OCT/facetracker.py

from picamera.array import PiRGBArray
from picamera import PiCamera
from datetime import datetime
import time
import cv2
import sys
import imutils
import math # for sqrt distance formula

# Create the haar cascade
frontalfaceCascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
profilefaceCascade = cv2.CascadeClassifier("haarcascade_profileface.xml")

face = [0,0,0,0]	# This will hold the array that OpenCV returns when it finds a face: (makes a rectangle)
center = [0,0]		# Center of the face: a point calculated from the above variable
lastface = 0		# int 1-3 used to speed up detection. The script is looking for a right profile face,-
			# 	a left profile face, or a frontal face; rather than searching for all three every time,-
			# 	it uses this variable to remember which is last saw: and looks for that again. If it-
			# 	doesn't find it, it's set back to zero and on the next loop it will search for all three.-
			# 	This basically tripples the detect time so long as the face hasn't moved much.


# initialize the camera and grab a reference to the raw camera capture
camera = PiCamera()
#camera.resolution = (160, 120)
camera.resolution = (320,240)
cameracenter = (320/2, 240/2)
camera.framerate = 32
rawCapture = PiRGBArray(camera, size=(320, 240))

def distance(p0, p1):
    return math.sqrt((p0[0] - p1[0])**2 + (p0[1] - p1[1])**2)

def search_rightprofile(i):
    return profilefaceCascade.detectMultiScale(i,1.3,4,(cv2.cv.CV_HAAR_DO_CANNY_PRUNING + cv2.cv.CV_HAAR_FIND_BIGGEST_OBJECT + cv2.cv.CV_HAAR_DO_ROUGH_SEARCH),(30,30))

def search_leftprofile(i):
    revimage = cv2.flip(i, 1) # Flip the image
    return profilefaceCascade.detectMultiScale(i,1.3,4,(cv2.cv.CV_HAAR_DO_CANNY_PRUNING + cv2.cv.CV_HAAR_FIND_BIGGEST_OBJECT + cv2.cv.CV_HAAR_DO_ROUGH_SEARCH),(30,30))

def search_frontface(i):
    return frontalfaceCascade.detectMultiScale(i,1.3,4,(cv2.cv.CV_HAAR_DO_CANNY_PRUNING + cv2.cv.CV_HAAR_FIND_BIGGEST_OBJECT + cv2.cv.CV_HAAR_DO_ROUGH_SEARCH),(30,30))

# allow the camera to warmup
time.sleep(0.1)
lastTime = time.time()*1000.0

# capture frames from the camera
for frame in camera.capture_continuous(rawCapture, format="bgr", use_video_port=True):
	# grab the raw NumPy array representing the image, then initialize the timestamp
	# and occupied/unoccupied text
    image = frame.array
    
    faceFound = False	# This variable is set to true if, on THIS loop a face has already been found
				# We search for a face three diffrent ways, and if we have found one already-
				# there is no reason to keep looking.
    faces = ()

    # First Scan
    if lastface == 1:
	faces = search_rightprofile(image)
        if faces != ():
            faceFound=True
            lastface = 1
    elif lastface == 2:
        faces = search_leftprofile(image)
        if faces != ():
             faceFound=True
             lastface = 2
    else:
	faces = search_frontface(image)
        if faces != ():
            lastface = 3
            faceFound=True

    # Second scan
    if not faceFound:
	faces = search_frontface(image)
        if faces != ():
            lastface = 3
            faceFound=True
    elif lastface == 2:
	faces = search_rightprofile(image)
        if faces != ():
            faceFound=True
            lastface = 1
    else:
        faces = search_leftprofile(image)
        if faces != ():
             faceFound=True
             lastface = 2

    # Third scan
    if not faceFound:
        faces = search_leftprofile(image)
        if faces != ():
             faceFound=True
             lastface = 2
    elif lastface == 2:
	faces = search_frontface(image)
        if faces != ():
            lastface = 3
            faceFound=True
    else:
	faces = search_rightprofile(image)
        if faces != ():
            faceFound=True
            lastface = 1

    if not faceFound:
        print time.time()*1000.0-lastTime," no faces found."
        lastTime = time.time()*1000.0
        # clear the stream in preparation for the next frame
        rawCapture.truncate(0)
        continue; 

    print time.time()*1000.0-lastTime," {} faces found.".format(len(faces))
        
    lastTime = time.time()*1000.0

    # Draw a rectangle around the faces
    for (x, y, w, h) in faces:
        cv2.circle(image, (x+w/2, y+h/2), int((w+h)/3), (255, 255, 255), 1)

    # Temporary, save the image
    cv2.imwrite("tmp/img.{}.png".format(datetime.now().strftime("%Y%m%%d.%H%M%S.%f")), image)

    # clear the stream in preparation for the next frame
    rawCapture.truncate(0)
   
    # Find teh centermost face
    curdistance = 1000000 # Outside the dimensions of the picture
    for f in faces:
        x,y,w,h = f
        tmpcenter   = [(w/2+x),(h/2+y)]	# we are given an x,y corner point and a width and height, we need the center
        tmpdistance = distance(tmpcenter, cameracenter)
        if(tmpdistance < curdistance):
            print "Face closer to center detected. New target location: ({}, {}) - distance: {}".format(tmpcenter[0],tmpcenter[1],tmpdistance)
            center = tmpcenter;