//! Advanced Neural Vision Processing Demo
//!
//! This example demonstrates the state-of-the-art computer vision capabilities
//! including neural feature detection, HDR processing, super-resolution,
//! advanced denoising, and multi-object tracking.
//!
//! # Features Demonstrated
//!
//! 1. **Neural Feature Detection**: SuperPoint-like keypoint detection with learned descriptors
//! 2. **HDR Image Processing**: Multi-exposure fusion with advanced tone mapping
//! 3. **Super-Resolution**: AI-enhanced image upscaling with multiple algorithms
//! 4. **Advanced Denoising**: BM3D-inspired and neural denoising techniques
//! 5. **Multi-Object Tracking**: DeepSORT with Kalman filtering and appearance modeling
//! 6. **Attention-Based Matching**: Transformer-inspired feature matching
//!
//! # Usage
//!
//! ```bash
//! cargo run --example advanced_neural_vision_demo --features="simd,parallel,gpu"
//! ```

use scirs2_core::ndarray::{Array1, Array2};
use scirs2_vision::{
    error::Result,
    feature::{
        AdvancedDenoiser, AttentionFeatureMatcher, DeepSORT, DenoisingMethod, Detection,
        HDRProcessor, LearnedSIFT, NeuralFeatureConfig, NeuralFeatureMatcher, SIFTConfig,
        SuperPointNet, SuperResolutionMethod, SuperResolutionProcessor, ToneMappingMethod,
        TrackingBoundingBox,
    },
};
use std::time::Instant;

#[allow(dead_code)]
fn main() -> Result<()> {
    println!("🚀 Advanced Neural Vision Processing Demo");
    println!("==========================================");

    // Create synthetic test images
    let (testimage, hdrimages) = create_testimages()?;

    // 1. Neural Feature Detection Demo
    neural_feature_detection_demo(&testimage)?;

    // 2. HDR Processing Demo
    hdr_processing_demo(&hdrimages)?;

    // 3. Super-Resolution Demo
    super_resolution_demo(&testimage)?;

    // 4. Advanced Denoising Demo
    advanced_denoising_demo(&testimage)?;

    // 5. Multi-Object Tracking Demo
    multi_object_tracking_demo()?;

    // 6. Attention-Based Feature Matching Demo
    attention_matching_demo(&testimage)?;

    // 7. Learned SIFT Demo
    learned_sift_demo(&testimage)?;

    println!("\n✅ All advanced neural vision demos completed successfully!");
    println!("💡 This demonstrates the production-ready state of scirs2-vision");

    Ok(())
}

/// Neural feature detection using SuperPoint-like architecture
#[allow(dead_code)]
fn neural_feature_detection_demo(image: &Array2<f32>) -> Result<()> {
    println!("\n🧠 Neural Feature Detection (SuperPoint)");
    println!("-----------------------------------------");

    let start = Instant::now();

    // Create SuperPoint network with optimized configuration
    let config = NeuralFeatureConfig {
        input_size: (480, 640),
        max_keypoints: 1024,
        detection_threshold: 0.005,
        nms_radius: 4,
        descriptor_dim: 256,
        use_gpu: true,
        ..Default::default()
    };

    let superpoint = SuperPointNet::new(Some(config))?;

    // Resize image to network input size
    let resizedimage = resizeimage(image, (480, 640))?;

    // Detect features and compute descriptors
    let (keypoints, descriptors) = superpoint.detect_and_describe(&resizedimage.view())?;

    let elapsed = start.elapsed();

    println!(
        "  ✓ Detected {} keypoints in {:.2}ms",
        keypoints.len(),
        elapsed.as_millis()
    );
    println!(
        "  ✓ Generated {}-dimensional descriptors",
        descriptors.shape()[1]
    );
    println!(
        "  ✓ Average response: {:.4}",
        keypoints.iter().map(|kp| kp.response).sum::<f32>() / keypoints.len() as f32
    );

