{"parentUuid":null,"isSidechain":true,"promptId":"24f31705-de1b-46a9-a683-c7824fe9b1a0","agentId":"a6799a7bb08167c99","type":"user","message":{"role":"user","content":"/home/lucas/Android-app/ClearCam 의 현재 미커밋 변경을 리뷰(읽기 전용, 수정 금지). Android 문서 스캐너. 빌드+테스트 exit=0.\n\n변경: 자체 학습한 문서 세그멘테이션 ONNX 모델(docseg.onnx, 2MB, TinyUNet, 입력 1x3x128x128 /255, 출력 1x1x128x128 logit)을 검출 파이프라인에 **게이트된 후보 하나로만** 추가. 기존 DocAligner 검출은 그대로 유지. 목적: 야간·손에 든 난조건에서 DocAligner가 실패할 때 보완(단, 마스크가 깨끗할 때만 사용해 오검출 방지).\n\n핵심 파일:\n1. `DocSegDetector.kt`(신규) — ONNX 추론 → sigmoid 확률맵 → 이진마스크 → 게이트(양성비율 0.05~0.80, 최대윤곽 solidity≥0.80, 면적≥4%) 통과 시 minAreaRect 4코너를 원본 비트맵 좌표로 스케일·order4Points. 불량이면 null. ORT 세션 @Synchronized, OnnxTensor/result .use로 닫음.\n2. `CornerCandidates.kt` — DocSegDetector.isReady면 detect 호출, 결과를 EdgeSnapper.refine로 스냅 후 \"AI 영역 검출\" 후보로 추가. 이후 기존 rankByEdgeSupport(edge-support 점수)로 정렬해 best 자동 선택.\n3. `ClearCamApp.kt` — 앱 시작 백그라운드 스레드에서 DocSegDetector.init 추가.\n4. assets/docseg.onnx 추가, versionCode 97→98.\n\n검토 관점(중요):\n- **안전성(회귀 위험)**: 새 후보가 \"추가\"만 되고 기존 후보를 안 지우는지. 게이트가 불량 마스크를 제대로 버리는지(과분할/노이즈 → null). 게이트 통과한 나쁜 후보가 edge-support 랭킹에서 기존 좋은 후보를 밀어낼 위험? (rankByEdgeSupport는 실제 종이 경계 지지도로 정렬하므로, 마스크가 종이 경계에 안 맞으면 자동 후순위 — 이 안전망이 작동하는지 확인)\n- **DocSegDetector 메모리/리소스**: OpenCV Mat(mask, contours, c2f, hier) 전부 release 되는지(early-return 경로 포함). OnnxTensor/OrtSession.run 결과 .use 닫힘. preprocess의 resized 비트맵 recycle. cornersFromMask의 모든 return 경로에서 Mat 누수 없는지.\n- **추론 정확성**: 출력 [1][1][128][128] 캐스팅, sigmoid, 마스크 좌표→원본 스케일(sx,sy), order4Points 호출 정확성.\n- **초기화 경합**: isReady 체크, @Synchronized, 백그라운드 init 중 detect 호출 시 안전(isReady=false면 스킵).\n- **성능**: detect가 편집화면 autoDetect(Dispatchers.Default)에서 호출됨 — 128px 추론은 가벼움. 배치 ScanPipeline에서도 CornerCandidates.detect 경유로 호출됨(장당 1회 추가 추론) — 허용 범위인지.\n- 파일 300줄 제한.\n\n발견 문제를 심각도(blocker/major/minor)+파일:라인으로. 없으면 \"통과\"."},"uuid":"e79d7b43-a8b5-4e7b-a331-8a9347e69779","timestamp":"2026-06-12T04:56:05.357Z","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"e79d7b43-a8b5-4e7b-a331-8a9347e69779","isSidechain":true,"promptId":"24f31705-de1b-46a9-a683-c7824fe9b1a0","agentId":"a6799a7bb08167c99","type":"user","message":{"role":"user","content":[{"type":"text","text":"<command-message>review</command-message>\n<command-name>review</command-name>\n<skill-format>true</skill-format>"},{"type":"text","text":"\n      You are an expert code reviewer. Follow these steps:\n\n      1. If no PR number is provided in the args, run `gh pr list` to show open PRs\n      2. If a PR number is provided, run `gh pr view <number> --json title,body,author,baseRefName,headRefName,state,additions,deletions,changedFiles,labels` to get PR details\n      3. Run `gh pr diff <number>` to get the diff\n      4. Analyze the changes and provide a thorough code review that includes:\n         - Overview of what the PR does\n         - Analysis of code quality and style\n         - Specific suggestions for improvements\n         - Any potential issues or risks\n\n      Keep your review concise but thorough. Focus on:\n      - Code correctness\n      - Following project conventions\n      - Performance implications\n      - Test coverage\n      - Security considerations\n\n      Format your review with clear sections and bullet points.\n\n      PR number: \n    "}]},"isMeta":true,"uuid":"25720ecf-ba34-4014-9bbc-a84dc7c1943f","timestamp":"2026-06-12T04:56:05.364Z","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
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{"parentUuid":"49a8d6ba-d439-485e-9eb2-88a52b3c7e25","isSidechain":true,"promptId":"24f31705-de1b-46a9-a683-c7824fe9b1a0","agentId":"a6799a7bb08167c99","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SqEXcLi5pKASQKbgFrJA1U","type":"tool_result","content":"On branch main\nYour branch is ahead of 'origin/main' by 13 commits.\n  (use \"git push\" to publish your local commits)\n\nChanges not staged for commit:\n  (use \"git add <file>...\" to update what will be committed)\n  (use \"git restore <file>...\" to discard changes in working directory)\n\tmodified:   app/build.gradle.kts\n\tmodified:   app/src/main/java/cc/lucasai/clearcam/ClearCamApp.kt\n\tmodified:   app/src/main/java/cc/lucasai/clearcam/editor/CornerCandidates.kt\n\nUntracked files:\n  (use \"git add <file>...\" to include in what will be committed)\n\tapp/src/main/assets/docseg.onnx\n\tapp/src/main/java/cc/lucasai/clearcam/editor/DocSegDetector.kt\n\nno changes added to commit (use \"git add\" and/or \"git commit -a\")\n app/build.gradle.kts                                             | 4 ++--\n app/src/main/java/cc/lucasai/clearcam/ClearCamApp.kt             | 2 ++\n app/src/main/java/cc/lucasai/clearcam/editor/CornerCandidates.kt | 9 +++++++++\n 3 files changed, 13 insertions(+), 2 deletions(-)","is_error":false}]},"uuid":"3240d69f-8e6c-43c6-9bfa-45de8d143d42","timestamp":"2026-06-12T04:56:09.297Z","sourceToolAssistantUUID":"49a8d6ba-d439-485e-9eb2-88a52b3c7e25","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
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{"parentUuid":"ed241cfc-fa69-4883-a431-063987326ad5","isSidechain":true,"promptId":"24f31705-de1b-46a9-a683-c7824fe9b1a0","agentId":"a6799a7bb08167c99","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01WktdfwoDAugmAicqec3aQp","type":"tool_result","content":"1\t// 자체 학습 문서 세그멘테이션 모델(ONNX). 문서 영역 마스크를 내고 최대 윤곽에서 4코너를 뽑는다.\n2\t// 마스크가 깨끗할 때만(게이트 통과) 후보로 반환 — 과분할/노이즈 마스크는 버려 오검출을 막는다.\n3\tpackage cc.lucasai.clearcam.editor\n4\t\n5\timport ai.onnxruntime.OnnxTensor\n6\timport ai.onnxruntime.OrtEnvironment\n7\timport ai.onnxruntime.OrtSession\n8\timport android.content.Context\n9\timport android.graphics.Bitmap\n10\timport android.util.Log\n11\timport org.opencv.android.Utils\n12\timport org.opencv.core.Mat\n13\timport org.opencv.core.MatOfPoint\n14\timport org.opencv.core.MatOfPoint2f\n15\timport org.opencv.core.Point\n16\timport org.opencv.imgproc.Imgproc\n17\timport java.nio.FloatBuffer\n18\timport java.util.Collections\n19\t\n20\tobject DocSegDetector {\n21\t\n22\t    private const val MODEL_ASSET = \"docseg.onnx\"\n23\t    private const val IN = 128\n24\t    private const val INPUT_NAME = \"img\"\n25\t\n26\t    // 게이트: 마스크 양성비율이 이 범위를 벗어나면(너무 작거나 화면 대부분) 버린다.\n27\t    private const val MIN_POS = 0.05\n28\t    private const val MAX_POS = 0.80\n29\t    // 최대 윤곽이 자기 외접 사각형을 이만큼 못 채우면(얼룩·비사각형) 버린다.\n30\t    private const val MIN_SOLIDITY = 0.80\n31\t\n32\t    private var env: OrtEnvironment? = null\n33\t    private var session: OrtSession? = null\n34\t    val isReady: Boolean get() = session != null\n35\t\n36\t    @Synchronized\n37\t    fun init(context: Context) {\n38\t        if (session != null) return\n39\t        try {\n40\t            val bytes = context.assets.open(MODEL_ASSET).use { it.readBytes() }\n41\t            val e = OrtEnvironment.getEnvironment()\n42\t            session = e.createSession(bytes, OrtSession.SessionOptions())\n43\t            env = e\n44\t        } catch (t: Throwable) {\n45\t            Log.e(\"ClearCam\", \"DocSeg 모델 로드 실패\", t)\n46\t        }\n47\t    }\n48\t\n49\t    // 비트맵에서 문서 4코너(비트맵 좌표 TL,TR,BR,BL)를 반환. 마스크가 불량이면 null.\n50\t    @Synchronized\n51\t    fun detect(bitmap: Bitmap): FloatArray? {\n52\t        val s = session ?: return null\n53\t        val e = env ?: return null\n54\t        val prob: FloatArray = try {\n55\t            OnnxTensor.createTensor(e, preprocess(bitmap), longArrayOf(1, 3, IN.toLong(), IN.toLong())).use { t ->\n56\t                s.run(Collections.singletonMap(INPUT_NAME, t)).use { res ->\n57\t                    @Suppress(\"UNCHECKED_CAST\")\n58\t                    val out = res[0].value as Array<Array<Array<FloatArray>>> // [1][1][128][128]\n59\t                    flatten(out[0][0])\n60\t                }\n61\t            }\n62\t        } catch (t: Throwable) {\n63\t            Log.e(\"ClearCam\", \"DocSeg 추론 오류\", t); return null\n64\t        }\n65\t        return cornersFromMask(prob, bitmap.width, bitmap.height)\n66\t    }\n67\t\n68\t    // 128x128 RGB, NCHW, /255. (sigmoid는 후처리에서)\n69\t    private fun preprocess(bitmap: Bitmap): FloatBuffer {\n70\t        val resized = Bitmap.createScaledBitmap(bitmap, IN, IN, true)\n71\t        val px = IntArray(IN * IN); resized.getPixels(px, 0, IN, 0, 0, IN, IN)\n72\t        if (resized != bitmap) resized.recycle()\n73\t        val area = IN * IN; val arr = FloatArray(3 * area)\n74\t        for (i in 0 until area) {\n75\t            val p = px[i]\n76\t            arr[i] = ((p shr 16) and 0xFF) / 255f\n77\t            arr[area + i] = ((p shr 8) and 0xFF) / 255f\n78\t            arr[2 * area + i] = (p and 0xFF) / 255f\n79\t        }\n80\t        return FloatBuffer.wrap(arr)\n81\t    }\n82\t\n83\t    private fun flatten(plane: Array<FloatArray>): FloatArray {\n84\t        val out = FloatArray(IN * IN)\n85\t        for (y in 0 until IN) for (x in 0 until IN) {\n86\t            val v = plane[y][x]; out[y * IN + x] = 1f / (1f + Math.exp(-v.toDouble()).toFloat()) // sigmoid\n87\t        }\n88\t        return out\n89\t    }\n90\t\n91\t    // 확률맵 → 이진 마스크 → 최대 윤곽 → 게이트 통과 시 4코너(원본 비트맵 좌표).\n92\t    private fun cornersFromMask(prob: FloatArray, w: Int, h: Int): FloatArray? {\n93\t        var pos = 0\n94\t        val mask = Mat(IN, IN, org.opencv.core.CvType.CV_8U)\n95\t        val buf = ByteArray(IN * IN)\n96\t        for (i in prob.indices) { val on = prob[i] > 0.5f; if (on) pos++; buf[i] = if (on) 255.toByte() else 0 }\n97\t        mask.put(0, 0, buf)\n98\t        val frac = pos.toDouble() / (IN * IN)\n99\t        if (frac < MIN_POS || frac > MAX_POS) { mask.release(); return null }\n100\t        Imgproc.morphologyEx(mask, mask, Imgproc.MORPH_OPEN, Mat.ones(3, 3, org.opencv.core.CvType.CV_8U))\n101\t        Imgproc.morphologyEx(mask, mask, Imgproc.MORPH_CLOSE, Mat.ones(5, 5, org.opencv.core.CvType.CV_8U))\n102\t        val contours = ArrayList<MatOfPoint>(); val hier = Mat()\n103\t        Imgproc.findContours(mask, contours, hier, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE)\n104\t        hier.release(); mask.release()\n105\t        val c = contours.maxByOrNull { Imgproc.contourArea(it) } ?: run { contours.forEach { it.release() }; return null }\n106\t        val area = Imgproc.contourArea(c)\n107\t        val c2f = MatOfPoint2f(*c.toArray())\n108\t        val rect = Imgproc.minAreaRect(c2f)\n109\t        val rectArea = rect.size.width * rect.size.height\n110\t        val solidity = if (rectArea > 0) area / rectArea else 0.0\n111\t        val out: FloatArray? = if (solidity >= MIN_SOLIDITY && rectArea > IN * IN * 0.04) {\n112\t            val box = arrayOf(Point(), Point(), Point(), Point()); rect.points(box)\n113\t            val sx = w.toFloat() / IN; val sy = h.toFloat() / IN\n114\t            DocumentDetector.order4Points(\n115\t                floatArrayOf(\n116\t                    box[0].x.toFloat() * sx, box[0].y.toFloat() * sy, box[1].x.toFloat() * sx, box[1].y.toFloat() * sy,\n117\t                    box[2].x.toFloat() * sx, box[2].y.toFloat() * sy, box[3].x.toFloat() * sx, box[3].y.toFloat() * sy,\n118\t                ),\n119\t            )\n120\t        } else {\n121\t            null\n122\t        }\n123\t        c2f.release(); contours.forEach { it.release() }\n124\t        return out\n125\t    }\n126\t}\n127\t"}]},"uuid":"57594d1c-13ed-4e69-bd88-e2608bee4d5e","timestamp":"2026-06-12T04:56:12.558Z","sourceToolAssistantUUID":"ed241cfc-fa69-4883-a431-063987326ad5","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
