rustorch 0.6.29

//! Image transformations for computer vision
//! コンピュータビジョン用画像変換
//!
//! This module provides image transformation functions similar to torchvision.transforms,
//! including basic transformations, data augmentation, and composition utilities.
//!
//! このモジュールはtorchvision.transformsと同様の画像変換機能を提供し、
//! 基本変換、データ拡張、合成ユーティリティを含みます。

use crate::error::{RusTorchError, RusTorchResult};
use crate::tensor::Tensor;
use crate::vision::{Image, ImageFormat};
use num_traits::Float;
use rand::Rng;

/// Trait for image transformations
/// 画像変換のトレイト
pub trait Transform<T: Float>: std::fmt::Debug {
    /// Apply transformation to an image
    /// 画像に変換を適用
    fn apply(&self, image: &Image<T>) -> RusTorchResult<Image<T>>;
}

/// Resize transformation
/// リサイズ変換
#[derive(Debug, Clone)]
pub struct Resize {
    /// Target size (height, width)
    /// 目標サイズ (高さ, 幅)
    pub size: (usize, usize),
    /// Interpolation mode
    /// 補間モード
    pub interpolation: InterpolationMode,
}

/// Interpolation modes for resizing
/// リサイズ用補間モード
#[derive(Debug, Clone, Copy)]
pub enum InterpolationMode {
    /// Nearest neighbor interpolation
    /// 最近傍補間
    Nearest,
    /// Bilinear interpolation
    /// バイリニア補間
    Bilinear,
    /// Bicubic interpolation
    /// バイキュービック補間
    Bicubic,
}

impl Resize {
    /// Create new resize transformation
    /// 新しいリサイズ変換を作成
    pub fn new(size: (usize, usize)) -> Self {
        Self {
            size,
            interpolation: InterpolationMode::Bilinear,
        }
    }

    /// Set interpolation mode
    /// 補間モードを設定
    pub fn with_interpolation(mut self, mode: InterpolationMode) -> Self {
        self.interpolation = mode;
        self
    }
}

impl<T: Float + From<f32> + 'static + std::fmt::Debug> Transform<T> for Resize {
    fn apply(&self, image: &Image<T>) -> RusTorchResult<Image<T>> {
        let (target_height, target_width) = self.size;

        // Simple implementation: create new tensor with target size
        // 簡単な実装: 目標サイズの新しいテンソルを作成
        let new_shape = match image.format {
            ImageFormat::CHW => vec![image.channels, target_height, target_width],
            ImageFormat::HWC => vec![target_height, target_width, image.channels],
        };

        // For now, create a tensor filled with zeros - actual implementation would perform interpolation
        // 現在はゼロで埋められたテンソルを作成 - 実際の実装では補間を実行
        let resized_data = Tensor::zeros(&new_shape);

        Image::new(resized_data, image.format).map_err(|e| e.into())
    }
}

/// Center crop transformation
/// 中央クロップ変換
#[derive(Debug, Clone)]
pub struct CenterCrop {
    /// Crop size (height, width)
    /// クロップサイズ (高さ, 幅)
    pub size: (usize, usize),
}

impl CenterCrop {
    /// Create new center crop transformation
    /// 新しい中央クロップ変換を作成
    pub fn new(size: (usize, usize)) -> Self {
        Self { size }
    }
}

impl<T: Float + From<f32> + 'static + std::fmt::Debug> Transform<T> for CenterCrop {
    fn apply(&self, image: &Image<T>) -> RusTorchResult<Image<T>> {
        let (crop_height, crop_width) = self.size;

        if crop_height > image.height || crop_width > image.width {
            return Err(RusTorchError::InvalidTransformParams(format!(
                "Crop size ({}, {}) larger than image size ({}, {})",
                crop_height, crop_width, image.height, image.width
            ))
            .into());
        }

        // Calculate crop coordinates
        // クロップ座標を計算
        let _start_y = (image.height - crop_height) / 2;
        let _start_x = (image.width - crop_width) / 2;

        // Create new tensor with cropped data
        // クロップされたデータで新しいテンソルを作成
        let new_shape = match image.format {
            ImageFormat::CHW => vec![image.channels, crop_height, crop_width],
            ImageFormat::HWC => vec![crop_height, crop_width, image.channels],
        };

        let cropped_data = Tensor::zeros(&new_shape);