    // Demonstrate real-time performance
    if keypoints.len() > 100 {
        println!(
            "  🚀 Real-time capable: {} FPS estimated",
            1000 / elapsed.as_millis().max(1)
        );
    }

    Ok(())
}

/// HDR image processing with multiple tone mapping methods
#[allow(dead_code)]
fn hdr_processing_demo(_hdrimages: &[Array2<f32>]) -> Result<()> {
    println!("\n🌈 HDR Image Processing");
    println!("-----------------------");

    let exposures = vec![-2.0, 0.0, 2.0];

    // Test different tone mapping methods
    let tone_mapping_methods = [
        ("Reinhard", ToneMappingMethod::Reinhard),
        ("Adaptive Log", ToneMappingMethod::AdaptiveLog),
        ("Histogram Eq", ToneMappingMethod::HistogramEq),
        ("Drago", ToneMappingMethod::Drago),
        ("Mantiuk", ToneMappingMethod::Mantiuk),
    ];

    for (name, method) in &tone_mapping_methods {
        let start = Instant::now();

        let processor = HDRProcessor::new(exposures.clone(), *method);
        let hdr_views: Vec<_> = _hdrimages.iter().map(|img| img.view()).collect();
        let result = processor.create_hdr(&hdr_views)?;

        let elapsed = start.elapsed();

        // Compute dynamic range
        let min_val = result.iter().cloned().fold(f32::INFINITY, f32::min);
        let max_val = result.iter().cloned().fold(f32::NEG_INFINITY, f32::max);
        let dynamic_range = (max_val / min_val.max(1e-6)).log10();

        println!(
            "  ✓ {}: {:.2}ms, Dynamic Range: {:.1} stops",
            name,
            elapsed.as_millis(),
            dynamic_range * 3.32
        );
    }

    Ok(())
}

/// Super-resolution with multiple AI algorithms
#[allow(dead_code)]
fn super_resolution_demo(image: &Array2<f32>) -> Result<()> {
    println!("\n⬆️  Super-Resolution Enhancement");
    println!("-------------------------------");

    // Test different super-resolution methods
    let sr_methods = [
        ("Bicubic", SuperResolutionMethod::Bicubic),
        ("SRCNN", SuperResolutionMethod::SRCNN),
        ("ESRCNN", SuperResolutionMethod::ESRCNN),
        ("Real-time SR", SuperResolutionMethod::RealTimeSR),
    ];

    let scale_factor = 2;
    let smallimage = downsampleimage(image, 2)?;

    for (name, method) in &sr_methods {
        let start = Instant::now();

        let processor = SuperResolutionProcessor::new(scale_factor, *method)?;
        let upscaled = processor.upscale(&smallimage.view())?;

        let elapsed = start.elapsed();

        // Compute quality metrics
        let psnr = compute_psnr(image, &upscaled)?;

        println!(
            "  ✓ {}: {:.2}ms, PSNR: {:.2} dB, Size: {}x{}",
            name,
            elapsed.as_millis(),
            psnr,
            upscaled.shape()[1],
            upscaled.shape()[0]
        );
    }

    Ok(())
}

/// Advanced denoising with state-of-the-art methods
#[allow(dead_code)]
fn advanced_denoising_demo(image: &Array2<f32>) -> Result<()> {
    println!("\n🔧 Advanced Denoising");
    println!("---------------------");

    // Add synthetic noise
    let noisyimage = add_gaussian_noise(image, 0.05)?;

    // Test different denoising methods
    let denoising_methods = [
        ("BM3D", DenoisingMethod::BM3D),
        ("Non-Local Means", DenoisingMethod::NonLocalMeans),
        ("Wiener Filter", DenoisingMethod::WienerFilter),
        ("Total Variation", DenoisingMethod::TotalVariation),
        ("Neural Denoising", DenoisingMethod::NeuralDenoising),
    ];

    let original_psnr = compute_psnr(image, &noisyimage)?;
    println!("  📊 Original noise PSNR: {original_psnr:.2} dB");

    for (name, method) in &denoising_methods {
        let start = Instant::now();

        let denoiser = AdvancedDenoiser::new(*method, 0.0025);
        let denoised = denoiser.denoise(&noisyimage.view())?;

        let elapsed = start.elapsed();
        let denoised_psnr = compute_psnr(image, &denoised)?;
        let improvement = denoised_psnr - original_psnr;

        println!(
            "  ✓ {}: {:.2}ms, PSNR: {:.2} dB (+{:.2} dB)",
            name,
            elapsed.as_millis(),
            denoised_psnr,
            improvement
        );
    }

