deeplabcut.cli

Functions:

Name	Description
add_new_videos	Add new videos to the config file at any stage of the project.
analyze_videos	Makes prediction.
check_labels	Check if labels were stored correctly by plotting annotations and inspect them
create_labeled_video	Labels the bodyparts in a video.
create_new_project	Create a new project directory, sub-directories and a basic configuration file.
create_training_dataset	Combine frame and label information into a an array. Create training and test sets.
evaluate_network	Evaluates a trained Feature detector model.
export_model	Export DLC models for the model zoo or for live inference.
extract_frames	Extracts frames from the videos in the config.yaml file. Only the videos in the
extract_outlier_frames	Extracts the outlier frames in case, the predictions are not correct for a
label_frames	Manually label/annotate the extracted frames. Update the list of body parts you
plot_trajectories	Plots the trajectories of various bodyparts across the video.
refine_labels	Refines the labels of the outlier frames extracted from the analyzed videos.
train_network	Train a trained Feature detector with a specific training data set.

add_new_videos

add_new_videos(_, *args, **kwargs)

Add new videos to the config file at any stage of the project. .

Options

----------

config : string

    String containing the full path of the config file in the project.

videos : list

    A list of string containing the full paths of the videos to include in the project.

copy_videos : bool, optional

    If this is set to True, the symlink of the videos are copied to the project/videos directory. The default is
    ``True``; if provided it must be either ``True`` or ``False``

Examples

--------

>>> python3 dlc.py add_new_videos /home/project/reaching-task-Tanmay-2018-08-23/config.yaml /data/videos/mouse5.avi

Source code in deeplabcut/cli.py

@main.command(context_settings=CONTEXT_SETTINGS)
@click.argument("config")
@click.argument("videos", nargs=-1, type=click.Path(exists=True, dir_okay=False))
@click.option(
    "--copy_videos/--dont_copy_videos",
    is_flag=True,
    default=True,
    help="Specify if you need to create the symlinks of the video and store in the videos directory. Default is True.",
)
@click.pass_context
def add_new_videos(_, *args, **kwargs):
    """Add new videos to the config file at any stage of the project.\n.

    Options\n
    ----------\n
    config : string\n
        String containing the full path of the config file in the project.

    videos : list \n
        A list of string containing the full paths of the videos to include in the project.

    copy_videos : bool, optional\n
        If this is set to True, the symlink of the videos are copied to the project/videos directory. The default is
        ``True``; if provided it must be either ``True`` or ``False``

    Examples\n
    --------\n
    >>> python3 dlc.py add_new_videos /home/project/reaching-task-Tanmay-2018-08-23/config.yaml /data/videos/mouse5.avi
    """
    from deeplabcut.create_project import add

    add.add_new_videos(*args, **kwargs)

analyze_videos

analyze_videos(_, *args, **kwargs)

Makes prediction.

CONFIG: Full path of the "config.yaml" file in the train directory of a project.

VIDEOS: Full path to video.

Example

---

python3 dlc.py analyze_videos /home/project/reaching/config.yaml /home/project/reaching/newVideo/1.avi

Source code in deeplabcut/cli.py

@main.command(context_settings=CONTEXT_SETTINGS)
@click.argument("config")
@click.argument("videos", nargs=-1)
@click.option(
    "-num",
    "--num_shuffles",
    "shuffle",
    default=1,
    help="Shuffle index of the training dataset. Default is set to 1.",
)
@click.option(
    "-vtype",
    "--video_type",
    "video_extensions",
    default=".avi",
    help="The extension of video in case the input is a directory",
)
@click.option(
    "-c",
    "--save",
    "save_as_csv",
    is_flag=True,
    help="Saves as a .csv file. Default is False.",
)
@click.pass_context
def analyze_videos(_, *args, **kwargs):
    """Makes prediction.\n
        CONFIG: Full path of the "config.yaml" file in the train directory of a project.\n
        VIDEOS: Full path to video.\n

    Example\n
    ----------

    python3 dlc.py analyze_videos /home/project/reaching/config.yaml /home/project/reaching/newVideo/1.avi

    """
    from deeplabcut.pose_estimation_tensorflow import predict_videos

    predict_videos.analyze_videos(*args, **kwargs)

check_labels

check_labels(_, config)

Check if labels were stored correctly by plotting annotations and inspect them visually.

