| video_scores {transforEmotion} | R Documentation |
Run FER on YouTube video
Description
This function retrieves FER scores a specific number of frames extracted from YouTube video. It uses Python libraries for facial recognition and emotion detection in text, images, and videos.
Usage
video_scores(
video,
classes,
nframes = 100,
face_selection = "largest",
start = 0,
end = -1,
uniform = FALSE,
ffreq = 15,
save_video = FALSE,
save_frames = FALSE,
save_dir = "temp/",
video_name = "temp"
)
Arguments
video |
The URL of the YouTube video to analyze. |
classes |
A character vector specifying the classes to analyze. |
nframes |
The number of frames to analyze in the video. Default is 100. |
face_selection |
The method for selecting faces in the video. Options are "largest", "left", or "right". Default is "largest". |
start |
The start time of the video range to analyze. Default is 0. |
end |
The end time of the video range to analyze. Default is -1 and this means that video won't be cut. If end is a positive number greater than start, the video will be cut from start to end. |
uniform |
Logical indicating whether to uniformly sample frames from the video. Default is FALSE. |
ffreq |
The frame frequency for sampling frames from the video. Default is 15. |
save_video |
Logical indicating whether to save the analyzed video. Default is FALSE. |
save_frames |
Logical indicating whether to save the analyzed frames. Default is FALSE. |
save_dir |
The directory to save the analyzed frames. Default is "temp/". |
video_name |
The name of the analyzed video. Default is "temp". |
Value
A result object containing the analyzed video scores.
Author(s)
Aleksandar Tomašević <atomashevic@gmail.com>