    Ok(())
}

/// Multi-object tracking with DeepSORT
#[allow(dead_code)]
fn multi_object_tracking_demo() -> Result<()> {
    println!("\n🎯 Multi-Object Tracking (DeepSORT)");
    println!("-----------------------------------");

    // Create DeepSORT tracker
    let mut tracker = DeepSORT::new().with_params(0.7, 0.2, 30, 3);

    // Simulate detection sequence
    let detection_sequences = create_detection_sequences();

    let start = Instant::now();
    let mut total_detections = 0;
    let mut total_tracks = 0;

    for (frame_idx, detections) in detection_sequences.iter().enumerate() {
        let tracks = tracker.update(detections.clone())?;
        total_detections += detections.len();
        total_tracks = total_tracks.max(tracks.len());

        if frame_idx % 10 == 0 {
            println!(
                "  📹 Frame {}: {} detections, {} confirmed tracks",
                frame_idx,
                detections.len(),
                tracks.len()
            );
        }
    }

    let elapsed = start.elapsed();
    let fps = detection_sequences.len() as f32 / elapsed.as_secs_f32();

    println!(
        "  ✓ Processed {} frames in {:.2}s ({:.1} FPS)",
        detection_sequences.len(),
        elapsed.as_secs_f32(),
        fps
    );
    println!("  ✓ Total detections: {total_detections}, Max simultaneous tracks: {total_tracks}");

    Ok(())
}

/// Attention-based feature matching using transformer architecture
#[allow(dead_code)]
fn attention_matching_demo(image: &Array2<f32>) -> Result<()> {
    println!("\n🔍 Attention-Based Feature Matching");
    println!("-----------------------------------");

    let start = Instant::now();

    // Create attention matcher
    let matcher = AttentionFeatureMatcher::new(256, 8);

    // Generate synthetic keypoints and descriptors for two images
    let (keypoints1, descriptors1) = generate_synthetic_features(image, 100)?;
    let (keypoints2, descriptors2) = generate_synthetic_features(image, 120)?;

    // Perform attention-based matching
    let matches = matcher.match_with_attention(
        &keypoints1,
        &descriptors1.view(),
        &keypoints2,
        &descriptors2.view(),
    )?;

    let elapsed = start.elapsed();

    println!(
        "  ✓ Matched {} features in {:.2}ms",
        matches.len(),
        elapsed.as_millis()
    );
    println!(
        "  ✓ Match ratio: {:.1}%",
        matches.len() as f32 / keypoints1.len().min(keypoints2.len()) as f32 * 100.0
    );

    // Demonstrate geometric consistency
    let geometric_matches = filter_geometric_matches(&matches, &keypoints1, &keypoints2)?;
    println!(
        "  ✓ Geometrically consistent: {} matches ({:.1}%)",
        geometric_matches.len(),
        geometric_matches.len() as f32 / matches.len() as f32 * 100.0
    );

    Ok(())
}

/// Learned SIFT with neural enhancements
#[allow(dead_code)]
fn learned_sift_demo(image: &Array2<f32>) -> Result<()> {
    println!("\n🎓 Learned SIFT Features");
    println!("------------------------");

    let start = Instant::now();

    // Create Learned SIFT detector
    let config = SIFTConfig {
        num_octaves: 4,
        num_scales: 3,
        sigma: 1.6,
        edge_threshold: 10.0,
        peak_threshold: 0.03,
    };

    let learned_sift = LearnedSIFT::new(Some(config));

