# Scene Cut Detector
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This API Reference is based on DeGirum Tools version 1.2.0.
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## Scene Cut Detector Analyzer Module Overview
This module provides an analyzer (`SceneCutDetector`) for detecting scene cuts in video streams by comparing frame-to-frame differences using an adaptive thresholding approach. The detector is based on the PySceneDetect adaptive algorithm, which calculates differences in HSV color space and uses a rolling average to adapt to local changes.
Key Features
* **Adaptive Thresholding**: Uses rolling average of previous frames to adapt to gradual changes
* **HSV Color Space**: Analyzes differences in hue, saturation, and luminance channels
* **Fast Processing**: Supports luma-only mode and automatic frame resizing for performance
* **Configurable Parameters**: Adjustable sensitivity, minimum scene length, and window size
* **Real-time Detection**: Causal approach using only past frames for zero latency
* **Scene Cut Flag**: Adds `scene_cut` boolean attribute to inference results
Typical Usage
1. Create a `SceneCutDetector` instance with desired parameters
2. Attach it to a model or inference pipeline
3. Process video frames through the analyzer
4. Check `result.scene_cut` flag to detect scene transitions
5. Use scene cut information for downstream processing or triggering actions
Put it before ObjectTracker in the analyzer pipeline to ensure cuts are detected before tracking is applied, allowing you to reset object tracker on scene changes.
Integration Notes
* Works with any inference results that contain image data
* Can be combined with other analyzers in a pipeline
* Useful for video segmentation, activity detection, and content analysis
* Maintains internal state to track frame history
Configuration Options
* `adaptive_threshold`: Sensitivity ratio for detecting cuts (higher = less sensitive)
* `min_scene_len`: Minimum frames between detected cuts to avoid false positives
* `window_width`: Number of previous frames for rolling average calculation
* `min_content_val`: Minimum absolute change threshold for scene cuts
* `luma_only`: Use only brightness changes for faster processing
Key Classes
* `SceneCutDetector`: Main analyzer class for scene cut detection
## Classes
## SceneCutDetector
`SceneCutDetector`
Bases: `ResultAnalyzerBase`
Analyzer for detecting scene cuts using adaptive thresholding.
This analyzer examines consecutive frames and detects scene cuts when the frame-to-frame difference significantly exceeds the rolling average of recent frames. It uses HSV color space analysis to detect content changes while adapting to gradual variations like camera motion or lighting changes.
The detector adds a `scene_cut` boolean attribute to each inference result indicating whether a scene cut was detected at that frame.
Attributes:
| Name | Type | Description |
| -------------------- | ------- | ----------------------------------------------------------------------- |
| `adaptive_threshold` | `float` | Ratio threshold for scene cut detection. |
| `min_scene_len` | `int` | Minimum frames between consecutive scene cuts. |
| `window_width` | `int` | Number of previous frames used for rolling average. |
| `min_content_val` | `float` | Minimum absolute content change threshold. |
| `luma_only` | `bool` | Whether to use only luminance channel for comparison. |
| `resize_limit` | `int` | Maximum image dimension to apply frame resizing to improve performance. |
### SceneCutDetector Methods
#### \_\_init\_\_(\*, ...)
`__init__(*, adaptive_threshold=3.0, min_scene_len=15, window_width=4, min_content_val=15.0, luma_only=False, resize_limit=240)`
Initialize the scene cut detector.
Parameters:
| Name | Type | Description | Default |
| -------------------- | ------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------- |
| `adaptive_threshold` | `float` | Ratio that the frame score must exceed relative to the average of surrounding frames to trigger a cut. Default 3.0 means the frame must have 3x more change than its neighbors. | `3.0` |
| `min_scene_len` | `int` | Minimum number of frames between detected cuts. Default 15 frames. | `15` |
| `window_width` | `int` | Number of previous frames to use for computing the rolling average. Must be at least 1. Default 4. | `4` |
| `min_content_val` | `float` | Minimum absolute change threshold. Even if the adaptive ratio is exceeded, the absolute change must be at least this value. Default 15.0. | `15.0` |
| `luma_only` | `bool` | If True, only considers changes in luminance (brightness), ignoring color information for faster processing. Default False. | `False` |
| `resize_limit` | `int` | Maximum image dimension to apply frame resizing to improve performance. Frames larger than 1.5x this limit will be resized. Default 240. | `240` |
#### analyze(result)
`analyze(result)`
Analyze a frame and detect scene cuts using adaptive thresholding.
This method processes the image from the inference result, calculates the frame-to-frame content difference, and sets `result.scene_cut` to True if a scene cut is detected, False otherwise.
Uses a causal approach: compares the current frame score against the average of previous frame scores, enabling real-time detection with no latency.
Parameters:
| Name | Type | Description | Default |
| -------- | -------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------- | ---------- |
| `result` | [InferenceResults](https://docs.degirum.com/pysdk/user-guide-pysdk/api-ref/postprocessor#degirum.postprocessor.inferenceresults) | The inference result containing the image to analyze. | *required* |
Returns:
| Name | Type | Description |
| ------ | ------ | --------------------------------------------------------------- |
| `None` | `None` | Modifies `result` in place by adding the `scene_cut` attribute. |
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