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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"]