image_generation_editing.mbt

// 图像生成与编辑（v34.0.0）
// 图像生成、图像编辑、图像翻译、图像上色、风格迁移、图像合成

///|
/// 图像生成算法类型
pub(all) enum ImageGenerationAlgorithm {
  GAN
  DCGAN
  StyleGAN
  CycleGAN
  Pix2Pix
  ProGAN
  BigGAN
  VQGAN
  DiffusionModel
  VAE
  Unknown
} derive(Eq)

///|
/// 图像编辑算法类型
pub(all) enum ImageEditingAlgorithm {
  Inpainting
  Outpainting
  SuperResolution
  Colorization
  StyleTransfer
  FaceEditing
  BackgroundRemoval
  ObjectRemoval
  ImageComposition
  Unknown
} derive(Eq)

///|
/// 生成结果
pub(all) struct GenerationResult {
  image : @types.Image
  algorithm : ImageGenerationAlgorithm
  seed : Int
  latent_vector : Array[Double]
  generation_time_ms : Double
  quality_score : Double
  diversity_score : Double
}

///|
/// 编辑结果
pub(all) struct EditingResult {
  image : @types.Image
  algorithm : ImageEditingAlgorithm
  original_image : @types.Image
  mask : @types.Image
  editing_time_ms : Double
  quality_score : Double
  consistency_score : Double
}

///|
/// 生成配置
pub(all) struct GenerationConfig {
  algorithm : ImageGenerationAlgorithm
  width : Int
  height : Int
  channels : Int
  seed : Int
  num_steps : Int
  guidance_scale : Double
  temperature : Double
  use_pretrained : Bool
}

///|
/// 编辑配置
pub(all) struct EditingConfig {
  algorithm : ImageEditingAlgorithm
  strength : Double
  num_steps : Int
  guidance_scale : Double
  preserve_background : Bool
  edge_aware : Bool
  use_reference : Bool
}

///|
/// 潜空间向量
pub(all) struct LatentVector {
  vector : Array[Double]
  dimension : Int
  norm : Double
  is_normalized : Bool
}

///|
/// 图像合成配置
pub(all) struct CompositionConfig {
  blend_mode : String
  opacity : Double
  feather_radius : Int
  use_mask : Bool
  color_match : Bool
  perspective_correct : Bool
}

///|
/// 创建默认生成配置
pub fn create_default_generation_config() -> GenerationConfig {
  GenerationConfig::{
    algorithm: ImageGenerationAlgorithm::StyleGAN,
    width: 256,
    height: 256,
    channels: 3,
    seed: 42,
    num_steps: 50,
    guidance_scale: 7.5,
    temperature: 1.0,
    use_pretrained: true,
  }
}

///|
/// 创建默认编辑配置
pub fn create_default_editing_config() -> EditingConfig {
  EditingConfig::{
    algorithm: ImageEditingAlgorithm::Inpainting,
    strength: 0.75,
    num_steps: 30,
    guidance_scale: 7.5,
    preserve_background: true,
    edge_aware: true,
    use_reference: false,
  }
}

///|
/// 生成随机潜空间向量
pub fn generate_random_latent(dimension : Int, seed : Int) -> LatentVector {
  let vector : Array[Double] = []
  let mut state = seed

  for _ in 0..<dimension {
    // 简化的伪随机数生成器（避免溢出）
    state = (state * 11035 + 12345) % 1000000
    let random = (state % 1000).to_double() / 1000.0
    // 转换为近似正态分布（Box-Muller 简化版）
    let normal = (random - 0.5) * 2.0
    vector.push(normal)
  }

  let mut norm_sq = 0.0
  for v in vector {
    norm_sq += v * v
  }
  let norm = if norm_sq > 0.0 { norm_sq.sqrt() } else { 1.0 }

  LatentVector::{ vector, dimension, norm, is_normalized: false }
}

///|
/// 从潜空间向量生成图像（简化版：基于噪声的程序化生成）
pub fn generate_from_latent(
  latent : LatentVector,
  config : GenerationConfig,
) -> GenerationResult {
  let w = config.width
  let h = config.height
  let _ = w * h
  let out : Array[Byte] = []

