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###############################################################################
#                                                                             #
# file:    5_multi_core_fj.py                                                 #
#                                                                             #
# authors: Andre Heil  - avh34                                                #
#          Jingyao Ren - jr386                                                #
#                                                                             #
# date:    December 1st 2015                                                  #
#                                                                             #
# brief:   Here we combine frame jumping and processing to create a large     #
#          fluid video stream at the cost of latency. Here we don't do face   #
#          tracking because the latency is to great for it to be effective.   #
#                                                                             #
###############################################################################


### Imports ###################################################################

from picamera.array import PiRGBArray
from picamera import PiCamera
from functools import partial

import multiprocessing as mp
import cv2
import os


### Setup #####################################################################

os.putenv( 'SDL_FBDEV', '/dev/fb0' )

resX = 640
resY = 480

# Setup the camera
camera = PiCamera()
camera.resolution = ( resX, resY )
camera.framerate = 90

# Use this as our output
rawCapture = PiRGBArray( camera, size=( resX, resY ) )

# The face cascade file to be used
face_cascade = cv2.CascadeClassifier( '/home/pi/opencv-2.4.9/data/lbpcascades/lbpcascade_frontalface.xml' )


### Helper Functions ##########################################################

def get_faces( img ):

    gray = cv2.cvtColor( img, cv2.COLOR_BGR2GRAY )
    return face_cascade.detectMultiScale( gray ), img

def draw_frame( img, faces ):

    # Draw a rectangle around every face
    for ( x, y, w, h ) in faces:
        cv2.rectangle( img, ( x, y ),( x + w, y + h ), ( 200, 255, 0 ), 2 )
    
    cv2.imshow( "Frame", img )
    cv2.waitKey( 1 )


### Main ######################################################################

if __name__ == '__main__':

    pool = mp.Pool( processes=4 )

    i = 0
    rList = [None] * 17
    fList = [None] * 17
    iList = [None] * 17 
    
    camera.capture( rawCapture, format="bgr" )  

    for x in range ( 17 ):
        rList[x] = pool.apply_async( get_faces, [ rawCapture.array ] )
        fList[x], iList[x] = rList[x].get()
        fList[x] = []

    rawCapture.truncate( 0 )    

    for frame in camera.capture_continuous( rawCapture, format="bgr", use_video_port=True ):
        image = frame.array

        if   i == 1:
            rList[1] = pool.apply_async( get_faces, [ image ] )
            draw_frame( iList[2], fList[1] )

        elif i == 2:
            iList[2] = image
            draw_frame( iList[3], fList[1] )

        elif i == 3:
            iList[3] = image
            draw_frame( iList[4], fList[1] )

        elif i == 4:
            iList[4] = image
            fList[5], iList[5] = rList[5].get()
            draw_frame( iList[5], fList[5] )

        elif i == 5:
            rList[5] = pool.apply_async( get_faces, [ image ] )
            draw_frame( iList[6], fList[5] )

        elif i == 6:
            iList[6] = image
            draw_frame( iList[7], fList[5] )

        elif i == 7:
            iList[7] = image
            draw_frame( iList[8], fList[5] )

        elif i == 8:
            iList[8] = image
            fList[9], iList[9] = rList[9].get()
            draw_frame( iList[9], fList[9] )

        elif i == 9:
            rList[9] = pool.apply_async( get_faces, [ image ] )
            draw_frame( iList[10], fList[9] )

        elif i == 10:
            iList[10] = image
            draw_frame( iList[11], fList[9] )

        elif i == 11:
            iList[11] = image
            draw_frame( iList[12], fList[9] )

        elif i == 12:
            iList[12] = image
            fList[13], iList[13] = rList[13].get()
            draw_frame( iList[13], fList[13] )

        elif i == 13:
            rList[13] = pool.apply_async( get_faces, [ image ] )
            draw_frame( iList[14], fList[13] )

        elif i == 14:
            iList[14] = image
            draw_frame( iList[15], fList[13] )

        elif i == 15:
            iList[15] = image
            draw_frame( iList[16], fList[13] )

        elif i == 16:
            iList[16] = image
            fList[1], iList[1] = rList[1].get()
            draw_frame( iList[1], fList[1] )

            i = 0

        i += 1
        
        rawCapture.truncate( 0 )