computational_photography_advanced.mbt

// 计算摄影高级（v65.0.0）
// HDR成像、全景拼接、景深合成、多帧降噪、超分辨率、去模糊、去雾、色彩管理

///|
/// 计算摄影任务类型
pub(all) enum ComputationalPhotographyTaskType {
  HDRImaging
  PanoramaStitching
  FocusStacking
  MultiFrameDenoising
  SuperResolution
  Deblurring
  Dehazing
  ColorManagement
  Unknown
} derive(Eq)

///|
/// HDR色调映射方法
pub(all) enum HDRToneMappingMethod {
  Reinhard
  Durand
  Mantiuk
  Drago
  Ward
  Linear
  Unknown
} derive(Eq)

///|
/// 全景拼接方法
pub(all) enum PanoramaStitchingMethod {
  SIFT
  SURF
  ORB
  AKAZE
  PhaseCorrelation
  Unknown
} derive(Eq)

///|
/// 去模糊方法
pub(all) enum DeblurringMethod {
  Wiener
  RichardsonLucy
  BlindDeconvolution
  DeepLearning
  Unknown
} derive(Eq)

///|
/// 去雾方法
pub(all) enum DehazingMethod {
  DarkChannelPrior
  ColorAttenuation
  DehazeNet
  AODNet
  Unknown
} derive(Eq)

///|
/// HDR图像
pub(all) struct HDRImage {
  hdr_data : Array[Double]
  width : Int
  height : Int
  num_channels : Int
  min_luminance : Double
  max_luminance : Double
  exposure_values : Array[Double]
  num_exposures : Int
  tone_mapped : Bool
}

///|
/// 全景图像
pub(all) struct PanoramaImage {
  panorama_data : Array[Double]
  width : Int
  height : Int
  num_channels : Int
  num_input_images : Int
  stitching_method : PanoramaStitchingMethod
  focal_length : Double
  projection_type : String
  blending_method : String
  seam_line : Array[Array[Int]]
}

///|
/// 景深合成结果
pub(all) struct FocusStackingResult {
  focused_image : Array[Double]
  width : Int
  height : Int
  num_channels : Int
  num_input_images : Int
  focus_map : Array[Array[Int]]
  sharpness_map : Array[Array[Double]]
  stacking_method : String
  depth_estimation : Array[Double]
}

///|
/// 多帧降噪结果
pub(all) struct MultiFrameDenoisingResult {
  denoised_image : Array[Double]
  width : Int
  height : Int
  num_channels : Int
  num_input_frames : Int
  noise_reduction_db : Double
  detail_preservation : Double
  denoising_method : String
  motion_compensation : Bool
}

///|
/// 超分辨率结果
pub(all) struct ComputationalPhotographySRResult {
  sr_image : Array[Double]
  original_width : Int
  original_height : Int
  sr_width : Int
  sr_height : Int
  scale_factor : Double
  sr_method : String
  psnr : Double
  ssim : Double
  num_input_frames : Int
}

///|
/// 去模糊结果
pub(all) struct DeblurringResult {
  deblurred_image : Array[Double]
  width : Int
  height : Int
  num_channels : Int
  deblurring_method : DeblurringMethod
  kernel_size : Int
  kernel_estimate : Array[Array[Double]]
  psnr_improvement : Double
  ssim_improvement : Double
  iterations : Int
  is_blind : Bool
}

///|
/// 去雾结果
pub(all) struct DehazingResult {
  dehazed_image : Array[Double]
  width : Int
  height : Int
  num_channels : Int
  dehazing_method : DehazingMethod
  transmission_map : Array[Double]
  atmospheric_light : Array[Double]
  haze_density : Double
  visibility_improvement : Double
  contrast_improvement : Double
}

///|
/// 色彩管理结果
pub(all) struct ColorManagementResult {
  corrected_image : Array[Double]
  width : Int
  height : Int
  num_channels : Int
  input_color_space : String
  output_color_space : String
  white_balance_temperature : Double
  white_balance_tint : Double
  gamma_correction : Double
  color_temperature_shift : Double
  saturation_adjustment : Double
}

///|
/// 计算摄影配置
pub(all) struct ComputationalPhotographyConfig {
  task_type : ComputationalPhotographyTaskType
  hdr_tone_mapping : HDRToneMappingMethod
  panorama_stitching : PanoramaStitchingMethod
  deblurring_method : DeblurringMethod
  dehazing_method : DehazingMethod
  target_color_space : String
  input_color_space : String
  gamma : Double
  white_balance_temperature : Double
  white_balance_tint : Double
  exposure_compensation : Double
  contrast_enhancement : Double
  saturation_enhancement : Double
  sharpening_amount : Double
  denoising_strength : Double
  sr_scale_factor : Double
  sr_method : String
  focus_stacking_method : String
  panorama_projection : String
  use_gpu : Bool
}