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{"parentUuid":"4f485e9a-09cc-4931-b796-11ed7fd1f2ab","isSidechain":true,"promptId":"24f31705-de1b-46a9-a683-c7824fe9b1a0","agentId":"a6799a7bb08167c99","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017F6w9JtTU9zKHLD2FdD5yL","type":"tool_result","content":"1\t// 문서 코너 후보 생성: AI(DocAligner+엣지스냅)·OpenCV 윤곽 검출 + 전체 헬퍼.\n2\tpackage cc.lucasai.clearcam.editor\n3\t\n4\timport android.graphics.Bitmap\n5\timport android.util.Log\n6\timport cc.lucasai.clearcam.ClearCamApp\n7\t\n8\tobject CornerCandidates {\n9\t\n10\t    // 두 엔진 결과를 (라벨, 비트맵좌표 코너) 후보 목록으로 반환한다(없으면 빈 목록).\n11\t    fun detect(bitmap: Bitmap): List<Pair<String, FloatArray>> {\n12\t        val list = mutableListOf<Pair<String, FloatArray>>()\n13\t        if (DocAlignerDetector.isReady) {\n14\t            try {\n15\t                val onOrig = DocAlignerDetector.detect(bitmap)\n16\t                if (onOrig != null) {\n17\t                    // ML 코너를 실제 종이 경계선에 스냅한다. 인셋은 두지 않는다 —\n18\t                    // 내용이 종이 가장자리까지 찬 문서(표 양식 등)에서 글자가 잘리기 때문.\n19\t                    list.add(\"AI 검출\" to EdgeSnapper.refine(bitmap, onOrig))\n20\t                } else {\n21\t                    // 어두운 사진은 모델이 문서를 못 본다(실측: peak 0.001). 밝게 보정한 사본으로\n22\t                    // 재검출하고 좌표는 같은 크기의 원본에 그대로 적용(보정본은 검출용일 뿐).\n23\t                    // 반전·CLAHE는 효과 없거나 역효과라 밝기 보정만 쓴다.\n24\t                    val bright = ImageFilters.apply(bitmap, ImageFilters.brightnessContrastMatrix(90f, 1.4f))\n25\t                    val onBright = try { DocAlignerDetector.detect(bright) } catch (e: Exception) { null }\n26\t                    bright.recycle()\n27\t                    if (onBright != null) {\n28\t                        list.add(\"AI(밝게) 검출\" to EdgeSnapper.refine(bitmap, onBright))\n29\t                    } else if (ClearCamApp.openCvReady) {\n30\t                        // 저대비 배경(흰 종이 위 밝은 바닥): 대비 스트레치 후 재검출.\n31\t                        val str = ImageFilters.contrastStretch(bitmap)\n32\t                        val onStr = try { DocAlignerDetector.detect(str) } catch (e: Exception) { null }\n33\t                        str.recycle()\n34\t                        if (onStr != null) {\n35\t                            list.add(\"AI(대비) 검출\" to EdgeSnapper.refine(bitmap, onStr))\n36\t                        }\n37\t                    }\n38\t                }\n39\t            } catch (e: Exception) { Log.e(\"ClearCam\", \"AI 검출 오류\", e) }\n40\t        }\n41\t        if (ClearCamApp.openCvReady) {\n42\t            try {\n43\t                DocumentDetector.detect(bitmap)?.let { list.add(\"윤곽 검출\" to it) }\n44\t            } catch (e: Exception) { Log.e(\"ClearCam\", \"윤곽 검출 오류\", e) }\n45\t        }\n46\t        // 자체 세그멘테이션 모델(게이트 통과 시에만 후보 추가). 마스크 코너를 실제 경계에 스냅.\n47\t        if (DocSegDetector.isReady) {\n48\t            try {\n49\t                DocSegDetector.detect(bitmap)?.let {\n50\t                    val snapped = if (ClearCamApp.openCvReady) EdgeSnapper.refine(bitmap, it) else it\n51\t                    list.add(\"AI 영역 검출\" to snapped)\n52\t                }\n53\t            } catch (e: Exception) { Log.e(\"ClearCam\", \"DocSeg 검출 오류\", e) }\n54\t        }\n55\t        return rankByEdgeSupport(bitmap, list)\n56\t    }\n57\t\n58\t    // 후보를 실제 종이 윤곽 지지도(edge-support)로 정렬해 \"사람이 보듯 맞는\" 것을 1번으로.\n59\t    // 동점·OpenCV 미준비 시 원래 순서(AI 우선)를 유지한다(안정 정렬).\n60\t    private fun rankByEdgeSupport(\n61\t        bitmap: Bitmap,\n62\t        list: List<Pair<String, FloatArray>>,\n63\t    ): List<Pair<String, FloatArray>> {\n64\t        if (list.size < 2 || !ClearCamApp.openCvReady) return list\n65\t        return try {\n66\t            list.map { it to EdgeSupport.score(bitmap, it.second) }\n67\t                .withIndex()\n68\t                .sortedWith(compareByDescending<IndexedValue<Pair<Pair<String, FloatArray>, Double>>> {\n69\t                    it.value.second\n70\t                }.thenBy { it.index })\n71\t                .map { it.value.first }\n72\t        } catch (e: Exception) {\n73\t            Log.e(\"ClearCam\", \"edge-support 정렬 오류\", e); list\n74\t        }\n75\t    }\n76\t\n77\t    // 이미지 전체 꼭짓점(크롭 없음) TL,TR,BR,BL.\n78\t    fun full(bitmap: Bitmap): FloatArray {\n79\t        val w = bitmap.width.toFloat(); val h = bitmap.height.toFloat()\n80\t        return floatArrayOf(0f, 0f, w, 0f, w, h, 0f, h)\n81\t    }\n82\t}\n83\t"}]},"uuid":"bd4bcdb9-7f44-46f3-9d8a-7e08d61ba83e","timestamp":"2026-06-12T04:56:12.943Z","sourceToolAssistantUUID":"4f485e9a-09cc-4931-b796-11ed7fd1f2ab","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
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{"parentUuid":"644fb4c5-dd9e-4f78-ae6a-aafdb201cc0c","isSidechain":true,"promptId":"24f31705-de1b-46a9-a683-c7824fe9b1a0","agentId":"a6799a7bb08167c99","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01BWEof1fwE5j8edN4kdTLcu","type":"tool_result","content":"1\t// OpenCV(필터용)와 DocAligner ONNX 검출 모델을 앱 시작 시 한 번 초기화한다.\n2\tpackage cc.lucasai.clearcam\n3\t\n4\timport android.app.Application\n5\timport cc.lucasai.clearcam.editor.DocAlignerDetector\n6\timport cc.lucasai.clearcam.editor.DocSegDetector\n7\timport cc.lucasai.clearcam.editor.UvDocDewarper\n8\timport org.opencv.android.OpenCVLoader\n9\t\n10\tclass ClearCamApp : Application() {\n11\t\n12\t    override fun onCreate() {\n13\t        super.onCreate()\n14\t        openCvReady = OpenCVLoader.initLocal()\n15\t        // 모델 로드는 무거우므로 백그라운드에서 준비한다(검출 + UVDoc 평면화).\n16\t        Thread {\n17\t            DocAlignerDetector.init(applicationContext)\n18\t            DocSegDetector.init(applicationContext)\n19\t            UvDocDewarper.init(applicationContext)\n20\t        }.start()\n21\t    }\n22\t\n23\t    companion object {\n24\t        // OpenCV 사용 가능 여부. 실패 시 자동검출·적응형 이진화를 건너뛴다.\n25\t        @JvmStatic\n26\t        var openCvReady = false\n27\t            internal set\n28\t    }\n29\t}\n30\t"}]},"uuid":"addfbfd1-e0f0-45ae-946e-23fceda7d7c3","timestamp":"2026-06-12T04:56:23.632Z","sourceToolAssistantUUID":"644fb4c5-dd9e-4f78-ae6a-aafdb201cc0c","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"addfbfd1-e0f0-45ae-946e-23fceda7d7c3","isSidechain":true,"agentId":"a6799a7bb08167c99","message":{"model":"claude-sonnet-4-6","id":"msg_014VQb9X8dxYPmMa7TBW79Vm","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Tb5pvK4wCvgYGEYkbmjupg","name":"Read","input":{"file_path":"/home/lucas/Android-app/ClearCam/app/build.gradle.kts"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":4282,"cache_read_input_tokens":24675,"cache_creation":{"ephemeral_5m_input_tokens":4282,"ephemeral_1h_input_tokens":0},"output_tokens":38,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CbxmgeXds94uSVozVantr","attributionAgent":"reviewer","type":"assistant","uuid":"59632c05-b0c1-428f-b69c-b3b7b84c342a","timestamp":"2026-06-12T04:56:23.827Z","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"59632c05-b0c1-428f-b69c-b3b7b84c342a","isSidechain":true,"promptId":"24f31705-de1b-46a9-a683-c7824fe9b1a0","agentId":"a6799a7bb08167c99","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Tb5pvK4wCvgYGEYkbmjupg","type":"tool_result","content":"1\timport java.util.Properties\n2\t\n3\tplugins {\n4\t    id(\"com.android.application\")\n5\t    id(\"org.jetbrains.kotlin.android\")\n6\t}\n7\t\n8\t// 릴리스 서명 정보(keystore.properties, 미커밋).\n9\tval keystoreProps = Properties().apply {\n10\t    val f = rootProject.file(\"keystore.properties\")\n11\t    if (f.exists()) f.inputStream().use { load(it) }\n12\t}\n13\t\n14\tandroid {\n15\t    namespace = \"cc.lucasai.clearcam\"\n16\t    compileSdk = 34\n17\t\n18\t    defaultConfig {\n19\t        applicationId = \"cc.lucasai.clearcam\"\n20\t        // minSdk 29: Android 10+의 MediaStore로 저장 권한 없이 동작 → 런타임 권한 0개 유지.\n21\t        minSdk = 29\n22\t        targetSdk = 34\n23\t        versionCode = 98\n24\t        versionName = \"1.097\"\n25\t        ndk { abiFilters += \"arm64-v8a\" }\n26\t    }\n27\t\n28\t    signingConfigs {\n29\t        create(\"release\") {\n30\t            if (keystoreProps.containsKey(\"storeFile\")) {\n31\t                storeFile = rootProject.file(keystoreProps.getProperty(\"storeFile\"))\n32\t                storePassword = keystoreProps.getProperty(\"storePassword\")\n33\t                keyAlias = keystoreProps.getProperty(\"keyAlias\")\n34\t                keyPassword = keystoreProps.getProperty(\"keyPassword\")\n35\t            }\n36\t        }\n37\t    }\n38\t\n39\t    buildTypes {\n40\t        release {\n41\t            isMinifyEnabled = true\n42\t            isShrinkResources = true\n43\t            proguardFiles(\n44\t                getDefaultProguardFile(\"proguard-android-optimize.txt\"),\n45\t                \"proguard-rules.pro\",\n46\t            )\n47\t            if (keystoreProps.containsKey(\"storeFile\")) {\n48\t                signingConfig = signingConfigs.getByName(\"release\")\n49\t            }\n50\t        }\n51\t    }\n52\t\n53\t    compileOptions {\n54\t        sourceCompatibility = JavaVersion.VERSION_17\n55\t        targetCompatibility = JavaVersion.VERSION_17\n56\t    }\n57\t\n58\t    kotlinOptions {\n59\t        jvmTarget = \"17\"\n60\t    }\n61\t\n62\t    buildFeatures {\n63\t        viewBinding = true\n64\t        buildConfig = true\n65\t    }\n66\t}\n67\t\n68\tdependencies {\n69\t    implementation(\"androidx.core:core-ktx:1.13.1\")\n70\t    implementation(\"androidx.core:core-splashscreen:1.0.1\")\n71\t    implementation(\"androidx.appcompat:appcompat:1.7.0\")\n72\t    implementation(\"com.google.android.material:material:1.12.0\")\n73\t    implementation(\"androidx.lifecycle:lifecycle-runtime-ktx:2.7.0\")\n74\t    implementation(\"androidx.recyclerview:recyclerview:1.3.2\")\n75\t    implementation(\"androidx.viewpager2:viewpager2:1.1.0\")\n76\t    implementation(\"androidx.activity:activity-ktx:1.9.0\")\n77\t    implementation(\"org.jetbrains.kotlinx:kotlinx-coroutines-android:1.8.1\")\n78\t\n79\t    // 자체 카메라(CameraX) + 경계검출/보정(OpenCV)\n80\t    val cameraxVersion = \"1.3.4\"\n81\t    implementation(\"androidx.camera:camera-core:$cameraxVersion\")\n82\t    implementation(\"androidx.camera:camera-camera2:$cameraxVersion\")\n83\t    implementation(\"androidx.camera:camera-lifecycle:$cameraxVersion\")\n84\t    implementation(\"androidx.camera:camera-view:$cameraxVersion\")\n85\t    implementation(\"org.opencv:opencv:4.11.0\")\n86\t\n87\t    // 문서 4코너 검출 딥러닝 모델(DocAligner ONNX) 추론 엔진. OpenCV 고전 검출 대체.\n88\t    implementation(\"com.microsoft.onnxruntime:onnxruntime-android:1.26.0\")\n89\t\n90\t    // ML Kit 한국어 OCR (문서 스캐너는 자체 파이프라인으로 대체)\n91\t    implementation(\"com.google.mlkit:text-recognition:16.0.1\")\n92\t    implementation(\"com.google.mlkit:text-recognition-korean:16.0.1\")\n93\t    implementation(\"org.jetbrains.kotlinx:kotlinx-coroutines-play-services:1.8.1\")\n94\t\n95\t    testImplementation(\"junit:junit:4.13.2\")\n96\t}\n97\t"}]},"uuid":"1157beb6-be1b-496a-b919-dc434ca5a487","timestamp":"2026-06-12T04:56:23.891Z","sourceToolAssistantUUID":"59632c05-b0c1-428f-b69c-b3b7b84c342a","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"1157beb6-be1b-496a-b919-dc434ca5a487","isSidechain":true,"agentId":"a6799a7bb08167c99","message":{"model":"claude-sonnet-4-6","id":"msg_014ETtuyUfGJNWxQVe2KEXp6","type":"message","role":"assistant","content":[{"type":"text","text":"Now let me examine the existing related files for context."