Added automatic rotation detection.

README.md

@@ -1,25 +1,25 @@
 # Splitter
 
-Splitter is a high‑performance command line tool for cutting one or more video files into equal or fixed‑length segments using multi‑threaded FFmpeg execution.  
+Splitter is a high‑performance command line tool for cutting one or more video files into equal or fixed‑length segments using multi‑threaded FFmpeg execution.
 It supports batch input, flexible duration formats, rotation, smart face/body‑aware cropping, ETA and speed reporting, and both rich and plain‑text terminal output.
 
 ![Splitter](splitter.png)
 
 ## Features
 
-- Multi‑threaded FFmpeg splitting for maximum throughput  
+- Multi‑threaded FFmpeg splitting for maximum throughput
-- Equal or fixed‑length segmentation  
+- Equal or fixed‑length segmentation
-- Batch input via file masks or list files  
+- Batch input via file masks or list files
-- Smart cropping with face/body tracking  
+- Smart cropping with face/body tracking
-- Rotation correction  
+- Rotation correction
-- ETA, speed, and progress display  
+- ETA, speed, and progress display
-- FFmpeg passthrough for advanced control  
+- FFmpeg passthrough for advanced control
 - [Potentially] Cross‑platform (.NET 10)
 
 ## Requirements
 
-- FFmpeg and FFprobe available in system PATH  
+- FFmpeg and FFprobe available in system PATH
-- .NET 10 Runtime or newer  
+- .NET 10 Runtime or newer
 
 If you want to update model:
 
@@ -37,74 +37,90 @@ model.export(format="onnx", opset=12, half=False)   # FP32 ONNX
 
 ## How It Works
 
-1. Reads total duration using ffprobe  
+1. Reads total duration using ffprobe
-2. Parses target duration  
+2. Parses target duration
-3. Computes number of segments  
+3. Computes number of segments
-4. If not forced, equalizes segment lengths  
+4. If not forced, equalizes segment lengths
-5. Runs multiple FFmpeg processes in parallel  
+5. Runs multiple FFmpeg processes in parallel
-6. Applies rotation, crop, and tracking if enabled  
+6. Applies rotation, crop, and tracking if enabled
-7. Displays progress, ETA, and speed  
+7. Displays progress, ETA, and speed
 
----
 ## Face Tracking vs Body Tracking
 
-Face tracking and body tracking serve different purposes, and Splitter supports both because each 
+Face tracking and body tracking serve different purposes, and Splitter supports both because each
-excels in different recording environments. When converting horizontal footage into vertical clips, 
+excels in different recording environments. When converting horizontal footage into vertical clips,
 the choice of detector determines how stable, reliable, and natural the automated camera motion will be.
 
 ![Face vs Body Tracking](tracking.png)
 
 ### Face Tracking Using UltraFace 320
 
-Splitter uses the UltraFace 320 ONNX model to perform lightweight, real‑time face detection on each 
+Splitter uses the UltraFace 320 ONNX model to perform lightweight, real‑time face detection on each
-frame of the input video. The detector produces bounding boxes for visible faces, and the tracking 
+frame of the input video. The detector produces bounding boxes for visible faces, and the tracking
 system maintains a stable, smoothed target region across time. This is achieved by combining per‑frame
-detections with temporal smoothing (EMA), dropout tolerance, and camera easing. The result is a 
+detections with temporal smoothing (EMA), dropout tolerance, and camera easing. The result is a
 continuous, stable crop window that follows the performer even when the face is partially occluded,
 briefly lost, or moving rapidly.
 
-During segmentation, the crop window is recalculated for every frame, ensuring that each output 
+During segmentation, the crop window is recalculated for every frame, ensuring that each output
-segment inherits the same smooth camera motion. This makes the vertical clips appear as if they 
+segment inherits the same smooth camera motion. This makes the vertical clips appear as if they
-were recorded with a dedicated portrait‑oriented camera operator. The UltraFace 320 model is 
+were recorded with a dedicated portrait‑oriented camera operator. The UltraFace 320 model is
-fast enough to run alongside multi‑threaded FFmpeg splitting without becoming a bottleneck, 
+fast enough to run alongside multi‑threaded FFmpeg splitting without becoming a bottleneck,
 making it suitable for long recordings and batch processing.
 
