app/utils/onnx_processing.py

"""

ONNX-based batch image processing for the Image Tagger application.

"""

import os
import json
import time
import traceback
import numpy as np
import glob
import onnxruntime as ort
from PIL import Image
import torchvision.transforms as transforms
from concurrent.futures import ThreadPoolExecutor

def preprocess_image(image_path, image_size=512):
    """Process an image for inference"""
    if not os.path.exists(image_path):
        raise ValueError(f"Image not found at path: {image_path}")
   
    # Initialize transform
    transform = transforms.Compose([
        transforms.ToTensor(),
    ])
   
    try:
        with Image.open(image_path) as img:
            # Convert RGBA or Palette images to RGB
            if img.mode in ('RGBA', 'P'):
                img = img.convert('RGB')
           
            # Get original dimensions
            width, height = img.size
            aspect_ratio = width / height
           
            # Calculate new dimensions to maintain aspect ratio
            if aspect_ratio > 1:
                new_width = image_size
                new_height = int(new_width / aspect_ratio)
            else:
                new_height = image_size
                new_width = int(new_height * aspect_ratio)
           
            # Resize with LANCZOS filter
            img = img.resize((new_width, new_height), Image.Resampling.LANCZOS)
           
            # Create new image with padding
            new_image = Image.new('RGB', (image_size, image_size), (0, 0, 0))
            paste_x = (image_size - new_width) // 2
            paste_y = (image_size - new_height) // 2
            new_image.paste(img, (paste_x, paste_y))
           
            # Apply transforms
            img_tensor = transform(new_image)
            return img_tensor.numpy()
    except Exception as e:
        raise Exception(f"Error processing {image_path}: {str(e)}")

def process_single_image_onnx(image_path, model_path, metadata, threshold_profile="Overall", 

                           active_threshold=0.35, active_category_thresholds=None, 

                           min_confidence=0.1):
    """

    Process a single image using ONNX model

    

    Args:

        image_path: Path to the image file

        model_path: Path to the ONNX model file

        metadata: Model metadata dictionary

        threshold_profile: The threshold profile being used

        active_threshold: Overall threshold value

        active_category_thresholds: Category-specific thresholds

        min_confidence: Minimum confidence to include in results

        

    Returns:

        Dictionary with tags and probabilities

    """
    import time
    
    try:
        # Create ONNX tagger for this image (or reuse an existing one)
        if hasattr(process_single_image_onnx, 'tagger'):
            tagger = process_single_image_onnx.tagger
        else:
            # Get metadata path from model_path
            metadata_path = model_path.replace('.onnx', '_metadata.json')
            if not os.path.exists(metadata_path):
                metadata_path = model_path.replace('.onnx', '') + '_metadata.json'
                
            # Create new tagger
            tagger = ONNXImageTagger(model_path, metadata_path)
            # Cache it for future calls
            process_single_image_onnx.tagger = tagger
        
        # Preprocess the image
        start_time = time.time()
        img_array = preprocess_image(image_path)
        
        # Run inference
        results = tagger.predict_batch(
            [img_array], 
            threshold=active_threshold,
            category_thresholds=active_category_thresholds,
            min_confidence=min_confidence
        )
        inference_time = time.time() - start_time
        
        if results:
            result = results[0]
            result['inference_time'] = inference_time
            return result
        else:
            return {
                'success': False,
                'error': 'Failed to process image',
                'all_tags': [],
                'all_probs': {},
                'tags': {}
            }
    
    except Exception as e:
        import traceback
        print(f"Error in process_single_image_onnx: {str(e)}")
        traceback.print_exc()
        return {
            'success': False,
            'error': str(e),
            'all_tags': [],
            'all_probs': {},
            'tags': {}
        }

def preprocess_images_parallel(image_paths, image_size=512, max_workers=8):
    """Process multiple images in parallel"""
    processed_images = []
    valid_paths = []
    
    # Define a worker function
    def process_single_image(path):
        try:
            return preprocess_image(path, image_size), path
        except Exception as e:
            print(f"Error processing {path}: {str(e)}")
            return None, path
    
    # Process images in parallel
    with ThreadPoolExecutor(max_workers=max_workers) as executor:
        results = list(executor.map(process_single_image, image_paths))
    
    # Filter results
    for img_array, path in results:
        if img_array is not None:
            processed_images.append(img_array)
            valid_paths.append(path)
    
    return processed_images, valid_paths

def apply_category_limits(result, category_limits):
    """

    Apply category limits to a result dictionary.

