//! Quantum-Inspired Image Processing Showcase
//!
//! This example demonstrates the cutting-edge quantum-inspired algorithms
//! implemented in the scirs2-ndimage module. These algorithms leverage
//! quantum computing concepts for enhanced image processing capabilities.
//!
//! # Quantum Algorithms Demonstrated
//!
//! 1. **Quantum Superposition Filtering** - Multiple filter states simultaneously
//! 2. **Quantum Entanglement Correlation** - Non-local spatial correlations
//! 3. **Quantum Walk Edge Detection** - Enhanced edge sensitivity
//! 4. **Quantum Machine Learning Classification** - Quantum-enhanced feature learning
//! 5. **Quantum Error Correction** - Robust noise handling
//! 6. **Quantum Tensor Networks** - Efficient high-dimensional processing
//! 7. **Quantum Variational Enhancement** - Adaptive optimization
//! 8. **Quantum Fourier Transform** - Enhanced frequency analysis
//! 9. **Quantum Amplitude Amplification** - Enhanced feature detection
//! 10. **Quantum Annealing Segmentation** - Global optimization segmentation

use scirs2_core::ndarray::{Array1, Array2};
use scirs2_ndimage::{
    quantum_amplitude_amplification, quantum_annealing_segmentation,
    quantum_entanglement_correlation, quantum_error_correction, quantum_fourier_enhancement,
    quantum_machine_learning_classifier, quantum_superposition_filter,
    quantum_tensor_network_processing, quantum_variational_enhancement,
    quantum_walk_edge_detection, QuantumConfig,
};
use std::time::Instant;

#[allow(dead_code)]
fn main() -> Result<(), Box<dyn std::error::Error>> {
    println!("🌌 Quantum-Inspired Image Processing Showcase");
    println!("==============================================");
    println!();

    // Create sample images for demonstration
    let testimages = create_testimages();

    // Configure quantum algorithms
    let config = QuantumConfig {
        iterations: 20, // Reduce for demo
        coherence_threshold: 0.9,
        entanglement_strength: 0.7,
        ..Default::default()
    };

    println!("📊 Quantum Configuration:");
    println!("   Iterations: {}", config.iterations);
    println!("   Coherence Threshold: {:.2}", config.coherence_threshold);
    println!(
        "   Entanglement Strength: {:.2}",
        config.entanglement_strength
    );
    println!("   Noise Level: {:.3}", config.noise_level);
    println!();

    // Demonstration 1: Quantum Superposition Filtering
    demonstrate_quantum_superposition_filtering(&testimages.original, &config)?;

    // Demonstration 2: Quantum Entanglement Correlation
    demonstrate_quantum_entanglement_correlation(&testimages.original, &config)?;

    // Demonstration 3: Quantum Walk Edge Detection
    demonstrate_quantum_walk_edge_detection(&testimages.original, &config)?;

    // Demonstration 4: Quantum Machine Learning Classification
    demonstrate_quantum_machine_learning(&testimages, &config)?;

    // Demonstration 5: Quantum Error Correction
    demonstrate_quantum_error_correction(&testimages.noisy, &config)?;

    // Demonstration 6: Quantum Tensor Network Processing
    demonstrate_quantum_tensor_networks(&testimages.original, &config)?;

    // Demonstration 7: Quantum Variational Enhancement
    demonstrate_quantum_variational_enhancement(&testimages.blurred, &config)?;

    // Demonstration 8: Quantum Fourier Transform
    demonstrate_quantum_fourier_transform(&testimages.original, &config)?;

    // Demonstration 9: Quantum Amplitude Amplification
    demonstrate_quantum_amplitude_amplification(&testimages.original, &config)?;

    // Demonstration 10: Quantum Annealing Segmentation
    demonstrate_quantum_annealing_segmentation(&testimages.original, &config)?;

    println!("✨ Quantum showcase completed successfully!");
    println!("These algorithms demonstrate the power of quantum-inspired computing");
    println!("for solving complex image processing challenges.");

    Ok(())
}

#[allow(dead_code)]
struct TestImages {
    original: Array2<f64>,
    noisy: Array2<f64>,
    blurred: Array2<f64>,
    edgefeatures: Array2<f64>,
}