        Image::new(cropped_data, image.format).map_err(|e| e.into())
    }
}

/// Random crop transformation
/// ランダムクロップ変換
#[derive(Debug, Clone)]
pub struct RandomCrop {
    /// Crop size (height, width)
    /// クロップサイズ (高さ, 幅)
    pub size: (usize, usize),
    /// Padding size
    /// パディングサイズ
    pub padding: Option<(usize, usize)>,
}

impl RandomCrop {
    /// Create new random crop transformation
    /// 新しいランダムクロップ変換を作成
    pub fn new(size: (usize, usize)) -> Self {
        Self {
            size,
            padding: None,
        }
    }

    /// Set padding
    /// パディングを設定
    pub fn with_padding(mut self, padding: (usize, usize)) -> Self {
        self.padding = Some(padding);
        self
    }
}

impl<T: Float + From<f32> + 'static + std::fmt::Debug> Transform<T> for RandomCrop {
    fn apply(&self, image: &Image<T>) -> RusTorchResult<Image<T>> {
        let (crop_height, crop_width) = self.size;
        let mut rng = rand::thread_rng();

        // Apply padding if specified
        // 指定されている場合はパディングを適用
        let working_image = if let Some((_pad_h, _pad_w)) = self.padding {
            // Simplified: return original image for now
            // 簡略化: 現在は元の画像を返す
            image.clone()
        } else {
            image.clone()
        };

        if crop_height > working_image.height || crop_width > working_image.width {
            return Err(RusTorchError::InvalidTransformParams(format!(
                "Crop size ({}, {}) larger than image size ({}, {})",
                crop_height, crop_width, working_image.height, working_image.width
            ))
            .into());
        }

        // Random crop coordinates
        // ランダムクロップ座標
        let max_y = working_image.height - crop_height;
        let max_x = working_image.width - crop_width;
        let _start_y = rng.gen_range(0..=max_y);
        let _start_x = rng.gen_range(0..=max_x);

        let new_shape = match image.format {
            ImageFormat::CHW => vec![image.channels, crop_height, crop_width],
            ImageFormat::HWC => vec![crop_height, crop_width, image.channels],
        };

        let cropped_data = Tensor::zeros(&new_shape);

        Image::new(cropped_data, image.format).map_err(|e| e.into())
    }
}

/// Random horizontal flip transformation
/// ランダム水平反転変換
#[derive(Debug, Clone)]
pub struct RandomHorizontalFlip {
    /// Probability of flipping
    /// 反転の確率
    pub probability: f64,
}

impl RandomHorizontalFlip {
    /// Create new random horizontal flip transformation
    /// 新しいランダム水平反転変換を作成
    pub fn new(probability: f64) -> Self {
        Self { probability }
    }
}

impl<T: Float + From<f32> + 'static + std::fmt::Debug> Transform<T> for RandomHorizontalFlip {
    fn apply(&self, image: &Image<T>) -> RusTorchResult<Image<T>> {
        let mut rng = rand::thread_rng();

        if rng.gen::<f64>() < self.probability {
            // Apply horizontal flip
            // 水平反転を適用
            // For now, return cloned image - actual implementation would flip the tensor
            // 現在はクローン画像を返す - 実際の実装ではテンソルを反転
            Ok(image.clone())
        } else {
            Ok(image.clone())
        }
    }
}

/// Random rotation transformation
/// ランダム回転変換
#[derive(Debug, Clone)]
pub struct RandomRotation {
    /// Rotation angle range in degrees
    /// 回転角度範囲（度）
    pub degrees: (f64, f64),
    /// Fill value for empty pixels
    /// 空ピクセルの填込値
    pub fill: Option<f64>,
}

impl RandomRotation {
    /// Create new random rotation transformation
    /// 新しいランダム回転変換を作成
    pub fn new(degrees: (f64, f64)) -> Self {
        Self {
            degrees,
            fill: None,
        }
    }

    /// Set fill value
    /// 填込値を設定
    pub fn with_fill(mut self, fill: f64) -> Self {
        self.fill = Some(fill);
        self
    }
}

impl<T: Float + From<f32> + From<f64> + 'static + std::fmt::Debug> Transform<T> for RandomRotation {
    fn apply(&self, image: &Image<T>) -> RusTorchResult<Image<T>> {
        let mut rng = rand::thread_rng();
        let _angle = rng.gen_range(self.degrees.0..=self.degrees.1);

        // For now, return cloned image - actual implementation would rotate the tensor
        // 現在はクローン画像を返す - 実際の実装ではテンソルを回転
        Ok(image.clone())
    }
}

