Example: Detecting and Splitting Scenes in Movie Clip

As a concrete example to become familiar with PySceneDetect, let's use the following short clip from the James Bond movie, GoldenEye (Copyright © 1995 MGM):

https://www.youtube.com/watch?v=OMgIPnCnlbQ

You can download the clip from here (may have to right-click and save-as, put the video in your working directory as goldeneye.mp4). We will first demonstrate using the default parameters, then how to find the optimal threshold/sensitivity for a given video, and lastly, using the PySceneDetect output to split the video into individual scenes/clips.

Content-Aware Detection with Default Parameters

In this case, we want to split this clip up into each individual scene - at each location where a fast cut occurs. This means we need to use content-aware detecton mode (-d content). Using the following command, let's run PySceneDetect on the video using the default threshold/sensitivity:

scenedetect --input goldeneye.mp4 detect-content list-scenes save-images

Running the above command, in the working directory, you should see a file goldeneye.scenes.csv, as well as thumbnails for the start/middle/end of each scene as goldeneye-XXXX-00/01.jpg (the output directory can be specified with the -o/--output option after the save-images command, or after scenedetect to specify the output for all files). The results should appear as follows:

Scene # Start Time Preview
1 00:00:03.502
2 00:00:04.144
3 00:00:04.144
4 00:00:04.144
5 00:00:04.144
6 00:00:04.144
7 00:00:04.144
8 00:00:04.144
9 00:00:04.144
10 00:00:04.144
11 00:00:04.144
12 00:00:04.144
13 00:00:04.144
14 00:00:04.144
15 00:00:04.144
16 00:00:04.144
17 00:00:04.144
18 00:00:04.144
19 00:00:04.144
20 00:00:04.144

Note that this is almost perfect - however, one of the scene cuts/breaks in scene 17 was not detected. We will now generate a statistics file for the goldeneye.mp4 video to determine the optimal detection threshold (--threshold 27 ends up being the optimal value for goldeneye.mp4 when using detect-content, versus the default value of 30). Finally, we will use the output from PySceneDetect to split the original video into individual files/clips.

Finding Optimal Threshold/Sensitivity Value

We now know that a threshold of 30 does not work in all cases for our video, as per scene 17 detected above (note the last image is from a different scene):

We can determine the proper threshold in this case by generating a statistics file (with the -s / --stats option) for the video goldeneye.mp4, and looking at the behaviour of the values where we expect the scene break/cut to occur in scene 17:

scenedetect --input goldeneye.mp4 --stats goldeneye.stats.csv detect-content list-scenes save-images

After examining the file and determining an optimal value of 27 for detect-content, we can set the threshold for the detector via:

scenedetect --input goldeneye.mp4 --stats goldeneye.stats.csv detect-content --threshold 27 list-scenes save-images

Note that specifying the same --stats file again will make parsing the scenes significantly quicker, as the frame metrics stored in this file are re-used as a cache instead of computing them again. Finally, our updated scene list appears as follows (similar entries skipped for brevity):

Scene # Start Time Preview
... ... ...
16 00:00:04.144
17 00:00:04.144
18 00:00:04.144
19 00:00:04.144
20 00:00:04.144
21 00:00:04.144

Now the missing scene (scene number 18, in this case) has been detected properly, and our scene list is larger now due to the added cuts.

Splitting/Cutting Video into Clips

The last step to automatically split the input file into clips is to specify the split-video command. This will pass a list of the detected scene timecodes to ffmpeg if installed, splitting the input video into scenes.

You may also want to use the -c/--copy option to ensure that no re-encoding is performed (using mkvmerge instead), at the expense of frame-accurate scene cuts, since when copying, cuts can sometimes only be generated on keyframes. You can also pass the -h/--high-quality option to ensure the output videos are visually identical to the input (at the expense of longer processing time and greater filesize).

Thus, to generate a sequence of files goldeneye-scene-001.mp4, goldeneye-scene-002.mp4, goldeneye-scene-003.mp4..., our full command becomes:

scenedetect -i goldeneye.mp4 -o output_dir detect-content -t 27 list-scenes save-images split-video

The scene number -001 will be added to the output filename automatically.