Neural Network Inference

def inference(model_path: str, img_path: str, step_size: int = 128, batch_size: int = 16, manual_th: Optional[Any] = None, use_tf: bool = False) -> Tuple[ndarray, ndarray]:
    """
    Run neural network inference on image patches using sliding window approach.
    
    Parameters:
    - model_path (str): Path to model checkpoint directory containing model files
    - img_path (str): Path to input image file
    - step_size (int): Sliding window step size in pixels (default: 128)
    - batch_size (int): Number of patches to process in each batch (default: 16)
    - manual_th (Optional[Any]): Manual threshold for prediction binarization
    - use_tf (bool): Use TensorFlow instead of ONNX runtime (default: False)
    
    Returns:
    Tuple containing:
    - predictions (ndarray): Segmentation predictions with class labels
    - metadata (ndarray): Additional prediction metadata
    
    Raises:
    FileNotFoundError: If model files or input image are not found
    RuntimeError: If inference fails due to model or memory issues
    """

def resize_image(image: Image.Image) -> Image.Image:
    """
    Resize image to optimal dimensions for neural network processing.
    
    Maintains aspect ratio while ensuring dimensions are compatible
    with the model's expected input size requirements.
    
    Parameters:
    - image (PIL.Image.Image): Input image to resize
    
    Returns:
    PIL.Image.Image: Resized image optimized for model inference
    """

def predict(region: ndarray, model_name: str) -> str:
    """
    Predict symbol type using trained sklearn classification models.
    
    Used for distinguishing between similar symbols that neural networks
    cannot reliably differentiate (e.g., different clef types, accidentals).
    
    Parameters:
    - region (ndarray): Image region containing the symbol to classify
    - model_name (str): Name of the sklearn model to use for prediction
    
    Returns:
    str: Predicted symbol class label
    
    Raises:
    ValueError: If model_name is not recognized
    FileNotFoundError: If sklearn model file is not found
    """

from oemer.inference import inference
import numpy as np

# Run inference on a music sheet image
model_path = "oemer/checkpoints/unet_big"
img_path = "sheet_music.jpg"

# Generate predictions
predictions, metadata = inference(
    model_path=model_path,
    img_path=img_path,
    step_size=128,
    batch_size=16,
    use_tf=False  # Use ONNX runtime
)

# Extract staff and symbol predictions
staff_mask = np.where(predictions == 1, 1, 0)
symbol_mask = np.where(predictions == 2, 1, 0)

print(f"Predictions shape: {predictions.shape}")
print(f"Staff pixels: {np.sum(staff_mask)}")
print(f"Symbol pixels: {np.sum(symbol_mask)}")

from oemer.inference import inference, resize_image
from PIL import Image
import os

def run_complete_inference(img_path: str, use_tf: bool = False):
    """Run both inference models on an image."""
    
    # Resize image for optimal processing
    image = Image.open(img_path)
    resized_image = resize_image(image)
    temp_path = "temp_resized.jpg"
    resized_image.save(temp_path)
    
    try:
        # Stage 1: Staff vs. symbols segmentation
        print("Running stage 1 inference (staff vs symbols)...")
        staff_symbols, _ = inference(
            model_path="oemer/checkpoints/unet_big",
            img_path=temp_path,
            step_size=128,
            batch_size=16,
            use_tf=use_tf
        )
        
        # Stage 2: Detailed symbol classification
        print("Running stage 2 inference (symbol details)...")
        symbol_details, _ = inference(
            model_path="oemer/checkpoints/seg_net",
            img_path=temp_path,
            step_size=128,
            batch_size=16,
            use_tf=use_tf
        )
        
        # Process results
        staff = np.where(staff_symbols == 1, 1, 0)
        symbols = np.where(staff_symbols == 2, 1, 0)
        stems_rests = np.where(symbol_details == 1, 1, 0)
        noteheads = np.where(symbol_details == 2, 1, 0)
        clefs_keys = np.where(symbol_details == 3, 1, 0)
        
        return {
            'staff': staff,
            'symbols': symbols,
            'stems_rests': stems_rests,
            'noteheads': noteheads,
            'clefs_keys': clefs_keys
        }
        
    finally:
        # Clean up temporary file
        if os.path.exists(temp_path):
            os.remove(temp_path)

# Run complete inference
results = run_complete_inference("my_sheet_music.jpg")
for key, mask in results.items():
    print(f"{key}: {mask.shape}, pixels: {np.sum(mask)}")

from oemer.inference import inference

# High-precision inference with smaller steps
high_precision_predictions, _ = inference(
    model_path="oemer/checkpoints/unet_big",
    img_path="complex_score.jpg",
    step_size=64,      # Smaller steps for more overlap
    batch_size=8,      # Smaller batches to reduce memory usage
    use_tf=True        # Use TensorFlow for potentially better precision
)

# Fast inference with larger steps  
fast_predictions, _ = inference(
    model_path="oemer/checkpoints/unet_big", 
    img_path="simple_score.jpg",
    step_size=256,     # Larger steps for faster processing
    batch_size=32,     # Larger batches if memory allows
    use_tf=False       # ONNX is typically faster
)

from oemer.inference import predict
import cv2
import numpy as np

# Extract a symbol region from the image
image = cv2.imread("sheet_music.jpg", cv2.IMREAD_GRAYSCALE)
symbol_region = image[100:150, 200:250]  # Extract 50x50 region

# Classify the symbol using trained sklearn models
try:
    # Predict clef type
    clef_type = predict(symbol_region, "clef_classifier")
    print(f"Detected clef: {clef_type}")
    
    # Predict accidental type  
    accidental_type = predict(symbol_region, "accidental_classifier")
    print(f"Detected accidental: {accidental_type}")
    
    # Predict rest type
    rest_type = predict(symbol_region, "rest_classifier")
    print(f"Detected rest: {rest_type}")
    
except ValueError as e:
    print(f"Classification error: {e}")

# Quality-focused settings (slower)
quality_settings = {
    'step_size': 64,
    'batch_size': 8,
    'use_tf': True
}

# Speed-focused settings (faster)  
speed_settings = {
    'step_size': 256,
    'batch_size': 32,
    'use_tf': False
}

# Balanced settings (recommended)
balanced_settings = {
    'step_size': 128,
    'batch_size': 16, 
    'use_tf': False
}