 ### Benefits of Full‑Body Detection Using YOLOv8s for Live Gig Recordings
 
-When recording concerts or live gigs, performers often move unpredictably, turn away from the 
+When recording concerts or live gigs, performers often move unpredictably, turn away from the
-camera, or become partially obscured by lighting, instruments, or stage effects. 
+camera, or become partially obscured by lighting, instruments, or stage effects.
-Full‑body detection using a YOLOv8s ONNX model provides a more reliable tracking anchor than 
+Full‑body detection using a YOLOv8s ONNX model provides a more reliable tracking anchor than
 face detection alone. Because YOLOv8s can detect the entire human silhouette, the tracker
-maintains stable framing even when the face is not visible, when the performer is far from 
+maintains stable framing even when the face is not visible, when the performer is far from
-the camera, or when stage lighting makes facial features hard to detect. This produces vertical 
+the camera, or when stage lighting makes facial features hard to detect. This produces vertical
 clips that feel intentional and professionally framed, with fewer sudden jumps or lost‑tracking
-moments. For creators converting horizontal gig footage into short vertical clips for YouTube 
+moments. For creators converting horizontal gig footage into short vertical clips for YouTube
-Shorts or TikTok, body‑based tracking significantly improves consistency, reduces manual editing, 
+Shorts or TikTok, body‑based tracking significantly improves consistency, reduces manual editing,
 and preserves the energy and motion of the performance.
 
 ### Automated Camera Control
 
-Splitter includes an automated camera control system that simulates the behavior of a virtual 
+Splitter includes an automated camera control system that simulates the behavior of a virtual
-camera operator when generating vertical crops from horizontal footage. The goal is to maintain 
+camera operator when generating vertical crops from horizontal footage. The goal is to maintain
 smooth, intentional framing around the tracked subject, even when detections are noisy, intermittent,
 or temporarily lost.
 
-The controller receives object detections (face or body) and converts them into a stable crop 
+The controller receives object detections (face or body) and converts them into a stable crop
-window using a combination of Kalman filtering, exponential smoothing, dropout tolerance, 
+window using a combination of Kalman filtering, exponential smoothing, dropout tolerance,
-and a three‑state tracking model. The Kalman filter provides predictive motion smoothing, 
+and a three‑state tracking model. The Kalman filter provides predictive motion smoothing,
-while the EMA factor blends the predicted position with the previous camera center to avoid jitter. 
+while the EMA factor blends the predicted position with the previous camera center to avoid jitter.
-The camera easing value controls how quickly the virtual camera follows the subject, producing 
+The camera easing value controls how quickly the virtual camera follows the subject, producing
 natural‑looking motion rather than abrupt jumps.
 
-When detections disappear, the controller enters one of two fallback modes. In LostFreeze mode, 
+When detections disappear, the controller enters one of two fallback modes. In LostFreeze mode,
-the camera holds its last known position for a configurable number of frames, preventing sudden 
+the camera holds its last known position for a configurable number of frames, preventing sudden
-jumps during brief occlusions. If the subject remains lost beyond that threshold, the controller 
+jumps during brief occlusions. If the subject remains lost beyond that threshold, the controller
-transitions to LostDrift mode, slowly drifting the camera back toward a neutral center position. 
+transitions to LostDrift mode, slowly drifting the camera back toward a neutral center position.
-This prevents the crop from drifting off‑screen and ensures that the output remains usable even 
+This prevents the crop from drifting off‑screen and ensures that the output remains usable even
 when tracking fails. All positions are clamped to valid bounds, guaranteeing that the crop window
 never leaves the video frame.
 
----
+### Automatic rotation detection
+
+The rotation‑estimation method is based on analyzing the distribution of gradient orientations within 
+a video frame. After converting the frame to grayscale, the algorithm computes horizontal and vertical 
+image gradients using Sobel operators and derives per‑pixel gradient magnitudes and orientations. 
+These orientations are folded into the range [0, 180) and accumulated into a fixed‑size, 
+magnitude‑weighted histogram. The histogram represents the structural edge distribution of the frame, 
+independent of brightness fluctuations or local lighting artifacts. By comparing the total gradient
+energy concentrated near 0 degrees (vertical edges) with the energy near 90 degrees (horizontal edges), 
+the method determines whether the frame is more consistent with an upright or sideways orientation.
+
+This approach is designed for environments where brightness‑based cues are unreliable, such as
+live concerts with strobe lights, LED walls, haze, and crowd movement. It relies solely on geometric 
+edge structure, which remains stable even under extreme lighting variation. The implementation is 
+optimized for high‑throughput video processing: all intermediate Mats, buffers, and histograms are 
+preallocated, and pixel data is accessed directly through pointers to avoid per‑frame memory 
+allocation. The method is intentionally biased toward the upright orientation, returning a sideways
+classification only when the horizontal‑edge energy significantly exceeds the vertical‑edge energy.
 