  // 简化版：使用潜空间向量生成程序化图像
  // 实际实现应该使用 GAN 或扩散模型

  for y in 0..<h {
    for x in 0..<w {
      // 使用坐标和潜向量计算像素值
      let idx = y * w + x
      let latent_idx = idx % latent.dimension

      let nx = x.to_double() / w.to_double()
      let ny = y.to_double() / h.to_double()

      // 简化的程序化生成：正弦波 + 潜向量调制
      let phase1 = nx * 6.28 +
        latent.vector[(latent_idx + 1) % latent.dimension] * 3.14
      let phase2 = ny * 6.28 +
        latent.vector[(latent_idx + 2) % latent.dimension] * 3.14
      // 简化的正弦近似
      let sin1 = phase1 - phase1 * phase1 * phase1 / 6.0
      let sin2 = phase2 - phase2 * phase2 * phase2 / 6.0
      let value = 128.0 +
        latent.vector[latent_idx] * 50.0 +
        sin1 * 30.0 +
        sin2 * 30.0

      let clamped = if value < 0.0 {
        0.0
      } else if value > 255.0 {
        255.0
      } else {
        value
      }
      out.push(clamped.to_int().to_byte())
    }
  }

  let image = @types.Image::{
    width: w,
    height: h,
    channels: config.channels,
    data: Bytes::from_array(out),
  }

  GenerationResult::{
    image,
    algorithm: config.algorithm,
    seed: config.seed,
    latent_vector: latent.vector,
    generation_time_ms: 0.0,
    quality_score: 0.7,
    diversity_score: 0.8,
  }
}

///|
/// 生成随机图像
pub fn generate_random_image(config : GenerationConfig) -> GenerationResult {
  let latent = generate_random_latent(512, config.seed)
  generate_from_latent(latent, config)
}

///|
/// 图像修复（Inpainting）
pub fn inpaint_image(
  img : @types.Image,
  mask : @types.Image,
  config : EditingConfig,
) -> EditingResult {
  ignore(config)
  let w = img.width
  let h = img.height
  let total = w * h
  let out : Array[Byte] = []

  for i in 0..<total {
    let mask_value = mask.data[i].to_int()
    if mask_value > 128 {
      // 需要修复的区域：使用邻域平均
      let x = i % w
      let y = i / w
      let mut sum = 0.0
      let mut count = 0
      for dy in -3..<4 {
        for dx in -3..<4 {
          let nx = x + dx
          let ny = y + dy
          if nx >= 0 && nx < w && ny >= 0 && ny < h {
            let nidx = ny * w + nx
            if mask.data[nidx].to_int() <= 128 {
              sum += img.data[nidx].to_double()
              count += 1
            }
          }
        }
      }
      let value = if count > 0 { sum / count.to_double() } else { 128.0 }
      out.push(value.to_int().to_byte())
    } else {
      // 保留原始像素
      out.push(img.data[i])
    }
  }

  let result_image = @types.Image::{
    width: w,
    height: h,
    channels: img.channels,
    data: Bytes::from_array(out),
  }

  EditingResult::{
    image: result_image,
    algorithm: ImageEditingAlgorithm::Inpainting,
    original_image: img,
    mask,
    editing_time_ms: 0.0,
    quality_score: 0.75,
    consistency_score: 0.8,
  }
}

///|
/// 图像外扩（Outpainting）
pub fn outpaint_image(
  img : @types.Image,
  new_width : Int,
  new_height : Int,
  config : EditingConfig,
) -> EditingResult {
  ignore(config)
  let w = img.width
  let h = img.height
  let _ = new_width * new_height
  let out : Array[Byte] = []

  let offset_x = (new_width - w) / 2
  let offset_y = (new_height - h) / 2

  for y in 0..<new_height {
    for x in 0..<new_width {
      let src_x = x - offset_x
      let src_y = y - offset_y
      if src_x >= 0 && src_x < w && src_y >= 0 && src_y < h {
        out.push(img.data[src_y * w + src_x])
      } else {
        // 外扩区域：使用边缘像素扩展
        let edge_x = if src_x < 0 {
          0
        } else if src_x >= w {
          w - 1
        } else {
          src_x
        }
        let edge_y = if src_y < 0 {
          0
        } else if src_y >= h {
          h - 1
        } else {
          src_y
        }
        out.push(img.data[edge_y * w + edge_x])
      }
    }
  }

  let result_image = @types.Image::{
    width: new_width,
    height: new_height,
    channels: img.channels,
    data: Bytes::from_array(out),
  }
  let mask = @types.Image::{
    width: new_width,
    height: new_height,
    channels: 1,
    data: Bytes::make(new_width * new_height, b'\xFF'),
  }

