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searchModule.py
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searchModule.py
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#
# Copyright (c) IEE, University of Luxembourg 2021-2022.
# Created by Hazem FAHMY, [email protected], SNT, 2022.
#
import subprocess as sp
import pathlib as pl
from imports import np, random, makedirs, torch, pd, join, basename, exists, isfile, sys, os, cv2, dlib, dirname, \
imageio, subprocess, shutil, math, random, distance, math, time, plt
from HeatmapModule import generateHeatMap, doDistance
from assignModule import getClusterData
from testModule import testModelForImg
#from dnnModels import ConvModel
#import ieedatavendor as ieeDV
import logging
# import mosa
from pymoo.model.problem import Problem
from pymoo.algorithms.nsga2 import NSGA2
#from pymoo.algorithms.nsga3 import NSGA3
from pymoo.optimize import minimize
from pymoo.factory import get_problem, get_sampling, get_crossover, get_mutation, get_reference_directions
#from pymoo.visualization.scatter import Scatter
from pymoo.model.population import Population
from pymoo.model.individual import Individual
import nsga2P1
import nsga2plus
import pickle
import config as cfg
components = cfg.components
blenderPath = cfg.blenderPath
nVar = cfg.nVar
indvdSize = cfg.indvdSize
BL = cfg.BL
width = cfg.width
height = cfg.height
globalCounter = random.randint(1, 999999999)
def search(caseFile, clusters, centroidHM, clusterRadius, popSize, nGen):
global layer
global prevPOP
layer = int(caseFile["selectedLayer"].replace("Layer", ""))
outPath = join(caseFile["filesPath"], "Pool")
caseFile["SimDataPath"] = outPath
if not exists(outPath):
makedirs(outPath)
r = random.randint(1, 1000)
print(r)
GI_path = join(caseFile["filesPath"], "GeneratedImages")
caseFile["GI"] = GI_path
caseFile["iee_version"] = int(caseFile["iee_version"])
if not exists(GI_path):
makedirs(GI_path)
dp = join(GI_path, "details" + str(r) + ".txt")
open(dp, "w")
for ID in clusters:
file = open(dp, "a")
file.write("Cluster:" + str(ID) + "\n")
print("Cluster:" + str(ID) + "\n")
logging.info("Searching images for 5Cluster:", ID)
# evalImages(clsData, caseFile)
caseFile["CR"] = clusterRadius[ID]
caseFile["CH"] = centroidHM[ID]
caseFile["ID"] = ID
caseFile["GI"] = GI_path
if not exists(join(GI_path, str(ID), "images")):
makedirs(join(GI_path, str(ID), "images"))
print("IEE Simulator", caseFile["iee_version"])
if int(caseFile["iee_version"]) == 2:
caseFile["xl"] = setX_2(1, "L")
caseFile["xu"] = setX_2(1, "U")
else:
caseFile["xl"] = setX(1, "L")
caseFile["xu"] = setX(1, "U")
initpop = ent = None
start1 = time.time()
for i in range(0, len(popSize)):
file.write("PopSize:" + str(popSize) + "\n")
file.write("Iterations:" + str(nGen) + "\n")
start = time.time()
initpop, ent = doProblem(popSize[i], popSize[i], nGen[i], caseFile, initpop, ent, i+1)
end = time.time()
file.write(doTime("Problem " + str(i + 1) + " Cost:", start-end) + "\n")
file.write(doTime("Total Cost:", start1) + "\n")
file.close()
def doProblem(pop_size, popS_new, n_gen, caseFile, prev_pop, prev_en, probNum):
dirPath = join(caseFile["GI"], str(caseFile["ID"]))
if not exists(dirPath):
makedirs(dirPath)
CP = join(caseFile["GI"], str(caseFile["ID"]), "PF" + str(probNum) + ".pop")
init_ = [get_sampling("real_random"), prev_pop, prev_pop]
out_ = ['w', 'w', 'a']
noff_ = [1, 1, None]
csv = [False, True, True]
res = solveProblem(pop_size=pop_size, n_gen=n_gen, n_offsprings=noff_[probNum - 1], sampling=init_[probNum - 1],
caseFile=caseFile, probNum=probNum, initpop=prev_pop, ent=prev_en)
# caseFile=caseFile, probNum=probNum - 1, initpop=prev_pop, ent=prev_en)
if hasattr(res, "pop"):
res = res.pop
#PF = getPF(res.pop, CP)
exportResults_2(res, "/PF/" + str(probNum) + ".txt", "/PF/" + str(probNum), probNum, dirPath,
# exportResults_2(res.pop, "/PF/" + str(probNum - 1) + ".txt", "/PF/" + str(probNum - 1), probNum - 1, dirPath,
open(dirPath + "/results.csv", out_[probNum - 1]), caseFile, prev_en, prev_pop, csv[probNum - 1])
