Confidence Scores

import cv2
import numpy as np

def filter_detections(detections, confidence_threshold=0.5):
    """
    Filter detections by confidence threshold.
    Only keep detections where the model is confident enough.
    """
    filtered = [d for d in detections if d["confidence"] >= confidence_threshold]
    print(f"Before filtering: {len(detections)} detections")
    print(f"After filtering (threshold={confidence_threshold}): {len(filtered)}")
    return filtered

# Simulated detection output (confidence from a model)
raw_detections = [
    {"class": "person",   "confidence": 0.95, "bbox": (50, 30, 80, 200)},
    {"class": "car",      "confidence": 0.88, "bbox": (200, 100, 300, 180)},
    {"class": "bicycle",  "confidence": 0.45, "bbox": (150, 200, 70, 90)},
    {"class": "dog",      "confidence": 0.32, "bbox": (400, 300, 60, 80)},
    {"class": "cat",      "confidence": 0.71, "bbox": (320, 150, 50, 70)},
]

# Filter at different thresholds
strict  = filter_detections(raw_detections, 0.8)   # High confidence only
medium  = filter_detections(raw_detections, 0.5)   # Balanced
lenient = filter_detections(raw_detections, 0.3)   # Accept more detections

# The confidence threshold trades off precision vs. recall:
# Higher threshold → fewer detections, fewer false positives
# Lower threshold  → more detections, more false positives
print("\nGuideline: start with 0.5, adjust based on false positive rate")