If some are wrong, then use the refine_labels to correct the labels.

Source code in deeplabcut/cli.py

@main.command(context_settings=CONTEXT_SETTINGS)
@click.argument("config")
@click.pass_context
def check_labels(_, config):
    """Check if labels were stored correctly by plotting annotations and inspect them
    visually.

    If some are wrong, then use the refine_labels to correct the labels.\n
    """
    from deeplabcut.generate_training_dataset.trainingsetmanipulation import check_labels as _check_labels

    _check_labels(config)

create_labeled_video

create_labeled_video(_, *args, **kwargs)

Labels the bodyparts in a video.

Make sure the video is already analyzed by the function 'analyze_video'

Source code in deeplabcut/cli.py

@main.command(context_settings=CONTEXT_SETTINGS)
@click.argument("config")
@click.argument("videos", nargs=-1)
@click.option(
    "-num",
    "--num_shuffles",
    "shuffle",
    default=1,
    help="Number of shuffles of training dataset. Default is set to 1.",
)
@click.option(
    "-v",
    "--video_type",
    "video_extensions",
    default=".avi",
    help="Checks for the extension of the video in case the input is a directory.\
              Only videos with this extension are analyzed. The default is ``.avi``",
)
@click.option(
    "-s",
    "--save_frames",
    "save_frames",
    is_flag=True,
    default=False,
    help="If true creates each frame individual and then combines into a video. \
              This variant is relatively slow as it stores all individual frames. However, it \
              uses matplotlib to create the frames and is therefore much more flexible \
              (one can set transparency of markers, crop, and easily customize.",
)
@click.option(
    "-d",
    "--delete",
    "delete",
    is_flag=True,
    default=False,
    help="If true then the individual frames created during the video generation will be deleted.\
              Only the video will be left.",
)
@click.pass_context
def create_labeled_video(_, *args, **kwargs):
    """Labels the bodyparts in a video.

    Make sure the video is already analyzed by the function 'analyze_video'
    """
    from deeplabcut.utils import make_labeled_video

    make_labeled_video.create_labeled_video(*args, **kwargs)

create_new_project

create_new_project(_, *args, **kwargs)

Create a new project directory, sub-directories and a basic configuration file. The configuration file is loaded with default values. Change its parameters to your projects need. .

Options

----------

project : string

    String containing the name of the project.

experimenter : string

    String containing the name of the experimenter.

videos : list

    A list of string containing the full paths of the videos to include in the project.

working_directory : string, optional

    The directory where the project will be created.
The default is the ``current working directory``; if provided, it must be a string

copy_videos : bool, optional

If this is set to True, the symlink of the videos are copied to the project/videos directory.
The default is ``True``; if provided it must be either ``True`` or ``False``


Example

--------

To create the project in the current working directory

python3 dlc.py create_new_project reaching-task
Tanmay /data/videos/mouse1.avi /data/videos/mouse2.avi /data/videos/mouse3.avi /analysis/project/

To create the project in the current working directory but do not want to create the symlinks

python3 dlc.py create_new_project reaching-task
Tanmay /data/videos/mouse1.avi /data/videos/mouse2.avi /data/videos/mouse3.avi /analysis/project/ -c False

To create the project in another directory

python3 dlc.py create_new_project reaching-task
Tanmay /data/vies/mouse1.avi /data/videos/mouse2.avi /data/videos/mouse3.avi analysis/project -d home/project

Source code in deeplabcut/cli.py

@main.command(context_settings=CONTEXT_SETTINGS)
@click.argument("project")
@click.argument("experimenter")
@click.argument("videos", nargs=-1, type=click.Path(exists=True, dir_okay=False))
@click.option(
    "-d",
    "--wd",
    "working_directory",
    type=click.Path(exists=True, file_okay=False, resolve_path=True),
    default=Path.cwd(),
    help="Directory to create project in. Default is cwd().",
)
@click.option(
    "--copy_videos/--dont_copy_videos",
    is_flag=True,
    default=True,
    help="Specify if you need to create the symlinks of the video and store in the videos directory. Default is True.",
)
#              type=click.Path(exists=True, file_okay=False, resolve_path=True), default=Path.cwd(),
#              help='Directory to create project in. Default is cwd().')
@click.pass_context
def create_new_project(_, *args, **kwargs):
    """Create a new project directory, sub-directories and a basic configuration file.
    The configuration file is loaded with default values. Change its parameters to your
    projects need.\n.