    

    Args:

        result: Result dictionary containing tags and all_tags

        category_limits: Dictionary mapping categories to their tag limits

                         (0 = exclude category, -1 = no limit/include all)

        

    Returns:

        Updated result dictionary with limits applied

    """
    if not category_limits or not result['success']:
        return result
    
    # Get the filtered tags
    filtered_tags = result['tags']
    
    # Apply limits to each category
    for category, cat_tags in list(filtered_tags.items()):
        # Get limit for this category, default to -1 (no limit)
        limit = category_limits.get(category, -1)
        
        if limit == 0:
            # Exclude this category entirely
            del filtered_tags[category]
        elif limit > 0 and len(cat_tags) > limit:
            # Limit to top N tags for this category
            filtered_tags[category] = cat_tags[:limit]
    
    # Regenerate all_tags list after applying limits
    all_tags = []
    for category, cat_tags in filtered_tags.items():
        for tag, _ in cat_tags:
            all_tags.append(tag)
    
    # Update the result with limited tags
    result['tags'] = filtered_tags
    result['all_tags'] = all_tags
    
    return result

class ONNXImageTagger:
    """ONNX-based image tagger for fast batch inference"""
    
    def __init__(self, model_path, metadata_path):
        # Load model
        self.model_path = model_path
        try:
            self.session = ort.InferenceSession(
                model_path, 
                providers=['CUDAExecutionProvider', 'CPUExecutionProvider']
            )
            print(f"Using providers: {self.session.get_providers()}")
        except Exception as e:
            print(f"CUDA not available, using CPU: {e}")
            self.session = ort.InferenceSession(
                model_path, 
                providers=['CPUExecutionProvider']
            )
            print(f"Using providers: {self.session.get_providers()}")
        
        # Load metadata
        with open(metadata_path, 'r') as f:
            self.metadata = json.load(f)
            
        # Get input name
        self.input_name = self.session.get_inputs()[0].name
        print(f"Model loaded successfully. Input name: {self.input_name}")
        
    def predict_batch(self, image_arrays, threshold=0.325, category_thresholds=None, min_confidence=0.1):
        """Run batch inference on preprocessed image arrays"""
        # Stack arrays into batch
        batch_input = np.stack(image_arrays)
        
        # Run inference
        start_time = time.time()
        outputs = self.session.run(None, {self.input_name: batch_input})
        inference_time = time.time() - start_time
        print(f"Batch inference completed in {inference_time:.4f} seconds ({inference_time/len(image_arrays):.4f} s/image)")
        
        # Process outputs
        initial_probs = 1.0 / (1.0 + np.exp(-outputs[0]))  # Apply sigmoid
        refined_probs = 1.0 / (1.0 + np.exp(-outputs[1])) if len(outputs) > 1 else initial_probs
        
        # Apply thresholds and extract tags for each image
        batch_results = []
        
        for i in range(refined_probs.shape[0]):
            probs = refined_probs[i]
            
            # Extract and organize all probabilities
            all_probs = {}
            for idx in range(probs.shape[0]):
                prob_value = float(probs[idx])
                if prob_value >= min_confidence:
                    idx_str = str(idx)
                    tag_name = self.metadata['idx_to_tag'].get(idx_str, f"unknown-{idx}")
                    category = self.metadata['tag_to_category'].get(tag_name, "general")
                    
                    if category not in all_probs:
                        all_probs[category] = []
                    
                    all_probs[category].append((tag_name, prob_value))
            
            # Sort tags by probability within each category
            for category in all_probs:
                all_probs[category] = sorted(
                    all_probs[category], 
                    key=lambda x: x[1], 
                    reverse=True
                )
            
