ALFaceDetection Tutorial

NAOqi People Perception - Overview | API | Tutorial


Introduction

This tutorial explains how to run the ALFaceDetection module using Python. We use the following approach: we regularly check the ALMemory’s result variable. Information about the detected faces is printed on the screen.

vision_faceDetection.py

Getting a proxy to ALFaceDetection

After some initialization steps, we first instantiate a proxy to the ALFaceDetection module.


            
             # This test demonstrates how to use the ALFaceDetection module.
# Note that you might not have this module depending on your distribution
#
# - We first instantiate a proxy to the ALFaceDetection module
#     Note that this module should be loaded on the robot's NAOqi.
#     The module output its results in ALMemory in a variable
#     called "FaceDetected"
# - We then read this ALMemory value and check whether we get
#   interesting things.
import time
from naoqi import ALProxy

# Replace this with your robot's IP address
IP = "10.0.252.91"
PORT = 9559

# Create a proxy to ALFaceDetection
try:
  faceProxy = ALProxy("ALFaceDetection", IP, PORT)
except Exception, e:
  print "Error when creating face detection proxy:"
  print str(e)
  exit(1)

# Subscribe to the ALFaceDetection proxy
# This means that the module will write in ALMemory with
# the given period below
period = 500
faceProxy.subscribe("Test_Face", period, 0.0 )

            
           

Reading the results in the ALMemory variable

Now we need to get a proxy to ALMemory and check the ALFaceDetection output variable.


            
             # ALMemory variable where the ALFaceDetection module
# outputs its results.
memValue = "FaceDetected"

# Create a proxy to ALMemory
try:
  memoryProxy = ALProxy("ALMemory", IP, PORT)
  except Exception, e:
  print "Error when creating memory proxy:"
  print str(e)
  exit(1)

# A simple loop that reads the memValue and checks whether faces are detected.
for i in range(0, 20):
  time.sleep(0.5)
  val = memoryProxy.getData(memValue, 0)
  print ""
  print "\*****"
  print ""

# Check whether we got a valid output: a list with two fields.
if(val and isinstance(val, list) and len(val) == 2):
  # We detected faces !
  # For each face, we can read its shape info and ID.
  # First Field = TimeStamp.
  timeStamp = val[0]
  # Second Field = array of face_Info's.
  faceInfoArray = val[1]

  try:
  # Browse the faceInfoArray to get info on each detected face.
    for faceInfo in faceInfoArray:
    # First Field = Shape info.
    faceShapeInfo = faceInfo[0]
    # Second Field = Extra info (empty for now).
    faceExtraInfo = faceInfo[1]
    print "  alpha %.3f - beta %.3f" % (faceShapeInfo[1], faceShapeInfo[2])
    print "  width %.3f - height %.3f" % (faceShapeInfo[3], faceShapeInfo[4])
  except Exception, e:
    print "faces detected, but it seems getData is invalid. ALValue ="
    print val
    print "Error msg %s" % (str(e))
else:
  print "Error with getData. ALValue = %s" % (str(val))
  # Unsubscribe the module.

faceProxy.unsubscribe("Test_Face")
print "Test terminated successfully."

            
           

Results

Here is what you get when you execute the above script. We get different results as we occult or present new faces to the robot.


            
             \*****
alpha 0.243 - beta 0.005
width 0.167 - height 0.167
\*****
alpha 0.243 - beta 0.005
width 0.167 - height 0.167
\*****
alpha 0.243 - beta 0.005
width 0.167 - height 0.167