KotenOCR: 近代OCRの検出重複を解消する（NMS追加とクラスフィルタリング）

// DEIMDetector.swift — postprocess() の末尾

// Sort by score descending
let sorted = detections.sorted { $0.score > $1.score }

// Apply NMS to remove overlapping detections
let nmsResult = applyNMS(sorted, iouThreshold: iouThreshold)

return Array(nmsResult.prefix(maxDetections))

// DEIMDetector.swift

/// Apply Non-Maximum Suppression to remove overlapping detections.
private func applyNMS(_ detections: [Detection], iouThreshold: Float) -> [Detection] {
    var kept: [Detection] = []

    for det in detections {
        var shouldKeep = true
        for existing in kept {
            if computeIoU(det.box, existing.box) > iouThreshold {
                shouldKeep = false
                break
            }
        }
        if shouldKeep {
            kept.append(det)
        }
    }

    return kept
}

/// Compute Intersection over Union between two boxes [x1, y1, x2, y2].
private func computeIoU(_ a: [Int], _ b: [Int]) -> Float {
    let x1 = max(a[0], b[0])
    let y1 = max(a[1], b[1])
    let x2 = min(a[2], b[2])
    let y2 = min(a[3], b[3])

    let interW = max(0, x2 - x1)
    let interH = max(0, y2 - y1)
    let interArea = Float(interW * interH)

    let areaA = Float((a[2] - a[0]) * (a[3] - a[1]))
    let areaB = Float((b[2] - b[0]) * (b[3] - b[1]))
    let unionArea = areaA + areaB - interArea

    guard unionArea > 0 else { return 0 }
    return interArea / unionArea
}

// OCREngine.swift — processNDL()

// Step 1: Layout detection
let allDetections = try await Task.detached(priority: .userInitiated) {
    try detector.detect(image: image)
}.value

// Filter to line_* classes only (text_block and block_* are structural, not for OCR)
let detections = allDetections.filter { $0.className.hasPrefix("line_") }

// OCREngine.swift — loadNDLModels()

self.ndlDetector = try DEIMDetector(
    env: env, modelPath: detModelPath, configPath: configPath,
    scoreThreshold: 0.2, confThreshold: 0.25,
    iouThreshold: 0.2, maxDetections: 100
)

// DEIMDetector.swift

init(env: ORTEnv, modelPath: String, configPath: String,
     scoreThreshold: Float = 0.2, confThreshold: Float = 0.25,
     iouThreshold: Float = 0.2, maxDetections: Int = 100) throws {