            # Get the filtered tags based on the selected threshold
            tags = {}
            for category, cat_tags in all_probs.items():
                # Use category-specific threshold if available
                if category_thresholds and category in category_thresholds:
                    cat_threshold = category_thresholds[category]
                else:
                    cat_threshold = threshold
                    
                tags[category] = [(tag, prob) for tag, prob in cat_tags if prob >= cat_threshold]
            
            # Create a flat list of all tags above threshold
            all_tags = []
            for category, cat_tags in tags.items():
                for tag, _ in cat_tags:
                    all_tags.append(tag)
            
            batch_results.append({
                'tags': tags,
                'all_probs': all_probs,
                'all_tags': all_tags,
                'success': True
            })
            
        return batch_results

def batch_process_images_onnx(folder_path, model_path, metadata_path, threshold_profile, 

                            active_threshold, active_category_thresholds, save_dir=None, 

                            progress_callback=None, min_confidence=0.1, batch_size=16,

                            category_limits=None):
    """

    Process all images in a folder using the ONNX model.

    

    Args:

        folder_path: Path to folder containing images

        model_path: Path to the ONNX model file

        metadata_path: Path to the model metadata file

        threshold_profile: Selected threshold profile

        active_threshold: Overall threshold value

        active_category_thresholds: Category-specific thresholds

        save_dir: Directory to save tag files (if None uses default)

        progress_callback: Optional callback for progress updates

        min_confidence: Minimum confidence threshold

        batch_size: Number of images to process at once

        category_limits: Dictionary mapping categories to their tag limits (0 = unlimited)

        

    Returns:

        Dictionary with results for each image

    """
    from utils.file_utils import save_tags_to_file  # Import here to avoid circular imports
    
    # Find all image files in the folder
    image_extensions = ['*.jpg', '*.jpeg', '*.png']
    image_files = []
    
    for ext in image_extensions:
        image_files.extend(glob.glob(os.path.join(folder_path, ext)))
        image_files.extend(glob.glob(os.path.join(folder_path, ext.upper())))
    # Use a set to remove duplicate files (Windows filesystems are case-insensitive)
    if os.name == 'nt':  # Windows
        # Use lowercase paths for comparison on Windows
        unique_paths = set()
        unique_files = []
        for file_path in image_files:
            normalized_path = os.path.normpath(file_path).lower()
            if normalized_path not in unique_paths:
                unique_paths.add(normalized_path)
                unique_files.append(file_path)
        image_files = unique_files
    
    if not image_files:
        return {
            'success': False,
            'error': f"No images found in {folder_path}",
            'results': {}
        }
    
    # Use the provided save directory or create a default one
    if save_dir is None:
        app_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
        save_dir = os.path.join(app_dir, "saved_tags")
    
    # Ensure the directory exists
    os.makedirs(save_dir, exist_ok=True)
    
    # Create ONNX tagger
    tagger = ONNXImageTagger(model_path, metadata_path)
    
    # Process images in batches
    results = {}
    total_images = len(image_files)
    processed = 0
    
    start_time = time.time()
    
    # Process in batches
    for i in range(0, total_images, batch_size):
        batch_start = time.time()
        
        # Get current batch of images
        batch_files = image_files[i:i+batch_size]
        batch_size_actual = len(batch_files)
        
        # Update progress if callback provided
        if progress_callback:
            progress_callback(processed, total_images, batch_files[0] if batch_files else None)
        
        print(f"Processing batch {i//batch_size + 1}/{(total_images + batch_size - 1)//batch_size}: {batch_size_actual} images")
        
        try:
            # Preprocess images in parallel
            processed_images, valid_paths = preprocess_images_parallel(batch_files)
            
            if processed_images:
                # Run batch prediction
                batch_results = tagger.predict_batch(
                    processed_images, 
                    threshold=active_threshold,
                    category_thresholds=active_category_thresholds,
                    min_confidence=min_confidence
                )
                
            # Process results for each image
            for j, (image_path, result) in enumerate(zip(valid_paths, batch_results)):
                # Update progress if callback provided
                if progress_callback:
                    progress_callback(processed + j, total_images, image_path)
                
