torsh-vision 0.1.2

// Framework infrastructure - components designed for future use
#![allow(dead_code)]
use crate::hardware::{GpuTransform, HardwareContext};
use crate::transforms::Transform;
use crate::{Result, VisionError};
use std::path::Path;
use torsh_core::device::{Device, DeviceType};
use torsh_core::dtype::DType;
use torsh_tensor::Tensor;

/// Video frame representation
#[derive(Debug, Clone)]
pub struct VideoFrame {
    pub data: Tensor<f32>,
    pub timestamp: f64,
    pub frame_index: usize,
}

impl VideoFrame {
    pub fn new(data: Tensor<f32>, timestamp: f64, frame_index: usize) -> Self {
        Self {
            data,
            timestamp,
            frame_index,
        }
    }

    pub fn width(&self) -> usize {
        self.data.shape().dims()[2]
    }

    pub fn height(&self) -> usize {
        self.data.shape().dims()[1]
    }

    pub fn channels(&self) -> usize {
        self.data.shape().dims()[0]
    }
}

/// Video reader trait for different video formats
pub trait VideoReader {
    fn read_frame(&mut self) -> Result<Option<VideoFrame>>;
    fn seek(&mut self, frame_index: usize) -> Result<()>;
    fn total_frames(&self) -> usize;
    fn fps(&self) -> f32;
    fn duration(&self) -> f64;
}

/// Simple video reader implementation (placeholder)
pub struct SimpleVideoReader {
    frames: Vec<VideoFrame>,
    current_frame: usize,
    fps: f32,
}

impl SimpleVideoReader {
    pub fn from_images<P: AsRef<Path>>(image_paths: &[P], fps: f32) -> Result<Self> {
        let mut frames = Vec::new();

        for (i, path) in image_paths.iter().enumerate() {
            // Load image and convert to tensor
            let image = image::open(path).map_err(VisionError::ImageError)?;
            let tensor = crate::ops::to_tensor(&image)?;
            let timestamp = i as f64 / fps as f64;
            frames.push(VideoFrame::new(tensor, timestamp, i));
        }

        Ok(Self {
            frames,
            current_frame: 0,
            fps,
        })
    }

    pub fn from_tensors(tensors: Vec<Tensor<f32>>, fps: f32) -> Self {
        let frames = tensors
            .into_iter()
            .enumerate()
            .map(|(i, tensor)| {
                let timestamp = i as f64 / fps as f64;
                VideoFrame::new(tensor, timestamp, i)
            })
            .collect();

        Self {
            frames,
            current_frame: 0,
            fps,
        }
    }
}

impl VideoReader for SimpleVideoReader {
    fn read_frame(&mut self) -> Result<Option<VideoFrame>> {
        if self.current_frame >= self.frames.len() {
            Ok(None)
        } else {
            let frame = self.frames[self.current_frame].clone();
            self.current_frame += 1;
            Ok(Some(frame))
        }
    }

    fn seek(&mut self, frame_index: usize) -> Result<()> {
        if frame_index >= self.frames.len() {
            return Err(VisionError::InvalidArgument(format!(
                "Frame index {} out of bounds (total frames: {})",
                frame_index,
                self.frames.len()
            )));
        }
        self.current_frame = frame_index;
        Ok(())
    }

    fn total_frames(&self) -> usize {
        self.frames.len()
    }

    fn fps(&self) -> f32 {
        self.fps
    }

    fn duration(&self) -> f64 {
        self.frames.len() as f64 / self.fps as f64
    }
}

/// Video writer trait for different video formats
pub trait VideoWriter {
    fn write_frame(&mut self, frame: &VideoFrame) -> Result<()>;
    fn finalize(&mut self) -> Result<()>;
}

/// Simple video writer implementation (placeholder)
pub struct SimpleVideoWriter {
    frames: Vec<VideoFrame>,
    output_path: String,
    fps: f32,
}

impl SimpleVideoWriter {
    pub fn new<P: AsRef<Path>>(output_path: P, fps: f32) -> Self {
        Self {
            frames: Vec::new(),
            output_path: output_path.as_ref().to_string_lossy().to_string(),
            fps,
        }
    }
}

impl VideoWriter for SimpleVideoWriter {
    fn write_frame(&mut self, frame: &VideoFrame) -> Result<()> {
        self.frames.push(frame.clone());
        Ok(())
    }

    fn finalize(&mut self) -> Result<()> {
        // In a real implementation, this would write frames to a video file
        println!(
            "Writing {} frames to {}",
            self.frames.len(),
            self.output_path
        );
        Ok(())
    }
}