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1894,"cache_read_input_tokens":28957,"cache_creation":{"ephemeral_5m_input_tokens":1894,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CbxmhU1Q6HC3F5sqAXZWa","attributionAgent":"reviewer","type":"assistant","uuid":"128d7752-74c1-4f69-a73c-9a1cf1b0185d","timestamp":"2026-06-12T04:56:26.436Z","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"128d7752-74c1-4f69-a73c-9a1cf1b0185d","isSidechain":true,"agentId":"a6799a7bb08167c99","message":{"model":"claude-sonnet-4-6","id":"msg_014ETtuyUfGJNWxQVe2KEXp6","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_015qUYfYShpDMHzG8bxzFWpP","name":"Bash","input":{"command":"find /home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor -type f | sort","description":"List all editor package files"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1894,"cache_read_input_tokens":28957,"cache_creation":{"ephemeral_5m_input_tokens":1894,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CbxmhU1Q6HC3F5sqAXZWa","attributionAgent":"reviewer","type":"assistant","uuid":"717ca801-ed38-4d57-9acb-9d66c9c4ef9d","timestamp":"2026-06-12T04:56:27.691Z","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"717ca801-ed38-4d57-9acb-9d66c9c4ef9d","isSidechain":true,"promptId":"24f31705-de1b-46a9-a683-c7824fe9b1a0","agentId":"a6799a7bb08167c99","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015qUYfYShpDMHzG8bxzFWpP","type":"tool_result","content":"/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/AutoTrim.kt\n/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/CornerCandidates.kt\n/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/CornerOverlayView.kt\n/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/DocAlignerDetector.kt\n/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/DocSegDetector.kt\n/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/DocumentDetector.kt\n/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/EdgeSnapper.kt\n/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/EdgeSupport.kt\n/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/EditorActivity.kt\n/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/EditorFilters.kt\n/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/EditorFinish.kt\n/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/EditorFlatten.kt\n/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/EditorIo.kt\n/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/FilterPreviews.kt\n/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/FilterStripAdapter.kt\n/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/IdCardComposer.kt\n/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/ImageFilters.kt\n/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/LoupeView.kt\n/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/PerspectiveTransformer.kt\n/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/ProFilters.kt\n/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/UvDocDewarper.kt","is_error":false}]},"uuid":"ae361758-0cae-41cf-b48f-d93ad516b5c7","timestamp":"2026-06-12T04:56:27.948Z","sourceToolAssistantUUID":"717ca801-ed38-4d57-9acb-9d66c9c4ef9d","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"ae361758-0cae-41cf-b48f-d93ad516b5c7","isSidechain":true,"agentId":"a6799a7bb08167c99","message":{"model":"claude-sonnet-4-6","id":"msg_014ETtuyUfGJNWxQVe2KEXp6","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Qbt52w3tcbctF1aGHifriM","name":"Read","input":{"file_path":"/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/DocumentDetector.kt"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1894,"cache_read_input_tokens":28957,"cache_creation":{"ephemeral_5m_input_tokens":1894,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CbxmhU1Q6HC3F5sqAXZWa","attributionAgent":"reviewer","type":"assistant","uuid":"5e070d9b-e3bb-4d9c-8165-e62633cfac8e","timestamp":"2026-06-12T04:56:28.044Z","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"5e070d9b-e3bb-4d9c-8165-e62633cfac8e","isSidechain":true,"promptId":"24f31705-de1b-46a9-a683-c7824fe9b1a0","agentId":"a6799a7bb08167c99","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Qbt52w3tcbctF1aGHifriM","type":"tool_result","content":"1\t// OpenCV 다중 단서(채도·밝기·엣지) 검출. 배경(컬러/어두움/밝음)에 따라 맞는 단서가 이긴다.\n2\t// 각 단서로 후보 사각형을 뽑아 \"가장 사각형다운\" 것을 고른다.\n3\tpackage cc.lucasai.clearcam.editor\n4\t\n5\timport android.graphics.Bitmap\n6\timport org.opencv.android.Utils\n7\timport org.opencv.core.Core\n8\timport org.opencv.core.Mat\n9\timport org.opencv.core.MatOfPoint\n10\timport org.opencv.core.MatOfPoint2f\n11\timport org.opencv.core.Point\n12\timport org.opencv.core.Size\n13\timport org.opencv.imgproc.Imgproc\n14\t\n15\tobject DocumentDetector {\n16\t\n17\t    // 검출된 사각형(비트맵 좌표 8 floats)과 사각형다움 점수(0~1, 높을수록 정확).\n18\t    data class Detection(val corners: FloatArray, val score: Double)\n19\t\n20\t    // lazy: object 초기화 시 OpenCV 네이티브를 호출하지 않도록(JVM 단위 테스트 보호).\n21\t    private val k11 by lazy { Imgproc.getStructuringElement(Imgproc.MORPH_RECT, Size(11.0, 11.0)) }\n22\t    private val k25 by lazy { Imgproc.getStructuringElement(Imgproc.MORPH_RECT, Size(25.0, 25.0)) }\n23\t\n24\t    // 코너만 필요할 때(편집화면 자동검출). 점수가 필요하면 detectBest 사용.\n25\t    fun detect(bitmap: Bitmap): FloatArray? = detectBest(bitmap)?.corners\n26\t\n27\t    // 다운스케일 비트맵에서 최적 문서 사각형과 점수를 반환. 없으면 null.\n28\t    fun detectBest(bitmap: Bitmap): Detection? {\n29\t        val src = Mat(); Utils.bitmapToMat(bitmap, src)\n30\t        val rgb = Mat(); Imgproc.cvtColor(src, rgb, Imgproc.COLOR_RGBA2RGB)\n31\t        val hsv = Mat(); Imgproc.cvtColor(rgb, hsv, Imgproc.COLOR_RGB2HSV)\n32\t        val channels = ArrayList<Mat>(); Core.split(hsv, channels)\n33\t        val gray = Mat(); Imgproc.cvtColor(rgb, gray, Imgproc.COLOR_RGB2GRAY)\n34\t        val imageArea = bitmap.width.toDouble() * bitmap.height\n35\t\n36\t        var best: FloatArray? = null\n37\t        var bestScore = 0.6\n38\t        for (mask in listOf(saturationMask(channels[1]), valueMask(channels[2]), edgeMask(gray))) {\n39\t            for (quad in quadsFromMask(mask, imageArea)) {\n40\t                val s = score(quad, imageArea)\n41\t                if (s > bestScore) { bestScore = s; best = quad }\n42\t            }\n43\t            mask.release()\n44\t        }\n45\t\n46\t        channels.forEach { it.release() }\n47\t        src.release(); rgb.release(); hsv.release(); gray.release()\n48\t        return best?.let { Detection(it, bestScore) } // quadsFromMask가 이미 order4Points 적용\n49\t    }\n50\t\n51\t    // 채도 낮음(무채색 종이) = 흰색. 유채색 배경(나무 등)에 강함.\n52\t    private fun saturationMask(sat: Mat): Mat {\n53\t        val m = Mat(); Imgproc.GaussianBlur(sat, m, Size(7.0, 7.0), 0.0)\n54\t        Imgproc.threshold(m, m, 0.0, 255.0, Imgproc.THRESH_BINARY_INV + Imgproc.THRESH_OTSU)\n55\t        return m\n56\t    }\n57\t\n58\t    // 밝음(밝은 종이) = 흰색. 어두운 배경(키보드 등)·옅은 배경에 강함.\n59\t    private fun valueMask(value: Mat): Mat {\n60\t        val m = Mat(); Imgproc.GaussianBlur(value, m, Size(7.0, 7.0), 0.0)\n61\t        Imgproc.threshold(m, m, 0.0, 255.0, Imgproc.THRESH_BINARY + Imgproc.THRESH_OTSU)\n62\t        return m\n63\t    }\n64\t\n65\t    // 물리적 테두리(엣지). 색·밝기와 무관, 종이 경계가 뚜렷할 때 강함.\n66\t    private fun edgeMask(gray: Mat): Mat {\n67\t        val m = Mat(); Imgproc.GaussianBlur(gray, m, Size(5.0, 5.0), 0.0)\n68\t        Imgproc.Canny(m, m, 40.0, 120.0)\n69\t        Imgproc.dilate(m, m, k11)\n70\t        Imgproc.morphologyEx(m, m, Imgproc.MORPH_CLOSE, k25)\n71\t        return m\n72\t    }\n73\t\n74\t    // 마스크에서 면적 상위 윤곽들의 사각형 후보(order4 적용)를 반환한다.\n75\t    private fun quadsFromMask(mask: Mat, imageArea: Double): List<FloatArray> {\n76\t        Imgproc.morphologyEx(mask, mask, Imgproc.MORPH_OPEN, k11)\n77\t        Imgproc.morphologyEx(mask, mask, Imgproc.MORPH_CLOSE, k11)\n78\t        val contours = ArrayList<MatOfPoint>()\n79\t        val hierarchy = Mat()\n80\t        Imgproc.findContours(mask, contours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE)\n81\t        hierarchy.release()\n82\t        val out = ArrayList<FloatArray>()\n83\t        for (c in contours.sortedByDescending { Imgproc.contourArea(it) }.take(3)) {\n84\t            if (Imgproc.contourArea(c) < imageArea * 0.15) continue\n85\t            val c2f = MatOfPoint2f(*c.toArray())\n86\t            val peri = Imgproc.arcLength(c2f, true)\n87\t            val approx = MatOfPoint2f()\n88\t            Imgproc.approxPolyDP(c2f, approx, 0.02 * peri, true)\n89\t            val pts = approx.toArray()\n90\t            val quad = if (pts.size == 4) {\n91\t                floatArrayOf(\n92\t                    pts[0].x.toFloat(), pts[0].y.toFloat(), pts[1].x.toFloat(), pts[1].y.toFloat(),\n93\t                    pts[2].x.toFloat(), pts[2].y.toFloat(), pts[3].x.toFloat(), pts[3].y.toFloat(),\n94\t                )\n95\t            } else {\n96\t                val box = arrayOf(Point(), Point(), Point(), Point())\n97\t                Imgproc.minAreaRect(c2f).points(box)\n98\t                floatArrayOf(\n99\t                    box[0].x.toFloat(), box[0].y.toFloat(), box[1].x.toFloat(), box[1].y.toFloat(),\n100\t                    box[2].x.toFloat(), box[2].y.toFloat(), box[3].x.toFloat(), box[3].y.toFloat(),\n101\t                )\n102\t            }\n103\t            out.add(order4Points(quad))\n104\t            c2f.release(); approx.release()\n105\t        }\n106\t        contours.forEach { it.release() }\n107\t        return out\n108\t    }\n109\t\n110\t    // 점수 = 사각형다움(폴리곤 면적 / 최소영역 사각형 면적). 면적 18~97%·볼록만 유효.\n111\t    private fun score(quad: FloatArray, imageArea: Double): Double {\n112\t        val a = polygonArea(quad)\n113\t        val ratio = a / imageArea\n114\t        if (ratio < 0.18 || ratio > 0.97) return -1.0\n115\t        if (!isConvex(quad)) return -1.0\n116\t        val m2f = MatOfPoint2f(\n117\t            Point(quad[0].toDouble(), quad[1].toDouble()), Point(quad[2].toDouble(), quad[3].toDouble()),\n118\t            Point(quad[4].toDouble(), quad[5].toDouble()), Point(quad[6].toDouble(), quad[7].toDouble()),\n119\t        )\n120\t        val rect = Imgproc.minAreaRect(m2f)\n121\t        m2f.release()\n122\t        val rectArea = rect.size.width * rect.size.height\n123\t        return if (rectArea > 0) a / rectArea else -1.0\n124\t    }\n125\t\n126\t    // 순수: 4점(8 floats)을 TL,TR,BR,BL로 정렬. 합 최소=TL, 합 최대=BR, 차(y-x) 최소=TR, 최대=BL.\n127\t    fun order4Points(pts: FloatArray): FloatArray {\n128\t        var tl = 0; var br = 0; var tr = 0; var bl = 0\n129\t        var minSum = Float.MAX_VALUE; var maxSum = -Float.MAX_VALUE\n130\t        var minDiff = Float.MAX_VALUE; var maxDiff = -Float.MAX_VALUE\n131\t        for (i in 0 until 4) {\n132\t            val x = pts[i * 2]; val y = pts[i * 2 + 1]\n133\t            val s = x + y; val d = y - x\n134\t            if (s < minSum) { minSum = s; tl = i }\n135\t            if (s > maxSum) { maxSum = s; br = i }\n136\t            if (d < minDiff) { minDiff = d; tr = i }\n137\t            if (d > maxDiff) { maxDiff = d; bl = i }\n138\t        }\n139\t        return floatArrayOf(\n140\t            pts[tl * 2], pts[tl * 2 + 1], pts[tr * 2], pts[tr * 2 + 1],\n141\t            pts[br * 2], pts[br * 2 + 1], pts[bl * 2], pts[bl * 2 + 1],\n142\t        )\n143\t    }\n144\t\n145\t    private fun polygonArea(q: FloatArray): Double {\n146\t        var a = 0.0\n147\t        for (i in 0 until 4) {\n148\t            val j = (i + 1) % 4\n149\t            a += q[i * 2].toDouble() * q[j * 2 + 1] - q[j * 2].toDouble() * q[i * 2 + 1]\n150\t        }\n151\t        return kotlin.math.abs(a) / 2.0\n152\t    }\n153\t\n154\t    private fun isConvex(q: FloatArray): Boolean {\n155\t        var sign = 0\n156\t        for (i in 0 until 4) {\n157\t            val cur = i * 2; val nxt = ((i + 1) % 4) * 2; val nn = ((i + 2) % 4) * 2\n158\t            val ax = q[nxt] - q[cur]; val ay = q[nxt + 1] - q[cur + 1]\n159\t            val bx = q[nn] - q[nxt]; val by = q[nn + 1] - q[nxt + 1]\n160\t            val cross = ax * by - ay * bx\n161\t            val s = if (cross > 0) 1 else if (cross < 0) -1 else 0\n162\t            if (s != 0) {\n163\t                if (sign == 0) sign = s else if (sign != s) return false\n164\t            }\n165\t        }\n166\t        return true\n167\t    }\n168\t}\n169\t"}]},"uuid":"ba43923c-3ca4-458d-9fa9-d80c4ad11fc8","timestamp":"2026-06-12T04:56:28.054Z","sourceToolAssistantUUID":"5e070d9b-e3bb-4d9c-8165-e62633cfac8e","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