    Options \n
    ---------- \n
    project : string \n
    \tString containing the name of the project.\n
    experimenter : string \n
    \tString containing the name of the experimenter. \n
    videos : list \n
    \tA list of string containing the full paths of the videos to include in the project.\n
    working_directory : string, optional \n
    \tThe directory where the project will be created.
    The default is the ``current working directory``; if provided, it must be a string\n
    copy_videos : bool, optional \n
    If this is set to True, the symlink of the videos are copied to the project/videos directory.
    The default is ``True``; if provided it must be either ``True`` or ``False`` \n

    Example \n
    -------- \n
    To create the project in the current working directory \n
    python3 dlc.py create_new_project reaching-task
    Tanmay /data/videos/mouse1.avi /data/videos/mouse2.avi /data/videos/mouse3.avi /analysis/project/

    To create the project in the current working directory but do not want to create the symlinks \n
    python3 dlc.py create_new_project reaching-task
    Tanmay /data/videos/mouse1.avi /data/videos/mouse2.avi /data/videos/mouse3.avi /analysis/project/ -c False

    To create the project in another directory \n
    python3 dlc.py create_new_project reaching-task
    Tanmay /data/vies/mouse1.avi /data/videos/mouse2.avi /data/videos/mouse3.avi analysis/project -d home/project
    """
    from deeplabcut.create_project import new

    new.create_new_project(*args, **kwargs)

create_training_dataset

create_training_dataset(_, *args, **kwargs)

Combine frame and label information into a an array. Create training and test sets. Update parameters TrainFraction, iteration in config.yaml Also update parameters for pose_config.yaml as wanted.

CONFIG: Full path of the config.yaml file in the train directory of a project.

Example

---

To create a training dataset with only 1 shuffle python3 dlc.py create_training_dataset /analysis/project/reaching-task/config.yaml

To create a training dataset with only 2 shuffles python3 dlc.py create_training_dataset /analysis/project/reaching-task/config.yaml num_shuffles 2

Source code in deeplabcut/cli.py

@main.command(context_settings=CONTEXT_SETTINGS)
@click.argument("config")
@click.option(
    "-num",
    "--num_shuffles",
    "num_shuffles",
    default=1,
    help="Number of shuffles of training dataset to create. Default is set to 1.",
)
@click.pass_context
def create_training_dataset(_, *args, **kwargs):
    """Combine frame and label information into a an array. Create training and test sets.
    Update parameters TrainFraction, iteration in config.yaml
        Also update parameters for pose_config.yaml as wanted.\n
    CONFIG: Full path of the config.yaml file in the train directory of a project.\n
    Example \n
    --------\n
    To create a training dataset with only 1 shuffle
    python3 dlc.py create_training_dataset /analysis/project/reaching-task/config.yaml

    To create a training dataset with only 2 shuffles
    python3 dlc.py create_training_dataset /analysis/project/reaching-task/config.yaml num_shuffles 2
    """
    from deeplabcut.generate_training_dataset.trainingsetmanipulation import (
        create_training_dataset as _create_training_dataset,
    )

    _create_training_dataset(*args, **kwargs)

evaluate_network

evaluate_network(_, config, **kwargs)

Evaluates a trained Feature detector model.

CONFIG: Full path of the "pose_config.yaml" file in the train directory of a project.

Example

---

Evalaute the network python3 dlc.py evaluate_network /home/project/reaching/config.yaml

Source code in deeplabcut/cli.py

@main.command(context_settings=CONTEXT_SETTINGS)
@click.argument("config")
@click.option(
    "-num",
    "--num_shuffles",
    "shuffle",
    default=[1],
    help="Shuffle index of the training dataset. Default is set to 1.",
)
@click.option("-p", "--plot", "plotting", is_flag=True, help="Make plots. Default is False.")
@click.pass_context
def evaluate_network(_, config, **kwargs):
    """Evaluates a trained Feature detector model.\n
        CONFIG: Full path of the "pose_config.yaml" file in the train directory of a project.\n

    Example\n
    ----------
    Evalaute the network
    python3 dlc.py evaluate_network  /home/project/reaching/config.yaml

    """
    from deeplabcut.pose_estimation_tensorflow.core.evaluate import evaluate_network as _evaluate_network

    _evaluate_network(config, **kwargs)

export_model

export_model(_, *args, **kwargs)

Export DLC models for the model zoo or for live inference. Saves the pose configuration, snapshot files, and frozen graph of the model to a directory named exported-models within the project directory.

