"""
    Python3 script for YOLO prediction and sizing of oyster objects.
    
    """
    
    from ultralytics import YOLO
    
    # Locally written directories
    results_dir = 'oyster_yolo_results'
    predict_name = 'example_oyster_results'
    
    # Files from this aquaculture_oysters HF repository
    model = YOLO('aquaculture_oysters.pt')
    oyster_img =  'oyster_sample.jpg'
    
    # The size standard disk diameter. In this case, as millimeters, 
    #  using a cutout from a 3 inch hockey puck.
    std_mm = 76.2
    
    # Run inference with these arguments
    prediction_kwargs = dict(
        save=True,  # saves image to project/name
        save_txt=True,  # saves relative bounding box coordinates
        save_conf=True,  # includes conf scores
        augment=False,
        show_labels=True,
        line_width=2,
        project=results_dir,
        name=predict_name,
        exist_ok=False,
    )
    
    results = model.predict(
        source=oyster_img,
        half=False,
        imgsz=960,
        conf=0.7,
        device='cpu',
        iou=0.7,
        max_det=400,
        classes=[0,1],  #  0 is 'disk', 1 is 'oyster'.
        **prediction_kwargs
    )
    
    # Display detection bounding boxes with class names and confidence values.
    results[0].show(line_width=2)
    
    # Calculate and print size metrics, Are based on longest bounding box dimension.
    # Sizes do not account for longer lengths of oysters oriented on the diagonal.
    #  An empirically determined correction factor is recommended for better accuracy.
    #  A good correction function is used in the companion GitHub repository.
    boxes_tensor = results[0].boxes.xywh
    boxes_numpy = boxes_tensor.cpu().numpy().astype(int)
    w_h = boxes_numpy[:, 2:4]
    px_lengths = w_h.max(axis=1)
    
    classes = results[0].boxes.cls
    class_distribution = classes.cpu().numpy().astype(int)
    
    num_disks = len(class_distribution[class_distribution == 0])
    num_oysters = len(class_distribution[class_distribution == 1])
    oyster_px_sizes = px_lengths[class_distribution == 1]
    disk_px_sizes = px_lengths[class_distribution == 0]
    px2mm_factor = std_mm / disk_px_sizes.mean()
    oyster_mean_mm = round(oyster_px_sizes.mean() * px2mm_factor, 2)
    smallest = round(oyster_px_sizes.min() * px2mm_factor, 2)
    largest = round(oyster_px_sizes.max() * px2mm_factor, 2)
    
    print(f'Mean oyster size, mm: {oyster_mean_mm}')
    print(f'Oyster size range, mm: [{smallest} -- {largest}]')

hyperparam_args233 = {
    'lr0': 0.00242,
    'lrf': 0.01009,
    'momentum': 0.92746,
    'weight_decay': 0.00038,
    'warmup_epochs': 1.94011,
    'warmup_momentum': 0.47865,
    'box': 5.89996,
    'cls': 0.27165,
    'dfl': 1.23119,
    'hsv_h': 0.01303,
    'hsv_s': 0.32101,
    'hsv_v': 0.40969,
    'degrees': 0.0,
    'translate': 0.15574,
    'scale': 0.75304,
    'shear': 0.0,
    'perspective': 0.0,
    'flipud': 0.0,
    'fliplr': 0.58715,
    'bgr': 0.0,
    'mosaic': 1.0,
    'mixup': 0.0,
    'copy_paste': 0.0,
}

image 1/1 /home/craig/Desktop/oyster_sample.jpg: 960x768 3 disks, 105 oysters, 2903.9ms
Speed: 13.3ms preprocess, 2903.9ms inference, 28.1ms postprocess per image at shape (1, 3, 960, 768)
Results saved to oyster_yolo_results/example_oyster_results
1 label saved to oyster_yolo_results/example_oyster_results/labels
Mean oyster size, mm: 58.71
Oyster size range, mm: [47.22 -- 69.1]