/// Video transform that applies image transforms to each frame
pub struct VideoTransform<T: Transform> {
    transform: T,
}

impl<T: Transform> VideoTransform<T> {
    pub fn new(transform: T) -> Self {
        Self { transform }
    }

    pub fn apply(&self, frame: &VideoFrame) -> Result<VideoFrame> {
        let transformed_data = self.transform.forward(&frame.data)?;
        Ok(VideoFrame::new(
            transformed_data,
            frame.timestamp,
            frame.frame_index,
        ))
    }

    pub fn apply_to_video<R: VideoReader, W: VideoWriter>(
        &self,
        reader: &mut R,
        writer: &mut W,
    ) -> Result<()> {
        while let Some(frame) = reader.read_frame()? {
            let transformed_frame = self.apply(&frame)?;
            writer.write_frame(&transformed_frame)?;
        }
        writer.finalize()?;
        Ok(())
    }
}

/// Video dataset for loading video sequences
pub struct VideoDataset {
    videos: Vec<Box<dyn VideoReader>>,
    current_video: usize,
    sequence_length: usize,
    overlap: usize,
}

impl VideoDataset {
    pub fn new(videos: Vec<Box<dyn VideoReader>>, sequence_length: usize, overlap: usize) -> Self {
        Self {
            videos,
            current_video: 0,
            sequence_length,
            overlap,
        }
    }

    pub fn get_sequence(
        &mut self,
        video_index: usize,
        start_frame: usize,
    ) -> Result<Vec<VideoFrame>> {
        if video_index >= self.videos.len() {
            return Err(VisionError::InvalidArgument(format!(
                "Video index {} out of bounds",
                video_index
            )));
        }

        let reader = &mut self.videos[video_index];
        reader.seek(start_frame)?;

        let mut sequence = Vec::new();
        for _ in 0..self.sequence_length {
            if let Some(frame) = reader.read_frame()? {
                sequence.push(frame);
            } else {
                break;
            }
        }

        Ok(sequence)
    }

    pub fn total_sequences(&self) -> usize {
        self.videos
            .iter()
            .map(|reader| {
                let total_frames = reader.total_frames();
                if total_frames >= self.sequence_length {
                    (total_frames - self.sequence_length) / (self.sequence_length - self.overlap)
                        + 1
                } else {
                    0
                }
            })
            .sum()
    }
}

/// Optical flow computation
pub struct OpticalFlow {
    device: DeviceType,
}

impl OpticalFlow {
    pub fn new(device: DeviceType) -> Self {
        Self { device }
    }

    /// Compute optical flow between two frames using Lucas-Kanade method
    pub fn lucas_kanade(
        &self,
        frame1: &Tensor<f32>,
        frame2: &Tensor<f32>,
    ) -> Result<(Tensor<f32>, Tensor<f32>)> {
        // Convert to grayscale if needed
        let gray1 = if frame1.shape().dims().len() == 3 && frame1.shape().dims()[0] == 3 {
            crate::ops::rgb_to_grayscale(frame1)?
        } else {
            frame1.clone()
        };

        let gray2 = if frame2.shape().dims().len() == 3 && frame2.shape().dims()[0] == 3 {
            crate::ops::rgb_to_grayscale(frame2)?
        } else {
            frame2.clone()
        };

        // Compute gradients
        let grad_x1 = self.compute_gradient_x(&gray1)?;
        let grad_y1 = self.compute_gradient_y(&gray1)?;
        let grad_t = gray2.sub(&gray1)?;

        let gray1_shape = gray1.shape();
        let gray1_dims = gray1_shape.dims();
        let (height, width) = if gray1_dims.len() == 2 {
            // Handle 2D tensors [height, width]
            (gray1_dims[0], gray1_dims[1])
        } else if gray1_dims.len() == 3 {
            // Handle 3D tensors [channels, height, width]
            (gray1_dims[1], gray1_dims[2])
        } else {
            return Err(VisionError::TensorError(
                torsh_core::error::TorshError::InvalidArgument(format!(
                    "Expected 2D or 3D tensor, got {}D",
                    gray1_dims.len()
                )),
            ));
        };
        let flow_x = Tensor::zeros(&[height, width], self.device)?;
        let flow_y = Tensor::zeros(&[height, width], self.device)?;

        // Window size for Lucas-Kanade
        let window_size = 5;
        let half_window = window_size / 2;

        for y in half_window..(height - half_window) {
            for x in half_window..(width - half_window) {
                let (fx, fy) = self.compute_optical_flow_at_point(
                    &grad_x1,
                    &grad_y1,
                    &grad_t,
                    x,
                    y,
                    window_size,
                )?;

                flow_x.set(&[y, x], fx)?;
                flow_y.set(&[y, x], fy)?;
            }
        }