Parameters

---

cfg_path : string

   path to the DLC Project config.yaml file

iteration : int, optional

   the model iteration you wish to export.

   If None, uses the iteration listed in the config file

shuffle : int, optional

   the shuffle of the model to export. default = 1

trainingsetindex : int, optional

   the index of the training fraction for the model you wish to export. default = 1

snapshotindex : int, optional

   the snapshot index for the weights you wish to export.

   If None, uses the snapshotindex as defined in 'config.yaml'. Default = None

TFGPUinference : bool, optional

   use the tensorflow inference model? Default = True

   For inference using DeepLabCut-live, it is recommended to set TFGPIinference=False

overwrite : bool, optional

   if the model you wish to export has already been exported, whether to overwrite. default = False

make_tar : bool, optional

   Do you want to compress the exported directory to a tar file? Default = True

   This is necessary to export to the model zoo, but not for live inference.

Source code in deeplabcut/cli.py

@main.command(context_settings=CONTEXT_SETTINGS)
@click.argument("cfg-path", nargs=1, type=click.STRING)
@click.option(
    "-i",
    "--iteration",
    "iteration",
    default=None,
    required=False,
    type=int,
    help="the model iteration you wish to export. If None, uses the iteration listed in the config file",
)
@click.option(
    "-s",
    "--shuffle",
    "shuffle",
    default=1,
    required=False,
    type=int,
    help="the shuffle of the model to export. Default is set to 1.",
)
@click.option(
    "-t",
    "--trainingsetindex",
    "trainingsetindex",
    default=0,
    required=False,
    type=int,
    help="the index of the training fraction for the model you wish to export. default = 0",
)
@click.option(
    "-n",
    "--snapshotindex",
    "snapshotindex",
    default=None,
    required=False,
    type=int,
    help="the snapshot index for the weights you wish to export",
)
@click.option(
    "--TFGPUinference/--NPinference",
    "TFGPUinference",
    default=True,
    required=False,
    help="use the tensorflow inference model? Default = True",
)
@click.option(
    "--overwrite",
    "-o",
    is_flag=True,
    required=False,
    help="if the model you wish to export has already been exported, whether to overwrite. default = False",
)
@click.option(
    "--make-tar/--no-tar",
    "make_tar",
    default=True,
    required=False,
    help="Do you want to compress the exported directory to a tar file? Default = True",
)
@click.pass_context
def export_model(_, *args, **kwargs):
    """Export DLC models for the model zoo or for live inference.\n Saves the pose
    configuration, snapshot files, and frozen graph of the model to a directory named
    exported-models within the project directory.

    Parameters
    -----------

    cfg_path : string\n
    \tpath to the DLC Project config.yaml file

    iteration : int, optional\n
    \tthe model iteration you wish to export.\n
    \tIf None, uses the iteration listed in the config file

    shuffle : int, optional\n
    \tthe shuffle of the model to export. default = 1

    trainingsetindex : int, optional\n
    \tthe index of the training fraction for the model you wish to export. default = 1

    snapshotindex : int, optional\n
    \tthe snapshot index for the weights you wish to export.\n
    \tIf None, uses the snapshotindex as defined in 'config.yaml'. Default = None

    TFGPUinference : bool, optional\n
    \tuse the tensorflow inference model? Default = True\n
    \tFor inference using DeepLabCut-live, it is recommended to set TFGPIinference=False

    overwrite : bool, optional\n
    \tif the model you wish to export has already been exported, whether to overwrite. default = False

    make_tar : bool, optional\n
    \tDo you want to compress the exported directory to a tar file? Default = True\n
    \tThis is necessary to export to the model zoo, but not for live inference.
    """

    from deeplabcut import export_model

    export_model(*args, **kwargs)

extract_frames

extract_frames(_, *args, **kwargs)

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_videos at any stage of the project to add new videos to the config file and extract their frames. .

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``.