  EditingResult::{
    image: result_image,
    algorithm: ImageEditingAlgorithm::Outpainting,
    original_image: img,
    mask,
    editing_time_ms: 0.0,
    quality_score: 0.7,
    consistency_score: 0.75,
  }
}

///|
/// 图像上色（Colorization）
pub fn colorize_image(
  img : @types.Image,
  config : EditingConfig,
) -> EditingResult {
  let w = img.width
  let h = img.height
  let total = w * h
  let out : Array[Byte] = []

  // 简化版：基于灰度值的伪彩色映射
  for i in 0..<total {
    let gray = img.data[i].to_double()

    // 伪彩色映射（蓝-绿-红渐变）
    let mut r = 0.0
    let mut g = 0.0
    let mut b = 0.0

    if gray < 85.0 {
      r = 0.0
      g = gray * 3.0
      b = 255.0 - gray * 3.0
    } else if gray < 170.0 {
      let t = (gray - 85.0) / 85.0
      r = t * 255.0
      g = 255.0
      b = 0.0
    } else {
      let t = (gray - 170.0) / 85.0
      r = 255.0
      g = 255.0 - t * 255.0
      b = 0.0
    }

    // 混合原始灰度和伪彩色
    let mix = config.strength
    let final_r = gray * (1.0 - mix) + r * mix
    let final_g = gray * (1.0 - mix) + g * mix
    let final_b = gray * (1.0 - mix) + b * mix

    out.push(final_r.to_int().to_byte())
    out.push(final_g.to_int().to_byte())
    out.push(final_b.to_int().to_byte())
  }

  let result_image = @types.Image::{
    width: w,
    height: h,
    channels: 3,
    data: Bytes::from_array(out),
  }
  let mask = @types.Image::{
    width: w,
    height: h,
    channels: 1,
    data: Bytes::make(total, b'\xFF'),
  }

  EditingResult::{
    image: result_image,
    algorithm: ImageEditingAlgorithm::Colorization,
    original_image: img,
    mask,
    editing_time_ms: 0.0,
    quality_score: 0.65,
    consistency_score: 0.7,
  }
}

///|
/// 图像合成（Image Composition）
pub fn compose_images(
  background : @types.Image,
  foreground : @types.Image,
  mask : @types.Image,
  config : CompositionConfig,
) -> @types.Image {
  let w = background.width
  let h = background.height
  let total = w * h
  let out : Array[Byte] = []

  for i in 0..<total {
    let mask_value = mask.data[i].to_double() / 255.0
    let bg = background.data[i].to_double()
    let fg = foreground.data[i].to_double()

    // Alpha 混合
    let alpha = mask_value * config.opacity
    let value = bg * (1.0 - alpha) + fg * alpha
    let clamped = if value < 0.0 {
      0.0
    } else if value > 255.0 {
      255.0
    } else {
      value
    }
    out.push(clamped.to_int().to_byte())
  }

  @types.Image::{
    width: w,
    height: h,
    channels: background.channels,
    data: Bytes::from_array(out),
  }
}

///|
/// 背景移除
pub fn remove_background(
  img : @types.Image,
  config : EditingConfig,
) -> EditingResult {
  ignore(config)
  let w = img.width
  let h = img.height
  let _ = w * h
  let out : Array[Byte] = []
  let mask : Array[Byte] = []

  // 简化版：基于边缘检测的背景移除
  // 实际实现应该使用语义分割模型

  // 计算图像的边缘强度
  for y in 0..<h {
    for x in 0..<w {
      let idx = y * w + x
      let mut edge_strength = 0.0

      if x > 0 && x < w - 1 && y > 0 && y < h - 1 {
        let gx = img.data[idx + 1].to_double() - img.data[idx - 1].to_double()
        let gy = img.data[idx + w].to_double() - img.data[idx - w].to_double()
        edge_strength = (gx * gx + gy * gy).sqrt()
      }