 ## Usage
 
@@ -114,11 +130,10 @@ splitter [<input.mp4> ...] [options] [--] <ffmpeg passthrough>
 
 Inputs may be provided directly, via `--file=...`, or using file masks such as `videos/*.mp4`.
 
----
 
 ## Options
 
-Below is a clean, ASCII‑only **options table** version of your content.  
+Below is a clean, ASCII‑only **options table** version of your content.
 All option names are preserved exactly, and descriptions are consolidated for clarity.
 
 ---
@@ -133,6 +148,7 @@ All option names are preserved exactly, and descriptions are consolidated for cl
 | **--duration=<value>** | Override target segment duration. Formats: `Ns`, `NmMs`, `N`. Examples: `--duration=90s`, `--duration=2m30s`, `--duration=45`. Without `--force`: max 58 seconds, equalized across segments. |
 | **--force** | Use the duration exactly as provided. Last segment may be shorter. |
 | **--rotate=<degrees>** | Rotate video by 90, 180, or 270 degrees. Useful for correcting orientation metadata. |
+| **--rotate-auto** | Use automatic rotation detection. |
 | **--estimate** | Print calculated segment information and exit. No splitting is performed. |
 | **--crop[=<w:h>]** | Crop video to a target width and height with face/body tracking. Default: 607x1080. Ideal for Shorts, TikTok, Reels. |
 | **--detect=<name>** | Object detector for tracking. Values: `face` (UltraFace), `body` (YoloOnnx, default), `none` (center crop). |
@@ -142,8 +158,6 @@ All option names are preserved exactly, and descriptions are consolidated for cl
 | **--debug** | Show debug overlay during tracking. No cropping performed, but crop region shown. |
 | **-p:<name>=<value>** | Set custom parameters for the object detector. Example: `-p:confidence=0.5`. Defaults: DropoutToleranceFrames=20, EmaFactor=0.65, CameraEasing=0.03, LostFreezeFrames=60. |
 
