import numpy as np
import cv2
from PIL import Image, ImageEnhance, ImageFilter
import os

def enhance_image_quality(image, brightness=1.1, contrast=1.1, sharpness=1.1):
    """Enhance image quality for better virtual try-on results"""
    if image is None:
        return None
    
    pil_image = Image.fromarray(image)
    
    # Adjust brightness
    if brightness != 1.0:
        brightness_enhancer = ImageEnhance.Brightness(pil_image)
        pil_image = brightness_enhancer.enhance(brightness)
    
    # Adjust contrast
    if contrast != 1.0:
        contrast_enhancer = ImageEnhance.Contrast(pil_image)
        pil_image = contrast_enhancer.enhance(contrast)
    
    # Adjust sharpness
    if sharpness != 1.0:
        sharpness_enhancer = ImageEnhance.Sharpness(pil_image)
        pil_image = sharpness_enhancer.enhance(sharpness)
    
    return np.array(pil_image)

def remove_background_simple(image):
    """Simple background removal using color clustering"""
    if image is None:
        return None, None
    
    # Convert to PIL for easier processing
    pil_image = Image.fromarray(image)
    
    # Convert to RGBA
    if pil_image.mode != 'RGBA':
        pil_image = pil_image.convert('RGBA')
    
    # Simple approach: make white/light colors transparent
    data = np.array(pil_image)
    
    # Create mask for light colors (potential background)
    light_mask = np.all(data[:, :, :3] > 200, axis=2)
    
    # Create transparent version
    data[light_mask] = [255, 255, 255, 0]  # Make light colors transparent
    
    return np.array(Image.fromarray(data, 'RGBA')), light_mask

def get_image_info(image):
    """Get basic information about an image"""
    if image is None:
        return "No image loaded"
    
    height, width = image.shape[:2]
    channels = image.shape[2] if len(image.shape) > 2 else 1
    
    return f"Size: {width}x{height}, Channels: {channels}, Data type: {image.dtype}"

def resize_image_preserve_aspect(image, target_width, target_height):
    """Resize image while preserving aspect ratio"""
    if image is None:
        return None
    
    height, width = image.shape[:2]
    aspect_ratio = width / height
    
    if target_width / target_height > aspect_ratio:
        # Height is the limiting factor
        new_height = target_height
        new_width = int(target_height * aspect_ratio)
    else:
        # Width is the limiting factor
        new_width = target_width
        new_height = int(target_width / aspect_ratio)
    
    resized = cv2.resize(image, (new_width, new_height))
    
    # Create canvas and center the image
    canvas = np.zeros((target_height, target_width, 3), dtype=np.uint8)
    y_offset = (target_height - new_height) // 2
    x_offset = (target_width - new_width) // 2
    canvas[y_offset:y_offset+new_height, x_offset:x_offset+new_width] = resized
    
    return canvas

This is a comprehensive virtual cloth trial room application! Here's what it includes:

## Features:

1. **Virtual Try-On System**: Upload person and clothing images to see a virtual try-on result
2. **Real-time Adjustments**: Use sliders to adjust clothing opacity and vertical position
3. **Simple AI Processing**: Uses computer vision techniques to blend clothing with person images
4. **User-Friendly Interface**: Clear instructions and status feedback
5. **Reset Functionality**: Start over with new images easily

## How It Works:

1. **Image Loading**: Upload photos of yourself and the clothing you want to try
2. **Preprocessing**: Images are resized and optimized for processing
3. **Virtual Try-On**: The system blends the clothing onto the person using mask-based blending
4. **Adjustments**: Fine-tune the result with opacity and position controls

## Key Technologies:

- **PIL/Pillow**: Image processing and enhancement
- **OpenCV**: Computer vision operations
- **NumPy**: Numerical operations for image arrays
- **Gradio**: User interface and web app framework

## Usage Tips:

- Use high-quality, well-lit images for best results
- Person should be positioned upright and clearly visible
- Clothing should have good contrast with its background
- The app works best with frontal-facing photos

The application is now ready to run! It provides a functional virtual try-on experience with intuitive controls and helpful feedback for users.