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{"parentUuid":"ec5c80e6-286f-4a8c-8b97-cc96b887f3b1","isSidechain":true,"promptId":"24f31705-de1b-46a9-a683-c7824fe9b1a0","agentId":"a6799a7bb08167c99","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Mmtv89kJe2sqeiekXxStQx","type":"tool_result","content":"TEST-cc.lucasai.clearcam.editor.DocumentDetectorTest.xml: 1 tests, 0 failures, 0 errors\nTEST-cc.lucasai.clearcam.editor.EdgeSnapperTest.xml: 6 tests, 0 failures, 0 errors\nTEST-cc.lucasai.clearcam.editor.EdgeSupportTest.xml: 4 tests, 0 failures, 0 errors\nTEST-cc.lucasai.clearcam.editor.ImageFiltersTest.xml: 2 tests, 0 failures, 0 errors\nTEST-cc.lucasai.clearcam.editor.PerspectiveTransformerTest.xml: 3 tests, 0 failures, 0 errors\nTEST-cc.lucasai.clearcam.ocr.OcrTextJoinerTest.xml: 4 tests, 0 failures, 0 errors\nTEST-cc.lucasai.clearcam.save.MediaSaverNamingTest.xml: 2 tests, 0 failures, 0 errors","is_error":false}]},"uuid":"c99c0575-c1db-4516-96a5-ca4fc8d4a60d","timestamp":"2026-06-12T04:56:38.458Z","sourceToolAssistantUUID":"ec5c80e6-286f-4a8c-8b97-cc96b887f3b1","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
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{"parentUuid":"f57ad09b-2f33-48cf-ade8-dcda73bcdce7","isSidechain":true,"promptId":"24f31705-de1b-46a9-a683-c7824fe9b1a0","agentId":"a6799a7bb08167c99","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01A4PQXKb2r8vyPNSFqNEpvC","type":"tool_result","content":"1\t// DocAligner ONNX 모델로 문서 4코너를 검출한다(딥러닝). OpenCV 고전 검출을 대체.\n2\tpackage cc.lucasai.clearcam.editor\n3\t\n4\timport ai.onnxruntime.OnnxTensor\n5\timport ai.onnxruntime.OrtEnvironment\n6\timport ai.onnxruntime.OrtSession\n7\timport android.content.Context\n8\timport android.graphics.Bitmap\n9\timport android.util.Log\n10\timport java.nio.FloatBuffer\n11\timport java.util.Collections\n12\t\n13\tobject DocAlignerDetector {\n14\t\n15\t    private const val MODEL_ASSET = \"doc_corners.onnx\"\n16\t    private const val IN = 256       // 모델 입력 한 변(px)\n17\t    private const val INPUT_NAME = \"img\"\n18\t\n19\t    // 코너로 인정할 히트맵 peak 하한. 이보다 낮으면 문서가 없다고 본다.\n20\t    private const val MIN_PEAK = 0.25\n21\t\n22\t    private var env: OrtEnvironment? = null\n23\t    private var session: OrtSession? = null\n24\t\n25\t    val isReady: Boolean get() = session != null\n26\t\n27\t    // 앱 시작 시 1회. assets의 모델을 메모리로 읽어 ORT 세션을 만든다.\n28\t    @Synchronized\n29\t    fun init(context: Context) {\n30\t        if (session != null) return\n31\t        try {\n32\t            val bytes = context.assets.open(MODEL_ASSET).use { it.readBytes() }\n33\t            val e = OrtEnvironment.getEnvironment()\n34\t            session = e.createSession(bytes, OrtSession.SessionOptions())\n35\t            env = e\n36\t        } catch (t: Throwable) {\n37\t            Log.e(\"ClearCam\", \"DocAligner 모델 로드 실패\", t)\n38\t        }\n39\t    }\n40\t\n41\t    // 코너만 필요할 때(편집화면 자동검출).\n42\t    fun detect(bitmap: Bitmap): FloatArray? = detectBest(bitmap)?.corners\n43\t\n44\t    // 비트맵에서 최적 문서 사각형과 신뢰도(최소 코너 peak)를 반환. 없으면 null.\n45\t    @Synchronized\n46\t    fun detectBest(bitmap: Bitmap): DocumentDetector.Detection? {\n47\t        val s = session ?: return null\n48\t        val e = env ?: return null\n49\t        val input = preprocess(bitmap)\n50\t        val tensor = OnnxTensor.createTensor(e, input, longArrayOf(1, 3, IN.toLong(), IN.toLong()))\n51\t        val corners: FloatArray\n52\t        val peak: Double\n53\t        try {\n54\t            tensor.use { t ->\n55\t                s.run(Collections.singletonMap(INPUT_NAME, t)).use { res ->\n56\t                    @Suppress(\"UNCHECKED_CAST\")\n57\t                    val hm = res[0].value as Array<Array<Array<FloatArray>>> // [1][4][H][W]\n58\t                    val r = postprocess(hm[0], bitmap.width, bitmap.height)\n59\t                    corners = r.first; peak = r.second\n60\t                }\n61\t            }\n62\t        } catch (t: Throwable) {\n63\t            Log.e(\"ClearCam\", \"DocAligner 추론 오류\", t)\n64\t            return null\n65\t        }\n66\t        if (peak < MIN_PEAK) return null\n67\t        return DocumentDetector.Detection(DocumentDetector.order4Points(corners), peak)\n68\t    }\n69\t\n70\t    // 비트맵 → 256x256 RGB, NCHW, /255 정규화 FloatBuffer.\n71\t    private fun preprocess(bitmap: Bitmap): FloatBuffer {\n72\t        val resized = Bitmap.createScaledBitmap(bitmap, IN, IN, true)\n73\t        val px = IntArray(IN * IN)\n74\t        resized.getPixels(px, 0, IN, 0, 0, IN, IN)\n75\t        if (resized != bitmap) resized.recycle()\n76\t        val area = IN * IN\n77\t        val arr = FloatArray(3 * area)\n78\t        for (i in 0 until area) {\n79\t            val p = px[i]\n80\t            arr[i] = ((p shr 16) and 0xFF) / 255f          // R 평면\n81\t            arr[area + i] = ((p shr 8) and 0xFF) / 255f     // G 평면\n82\t            arr[2 * area + i] = (p and 0xFF) / 255f          // B 평면\n83\t        }\n84\t        return FloatBuffer.wrap(arr)\n85\t    }\n86\t\n87\t    // 히트맵 [4][H][W]에서 채널별 \"피크 주변 가중 중심\"(서브픽셀)으로 코너를 구한다.\n88\t    // argmax보다 양자화 오차가 작아 코너가 더 정밀하게 잡힌다. 신뢰도 = 최소 코너 peak.\n89\t    private fun postprocess(planes: Array<Array<FloatArray>>, w: Int, h: Int): Pair<FloatArray, Double> {\n90\t        val hgt = planes[0].size\n91\t        val wid = planes[0][0].size\n92\t        val corners = FloatArray(8)\n93\t        var minPeak = 1f\n94\t        for (ch in 0 until 4) {\n95\t            val plane = planes[ch]\n96\t            // 1) 최대값 위치·크기\n97\t            var best = -1f\n98\t            for (y in 0 until hgt) {\n99\t                val row = plane[y]\n100\t                for (x in 0 until wid) if (row[x] > best) best = row[x]\n101\t            }\n102\t            // 2) 최대값의 절반 이상인 셀들의 값 가중 중심 = 서브픽셀 코너\n103\t            val thr = best * 0.5f\n104\t            var sx = 0.0; var sy = 0.0; var sw = 0.0\n105\t            for (y in 0 until hgt) {\n106\t                val row = plane[y]\n107\t                for (x in 0 until wid) {\n108\t                    val v = row[x]\n109\t                    if (v >= thr) { sx += x * v; sy += y * v; sw += v }\n110\t                }\n111\t            }\n112\t            val cx = if (sw > 0) (sx / sw) else 0.0\n113\t            val cy = if (sw > 0) (sy / sw) else 0.0\n114\t            corners[ch * 2] = (cx / wid * w).toFloat()\n115\t            corners[ch * 2 + 1] = (cy / hgt * h).toFloat()\n116\t            if (best < minPeak) minPeak = best\n117\t        }\n118\t        return corners to minPeak.toDouble()\n119\t    }\n120\t}\n121\t"}]},"uuid":"87ba874b-da54-418f-9183-f9a19a0e29de","timestamp":"2026-06-12T04:56:41.038Z","sourceToolAssistantUUID":"f57ad09b-2f33-48cf-ade8-dcda73bcdce7","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
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{"parentUuid":"07f35cf2-2ab6-4b4a-bcf0-17a0e1315160","isSidechain":true,"promptId":"24f31705-de1b-46a9-a683-c7824fe9b1a0","agentId":"a6799a7bb08167c99","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SjNZRhcVeeRUv3hnMTZw6V","type":"tool_result","content":"1\t// ML이 준 대략적 4코너를, 그 근처에서 찾은 실제 종이 경계선에 스냅해 정밀하게 보정한다.\n2\t// 사람이 종이 실루엣을 보듯 \"안쪽으로 밝아지는(배경→종이) 전이 중 가장 바깥쪽\"을 경계로 삼아,\n3\t// 종이 안쪽의 더 강한 표 테두리·텍스트나 종이 밖 그림자 경계에 끌려가지 않는다.\n4\t// 변마다 경계점을 모아 이상치 제거 직선 적합 → 인접 직선 교점 = 정밀 코너.\n5\t// 경계가 약하거나 ML에서 너무 멀면 ML 코너로 폴백(모호한 겹친 문서에서 안전).\n6\tpackage cc.lucasai.clearcam.editor\n7\t\n8\timport android.graphics.Bitmap\n9\timport cc.lucasai.clearcam.ClearCamApp\n10\timport org.opencv.android.Utils\n11\timport org.opencv.core.Core\n12\timport org.opencv.core.CvType\n13\timport org.opencv.core.Mat\n14\timport org.opencv.core.Size\n15\timport org.opencv.imgproc.Imgproc\n16\timport kotlin.math.abs\n17\timport kotlin.math.atan2\n18\timport kotlin.math.cos\n19\timport kotlin.math.hypot\n20\timport kotlin.math.sin\n21\t\n22\tobject EdgeSnapper {\n23\t\n24\t    private const val BAND_FRAC = 0.03      // 경계 탐색 폭(짧은 변 대비)\n25\t    private const val CLAMP_FRAC = 0.07     // ML 코너에서 이보다 멀면 폴백\n26\t    private const val SAMPLES = 24          // 변당 샘플 수\n27\t\n28\t    // corners: 비트맵 좌표 8 floats(TL,TR,BR,BL). 보정된 8 floats 반환.\n29\t    fun refine(bitmap: Bitmap, corners: FloatArray): FloatArray {\n30\t        if (!ClearCamApp.openCvReady) return corners\n31\t        val src = Mat(); val gray = Mat(); val gx = Mat(); val gy = Mat(); val grad = Mat()\n32\t        try {\n33\t            Utils.bitmapToMat(bitmap, src)\n34\t            Imgproc.cvtColor(src, gray, Imgproc.COLOR_RGBA2GRAY)\n35\t            Imgproc.Sobel(gray, gx, CvType.CV_32F, 1, 0, 3)\n36\t            Imgproc.Sobel(gray, gy, CvType.CV_32F, 0, 1, 3)\n37\t            Core.magnitude(gx, gy, grad)\n38\t            Imgproc.GaussianBlur(grad, grad, Size(5.0, 5.0), 0.0)\n39\t            // 방향성 판단용 부드러운 그래디언트(법선과 내적해 \"안쪽으로 밝아짐\"을 검사).\n40\t            Imgproc.GaussianBlur(gx, gx, Size(5.0, 5.0), 0.0)\n41\t            Imgproc.GaussianBlur(gy, gy, Size(5.0, 5.0), 0.0)\n42\t            val w = gray.cols(); val h = gray.rows()\n43\t            val g = FloatArray(w * h); grad.get(0, 0, g)\n44\t            val dgx = FloatArray(w * h); gx.get(0, 0, dgx)\n45\t            val dgy = FloatArray(w * h); gy.get(0, 0, dgy)\n46\t            val gmean = Core.mean(grad).`val`[0]\n47\t            val band = maxOf(6, (minOf(w, h) * BAND_FRAC).toInt())\n48\t            val thr = gmean * 3.0\n49\t\n50\t            // 변: top(0,1) right(1,2) bottom(2,3) left(3,0). 각 변의 직선 [mx,my,dx,dy] 또는 null.\n51\t            val edges = arrayOf(intArrayOf(0, 1), intArrayOf(1, 2), intArrayOf(2, 3), intArrayOf(3, 0))\n52\t            val lines = arrayOfNulls<DoubleArray>(4)\n53\t            for (e in 0 until 4) {\n54\t                lines[e] = fitEdge(corners, edges[e][0], edges[e][1], g, dgx, dgy, w, h, band, thr)\n55\t            }\n56\t            // 코너 i = (이전 변 ∩ 다음 변).\n57\t            val meet = arrayOf(intArrayOf(3, 0), intArrayOf(0, 1), intArrayOf(1, 2), intArrayOf(2, 3))\n58\t            val out = corners.copyOf()\n59\t            val clamp = minOf(w, h) * CLAMP_FRAC\n60\t            for (ci in 0 until 4) {\n61\t                val l1 = lines[meet[ci][0]]; val l2 = lines[meet[ci][1]]\n62\t                if (l1 != null && l2 != null) {\n63\t                    val ip = intersect(l1, l2) ?: continue\n64\t                    val dx = ip[0] - corners[ci * 2]; val dy = ip[1] - corners[ci * 2 + 1]\n65\t                    if (hypot(dx, dy) < clamp) { out[ci * 2] = ip[0].toFloat(); out[ci * 2 + 1] = ip[1].toFloat() }\n66\t                }\n67\t            }\n68\t            return out\n69\t        } catch (e: Exception) {\n70\t            return corners\n71\t        } finally {\n72\t            src.release(); gray.release(); gx.release(); gy.release(); grad.release()\n73\t        }\n74\t    }\n75\t\n76\t    // 한 변을 따라 법선 스캔으로 경계점을 모아 이상치 제거 총최소제곱 직선을 적합한다.\n77\t    // 각 스캔(바깥→안)에서 \"안쪽으로 밝아지는\" 전이(d≥thr) 중 가장 바깥 점을 우선하고,\n78\t    // 없으면 최강 그래디언트 크기(>thr)로 폴백한다. TL,TR,BR,BL 시계방향이면 +법선=안쪽.\n79\t    internal fun fitEdge(\n80\t        c: FloatArray, ia: Int, ib: Int,\n81\t        g: FloatArray, dgx: FloatArray, dgy: FloatArray,\n82\t        w: Int, h: Int, band: Int, thr: Double,\n83\t    ): DoubleArray? {\n84\t        val ax = c[ia * 2].toDouble(); val ay = c[ia * 2 + 1].toDouble()\n85\t        val bx = c[ib * 2].toDouble(); val by = c[ib * 2 + 1].toDouble()\n86\t        var evx = bx - ax; var evy = by - ay; val len = hypot(evx, evy)\n87\t        if (len < 2) return null\n88\t        evx /= len; evy /= len\n89\t        val nx = -evy; val ny = evx\n90\t        val px = ArrayList<Double>(); val py = ArrayList<Double>()\n91\t        for (i in 0 until SAMPLES) {\n92\t            val t = 0.12 + (0.88 - 0.12) * i / (SAMPLES - 1)\n93\t            val ox = ax + evx * (t * len); val oy = ay + evy * (t * len)\n94\t            var pickX = 0.0; var pickY = 0.0; var picked = false\n95\t            var bestV = thr; var fbX = 0.0; var fbY = 0.0; var fallback = false\n96\t            for (s in -band..band) { // 바깥(-)→안(+) 순서로 스캔\n97\t                val qx = ox + nx * s; val qy = oy + ny * s\n98\t                val xi = Math.round(qx).toInt(); val yi = Math.round(qy).toInt()\n99\t                if (xi in 0 until w && yi in 0 until h) {\n100\t                    val idx = yi * w + xi\n101\t                    if (!picked) {\n102\t                        val d = dgx[idx] * nx + dgy[idx] * ny // 안쪽으로 밝아지면 양수\n103\t                        if (d >= thr) { pickX = qx; pickY = qy; picked = true }\n104\t                    }\n105\t                    val v = g[idx].toDouble()\n106\t                    if (v > bestV) { bestV = v; fbX = qx; fbY = qy; fallback = true }\n107\t                }\n108\t            }\n109\t            if (picked) { px.add(pickX); py.add(pickY) }\n110\t            else if (fallback) { px.add(fbX); py.add(fbY) }\n111\t        }\n112\t        if (px.size < (SAMPLES * 0.6).toInt()) return null\n113\t        var fit = fitLine(px, py) ?: return null\n114\t        // 이상치 제거 후 1회 재적합: 잔차가 중앙값의 2배(최소 2px) 넘는 점을 버린다.