Run psychopy on anaconda12/4/2023 config_path=deeplabcut.create_new_project(.)) (TIP: you can also place config_path in front of deeplabcut.create_new_project to create a variable that holds the path to the config.yaml file, i.e. 'C:\\Users\\computername\\Videos\\reachingvideo1.avi' Windows users, you must input paths as: r'C:\Users\computername\Videos\reachingvideo1.avi' or create_new_project ( 'Name of the project', 'Name of the experimenter', , working_directory = 'Full path of the working directory', copy_videos = True / False, multianimal = True / False ) Each symbolic link creates a reference to a video and thus eliminates the need to copy the entire video to the video directory (if the videos remain at the original location).ĭeeplabcut. If the optional argument working_directory is unspecified, the project directory is created in the current working directory, and if copy_videos is unspecified symbolic links for the videos are created in the videos directory. Optional arguments specify the working directory, where the project directory will be created, and if the user wants to copy the videos (to the project directory). Thus, this function requires the user to input the name of the project, the name of the experimenter, and the full path of the videos that are (initially) used to create the training dataset. YourName), as well as the date at creation. Reaching), name of the experimenter (e.g. Each project is identified by the name of the project (e.g. The function create_new_project creates a new project directory, required subdirectories, and a basic project configuration file. Next, launch your conda env (i.e., for example conda activate DLC-CPU) and then type (windows/unix) ipython or (macOS) pythonw. We assume you have DeepLabCut installed (if not, go here). To begin, navigate to anaconda prompt and right-click to “open as admin “(windows), or simply launch “terminal” (unix/MacOS) on your computer. Thanks for using DeepLabCut! DeepLabCut in the Terminal: # Thus, we recommend you read over the protocol and then please look at the following documentation and the doctrings. Additional functions and features are continually added to the package. Read more here.Īs a reminder, the core functions are described in our Nature Protocols paper (published at the time of 2.0.6). While most functionality is available, advanced users might want the additional flexibility that command line interface offers. The below functions are available to you in an easy-to-use graphical user interface. Simply python -m deeplabcut or MacOS: pythonw -m deeplabcut. DeepLabCut Project Manager GUI (recommended for beginners) # To get started, you can use the GUI, or the terminal. If you have a complicated multi-animal scenario (i.e., they look the same), then please see our maDLC user guide. This document covers single/standard DeepLabCut use. Other functions, some are yet-to-be-documented:ĭeepLabCut User Guide (for single animal projects) #.Jupyter Notebooks for Demonstration of the DeepLabCut Workflow.(N) Refine Labels: Augmentation of the Training Dataset. (M) Optional Active Learning -> Network Refinement: Extract Outlier Frames.(J) Filter pose data data (RECOMMENDED!):.API Docs for deeplabcut.create_training_model_comparison.
0 Comments
Leave a Reply.AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |