A package to read in Vicon data for analysis. This package can be used to read in a CSV file generated from the Vicon motion capture system. It will automatically attempt to interpolate missing data, and can save data back to a csv.
- Python 3 (may work for Python 2, but deprecated and not supported)
- numpy
- matplotlib
- pandas
- scipy
This package requires:
- subject prefix removed from marker name i.e (subject:RKNEE -> RKNEE)
- New devices connected to the Vicon should extend the Device class
This package can be installed via pip:
pip install git+https://github.com/WPI-AIM/AIM_Vicon.git(If you have both Python 2 and Python 3 installed you'll need to specify pip3 - pip defaults to Python 2 if installed.)
This package can be updated through pip:
pip install --upgrade git+https://github.com/WPI-AIM/AIM_Vicon.gitIf necessary, GaitCore can also be updated through pip:
pip install --upgrade git+https://github.com/WPI-AIM/AIM_GaitCore.git##Usage
Vicon automatically reads data from the provided file when constructed.
The constructor the following flags: verbose (defaults to False), interpolate (defaults to True),
maxnanstotal, (defaults to -1), maxnansrow (defaults to -1), and sanitize (defaults to True).
If verbose is set to True, it will print status updates and warnings while reading data.
If interpolate is set to True, it will attempt to interpolate missing data points. If interpolate is set to
False, or if a field cannot be interpolated, missing data points will be set to np.nan.
If maxnanstotal or maxnansrow are set to non-negative values, they will provide a cap on the maximum allowed
nans total or in a row, respectively. If a field violates either rule, that field will not be
interpolated. Setting either of these flags to 0 is equivalent to setting interpolate to False.
If sanitize is set to True, Vicon will replace any field consisting entirely of NaNs with
0s. Vicon will keep track of every object that contains a santized field. This can be checked through the is_sanitized
method.
Note: Some objects, as read by Vicon, contain empty fields named "" that contain no data.
These fields will be sanitized, but an object will not be marked as having been sanitized for having
one of these fields.
The Vicon.save() method will save the data previously read.
It accepts three flags: filename, which defaults to None, verbose, which defaults to False, and
mark_interpolated, which defaults to True.
If filename is not provided, it will default to the file path specified on construction. WARNING: Saving to a
file will overwrite it.
verbose controls whether or not the save method will print status updates and warnings.
If mark_interpolated is set to True, any values that were generated through interpolation will be preceded by '!'.
Vicon is able to read this, and a future Vicon object reading this value will display a warning with verbose set to True.
A Markers object can be obtained through the Vicon.get_markers() method.
It contains information about the markers' positions, and contains methods for calculating
information about the rigid bodies and the joint centers.
The smart_sort function will automatically group markers into their rigid bodies.
Once sorted, it is possible to retrieve the data of all markers associated with a given rigid body, using the get_rigid_bodies
function.
The auto_make_transform(frames) function will automatically make the transformation matrices for every
rigid body for which a frame of reference is provided.
A frame of reference is an array of points, which represent the locations of the markers on the rigid body
relative to 0,0 on that rigid body.
The get_frame function will return the transformation matrices for a given rigid body for all frames.
get_frame(RigidBody)[n] gives the transformation matrix for the specified rigid body during frame n.
The transformation matrices are of the form local to global - that is to say, where T = markers.get_frame(RigidBody)[n],
np.dot(T, [[0], [0], [0], [1]]) will return a vector representing the location of the specified rigid body during frame n.
There are a few static methods which automatically perform frame-shifting operations, requiring only that the user specify points and frames.
Markers.global_to_frame(frame, vector) transforms a vector in the global reference frame to the reference frame specified.
Markers.global_point_to_frame(frame, vector) transforms a Point object from the global frame to the provided frame.
Markers.local_to_global(frame, vector) is the inverse of global_to_frame, and likewise Markers.local_point_to_global(frame, point)
is the inverse of global_point_to_frame.
Markers.get_transform_btw_two_frames(parent_frame, child_frame) returns the transformation matrix from the parent
frame of reference to the child frame of reference.
The def_joint function allows the user to define their own joints with the rigid bodies in the data.
def_joint("r_hip", "hip", "r_femur", ballJoint=True) creates a ball joint named r_hip between the rigid bodies hip and r_femur.
The calculate_joints function will automatically calculate the positions of all defined joint locations.
Additionally, joints may be calculated directly through the _calc_ball_joint and _calc_hinge_joint methods.
