video_scores: 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"
)

Value

A result object containing the analyzed video scores.

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".

Author

Aleksandar Tomasevic <atomashevic@gmail.com>