----
-
 ## FFmpeg Passthrough
 
 Anything after `--` is passed directly to FFmpeg.
@@ -153,16 +167,12 @@ Example:
 splitter video.mp4 --force --duration=45 -- -an -sn
 ```
 
----
-
 ## Input and Output Behavior
 
-- `input.mp4` may be a file mask (`videos/*.mp4`)  
+- `input.mp4` may be a file mask (`videos/*.mp4`)
-- Output filenames follow the `--mask` pattern  
+- Output filenames follow the `--mask` pattern
 - Output folder defaults to `<input folder>/Splitter` unless overridden
 
----
-
 ## Examples
 
 Split into equal 60‑second segments:

splitter-cli/CameraController.cs

@@ -1,5 +1,4 @@
-using System;
+using OpenCvSharp;
-using OpenCvSharp;
 
 namespace splitter;
 

splitter-cli/CommandLine.cs

@@ -1,8 +1,4 @@
-using System;
+using System.Globalization;
-using System.Collections.Generic;
-using System.Globalization;
-using System.Text;
-using OpenCvSharp;
 
 namespace splitter;
 
@@ -22,6 +18,7 @@ public class SingleJob
     public bool                       ForceFixed             { get; set; }
     public bool                       SingleThreaded         { get; set; }
     public int?                       Rotate                 { get; set; }
+    public bool                       RotateAuto             { get; set; }
     public Dictionary<string, string> Parameters             { get; set; } = [];
 
     public void Override<T>(ref T member, string name)
@@ -143,6 +140,10 @@ public sealed class CommandLine
             {
                 Master.SingleThreaded = true;
             }
+            else if (arg == "--rotate-auto")
+            {
+                Master.RotateAuto = true;
+            }
             else if (arg.StartsWith("--gravitate="))
             {
                 var val = arg.Substring("--gravitate=".Length);
@@ -197,6 +198,7 @@ public sealed class CommandLine
             ForceFixed             = Master.ForceFixed,
             SingleThreaded         = Master.SingleThreaded,
             Rotate                 = Master.Rotate,
+            RotateAuto             = Master.RotateAuto,
             Parameters             = new Dictionary<string, string>(Master.Parameters)
         }).ToArray();
 
@@ -371,6 +373,9 @@ Options:
   --rotate=<degrees>     Rotate video by specified degrees (90, 180, 270).
                          Useful for videos with incorrect orientation metadata.
 
+  --rotate-auto          Auto-detect rotation and rotate accordingly.
+                         Uses edge orientation statistics to determine if video is rotated.
+
   --estimate             Print calculated segment information and exit.
                          No splitting is performed.
 
@@ -393,7 +398,7 @@ Options:
   --debug                Show debug overlay during face tracking.
 
   -p:<name>=<value>      Set a custom parameter for the object detector.
-                         Example: -p:confidence=0.5
+                         Example: -p:EmaFactor=0.65
 
                          Tracking splitter defaults:
                             DropoutToleranceFrames = 20;
@@ -401,6 +406,12 @@ Options:
                             CameraEasing           = 0.03;
                             LostFreezeFrames       = 60;   
 
+                         Rotation detector defaults:
+                            RotationDetectorSampleCount  = 5;
+                            RotationDetectorSampleLength = 0.15; 
+                            RotationDetectorFrameWidth   = 320;
+                            RotationDetectorFrameHeight  = 180;
+
 Passthrough:
   Anything after -- is passed directly to ffmpeg.
 

splitter-cli/FrameRotationDetector.cs

@@ -0,0 +1,102 @@
+using OpenCvSharp;
+
+namespace splitter;
+
+public sealed class FrameRotationDetector
+{
+    private readonly Mat _gray;
+    private readonly Mat _gx;
+    private readonly Mat _gy;
+    private readonly Mat _mag;
+    private readonly Mat _angle;
+
+    private readonly float[] _hist;
+
+    private readonly int _w;
+    private readonly int _h;
+    private readonly int _bins;
+
+    public FrameRotationDetector(int width = 320, int height = 180, int bins = 36)
+    {
+        _w = width;
+        _h = height;
+        _bins = bins;
+
+        _gray = new Mat(height, width, MatType.CV_8UC1);
+        _gx = new Mat(height, width, MatType.CV_32F);
+        _gy = new Mat(height, width, MatType.CV_32F);