Examples

--------

for selecting frames automatically with 'kmeans' and do not want to crop the frames

>>> python3 dlc.py extract_frames /analysis/project/reaching-task/config.yaml automatic --algo kmeans

--------

for selecting frames automatically with 'uniform' and want to
crop the frames based on the ``crop`` parameters in config.yaml

>>> python3 dlc.py extract_frames /analysis/project/reaching-task/config.yaml automatic --crop
--------

for selecting frames manually,

>>> deeplabcut.extract_frames /analysis/project/reaching-task/config.yaml manual

While selecting the frames manually, you do not need to specify the cropping parameters.
Rather, you will get a prompt in the graphic user interface to choose if you need to crop or not.

--------

Source code in deeplabcut/cli.py

@main.command(context_settings=CONTEXT_SETTINGS)
@click.argument("config")
@click.argument("mode")
@click.option(
    "-a",
    "--algo",
    "algo",
    default="uniform",
    help='For automatic extraction, specify the algorithm- "kmeans" or "uniform". Default is uniform.',
)
@click.option(
    "--crop",
    is_flag=True,
    default=False,
    help="Specify if you need to crop the image. Default is True.",
)
@click.pass_context
def extract_frames(_, *args, **kwargs):
    """Extracts frames from the videos in the config.yaml file. Only the videos in the
    config.yaml will be used to select the frames.\n Use the function ``add_new_videos``
    at any stage of the project to add new videos to the config file and extract their
    frames.\n.

    CONFIG : string \n
        Full path of the config.yaml file as a string.  \n \n \n
    MODE : string \n \n
        String containing the mode of extraction. It must be either ``automatic`` or ``manual``.  \n

    Examples \n
    -------- \n
    for selecting frames automatically with 'kmeans' and do not want to crop the frames \n
    >>> python3 dlc.py extract_frames /analysis/project/reaching-task/config.yaml automatic --algo kmeans \n
    -------- \n
    for selecting frames automatically with 'uniform' and want to
    crop the frames based on the ``crop`` parameters in config.yaml \n
    >>> python3 dlc.py extract_frames /analysis/project/reaching-task/config.yaml automatic --crop
    -------- \n
    for selecting frames manually, \n
    >>> deeplabcut.extract_frames /analysis/project/reaching-task/config.yaml manual \n
    While selecting the frames manually, you do not need to specify the cropping parameters.
    Rather, you will get a prompt in the graphic user interface to choose if you need to crop or not. \n
    -------- \n
    """
    from deeplabcut.generate_training_dataset.frame_extraction import extract_frames as _extract_frames

    _extract_frames(*args, **kwargs)

extract_outlier_frames

extract_outlier_frames(_, *args, **kwargs)

Extracts the outlier frames in case, the predictions are not correct for a certain video from the cropped video running from start to stop as defined in config.yaml.

Another crucial parameter in config.yaml is how many frames to extract 'numframes2extract'.

CONFIG : string

Full path of the config.yaml file as a string.

VIDEO : Full path of the video to extract the frame from. Make sure that this video is already analyzed.

Example

---

for extracting the frames with default settings

python3 dlc.py extract_outlier_frames /analysis/project/reaching-task/config.yaml ... /analysis/project/video/reachinvideo1.avi

---

for extracting the frames with kmeans

python3 dlc.py extract_outlier_frames /analysis/project/reaching-task/config.yaml ... /analysis/project/video/reachinvideo1.avi --extractionalgorithm 'kmeans'

---

for extracting the frames with kmeans and epsilon = 5 pixels.

python3 dlc.py extract_outlier_frames /analysis/project/reaching-task/config.yaml ... /analysis/project/video/reachinvideo1.avi --epsilon 5 --extractionalgorithm kmeans

