Extract_frames with 3000 videos

I am new to using DeepLabCut. I have 3,000+ videos ranging from 30 seconds to 5 minutes long (over a terabyte of data). Videos are stored as mj2000 compressed .avi files.

Based on the large number of videos, I opted to use “uniform” frame extraction. However, I am being prompted for a Y/N response on whether I would like to extract frames for each individual video, AND each video is taking several minutes to extract frames from.

Am I doing something wrong? Is there an option I could have selected so that DeepLabCut would automatically extract frames from the whole dataset without giving me a yes/no prompt for each individual file? I don’t want to sit here and check my python terminal every couple minutes to press “Y” for the next week.

Thanks in advance for any advice.

if you look at the docstrings for each call, there are many options. I. e “userfeedback” is one of them:

deeplabcut.extractframes?

Signature: deeplabcut.extract_frames(config, mode='automatic', algo='kmeans', crop=False, userfeedback=True, cluster_step=1, cluster_resizewidth=30, cluster_color=False, opencv=True)
Docstring:
Extracts frames from the videos in the config.yaml file. Only the videos in the config.yaml will be used to select the frames.

Use the function ``add_new_video`` at any stage of the project to add new videos to the config file and extract their frames.

The provided function either selects frames from the videos in a randomly and temporally uniformly distributed way (uniform), 

by clustering based on visual appearance (k-means), or by manual selection. 

Three important parameters for automatic extraction: numframes2pick, start and stop are set in the config file. 

Please refer to the user guide for more details on methods and parameters https://www.biorxiv.org/content/biorxiv/early/2018/11/24/476531.full.pdf

Parameters
----------
config : string
    Full path of the config.yaml file as a string.
    
mode : string
    String containing the mode of extraction. It must be either ``automatic`` or ``manual``.
    
algo : string 
    String specifying the algorithm to use for selecting the frames. Currently, deeplabcut supports either ``kmeans`` or ``uniform`` based selection. This flag is
    only required for ``automatic`` mode and the default is ``uniform``. For uniform, frames are picked in temporally uniform way, kmeans performs clustering on downsampled frames (see user guide for details).
    Note: color information is discarded for kmeans, thus e.g. for camouflaged octopus clustering one might want to change this. 
    
crop : bool, optional
    If this is set to True, the selected frames are cropped based on the ``crop`` parameters in the config.yaml file. 
    The default is ``False``; if provided it must be either ``True`` or ``False``.
        
userfeedback: bool, optional
    If this is set to false during automatic mode then frames for all videos are extracted. The user can set this to true, which will result in a dialog,
    where the user is asked for each video if (additional/any) frames from this video should be extracted. Use this, e.g. if you have already labeled
    some folders and want to extract data for new videos. 

cluster_resizewidth: number, default: 30
    For k-means one can change the width to which the images are downsampled (aspect ratio is fixed).

cluster_step: number, default: 1
    By default each frame is used for clustering, but for long videos one could only use every nth frame (set by: cluster_step). This saves memory before clustering can start, however, 
    reading the individual frames takes longer due to the skipping.

cluster_color: bool, default: False
    If false then each downsampled image is treated as a grayscale vector (discarding color information). If true, then the color channels are considered. This increases 
    the computational complexity. 

opencv: bool, default: True
    Uses openCV for loading & extractiong (otherwise moviepy (legacy))

(ps - if you videos are sufficiently similar, no need to use all for training! The idea to to make a network that can be used on novel videos)