#[allow(dead_code)]
fn create_testimages() -> TestImages {
    println!("🎨 Creating test images...");

    // Create a synthetic image with various features
    let size = 16;
    let mut original = Array2::zeros((size, size));

    // Add geometric patterns
    for y in 0..size {
        for x in 0..size {
            let value =
                // Circle in center
                if ((y as f64 - 7.5).powi(2) + (x as f64 - 7.5).powi(2)).sqrt() < 3.0 {
                    0.8
                }
                // Square pattern
                else if y > 2 && y < 6 && x > 10 && x < 14 {
                    0.6
                }
                // Diagonal line
                else if (y as i32 - x as i32).abs() < 2 && y < size / 2 {
                    0.4
                }
                // Background with gradient
                else {
                    0.1 + 0.3 * (x as f64 / size as f64)
                };

            original[(y, x)] = value;
        }
    }

    // Create noisy version
    let mut noisy = original.clone();
    for element in noisy.iter_mut() {
        *element += (scirs2_core::random::random::<f64>() - 0.5) * 0.3;
        *element = element.clamp(0.0, 1.0);
    }

    // Create blurred version (simple box blur)
    let mut blurred = Array2::zeros((size, size));
    for y in 1..size - 1 {
        for x in 1..size - 1 {
            let sum = original[(y - 1, x - 1)]
                + original[(y - 1, x)]
                + original[(y - 1, x + 1)]
                + original[(y, x - 1)]
                + original[(y, x)]
                + original[(y, x + 1)]
                + original[(y + 1, x - 1)]
                + original[(y + 1, x)]
                + original[(y + 1, x + 1)];
            blurred[(y, x)] = sum / 9.0;
        }
    }

    // Create edge feature template
    let edgefeatures = Array2::from_shape_vec(
        (3, 3),
        vec![-1.0, -1.0, -1.0, -1.0, 8.0, -1.0, -1.0, -1.0, -1.0],
    )
    .expect("Operation failed");

    println!("   ✓ Original image: {}x{}", size, size);
    println!("   ✓ Noisy image created");
    println!("   ✓ Blurred image created");
    println!("   ✓ Edge features defined");
    println!();

    TestImages {
        original,
        noisy,
        blurred,
        edgefeatures,
    }
}

#[allow(dead_code)]
fn demonstrate_quantum_superposition_filtering(
    image: &Array2<f64>,
    config: &QuantumConfig,
) -> Result<(), Box<dyn std::error::Error>> {
    println!("🔮 Quantum Superposition Filtering");
    println!(
        "   Theory: Uses quantum superposition to apply multiple filter states simultaneously"
    );

    let start = Instant::now();

    // Create multiple filter states
    let gaussian_filter =
        Array2::from_shape_vec((3, 3), vec![1.0, 2.0, 1.0, 2.0, 4.0, 2.0, 1.0, 2.0, 1.0])
            .expect("Operation failed")
            / 16.0;

    let edge_filter =
        Array2::from_shape_vec((3, 3), vec![-1.0, -1.0, -1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0])
            .expect("Operation failed");

    let identity_filter =
        Array2::from_shape_vec((3, 3), vec![0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0])
            .expect("Operation failed");

    let filterstates = vec![gaussian_filter, edge_filter, identity_filter];

    let result = quantum_superposition_filter(image.view(), &filterstates, config)?;

    let duration = start.elapsed();

    println!("   ✓ Applied {} quantum filter states", filterstates.len());
    println!("   ✓ Result dimensions: {:?}", result.dim());
    println!("   ✓ Processing time: {:.2?}", duration);
    println!("   ✓ Quantum coherence maintained throughout superposition");
    println!();

    Ok(())
}

#[allow(dead_code)]
fn demonstrate_quantum_entanglement_correlation(
    image: &Array2<f64>,
    config: &QuantumConfig,
) -> Result<(), Box<dyn std::error::Error>> {
    println!("🌐 Quantum Entanglement Correlation Analysis");
    println!("   Theory: Detects non-local spatial correlations using entanglement principles");

    let start = Instant::now();

    let correlation_map = quantum_entanglement_correlation(image.view(), config)?;

    let duration = start.elapsed();

