scirs2_vision/segmentation/semantic/

legacy.rs

//! Semantic segmentation with deep learning integration
//!
//! This module provides semantic segmentation capabilities using deep learning models
//! integrated with scirs2-neural for neural network inference.

use crate::error::{Result, VisionError};
use image::{DynamicImage, Rgb, RgbImage};
use scirs2_core::ndarray::{Array2, Array3, Array4};

/// Semantic segmentation model architecture
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum SegmentationArchitecture {
    /// DeepLab v3+ with Atrous Spatial Pyramid Pooling
    DeepLabV3Plus,
    /// U-Net for biomedical image segmentation
    UNet,
    /// Fully Convolutional Network
    FCN,
    /// Mask R-CNN for instance segmentation
    MaskRCNN,
    /// Segmentation Transformer
    SegFormer,
}

/// Segmentation class definition
#[derive(Debug, Clone)]
pub struct SegmentationClass {
    /// Class ID
    pub id: usize,
    /// Class name
    pub name: String,
    /// RGB color for visualization
    pub color: (u8, u8, u8),
}

/// Semantic segmentation result
#[derive(Debug, Clone)]
pub struct SegmentationResult {
    /// Class predictions per pixel (height × width)
    pub class_map: Array2<usize>,
    /// Confidence scores per pixel per class (height × width × num_classes)
    pub confidence: Array3<f32>,
    /// Class definitions
    pub classes: Vec<SegmentationClass>,
}

impl SegmentationResult {
    /// Convert segmentation to color visualization
    pub fn to_color_image(&self) -> Result<RgbImage> {
        let (height, width) = self.class_map.dim();
        let mut img = RgbImage::new(width as u32, height as u32);

        for y in 0..height {
            for x in 0..width {
                let class_id = self.class_map[[y, x]];
                let color = if class_id < self.classes.len() {
                    let (r, g, b) = self.classes[class_id].color;
                    Rgb([r, g, b])
                } else {
                    Rgb([0, 0, 0])
                };
                img.put_pixel(x as u32, y as u32, color);
            }
        }

        Ok(img)
    }

    /// Get confidence for a specific class at a pixel
    pub fn get_class_confidence(&self, y: usize, x: usize, class_id: usize) -> Option<f32> {
        if y < self.confidence.dim().0
            && x < self.confidence.dim().1
            && class_id < self.confidence.dim().2
        {
            Some(self.confidence[[y, x, class_id]])
        } else {
            None
        }
    }

    /// Apply conditional random field (CRF) post-processing for refinement
    pub fn refine_with_crf(
        &mut self,
        original_img: &DynamicImage,
        iterations: usize,
    ) -> Result<()> {
        // Simplified CRF refinement
        let (height, width) = self.class_map.dim();
        let num_classes = self.classes.len();

        for _iter in 0..iterations {
            let mut new_confidence = self.confidence.clone();

            for y in 1..height - 1 {
                for x in 1..width - 1 {
                    // Consider spatial smoothness and appearance consistency
                    for c in 0..num_classes {
                        let mut sum = self.confidence[[y, x, c]];
                        let mut count = 1.0;

                        // Neighbor contributions
                        for (dy, dx) in &[(-1, 0), (1, 0), (0, -1), (0, 1)] {
                            let ny = (y as i32 + dy) as usize;
                            let nx = (x as i32 + dx) as usize;

                            sum += self.confidence[[ny, nx, c]] * 0.1;
                            count += 0.1;
                        }

                        new_confidence[[y, x, c]] = sum / count;
                    }

                    // Normalize
                    let sum: f32 = (0..num_classes).map(|c| new_confidence[[y, x, c]]).sum();
                    if sum > 0.0 {
                        for c in 0..num_classes {
                            new_confidence[[y, x, c]] /= sum;
                        }
                    }
                }
            }

            self.confidence = new_confidence;

            // Update class map
            for y in 0..height {
                for x in 0..width {
                    let mut max_conf = 0.0f32;
                    let mut max_class = 0;
                    for c in 0..num_classes {
                        if self.confidence[[y, x, c]] > max_conf {
                            max_conf = self.confidence[[y, x, c]];
                            max_class = c;
                        }
                    }
                    self.class_map[[y, x]] = max_class;
                }
            }
        }

        Ok(())
    }
}

/// DeepLab v3+ segmentation model
pub struct DeepLabV3Plus {
    num_classes: usize,
    input_size: (usize, usize),
    classes: Vec<SegmentationClass>,
}

impl DeepLabV3Plus {
    /// Create a new DeepLab v3+ model
    ///
    /// # Arguments
    ///
    /// * `num_classes` - Number of segmentation classes
    /// * `input_size` - Expected input image size (height, width)
    /// * `classes` - Class definitions
    pub fn new(
        num_classes: usize,
        input_size: (usize, usize),
        classes: Vec<SegmentationClass>,
    ) -> Self {
        Self {
            num_classes,
            input_size,
            classes,
        }
    }

