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213 lines
10 KiB
Markdown
213 lines
10 KiB
Markdown
# Splitter
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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.
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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.
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## Features
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- Multi-threaded FFmpeg splitting for maximum throughput
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- Equal or fixed-length segmentation
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- Batch input via file masks or list files
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- Smart cropping with face/body tracking
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- Rotation correction
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- ETA, speed, and progress display
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- FFmpeg passthrough for advanced control
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- [Potentially] Cross-platform (.NET 10)
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## Requirements
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- FFmpeg and FFprobe available in system PATH
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- .NET 10 Runtime or newer
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If you want to update model:
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- 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)
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- For body detection: [yolov8s.pt · Ultralytics/YOLOv8 at main](https://huggingface.co/Ultralytics/YOLOv8/blob/main/yolov8s.pt)
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To convert models from PyTorch to ONNX, you can use the following command:
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```python
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from ultralytics import YOLO
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model = YOLO("yolov8x.pt")
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model.export(format="onnx", opset=12, half=False) # FP32 ONNX
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```
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## How It Works
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1. Reads total duration using ffprobe
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2. Parses target duration
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3. Computes number of segments
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4. If not forced, equalizes segment lengths
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5. Runs multiple FFmpeg processes in parallel
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6. Applies rotation, crop, and tracking if enabled
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7. Displays progress, ETA, and speed
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## Face Tracking vs Body Tracking
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Face tracking and body tracking serve different purposes, and Splitter supports both because each
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excels in different recording environments. When converting horizontal footage into vertical clips,
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the choice of detector determines how stable, reliable, and natural the automated camera motion will be.
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### Face Tracking Using UltraFace 320
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Splitter uses the UltraFace 320 ONNX model to perform lightweight, real-time face detection on each
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frame of the input video. The detector produces bounding boxes for visible faces, and the tracking
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system maintains a stable, smoothed target region across time. This is achieved by combining per-frame
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detections with temporal smoothing (EMA), dropout tolerance, and camera easing. The result is a
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continuous, stable crop window that follows the performer even when the face is partially occluded,
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briefly lost, or moving rapidly.
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During segmentation, the crop window is recalculated for every frame, ensuring that each output
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segment inherits the same smooth camera motion. This makes the vertical clips appear as if they
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were recorded with a dedicated portrait-oriented camera operator. The UltraFace 320 model is
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fast enough to run alongside multi-threaded FFmpeg splitting without becoming a bottleneck,
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making it suitable for long recordings and batch processing.
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### Benefits of Full-Body Detection Using YOLOv8s for Live Gig Recordings
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When recording concerts or live gigs, performers often move unpredictably, turn away from the
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camera, or become partially obscured by lighting, instruments, or stage effects.
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Full-body detection using a YOLOv8s ONNX model provides a more reliable tracking anchor than
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face detection alone. Because YOLOv8s can detect the entire human silhouette, the tracker
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maintains stable framing even when the face is not visible, when the performer is far from
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the camera, or when stage lighting makes facial features hard to detect. This produces vertical
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clips that feel intentional and professionally framed, with fewer sudden jumps or lost-tracking
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moments. For creators converting horizontal gig footage into short vertical clips for YouTube
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Shorts or TikTok, body-based tracking significantly improves consistency, reduces manual editing,
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and preserves the energy and motion of the performance.
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### Automated Camera Control
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Splitter includes an automated camera control system that simulates the behavior of a virtual
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camera operator when generating vertical crops from horizontal footage. The goal is to maintain
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smooth, intentional framing around the tracked subject, even when detections are noisy, intermittent,
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or temporarily lost.
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The controller receives object detections (face or body) and converts them into a stable crop
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window using a combination of Kalman filtering, exponential smoothing, dropout tolerance,
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and a three-state tracking model. The Kalman filter provides predictive motion smoothing,
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while the EMA factor blends the predicted position with the previous camera center to avoid jitter.
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The camera easing value controls how quickly the virtual camera follows the subject, producing
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natural-looking motion rather than abrupt jumps.
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When detections disappear, the controller enters one of two fallback modes. In LostFreeze mode,
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the camera holds its last known position for a configurable number of frames, preventing sudden
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jumps during brief occlusions. If the subject remains lost beyond that threshold, the controller
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transitions to LostDrift mode, slowly drifting the camera back toward a neutral center position.
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This prevents the crop from drifting off-screen and ensures that the output remains usable even
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when tracking fails. All positions are clamped to valid bounds, guaranteeing that the crop window
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never leaves the video frame.
