"""
Image processing utility functions
"""
from PIL import Image
import os


def remove_image_metadata(image_path: str) -> None:
    """
    Remove metadata information from image file
    
    Args:
        image_path: Path to the image file
    """
    if not os.path.exists(image_path):
        print(f"Warning: Image file does not exist: {image_path}")
        return
    
    try:
        # Load original image
        img = Image.open(image_path)
        
        # Create new image without metadata
        # Preserve image mode and size
        img_without_metadata = Image.new(img.mode, img.size)
        img_without_metadata.putdata(list(img.getdata()))
        
        # Save without any metadata
        img_without_metadata.save(image_path, format='PNG')
        
        print(f"Removed image metadata: {image_path}")
    except Exception as e:
        print(f"Failed to remove metadata ({image_path}): {str(e)}")


def remove_multiple_images_metadata(image_paths: list) -> None:
    """
    Batch remove metadata from multiple image files
    
    Args:
        image_paths: List of image file paths
    """
    for path in image_paths:
        remove_image_metadata(path)


def resize_and_optimize_image(image_path: str, target_long_edge: int = 1536) -> None:
    """
    Resize image (long edge) and optimize PNG compression, while removing metadata
    
    Args:
        image_path: Path to the image file
        target_long_edge: Target long edge size, default 1536 (768×2)
    """
    if not os.path.exists(image_path):
        print(f"Warning: Image file does not exist: {image_path}")
        return
    
    try:
        # Load original image
        img = Image.open(image_path)
        original_size = img.size
        
        # Calculate scaling ratio
        width, height = img.size
        long_edge = max(width, height)
        
        # If long edge is already smaller than target, only optimize compression
        if long_edge <= target_long_edge:
            print(f"Image size {original_size} is already smaller than target, only optimizing compression: {image_path}")
            img.save(
                image_path, 
                format='PNG',
                compress_level=9,  # Maximum compression level (0-9)
                optimize=True      # Enable optimization
            )
            return
        
        # Calculate new dimensions
        scale = target_long_edge / long_edge
        new_width = int(width * scale)
        new_height = int(height * scale)
        
        # Resize using high-quality Lanczos resampling algorithm
        img_resized = img.resize((new_width, new_height), Image.Resampling.LANCZOS)
        
        # Save optimized image (without metadata)
        img_resized.save(
            image_path,
            format='PNG',
            compress_level=9,  # Maximum compression level
            optimize=True      # Enable optimization
        )
        
        print(f"Image resized and optimized: {image_path}")
        print(f"  Original size: {original_size} -> New size: ({new_width}, {new_height})")
        
        # Display file size
        if os.path.exists(image_path):
            file_size_mb = os.path.getsize(image_path) / (1024 * 1024)
            print(f"  File size: {file_size_mb:.2f} MB")
            
    except Exception as e:
        print(f"Failed to resize and optimize image ({image_path}): {str(e)}")