+        _mag = new Mat(height, width, MatType.CV_32F);
+        _angle = new Mat(height, width, MatType.CV_32F);
+
+        _hist = new float[bins];   // allocated once
+    }
+
+    public int GetRotation(Mat frame)
+    {
+        // 1. Grayscale
+        Cv2.CvtColor(frame, _gray, ColorConversionCodes.BGR2GRAY);
+
+        // 2. Sobel
+        Cv2.Sobel(_gray, _gx, MatType.CV_32F, 1, 0, 3);
+        Cv2.Sobel(_gray, _gy, MatType.CV_32F, 0, 1, 3);
+
+        // 3. Magnitude + angle
+        Cv2.CartToPolar(_gx, _gy, _mag, _angle, angleInDegrees: true);
+
+        // 4. Clear histogram
+        for (int i = 0; i < _bins; i++)
+            _hist[i] = 0;
+
+        float binSize = 180f / _bins;
+
+        unsafe
+        {
+            float* anglePtr = (float*)_angle.Data;
+            float* magPtr   = (float*)_mag.Data;
+
+            int total = _w * _h;
+
+            for (int i = 0; i < total; i++)
+            {
+                float m = magPtr[i];
+                if (m < 5f) continue; // ignore weak gradients
+
+                float a = anglePtr[i];
+                if (a < 0) a += 360f;
+                a = a % 180f;
+
+                int bin = (int)(a / binSize);
+                if (bin < 0) bin = 0;
+                if (bin >= _bins) bin = _bins - 1;
+
+                _hist[bin] += m;
+            }
+        }
+
+        // 5. Energy around 0° vs 90°
+        float e0 = 0, e90 = 0;
+        int window = 3;
+
+        int bin0  = 0;
+        int bin90 = _bins / 2;
+
+        for (int i = -window; i <= window; i++)
+        {
+            e0 += _hist[Wrap(bin0 + i)];
+            e90 += _hist[Wrap(bin90 + i)];
+        }
+
+        // 6. Decide upright vs sideways
+        if (e90 > e0 * 1.6f)
+            return 90;   // sideways
+
+        return 0;         // upright (concert default)
+    }
+
+    private int Wrap(int b)
+    {
+        if (b < 0) return b + _bins;
+        if (b >= _bins) return b - _bins;
+        return b;
+    }
+}

splitter-cli/IObjectDetector.cs

@@ -4,5 +4,5 @@ namespace splitter;
 
 public interface IObjectDetector : IDisposable
 {
-    List<(Rect box, Point2f center)> DetectAll(Mat frameCont, int width, int height);
+    List<(Rect box, Point2f center)> DetectAll(Mat frameCont);
 }

splitter-cli/ISegmentProcessor.cs

@@ -1,8 +1,4 @@
-using System;
+namespace splitter;
-using System.Collections.Generic;
-using System.Text;
-
-namespace splitter;
 
 public interface ISegmentProcessor
 {

splitter-cli/LoggingBase.cs

@@ -1,5 +1,4 @@
-using System;
+namespace splitter;
-namespace splitter;
 
 public abstract class LoggingBase(ILogger _logger, int _progressLine)
 {

splitter-cli/ProbeVideo.cs

@@ -1,8 +1,5 @@
-using System;
+using System.Diagnostics;
-using System.Collections.Generic;
-using System.Diagnostics;
 using System.Globalization;
-using System.Text;
 
 namespace splitter;
 
@@ -11,12 +8,31 @@ public record VideoInfo(
     int Width,
     int Height,
     double Fps,
-    double Bitrate
+    double Bitrate,
+    int Rotation = 0
 );
 
 public static class ProbeVideo
 {
-    public static VideoInfo Probe(string inputFile)
+    public static async Task<VideoInfo> Probe(SingleJob job)
+    {
+        var info = ProbeSize(job.InputFile);
+        if ( job.RotateAuto)
+        {
+            var rotation = await ProbeRotation(job, info.Duration);
+            info = info with { Rotation = rotation };
+        }
+
+        return info;
+    }
+
+    private static async Task<int> ProbeRotation(SingleJob job, double duration)
+    {
+        var rotation = await new VideoRotationSampler(job).DetectRotationAsync(job.InputFile, duration);
+        return rotation;
+    }
+
+    private static VideoInfo ProbeSize(string inputFile)
     {
         var args =
         "-v error " +

splitter-cli/Properties/launchSettings.json

@@ -10,7 +10,7 @@
     },
     "Debug": {
       "commandName": "Project",
-      "commandLineArgs": "\"C:\\Users\\uncls\\Pictures\\2026\\2026 - Secret Rule\\20260426_212004.mp4\" --crop --detect=body --debug --single-thread --text"
+      "commandLineArgs": "\"C:\\Users\\uncls\\Pictures\\2026\\2026 - Laura Cox\\Video\\MAH00041.MP4\" --rotate-auto"
     }
   }
 }

splitter-cli/SimpleSplitter.cs

@@ -1,9 +1,5 @@
-using System;
+using System.Diagnostics;
-using System.Collections.Generic;
-using System.Diagnostics;
 using System.Globalization;
