Understanding Detection Results

import numpy as np

def calculate_iou(box1, box2):
    """
    Calculate Intersection over Union between two bounding boxes.
    Format: (x1, y1, x2, y2)
    IoU = 1.0: perfect overlap (identical boxes)
    IoU = 0.0: no overlap at all
    """
    # Intersection rectangle
    inter_x1 = max(box1[0], box2[0])
    inter_y1 = max(box1[1], box2[1])
    inter_x2 = min(box1[2], box2[2])
    inter_y2 = min(box1[3], box2[3])

    # Check if there is any overlap
    if inter_x2 < inter_x1 or inter_y2 < inter_y1:
        return 0.0

    # Areas
    inter_area = (inter_x2 - inter_x1) * (inter_y2 - inter_y1)
    box1_area  = (box1[2] - box1[0]) * (box1[3] - box1[1])
    box2_area  = (box2[2] - box2[0]) * (box2[3] - box2[1])
    union_area = box1_area + box2_area - inter_area

    return inter_area / union_area

# Example
box_a = (50, 50, 200, 200)   # Ground truth
box_b = (80, 80, 220, 220)   # Detection

iou = calculate_iou(box_a, box_b)
print(f"IoU: {iou:.3f}")
print(f"Detection quality: {'Good (>0.5)' if iou > 0.5 else 'Poor (<0.5)'}")