nsfwapp/frontend/src/components/ui/videoHeatmap.ts

// frontend\src\components\ui\videoHeatmap.ts

import type {
  RecordJob,
  VideoHeatmapSegment,
  VideoHeatmapSource,
} from '../../types'
import {
  aiLabelGroup,
  normalizeAiLabel,
  prettyAiLabel,
} from '../../aiLabels'

function isPlainObject(value: unknown): value is Record<string, any> {
  return !!value && typeof value === 'object' && !Array.isArray(value)
}

export function parseHeatmapMeta(metaRaw: unknown): Record<string, any> | null {
  if (!metaRaw) return null

  if (typeof metaRaw === 'string') {
    try {
      const parsed = JSON.parse(metaRaw)
      return isPlainObject(parsed) ? parsed : null
    } catch {
      return null
    }
  }

  return isPlainObject(metaRaw) ? metaRaw : null
}

function normalizeDurationSeconds(value: unknown): number {
  const n = Number(value)
  if (!Number.isFinite(n) || n <= 0) return 0
  return n > 24 * 60 * 60 ? n / 1000 : n
}

export function heatmapDurationForJob(
  job: RecordJob,
  fallbackDurationSeconds?: number
): number {
  const meta = parseHeatmapMeta((job as any)?.meta)

  return normalizeDurationSeconds(
    meta?.media?.durationSeconds ??
      meta?.file?.durationSeconds ??
      meta?.durationSeconds ??
      (job as any)?.durationSeconds ??
      fallbackDurationSeconds
  )
}

function readAiNode(meta: Record<string, any> | null): Record<string, any> | null {
  if (!meta) return null

  const ai =
    meta?.analysis?.highlights ??
    meta?.analysis?.ai ??
    meta?.ai ??
    null

  return isPlainObject(ai) ? ai : null
}

function clamp(n: number, min: number, max: number) {
  return Math.max(min, Math.min(max, n))
}

function readScore(value: unknown): number {
  const n = Number(value)
  if (!Number.isFinite(n)) return 0.35

  if (n >= 0 && n <= 1) return clamp(n, 0, 1)
  if (n > 1 && n <= 100) return clamp(n / 100, 0, 1)

  return 0.35
}

function readOptionalScore(value: unknown): number | null {
  if (value == null || value === '') return null

  const n = Number(value)
  if (!Number.isFinite(n)) return null

  if (n >= 0 && n <= 1) return clamp(n, 0, 1)
  if (n > 1 && n <= 100) return clamp(n / 100, 0, 1)

  return null
}

type WeightedDetectedLabel = {
  label: string
  scoreWeight: number
  priority: number
  source: VideoHeatmapSource
}

function weightedLabelFromValue(value: unknown): WeightedDetectedLabel | null {
  const raw = String(value ?? '').trim()
  if (!raw) return null

  const lower = raw.toLowerCase()

  if (lower.startsWith('combo:')) {
    const parts = lower.slice('combo:'.length).split('+')

    let best: WeightedDetectedLabel | null = null

    for (const part of parts) {
      const found = weightedLabelFromValue(part)
      if (!found) continue

      if (!best || found.priority > best.priority) {
        best = found
      }
    }

    return best
  }

  const normalized = normalizeAiLabel(raw)
  if (!normalized || normalized === 'unknown') return null

  const group = aiLabelGroup(normalized)

  if (group === 'position') {
    return {
      label: prettyAiLabel(normalized),
      scoreWeight: 0.82,
      priority: 4,
      source: 'position',
    }
  }

  if (group === 'body') {
    return {
      label: prettyAiLabel(normalized),
      scoreWeight: 0.50,
      priority: 3,
      source: 'other',
    }
  }

  if (group === 'object') {
    return {
      label: prettyAiLabel(normalized),
      scoreWeight: 0.15,
      priority: 2,
      source: 'other',
    }
  }

  if (group === 'clothing') {
    return {
      label: prettyAiLabel(normalized),
      scoreWeight: 0.28,
      priority: 1,
      source: 'clothing',
    }
  }

  return null
}

function readWeightedLabel(item: any): WeightedDetectedLabel | null {
  const directValues = [
    item?.position,
    item?.Position,
    item?.label,
    item?.Label,
    item?.title,
    item?.name,
    item?.category,
    item?.type,
    item?.kind,
  ]