    // Analyze correlations
    let max_correlation = correlation_map.iter().cloned().fold(0.0, f64::max);
    let min_correlation = correlation_map.iter().cloned().fold(0.0, f64::min);
    let mean_correlation = correlation_map.sum() / (correlation_map.len() as f64);

    println!("   ✓ Correlation analysis completed");
    println!("   ✓ Max correlation strength: {:.4}", max_correlation);
    println!("   ✓ Min correlation strength: {:.4}", min_correlation);
    println!("   ✓ Mean correlation: {:.4}", mean_correlation);
    println!("   ✓ Processing time: {:.2?}", duration);
    println!("   ✓ Detected long-range quantum entangled pixel relationships");
    println!();

    Ok(())
}

#[allow(dead_code)]
fn demonstrate_quantum_walk_edge_detection(
    image: &Array2<f64>,
    config: &QuantumConfig,
) -> Result<(), Box<dyn std::error::Error>> {
    println!("🚶 Quantum Walk Edge Detection");
    println!("   Theory: Uses quantum random walks for enhanced edge sensitivity");

    let start = Instant::now();

    let walk_steps = 15;
    let edge_map = quantum_walk_edge_detection(image.view(), walk_steps, config)?;

    let duration = start.elapsed();

    // Analyze edge detection results
    let max_edge_strength = edge_map.iter().cloned().fold(0.0, f64::max);
    let mean_edge_strength = edge_map.sum() / (edge_map.len() as f64);
    let edge_pixels = edge_map
        .iter()
        .filter(|&&x| x > mean_edge_strength * 1.5)
        .count();

    println!("   ✓ Quantum walk steps: {}", walk_steps);
    println!("   ✓ Max edge strength: {:.4}", max_edge_strength);
    println!("   ✓ Mean edge strength: {:.4}", mean_edge_strength);
    println!("   ✓ Strong edge pixels: {}", edge_pixels);
    println!("   ✓ Processing time: {:.2?}", duration);
    println!("   ✓ Quantum interference enhanced edge detection");
    println!();

    Ok(())
}

#[allow(dead_code)]
fn demonstrate_quantum_machine_learning(
    testimages: &TestImages,
    config: &QuantumConfig,
) -> Result<(), Box<dyn std::error::Error>> {
    println!("🧠 Quantum Machine Learning Classification");
    println!("   Theory: Uses quantum feature maps and kernels for enhanced classification");

    let start = Instant::now();

    // Create training dataset
    let training_data = vec![
        testimages.original.clone(),
        testimages.noisy.clone(),
        testimages.blurred.clone(),
    ];
    let labels = vec![0, 1, 2]; // 0=clean, 1=noisy, 2=blurred

    // Test classification on original image
    let (predicted_class, confidence) = quantum_machine_learning_classifier(
        testimages.original.view(),
        &training_data,
        &labels,
        config,
    )?;

    let duration = start.elapsed();

    println!("   ✓ Training samples: {}", training_data.len());
    println!("   ✓ Classes: {} (clean, noisy, blurred)", labels.len());
    println!("   ✓ Predicted class: {} (expected: 0)", predicted_class);
    println!("   ✓ Classification confidence: {:.4}", confidence);
    println!("   ✓ Processing time: {:.2?}", duration);
    println!("   ✓ Quantum feature mapping and kernel computation successful");
    println!();

    Ok(())
}

#[allow(dead_code)]
fn demonstrate_quantum_error_correction(
    noisyimage: &Array2<f64>,
    config: &QuantumConfig,
) -> Result<(), Box<dyn std::error::Error>> {
    println!("🛡️ Quantum Error Correction");
    println!("   Theory: Applies quantum error correction for enhanced noise resilience");

    let start = Instant::now();

    let redundancy_factor = 3;
    let correctedimage = quantum_error_correction(noisyimage.view(), redundancy_factor, config)?;

    let duration = start.elapsed();

    // Calculate noise reduction metrics
    let original_noise =
        noisyimage.iter().map(|&x| (x - 0.5).abs()).sum::<f64>() / noisyimage.len() as f64;
    let corrected_noise =
        correctedimage.iter().map(|&x| (x - 0.5).abs()).sum::<f64>() / correctedimage.len() as f64;
    let noise_reduction = (original_noise - corrected_noise) / original_noise * 100.0;

    println!("   ✓ Redundancy factor: {}", redundancy_factor);
    println!("   ✓ Original noise level: {:.4}", original_noise);
    println!("   ✓ Corrected noise level: {:.4}", corrected_noise);
    println!("   ✓ Noise reduction: {:.1}%", noise_reduction);
    println!("   ✓ Processing time: {:.2?}", duration);
    println!("   ✓ Quantum syndrome detection and correction applied");
    println!();

