The Python API is documented using Sphinx and can be found here.
Scene Detection Algorithms
This page discusses the scene detection methods/algorithms available for use in PySceneDetect, including details describing the operation of the detection method, as well as relevant command-line arguments and recommended values.
The content-aware scene detector (
detect-content) detects jump cuts in the input video. This is typically what people think of as "cuts" between scenes in a movie - given two adjacent frames, do they belong to the same scene? The content-aware scene detector finds areas where the difference between two subsequent frames exceeds the threshold value that is set (a good value to start with is
Internally, this detector functions by converting the colorspace of each decoded frame from RGB into HSV. It then takes the average difference across all channels (or optionally just the value channel) from frame to frame. When this exceeds a set threshold, a scene change is triggered.
detect-content also has edge detection, which can be enabled by providing a set of 4 numbers in the form (delta_hue, delta_sat, delta_lum, delta_edges). Changes in edges are typically larger than the other components, so threshold may need to be increased accordingly. For example,
-w 1.0 0.5 1.0 0.2 -t 32 is a good starting point to use with edge detection. The default weights are
--weights 1.0 1.0 1.0 0.0 which does not include edges, but this may change in the future.
See the documentation for detect-content for details.
Adaptive Content Detector
The adaptive content detector (
detect-adaptive) compares the difference in content between adjacent frames similar to
detect-content but instead using a rolling average of adjacent frame changes. This helps mitigate false detections where there is fast camera motion.
The threshold-based scene detector (
detect-threshold) is how most traditional scene detection methods work (e.g. the
ffmpeg blackframe filter), by comparing the intensity/brightness of the current frame with a set threshold, and triggering a scene cut/break when this value crosses the threshold. In PySceneDetect, this value is computed by averaging the R, G, and B values for every pixel in the frame, yielding a single floating point number representing the average pixel value (from 0.0 to 255.0).
Creating New Detection Algorithms
All scene detection algorithms must inherit from the base
SceneDetector class. Note that the current SceneDetector API is under development and expected to change somewhat before v1.0 is released, so make sure to pin your
scenedetect dependency to the correct API version (e.g.
scenedetect < 0.6,
scenedetect < 0.7, etc...).
Creating a new scene detection method can be as simple as implementing the
process_frame function, and optionally
from scenedetect.scene_detector import SceneDetector class CustomDetector(SceneDetector): """CustomDetector class to implement a scene detection algorithm.""" def __init__(self): pass def process_frame(self, frame_num, frame_img, frame_metrics, scene_list): """Computes/stores metrics and detects any scene changes. Returns: A list containing 1 or more the frame numbers of any detected scenes. """ return  def post_process(self, scene_list): pass
process_frame is called on every frame in the input video, which will be called after the final frame of the video is passed to
process_frame. This may be useful for multi-pass algorithms, or detectors which are waiting on some condition but still wish to output an event on the final frame.
For example, a detector may output at most 1 cuts for every call to
process_frame, it may output the entire scene list in
post_process, or a combination of both. Note that the latter will not work in cases where a live video stream or camera input device is being used. See the API documentation for the
SceneDetector class for details. Alternatively, you can call
help(SceneDetector) from a Python REPL. For examples of actual detection algorithm implementations, see the source files in the
scenedetect/detectors/ directory (e.g.
Processing is done by calling the
process_frame(...) function for all frames in the video, followed by
post_process(...) (optional) after the final frame. Scene cuts are detected and added to the passed list object in both cases.
process_frame(...) is called for each frame in sequence, passing the following arguments:
frame_num: the number of the current frame being processed
frame_img: frame returned video file or stream (accessible as NumPy array)
frame_metrics: dictionary for memoizing results of detection algorithm calculations for quicker subsequent analyses (if possible)
scene_list: List containing the frame numbers where all scene cuts/breaks occur in the video.
post_process(...) is called after the final frame has been processed, to allow for any stored scene cuts to be written if required (e.g. in the case of the
You may also want to look into the implementation of current detectors to understand how frame metrics are saved/loaded to/from a
StatsManager for caching and allowing values to be written to a stats file for users to graph and find trends in to tweak detector options. Also see the documentation for the
SceneManager for details.