    // Detect keypoints
    let keypoints = learned_sift.detect_keypoints(&image.view())?;

    if !keypoints.is_empty() {
        // Compute enhanced descriptors
        let descriptors = learned_sift.compute_descriptors(&image.view(), &keypoints)?;

        let elapsed = start.elapsed();

        println!(
            "  ✓ Detected {} SIFT keypoints in {:.2}ms",
            keypoints.len(),
            elapsed.as_millis()
        );
        println!(
            "  ✓ Generated {}-dimensional enhanced descriptors",
            descriptors.shape()[1]
        );

        // Test descriptor matching
        let matcher = NeuralFeatureMatcher::new().with_params(0.7, 0.8);
        let self_matches = matcher.match_descriptors(&descriptors.view(), &descriptors.view())?;

        println!(
            "  ✓ Self-matching test: {} matches (expected: {})",
            self_matches.len(),
            keypoints.len()
        );
    } else {
        println!("  ℹ  No keypoints detected in this synthetic image");
    }

    Ok(())
}

// Utility functions for demo

#[allow(dead_code)]
fn create_testimages() -> Result<(Array2<f32>, Vec<Array2<f32>>)> {
    let width = 640;
    let height = 480;

    // Create main test image with various features
    let testimage = Array2::from_shape_fn((height, width), |(y, x)| {
        let fx = x as f32 / width as f32;
        let fy = y as f32 / height as f32;

        // Multiple frequency components to create interesting features
        let pattern1 = (fx * 20.0 * std::f32::consts::PI).sin() * 0.3;
        let pattern2 = (fy * 15.0 * std::f32::consts::PI).cos() * 0.2;
        let pattern3 = ((fx * fx + fy * fy).sqrt() * 10.0 * std::f32::consts::PI).sin() * 0.2;
        let noise = (x as f32 * 0.1 + y as f32 * 0.1).sin() * 0.1;

        (0.5 + pattern1 + pattern2 + pattern3 + noise).clamp(0.0, 1.0)
    });

    // Create HDR images with different exposures
    let hdrimages = vec![
        testimage.mapv(|x| (x * 0.3).clamp(0.0, 1.0)), // Underexposed
        testimage.clone(),                             // Normal
        testimage.mapv(|x| (x * 3.0).clamp(0.0, 1.0)), // Overexposed
    ];

    Ok((testimage, hdrimages))
}

#[allow(dead_code)]
fn resizeimage(image: &Array2<f32>, targetsize: (usize, usize)) -> Result<Array2<f32>> {
    let (src_height, src_width) = image.dim();
    let (dst_height, dst_width) = targetsize;

    let mut resized = Array2::zeros((dst_height, dst_width));

    let scale_y = src_height as f32 / dst_height as f32;
    let scale_x = src_width as f32 / dst_width as f32;

    for y in 0..dst_height {
        for x in 0..dst_width {
            let src_y = ((y as f32 * scale_y) as usize).min(src_height - 1);
            let src_x = ((x as f32 * scale_x) as usize).min(src_width - 1);
            resized[[y, x]] = image[[src_y, src_x]];
        }
    }

    Ok(resized)
}

#[allow(dead_code)]
fn downsampleimage(image: &Array2<f32>, factor: usize) -> Result<Array2<f32>> {
    let (height, width) = image.dim();
    let new_height = height / factor;
    let new_width = width / factor;

    let mut downsampled = Array2::zeros((new_height, new_width));

    for y in 0..new_height {
        for x in 0..new_width {
            downsampled[[y, x]] = image[[y * factor, x * factor]];
        }
    }

    Ok(downsampled)
}

#[allow(dead_code)]
fn add_gaussian_noise(image: &Array2<f32>, noisestd: f32) -> Result<Array2<f32>> {
    let mut noisy = image.clone();

    for pixel in noisy.iter_mut() {
        let noise = (scirs2_core::random::random::<f32>() - 0.5) * noisestd * 2.0;
        *pixel = (*pixel + noise).clamp(0.0, 1.0);
    }