  let best: WeightedDetectedLabel | null = null

  for (const value of directValues) {
    const found = weightedLabelFromValue(value)
    if (!found) continue

    if (!best || found.priority > best.priority) {
      best = found
    }
  }

  const arrayValues = [
    item?.tags,
    item?.labels,
    item?.classes,
    item?.flags,
  ]

  for (const values of arrayValues) {
    if (!Array.isArray(values)) continue

    for (const value of values) {
      const found = weightedLabelFromValue(value)
      if (!found) continue

      if (!best || found.priority > best.priority) {
        best = found
      }
    }
  }

  return best
}

function addSegment(
  out: VideoHeatmapSegment[],
  startRaw: unknown,
  endRaw: unknown,
  markerRaw: unknown,
  durationSec: number,
  label: string,
  scoreRaw: unknown,
  source: VideoHeatmapSource = 'other'
) {
  if (!Number.isFinite(durationSec) || durationSec <= 0) return

  let start = Number(startRaw)
  let end = Number(endRaw)

  if (!Number.isFinite(start) || start < 0) {
    const marker = Number(markerRaw)
    if (!Number.isFinite(marker) || marker < 0) return

    start = marker - 3
    end = marker + 3
  }

  if (!Number.isFinite(end) || end <= start) {
    end = start + 6
  }

  start = clamp(start, 0, durationSec)
  end = clamp(end, 0, durationSec)

  if (end <= start) return

  out.push({
    startSec: start,
    endSec: end,
    intensity: readScore(scoreRaw),
    label,
    source,
  })
}

export function buildVideoHeatmapSegments(
  job: RecordJob,
  fallbackDurationSeconds?: number
): VideoHeatmapSegment[] {
  const meta = parseHeatmapMeta((job as any)?.meta)
  const ai = readAiNode(meta)
  const durationSec = heatmapDurationForJob(job, fallbackDurationSeconds)

  if (!ai || durationSec <= 0) return []

  const out: VideoHeatmapSegment[] = []

  const segments = Array.isArray(ai?.segments ?? ai?.Segments)
    ? (ai?.segments ?? ai?.Segments)
    : []

  const appendItems = (items: unknown[]) => {
    for (const item of items) {
      if (!isPlainObject(item)) continue

      const detected = readWeightedLabel(item)
      if (!detected) continue

      const ratingIntensity = readOptionalScore(
        item.ratingIntensity ?? item.RatingIntensity
      )
      const rawScore = readScore(
          item.score ?? item.Score ?? item.confidence
      )
      const intensity =
        ratingIntensity ??
        clamp(rawScore * detected.scoreWeight, 0, 1)

      addSegment(
        out,
        item.startSeconds ?? item.StartSeconds ?? item.start ?? item.Start,
        item.endSeconds ?? item.EndSeconds ?? item.end ?? item.End,
        item.time ?? item.Time ?? item.at ?? item.timestamp,
        durationSec,
        detected.label,
        intensity,
        detected.source
      )
    }
  }

  appendItems(segments)

  // Raw hits are the source of the condensed segments and would otherwise
  // paint the same activity twice. Keep them only for legacy metadata.
  if (out.length === 0) {
    const hits = Array.isArray(ai?.hits ?? ai?.Hits)
      ? (ai?.hits ?? ai?.Hits)
      : []
    appendItems(hits)
  }

  return mergeHeatmapSegments(out, durationSec)
}

function mergeHeatmapSegments(
  input: VideoHeatmapSegment[],
  durationSec: number
): VideoHeatmapSegment[] {
  if (input.length <= 1) return input

  const sorted = [...input]
    .filter((s) => s.endSec > s.startSec)
    .sort((a, b) => a.startSec - b.startSec || b.endSec - a.endSec)

  const out: VideoHeatmapSegment[] = []

  for (const seg of sorted) {
    const last = out[out.length - 1]

    // Nur sehr nahe/überlappende Bereiche zusammenfassen.
    if (
    !last ||
    last.source !== seg.source ||
    seg.startSec > last.endSec + 1.5
    ) {
      out.push({ ...seg })
      continue
    }

    last.endSec = clamp(Math.max(last.endSec, seg.endSec), 0, durationSec)
    last.intensity = Math.max(last.intensity, seg.intensity)

    if (!last.label && seg.label) {
      last.label = seg.label
    }
  }

  return out
}