README updated. Fixed bug with OutputFolder.

README.md

@@ -1,191 +1,202 @@
 # Splitter
 
-Splitter is a command line tool for cutting video files into equal length or fixed length segments using FFmpeg.  
-It supports multi threaded splitting, ETA and speed reporting, flexible duration formats, and a simple terminal user interface.
+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.
 
-This tool is intended for creators, archivists, and automation workflows that need fast and predictable video segmentation.
-
----
+![Splitter](splitter.png)
 
 ## Features
 
-- Multi threaded splitting for high performance
-- Equal length segments (default)
-- Fixed length segments with the `--force` option
-- Flexible duration formats (`Ns`, `NmMs`, or plain seconds)
-- Estimate only mode (`--estimate`)
-- Terminal progress display with ETA and speed
-- Custom output filename masks
-- FFmpeg passthrough for advanced users
-- Cross platform (.NET 10)
-
----
+- Multi‑threaded FFmpeg splitting for maximum throughput  
+- Equal or fixed‑length segmentation  
+- Batch input via file masks or list files  
+- Smart cropping with face/body tracking  
+- Rotation correction  
+- ETA, speed, and progress display  
+- FFmpeg passthrough for advanced control  
+- [Potentially] Cross‑platform (.NET 10)
 
 ## Requirements
 
-- FFmpeg and FFprobe installed and available in the system PATH
-- .NET 10 SDK or newer
+- FFmpeg and FFprobe available in system PATH  
+- .NET 10 Runtime or newer  
 
->Note: You must download YuNet ONNX model:
-https://github.com/opencv/opencv_zoo/tree/master/models/face_detection_yunet (github.com in Bing)
+If you want to update model:
+
+- For face detection: [opencv_zoo/models/face_detection_yunet at main · opencv/opencv_zoo](https://github.com/opencv/opencv_zoo/tree/main/models/face_detection_yunet)
+- For body detection: [yolov8s.pt · Ultralytics/YOLOv8 at main](https://huggingface.co/Ultralytics/YOLOv8/blob/main/yolov8s.pt)
+
+To convert models from PyTorch to ONNX, you can use the following command:
+
+```python
+from ultralytics import YOLO
+
+model = YOLO("yolov8x.pt")
+model.export(format="onnx", opset=12, half=False)   # FP32 ONNX
+```
+
+## How It Works
+
+1. Reads total duration using ffprobe  
+2. Parses target duration  
+3. Computes number of segments  
+4. If not forced, equalizes segment lengths  
+5. Runs multiple FFmpeg processes in parallel  
+6. Applies rotation, crop, and tracking if enabled  
+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 
+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 
+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 
+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 
+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 
+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 
+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 
+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 
+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 
+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 
+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 
+window using a combination of Kalman filtering, exponential smoothing, dropout tolerance, 
+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. 
+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, 
+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 
+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 
+when tracking fails. All positions are clamped to valid bounds, guaranteeing that the crop window
+never leaves the video frame.
 
 ---
 
 ## Usage
 
 ```
-splitter <input.mp4> <output_folder> [options] [--] <ffmpeg passthrough>
+splitter [<input.mp4> ...] [options] [--] <ffmpeg passthrough>
 ```
 
+Inputs may be provided directly, via `--file=...`, or using file masks such as `videos/*.mp4`.
+
 ---
 
 ## Options
 
-### --duration=<value>
-
-Overrides the default 60 second target chunk size.
-
-Accepted formats:
-
-| Format | Meaning |
-|--------|---------|
-| Ns | N seconds |
-| NmMs | N minutes and M seconds |
-| N | N seconds (plain number) |
-
-Examples:
-
-```
---duration=90s
---duration=2m30s
---duration=45
-```
-
-Without `--force`, the program adjusts the segment length so that all segments are equal.
+Below is a clean, ASCII‑only **options table** version of your content.  
+All option names are preserved exactly, and descriptions are consolidated for clarity.
 