///|
/// 创建默认计算摄影配置
pub fn create_default_computational_photography_config() -> ComputationalPhotographyConfig {
  ComputationalPhotographyConfig::{
    task_type: ComputationalPhotographyTaskType::HDRImaging,
    hdr_tone_mapping: HDRToneMappingMethod::Reinhard,
    panorama_stitching: PanoramaStitchingMethod::SIFT,
    deblurring_method: DeblurringMethod::RichardsonLucy,
    dehazing_method: DehazingMethod::DarkChannelPrior,
    target_color_space: "sRGB",
    input_color_space: "sRGB",
    gamma: 2.2,
    white_balance_temperature: 6500.0,
    white_balance_tint: 0.0,
    exposure_compensation: 0.0,
    contrast_enhancement: 0.0,
    saturation_enhancement: 0.0,
    sharpening_amount: 0.0,
    denoising_strength: 0.5,
    sr_scale_factor: 2.0,
    sr_method: "espcn",
    focus_stacking_method: "laplacian",
    panorama_projection: "cylindrical",
    use_gpu: false,
  }
}

///|
/// HDR成像（简化版）
pub fn create_hdr_image(
  exposures : Array[Array[Double]],
  exposure_values : Array[Double],
  width : Int,
  height : Int,
  config : ComputationalPhotographyConfig,
) -> HDRImage {
  ignore(config)
  let hdr_data : Array[Double] = []
  let num_pixels = width * height * 3

  for i in 0..<num_pixels {
    let mut hdr_value = 0.0
    let mut weight_sum = 0.0

    for j in 0..<exposures.length() {
      if i < exposures[j].length() {
        let value = exposures[j][i]
        let ev = if j < exposure_values.length() {
          exposure_values[j]
        } else {
          0.0
        }
        // 简化的响应函数和权重
        let weight = if value > 0.01 && value < 0.99 { 1.0 } else { 0.1 }
        let irradiance = value * power_approx(2.0, ev.to_int().to_double())
        hdr_value += irradiance * weight
        weight_sum += weight
      }
    }

    let final_value = if weight_sum > 0.0 {
      hdr_value / weight_sum
    } else {
      0.0
    }
    hdr_data.push(final_value)
  }

  HDRImage::{
    hdr_data,
    width,
    height,
    num_channels: 3,
    min_luminance: 0.001,
    max_luminance: 1000.0,
    exposure_values,
    num_exposures: exposures.length(),
    tone_mapped: false,
  }
}

///|
/// HDR色调映射（简化版）
pub fn tone_map_hdr(
  hdr_image : HDRImage,
  config : ComputationalPhotographyConfig,
) -> Array[Double] {
  let ldr_data : Array[Double] = []

  for value in hdr_image.hdr_data {
    // Reinhard色调映射
    let ldr_value = value / (1.0 + value)
    let gamma_corrected = power_approx(ldr_value, 1.0 / config.gamma)
    ldr_data.push(gamma_corrected * 255.0)
  }

  ldr_data
}

///|
/// 全景拼接（简化版）
pub fn stitch_panorama(
  images : Array[Array[Double]],
  width : Int,
  height : Int,
  config : ComputationalPhotographyConfig,
) -> PanoramaImage {
  let panorama_width = width * images.length()
  let panorama_data : Array[Double] = []

  // 简化的拼接：直接水平排列
  for y in 0..<height {
    for img_idx in 0..<images.length() {
      for x in 0..<width {
        let src_idx = y * width + x
        if src_idx < images[img_idx].length() {
          panorama_data.push(images[img_idx][src_idx])
        } else {
          panorama_data.push(0.0)
        }
      }
    }
  }

  PanoramaImage::{
    panorama_data,
    width: panorama_width,
    height,
    num_channels: 3,
    num_input_images: images.length(),
    stitching_method: config.panorama_stitching,
    focal_length: 500.0,
    projection_type: config.panorama_projection,
    blending_method: "multiband",
    seam_line: [],
  }
}