Joint positions calculated through the calculate_joints function can be accessed using the get_joint(name) method.
Joint positions are represented as a 2D array, consisting of the [x, y, z] position of a joint for each timestep.
The joint position relative to the parent and child rigid bodies can be accessed through the get_joint_rel and
get_joint_rel_child methods, respectively. Each returns a 1D array of the [x, y, z] position of the joint center,
relative to either the parent or child rigid body, in that rigid body's reference frame.
The play function will create a matplotlib animation of the markers. If the calculate_joints
function has been run, and the joints flag is set to True, this animation will include the calculated joint locations
in green. If the center flag is set to True, the body will be anchored to the center of the screen. This is highly recommended
for any dataset where the markers move a large distance from their starting position.
Vicon's behavior upon encountering missing data is highly customizable.
With no flags specified, Vicon will attempt to interpolate any amount of missing data in all fields.
import Vicon
data = Vicon.Vicon("path/to/file")Vicon can be configured to never attempt to interpolate missing data, instead filling any holes with np.nan.
import Vicon
data = Vicon.Vicon("path/to/file", interpolate=False) # No interpolation!By using the maxnansrow field, Vicon can be configured to only attempt to interpolate fields
if they do not have any holes larger than specified.
import Vicon
data = Vicon.Vicon("path/to/file", maxnansrow=100) # If any field is missing more than 100 values in a row, it will not be interpolated.By using the maxnanstotal field, Vicon will only attempt to interpolate a field if it does not have
too many missing values.
import Vicon
data = Vicon.Vicon("path/to/file", maxnanstotal=1000) # If any field is missing more than 1000 values in total, it will not be interpolated.Vicon can save data into a CSV file. This can be done to save the results of any interpolation, or perhaps to copy a CSV file very inefficiently.
In the default case, Vicon will overwrite the file that it read from on creation. Any values produced via interpolation will be preceded with '!'. If Vicon encounters such a value while in verbose mode, it will print a warning.
import Vicon
data = Vicon.Vicon("path/to/file")
...
data.save()Vicon can write saved data to a new file.
import Vicon
data = Vicon.Vicon("path/to/file")
...
data.save(filename="path/to/new/file")Vicon can be configured to not mark previously interpolated values. If this is done, any future Vicon object will not be able to distinguish previously interpolated values from real data.
import Vicon
data = Vicon.Vicon("path/to/file")
...
data.save(mark_interpolated=False)import Vicon
file = "path to CSV file"
data = Vicon.Vicon(file)
markers = data.get_markers()
markers.smart_sort() # sort the markers into bodies by the names
markers.play()import Vicon
import Core
import Markers
v = Vicon.Vicon("Path/To/File")
markers = v.get_markers()
markers.smart_sort()
frames = {"Root": [Core.Point.Point(0, 14, 0),
Core.Point.Point(56, 0, 0),
Core.Point.Point(14, 63, 0),
Core.Point.Point(56, 63, 0)], "L_Foot": [Core.Point.Point(0, 0, 0),
Core.Point.Point(70, 0, 0),
Core.Point.Point(28, 70, 0),
Core.Point.Point(70, 63, 0)],
"L_Tibia": [Core.Point.Point(0, 0, 0),
Core.Point.Point(0, 63, 0),
Core.Point.Point(70, 14, 0),
Core.Point.Point(35, 49, 0)], "L_Femur": [Core.Point.Point(0, 0, 0),
Core.Point.Point(70, 0, 0),
Core.Point.Point(0, 42, 0),
Core.Point.Point(70, 56, 0)],
"R_Foot": [Core.Point.Point(0, 0, 0),
Core.Point.Point(56, 0, 0),
Core.Point.Point(0, 49, 0),
Core.Point.Point(42, 70, 0)], "R_Tibia": [Core.Point.Point(0, 0, 0),
Core.Point.Point(42, 0, 0),
Core.Point.Point(7, 49, 0),
Core.Point.Point(63, 70, 0)],
"R_Femur": [Core.Point.Point(7, 0, 0),
Core.Point.Point(56, 0, 0),
Core.Point.Point(0, 70, 0),
Core.Point.Point(42, 49, 0)]}
markers.auto_make_transform(frames)
# If any of the markers on the rigid bodies are missing data, the joint calculation will be inaccurate.
# With interpolation and sanitizing on, markers will only be missing data if they have been sanitized.
for body in markers._rigid_body.keys():
if ("R_" in body or "L_" in body) and v.is_sanitized("Trajectories", body):
print(body + " is missing data! Adjacent joint locations might not be correct!")