-using System.Text;
-using FFmpeg.AutoGen;
 
 namespace splitter;
 

splitter-cli/SpectreConsoleLogger.cs

@@ -1,9 +1,4 @@
-using System;
+using System.Text;
-using System.Collections.Generic;
-using System.Linq;
-using System.Text;
-using System.Threading;
-using System.Threading.Tasks;
 using Spectre.Console;
 using Spectre.Console.Rendering;
 

splitter-cli/TrackingSplitter.cs

@@ -1,9 +1,6 @@
-using System;
+using System.Diagnostics;
-using System.Diagnostics;
 using System.Globalization;
-using System.IO;
 using System.Runtime.InteropServices;
-using System.Threading.Tasks;
 using OpenCvSharp;
 
 namespace splitter;
@@ -112,7 +109,7 @@ public class TrackingSplitter : LoggingBase, ISegmentProcessor, IDisposable
             // input frame → Mat
             Marshal.Copy(inBuffer, 0, frameMat.Data, inBytes);
 
-            var objects = _detector.DetectAll(frameMat, videoWidth, videoHeight);
+            var objects = _detector.DetectAll(frameMat);
             var primary = SelectTrackedObject(objects, kalman.LastMeasurement);
 
             camera.Update(primary);

splitter-cli/UltraFaceDetector.cs

@@ -1,5 +1,4 @@
 using System.Runtime.InteropServices;
-using NcnnDotNet.Layers;
 using OpenCvSharp;
 using UltraFaceDotNet;
 
@@ -25,7 +24,7 @@ public sealed class UltraFaceDetector: LoggingBase, IDisposable, IObjectDetector
         _ultraFace = UltraFace.Create(param);
     }
 
-    public List<(Rect box, Point2f center)> DetectAll(Mat frameCont, int width, int height)
+    public List<(Rect box, Point2f center)> DetectAll(Mat frameCont)
     {
         // Convert to byte[] for UltraFace
         var bytesFull = frameCont.Rows * frameCont.Cols * frameCont.ElemSize();
@@ -44,8 +43,8 @@ public sealed class UltraFaceDetector: LoggingBase, IDisposable, IObjectDetector
                 using var mat = NcnnDotNet.Mat.FromPixels(
                 (IntPtr)p,
                 NcnnDotNet.PixelType.Bgr,     // BGR24 input
-                width,
+                frameCont.Width,
-                height);
+                frameCont.Height);
 
                 var faces = _ultraFace.Detect(mat);
                 if (faces == null)

splitter-cli/VideoRotationSampler.cs

@@ -0,0 +1,169 @@
+using OpenCvSharp;
+using System.Diagnostics;
+
+namespace splitter;
+
+public sealed class VideoRotationSampler
+{
+    private readonly FrameRotationDetector _detector = new FrameRotationDetector();
+
+    public static int    RotationDetectorSampleCount  = 20;
+    public static double RotationDetectorSampleLength = 0.15;  // seconds to decode per probe
+    public static int    RotationDetectorFrameWidth   = 320;
+    public static int    RotationDetectorFrameHeight  = 180;
+
+    // --- Zero-allocation buffers ---
+    private readonly byte[] _buffer;
+    private readonly Mat _frameMat;
+
+    public VideoRotationSampler(SingleJob _master)
+    {
+        if (_master.Parameters.TryGetValue("RotationDetectorSampleCount", out var s))
+            RotationDetectorSampleCount  = int.Parse(s);
+        if (_master.Parameters.TryGetValue("RotationDetectorSampleLength", out s))
+            RotationDetectorSampleLength = double.Parse(s);
+        if (_master.Parameters.TryGetValue("RotationDetectorFrameWidth", out s))
+            RotationDetectorFrameWidth   = int.Parse(s);
+        if (_master.Parameters.TryGetValue("RotationDetectorFrameHeight", out s))
+            RotationDetectorFrameHeight  = int.Parse(s);
+
+        int w = RotationDetectorFrameWidth;
+        int h = RotationDetectorFrameHeight;
+
+        _buffer    = new byte[w * h * 3];                  // raw BGR24 buffer
+        _frameMat  = new Mat(h, w, MatType.CV_8UC3);       // wraps buffer
+    }
+
+    public async Task<int> DetectRotationAsync(
+         string inputFile,
+         double videoLengthSeconds)
+    {
+        if (videoLengthSeconds <= 0)
+            return 0;
+
+        var rotations = new List<int>();
+
+        for (int i = 0; i < RotationDetectorSampleCount; i++)
+        {
+            double t = videoLengthSeconds * (i + 1) / (RotationDetectorSampleCount + 1);
+
+            var frame = await DecodeSingleFrameAsync(
+                inputFile,
+                t,
+                RotationDetectorSampleLength,
+                RotationDetectorFrameWidth,
+                RotationDetectorFrameHeight);
+