if probNum != 3:
# new_pop, new_en = prepareINIT(res.pop, caseFile, dirPath, probNum + 1, popS_new)
new_pop, new_en = prepareINIT(res, caseFile, dirPath, probNum, popS_new)
#for member in new_pop:
# print(min(member.F))
return new_pop, new_en
#return None, None
else:
return None, None
def solveProblem(pop_size, n_gen, n_offsprings, sampling, caseFile, probNum, ent, initpop):
problem = HUDDProblem_N(caseFile, probNum, ent, initpop, n_gen, pop_size)
CP = join(caseFile["GI"], str(caseFile["ID"]), "res" + str(probNum) + ".pop")
CP2 = join(caseFile["GI"], str(caseFile["ID"]), "res" + str(probNum) + ".ckpt.npy")
problem.CP = CP
# if probNum == 0:
# algorithm = nsga2P0.NSGA2P0(pop_size=pop_size, sampling=sampling,
# crossover=get_crossover("real_sbx", prob=0.7, eta=20),
# mutation=get_mutation("real_pm", prob=0.3), eliminate_duplicates=True)
if probNum == 1:
algorithm = nsga2P1.NSGA2P1(pop_size=pop_size, sampling=sampling,
crossover=get_crossover("real_sbx", prob=0.7, eta=20),
mutation=get_mutation("real_pm", prob=0.3), eliminate_duplicates=True)
#algorithm = NSGA2(pop_size=pop_size, sampling=sampling,
# crossover=get_crossover("real_sbx", prob=0.7, eta=20),
# mutation=get_mutation("real_pm", prob=0.3), eliminate_duplicates=True)
elif probNum == 2:
algorithm = nsga2plus.NSGA2Plus(pop_size=pop_size, sampling=sampling,
crossover=get_crossover("real_sbx", prob=0.3, eta=20),
mutation=get_mutation("real_pm", prob=0.3), eliminate_duplicates=True)
# algorithm = NSGA3(ref_dirs=get_reference_directions("energy", pop_size, pop_size, seed=1), pop_size=pop_size,
# sampling=sampling, crossover=get_crossover("real_sbx", prob=0.7, eta=20),
# mutation=get_mutation("real_pm", prob=0.3), eliminate_duplicates=True)
elif probNum == 3:
algorithm = nsga2plus.NSGA2Plus(pop_size=pop_size, sampling=sampling,
crossover=get_crossover("real_sbx", prob=0.3, eta=20),
mutation=get_mutation("real_pm", prob=0.3), eliminate_duplicates=True)
# algorithm = NSGA3(ref_dirs=get_reference_directions("energy", pop_size, pop_size, seed=1), pop_size=pop_size,
# sampling=sampling, crossover=get_crossover("real_sbx", prob=0.7, eta=20),
# mutation=get_mutation("real_pm", prob=0.3), eliminate_duplicates=True)
if isfile(CP):
res = loadCP(CP)
print("Loaded Checkpoint:", res)
else:
res = minimize(problem, algorithm, ('n_gen', n_gen), n_offsprings=n_offsprings, verbose=True)
res = res.pop
print(problem.dsim, problem.c)
np.save(join(caseFile["GI"], str(caseFile["ID"]), "diversity.npy"), problem.dsim) #avg sim params distance
#np.save(join(caseFile["GI"], str(caseFile["ID"]), "2.npy"), problem.dhm) #avg heatmap distances
np.save(join(caseFile["GI"], str(caseFile["ID"]), "individuals.npy"), problem.c) #individuals belonging to cluster
saveCP(res, CP)
if hasattr(res, "pop"):
res = res.pop
for member in res:
print(min(member.F))
#exportResults_2(res, "/Pop/" + str(probNum) + ".txt", "/Pop/" + str(probNum), probNum,
# join(caseFile["GI"], str(caseFile["ID"])), None,
# caseFile, ent, initpop, False)
return res
class HUDDProblem_N(Problem):
def __init__(self, caseFile, probID, prevEN, prevPOP, n_gen, pop_size, **kwargs):
self.outPath = caseFile["outputPath"]
self.problemDict = {}
self.n_gen = n_gen
self.pop_size = pop_size
self.clusterRadius = caseFile["CR"]
self.centroidHM = caseFile["CH"]
self.caseFile = caseFile
self.probID = probID
self.prevEN = prevEN
self.prevPOP = prevPOP
self.previousPop = prevPOP
self.t = time.time()
self.dsim = []
self.dhm = []
self.dind = []
self.c = []
self.counter = 0
dp = join(caseFile["GI"], str(caseFile["ID"]) + ".txt")
open(dp, "w")
self.file = open(dp, 'a')
if probID == 1:
# n_obj = 2
# elif probID == 0:
n_obj = 1
else:
n_obj = pop_size
if int(caseFile["iee_version"]) == 2:
xl = setX_2(1, "L")
xu = setX_2(1, "U")
else:
xl = setX(1, "L")
xu = setX(1, "U")
self.xl = xl
self.xu = xu
print("length of params", len(self.xl))
print("configured length of params", nVar)
assert len(self.xl) == nVar
assert len(self.xu) == nVar