    Ok(noisy)
}

#[allow(dead_code)]
fn compute_psnr(reference: &Array2<f32>, test: &Array2<f32>) -> Result<f32> {
    if reference.shape() != test.shape() {
        // Resize test image to match _reference
        let resized_test = resizeimage(test, reference.dim())?;
        return compute_psnr(reference, &resized_test);
    }

    let mut mse = 0.0;
    let mut count = 0;

    for (ref_val, test_val) in reference.iter().zip(test.iter()) {
        let diff = ref_val - test_val;
        mse += diff * diff;
        count += 1;
    }

    mse /= count as f32;

    if mse > 1e-10 {
        Ok(20.0 * (1.0 / mse.sqrt()).log10())
    } else {
        Ok(100.0) // Perfect match
    }
}

#[allow(dead_code)]
fn create_detection_sequences() -> Vec<Vec<Detection>> {
    let mut sequences = Vec::new();

    // Simulate 60 frames of detection data
    for frame in 0..60 {
        let mut detections = Vec::new();

        // Simulate 2-5 objects moving across the frame
        let num_objects = 2 + (frame % 4);

        for obj_id in 0..num_objects {
            let t = frame as f32 / 10.0;
            let base_x = 100.0 + obj_id as f32 * 150.0;
            let base_y = 100.0 + obj_id as f32 * 80.0;

            // Simple motion model
            let x = base_x + t * 20.0 + (t * 0.5).sin() * 30.0;
            let y = base_y + t * 10.0 + (t * 0.3).cos() * 20.0;

            let bbox =
                TrackingBoundingBox::new(x, y, 50.0, 80.0, 0.8 + obj_id as f32 * 0.05, obj_id);

            // Add synthetic appearance feature
            let feature = Array1::from_shape_fn(128, |i| {
                ((obj_id as f32 + i as f32) * 0.1).sin() * 0.5 + 0.5
            });

            let detection = Detection::with_feature(bbox, feature);
            detections.push(detection);
        }

        sequences.push(detections);
    }

    sequences
}

use scirs2_vision::feature::KeyPoint;

#[allow(dead_code)]
fn generate_synthetic_features(
    image: &Array2<f32>,
    num_features: usize,
) -> Result<(Vec<KeyPoint>, Array2<f32>)> {
    let (height, width) = image.dim();
    let mut keypoints = Vec::new();
    let mut descriptors = Array2::zeros((num_features, 256));

    for i in 0..num_features {
        let x = (i as f32 * 73.0) % width as f32;
        let y = (i as f32 * 97.0) % height as f32;

        keypoints.push(KeyPoint {
            x,
            y,
            response: 0.5 + (i as f32 * 0.1).sin() * 0.3,
            scale: 1.0 + (i as f32 * 0.05).cos() * 0.2,
            orientation: i as f32 * 0.1,
        });

        // Generate synthetic descriptor
        for j in 0..256 {
            descriptors[[i, j]] = ((i + j) as f32 * 0.1).sin() * 0.5 + 0.5;
        }
    }

    Ok((keypoints, descriptors))
}

#[allow(dead_code)]
fn filter_geometric_matches(
    matches: &[(usize, usize)],
    keypoints1: &[KeyPoint],
    keypoints2: &[KeyPoint],
) -> Result<Vec<(usize, usize)>> {
    let mut geometric_matches = Vec::new();

    // Simple geometric consistency check based on distance ratios
    for &(i1, i2) in matches {
        if i1 >= keypoints1.len() || i2 >= keypoints2.len() {
            continue;
        }

        let kp1 = &keypoints1[i1];
        let kp2 = &keypoints2[i2];

        // Check if the match is geometrically plausible
        let dx = kp2.x - kp1.x;
        let dy = kp2.y - kp1.y;
        let distance = (dx * dx + dy * dy).sqrt();

        // Simple heuristic: reasonable displacement
        if distance < 100.0 && kp1.response > 0.1 && kp2.response > 0.1 {
            geometric_matches.push((i1, i2));
        }
    }

    Ok(geometric_matches)
}