markers.calc_joints()
markers.play(joints=True)Rigid bodies are organized by marker then frame. The markers are of type Point.
import Vicon
file = "path to CSV file"
data = Vicon.Vicon(file)
markers = data.get_markers()
markers.smart_sort() # optional param to remove subject name
shank_frame = markers.get_rigid_body("name of body") # returns an array of markers
## Get the X corr of a marker 2 in frame 100
x = shank_frame[2][100].xRigid bodies are organized by marker then frame. The markers are of type Point.
import Vicon
import Core
import Markers
file = "path to CSV file"
data = Vicon.Vicon(file)
markers = data.get_markers()
markers.smart_sort() # optional param to remove subject name
# Do several bodies, use the marker location on the rigidbody
frames = {"Root": [Core.Point.Point(0, 14, 0),
Core.Point.Point(56, 0, 0),
Core.Point.Point(14, 63, 0),
Core.Point.Point(56, 63, 0)], "L_Foot": [Core.Point.Point(0, 0, 0),
Core.Point.Point(70, 0, 0),
Core.Point.Point(28, 70, 0),
Core.Point.Point(70, 63, 0)],
"L_Tibia": [Core.Point.Point(0, 0, 0),
Core.Point.Point(0, 63, 0),
Core.Point.Point(70, 14, 0),
Core.Point.Point(35, 49, 0)], "L_Femur": [Core.Point.Point(0, 0, 0),
Core.Point.Point(70, 0, 0),
Core.Point.Point(0, 42, 0),
Core.Point.Point(70, 56, 0)],
"R_Foot": [Core.Point.Point(0, 0, 0),
Core.Point.Point(56, 0, 0),
Core.Point.Point(0, 49, 0),
Core.Point.Point(42, 70, 0)], "R_Tibia": [Core.Point.Point(0, 0, 0),
Core.Point.Point(42, 0, 0),
Core.Point.Point(7, 49, 0),
Core.Point.Point(63, 70, 0)],
"R_Femur": [Core.Point.Point(7, 0, 0),
Core.Point.Point(56, 0, 0),
Core.Point.Point(0, 70, 0),
Core.Point.Point(42, 49, 0)]}
markers.auto_make_transform(frames)
hip_frames = markers.get_frame("Root")
l_femur = markers.get_rigid_body("L_Femur")
#Get the position of all the markers on the left femur rigid body relative to the hip rigid body on frame 0
rel_pos = [Markers.global_point_to_frame(hip_frames[0], l_femur[n][0]) for n in range(4)]Currently only works with lower body model.
import Vicon
file = "path to CSV file"
data = Vicon.Vicon(file)
model = data.get_model_output()
model.left_leg().hip.angle.ximport Vicon
file = "path to CSV file"
data = Vicon.Vicon(file)
fp = data.get_force_plate(1).get_forces() # pass in 1 or 2 to get the foce platesHere is a suggested way to develop Python modules, specifically the AIM-Vicon module. For this section, I will be using the project's GitHub URL, but this should be replaced with your personal URL if you are forking and creating a pull request.
- Clone the repository you will be working on.
git clone [email protected]:WPI-AIM/AIM_Vicon.gitNote: It is recommended to use a virtual environment. You can use venv, pipenv, conda, or anything else. This will keep the development environment separate from your main Python environment. Any new development won't affect the stable version of the module. For the sake of this tutorial, conda will be used, but you should be able to switch out the commands as you wish. If you are developing multiple VICON modules, you only need one environment. If you do not want to use a virtual environment, please skip to Step 3
- Set up your virtual environment. If you are using Conda, you can use this command:
conda create --name [environment_name] python=3.8Alternatively, you can use the environment file in the repository:
conda env create -f environment.ymlMake sure you enable the environment in every terminal you run code in by using the following command:
conda activate aim-vicon-dev # Replace [aim-vicon-dev] with the name of your environment- Perform a Pip development install. This will create a
.egg-infodirectory both in your pip install location and the root of your python module. Essentially this is a symbolic link pointing between your development environment and the Python module install directory, so that any changes you make to the module are immediately reflected in the installed module. Run this command to 'development install' the module
pip install -e [location_of_module_setup.py]
pip install -e /home/user/development/AIM_Vicon/. # An example- Time to write some code!