/// Normalize transformation
/// 正規化変換
#[derive(Debug, Clone)]
pub struct Normalize<T: Float> {
    /// Mean values for each channel
    /// 各チャンネルの平均値
    pub mean: Vec<T>,
    /// Standard deviation values for each channel
    /// 各チャンネルの標準偏差値
    pub std: Vec<T>,
}

impl<T: Float + From<f32> + Copy> Normalize<T> {
    /// Create new normalize transformation
    /// 新しい正規化変換を作成
    pub fn new(mean: Vec<T>, std: Vec<T>) -> RusTorchResult<Self> {
        if mean.len() != std.len() {
            return Err(RusTorchError::InvalidTransformParams(
                "Mean and std must have same length".to_string(),
            )
            .into());
        }

        Ok(Self { mean, std })
    }

    /// ImageNet normalization
    /// ImageNet正規化
    pub fn imagenet() -> Self {
        Self {
            mean: vec![
                <T as From<f32>>::from(0.485),
                <T as From<f32>>::from(0.456),
                <T as From<f32>>::from(0.406),
            ],
            std: vec![
                <T as From<f32>>::from(0.229),
                <T as From<f32>>::from(0.224),
                <T as From<f32>>::from(0.225),
            ],
        }
    }
}

impl<T: Float + From<f32> + Copy + 'static + std::fmt::Debug> Transform<T> for Normalize<T> {
    fn apply(&self, image: &Image<T>) -> RusTorchResult<Image<T>> {
        if self.mean.len() != image.channels {
            return Err(RusTorchError::InvalidTransformParams(format!(
                "Mean length {} doesn't match image channels {}",
                self.mean.len(),
                image.channels
            ))
            .into());
        }

        // For now, return cloned image - actual implementation would normalize the tensor
        // 現在はクローン画像を返す - 実際の実装ではテンソルを正規化
        Ok(image.clone())
    }
}

/// ToTensor transformation - converts PIL Image or numpy array to tensor
/// ToTensor変換 - PIL画像またはnumpy配列をテンソルに変換
#[derive(Debug, Clone)]
pub struct ToTensor {
    /// Target format for output tensor
    /// 出力テンソルの目標形式
    pub format: ImageFormat,
}

impl ToTensor {
    /// Create new ToTensor transformation
    /// 新しいToTensor変換を作成
    pub fn new() -> Self {
        Self {
            format: ImageFormat::CHW,
        }
    }

    /// Set output format
    /// 出力形式を設定
    pub fn with_format(mut self, format: ImageFormat) -> Self {
        self.format = format;
        self
    }
}

impl Default for ToTensor {
    fn default() -> Self {
        Self::new()
    }
}

impl<T: Float + From<f32> + 'static + std::fmt::Debug> Transform<T> for ToTensor {
    fn apply(&self, image: &Image<T>) -> RusTorchResult<Image<T>> {
        // Convert to target format
        // 目標形式に変換
        image.to_format(self.format).map_err(|e| e.into())
    }
}

/// Compose multiple transformations
/// 複数の変換を合成
#[derive(Debug)]
pub struct Compose<T: Float> {
    /// List of transformations to apply
    /// 適用する変換のリスト
    pub transforms: Vec<Box<dyn Transform<T>>>,
}

impl<T: Float> Compose<T> {
    /// Create new compose transformation
    /// 新しい合成変換を作成
    pub fn new(transforms: Vec<Box<dyn Transform<T>>>) -> Self {
        Self { transforms }
    }
}

impl<T: Float + 'static + std::fmt::Debug> Transform<T> for Compose<T> {
    fn apply(&self, image: &Image<T>) -> RusTorchResult<Image<T>> {
        let mut result = image.clone();

        for transform in &self.transforms {
            result = transform.apply(&result)?;
        }

        Ok(result)
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_resize_creation() {
        let resize = Resize::new((224, 224));
        assert_eq!(resize.size, (224, 224));
    }

    #[test]
    fn test_center_crop_creation() {
        let crop = CenterCrop::new((224, 224));
        assert_eq!(crop.size, (224, 224));
    }

    #[test]
    fn test_random_crop_creation() {
        let crop = RandomCrop::new((224, 224)).with_padding((4, 4));
        assert_eq!(crop.size, (224, 224));
        assert_eq!(crop.padding, Some((4, 4)));
    }

    #[test]
    fn test_normalize_creation() {
        let normalize = Normalize::new(vec![0.5f32], vec![0.5f32]).unwrap();
        assert_eq!(normalize.mean, vec![0.5f32]);
        assert_eq!(normalize.std, vec![0.5f32]);
    }

    #[test]
    fn test_normalize_imagenet() {
        let normalize: Normalize<f32> = Normalize::imagenet();
        assert_eq!(normalize.mean.len(), 3);
        assert_eq!(normalize.std.len(), 3);
    }

    #[test]
    fn test_to_tensor_creation() {
        let to_tensor = ToTensor::new();
        assert_eq!(to_tensor.format, ImageFormat::CHW);
    }
}