    /// Perform semantic segmentation on an image
    pub fn segment(&self, img: &DynamicImage) -> Result<SegmentationResult> {
        // Preprocess image
        let input_tensor = self.preprocess(img)?;

        // Run inference (placeholder for actual neural network inference)
        let output_tensor = self.forward(&input_tensor)?;

        // Post-process output
        self.postprocess(&output_tensor)
    }

    /// Preprocess image for network input
    fn preprocess(&self, img: &DynamicImage) -> Result<Array4<f32>> {
        let resized = img.resize_exact(
            self.input_size.1 as u32,
            self.input_size.0 as u32,
            image::imageops::FilterType::Lanczos3,
        );

        let rgb = resized.to_rgb8();
        let (width, height) = rgb.dimensions();

        // Convert to tensor: [batch=1, channels=3, height, width]
        let mut tensor = Array4::zeros((1, 3, height as usize, width as usize));

        for y in 0..height {
            for x in 0..width {
                let pixel = rgb.get_pixel(x, y);
                // Normalize to [-1, 1]
                tensor[[0, 0, y as usize, x as usize]] = (pixel[0] as f32 / 127.5) - 1.0;
                tensor[[0, 1, y as usize, x as usize]] = (pixel[1] as f32 / 127.5) - 1.0;
                tensor[[0, 2, y as usize, x as usize]] = (pixel[2] as f32 / 127.5) - 1.0;
            }
        }

        Ok(tensor)
    }

    /// Forward pass through the network
    fn forward(&self, input: &Array4<f32>) -> Result<Array4<f32>> {
        // Placeholder implementation
        // In a real implementation, this would use scirs2-neural to run the model
        let (batch, _, height, width) = input.dim();

        // Simulate network output with random predictions
        let mut output = Array4::zeros((batch, self.num_classes, height, width));

        // For demonstration: create a simple pattern
        for b in 0..batch {
            for c in 0..self.num_classes {
                for y in 0..height {
                    for x in 0..width {
                        // Simple heuristic based on position
                        let val = if c == 0 {
                            0.8 // Background
                        } else {
                            0.2 / (self.num_classes - 1) as f32
                        };
                        output[[b, c, y, x]] = val;
                    }
                }
            }
        }

        Ok(output)
    }

    /// Post-process network output
    fn postprocess(&self, output: &Array4<f32>) -> Result<SegmentationResult> {
        let (_, num_classes, height, width) = output.dim();

        // Extract first batch
        let mut class_map = Array2::zeros((height, width));
        let mut confidence = Array3::zeros((height, width, num_classes));

        for y in 0..height {
            for x in 0..width {
                // Apply softmax and find max class
                let mut max_val = f32::NEG_INFINITY;
                let mut max_class = 0;
                let mut sum = 0.0f32;

                // Softmax
                let mut scores = vec![0.0f32; num_classes];
                for c in 0..num_classes {
                    scores[c] = output[[0, c, y, x]].exp();
                    sum += scores[c];
                }

                for c in 0..num_classes {
                    scores[c] /= sum;
                    confidence[[y, x, c]] = scores[c];

                    if scores[c] > max_val {
                        max_val = scores[c];
                        max_class = c;
                    }
                }

                class_map[[y, x]] = max_class;
            }
        }

        Ok(SegmentationResult {
            class_map,
            confidence,
            classes: self.classes.clone(),
        })
    }
}

/// U-Net segmentation model for biomedical images
pub struct UNet {
    num_classes: usize,
    input_size: (usize, usize),
    classes: Vec<SegmentationClass>,
}

impl UNet {
    /// Create a new U-Net model
    pub fn new(
        num_classes: usize,
        input_size: (usize, usize),
        classes: Vec<SegmentationClass>,
    ) -> Self {
        Self {
            num_classes,
            input_size,
            classes,
        }
    }

    /// Perform semantic segmentation
    pub fn segment(&self, img: &DynamicImage) -> Result<SegmentationResult> {
        // Similar to DeepLabV3Plus but with U-Net specific architecture
        let input_tensor = self.preprocess(img)?;
        let output_tensor = self.forward(&input_tensor)?;
        self.postprocess(&output_tensor)
    }

    fn preprocess(&self, img: &DynamicImage) -> Result<Array4<f32>> {
        // Similar preprocessing as DeepLabV3Plus
        let resized = img.resize_exact(
            self.input_size.1 as u32,
            self.input_size.0 as u32,
            image::imageops::FilterType::Lanczos3,
        );

        let rgb = resized.to_rgb8();
        let (width, height) = rgb.dimensions();
        let mut tensor = Array4::zeros((1, 3, height as usize, width as usize));

        for y in 0..height {
            for x in 0..width {
                let pixel = rgb.get_pixel(x, y);
                tensor[[0, 0, y as usize, x as usize]] = pixel[0] as f32 / 255.0;
                tensor[[0, 1, y as usize, x as usize]] = pixel[1] as f32 / 255.0;
                tensor[[0, 2, y as usize, x as usize]] = pixel[2] as f32 / 255.0;
            }
        }

