def __init__(self, ):
        self.fetcher = Fetcher()

        self.actions = [[0, 0], [1, 0], [1, 1], [0, 1], [-1, 1], [-1, 0],
                        [-1, -1], [0, -1], [1, -1]]
        self.curr_score = 0
        self.curr_step = 0
示例#2
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def run(positions, plotEvery=1):
  encoder = OneDDepthEncoder(positions=positions,
                             radius=5,
                             wrapAround=True,
                             nPerPosition=28,
                             wPerPosition=3,
                             minVal=0,
                             maxVal=1)
  fetcher = Fetcher()
  plotter = Plotter(encoder)
  learner = QLearner(ACTIIONS, n=1008)

  lastState = None
  lastAction = None

  while True:
    outputData = fetcher.sync()

    if outputData is None:
      continue

    if fetcher.skippedTimesteps > 0:
      print ("Warning: skipped {0} timesteps, "
             "now at {1}").format(fetcher.skippedTimesteps, fetcher.timestep)

    if not ("reset" in outputData and
            "ForwardsSweepSensor" in outputData and
            "steer" in outputData):
      print ("Warning: Missing data on timestep {0}: {1}".format(
             fetcher.timestep, outputData))
      continue

    if outputData["reset"]:
      print "Reset."

    sensor = outputData["ForwardsSweepSensor"]
    steer = outputData["steer"]
    reward = outputData.get("reward") or 0

    encoding = encoder.encode(numpy.array(sensor))

    if lastState is not None:
      learner.update(lastState, str(lastAction), encoding, str(steer), reward)

    value = learner.value(encoding)

    qValues = {}
    for action in ACTIIONS:
      qValues[action] = learner.qValue(encoding, action)

    fetcher.inputData["qValues"] = qValues
    fetcher.inputData["bestAction"] = learner.bestAction(encoding)

    plotter.update(sensor, encoding, steer, reward, value, qValues)

    # if fetcher.timestep % plotEvery == 0:
    #   plotter.render()

    lastState = encoding
    lastAction = steer
示例#3
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def run():
  params = model_params.MODEL_PARAMS
  model = ModelFactory.create(params)
  model.enableInference({"predictedField": "vector"})

  anomalyLikelihoodHelper = anomaly_likelihood.AnomalyLikelihood(
    claLearningPeriod=100,
    estimationSamples=100
  )

  lastPosition = None
  lastTime = None
  fetcher = Fetcher()

  while True:
    data = fetcher.sync()

    if data is None:
      continue

    if fetcher.skippedTimesteps > 0:
      print "WARNING: Skipped {0} timesteps.".format(fetcher.skippedTimesteps)

    time = data["timestamp"]
    position = np.array([data["x"], data["y"], data["z"]]).astype(int)

    if lastPosition is None:
      lastPosition = position
      lastTime = time

    radius = calculateRadius(lastPosition, position, lastTime, time)
    modelInput = {
      "vector": (position, radius)
    }
    result = model.run(modelInput)

    anomalyScore = result.inferences["anomalyScore"]
    anomalyLikelihood = anomalyLikelihoodHelper.anomalyProbability(
      random.random(), anomalyScore
    )

    fetcher.inputData["anomalyScore"] = anomalyScore
    fetcher.inputData["anomalyLikelihood"] = anomalyLikelihood

    print anomalyScore, anomalyLikelihood

    lastPosition = position
    lastTime = time
示例#4
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def run():
  fetcher = Fetcher()

  while True:
    outputData = fetcher.sync()

    if outputData is None:
      continue

    print "Timestep: {0}".format(fetcher.timestep)
    print outputData

    if fetcher.skippedTimesteps > 0:
      print ("Warning: skipped {0} timesteps, "
             "now at {1}").format(fetcher.skippedTimesteps, fetcher.timestep)

    if outputData["reset"]:
      print "Reset."

    print "============"
示例#5
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def run():
    fetcher = Fetcher()

    while True:
        outputData = fetcher.sync()

        if outputData is None:
            continue

        print "Timestep: {0}".format(fetcher.timestep)
        print outputData

        if fetcher.skippedTimesteps > 0:
            print("Warning: skipped {0} timesteps, "
                  "now at {1}").format(fetcher.skippedTimesteps,
                                       fetcher.timestep)

        if outputData["reset"]:
            print "Reset."

        print "============"
def collectDepthData(outputDir=_OUTPUT_DIR):

	fetcher = Fetcher()

	holdingDownKey = False

	while True:

		outputData = fetcher.sync()

		if outputData is None or "collectKeyPressed" not in outputData:
		  holdingDownKey = False
		  continue

		if outputData["collectKeyPressed"] == 0:
			holdingDownKey = False
			continue

		if holdingDownKey:
			print "Stop holding down the key!"
			continue

		print "Collecting data."