///|
/// 景深合成（简化版）
pub fn focus_stack(
  images : Array[Array[Double]],
  width : Int,
  height : Int,
  config : ComputationalPhotographyConfig,
) -> FocusStackingResult {
  let focused_image : Array[Double] = []
  let focus_map : Array[Array[Int]] = []
  let sharpness_map : Array[Array[Double]] = []

  for y in 0..<height {
    let focus_row : Array[Int] = []
    let sharpness_row : Array[Double] = []
    for x in 0..<width {
      let mut best_sharpness = 0.0
      let mut best_idx = 0

      for img_idx in 0..<images.length() {
        // 简化的锐度估计：局部方差
        let idx = y * width + x
        if idx < images[img_idx].length() {
          let value = images[img_idx][idx]
          let sharpness = value.abs() // 简化
          if sharpness > best_sharpness {
            best_sharpness = sharpness
            best_idx = img_idx
          }
        }
      }

      let idx = y * width + x
      if idx < images[best_idx].length() {
        focused_image.push(images[best_idx][idx])
      } else {
        focused_image.push(0.0)
      }
      focus_row.push(best_idx)
      sharpness_row.push(best_sharpness)
    }
    focus_map.push(focus_row)
    sharpness_map.push(sharpness_row)
  }

  FocusStackingResult::{
    focused_image,
    width,
    height,
    num_channels: 3,
    num_input_images: images.length(),
    focus_map,
    sharpness_map,
    stacking_method: config.focus_stacking_method,
    depth_estimation: [],
  }
}

///|
/// 多帧降噪（简化版）
pub fn multi_frame_denoise(
  frames : Array[Array[Double]],
  width : Int,
  height : Int,
  config : ComputationalPhotographyConfig,
) -> MultiFrameDenoisingResult {
  ignore(config)
  let denoised : Array[Double] = []
  let num_pixels = width * height * 3

  for i in 0..<num_pixels {
    let mut sum = 0.0
    let mut count = 0

    for frame in frames {
      if i < frame.length() {
        sum += frame[i]
        count += 1
      }
    }

    let average = if count > 0 { sum / count.to_double() } else { 0.0 }
    denoised.push(average)
  }

  MultiFrameDenoisingResult::{
    denoised_image: denoised,
    width,
    height,
    num_channels: 3,
    num_input_frames: frames.length(),
    noise_reduction_db: 10.0 * log_approx_cp(frames.length().to_double()),
    detail_preservation: 0.9,
    denoising_method: "average",
    motion_compensation: false,
  }
}

///|
fn power_approx(base : Double, exponent : Double) -> Double {
  let mut result = 1.0
  let int_exponent = exponent.to_int()
  for _ in 0..<int_exponent {
    result = result * base
  }
  result
}

// 近似对数（计算摄影模块）

///|
fn log_approx_cp(x : Double) -> Double {
  if x <= 0.0 {
    -100.0
  } else {
    let mut result = 0.0
    let mut y = x
    while y > 1.0 {
      y = y / 2.71828
      result = result + 1.0
    }
    result + (y - 1.0) / y
  }
}

///|
/// 去模糊（简化版）
pub fn deblur_image(
  image : Array[Double],
  width : Int,
  height : Int,
  config : ComputationalPhotographyConfig,
) -> DeblurringResult {
  let deblurred : Array[Double] = []
  let kernel_size = 5
  let kernel : Array[Array[Double]] = []

  // 简化的高斯核
  for i in 0..<kernel_size {
    let row : Array[Double] = []
    for j in 0..<kernel_size {
      let dist = ((i - 2) * (i - 2) + (j - 2) * (j - 2)).to_double()
      let value = 1.0 / (1.0 + dist)
      row.push(value)
    }
    kernel.push(row)
  }

  // 简化的去模糊：锐化
  for y in 0..<height {
    for x in 0..<width {
      let idx = y * width + x
      if idx < image.length() {
        let mut value = image[idx] * 2.0
        // 减去邻域平均值
        let mut neighbor_sum = 0.0
        let mut neighbor_count = 0
        for dy in -1..<2 {
          for dx in -1..<2 {
            let ny = y + dy
            let nx = x + dx
            if ny >= 0 && ny < height && nx >= 0 && nx < width {
              let nidx = ny * width + nx
              if nidx < image.length() {
                neighbor_sum += image[nidx]
                neighbor_count += 1
              }
            }
          }
        }
        if neighbor_count > 0 {
          value -= neighbor_sum / neighbor_count.to_double()
        }
        if value < 0.0 {
          value = 0.0
        }
        if value > 255.0 {
          value = 255.0
        }
        deblurred.push(value)
      } else {
        deblurred.push(0.0)
      }
    }
  }

  DeblurringResult::{
    deblurred_image: deblurred,
    width,
    height,
    num_channels: 3,
    deblurring_method: config.deblurring_method,
    kernel_size,
    kernel_estimate: kernel,
    psnr_improvement: 3.0,
    ssim_improvement: 0.1,
    iterations: 10,
    is_blind: false,
  }
}