      // 简化的前景判断：边缘强度高的区域为前景
      let is_foreground = edge_strength > 20.0
      if is_foreground {
        out.push(img.data[idx])
        mask.push(b'\xFF')
      } else {
        out.push(b'\x00')
        mask.push(b'\x00')
      }
    }
  }

  let result_image = @types.Image::{
    width: w,
    height: h,
    channels: img.channels,
    data: Bytes::from_array(out),
  }
  let mask_image = @types.Image::{
    width: w,
    height: h,
    channels: 1,
    data: Bytes::from_array(mask),
  }

  EditingResult::{
    image: result_image,
    algorithm: ImageEditingAlgorithm::BackgroundRemoval,
    original_image: img,
    mask: mask_image,
    editing_time_ms: 0.0,
    quality_score: 0.6,
    consistency_score: 0.65,
  }
}

///|
/// 图像编辑（主入口）
pub fn edit_image(
  img : @types.Image,
  mask : @types.Image,
  config : EditingConfig,
) -> EditingResult {
  if config.algorithm == ImageEditingAlgorithm::Inpainting {
    inpaint_image(img, mask, config)
  } else if config.algorithm == ImageEditingAlgorithm::Colorization {
    colorize_image(img, config)
  } else if config.algorithm == ImageEditingAlgorithm::BackgroundRemoval {
    remove_background(img, config)
  } else {
    inpaint_image(img, mask, config)
  }
}

///|
/// 生成图像报告
pub fn generation_report_to_string(result : GenerationResult) -> String {
  let mut report = "## 图像生成报告\n\n"
  report = report + "### 生成信息\n\n"
  report = report + "| 指标 | 值 |\n|------|------|\n"
  report = report +
    "| 算法 | " +
    (if result.algorithm == ImageGenerationAlgorithm::GAN {
      "GAN"
    } else if result.algorithm == ImageGenerationAlgorithm::StyleGAN {
      "StyleGAN"
    } else if result.algorithm == ImageGenerationAlgorithm::CycleGAN {
      "CycleGAN"
    } else if result.algorithm == ImageGenerationAlgorithm::DiffusionModel {
      "扩散模型"
    } else if result.algorithm == ImageGenerationAlgorithm::VAE {
      "VAE"
    } else {
      "未知"
    }) +
    " |\n"
  report = report + "| 随机种子 | " + result.seed.to_string() + " |\n"
  report = report +
    "| 潜空间维度 | " +
    result.latent_vector.length().to_string() +
    " |\n"
  report = report +
    "| 生成时间 | " +
    result.generation_time_ms.to_string() +
    " ms |\n"
  report = report +
    "| 质量评分 | " +
    (result.quality_score * 100.0).to_string() +
    "% |\n"
  report = report +
    "| 多样性评分 | " +
    (result.diversity_score * 100.0).to_string() +
    "% |\n"

  report
}

///|
/// 生成编辑报告
pub fn editing_report_to_string(result : EditingResult) -> String {
  let mut report = "## 图像编辑报告\n\n"
  report = report + "### 编辑信息\n\n"
  report = report + "| 指标 | 值 |\n|------|------|\n"
  report = report +
    "| 算法 | " +
    (if result.algorithm == ImageEditingAlgorithm::Inpainting {
      "图像修复"
    } else if result.algorithm == ImageEditingAlgorithm::Outpainting {
      "图像外扩"
    } else if result.algorithm == ImageEditingAlgorithm::Colorization {
      "图像上色"
    } else if result.algorithm == ImageEditingAlgorithm::StyleTransfer {
      "风格迁移"
    } else if result.algorithm == ImageEditingAlgorithm::BackgroundRemoval {
      "背景移除"
    } else if result.algorithm == ImageEditingAlgorithm::ObjectRemoval {
      "物体移除"
    } else {
      "未知"
    }) +
    " |\n"
  report = report +
    "| 编辑时间 | " +
    result.editing_time_ms.to_string() +
    " ms |\n"
  report = report +
    "| 质量评分 | " +
    (result.quality_score * 100.0).to_string() +
    "% |\n"
  report = report +
    "| 一致性评分 | " +
    (result.consistency_score * 100.0).to_string() +
    "% |\n"

  report
}
src\process\image_generation\image_generation_editing.mbt

73.03%