		holdingDownKey = True

		sensor = outputData["ForwardsSweepSensor"]
		depthData = np.array(sensor)

		dt = time.strftime("%y%m%d%H%M%S")
		depthID = 'depth_'+dt+'.txt'

		depthOutputPath = os.path.join(os.path.dirname(__file__),
                                   outputDir,
                                   depthID)

		np.savetxt(depthOutputPath, depthData)

		print "Wrote out to ", depthOutputPath
示例#7
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def collectDepthData(outputDir=_OUTPUT_DIR):

	fetcher = Fetcher()

	holdingDownKey = False

	while True:

		outputData = fetcher.sync()

		if outputData is None or "collectKeyPressed" not in outputData:
		  holdingDownKey = False
		  continue

		if outputData["collectKeyPressed"] == 0:
			holdingDownKey = False
			continue

		if holdingDownKey:
			print "Stop holding down the key!"
			continue

		print "Collecting data."

		holdingDownKey = True

		sensor = outputData["ForwardsSweepSensor"]
		depthData = np.array(sensor)

		dt = time.strftime("%y%m%d%H%M%S")
		depthID = 'depth_'+dt+'.txt'

		depthOutputPath = os.path.join(os.path.dirname(__file__),
                                   outputDir,
                                   depthID)

		np.savetxt(depthOutputPath, depthData)

		print "Wrote out to ", depthOutputPath
class UnityEnvironment:
    def __init__(self, ):
        self.fetcher = Fetcher()

        self.actions = [[0, 0], [1, 0], [1, 1], [0, 1], [-1, 1], [-1, 0],
                        [-1, -1], [0, -1], [1, -1]]
        self.curr_score = 0
        self.curr_step = 0

    def reset(self):
        state, _, _ = self._get_obs()
        self.curr_score = 0
        self.curr_step = 0

        self.fetcher.inputData = {'Reset': True}
        self.fetcher.sync()
        time.sleep(0.04)
        state, _, _, _ = self.step(0)

        return state

    def step(self, action):
        hor = self.actions[action][0]
        vert = self.actions[action][1]
        self.fetcher.inputData = {'Horizontal': hor, 'Vertical': vert}
        self.fetcher.sync()

        time.sleep(0.04)
        self.curr_step += 1

        state, reward, terminal = self._get_obs()

        if self.curr_step > 1000:
            terminal = True

        return state, reward, terminal, []

    def numActions(self):
        return len(self.actions)

    def _get_obs(self):

        data = None
        count = 0
        while data is None:
            count += 1
            data = self.fetcher.sync()
            time.sleep(0.001)
            if count > 1000:
                print("Error getting data from server.")

        new_score = data.get('Score')
        if new_score is not None:
            reward = max(new_score - self.curr_score, 0)
            self.curr_score = new_score
        else:
            reward = 0

        terminal = data.get('Terminal') or False

        objects = []
        for k, v in data.items():
            if 'obj' in k:
                class_one_hot = [1.0, 0.0] if (v['tag']
                                               == "Player") else [0.0, 1.0]
                pos = v['position']
                vel = v['velocity']
                pos = [pos['y'], pos['x'], pos['z']]
                vel = [vel['y'], vel['x'], vel['z']]
                objects.append(np.array(class_one_hot + pos + vel))

        if (len(objects) == 0):
            objects = np.zeros((1, 8))

        return objects, reward, terminal
示例#9
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    if ellipsePos is not None:
      initPupPos = (np.round(ellipsePos[0][1]), np.round(ellipsePos[0][0]))
      if initPupPos[0] in ROIeye[0] and initPupPos[1] in ROIeye[1]:
        avgPup = cv2.mean(imAvgNorm[initPupPos[0]-ker:initPupPos[0]+ker, initPupPos[1]-ker:initPupPos[1]+ker])[0]
        if avgPup > THvis:
          print("Eye closed")
          return None
        else:
          print("TBD")
    else:
      print("Eye not in ROI")
  return ellipsePos


if __name__ == '__main__':
  fetcher = Fetcher()
  bitDepth = 8

  #fname = 1
  #vidr = cv2.VideoCapture(fname)
  ##vidr.set(3, 320)
  ##vidr.set(4, 240)
  ##vidr.set(5, 100)
  ##vidr.set(14, 63)
  #_, im = vidr.read()


  rDir = r"C:\Users\dp26312\Documents\GitHub\EyeTrackHFR\example"
  lDir =  glob.glob(os.path.join(rDir, r"*.tiff"))
  lDir.sort(key=lambda x: os.stat(os.path.join(rDir, x)).st_mtime)#sort by time created
  lDir = lDir