        Ok((flow_x, flow_y))
    }

    fn compute_gradient_x(&self, image: &Tensor<f32>) -> Result<Tensor<f32>> {
        // Simple gradient computation using Sobel operator
        let kernel = Tensor::from_data(
            vec![-1.0, 0.0, 1.0, -2.0, 0.0, 2.0, -1.0, 0.0, 1.0],
            vec![3, 3],
            self.device,
        )?;
        self.convolve_2d(image, &kernel)
    }

    fn compute_gradient_y(&self, image: &Tensor<f32>) -> Result<Tensor<f32>> {
        // Simple gradient computation using Sobel operator
        let kernel = Tensor::from_data(
            vec![-1.0, -2.0, -1.0, 0.0, 0.0, 0.0, 1.0, 2.0, 1.0],
            vec![3, 3],
            self.device,
        )?;
        self.convolve_2d(image, &kernel)
    }

    fn convolve_2d(&self, image: &Tensor<f32>, kernel: &Tensor<f32>) -> Result<Tensor<f32>> {
        // Simple 2D convolution implementation
        let image_shape = image.shape();
        let image_dims = image_shape.dims();
        let is_2d = image_dims.len() == 2;
        let (h, w) = if is_2d {
            // Handle 2D tensors [height, width]
            (image_dims[0], image_dims[1])
        } else if image_dims.len() == 3 {
            // Handle 3D tensors [channels, height, width]
            (image_dims[1], image_dims[2])
        } else {
            return Err(VisionError::TensorError(
                torsh_core::error::TorshError::InvalidArgument(format!(
                    "Expected 2D or 3D tensor, got {}D",
                    image_dims.len()
                )),
            ));
        };
        let (kh, kw) = (kernel.shape().dims()[0], kernel.shape().dims()[1]);
        let output = Tensor::zeros(&[h, w], self.device)?;

        let pad_h = kh / 2;
        let pad_w = kw / 2;

        for y in pad_h..(h - pad_h) {
            for x in pad_w..(w - pad_w) {
                let mut sum = 0.0;
                for ky in 0..kh {
                    for kx in 0..kw {
                        let img_y = y + ky - pad_h;
                        let img_x = x + kx - pad_w;
                        let img_val = if is_2d {
                            image.get(&[img_y, img_x])?
                        } else {
                            image.get(&[0, img_y, img_x])?
                        };
                        let kernel_val = kernel.get(&[ky, kx])?;
                        sum += img_val * kernel_val;
                    }
                }
                output.set(&[y, x], sum)?;
            }
        }

        Ok(output)
    }

    fn compute_optical_flow_at_point(
        &self,
        grad_x: &Tensor<f32>,
        grad_y: &Tensor<f32>,
        grad_t: &Tensor<f32>,
        x: usize,
        y: usize,
        window_size: usize,
    ) -> Result<(f32, f32)> {
        let half_window = window_size / 2;

        let mut a11 = 0.0;
        let mut a12 = 0.0;
        let mut a22 = 0.0;
        let mut b1 = 0.0;
        let mut b2 = 0.0;

        for dy in 0..window_size {
            for dx in 0..window_size {
                let px = x + dx - half_window;
                let py = y + dy - half_window;

                let gx = grad_x.get(&[py, px])?;
                let gy = grad_y.get(&[py, px])?;
                let gt = grad_t.get(&[py, px])?;

                a11 += gx * gx;
                a12 += gx * gy;
                a22 += gy * gy;
                b1 += -gx * gt;
                b2 += -gy * gt;
            }
        }

        // Solve 2x2 system
        let det = a11 * a22 - a12 * a12;
        if det.abs() < 1e-6 {
            return Ok((0.0, 0.0));
        }

        let flow_x = (a22 * b1 - a12 * b2) / det;
        let flow_y = (a11 * b2 - a12 * b1) / det;

        Ok((flow_x, flow_y))
    }
}

/// Video models for action recognition and temporal understanding
pub struct VideoModel {
    context: HardwareContext,
}

impl VideoModel {
    pub fn new(context: HardwareContext) -> Self {
        Self { context }
    }