\n115\t        val res = residuals(px, py, fit)\n116\t        val med = median(res)\n117\t        val cut = maxOf(2.0 * med, 2.0)\n118\t        val kx = ArrayList<Double>(); val ky = ArrayList<Double>()\n119\t        for (k in px.indices) if (res[k] <= cut) { kx.add(px[k]); ky.add(py[k]) }\n120\t        if (kx.size >= maxOf(4, px.size / 2)) {\n121\t            fit = fitLine(kx, ky) ?: fit\n122\t            val res2 = residuals(kx, ky, fit)\n123\t            if (res2.average() > band * 0.5) return null\n124\t            return fit\n125\t        }\n126\t        if (res.average() > band * 0.5) return null\n127\t        return fit\n128\t    }\n129\t\n130\t    // 총최소제곱(무게중심 + 주성분 방향) 직선 [mx,my,dx,dy].\n131\t    private fun fitLine(px: List<Double>, py: List<Double>): DoubleArray? {\n132\t        val n = px.size\n133\t        if (n < 2) return null\n134\t        var mx = 0.0; var my = 0.0\n135\t        for (k in 0 until n) { mx += px[k]; my += py[k] }\n136\t        mx /= n; my /= n\n137\t        var sxx = 0.0; var syy = 0.0; var sxy = 0.0\n138\t        for (k in 0 until n) {\n139\t            val dx = px[k] - mx; val dy = py[k] - my\n140\t            sxx += dx * dx; syy += dy * dy; sxy += dx * dy\n141\t        }\n142\t        val ang = 0.5 * atan2(2 * sxy, sxx - syy)\n143\t        return doubleArrayOf(mx, my, cos(ang), sin(ang))\n144\t    }\n145\t\n146\t    // 각 점의 직선까지 법선 거리.\n147\t    private fun residuals(px: List<Double>, py: List<Double>, l: DoubleArray): DoubleArray =\n148\t        DoubleArray(px.size) { k -> abs((px[k] - l[0]) * (-l[3]) + (py[k] - l[1]) * l[2]) }\n149\t\n150\t    private fun median(a: DoubleArray): Double {\n151\t        if (a.isEmpty()) return 0.0\n152\t        val s = a.sorted()\n153\t        return if (s.size % 2 == 1) s[s.size / 2] else (s[s.size / 2 - 1] + s[s.size / 2]) / 2.0\n154\t    }\n155\t\n156\t    // 점-방향 형식 두 직선의 교점.\n157\t    internal fun intersect(l1: DoubleArray, l2: DoubleArray): DoubleArray? {\n158\t        val det = l1[2] * (-l2[3]) - l1[3] * (-l2[2])\n159\t        if (abs(det) < 1e-6) return null\n160\t        val rx = l2[0] - l1[0]; val ry = l2[1] - l1[1]\n161\t        val t = (rx * (-l2[3]) - ry * (-l2[2])) / det\n162\t        return doubleArrayOf(l1[0] + l1[2] * t, l1[1] + l1[3] * t)\n163\t    }\n164\t}\n165\t"}]},"uuid":"80361ad9-3d5f-42d4-b4b7-892a3a37e2ae","timestamp":"2026-06-12T04:56:41.607Z","sourceToolAssistantUUID":"07f35cf2-2ab6-4b4a-bcf0-17a0e1315160","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
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{"parentUuid":"289b36d7-1ee0-4b1c-9fae-ed2ebecde07c","isSidechain":true,"promptId":"24f31705-de1b-46a9-a683-c7824fe9b1a0","agentId":"a6799a7bb08167c99","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01AMuK2M2TJxpw54GfWgDUtU","type":"tool_result","content":"1\t// 코너 후보가 \"실제 종이 경계에 얼마나 잘 붙어 있는가\"를 0~1로 점수화한다.\n2\t// 각 변을 따라 법선 방향으로 그래디언트 피크를 찾아, 임계 이상 지지점 비율을 센다.\n3\t// 진짜 경계 위 사각형은 네 변 모두 강한 엣지를 만나 점수가 높고,\n4\t// 내용 안쪽으로 잘린 후보(빈 곳을 변으로 삼음)나 배경으로 과확장한 후보는 낮다.\n5\t// → 여러 검출 후보 중 사람이 보듯 \"종이 윤곽에 맞는\" 것을 고른다.\n6\tpackage cc.lucasai.clearcam.editor\n7\t\n8\timport android.graphics.Bitmap\n9\timport cc.lucasai.clearcam.ClearCamApp\n10\timport org.opencv.android.Utils\n11\timport org.opencv.core.Core\n12\timport org.opencv.core.CvType\n13\timport org.opencv.core.Mat\n14\timport org.opencv.core.Size\n15\timport org.opencv.imgproc.Imgproc\n16\timport kotlin.math.hypot\n17\timport kotlin.math.roundToInt\n18\t\n19\tobject EdgeSupport {\n20\t\n21\t    private const val BAND_FRAC = 0.02\n22\t    private const val SAMPLES = 20\n23\t\n24\t    // 비트맵에서 quad(TL,TR,BR,BL 8 floats)의 경계 지지 점수(0~1). OpenCV 없으면 -1.\n25\t    fun score(bitmap: Bitmap, quad: FloatArray): Double {\n26\t        if (!ClearCamApp.openCvReady) return -1.0\n27\t        val src = Mat(); val gray = Mat(); val gx = Mat(); val gy = Mat(); val grad = Mat()\n28\t        try {\n29\t            Utils.bitmapToMat(bitmap, src)\n30\t            Imgproc.cvtColor(src, gray, Imgproc.COLOR_RGBA2GRAY)\n31\t            Imgproc.Sobel(gray, gx, CvType.CV_32F, 1, 0, 3)\n32\t            Imgproc.Sobel(gray, gy, CvType.CV_32F, 0, 1, 3)\n33\t            Core.magnitude(gx, gy, grad)\n34\t            Imgproc.GaussianBlur(grad, grad, Size(5.0, 5.0), 0.0)\n35\t            val w = gray.cols(); val h = gray.rows()\n36\t            val g = FloatArray(w * h); grad.get(0, 0, g)\n37\t            val band = maxOf(4, (minOf(w, h) * BAND_FRAC).toInt())\n38\t            val thr = Core.mean(grad).`val`[0] * 3.0\n39\t            return supportRatio(quad, g, w, h, band, thr)\n40\t        } catch (e: Exception) {\n41\t            return -1.0\n42\t        } finally {\n43\t            src.release(); gray.release(); gx.release(); gy.release(); grad.release()\n44\t        }\n45\t    }\n46\t\n47\t    // 순수 계산: 그래디언트 크기 배열에서 quad 네 변의 지지점 비율.\n48\t    internal fun supportRatio(\n49\t        quad: FloatArray, g: FloatArray, w: Int, h: Int, band: Int, thr: Double,\n50\t    ): Double {\n51\t        var hits = 0; var total = 0\n52\t        for (e in 0 until 4) {\n53\t            val ax = quad[e * 2].toDouble(); val ay = quad[e * 2 + 1].toDouble()\n54\t            val nIdx = ((e + 1) % 4)\n55\t            val bx = quad[nIdx * 2].toDouble(); val by = quad[nIdx * 2 + 1].toDouble()\n56\t            var evx = bx - ax; var evy = by - ay; val len = hypot(evx, evy)\n57\t            if (len < 2) continue\n58\t            evx /= len; evy /= len\n59\t            val nx = -evy; val ny = evx\n60\t            for (k in 0 until SAMPLES) {\n61\t                val t = 0.1 + 0.8 * k / (SAMPLES - 1)\n62\t                val ox = ax + evx * (t * len); val oy = ay + evy * (t * len)\n63\t                var best = 0.0\n64\t                for (s in -band..band) {\n65\t                    val qx = (ox + nx * s).roundToInt(); val qy = (oy + ny * s).roundToInt()\n66\t                    if (qx in 0 until w && qy in 0 until h) {\n67\t                        val v = g[qy * w + qx].toDouble()\n68\t                        if (v > best) best = v\n69\t                    }\n70\t                }\n71\t                total++\n72\t                if (best > thr) hits++\n73\t            }\n74\t        }\n75\t        return if (total > 0) hits.toDouble() / total else 0.0\n76\t    }\n77\t}\n78\t"}]},"uuid":"52e06353-7ee1-4144-82df-674844c1bfd9","timestamp":"2026-06-12T04:56:44.561Z","sourceToolAssistantUUID":"289b36d7-1ee0-4b1c-9fae-ed2ebecde07c","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"52e06353-7ee1-4144-82df-674844c1bfd9","isSidechain":true,"agentId":"a6799a7bb08167c99","message":{"model":"claude-sonnet-4-6","id":"msg_01V9zNCBtQ3by7hCbH332ons","type":"message","role":"assistant","content":[{"type":"text","text":"Now I have a complete picture. Let me do the final analysis."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1598,"cache_read_input_tokens":42352,"cache_creation":{"ephemeral_5m_input_tokens":1598,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CbxmiyQkJkQxN45NzsMjk","attributionAgent":"reviewer","type":"assistant","uuid":"d77e53c1-e699-4601-9cf4-a7dc51aec13d","timestamp":"2026-06-12T04:56:48.245Z","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"d77e53c1-e699-4601-9cf4-a7dc51aec13d","isSidechain":true,"agentId":"a6799a7bb08167c99","message":{"model":"claude-sonnet-4-6","id":"msg_01V9zNCBtQ3by7hCbH332ons","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01HG13TsWAkCVyZUapXBMcxQ","name":"Bash","input":{"command":"# Check morphologyEx kernel leak: Mat.ones() creates unnamed Mats — are they released?\ngrep -n \"Mat.ones\" /home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/DocSegDetector.kt","description":"Check Mat.ones kernel creation in morphologyEx calls"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1598,"cache_read_input_tokens":42352,"cache_creation":{"ephemeral_5m_input_tokens":1598,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CbxmiyQkJkQxN45NzsMjk","attributionAgent":"reviewer","type":"assistant","uuid":"f1e80288-ef65-41dd-a5cd-c9e45081d291","timestamp":"2026-06-12T04:56:50.127Z","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"f1e80288-ef65-41dd-a5cd-c9e45081d291","isSidechain":true,"promptId":"24f31705-de1b-46a9-a683-c7824fe9b1a0","agentId":"a6799a7bb08167c99","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01HG13TsWAkCVyZUapXBMcxQ","type":"tool_result","content":"100:        Imgproc.morphologyEx(mask, mask, Imgproc.MORPH_OPEN, Mat.ones(3, 3, org.opencv.core.CvType.CV_8U))\n101:        Imgproc.morphologyEx(mask, mask, Imgproc.MORPH_CLOSE, Mat.ones(5, 5, org.opencv.core.CvType.CV_8U))","is_error":false}]},"uuid":"79691fa0-3752-4088-b80d-09205345d285","timestamp":"2026-06-12T04:56:50.376Z","sourceToolAssistantUUID":"f1e80288-ef65-41dd-a5cd-c9e45081d291","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"79691fa0-3752-4088-b80d-09205345d285","isSidechain":true,"agentId":"a6799a7bb08167c99","message":{"model":"claude-sonnet-4-6","id":"msg_01V9zNCBtQ3by7hCbH332ons","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01HTiAhVpw8oskrpEAizhqVJ","name":"Bash","input":{"command":"# Check how DocAlignerDetector handles the structuring kernel - for comparison\ngrep -n \"Mat.ones\\|getStructuringElement\" /home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/DocAlignerDetector.kt /home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/DocumentDetector.kt","description":"Compare kernel management in other detectors"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1598,"cache_read_input_tokens":42352,"output_tokens":335,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1598},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":335,"cache_read_input_tokens":42352,"cache_creation_input_tokens":1598,"cache_creation":{"ephemeral_5m_input_tokens":1598,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CbxmiyQkJkQxN45NzsMjk","attributionAgent":"reviewer","type":"assistant","uuid":"d5ba8795-4130-4738-b673-c16c3b038c82","timestamp":"2026-06-12T04:56:52.023Z","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"d5ba8795-4130-4738-b673-c16c3b038c82","isSidechain":true,"promptId":"24f31705-de1b-46a9-a683-c7824fe9b1a0","agentId":"a6799a7bb08167c99","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01HTiAhVpw8oskrpEAizhqVJ","type":"tool_result","content":"/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/DocumentDetector.kt:21:    private val k11 by lazy { Imgproc.getStructuringElement(Imgproc.MORPH_RECT, Size(11.0, 11.0)) }\n/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/DocumentDetector.kt:22:    private val k25 by lazy { Imgproc.getStructuringElement(Imgproc.MORPH_RECT, Size(25.0, 25.0)) }","is_error":false}]},"uuid":"20470250-8360-4957-8d65-1060a69783fc","timestamp":"2026-06-12T04:56:52.313Z","sourceToolAssistantUUID":"d5ba8795-4130-4738-b673-c16c3b038c82","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"20470250-8360-4957-8d65-1060a69783fc","isSidechain":true,"agentId":"a6799a7bb08167c99","message":{"model":"claude-sonnet-4-6","id":"msg_011sKCYZaG9RLscGpF78SFUP","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01SC5JAKSELWiLjsrKR9tBdd","name":"Bash","input":{"command":"# Verify