This illustrates a use of a deep learning model called PoseNet. It defines blocks for obtaining up to 17 locations on one or more people (nose, elbow, knee, etc.). This program tracks the eyes, nose, and wrists and mirrors their movements. If a hand comes close to the face a warning is issued.data:image/png;base64,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:image/png;base64,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

Reports the distance between two pose elements if they are visible. Otherwise a big number is reported.

confidence threshold50distance threshold100222064

Reports a costume using the camera.

costume

Creates a costume from the video camera and passes it the 'process costume' blocks.

This reports either the confidence that the body part is correctly identified as a number between 0 and 100 or the location of a body part as two numbers. If 'stage coordinates' is true then the location matches the stage coordinates, otherwise it is between 0 and 100.

location confidenceleft right the aeye ear shoulder elbow wrist hip knee ankle nose persontruenose

confidence there is a nose

Only the nose has no left or right versions.property numberProperty numbers range from 0 to 34 to identify different information in a pose.property number

eyeearshoulderelbowwristhipkneeankle

4rightconfidence

truebody partbody part22221Confidence score

x2123

y2223

Stage100Stage100Coordinates adjusted to go from 0 to 100.

Open an interface page for different machine learning models.

training using cameratraining using camera training using microphone posenet tensorflow.js

Reports a list of poses. Use 'pose property' to get the locations of body parts and the confidences that the locations are correct.

de:ist _ leer?

false

Reports the value of the 'key' in a table that is a list of pairs of keys and values.

confidence it is a person confidence there is a nose location of nose confidence there is a left eye location of left eye confidence there is a right eye location of right eye confidence there is a left ear location of left ear confidence there is a right ear location of right ear confidence there is a left shoulder location of leff shoulder confidence there is a right shoulder location of right shoulder confidence there is a left elbow location of left elbow confidence there is a right elbow location of right elbow confidence there is a left wrist location of left wrist confidence there is a right wrist location of right wrist confidence there is a left hip location of left hip confidence there is a right hip location of right hip confidence there is a left knee location of left knee confidence there is a right knee location of right knee confidence there is a left ankle location of left ankle confidence there is a right ankle location of right ankletrue

Not sure if both eyes can be seen so just guess how far apart they are.11

50score,0.3880284232179881keypointsscore,0.9973439574241638part,nosepositionx,233.38382080078128y,202.66611063702504score,0.9994175434112549part,leftEyepositionx,258.85150863647465y,164.86884715394203score,0.9997450709342957part,rightEyepositionx,194.85158294677737y,158.51284797147193score,0.36794063448905945part,leftEarpositionx,284.68685775756836y,169.7789638827306score,0.9950804710388184part,rightEarpositionx,131.38760781019928y,156.64320714725469score,0.9883278012275696part,leftShoulderpositionx,279.8010054779053y,277.0630713279203score,0.8547376394271851part,rightShoulderpositionx,-8.157749443054199y,288.6677277310294score,0.2786438763141632part,leftElbowpositionx,289.58655227661137y,397.5086207419448score,0.01580936275422573part,rightElbowpositionx,-51.454463500976566y,390.0867824672912score,0.018496982753276825part,leftWristpositionx,288.03339950561525y,410.7967594691685score,0.016379455104470253part,rightWristpositionx,-16.95422233581543y,371.35251702729215score,0.0465381033718586part,leftHippositionx,218.51704421997073y,392.184262719954score,0.013741663657128811part,rightHippositionx,36.139731445312506y,390.1425436268682score,0.0020651237573474646part,leftKneepositionx,213.0106869506836y,395.5575452532087score,0.0010247377213090658part,rightKneepositionx,34.67103454589844y,373.4911501629752score,0.0008530699415132403part,leftAnklepositionx,212.24707748413087y,390.12607053199906score,0.00033770137815736234part,rightAnklepositionx,50.73942474365235y,384.8465065334154999999999999999999999100true1