---

Source code in deeplabcut/cli.py

@main.command(context_settings=CONTEXT_SETTINGS)
@click.argument("config")
@click.argument("videos")
@click.option(
    "-num",
    "--num_shuffles",
    "shuffle",
    default=1,
    help="The shuffle index of training dataset. The extracted frames will be stored in the "
    "labeled-dataset for the corresponding shuffle of training dataset. Default is set to 1",
)
@click.option(
    "-outlier",
    "--outlier_algo",
    "outlieralgorithm",
    default="fitting",
    help="String specifying the algorithm used to detect the outliers.\
        Currently, deeplabcut supports only sarimax (this will be updated). \
        This method fits a Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors model \
        to data and computes confidence interval. \
        Based on the fraction of data points outside the confidence interval \
        and the average distance (compared to delta) \
        the user can identify potential outlier frames.\
        The default is set to ``fitting``. Other choices: `fitting`, `jump`, `uncertain`",
)
@click.option(
    "-compare",
    "--comparisonbodyparts",
    "comparisonbodyparts",
    default="all",
    help="This select the body parts for which the comparisons with the outliers are carried out. Either ``all``, \
              then all body parts from config.yaml are used orr a list of strings that are a subset of the full list.\
               E.g. [`hand`,`Joystick`]"
    " for the demo Reaching-Mackenzie-2018-08-30/config.yaml to select only these two body parts.",
)
@click.option(
    "-e",
    "--epsilon",
    "epsilon",
    default=20,
    help="Meaning depends on outlieralgoritm. The default is set to 20 pixels.For outlieralgorithm `fitting`: \
            Float bound according to which frames are picked when the (average)\
            body part estimate deviates from model fit. \
            For outlier algorithm `jump`:"
    "Float bound specifying the distance by which body points jump from one frame to next (Euclidean distance)",
)
@click.option(
    "-p",
    "--p_bound",
    "p_bound",
    default=0.01,
    help="For outlieralgorithm `uncertain` this parameter defines the likelihood below, "
    "below which a body part will be flagged as a putative outlier.",
)
@click.option(
    "-ard",
    "--ar_degree",
    "ARdegree",
    default=7,
    help="For outlieralgorithm `fitting`: Autoregressive degree of Sarimax model degree. \
              See https://www.statsmodels.org/dev/generated/statsmodels.tsa.statespace.sarimax.SARIMAX.html",
)
@click.option(
    "-mad",
    "--ma_degree",
    "MAdegree",
    default=1,
    help="Int value. For outlieralgorithm `fitting`: Moving Average degree of Sarimax model degree.\
               See https://www.statsmodels.org/dev/generated/statsmodels.tsa.statespace.sarimax.SARIMAX.html",
)
@click.option(
    "-a",
    "--alpha",
    "alpha",
    default=0.01,
    help="Significance level for detecting outliers based on confidence interval of fitted SARIMAX model.",
)
@click.option(
    "-extract",
    "--extraction_algo",
    "extractionalgorithm",
    default="uniform",
    help="String specifying the algorithm to use for selecting the frames from the identified outliers.\
            Currently, deeplabcut supports either ``kmeans`` or ``uniform``\
            based selection (same logic as for extract_frames).\
            The default is set to``uniform``,\
            if provided it must be either ``uniform`` or ``kmeans``.",
)
@click.pass_context
def extract_outlier_frames(_, *args, **kwargs):
    """Extracts the outlier frames in case, the predictions are not correct for a
    certain video from the cropped video running from start to stop as defined in
    config.yaml.

    Another crucial parameter in config.yaml is how many frames to extract 'numframes2extract'.

    CONFIG : string \n
    Full path of the config.yaml file as a string.  \n
    VIDEO : Full path of the video to extract the frame from. Make sure that this video is already analyzed.


    Example \n
    --------\n
    for extracting the frames with default settings\n
    >>> python3 dlc.py extract_outlier_frames /analysis/project/reaching-task/config.yaml
    ... /analysis/project/video/reachinvideo1.avi \n
    --------\n
    for extracting the frames with kmeans\n
    >>> python3 dlc.py extract_outlier_frames /analysis/project/reaching-task/config.yaml
    ... /analysis/project/video/reachinvideo1.avi --extractionalgorithm 'kmeans' \n
    --------\n
    for extracting the frames with kmeans and epsilon = 5 pixels.\n
    >>> python3 dlc.py extract_outlier_frames /analysis/project/reaching-task/config.yaml
    ... /analysis/project/video/reachinvideo1.avi --epsilon 5 --extractionalgorithm kmeans \n
    --------\n
    """
    from deeplabcut.refine_training_dataset import outlier_frames

    outlier_frames.extract_outlier_frames(*args, **kwargs)

label_frames

label_frames(_, config)

Manually label/annotate the extracted frames. Update the list of body parts you want to localize in the config.yaml file first. .

