test_yolo_detection.py

#!/usr/bin/env python
"""Test YOLO detection with different confidence levels"""

import sys
from PIL import Image
import numpy as np

# Use full conda python path
sys.path.insert(0, r"c:\Users\barat\OneDrive\Desktop\model\plate-detector")

try:
    from ultralytics import YOLO
    from detector import detect_plate
    
    # Test with the test image
    test_image_path = r"C:\Users\barat\OneDrive\Desktop\image.jpg"
    
    print(f"Loading image from: {test_image_path}")
    image = Image.open(test_image_path)
    image_np = np.array(image.convert("RGB"))
    
    print(f"Image shape: {image_np.shape}")
    print(f"Image dtype: {image_np.dtype}")
    
    # Load YOLO model
    print("\nLoading YOLO model...")
    yolo = YOLO("license-plate-finetune-v1s.pt")
    print("✅ YOLO loaded")
    
    # Run detection with default settings
    print("\nRunning YOLO detection (conf=0.5)...")
    results = yolo(image_np)
    boxes = results[0].boxes
    
    print(f"Boxes found: {len(boxes) if boxes is not None else 0}")
    if boxes is not None:
        print(f"Confidences: {boxes.conf.cpu().numpy()}")
        print(f"Box coordinates: {boxes.xyxy.cpu().numpy()}")
    
    # Try with lower confidence
    print("\nRunning YOLO detection (conf=0.3)...")
    results = yolo(image_np, conf=0.3)
    boxes = results[0].boxes
    
    print(f"Boxes found: {len(boxes) if boxes is not None else 0}")
    if boxes is not None:
        print(f"Confidences: {boxes.conf.cpu().numpy()}")
        print(f"Box coordinates: {boxes.xyxy.cpu().numpy()}")
    
    # Try with very low confidence
    print("\nRunning YOLO detection (conf=0.1)...")
    results = yolo(image_np, conf=0.1)
    boxes = results[0].boxes
    
    print(f"Boxes found: {len(boxes) if boxes is not None else 0}")
    if boxes is not None:
        print(f"Confidences: {boxes.conf.cpu().numpy()}")
        print(f"Box coordinates: {boxes.xyxy.cpu().numpy()}")
    
    # Now test full detection pipeline
    print("\n" + "="*50)
    print("Testing full detection pipeline...")
    print("="*50)
    plate, state, vehicle_type, vehicle_conf, success = detect_plate(image)
    print(f"\nPlate: {plate}")
    print(f"State: {state}")
    print(f"Vehicle Type: {vehicle_type}")
    print(f"Vehicle Conf: {vehicle_conf}")
    print(f"Success: {success}")
    
except Exception as e:
    print(f"❌ Error: {e}")
    import traceback
    traceback.print_exc()