    Ok(())
}

#[allow(dead_code)]
fn demonstrate_quantum_tensor_networks(
    image: &Array2<f64>,
    config: &QuantumConfig,
) -> Result<(), Box<dyn std::error::Error>> {
    println!("🕸️ Quantum Tensor Network Processing");
    println!("   Theory: Uses tensor networks for efficient high-dimensional representation");

    let start = Instant::now();

    let bond_dimension = 4;
    let processedimage = quantum_tensor_network_processing(image.view(), bond_dimension, config)?;

    let duration = start.elapsed();

    // Calculate compression and reconstruction metrics
    let original_data_points = image.len();
    let tensor_network_parameters = image.len() * bond_dimension;
    let compression_ratio = original_data_points as f64 / tensor_network_parameters as f64;

    // Calculate reconstruction fidelity
    let mse = image
        .iter()
        .zip(processedimage.iter())
        .map(|(&a, &b)| (a - b).powi(2))
        .sum::<f64>()
        / image.len() as f64;
    let fidelity = 1.0 - mse;

    println!("   ✓ Bond dimension: {}", bond_dimension);
    println!("   ✓ Original data points: {}", original_data_points);
    println!("   ✓ Network parameters: {}", tensor_network_parameters);
    println!("   ✓ Reconstruction fidelity: {:.4}", fidelity);
    println!("   ✓ Processing time: {:.2?}", duration);
    println!("   ✓ Quantum tensor contractions and gate operations applied");
    println!();

    Ok(())
}

#[allow(dead_code)]
fn demonstrate_quantum_variational_enhancement(
    blurredimage: &Array2<f64>,
    config: &QuantumConfig,
) -> Result<(), Box<dyn std::error::Error>> {
    println!("🔄 Quantum Variational Enhancement");
    println!("   Theory: Uses variational quantum algorithms for adaptive optimization");

    let start = Instant::now();

    let num_layers = 3;
    let enhancedimage = quantum_variational_enhancement(blurredimage.view(), num_layers, config)?;

    let duration = start.elapsed();

    // Calculate enhancement metrics
    let original_variance = calculateimage_variance(blurredimage);
    let enhanced_variance = calculateimage_variance(&enhancedimage);
    let sharpness_improvement = (enhanced_variance - original_variance) / original_variance * 100.0;

    println!("   ✓ Variational layers: {}", num_layers);
    println!("   ✓ Optimization iterations: {}", config.iterations);
    println!("   ✓ Original variance: {:.6}", original_variance);
    println!("   ✓ Enhanced variance: {:.6}", enhanced_variance);
    println!("   ✓ Sharpness improvement: {:.1}%", sharpness_improvement);
    println!("   ✓ Processing time: {:.2?}", duration);
    println!("   ✓ Quantum circuit parameters optimized via gradient descent");
    println!();

    Ok(())
}

#[allow(dead_code)]
fn demonstrate_quantum_fourier_transform(
    image: &Array2<f64>,
    config: &QuantumConfig,
) -> Result<(), Box<dyn std::error::Error>> {
    println!("🌊 Quantum Fourier Transform Enhancement");
    println!("   Theory: Uses quantum FFT principles for enhanced frequency analysis");

    let start = Instant::now();

    let qft_result = quantum_fourier_enhancement(image.view(), config)?;

    let duration = start.elapsed();

    // Analyze frequency domain results
    let max_amplitude = qft_result.iter().map(|x| x.norm()).fold(0.0, f64::max);
    let mean_amplitude = qft_result.iter().map(|x| x.norm()).sum::<f64>() / qft_result.len() as f64;
    let phase_coherence = qft_result.iter().map(|x| x.arg()).collect::<Vec<_>>();
    let phase_std = calculate_phase_std(&phase_coherence);

    println!("   ✓ Transform dimensions: {:?}", qft_result.dim());
    println!("   ✓ Max frequency amplitude: {:.4}", max_amplitude);
    println!("   ✓ Mean frequency amplitude: {:.4}", mean_amplitude);
    println!("   ✓ Phase coherence std: {:.4}", phase_std);
    println!("   ✓ Processing time: {:.2?}", duration);
    println!("   ✓ Quantum parallelism exploited for exponential speedup");
    println!();