+            if (frame != null && !frame.Empty())
+            {
+                int rot = _detector.GetRotation(frame);
+                rotations.Add(rot);
+            }
+        }
+
+        if (rotations.Count == 0)
+            return 0;
+
+        return Majority(rotations);
+    }
+
+    private static int Majority(List<int> values)
+    {
+        var counts = new Dictionary<int, int>();
+        foreach (var v in values)
+        {
+            if (!counts.ContainsKey(v)) counts[v] = 0;
+            counts[v]++;
+        }
+
+        int best = 0;
+        int bestCount = 0;
+
+        foreach (var kv in counts)
+        {
+            if (kv.Value > bestCount)
+            {
+                best = kv.Key;
+                bestCount = kv.Value;
+            }
+        }
+
+        return best;
+    }
+
+    private async Task<Mat?> DecodeSingleFrameAsync(
+        string inputFile,
+        double start,
+        double length,
+        int width,
+        int height)
+    {
+        var p = StartFfmpegDecode(inputFile, start, length, rotate: null, plainText: false);
+
+        int needed = _buffer.Length;
+        int read = 0;
+
+        using var stdout = p.StandardOutput.BaseStream;
+
+        while (read < needed)
+        {
+            int r = await stdout.ReadAsync(_buffer, read, needed - read);
+            if (r == 0)
+                return null;
+            read += r;
+        }
+
+        try { p.Kill(); } catch { }
+
+        // Copy buffer → Mat (no new Mat)
+        System.Runtime.InteropServices.Marshal.Copy(_buffer, 0, _frameMat.Data, _buffer.Length);
+
+        return _frameMat;
+    }
+
+    private Process StartFfmpegDecode(
+        string inputFile,
+        double start,
+        double length,
+        int? rotate,
+        bool plainText)
+    {
+        var ss = start.ToString("0.###", System.Globalization.CultureInfo.InvariantCulture);
+        var t  = length.ToString("0.###", System.Globalization.CultureInfo.InvariantCulture);
+
+        // FFmpeg does the resize + format conversion
+        var args =
+            $"-ss {ss} -t {t} -i \"{inputFile}\" " +
+            "-an -sn " +
+            $"-vf scale={RotationDetectorFrameWidth}:{RotationDetectorFrameHeight},format=bgr24 " +
+            "-f rawvideo -";
+
+        var psi = new ProcessStartInfo
+        {
+            FileName               = "ffmpeg",
+            Arguments              = args,
+            RedirectStandardOutput = true,
+            RedirectStandardError  = true,
+            UseShellExecute        = false,
+            CreateNoWindow         = true
+        };
+
+        var p = new Process { StartInfo = psi };
+        p.Start();
+
+        // Optional stderr logging
+        _ = Task.Run(() =>
+        {
+            try
+            {
+                string? line;
+                while ((line = p.StandardError.ReadLine()) != null)
+                    if (plainText)
+                        Console.WriteLine($"[ffmpeg-decode] {line}");
+            }
+            catch { }
+        });
+
+        return p;
+    }
+}

splitter-cli/YoloOnnxObjectDetector.cs

@@ -1,8 +1,4 @@
-using System;
+using System.Runtime.CompilerServices;
-using System.Collections.Generic;
-using System.Linq;
-using System.Numerics;
-using System.Runtime.CompilerServices;
 using Microsoft.ML.OnnxRuntime;
 using Microsoft.ML.OnnxRuntime.Tensors;
 using OpenCvSharp;
@@ -83,7 +79,7 @@ public sealed class YoloOnnxObjectDetector : LoggingBase, IObjectDetector, IDisp
         _inputs.Add(NamedOnnxValue.CreateFromTensor(_inputName, _inputTensor));
     }
 
-    public List<(Rect box, Point2f center)> DetectAll(Mat frameCont, int width, int height)
+    public List<(Rect box, Point2f center)> DetectAll(Mat frameCont)
     {
         if (frameCont.Empty())
         {

splitter-cli/splitter.cs

@@ -1,7 +1,5 @@
 using System.Collections.Concurrent;
 using System.Diagnostics;
-using System.Globalization;
-using System.Text;
 using Spectre.Console;
 using splitter;
 
@@ -78,7 +76,7 @@ static partial class Program
         if (!Directory.Exists(job.OutputFolder))
             Directory.CreateDirectory(job.OutputFolder);
 
-        var info = ProbeVideo.Probe(job.InputFile);
+        var info = await ProbeVideo.Probe(job);
         if (info.Duration <= 0)
         {
             LogError($"{baseName}: Could not read duration.");