Limelight Lib Python
limelightlib-python is the easiest way to interface with Limelight devices. It works on all operating systems (MacOS, Windows, Linux) and architectures (x86, ARM).
Installation
pip install limelightlib-python
Usage
import limelight
import limelightresults
import json
import time
discovered_limelights = limelight.discover_limelights(debug=True)
print("discovered limelights:", discovered_limelights)
if discovered_limelights:
limelight_address = discovered_limelights[0]
ll = limelight.Limelight(limelight_address)
results = ll.get_results()
status = ll.get_status()
print("-----")
print("targeting results:", results)
print("-----")
print("status:", status)
print("-----")
print("temp:", ll.get_temp())
print("-----")
print("name:", ll.get_name())
print("-----")
print("fps:", ll.get_fps())
print("-----")
print("hwreport:", ll.hw_report())
ll.enable_websocket()
# print the current pipeline settings
print(ll.get_pipeline_atindex(0))
# update the current pipeline and flush to disk
pipeline_update = {
'area_max': 98.7,
'area_min': 1.98778
}
ll.update_pipeline(json.dumps(pipeline_update),flush=1)
print(ll.get_pipeline_atindex(0))
# switch to pipeline 1
ll.pipeline_switch(1)
# update custom user data
ll.update_python_inputs([4.2,0.1,9.87])
try:
while True:
result = ll.get_latest_results()
parsed_result = limelightresults.parse_results(result)
if parsed_result is not None:
print("valid targets: ", parsed_result.validity, ", pipelineIndex: ", parsed_result.pipeline_id,", Targeting Latency: ", parsed_result.targeting_latency)
#for tag in parsed_result.fiducialResults:
# print(tag.robot_pose_target_space, tag.fiducial_id)
time.sleep(1) # Set this to 0 for max fps
except KeyboardInterrupt:
print("Program interrupted by user, shutting down.")
finally:
ll.disable_websocket()
Methods
REST-based
- get_results(): Fetches latest results via HTTP GET.
- capture_snapshot(snapname): Captures a snapshot with a given name.
- upload_snapshot(snapname, image_path): Uploads a snapshot with a given name and image file.
- snapshot_manifest(): Retrieves the snapshot manifest via HTTP GET.
- delete_snapshots(): Deletes all snapshots via HTTP GET.
- upload_neural_network(nn_type, file_path): Uploads a neural network file with a specified type.
- hw_report(): Fetches hardware report via HTTP GET.
- cal_default(): Fetches default calibration data via HTTP GET.
- cal_file(): Fetches calibration data from file via HTTP GET.
- cal_eeprom(): Fetches calibration data from EEPROM via HTTP GET.
- cal_latest(): Fetches the latest calibration data via HTTP GET.
- update_cal_eeprom(cal_data): Updates calibration data in EEPROM via HTTP POST.
- update_cal_file(cal_data): Updates calibration data in file via HTTP POST.
- delete_cal_latest(): Deletes the latest calibration data via HTTP DELETE.
- delete_cal_eeprom(): Deletes calibration data from EEPROM via HTTP DELETE.
- delete_cal_file(): Deletes calibration data from file via HTTP DELETE.
Websocket-based
- enable_websocket(): Initializes and starts a WebSocket connection in another thread.
- disable_websocket(): Closes the WebSocket connection and joins the thread.
- get_latest_results(): Returns the latest results received from the WebSocket.
Parsing
- limelightresults.parse_results(): Parse results and return a GeneralResult object
Result Class Spec
class GeneralResult:
def __init__(self, results):
self.barcode = results.get("Barcode", [])
self.classifierResults = [ClassifierResult(item) for item in results.get("Classifier", [])]
self.detectorResults = [DetectorResult(item) for item in results.get("Detector", [])]
self.fiducialResults = [FiducialResult(item) for item in results.get("Fiducial", [])]
self.retroResults = [RetroreflectiveResult(item) for item in results.get("Retro", [])]
self.botpose = results.get("botpose", [])
self.botpose_wpiblue = results.get("botpose_wpiblue", [])
self.botpose_wpired = results.get("botpose_wpired", [])
self.capture_latency = results.get("cl", 0)
self.pipeline_id = results.get("pID", 0)
self.robot_pose_target_space = results.get("t6c_rs", [])
self.targeting_latency = results.get("tl", 0)
self.timestamp = results.get("ts", 0)
self.validity = results.get("v", 0)
self.parse_latency = 0.0
class RetroreflectiveResult:
def __init__(self, retro_data):
self.points = retro_data["pts"]
self.camera_pose_target_space = retro_data["t6c_ts"]
self.robot_pose_field_space = retro_data["t6r_fs"]
self.robot_pose_target_space = retro_data["t6r_ts"]
self.target_pose_camera_space = retro_data["t6t_cs"]
self.target_pose_robot_space = retro_data["t6t_rs"]
self.target_area = retro_data["ta"]
self.target_x_degrees = retro_data["tx"]
self.target_x_pixels = retro_data["txp"]
self.target_y_degrees = retro_data["ty"]
self.target_y_pixels = retro_data["typ"]
class FiducialResult:
def __init__(self, fiducial_data):
self.fiducial_id = fiducial_data["fID"]
self.family = fiducial_data["fam"]
self.points = fiducial_data["pts"]
self.skew = fiducial_data["skew"]
self.camera_pose_target_space = fiducial_data["t6c_ts"]
self.robot_pose_field_space = fiducial_data["t6r_fs"]
self.robot_pose_target_space = fiducial_data["t6r_ts"]
self.target_pose_camera_space = fiducial_data["t6t_cs"]
self.target_pose_robot_space = fiducial_data["t6t_rs"]
self.target_area = fiducial_data["ta"]
self.target_x_degrees = fiducial_data["tx"]
self.target_x_pixels = fiducial_data["txp"]
self.target_y_degrees = fiducial_data["ty"]
self.target_y_pixels = fiducial_data["typ"]
class DetectorResult:
def __init__(self, detector_data):
self.class_name = detector_data["class"]
self.class_id = detector_data["classID"]
self.confidence = detector_data["conf"]
self.points = detector_data["pts"]
self.target_area = detector_data["ta"]
self.target_x_degrees = detector_data["tx"]
self.target_x_pixels = detector_data["txp"]
self.target_y_degrees = detector_data["ty"]
self.target_y_pixels = detector_data["typ"]
class ClassifierResult:
def __init__(self, classifier_data):
self.class_name = classifier_data["class"]
self.class_id = classifier_data["classID"]
self.confidence = classifier_data["conf"]