contours release when c is not null but solidity gate fails\n# Trace the contours.forEach { it.release() } path at the bottom vs early-return paths\ngrep -n \"contours\\|c2f\\|\\.release\\|return null\" /home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/DocSegDetector.kt","description":"Trace resource release paths in cornersFromMask"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":673,"cache_read_input_tokens":43950,"cache_creation":{"ephemeral_5m_input_tokens":673,"ephemeral_1h_input_tokens":0},"output_tokens":38,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CbxmjXcVNUMEAJkkzMzZB","attributionAgent":"reviewer","type":"assistant","uuid":"034e16ff-8ffa-4579-bf61-6a23781031de","timestamp":"2026-06-12T04:56:57.501Z","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"034e16ff-8ffa-4579-bf61-6a23781031de","isSidechain":true,"promptId":"24f31705-de1b-46a9-a683-c7824fe9b1a0","agentId":"a6799a7bb08167c99","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SC5JAKSELWiLjsrKR9tBdd","type":"tool_result","content":"52:        val s = session ?: return null\n53:        val e = env ?: return null\n63:            Log.e(\"ClearCam\", \"DocSeg 추론 오류\", t); return null\n99:        if (frac < MIN_POS || frac > MAX_POS) { mask.release(); return null }\n102:        val contours = ArrayList<MatOfPoint>(); val hier = Mat()\n103:        Imgproc.findContours(mask, contours, hier, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE)\n104:        hier.release(); mask.release()\n105:        val c = contours.maxByOrNull { Imgproc.contourArea(it) } ?: run { contours.forEach { it.release() }; return null }\n107:        val c2f = MatOfPoint2f(*c.toArray())\n108:        val rect = Imgproc.minAreaRect(c2f)\n123:        c2f.release(); contours.forEach { it.release() }","is_error":false}]},"uuid":"1b13b0ae-711e-4ef8-8ec2-78b1ba7755d2","timestamp":"2026-06-12T04:56:57.738Z","sourceToolAssistantUUID":"034e16ff-8ffa-4579-bf61-6a23781031de","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"1b13b0ae-711e-4ef8-8ec2-78b1ba7755d2","isSidechain":true,"agentId":"a6799a7bb08167c99","message":{"model":"claude-sonnet-4-6","id":"msg_011sKCYZaG9RLscGpF78SFUP","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01XwPWA4gDYzs5P62sJ4nJ4L","name":"Bash","input":{"command":"# Verify the area gate (4% threshold) - IN=128, so IN*IN=16384. 4% = ~655 px²\n# The rect area gate is applied on the 128x128 coordinate space, not original bitmap\npython3 -c \"IN=128; print(f'4% of {IN}x{IN} = {IN*IN*0.04:.0f} px² in 128-space')\"","description":"Verify area gate threshold math"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":673,"cache_read_input_tokens":43950,"cache_creation":{"ephemeral_5m_input_tokens":673,"ephemeral_1h_input_tokens":0},"output_tokens":38,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CbxmjXcVNUMEAJkkzMzZB","attributionAgent":"reviewer","type":"assistant","uuid":"696656a3-e051-440b-8773-c100a40e072d","timestamp":"2026-06-12T04:57:00.424Z","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"696656a3-e051-440b-8773-c100a40e072d","isSidechain":true,"promptId":"24f31705-de1b-46a9-a683-c7824fe9b1a0","agentId":"a6799a7bb08167c99","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01XwPWA4gDYzs5P62sJ4nJ4L","type":"tool_result","content":"4% of 128x128 = 655 px² in 128-space","is_error":false}]},"uuid":"2984dcdf-ae76-416c-864b-25f1ee4d0c41","timestamp":"2026-06-12T04:57:00.681Z","sourceToolAssistantUUID":"696656a3-e051-440b-8773-c100a40e072d","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"2984dcdf-ae76-416c-864b-25f1ee4d0c41","isSidechain":true,"agentId":"a6799a7bb08167c99","message":{"model":"claude-sonnet-4-6","id":"msg_011sKCYZaG9RLscGpF78SFUP","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01J5jryeggjoFiUYUPHDSu24","name":"Bash","input":{"command":"# Check sigmoid computation for potential precision loss\n# The code does: 1f / (1f + Math.exp(-v.toDouble()).toFloat())\n# Is there a cast issue? Math.exp returns double, .toFloat() truncates, then added to 1f\npython3 -c \"\nimport math\n# Simulating: 1f / (1f + Math.exp(-v.toDouble()).toFloat())\n# vs correct:  1.0 / (1.0 + math.exp(-v))\nv = -100.0\ncorrect = 1.0 / (1.0 + math.exp(-v))\n# The kotlin code: 1f / (1f + float(math.exp(-v)))\n# For large positive v: exp(-v) is tiny, toFloat() = ~0, result = 1.0 OK\n# For large negative v: exp(-v) is huge, toFloat() = inf -> 1/(1+inf) = 0 OK in float\n# For v near 0: precision loss from toFloat() is minor (7 significant digits)\nprint(f'v={v}: correct sigmoid = {correct:.6f}')\nprint('Sigmoid impl has minor float precision reduction but is functionally correct')\nprint('The parenthesization: Math.exp(-v.toDouble()).toFloat() — converts exp result to float early')\nprint('Then 1f + thatFloat — still float arithmetic')\nprint('Result: acceptable for binary threshold at 0.5')\"","description":"Analyze sigmoid precision in flatten()"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":673,"cache_read_input_tokens":43950,"output_tokens":745,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":673},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":745,"cache_read_input_tokens":43950,"cache_creation_input_tokens":673,"cache_creation":{"ephemeral_5m_input_tokens":673,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CbxmjXcVNUMEAJkkzMzZB","attributionAgent":"reviewer","type":"assistant","uuid":"0a102103-ba6a-4382-a77d-fab607594129","timestamp":"2026-06-12T04:57:07.329Z","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"0a102103-ba6a-4382-a77d-fab607594129","isSidechain":true,"promptId":"24f31705-de1b-46a9-a683-c7824fe9b1a0","agentId":"a6799a7bb08167c99","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01J5jryeggjoFiUYUPHDSu24","type":"tool_result","content":"v=-100.0: correct sigmoid = 0.000000\nSigmoid impl has minor float precision reduction but is functionally correct\nThe parenthesization: Math.exp(-v.toDouble()).toFloat() — converts exp result to float early\nThen 1f + thatFloat — still float arithmetic\nResult: acceptable for binary threshold at 0.5","is_error":false}]},"uuid":"d0f6842c-2eb2-4a57-9f1a-62173c781c05","timestamp":"2026-06-12T04:57:07.585Z","sourceToolAssistantUUID":"0a102103-ba6a-4382-a77d-fab607594129","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"d0f6842c-2eb2-4a57-9f1a-62173c781c05","isSidechain":true,"agentId":"a6799a7bb08167c99","message":{"model":"claude-sonnet-4-6","id":"msg_0156QgGaHttmtCJggdEyD4pQ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_012KjkVTyTJFwb1hc3oXCWsA","name":"Bash","input":{"command":"# Verify cast chain: res[0].value is used as Array<Array<Array<FloatArray>>>\n# Model output is 1x1x128x128. So [1][1][128][128] is correct\n# DocAligner uses [1][4][H][W] cast, same pattern. Check it works for [1][1][128][128]\n# res[0].value as Array<Array<Array<FloatArray>>> = shape [1][1][128][128] ✓\n# out[0][0] = plane [128][128] — correct\n\n# Check if DocAlignerDetector does exact same cast pattern\ngrep -A3 \"UNCHECKED_CAST\" /home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/DocAlignerDetector.kt\necho \"---\"\ngrep -A3 \"UNCHECKED_CAST\" /home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/DocSegDetector.kt","description":"Compare ONNX output cast pattern between both detectors"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1193,"cache_read_input_tokens":44623,"cache_creation":{"ephemeral_5m_input_tokens":1193,"ephemeral_1h_input_tokens":0},"output_tokens":39,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011Cbxmkeif9Vjej45kZ1fkS","attributionAgent":"reviewer","type":"assistant","uuid":"c304108d-4bd9-4ba9-a55b-463c6148ce86","timestamp":"2026-06-12T04:57:14.860Z","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"c304108d-4bd9-4ba9-a55b-463c6148ce86","isSidechain":true,"promptId":"24f31705-de1b-46a9-a683-c7824fe9b1a0","agentId":"a6799a7bb08167c99","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012KjkVTyTJFwb1hc3oXCWsA","type":"tool_result","content":"                    @Suppress(\"UNCHECKED_CAST\")\n                    val hm = res[0].value as Array<Array<Array<FloatArray>>> // [1][4][H][W]\n                    val r = postprocess(hm[0], bitmap.width, bitmap.height)\n                    corners = r.first; peak = r.second\n---\n                    @Suppress(\"UNCHECKED_CAST\")\n                    val out = res[0].value as Array<Array<Array<FloatArray>>> // [1][1][128][128]\n                    flatten(out[0][0])\n                }","is_error":false}]},"uuid":"ddaac5dd-d37d-4b65-8163-52433dde0ac0","timestamp":"2026-06-12T04:57:15.106Z","sourceToolAssistantUUID":"c304108d-4bd9-4ba9-a55b-463c6148ce86","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"ddaac5dd-d37d-4b65-8163-52433dde0ac0","isSidechain":true,"agentId":"a6799a7bb08167c99","message":{"model":"claude-sonnet-4-6","id":"msg_0156QgGaHttmtCJggdEyD4pQ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01HojC7xdbweMuoDp49YXfKS","name":"Bash","input":{"command":"# Check if there's a DocSegDetector unit test\nfind /home/lucas/Android-app/ClearCam -name \"*DocSeg*\" -o -name \"*docseg*\" 2>/dev/null | grep -v build | grep -v assets","description":"Look for DocSegDetector tests"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1193,"cache_read_input_tokens":44623,"cache_creation":{"ephemeral_5m_input_tokens":1193,"ephemeral_1h_input_tokens":0},"output_tokens":39,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011Cbxmkeif9Vjej45kZ1fkS","attributionAgent":"reviewer","type":"assistant","uuid":"fad7b1e6-13ad-4db7-b4f9-046d4de819ae","timestamp":"2026-06-12T04:57:15.354Z","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"fad7b1e6-13ad-4db7-b4f9-046d4de819ae","isSidechain":true,"promptId":"24f31705-de1b-46a9-a683-c7824fe9b1a0","agentId":"a6799a7bb08167c99","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01HojC7xdbweMuoDp49YXfKS","type":"tool_result","content":"/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/DocSegDetector.kt","is_error":false}]},"uuid":"65f9916b-ca1e-4aba-8eb5-19cb0c5b9e40","timestamp":"2026-06-12T04:57:15.565Z","sourceToolAssistantUUID":"fad7b1e6-13ad-4db7-b4f9-046d4de819ae","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"65f9916b-ca1e-4aba-8eb5-19cb0c5b9e40","isSidechain":true,"agentId":"a6799a7bb08167c99","message":{"model":"claude-sonnet-4-6","id":"msg_01SwNaGRsnipQrDQXGZV4mFm","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01SUvwrn3ZscaBmCQm32Wd64","name":"Bash","input":{"command":"# Check the area gate condition: rectArea > IN * IN * 0.04\n# This checks rectArea in 128x128 coord space. If solidity < 0.80 OR rectArea <= 4%, return null.\n# But the original mask gate was frac (양성비율) based on pixel count, not rect area.\n# rectArea = rect.size.width * rect.size.height (128-space)\n# Minimum area doc at original bitmap = rectArea * sx * sy at minimum\n# This seems fine. Let's also check the 4% redundancy with the MIN_POS gate.\n# MIN_POS=0.05 means mask frac >= 5%, so after morphology contour might shrink.\n# The rectArea >= 4% of 128x128 = 655px² is a slightly looser fallback area check. OK.\n\n# Verify minAreaRect returns corners in float coordinates (OpenCV 4.x confirmed).\n# The box array from rect.points(box) is counterclockwise or clockwise depending on orientation.\n# order4Points() handles any ordering — safe.\n\necho \"Logic checks done\"\n\n# Now check: is @Synchronized on detect() needed given isReady check in CornerCandidates?\n# scenario: Thread A calls init() -> sets session. Thread B calls isReady -> true,\n# then calls detect() -> @Synchronized on same object -> safe.\n# But Thread B could call isReady=true, but init() is halfway done (session was just set).\n# Since session is set at end of init() (after env = e), and isReady checks session != null,\n# we need to be sure session isn't visible before env is.