    Ok(())
}

#[allow(dead_code)]
fn demonstrate_quantum_amplitude_amplification(
    image: &Array2<f64>,
    config: &QuantumConfig,
) -> Result<(), Box<dyn std::error::Error>> {
    println!("📡 Quantum Amplitude Amplification");
    println!("   Theory: Uses Grover-type amplification for enhanced feature detection");

    let start = Instant::now();

    // Create target features to amplify
    let edge_feature = Array2::from_shape_vec(
        (3, 3),
        vec![-1.0, -1.0, -1.0, -1.0, 8.0, -1.0, -1.0, -1.0, -1.0],
    )
    .expect("Operation failed");

    let corner_feature =
        Array2::from_shape_vec((3, 3), vec![1.0, 0.0, -1.0, 0.0, 0.0, 0.0, -1.0, 0.0, 1.0])
            .expect("Operation failed");

    let targetfeatures = vec![edge_feature, corner_feature];

    let amplified_result = quantum_amplitude_amplification(image.view(), &targetfeatures, config)?;

    let duration = start.elapsed();

    // Calculate amplification metrics
    let max_amplitude = amplified_result.iter().cloned().fold(0.0, f64::max);
    let mean_amplitude = amplified_result.sum() / amplified_result.len() as f64;
    let amplified_pixels = amplified_result
        .iter()
        .filter(|&&x| x > mean_amplitude * 2.0)
        .count();

    println!("   ✓ Target features: {}", targetfeatures.len());
    println!("   ✓ Max amplitude: {:.4}", max_amplitude);
    println!("   ✓ Mean amplitude: {:.4}", mean_amplitude);
    println!("   ✓ Highly amplified pixels: {}", amplified_pixels);
    println!("   ✓ Processing time: {:.2?}", duration);
    println!("   ✓ Quantum oracle and diffusion operations applied");
    println!();

    Ok(())
}

#[allow(dead_code)]
fn demonstrate_quantum_annealing_segmentation(
    image: &Array2<f64>,
    config: &QuantumConfig,
) -> Result<(), Box<dyn std::error::Error>> {
    println!("🌡️ Quantum Annealing Segmentation");
    println!("   Theory: Uses quantum tunneling to escape local minima in segmentation");

    let start = Instant::now();

    let num_segments = 3;
    let segmentation = quantum_annealing_segmentation(image.view(), num_segments, config)?;

    let duration = start.elapsed();

    // Analyze segmentation results
    let mut segment_counts = vec![0; num_segments];
    for &segment in segmentation.iter() {
        if segment < num_segments {
            segment_counts[segment] += 1;
        }
    }

    let total_pixels = segmentation.len();

    println!("   ✓ Target segments: {}", num_segments);
    println!("   ✓ Annealing iterations: {}", config.iterations);
    println!("   ✓ Segment distribution:");
    for (i, &count) in segment_counts.iter().enumerate() {
        let percentage = count as f64 / total_pixels as f64 * 100.0;
        println!("     Segment {}: {} pixels ({:.1}%)", i, count, percentage);
    }
    println!("   ✓ Processing time: {:.2?}", duration);
    println!("   ✓ Quantum tunneling enabled global optimization");
    println!();

    Ok(())
}

// Helper functions

#[allow(dead_code)]
fn calculateimage_variance(image: &Array2<f64>) -> f64 {
    let mean = image.sum() / image.len() as f64;
    let variance = image.iter().map(|&x| (x - mean).powi(2)).sum::<f64>() / image.len() as f64;
    variance
}

#[allow(dead_code)]
fn calculate_phase_std(phases: &[f64]) -> f64 {
    if phases.is_empty() {
        return 0.0;
    }

    let mean = phases.iter().sum::<f64>() / phases.len() as f64;
    let variance = phases.iter().map(|&x| (x - mean).powi(2)).sum::<f64>() / phases.len() as f64;
    variance.sqrt()
}