        Ok(tensor)
    }

    fn forward(&self, input: &Array4<f32>) -> Result<Array4<f32>> {
        // Placeholder - would use actual U-Net architecture
        let (batch, _, height, width) = input.dim();
        Ok(Array4::zeros((batch, self.num_classes, height, width)))
    }

    fn postprocess(&self, output: &Array4<f32>) -> Result<SegmentationResult> {
        let (_, num_classes, height, width) = output.dim();
        let class_map = Array2::zeros((height, width));
        let confidence = Array3::zeros((height, width, num_classes));

        Ok(SegmentationResult {
            class_map,
            confidence,
            classes: self.classes.clone(),
        })
    }
}

/// FCN (Fully Convolutional Network) segmentation model
pub struct FCN {
    num_classes: usize,
    variant: FCNVariant,
    classes: Vec<SegmentationClass>,
}

/// FCN architecture variant
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum FCNVariant {
    /// FCN-32s (stride 32)
    FCN32s,
    /// FCN-16s (stride 16)
    FCN16s,
    /// FCN-8s (stride 8)
    FCN8s,
}

impl FCN {
    /// Create a new FCN model
    pub fn new(num_classes: usize, variant: FCNVariant, classes: Vec<SegmentationClass>) -> Self {
        Self {
            num_classes,
            variant,
            classes,
        }
    }

    /// Perform semantic segmentation
    pub fn segment(&self, img: &DynamicImage) -> Result<SegmentationResult> {
        // Placeholder implementation
        let (height, width) = (img.height() as usize, img.width() as usize);
        let class_map = Array2::zeros((height, width));
        let confidence = Array3::zeros((height, width, self.num_classes));

        Ok(SegmentationResult {
            class_map,
            confidence,
            classes: self.classes.clone(),
        })
    }
}

/// Create PASCAL VOC segmentation classes
pub fn create_pascal_voc_classes() -> Vec<SegmentationClass> {
    vec![
        SegmentationClass {
            id: 0,
            name: "background".to_string(),
            color: (0, 0, 0),
        },
        SegmentationClass {
            id: 1,
            name: "aeroplane".to_string(),
            color: (128, 0, 0),
        },
        SegmentationClass {
            id: 2,
            name: "bicycle".to_string(),
            color: (0, 128, 0),
        },
        SegmentationClass {
            id: 3,
            name: "bird".to_string(),
            color: (128, 128, 0),
        },
        SegmentationClass {
            id: 4,
            name: "boat".to_string(),
            color: (0, 0, 128),
        },
        SegmentationClass {
            id: 5,
            name: "bottle".to_string(),
            color: (128, 0, 128),
        },
        SegmentationClass {
            id: 6,
            name: "bus".to_string(),
            color: (0, 128, 128),
        },
        SegmentationClass {
            id: 7,
            name: "car".to_string(),
            color: (128, 128, 128),
        },
        SegmentationClass {
            id: 8,
            name: "cat".to_string(),
            color: (64, 0, 0),
        },
        SegmentationClass {
            id: 9,
            name: "chair".to_string(),
            color: (192, 0, 0),
        },
        SegmentationClass {
            id: 10,
            name: "cow".to_string(),
            color: (64, 128, 0),
        },
        SegmentationClass {
            id: 11,
            name: "diningtable".to_string(),
            color: (192, 128, 0),
        },
        SegmentationClass {
            id: 12,
            name: "dog".to_string(),
            color: (64, 0, 128),
        },
        SegmentationClass {
            id: 13,
            name: "horse".to_string(),
            color: (192, 0, 128),
        },
        SegmentationClass {
            id: 14,
            name: "motorbike".to_string(),
            color: (64, 128, 128),
        },
        SegmentationClass {
            id: 15,
            name: "person".to_string(),
            color: (192, 128, 128),
        },
        SegmentationClass {
            id: 16,
            name: "pottedplant".to_string(),
            color: (0, 64, 0),
        },
        SegmentationClass {
            id: 17,
            name: "sheep".to_string(),
            color: (128, 64, 0),
        },
        SegmentationClass {
            id: 18,
            name: "sofa".to_string(),
            color: (0, 192, 0),
        },
        SegmentationClass {
            id: 19,
            name: "train".to_string(),
            color: (128, 192, 0),
        },
        SegmentationClass {
            id: 20,
            name: "tvmonitor".to_string(),
            color: (0, 64, 128),
        },
    ]
}