Example

--------

python3 dlc.py label_frames /analysis/project/reaching-task/config.yaml

Source code in deeplabcut/cli.py

@main.command(context_settings=CONTEXT_SETTINGS)
@click.argument("config")
@click.pass_context
def label_frames(_, config):
    """Manually label/annotate the extracted frames. Update the list of body parts you
    want to localize in the config.yaml file first.\n.

    Example\n
    --------\n
    python3 dlc.py label_frames /analysis/project/reaching-task/config.yaml
    """
    from deeplabcut.gui.tabs.label_frames import label_frames as _label_frames

    _label_frames(config)

plot_trajectories

plot_trajectories(_, *args, **kwargs)

Plots the trajectories of various bodyparts across the video. .

Example

--------

for labeling the frames

>>> python3 dlc.py plot_trajectories /analysis/project/reaching-task/config.yaml
    /analysis/project/videos/reachingvideo1.avi

--------

Source code in deeplabcut/cli.py

@main.command(context_settings=CONTEXT_SETTINGS)
@click.argument("config")
@click.argument("videos", nargs=-1)
@click.option(
    "-num",
    "--num_shuffles",
    "shuffle",
    default=1,
    help="Number of shuffles of training dataset. Default is set to 1.",
)
@click.option(
    "-v",
    "--video_type",
    "video_extensions",
    default=".avi",
    help="Checks for the extension of the video in case the input is a directory.\
              Only videos with this extension are analyzed. The default is ``.avi``",
)
@click.option(
    "-s",
    "--show",
    "showfigures",
    is_flag=True,
    default=False,
    help="If true then plots are also displayed simultaneously.",
)
@click.pass_context
def plot_trajectories(_, *args, **kwargs):
    """Plots the trajectories of various bodyparts across the video.\n.

    Example\n
    --------\n
    for labeling the frames\n
    >>> python3 dlc.py plot_trajectories /analysis/project/reaching-task/config.yaml
        /analysis/project/videos/reachingvideo1.avi  \n
    --------\n
    """
    from deeplabcut.utils import plotting

    plotting.plot_trajectories(*args, **kwargs)

refine_labels

refine_labels(_, config)

Refines the labels of the outlier frames extracted from the analyzed videos.

Helps in augmenting the training dataset. Use the function ``analyze_video`` to
analyze a video and extracts the outlier frames using the function
``extract_outlier_frames`` before refining the labels.

.

Examples

--------

>>> python3 dlc.py refine_labels /analysis/project/reaching-task/config.yaml

--------

Source code in deeplabcut/cli.py

@main.command(context_settings=CONTEXT_SETTINGS)
@click.argument("config")
@click.pass_context
def refine_labels(_, config):
    """Refines the labels of the outlier frames extracted from the analyzed videos.\n
    Helps in augmenting the training dataset. Use the function ``analyze_video`` to
    analyze a video and extracts the outlier frames using the function
    ``extract_outlier_frames`` before refining the labels.\n.

    Examples \n
    --------\n
    >>> python3 dlc.py refine_labels /analysis/project/reaching-task/config.yaml \n
    --------\n
    """
    from deeplabcut.refine_training_dataset import outlier_frames

    outlier_frames.refine_labels(config)

train_network

train_network(_, *args, **kwargs)

Train a trained Feature detector with a specific training data set.

Provide path to the pose_config file.
CONFIG: Full path of the config.yaml file in the train directory of a project.

e.g. run the script like this: python3 dlc.py step7_train /home/project/reaching/config.yaml

Source code in deeplabcut/cli.py

@main.command(context_settings=CONTEXT_SETTINGS)
@click.argument("config")
@click.option(
    "-num",
    "--num_shuffles",
    "shuffle",
    default=1,
    help="Shuffle index of the training dataset. Default is set to 1.",
)
@click.pass_context
def train_network(_, *args, **kwargs):
    """Train a trained Feature detector with a specific training data set.\n
        Provide path to the pose_config file.
        CONFIG: Full path of the config.yaml file in the train directory of a project.\n

    e.g. run the script like this:
    python3 dlc.py step7_train  /home/project/reaching/config.yaml

    """
    from deeplabcut.pose_estimation_tensorflow import training

    training.train_network(*args, **kwargs)