\n# In Kotlin, object fields without @Volatile could be reordered... Let me check.\ngrep -n \"volatile\\|@Volatile\" /home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/DocSegDetector.kt","description":"Check volatile annotations and thread safety"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":707,"cache_read_input_tokens":45816,"output_tokens":543,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":707},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":543,"cache_read_input_tokens":45816,"cache_creation_input_tokens":707,"cache_creation":{"ephemeral_5m_input_tokens":707,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CbxmmEuiEq69dXk42qEJC","attributionAgent":"reviewer","type":"assistant","uuid":"1cd2654c-d4ac-43f0-aa71-4724a2992bbd","timestamp":"2026-06-12T04:57:28.566Z","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"1cd2654c-d4ac-43f0-aa71-4724a2992bbd","isSidechain":true,"promptId":"24f31705-de1b-46a9-a683-c7824fe9b1a0","agentId":"a6799a7bb08167c99","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SUvwrn3ZscaBmCQm32Wd64","type":"tool_result","content":"Logic checks done","is_error":false}]},"uuid":"7a912f33-71bf-4c9e-aca4-45d3e07907a9","timestamp":"2026-06-12T04:57:28.804Z","sourceToolAssistantUUID":"1cd2654c-d4ac-43f0-aa71-4724a2992bbd","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"7a912f33-71bf-4c9e-aca4-45d3e07907a9","isSidechain":true,"agentId":"a6799a7bb08167c99","message":{"model":"claude-sonnet-4-6","id":"msg_01NaX4CZPK33LivKrrfnFoeJ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LUpF5pckN447x9bqNuDg1c","name":"Bash","input":{"command":"grep -n \"volatile\\|@Volatile\\|@Synchronized\" /home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/DocSegDetector.kt\necho \"---DocAligner---\"\ngrep -n \"volatile\\|@Volatile\\|@Synchronized\" /home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/DocAlignerDetector.kt","description":"Check all synchronization annotations in both detectors"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":558,"cache_read_input_tokens":46523,"output_tokens":200,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":558},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":200,"cache_read_input_tokens":46523,"cache_creation_input_tokens":558,"cache_creation":{"ephemeral_5m_input_tokens":558,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CbxmnDTg4fRx9fASZWrqB","attributionAgent":"reviewer","type":"assistant","uuid":"d91ed217-1f0d-45be-86dc-2cceec056275","timestamp":"2026-06-12T04:57:31.856Z","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"d91ed217-1f0d-45be-86dc-2cceec056275","isSidechain":true,"promptId":"24f31705-de1b-46a9-a683-c7824fe9b1a0","agentId":"a6799a7bb08167c99","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01LUpF5pckN447x9bqNuDg1c","type":"tool_result","content":"36:    @Synchronized\n50:    @Synchronized\n---DocAligner---\n28:    @Synchronized\n45:    @Synchronized","is_error":false}]},"uuid":"4db845f6-2258-4f39-bb32-918559add415","timestamp":"2026-06-12T04:57:32.065Z","sourceToolAssistantUUID":"d91ed217-1f0d-45be-86dc-2cceec056275","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"4db845f6-2258-4f39-bb32-918559add415","isSidechain":true,"agentId":"a6799a7bb08167c99","message":{"model":"claude-sonnet-4-6","id":"msg_019iLemi2b6W13rK6XbJf6Gq","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01G5dhvxtWyftsmWbfwNZW9M","name":"Bash","input":{"command":"# Key question: isReady is NOT @Synchronized and reads session directly.\n# If init() is @Synchronized and detect() is @Synchronized on the same object,\n# can isReady(=session != null) see a stale value (null) AFTER session was set?\n# In Java/JVM: @Synchronized uses the monitor of the enclosing object (the companion object).\n# isReady is a plain getter — no synchronization. So there's a visibility gap:\n# Thread A: init() sets session under lock, releases lock\n# Thread B: reads session via isReady WITHOUT lock — may see null due to no happens-before\n# However this is the same pattern as DocAlignerDetector (already in prod).\n# The race consequence: isReady=false → detect skipped → no candidate from DocSeg.\n# Not a correctness/crash risk, only a missed-detection on first few frames. Benign.\necho \"Visibility gap on isReady exists in both detectors — same pattern, benign\"\n\n# Check the kotlin object monitor: @Synchronized on an object function uses the object instance as monitor\n# DocSegDetector is a Kotlin object (singleton), so the monitor is the DocSegDetector class instance.\n# Both init() and detect() lock on the same monitor. isReady is unguarded.\necho \"Confirmed: isReady has no memory barrier — potential stale read, same as DocAlignerDetector\"","description":"Analyze thread safety of isReady getter"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":245,"cache_read_input_tokens":47081,"output_tokens":409,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":245},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":409,"cache_read_input_tokens":47081,"cache_creation_input_tokens":245,"cache_creation":{"ephemeral_5m_input_tokens":245,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CbxmnTRozr8hcx72Knop3","attributionAgent":"reviewer","type":"assistant","uuid":"26bb07dc-e15d-4da0-bcfa-5bd2b32e6eb7","timestamp":"2026-06-12T04:57:41.388Z","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"26bb07dc-e15d-4da0-bcfa-5bd2b32e6eb7","isSidechain":true,"promptId":"24f31705-de1b-46a9-a683-c7824fe9b1a0","agentId":"a6799a7bb08167c99","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01G5dhvxtWyftsmWbfwNZW9M","type":"tool_result","content":"Visibility gap on isReady exists in both detectors — same pattern, benign\nConfirmed: isReady has no memory barrier — potential stale read, same as DocAlignerDetector","is_error":false}]},"uuid":"3a94f482-c8c6-48cf-af9c-924b1cef91d6","timestamp":"2026-06-12T04:57:41.596Z","sourceToolAssistantUUID":"26bb07dc-e15d-4da0-bcfa-5bd2b32e6eb7","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"3a94f482-c8c6-48cf-af9c-924b1cef91d6","isSidechain":true,"agentId":"a6799a7bb08167c99","message":{"model":"claude-sonnet-4-6","id":"msg_01Wcptxppo9Ec37siXMznimC","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01MqyC7rMiBz4yjAR3AUFh5P","name":"Bash","input":{"command":"# Check the Mat.ones() kernel leak more carefully\n# Lines 100-101: Mat.ones(3,3,...) and Mat.ones(5,5,...) are created as temporary arguments\n# They are Java objects on the heap. OpenCV Mat.ones() returns a new Mat backed by native memory.\n# These Mats are passed to morphologyEx but NEVER released.\n# Each call to cornersFromMask() leaks 2 small native Mat allocations.\n# Let's check if this is a concern for scan pipelines (called per page).\n\n# For comparison, check DocumentDetector - it uses lazy vals (k11, k25) to avoid this.\necho \"Mat.ones() leak: each detect() call leaks 2 kernel Mats (3x3 and 5x5)\"\necho \"These are small (~9 and ~25 bytes native) but never freed\"\necho \"In a batch scan of 10 pages = 20 leaked native Mat headers\"\n\n# Check if DocumentDetector pattern for structuring elements is the established approach\ngrep -B2 -A2 \"k11\\|k25\" /home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/DocumentDetector.kt | head -10","description":"Evaluate the Mat.ones kernel leak severity"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":464,"cache_read_input_tokens":47326,"output_tokens":378,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":464},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":378,"cache_read_input_tokens":47326,"cache_creation_input_tokens":464,"cache_creation":{"ephemeral_5m_input_tokens":464,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CbxmoAAm3o2k8V3NBegr5","attributionAgent":"reviewer","type":"assistant","uuid":"c05673ef-01a8-4c60-a877-2accd7754cba","timestamp":"2026-06-12T04:57:50.465Z","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"c05673ef-01a8-4c60-a877-2accd7754cba","isSidechain":true,"promptId":"24f31705-de1b-46a9-a683-c7824fe9b1a0","agentId":"a6799a7bb08167c99","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01MqyC7rMiBz4yjAR3AUFh5P","type":"tool_result","content":"Mat.ones() leak: each detect() call leaks 2 kernel Mats (3x3 and 5x5)\nThese are small (~9 and ~25 bytes native) but never freed\nIn a batch scan of 10 pages = 20 leaked native Mat headers\n\n    // lazy: object 초기화 시 OpenCV 네이티브를 호출하지 않도록(JVM 단위 테스트 보호).\n    private val k11 by lazy { Imgproc.getStructuringElement(Imgproc.MORPH_RECT, Size(11.0, 11.0)) }\n    private val k25 by lazy { Imgproc.getStructuringElement(Imgproc.MORPH_RECT, Size(25.0, 25.0)) }\n\n    // 코너만 필요할 때(편집화면 자동검출). 점수가 필요하면 detectBest 사용.\n--\n        val m = Mat(); Imgproc.GaussianBlur(gray, m, Size(5.0, 5.0), 0.0)\n        Imgproc.Canny(m, m, 40.0, 120.0)\n        Imgproc.dilate(m, m, k11)","is_error":false}]},"uuid":"5ab7dc28-b620-46b8-bbec-197197773056","timestamp":"2026-06-12T04:57:50.702Z","sourceToolAssistantUUID":"c05673ef-01a8-4c60-a877-2accd7754cba","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"5ab7dc28-b620-46b8-bbec-197197773056","isSidechain":true,"agentId":"a6799a7bb08167c99","message":{"model":"claude-sonnet-4-6","id":"msg_014y63VU7AWic3UYeV1UxhU7","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01V8w7oc1Nv4Dpok6KqRe8sk","name":"Bash","input":{"command":"# Check the area gate more carefully for the 4% on 128x128 space\n# MIN_POS = 5% means after morphology, contour area *could* be smaller than 4%\n# Scenario: mask frac = 6% (passes MIN_POS), but after OPEN+CLOSE, largest contour's\n# minAreaRect might be > mask frac (rect includes interior). So rectArea >= contourArea >= pos count.\n# The 4% area gate on rectArea is therefore mostly redundant with 5% MIN_POS gate — benign.\n\n# Final check: the \"AI 영역 검출\" label in candidates list — \n# verify it doesn't conflict with \"AI 검출\" label used elsewhere\ngrep -rn '\"AI' /home/lucas/Android-app/ClearCam/app/src/main/java/ | grep -v \"\\.class\" | grep -v build","description":"Check label uniqueness in candidate list"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":711,"cache_read_input_tokens":47790,"cache_creation":{"ephemeral_5m_input_tokens":711,"ephemeral_1h_input_tokens":0},"output_tokens":51,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011Cbxmoq8odKXGfi6WwhSoS","attributionAgent":"reviewer","type":"assistant","uuid":"3946ea6b-9bd6-48da-94c6-c9abe71ff5ef","timestamp":"2026-06-12T04:57:57.477Z","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"3946ea6b-9bd6-48da-94c6-c9abe71ff5ef","isSidechain":true,"promptId":"24f31705-de1b-46a9-a683-c7824fe9b1a0","agentId":"a6799a7bb08167c99","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01V8w7oc1Nv4Dpok6KqRe8sk","type":"tool_result","content":"/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/CornerCandidates.kt:19:                    list.add(\"AI 검출\" to EdgeSnapper.refine(bitmap, onOrig))\n/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/CornerCandidates.kt:28:                        list.add(\"AI(밝게) 검출\" to EdgeSnapper.refine(bitmap, onBright))\n/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/CornerCandidates.kt:35:                            list.add(\"AI(대비) 검출\" to EdgeSnapper.refine(bitmap, onStr))\n/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/CornerCandidates.kt:39:            } catch (e: Exception) { Log.e(\"ClearCam\", \"AI 검출 오류\", e) }\n/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/CornerCandidates.kt:51:                    list.add(\"AI 영역 검출\" to snapped)","is_error":false}]},"uuid":"53d80c2f-85e6-4284-98c1-81b79ee9685e","timestamp":"2026-06-12T04:57:57.683Z","sourceToolAssistantUUID":"3946ea6b-9bd6-48da-94c6-c9abe71ff5ef","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"53d80c2f-85e6-4284-98c1-81b79ee9685e","isSidechain":true,"agentId":"a6799a7bb08167c99","message":{"model":"claude-sonnet-4-6","id":"msg_016j7JmPsR1DQujkRJfRHcP3","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_012C7T9Uj8LVvkNf2QDc8vo5","name":"Bash","input":{"command":"# Check how labels are used downstream (displayed to user? used for sorting logic?)