Part 1: Create a DeepLabCut Project

Note: Open this jupyter notebook in your DeepLabCut environment.

Create new Project

Import python libraries needed for this notebook

try:
    import deeplabcut
    import tensorflow
    import tkinter
    from tkinter import filedialog

    print(f'Using DeepLabCut version: {deeplabcut. __version__}')
    print(f'Using TensorFlow version: {tensorflow. __version__}')   

except:
    print("Please run the notebook in in your local environment")

Start by selecting the list of videos to be included in the model. You could manually type the full path of each video in a python list as argument of the deeplabcut.create_new_project() function, like so:

['C:\\Users\\computername\\Videos\\reachingvideo1.avi', 
 'C:\\Users\\computername\\Videos\\reachingvideo2.avi', 
 'C:\\Users\\computername\\Videos\\reachingvideo3.avi']

Note

Windows users need to use the double backslash for path directories or a python raw filestring.

Instead, we use tkinter to open a file dialoge and save the file paths in a python list called videolist:

video_files = filedialog.askopenfilenames(title='Choose new video files to analyze in DeepLabCut:')
videolist = list(video_files)

print(f'{len(videolist)} videos selected:')
for i in range(len(videolist)): 
    print(videolist[i])

Now we create a new project using the video paths in videolist, give the project a name and set a few parameters:

config_path = deeplabcut.create_new_project('Name of project', 'Name of Experimenter', videolist, working_directory='path working directory', copy_videos=True, multianimal=False)

Note

You can load existing DeepLabCut projects by specifying the config_path as below:

config_path = filedialog.askopenfilename(title='Choose DeepLabCut config.yaml file:')

Configure Project

Now that a new project has been created with a specific directory structure and configuration file, we need to tweak some parameters to tailor the bodyparts we want to track:

import webbrowser
webbrowser.open(config_path)
print('Please edit bodyparts list to be tracked')

Once happy with all bodyparts, skeleton: and numframes2pick: settings, start extracting frames to label:

deeplabcut.extract_frames(config_path, mode='automatic', algo='kmeans', userfeedback=False, crop=False)

Label Frames

deeplabcut.label_frames(config_path)

You can plot your labeled frames to check your annotation accuracy.

deeplabcut.check_labels(config_path)

Train Model

deeplabcut.create_training_dataset(config_path, num_shuffles=1, net_type='resnet_50', augmenter_type='imgaug')

deeplabcut.train_network(config_path, shuffle=1, trainingsetindex=0, max_snapshots_to_keep=5, displayiters=200, saveiters=20000, maxiters=1030000, allow_growth=True)

Evaluate Model

deeplabcut.evaluate_network(config_path,Shuffles=[1], plotting=True)

Analyze Videos

deeplabcut.analyze_videos(config_path, videolist, shuffle=1, save_as_csv=True, videotype='mp4' )

deeplabcut.filterpredictions(config_path,videolist, videotype='.mp4',filtertype= 'arima',ARdegree=5,MAdegree=2)

deeplabcut.analyzeskeleton(config_path, videolist, videotype='.mp4', shuffle=1, trainingsetindex=0, save_as_csv=False, destfolder=None)

Create labeled videos

deeplabcut.create_labeled_video(config_path, videolist, videotype='.mp4', filtered=False, trailpoints=10, draw_skeleton = True)

deeplabcut.create_labeled_video(config_path, videolist, videotype='.mp4', filtered=True, trailpoints=10, draw_skeleton = True, keypoints_only=True)

deeplabcut.plot_trajectories(config_path, videolist)