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### Automatic rotation detection
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The rotation-estimation method is based on analyzing the distribution of gradient orientations within
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a video frame. After converting the frame to grayscale, the algorithm computes horizontal and vertical
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image gradients using Sobel operators and derives per-pixel gradient magnitudes and orientations.
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These orientations are folded into the range [0, 180) and accumulated into a fixed-size,
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magnitude-weighted histogram. The histogram represents the structural edge distribution of the frame,
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independent of brightness fluctuations or local lighting artifacts. By comparing the total gradient
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energy concentrated near 0 degrees (vertical edges) with the energy near 90 degrees (horizontal edges),
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the method determines whether the frame is more consistent with an upright or sideways orientation.
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This approach is designed for environments where brightness-based cues are unreliable, such as
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live concerts with strobe lights, LED walls, haze, and crowd movement. It relies solely on geometric
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edge structure, which remains stable even under extreme lighting variation. The implementation is
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optimized for high-throughput video processing: all intermediate Mats, buffers, and histograms are
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preallocated, and pixel data is accessed directly through pointers to avoid per-frame memory
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allocation. The method is intentionally biased toward the upright orientation, returning a sideways
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classification only when the horizontal-edge energy significantly exceeds the vertical-edge energy.
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## Usage
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```
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splitter [<input.mp4> ...] [options] [--] <ffmpeg passthrough>
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```
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Inputs may be provided directly, via `--file=...`, or using file masks such as `videos/*.mp4`.
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## Options
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Below is a clean, ASCII-only **options table** version of your content.
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All option names are preserved exactly, and descriptions are consolidated for clarity.
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---
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## Options
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| Option | Description |
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|--------|-------------|
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| **--out=<folder>** | Output folder for generated segments. Default: `<input folder>/Splitter`. |
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| **--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`. |
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| **--mask=<pattern>** | Custom output filename pattern. Default: `[NAME]_seg[NN].[EXT]`. Supports `[NAME]`, `[N]`, `[NN]`, `[NNN]`, `[NNNN]`, `[EXT]`. Example: `--mask="[NAME]_[NNNN].mp4"`. |
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| **--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. |
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| **--force** | Use the duration exactly as provided. Last segment may be shorter. |
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| **--rotate=<degrees>** | Rotate video by 90, 180, or 270 degrees. Useful for correcting orientation metadata. |
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| **--rotate-auto** | Use automatic rotation detection. |
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| **--estimate** | Print calculated segment information and exit. No splitting is performed. |
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| **--crop[=<w:h>]** | Crop video to a target width and height with face/body tracking. Default: 607x1080. Ideal for Shorts, TikTok, Reels. |
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| **--detect=<name>** | Object detector for tracking. Values: `face` (UltraFace), `body` (YoloOnnx, default), `none` (center crop). |
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| **--gravitate=<x:y>** | Bias the crop window toward a normalized point in the frame. Example: `--gravitate=0.2:0.5`. |
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| **--text** | Use plain-text logging instead of the rich terminal UI. |
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| **--single-thread** | Disable parallel FFmpeg execution. Useful for debugging or low-resource systems. |
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| **--debug** | Show debug overlay during tracking. No cropping performed, but crop region shown. |
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| **-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. |
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## FFmpeg Passthrough
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Anything after `--` is passed directly to FFmpeg.
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Example:
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```
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splitter video.mp4 --force --duration=45 -- -an -sn
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```
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## Input and Output Behavior
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- `input.mp4` may be a file mask (`videos/*.mp4`)
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- Output filenames follow the `--mask` pattern
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- Output folder defaults to `<input folder>/Splitter` unless overridden
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## Examples
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Split into equal 60-second segments:
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```
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splitter vertical-video.mp4
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```
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Split into equal 90-second segments:
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```
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splitter vertical-video.mp4 --duration=90s
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```
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Custom naming:
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```
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splitter vertical-video.mp4 --duration=2m30s --mask="[NAME]_[NNNN].mp4"
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```
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Estimate only:
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```
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splitter vertical-video.mp4 --estimate
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```
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Fixed 45-second segments with passthrough:
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```
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splitter vertical-video.mp4 --force --duration=45 -- -an -sn
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```
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Smart crop for Shorts:
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```
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splitter horizontal-video.mp4 --out=Cropped/ --crop
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```
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Batch processing with body tracking:
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```
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splitter --file=file_names.txt --out=Cropped/ --crop --detect=body
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```
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