                # Debug print to track what's happening
                print(f"Before limiting - Tags for {os.path.basename(image_path)}: {len(result['all_tags'])} tags")
                print(f"Category limits applied: {category_limits}")
                
                # Make sure we apply limits right before saving
                if category_limits and result['success']:
                    # Before counts for debugging
                    before_counts = {cat: len(tags) for cat, tags in result['tags'].items()}
                    
                    # Apply the limits
                    result = apply_category_limits(result, category_limits)
                    
                    # After counts for debugging
                    after_counts = {cat: len(tags) for cat, tags in result['tags'].items()}
                    
                    # Print the effect of limits
                    print(f"Before limits: {before_counts}")
                    print(f"After limits: {after_counts}")
                    print(f"After limiting - Tags for {os.path.basename(image_path)}: {len(result['all_tags'])} tags")
                
                # Save the tags to a file
                if result['success']:
                    output_path = save_tags_to_file(
                        image_path=image_path,
                        all_tags=result['all_tags'],
                        custom_dir=save_dir,
                        overwrite=True
                    )
                    result['output_path'] = str(output_path)
                
                # Store the result
                results[image_path] = result
            
            processed += batch_size_actual
            
            # Calculate batch timing
            batch_end = time.time()
            batch_time = batch_end - batch_start
            print(f"Batch processed in {batch_time:.2f} seconds ({batch_time/batch_size_actual:.2f} seconds per image)")
            
        except Exception as e:
            print(f"Error processing batch: {str(e)}")
            traceback.print_exc()
            
            # Process failed images one by one as fallback
            for image_path in batch_files:
                try:
                    # Update progress if callback provided
                    if progress_callback:
                        progress_callback(processed + j, total_images, image_path)
                    
                    # Debug print to track what's happening
                    print(f"Before limiting - Tags for {os.path.basename(image_path)}: {len(result['all_tags'])} tags")
                    print(f"Category limits applied: {category_limits}")
                    
                    # Make sure we apply limits right before saving
                    if category_limits and result['success']:
                        # Before counts for debugging
                        before_counts = {cat: len(tags) for cat, tags in result['tags'].items()}
                        
                        # Apply the limits
                        result = apply_category_limits(result, category_limits)
                        
                        # After counts for debugging
                        after_counts = {cat: len(tags) for cat, tags in result['tags'].items()}
                        
                        # Print the effect of limits
                        print(f"Before limits: {before_counts}")
                        print(f"After limits: {after_counts}")
                        print(f"After limiting - Tags for {os.path.basename(image_path)}: {len(result['all_tags'])} tags")
                                           
                    # Preprocess single image
                    img_array = preprocess_image(image_path)
                    
                    # Run inference on single image
                    single_results = tagger.predict_batch(
                        [img_array], 
                        threshold=active_threshold,
                        category_thresholds=active_category_thresholds,
                        min_confidence=min_confidence
                    )
                    
                    if single_results:
                        result = single_results[0]
                                            
                    # Save the tags to a file
                    if result['success']:
                        output_path = save_tags_to_file(
                            image_path=image_path,
                            all_tags=result['all_tags'],
                            custom_dir=save_dir,
                            overwrite=True  # Add this to be consistent
                        )
                        result['output_path'] = str(output_path)
                        
                        # Store the result
                        results[image_path] = result
                    else:
                        results[image_path] = {
                            'success': False,
                            'error': 'Failed to process image',
                            'all_tags': []
                        }
                        
                except Exception as img_e:
                    print(f"Error processing single image {image_path}: {str(img_e)}")
                    results[image_path] = {
                        'success': False,
                        'error': str(img_e),
                        'all_tags': []
                    }
                
                processed += 1
    
    # Final progress update
    if progress_callback:
        progress_callback(total_images, total_images, None)
    
    end_time = time.time()
    total_time = end_time - start_time
    print(f"Batch processing finished. Total time: {total_time:.2f} seconds, Average: {total_time/total_images:.2f} seconds per image")
    
    return {
        'success': True,
        'total': total_images,
        'processed': len(results),
        'results': results,
        'save_dir': save_dir,
        'time_elapsed': end_time - start_time
    }