_label.replace('_', ' ')}")
else:
intersecting_parts.append(f"{count} {obj_label.replace('_', ' ')}s")
intersecting_desc = ", ".join(intersecting_parts)
narrative_parts.append(
f"I am about {confidence_score*100:.1f}% confident that, in {grid_region} region, "
f"{intersecting_desc} found overlapping around the {object_type.replace('_', ' ')} "
f"object at location (top, left) = {object_location}.\n"
)
# Only add nearest neighbor information if it's a DIFFERENT object type
if rel['nearest_neighbor'] and rel['nearest_neighbor'] != object_type:
narrative_parts.append(
f"I am about {confidence_score*100:.1f}% confident that, in {grid_region} region, "
f"around the {object_type.replace('_', ' ')} at location (top, left) = {object_location} "
f"the nearest neighbor is a {rel['nearest_neighbor'].replace('_', ' ')}.\n"
)
if narrative_parts:
# Remove duplicates while preserving order
unique_narratives = []
seen = set()
for part in narrative_parts:
if part not in seen:
unique_narratives.append(part)
seen.add(part)
return " ".join(unique_narratives)
else:
return f"Spatial analysis completed for {len(relationships)} objects with confidence >= {confidence_threshold}, but no significant spatial relationships between different object types detected."
def get_detection_statistics(self) -> Dict[str, Any]:
"""
Get comprehensive detection statistics.
Returns:
Dictionary with overall statistics
"""
if not self.detections:
return {"total_count": 0}
# Basic counts and confidence
total_count = len(self.detections)
scores = [d.get('score', 0.0) for d in self.detections]
overall_confidence = np.mean(scores)
# Size statistics
areas = [d['area'] for d in self.detections]
avg_area = np.mean(areas)
min_area = np.min(areas)
max_area = np.max(areas)
total_area = np.sum(areas)
# Label distribution
labels = [d.get('label', 'unknown') for d in self.detections]
# Handle classification labels for trees
classified_labels = []
for d in self.detections:
if 'classification_label' in d and d['classification_label'] and str(d['classification_label']).lower() != 'nan':
classified_labels.append(d['classification_label'])
else:
classified_labels.append(d.get('label', 'unknown'))
from collections import Counter
label_counts = Counter(classified_labels)
return {
"total_count": total_count,
"overall_confidence": overall_confidence,
"size_stats": {
"avg_area": avg_area,
"min_area": min_area,
"max_area": max_area,
"total_area_covered": total_area
},
"label_distribution": dict(label_counts),
"confidence_distribution": {
"low_count": len([s for s in scores if s < 0.3]),
"medium_count": len([s for s in scores if 0.3 <= s < 0.7]),
"high_count": len([s for s in scores if s >= 0.7])
}
} |