\ngrep -rn \"Pair\\|label\\|first\\|second\\|검출\" /home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/EditorActivity.kt | head -20","description":"Check how candidate labels are consumed in EditorActivity"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":663,"cache_read_input_tokens":48501,"cache_creation":{"ephemeral_5m_input_tokens":663,"ephemeral_1h_input_tokens":0},"output_tokens":41,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CbxmpLxRvBKfNcZr5g97y","attributionAgent":"reviewer","type":"assistant","uuid":"7c2a883f-4e5b-4624-864d-5fb7cc9aa401","timestamp":"2026-06-12T04:58:01.407Z","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"7c2a883f-4e5b-4624-864d-5fb7cc9aa401","isSidechain":true,"promptId":"24f31705-de1b-46a9-a683-c7824fe9b1a0","agentId":"a6799a7bb08167c99","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012C7T9Uj8LVvkNf2QDc8vo5","type":"tool_result","content":"/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/EditorActivity.kt:49:    // 자동검출 후보(라벨, 비트맵좌표 코너). \"영역\" 버튼으로 순환한다. AI/윤곽 두 엔진 결과.\n/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/EditorActivity.kt:50:    private val autoCandidates = mutableListOf<Pair<String, FloatArray>>()\n/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/EditorActivity.kt:99:    // 원본을 지정 각도(±90)로 회전하고 코너를 다시 잡은 뒤 자동검출을 다시 시도한다.\n/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/EditorActivity.kt:112:    // 검출·표시는 축소본으로 했지만, 펴기는 고해상도 원본에서 한다(글씨 선명도 보존).\n/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/EditorActivity.kt:147:    // 처리 중 오버레이를 띄우고, 회전·재검출 등 소스를 바꾸는 도구 입력을 막는다(스케일 어긋남 방지).\n/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/EditorActivity.kt:234:                binding.overlay.setBitmapCorners(autoCandidates[0].second)\n/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/EditorActivity.kt:241:    // \"영역\": 자동 검출 후보를 순환 적용한다(AI ↔ 윤곽).\n/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/EditorActivity.kt:246:        val (label, c) = autoCandidates[candidateIndex]\n/home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/EditorActivity.kt:247:        binding.overlay.setBitmapCorners(c); toast(label)","is_error":false}]},"uuid":"fc3d6992-6d12-4842-935c-9392c0fe268c","timestamp":"2026-06-12T04:58:01.616Z","sourceToolAssistantUUID":"7c2a883f-4e5b-4624-864d-5fb7cc9aa401","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"fc3d6992-6d12-4842-935c-9392c0fe268c","isSidechain":true,"agentId":"a6799a7bb08167c99","message":{"model":"claude-sonnet-4-6","id":"msg_016j7JmPsR1DQujkRJfRHcP3","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_012p3tP9qNhpPVzocSvUsS7V","name":"Bash","input":{"command":"grep -n \"CornerCandidates\\|candidates\\|detect\" /home/lucas/Android-app/ClearCam/app/src/main/java/cc/lucasai/clearcam/editor/EditorActivity.kt | head -20","description":"Find CornerCandidates usage in EditorActivity"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":663,"cache_read_input_tokens":48501,"cache_creation":{"ephemeral_5m_input_tokens":663,"ephemeral_1h_input_tokens":0},"output_tokens":41,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CbxmpLxRvBKfNcZr5g97y","attributionAgent":"reviewer","type":"assistant","uuid":"c74d4f85-cdd2-4cd8-9f5e-80a29e52d46a","timestamp":"2026-06-12T04:58:02.580Z","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"c74d4f85-cdd2-4cd8-9f5e-80a29e52d46a","isSidechain":true,"promptId":"24f31705-de1b-46a9-a683-c7824fe9b1a0","agentId":"a6799a7bb08167c99","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012p3tP9qNhpPVzocSvUsS7V","type":"tool_result","content":"231:            val found = withContext(Dispatchers.Default) { CornerCandidates.detect(sourceBitmap) }\n236:                toast(getString(R.string.detect_none))\n243:        if (autoCandidates.isEmpty()) { toast(getString(R.string.detect_none)); return }\n244:        if (autoCandidates.size < 2) { toast(getString(R.string.detect_no_more)); return }\n252:        binding.overlay.setBitmapCorners(CornerCandidates.full(sourceBitmap))","is_error":false}]},"uuid":"03d079aa-2f1a-4679-a4de-1ff59fb7ff96","timestamp":"2026-06-12T04:58:02.768Z","sourceToolAssistantUUID":"c74d4f85-cdd2-4cd8-9f5e-80a29e52d46a","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}
{"parentUuid":"03d079aa-2f1a-4679-a4de-1ff59fb7ff96","isSidechain":true,"agentId":"a6799a7bb08167c99","message":{"model":"claude-sonnet-4-6","id":"msg_01Y4jD9FgQTcGa8RHuQxJT8n","type":"message","role":"assistant","content":[{"type":"text","text":"I now have a complete picture of all the changes. Let me compile the review.\n\n---\n\n## Code Review Results\n\n### Summary\n\nChanges: DocSeg ONNX detector added as a gated fallback candidate in the detection pipeline. 4 files changed (2 modified, 1 new source, 1 new binary asset).\n\nReview result: **REQUEST_CHANGES**\n\n---\n\n### TRUST 5 Score\n\n| Item | Status | Notes |\n|------|--------|-------|\n| Tested | WARN | No unit test for `DocSegDetector` (cornersFromMask logic, gate conditions, order4Points wiring). All 22 existing tests pass. New code path has zero test coverage. |\n| Readable | PASS | 126 lines, comments are clear, intent is well documented. |\n| Unified | PASS | Consistent with project style (same ORT pattern as DocAlignerDetector, same Kotlin object singleton). |\n| Secured | PASS | No external input surface, no credentials, no file path traversal. |\n| Trackable | N/A | Not yet committed. |\n\n---\n\n### Mandatory Fixes\n\n**1. [MAJOR] `DocSegDetector.kt:100-101` — Mat kernel leak on every `detect()` call**\n\n`Mat.ones(3,3,...)` and `Mat.ones(5,5,...)` are created inline as arguments to `morphologyEx` and are never `.release()`d. Each `cornersFromMask` invocation leaks two native OpenCV Mat allocations. This compounds on every live-preview autodetect frame and every page in a batch scan.\n\nThe established project pattern (see `DocumentDetector.kt:21-22`) is `private val kN by lazy { Imgproc.getStructuringElement(...) }` — a singleton kernel that is allocated once and reused. The `DocSegDetector` object should follow the same pattern.\n\nFix: replace the inline `Mat.ones(...)` calls with two `private val` lazy-initialized structuring element Mats.\n\n**2. [MINOR] `DocSegDetector.kt:34` — `isReady` lacks a memory-visibility guarantee**\n\n`isReady` reads `session` directly without acquiring the object monitor. `init()` and `detect()` are `@Synchronized`, so `session` writes are fenced — but `isReady` reads are not. A thread calling `isReady` could observe a stale `null` even after `init()` completes, causing the first N frames to miss the DocSeg candidate.\n\nThis is the same pattern as `DocAlignerDetector` (already in production), so the impact is identical and known-benign: a missed candidate on early frames only. Calling `detect()` when `isReady=false` does not crash. However, for correctness the fix is to annotate `session` as `@Volatile`, which removes the need for `@Synchronized` on `detect()` while maintaining safety. Since DocAlignerDetector has the same issue this is a minor consistency concern, not a blocker.\n\n---\n\n### Safety / Regression Analysis (all pass)\n\n**New candidate is additive only.** `DocSegDetector.detect(bitmap)` returns `null` when the gate fails; `CornerCandidates.kt:49` wraps the call in `?.let { }`, so nothing is added to `list` on `null`. Existing DocAligner and OpenCV contour candidates are unaffected regardless of DocSeg result.\n\n**Gate quality.** The three-tier gate (양성비율 5-80%, solidity >= 0.80, rectArea >= 4% of 128x128) is conservative. A noisy or over-segmented mask will either fail the fraction gate, fail the solidity gate after OPEN+CLOSE, or produce a null from `maxByOrNull`. All early-return paths through `cornersFromMask` correctly release `mask` (line 99) and iterate `contours.forEach { it.release() }` (line 105) before returning null. The single-Mat `hier` is released on line 104, `mask` on line 104, `c2f` and `contours` on line 123. No Mat leak on any control path **except the kernel Mats in items 1 above**.\n\n**edge-support ranking as safety net.** A DocSeg candidate that passes the gate but has poor boundary alignment will score lower than a well-aligned DocAligner candidate in `rankByEdgeSupport`. The `EdgeSupport.score` function measures actual gradient support per edge, so a mask-derived quadrilateral that lands inside the paper (missing margin) will lose to a DocAligner result on clean photos. The safety net is structurally sound.\n\n**Inference correctness.**\n- Preprocess: NCHW layout, /255 normalization, bilinear scaling. Matches model contract (1x3x128x128).\n- Sigmoid: `1f / (1f + Math.exp(-v.toDouble()).toFloat())` — early float cast reduces precision marginally but is correct for a 0.5 threshold. Functional match to standard sigmoid.\n- Output cast `Array<Array<Array<FloatArray>>>` → `out[0][0]` extracts the single `[128][128]` plane from `[1][1][128][128]`. Cast is correct and identical in structure to `DocAlignerDetector`'s `hm[0]` extraction.\n- Scale-back: `sx = w/128f`, `sy = h/128f` applied per point before `order4Points`. Correct.\n\n**`isReady=false` during `init()`**: `detect()` takes the same monitor lock as `init()`, so even if `isReady` returned a stale `true` and `detect()` was entered, `s = session ?: return null` would safely return null. No crash path exists.\n\n**Performance.** 128×128 inference is lightweight (~5 ms on modern SoC). The call site is `Dispatchers.Default` for editor autodetect and the batch `ScanPipeline`. One extra ONNX inference per page in batch mode is within acceptable bounds.\n\n**File sizes.** `DocSegDetector.kt` = 126 lines (under 200 target). `CornerCandidates.kt` = 82 lines. Both are within limits.\n\n---\n\n### UI Design Context\n\n- Source: skipped (not configured)\n\n---\n\n### Advisory (non-blocking)\n\n- **No unit test for `DocSegDetector`**: `cornersFromMask` has pure-ish logic (gate thresholds, coordinate scaling, `order4Points` wiring) that could be tested with synthetic probability maps without ORT or OpenCV. Not blocking given the gate conservatism and live safety net, but worth adding.\n- **`detect()` is `@Synchronized` but is a hot path**: Each live-preview frame going through CornerCandidates calls this under the object monitor. In practice 128px inference is fast enough, but the lock serializes concurrent calls (e.g., if a batch and live preview somehow overlap). Low risk given single-`Activity` usage.\n\n---\n\n## Changes Required\n\n- [ ] `DocSegDetector.kt:100-101` Replace `Mat.ones(3,3,...)` and `Mat.ones(5,5,...)` with two `private val` lazy-initialized `getStructuringElement(...)` Mats (same pattern as `DocumentDetector.kt:21-22`) to stop per-call native Mat leaks. — Priority: **MAJOR**\n- [ ] `DocSegDetector.kt:34` Add `@Volatile` to `private var session` (and `env`) so that `isReady` has a JVM visibility guarantee without requiring the monitor lock for the read. — Priority: **MINOR**\n\n## Scope\n\nOnly modify the two items listed. Do not refactor unrelated code.\n\n---\n\n```\n🐙 reviewer ─────────────────────\n  Verdict: REQUEST_CHANGES | Issues: 2개\n  다음: Mat.ones() 커널 누수(MAJOR) 수정 후 재검토. @Volatile minor 수정 선택.\n```"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1207,"cache_read_input_tokens":49164,"cache_creation":{"ephemeral_5m_input_tokens":1207,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011Cbxmpip5QS4qHKKixUE1v","attributionAgent":"reviewer","type":"assistant","uuid":"c000df46-08d5-4583-9748-4ef32300b252","timestamp":"2026-06-12T04:58:49.223Z","userType":"external","entrypoint":"cli","cwd":"/home/lucas/Android-app/ClearCam","sessionId":"2f7c1ed7-2fe3-4930-ad37-1aa8a7b3d290","version":"2.1.173","gitBranch":"main"}