    /// Simple 3D convolution for video processing
    pub fn conv3d(
        &self,
        input: &Tensor<f32>,
        kernel: &Tensor<f32>,
        stride: (usize, usize, usize),
        padding: (usize, usize, usize),
    ) -> Result<Tensor<f32>> {
        // Simplified 3D convolution implementation
        let input_shape = input.shape();
        let kernel_shape = kernel.shape();

        if input_shape.dims().len() != 5 || kernel_shape.dims().len() != 5 {
            return Err(VisionError::InvalidShape(
                "Expected 5D tensors for 3D convolution (N, C, T, H, W)".to_string(),
            ));
        }

        let (batch_size, _in_channels, time_steps, height, width) = (
            input_shape.dims()[0],
            input_shape.dims()[1],
            input_shape.dims()[2],
            input_shape.dims()[3],
            input_shape.dims()[4],
        );

        let (out_channels, _, kernel_t, kernel_h, kernel_w) = (
            kernel_shape.dims()[0],
            kernel_shape.dims()[1],
            kernel_shape.dims()[2],
            kernel_shape.dims()[3],
            kernel_shape.dims()[4],
        );

        let (stride_t, stride_h, stride_w) = stride;
        let (pad_t, pad_h, pad_w) = padding;

        let out_t = (time_steps + 2 * pad_t - kernel_t) / stride_t + 1;
        let out_h = (height + 2 * pad_h - kernel_h) / stride_h + 1;
        let out_w = (width + 2 * pad_w - kernel_w) / stride_w + 1;

        let output = Tensor::zeros(
            &[batch_size, out_channels, out_t, out_h, out_w],
            input.device(),
        )?;

        // For now, return zeros as full 3D convolution is complex
        Ok(output)
    }

    /// Temporal pooling for video sequences
    pub fn temporal_pool(
        &self,
        input: &Tensor<f32>,
        pool_type: TemporalPoolType,
    ) -> Result<Tensor<f32>> {
        let shape = input.shape();
        if shape.dims().len() != 5 {
            return Err(VisionError::InvalidShape(
                "Expected 5D tensor (N, C, T, H, W)".to_string(),
            ));
        }

        let (_batch_size, _channels, _time_steps, _height, _width) = (
            shape.dims()[0],
            shape.dims()[1],
            shape.dims()[2],
            shape.dims()[3],
            shape.dims()[4],
        );

        match pool_type {
            TemporalPoolType::Average => {
                // Average pooling across time dimension
                let pooled = input.mean(Some(&[2]), false)?;
                Ok(pooled)
            }
            TemporalPoolType::Max => {
                // Max pooling across time dimension
                let pooled = input.max_dim(2, false)?;
                Ok(pooled)
            }
            TemporalPoolType::Last => {
                // Take the last frame
                let last_frame = input.select(2, -1)?;
                Ok(last_frame)
            }
        }
    }
}

#[derive(Debug, Clone, Copy)]
pub enum TemporalPoolType {
    Average,
    Max,
    Last,
}

/// Video augmentation transforms
pub struct VideoAugmentation {
    transforms: Vec<Box<dyn Transform>>,
    temporal_transforms: Vec<Box<dyn TemporalTransform>>,
}

impl VideoAugmentation {
    pub fn new() -> Self {
        Self {
            transforms: Vec::new(),
            temporal_transforms: Vec::new(),
        }
    }

    pub fn add_frame_transform<T: Transform + 'static>(&mut self, transform: T) {
        self.transforms.push(Box::new(transform));
    }

    pub fn add_temporal_transform<T: TemporalTransform + 'static>(&mut self, transform: T) {
        self.temporal_transforms.push(Box::new(transform));
    }

    pub fn apply_to_sequence(&self, sequence: &[VideoFrame]) -> Result<Vec<VideoFrame>> {
        let mut result = sequence.to_vec();

        // Apply temporal transforms first
        for temporal_transform in &self.temporal_transforms {
            result = temporal_transform.apply_temporal(&result)?;
        }

        // Apply frame-wise transforms
        for transform in &self.transforms {
            for frame in &mut result {
                frame.data = transform.forward(&frame.data)?;
            }
        }

        Ok(result)
    }
}

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

/// Trait for temporal transforms that operate on video sequences
pub trait TemporalTransform: Send + Sync {
    fn apply_temporal(&self, sequence: &[VideoFrame]) -> Result<Vec<VideoFrame>>;
}

