ED_LIVE.py

import random
import cv2
import math
import imageio
import os
import threading

MAX_NUMBER_OF_FRAMES = 300

TOTAL_KEY_FRAMES = 10

STOPPING_ITERATION = 10

NUMBER_OF_NP_CANDIDATES = 10

BUFFER_READ_LOCATION = "/path/to/location/_"

WRITE_GIF_LOCATION = "/path/to/location/SAMPLE-"


# Population matrix.
NP = []

# Mutation vector.
MV = []

# Trail vector.
TV = []

# Scale factor. (prev .2) (pretty good 0.6)
F = 0.9

# Cr probability value. (prev .5) (pretty good 0.9)
Cr = 0.6

# Calculate AED for a chromosome.
def getAED( KF ):
    ED_sum = 0
    for i in range(1, TOTAL_KEY_FRAMES - 1):
        while True:
            try:
                im1 = cv2.imread(BUFFER_READ_LOCATION + str(KF[i]) + ".jpg",0)
                im2 = cv2.imread(BUFFER_READ_LOCATION + str(KF[i+1]) + ".jpg",0)
                #print KF[i], KF[i+1], ssim(im1, im2)
                ED_sum += cv2.norm(im1, im2, cv2.NORM_L2)
            except:
                print i, KF, KF[i], KF[i+1]
                continue
            break
    return ED_sum/(TOTAL_KEY_FRAMES - 1)

# INITIALISATION : Generates population NP of 10 parent vectors (and ASSIMs).
def initialize_NP():
    for i in range(NUMBER_OF_NP_CANDIDATES):
        NP.append(sorted(random.sample(range(1, MAX_NUMBER_OF_FRAMES+1), TOTAL_KEY_FRAMES)))
        NP[-1].append(getAED(NP[-1]))
        print NP[-1]

def initialize_NP_without_AED():
    for i in range(NUMBER_OF_NP_CANDIDATES):
        NP.append(sorted(random.sample(range(1, MAX_NUMBER_OF_FRAMES+1), TOTAL_KEY_FRAMES)))


def append_AED_for_parents():
    for i in range(NUMBER_OF_NP_CANDIDATES):
	NP[i].append(getAED(NP[-1]))

# MUTATION
def mutation(num):
    R = random.sample(NP,3)
    global MV
    MV[:] = []
    MV_value = 0
    for i in range(TOTAL_KEY_FRAMES):
        MV_value = int(NP[num][i] + F*(R[1][i] - R[2][i]))
        if(MV_value < 1):
            MV.append(1)
        elif(MV_value > MAX_NUMBER_OF_FRAMES):
            MV.append(MAX_NUMBER_OF_FRAMES)
        else:
            MV.append(MV_value)
    MV.sort()
    MV.append(getAED(MV))

# CROSSOVER (uniform crossover with Cr = 0.6).
def crossover(parent, mutant):
    print "mutant: ", mutant
    print "parent: ", parent
    for j in range(TOTAL_KEY_FRAMES) :
        if(random.uniform(0,1) < Cr) :
            TV.append(mutant[j])
        else:
            TV.append(parent[j])
    TV.sort()
    TV.append(getAED(TV))
    print "TV    : ", TV

# SELECTION : Selects offspring / parent based on higher AED value.
def selection(parent, trail_vector):
    if(trail_vector[-1] > parent[-1]):
        parent[:] = trail_vector
        print "yes", parent
    else:
        print "no"

# bestParent returns the parent with then maximum AED value.
def bestParent(population):
    Max_AED_value = population[0][-1]
    Best_Parent_Index = population[0]
    for parent in population:
        if (parent[-1] > Max_AED_value):
            Max_AED_value = parent[-1]
            Best_Parent_Index = parent
    return Best_Parent_Index

count = 0
while True:
    NP=[]
    t1 = threading.Thread(target=initialize_NP_without_AED)
    # capture_xframes is a python script to capture webcam stream and save images to
    # a file. It follows a naming convention ( _NUMBER.jpg eg. _20.jpg, _21.jpg).
    t2 = threading.Thread(target=os.system,args=('python capture_xframes.py',))
    t2.start()
    t1.start()
    t2.join()
    t1.join()
    append_AED_for_parents()
    #initialize_NP()
    for GENERATION in range(STOPPING_ITERATION):
        for i in range(NUMBER_OF_NP_CANDIDATES):
            print "---------------------", "PARENT:", i+1 , "GENERATION:", GENERATION+1, "---------------------"
            mutation(i)
            crossover(NP[i], MV)
            selection(NP[i], TV)
            print NP[i]
            TV[:] = []
            print ""
        print ""
    best_parent = bestParent(NP)
    print "best solution is: ", best_parent
    images_for_gif = []
    for frame_number in best_parent[:-1]:
        images_for_gif.append(imageio.imread(BUFFER_READ_LOCATION + str(frame_number) + '.jpg'))
    imageio.mimsave(WRITE_GIF_LOCATION + str(count)+'.gif', images_for_gif)
    count+=1