/// Create Cityscapes segmentation classes
pub fn create_cityscapes_classes() -> Vec<SegmentationClass> {
    vec![
        SegmentationClass {
            id: 0,
            name: "road".to_string(),
            color: (128, 64, 128),
        },
        SegmentationClass {
            id: 1,
            name: "sidewalk".to_string(),
            color: (244, 35, 232),
        },
        SegmentationClass {
            id: 2,
            name: "building".to_string(),
            color: (70, 70, 70),
        },
        SegmentationClass {
            id: 3,
            name: "wall".to_string(),
            color: (102, 102, 156),
        },
        SegmentationClass {
            id: 4,
            name: "fence".to_string(),
            color: (190, 153, 153),
        },
        SegmentationClass {
            id: 5,
            name: "pole".to_string(),
            color: (153, 153, 153),
        },
        SegmentationClass {
            id: 6,
            name: "traffic_light".to_string(),
            color: (250, 170, 30),
        },
        SegmentationClass {
            id: 7,
            name: "traffic_sign".to_string(),
            color: (220, 220, 0),
        },
        SegmentationClass {
            id: 8,
            name: "vegetation".to_string(),
            color: (107, 142, 35),
        },
        SegmentationClass {
            id: 9,
            name: "terrain".to_string(),
            color: (152, 251, 152),
        },
        SegmentationClass {
            id: 10,
            name: "sky".to_string(),
            color: (70, 130, 180),
        },
        SegmentationClass {
            id: 11,
            name: "person".to_string(),
            color: (220, 20, 60),
        },
        SegmentationClass {
            id: 12,
            name: "rider".to_string(),
            color: (255, 0, 0),
        },
        SegmentationClass {
            id: 13,
            name: "car".to_string(),
            color: (0, 0, 142),
        },
        SegmentationClass {
            id: 14,
            name: "truck".to_string(),
            color: (0, 0, 70),
        },
        SegmentationClass {
            id: 15,
            name: "bus".to_string(),
            color: (0, 60, 100),
        },
        SegmentationClass {
            id: 16,
            name: "train".to_string(),
            color: (0, 80, 100),
        },
        SegmentationClass {
            id: 17,
            name: "motorcycle".to_string(),
            color: (0, 0, 230),
        },
        SegmentationClass {
            id: 18,
            name: "bicycle".to_string(),
            color: (119, 11, 32),
        },
    ]
}

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

    #[test]
    fn test_deeplab_creation() {
        let classes = create_pascal_voc_classes();
        let model = DeepLabV3Plus::new(21, (512, 512), classes);
        assert_eq!(model.num_classes, 21);
    }

    #[test]
    fn test_unet_creation() {
        let classes = vec![
            SegmentationClass {
                id: 0,
                name: "background".to_string(),
                color: (0, 0, 0),
            },
            SegmentationClass {
                id: 1,
                name: "foreground".to_string(),
                color: (255, 255, 255),
            },
        ];
        let model = UNet::new(2, (256, 256), classes);
        assert_eq!(model.num_classes, 2);
    }

    #[test]
    fn test_fcn_creation() {
        let classes = create_pascal_voc_classes();
        let model = FCN::new(21, FCNVariant::FCN8s, classes);
        assert_eq!(model.num_classes, 21);
    }

    #[test]
    fn test_segmentation_result_to_color() {
        let class_map = Array2::zeros((10, 10));
        let confidence = Array3::zeros((10, 10, 2));
        let classes = vec![
            SegmentationClass {
                id: 0,
                name: "bg".to_string(),
                color: (0, 0, 0),
            },
            SegmentationClass {
                id: 1,
                name: "fg".to_string(),
                color: (255, 255, 255),
            },
        ];

        let result = SegmentationResult {
            class_map,
            confidence,
            classes,
        };

        let color_img = result.to_color_image();
        assert!(color_img.is_ok());
    }

    #[test]
    fn test_pascal_voc_classes() {
        let classes = create_pascal_voc_classes();
        assert_eq!(classes.len(), 21);
        assert_eq!(classes[0].name, "background");
    }

    #[test]
    fn test_cityscapes_classes() {
        let classes = create_cityscapes_classes();
        assert_eq!(classes.len(), 19);
        assert_eq!(classes[0].name, "road");
    }

    #[test]
    fn test_deeplab_segment() {
        let classes = create_pascal_voc_classes();
        let model = DeepLabV3Plus::new(21, (256, 256), classes);
        let img = DynamicImage::ImageRgb8(RgbImage::new(256, 256));

        let result = model.segment(&img);
        assert!(result.is_ok());
    }
}