 ---
 
-### --force
+## Options
 
-Uses the duration exactly as provided.  
-The last segment may be shorter.
-
-Example:
-
-```
---duration=45 --force
-```
+| Option | Description |
+|--------|-------------|
+| **--out=<folder>** | Output folder for generated segments. Default: `<input folder>/Splitter`. |
+| **--file=<path>** | Input file list or file mask. If omitted, the first non‑option argument is used as input. Examples: `--file=videos/*.mp4`, `--file=file_list.txt`. |
+| **--mask=<pattern>** | Custom output filename pattern. Default: `[NAME]_seg[NN].[EXT]`. Supports `[NAME]`, `[N]`, `[NN]`, `[NNN]`, `[NNNN]`, `[EXT]`. Example: `--mask="[NAME]_[NNNN].mp4"`. |
+| **--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. |
+| **--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). |
+| **--gravitate=<x:y>** | Bias the crop window toward a normalized point in the frame. Example: `--gravitate=0.2:0.5`. |
+| **--text** | Use plain‑text logging instead of the rich terminal UI. |
+| **--single-thread** | Disable parallel FFmpeg execution. Useful for debugging or low‑resource systems. |
+| **--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. |
 
 ---
 
-### --estimate
-
-Prints calculated segment information and exits.  
-No splitting is performed.
-
-Example:
-
-```
-splitter video.mp4 out/ --duration=2m30s --estimate
-```
-
----
-
-### --mask=<pattern>
-
-Custom output filename pattern.
-
-Default:
-
-```
-<OriginalName>_Seg%03d.mp4
-```
-
-Supports:
-
-- `%03d` for zero padded index
-- `%d` for plain index
-
-Example:
-
-```
---mask="Part_%03d.mp4"
-```
-
----
-
-### FFmpeg passthrough
+## FFmpeg Passthrough
 
 Anything after `--` is passed directly to FFmpeg.
 
 Example:
-
 ```
-splitter video.mp4 out/ -- -an -sn
+splitter video.mp4 --force --duration=45 -- -an -sn
 ```
 
 ---
 
-## How It Works
+## Input and Output Behavior
 
-1. Reads total duration using ffprobe  
-2. Parses the target duration (default 60 seconds or from `--duration=`)  
-3. Computes the number of segments  
-4. If not forced, adjusts segment length so all segments are equal  
-5. Runs multiple FFmpeg processes in parallel  
-6. Displays progress, ETA, and speed in the terminal  
+- `input.mp4` may be a file mask (`videos/*.mp4`)  
+- Output filenames follow the `--mask` pattern  
+- Output folder defaults to `<input folder>/Splitter` unless overridden
 
 ---
 
 ## Examples
 
-Split into equal 60 second segments:
-
+Split into equal 60‑second segments:
 ```
-splitter video.mp4 out/
+splitter vertical-video.mp4
 ```
 
-Split into equal 90 second segments:
-
+Split into equal 90‑second segments:
 ```
-splitter video.mp4 out/ --duration=90s
+splitter vertical-video.mp4 --duration=90s
 ```
 
-Split into fixed 45 second segments:
-
+Custom naming:
 ```
-splitter video.mp4 out/ --duration=45 --force
+splitter vertical-video.mp4 --duration=2m30s --mask="[NAME]_[NNNN].mp4"
 ```
 
 Estimate only:
-
 ```
-splitter video.mp4 out/ --duration=2m30s --estimate
+splitter vertical-video.mp4 --estimate
 ```
 
-Custom output names:
-
+Fixed 45‑second segments with passthrough:
 ```
-splitter video.mp4 out/ --mask="Clip_%03d.mp4"
+splitter vertical-video.mp4 --force --duration=45 -- -an -sn
 ```
 
-Pass extra flags to FFmpeg:
-
+Smart crop for Shorts:
 ```
-splitter video.mp4 out/ -- -an -sn
+splitter horizontal-video.mp4 --out=Cropped/ --crop
 ```
 
----
-
-## Building
-
+Batch processing with body tracking:
 ```
-dotnet build
-```
-
-Or run directly:
-
-```
-dotnet run -- video.mp4 out/
+splitter --file=file_names.txt --out=Cropped/ --crop --detect=body
 ```
 

splitter-cli/CommandLine.cs

@@ -211,6 +211,8 @@ public sealed class CommandLine
         {
             var firstInput = Jobs[0].InputFile;
             Master.OutputFolder = Path.Combine(Path.GetDirectoryName(Path.GetFullPath(firstInput))!, "Splitter");
+            foreach (var job in Jobs)
+                job.OutputFolder = Master.OutputFolder;
             Console.WriteLine($"Using default output folder: {Master.OutputFolder}");
         }
     }

splitter.slnx

@@ -1,3 +1,9 @@
 <Solution>
+  <Folder Name="/Solution items/">
+    <File Path=".github/workflows/publish.yml" />
+    <File Path=".gitignore" />
+    <File Path="LICENSE.txt" />
+    <File Path="README.md" />
+  </Folder>
   <Project Path="splitter-cli/splitter.csproj" />
 </Solution>