/// Temporal sampling transform
pub struct TemporalSampling {
    num_frames: usize,
    strategy: SamplingStrategy,
}

impl TemporalSampling {
    pub fn new(num_frames: usize, strategy: SamplingStrategy) -> Self {
        Self {
            num_frames,
            strategy,
        }
    }
}

impl TemporalTransform for TemporalSampling {
    fn apply_temporal(&self, sequence: &[VideoFrame]) -> Result<Vec<VideoFrame>> {
        if sequence.len() <= self.num_frames {
            return Ok(sequence.to_vec());
        }

        let indices = match self.strategy {
            SamplingStrategy::Uniform => {
                let step = sequence.len() as f32 / self.num_frames as f32;
                (0..self.num_frames)
                    .map(|i| (i as f32 * step) as usize)
                    .collect()
            }
            SamplingStrategy::Random => {
                use scirs2_core::random::Random;
                let mut rng = Random::seed(42); // Use fixed seed for deterministic results
                let mut indices: Vec<usize> = (0..sequence.len()).collect();
                // Fisher-Yates shuffle algorithm
                for i in (1..indices.len()).rev() {
                    let j = rng.gen_range(0..=i);
                    indices.swap(i, j);
                }
                indices.truncate(self.num_frames);
                indices.sort();
                indices
            }
            SamplingStrategy::Center => {
                let start = (sequence.len() - self.num_frames) / 2;
                (start..start + self.num_frames).collect()
            }
        };

        Ok(indices.into_iter().map(|i| sequence[i].clone()).collect())
    }
}

#[derive(Debug, Clone, Copy)]
pub enum SamplingStrategy {
    Uniform,
    Random,
    Center,
}

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

    #[test]
    fn test_video_frame() {
        let tensor = zeros(&[3, 224, 224]).unwrap();
        let frame = VideoFrame::new(tensor, 0.0, 0);
        assert_eq!(frame.width(), 224);
        assert_eq!(frame.height(), 224);
        assert_eq!(frame.channels(), 3);
    }

    #[test]
    fn test_simple_video_reader() {
        let tensors = vec![
            zeros(&[3, 64, 64]).unwrap(),
            zeros(&[3, 64, 64]).unwrap(),
            zeros(&[3, 64, 64]).unwrap(),
        ];
        let mut reader = SimpleVideoReader::from_tensors(tensors, 30.0);

        assert_eq!(reader.total_frames(), 3);
        assert_eq!(reader.fps(), 30.0);

        let frame1 = reader.read_frame().unwrap().unwrap();
        assert_eq!(frame1.frame_index, 0);

        let frame2 = reader.read_frame().unwrap().unwrap();
        assert_eq!(frame2.frame_index, 1);

        reader.seek(0).unwrap();
        let frame_again = reader.read_frame().unwrap().unwrap();
        assert_eq!(frame_again.frame_index, 0);
    }

    #[test]
    fn test_video_transform() {
        let transform = crate::transforms::Resize::new((128, 128));
        let video_transform = VideoTransform::new(transform);

        let tensor = zeros(&[3, 64, 64]).unwrap();
        let frame = VideoFrame::new(tensor, 0.0, 0);

        let transformed_frame = video_transform.apply(&frame).unwrap();
        assert_eq!(transformed_frame.data.shape().dims(), &[3, 128, 128]);
    }

    #[test]
    fn test_optical_flow() {
        let device = DeviceType::Cpu;
        let optical_flow = OpticalFlow::new(device);

        let frame1 = zeros(&[32, 32]).unwrap();
        let frame2 = zeros(&[32, 32]).unwrap();

        let (flow_x, flow_y) = optical_flow.lucas_kanade(&frame1, &frame2).unwrap();
        assert_eq!(flow_x.shape().dims(), &[32, 32]);
        assert_eq!(flow_y.shape().dims(), &[32, 32]);
    }

    #[test]
    fn test_temporal_sampling() {
        let frames = (0..10)
            .map(|i| VideoFrame::new(zeros(&[3, 32, 32]).unwrap(), i as f64 * 0.1, i))
            .collect::<Vec<_>>();

        let sampling = TemporalSampling::new(5, SamplingStrategy::Uniform);
        let sampled = sampling.apply_temporal(&frames).unwrap();

        assert_eq!(sampled.len(), 5);
    }

    #[test]
    fn test_video_augmentation() {
        let mut augmentation = VideoAugmentation::new();
        augmentation.add_frame_transform(crate::transforms::Resize::new((64, 64)));
        augmentation.add_temporal_transform(TemporalSampling::new(3, SamplingStrategy::Center));

        let frames = (0..5)
            .map(|i| VideoFrame::new(zeros(&[3, 32, 32]).unwrap(), i as f64 * 0.1, i))
            .collect::<Vec<_>>();

        let augmented = augmentation.apply_to_sequence(&frames).unwrap();
        assert_eq!(augmented.len(), 3);
        assert_eq!(augmented[0].data.shape().dims(), &[3, 64, 64]);
    }
}