pose_format.pose module
Classes:
|
File IO for '.pose' file format, including the header and body. |
- class pose_format.pose.Pose(header, body)[source]
Bases:
objectFile IO for ‘.pose’ file format, including the header and body.
- Parameters:
header (PoseHeader) – Header information for the pose.
body (PoseBody) – Body data for the pose.
Methods:
bbox()Calculates bounding box for Pose.
focus()Gets the pose to start at (0,0) and have dimensions as big as needed
frame_dropout_normal([dropout_mean, dropout_std])Normal frame dropout on Pose.
frame_dropout_uniform([dropout_min, dropout_max])Perform uniform frame dropout on Pose
get_components(components[, points])get pose components based on criteria.
normalize(info[, scale_factor])Normalize the points to a fixed distance between two particular points.
normalize_distribution([mu, std, axis])Normalize points distribution.
read(buffer[, pose_body])Read Pose object from buffer.
unnormalize_distribution(mu, std)Given mean, standard deviationn unnormalization applied to the pose points distribution.
write(buffer)Write Pose object to buffer.
Attributes:
A set of method names which define actions that can be applied to the pose data.
- bbox()[source]
Calculates bounding box for Pose.
- Returns:
Pose object representing bounding box (bbox).
- Return type:
- frame_dropout_normal(dropout_mean=0.5, dropout_std=0.1)[source]
Normal frame dropout on Pose.
- Parameters:
dropout_mean (float, optional) – Mean value for dropout. Defaults to 0.5.
dropout_std (float, optional) – Standard deviation for dropout. Defaults to 0.1.
- Returns:
a tuple with Pose of dropped frames and a list of selected indexes.
- Return type:
tuple
- frame_dropout_uniform(dropout_min=0.2, dropout_max=1.0)[source]
Perform uniform frame dropout on Pose
- Parameters:
dropout_min (float, optional) – Minimum dropout value. Defaults to 0.2.
dropout_max (float, optional) – Maximum dropout value. Defaults to 1.0.
- Returns:
a tuple containing Pose with dropped frames and a list of selected indexes.
- Return type:
tuple
- get_components(components, points=None)[source]
get pose components based on criteria.
- Parameters:
components (List[str]) – List of component names to get.
points (Dict[str, List[str]], optional) – Mapping of component names to lists of point names to get.
- Returns:
Pose object containing new components
- Return type:
- normalize(info, scale_factor=1)[source]
Normalize the points to a fixed distance between two particular points.
- Parameters:
info (PoseNormalizationInfo) – Information for normalization.
scale_factor (float, optional) – Scaling factor. Defaults to 1.
- Returns:
The normalized Pose object.
- Return type:
- normalize_distribution(mu=None, std=None, axis=(0, 1))[source]
Normalize points distribution.
- Parameters:
mu (np.ndarray, optional) – Mean values for normalization. If None, it will be computed.
std (np.ndarray, optional) – Standard deviation values for normalization. If None, it will be computed.
axis (tuple of int, optional) – Axes for mean and std computation. Defaults to (0, 1).
- Returns:
Calculated mean and standard deviation.
- Return type:
tuple of np.ndarray
- pass_through_methods = {'augment2d', 'flip', 'interpolate', 'slice_step', 'tensorflow', 'torch'}
A set of method names which define actions that can be applied to the pose data.
- Parameters:
augment2d (str) – Represents a method to augment 2D points.
flip (str) – Represents a method to flip the pose on an axis.
interpolate (str) – Represents a method to interpolate missing pose points.
torch (str) – Represents a method to convert the body data to torch format.
tensorflow (str) – Represents a method to convert the body data to TensorFlow format.
slice_step (str) – Represents a method to step through the data.
- static read(buffer, pose_body=<class 'pose_format.numpy.pose_body.NumPyPoseBody'>, **kwargs)[source]
Read Pose object from buffer.