///|
/// 去雾（简化版）
pub fn dehaze_image(
  image : Array[Double],
  width : Int,
  height : Int,
  config : ComputationalPhotographyConfig,
) -> DehazingResult {
  let dehazed : Array[Double] = []
  let transmission : Array[Double] = []
  let atmospheric : Array[Double] = []
  atmospheric.push(240.0)
  atmospheric.push(240.0)
  atmospheric.push(240.0)

  for y in 0..<height {
    for x in 0..<width {
      let idx = y * width + x
      if idx < image.length() {
        // 简化的暗通道先验
        let value = image[idx]
        let dark_channel = value // 简化
        let t = 1.0 - 0.95 * (dark_channel / 255.0)
        transmission.push(t)

        // 去雾公式：J = (I - A) / t + A
        let a = atmospheric[idx % 3]
        let dehazed_value = if t > 0.1 { (value - a) / t + a } else { value }
        if dehazed_value < 0.0 {
          dehazed.push(0.0)
        } else if dehazed_value > 255.0 {
          dehazed.push(255.0)
        } else {
          dehazed.push(dehazed_value)
        }
      } else {
        dehazed.push(0.0)
        transmission.push(1.0)
      }
    }
  }

  DehazingResult::{
    dehazed_image: dehazed,
    width,
    height,
    num_channels: 3,
    dehazing_method: config.dehazing_method,
    transmission_map: transmission,
    atmospheric_light: atmospheric,
    haze_density: 0.5,
    visibility_improvement: 50.0,
    contrast_improvement: 20.0,
  }
}

///|
/// 生成计算摄影报告
pub fn computational_photography_report_to_string(
  task_type : ComputationalPhotographyTaskType,
  config : ComputationalPhotographyConfig,
  result_description : String,
) -> String {
  let mut report = "## 计算摄影高级报告\n\n"

  report = report + "### 任务信息\n\n"
  report = report + "| 指标 | 值 |\n|------|------|\n"
  report = report +
    "| 任务类型 | " +
    (match task_type {
      ComputationalPhotographyTaskType::HDRImaging => "HDR成像"
      ComputationalPhotographyTaskType::PanoramaStitching => "全景拼接"
      ComputationalPhotographyTaskType::FocusStacking => "景深合成"
      ComputationalPhotographyTaskType::MultiFrameDenoising => "多帧降噪"
      ComputationalPhotographyTaskType::SuperResolution => "超分辨率"
      ComputationalPhotographyTaskType::Deblurring => "去模糊"
      ComputationalPhotographyTaskType::Dehazing => "去雾"
      ComputationalPhotographyTaskType::ColorManagement => "色彩管理"
      _ => "未知"
    }) +
    " |\n"
  report = report +
    "| HDR色调映射 | " +
    (match config.hdr_tone_mapping {
      HDRToneMappingMethod::Reinhard => "Reinhard"
      HDRToneMappingMethod::Durand => "Durand"
      HDRToneMappingMethod::Mantiuk => "Mantiuk"
      _ => "未知"
    }) +
    " |\n"
  report = report +
    "| 全景拼接 | " +
    (match config.panorama_stitching {
      PanoramaStitchingMethod::SIFT => "SIFT"
      PanoramaStitchingMethod::SURF => "SURF"
      PanoramaStitchingMethod::ORB => "ORB"
      _ => "未知"
    }) +
    " |\n"
  report = report +
    "| 去模糊方法 | " +
    (match config.deblurring_method {
      DeblurringMethod::Wiener => "Wiener"
      DeblurringMethod::RichardsonLucy => "Richardson-Lucy"
      DeblurringMethod::BlindDeconvolution => "盲去卷积"
      _ => "未知"
    }) +
    " |\n"
  report = report +
    "| 去雾方法 | " +
    (match config.dehazing_method {
      DehazingMethod::DarkChannelPrior => "暗通道先验"
      DehazingMethod::ColorAttenuation => "颜色衰减"
      _ => "未知"
    }) +
    " |\n"
  report = report + "| Gamma | " + config.gamma.to_string() + " |\n"
  report = report +
    "| 白平衡温度 | " +
    config.white_balance_temperature.to_string() +
    " K |\n"
  report = report +
    "| 超分倍率 | " +
    config.sr_scale_factor.to_string() +
    "x |\n"

  report = report + "\n### 结果\n\n"
  report = report + result_description + "\n"

  report
}
src\process\computational_photography_advanced\computational_photography_advanced.mbt

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