self.file.write("Solving Problem .. " + str(probID) + "\n")
logging.info("Number of Variables", nVar)
logging.info("Number of Objectives", nVar)
super().__init__(n_var=nVar, n_obj=n_obj, n_constr=0, xl=xl, xu=xu, elementwise_evaluation=False, **kwargs,
type_var=np.float)
def _evaluate(self, X, out, *args, **kwargs): # Batchwise
if not exists(self.outPath):
makedirs(self.outPath)
if self.probID == 1:
# print("prevPop", self.previousPop)
#F = HUDDevaluate_Pop1(X, self.caseFile, self.previousPop) #SEDE
F = HUDDevaluate_Pop1N(X, self.caseFile, self.previousPop) #NSGA2
elif self.probID == 2:
F = HUDDevaluate_Pop2(X, self.caseFile, self.previousPop)
elif self.probID == 3:
F = HUDDevaluate_Pop3(X, self.caseFile, self.prevPOP)
elif self.probID == 0:
F = HUDDevaluate_Pop1(X, self.caseFile, self.previousPop)
if out is None:
return F
else:
out["F"] = np.array(F)
self.file.write("Evaluation" + "\n")
print("Offspring Evaluation:")
#if len(F[0]) > 1:
# for n in F:
# self.file.write("F: " + str(min(n)) + "\n")
# print("F: " + str(min(n)))
#else:
# self.file.write("F: " + str(F) + "\n")
# print("F:" + str(F))
print(out["F"])
d_sim = []
d_hm = []
d_ind = []
if hasattr(self, "pop"): #NSGA
self.previousPop = self.pop #NSGA
# print("has pop")
#if self.previousPop is not None:
# print("Comparing Current vs. Prev., F=1-2ndClosest")
# print("here")
for x in self.previousPop:
if int(self.caseFile["iee_version"]) == 2:
imgPath, F = generateHuman(x.X, self.caseFile)
else:
imgPath, F = generateAnImage(x.X, self.caseFile)
imgPath += ".png"
if F:
N, DNNResult, P, L, D3, HM = doImage(imgPath, self.caseFile, self.caseFile["CH"])
print(D3 / self.caseFile["CR"])
if D3 /self.caseFile["CR"] <= 1:
d_ind.append(D3/self.caseFile["CR"])
for x3 in self.previousPop:
if int(self.caseFile["iee_version"]) == 2:
imgPath, F3 = generateHuman(x3.X, self.caseFile)
else:
imgPath, F3 = generateAnImage(x3.X, self.caseFile)
imgPath += ".png"
if F3:
N, DNNResult, P, L, D3, _ = doImage(imgPath, self.caseFile, HM[layer])
f3 = D3 / self.caseFile["CR"]
fp = doParamDist(x.X, x3.X, self.xl, self.xu)
if fp !=0 and f3 <= 1:
d_sim.append(fp)
if D3 != 0:
d_hm.append(D3)
if len(d_sim) > 0:
dsim_avg = sum(d_sim) / len(d_sim)
else:
dsim_avg = 0
if len(d_hm)> 0:
dhm_avg = sum(d_hm) / len(d_hm)
else:
dhm_avg = 0
self.counter += 1
self.dsim.append(dsim_avg)
self.dhm.append(dhm_avg)
self.c.append(self.counter)
self.dind.append(100 * (len(d_ind)))
print(d_ind, self.dind[-1], d_sim, d_hm, dsim_avg, dhm_avg,self.counter)
self.file.write("Selected Population" + "\n")
print("Selected Population:")
if self.previousPop is not None:
saveCP(self.previousPop, self.CP)
for ind in self.previousPop:
if len(ind.F) > 1:
self.file.write("F: " + str(min(ind.F)) + "\n")
print("F: " + str(min(ind.F)))
else:
self.file.write("F: " + str(ind.F) + "\n")
print("F: " + str(ind.F))
def _evaluate_EW(self, x, out, *args, **kwargs): # Elementwise
self.counter += 1
print(str(100 * (self.counter / (self.n_gen * self.pop_size)))[0:5] + "%", end="\r")
if not exists(self.outPath):
makedirs(self.outPath)
if self.probID == 1:
problemDict = HUDDevaluate_N(x, self.caseFile, self.prevEN, self.prevPOP)
self.problemDict[processX(x)] = problemDict
out["F"] = np.array([problemDict["F1"]])
elif self.probID == 0:
problemDict = HUDDevaluate_N(x, self.caseFile, None, None)
self.problemDict[processX(x)] = problemDict
out["F"] = np.array([problemDict["F1"]])
else:
F_list = []
for i in range(0, len(self.prevPOP)):
problemDict = HUDDevaluate_N(x, self.caseFile, self.prevEN, self.prevPOP[i].X)
self.problemDict[processX(x)] = problemDict
if self.probID == 2:
F_list.append(problemDict["FY"])
out["F"] = np.array([problemDict["FY"]])
elif self.probID == 3:
F_list.append(problemDict["FY_"])
out["F"] = np.array([problemDict["FY_"]])
out["F"] = np.array(F_list)
print(out["F"])
# if time.time() - self.t > 2h:
# terminate
#NSGA2
def HUDDevaluate_Pop1N(X, caseFile, Y):
xl = caseFile["xl"]
xu = caseFile["xu"]
CR = caseFile["CR"]
CH = caseFile["CH"]
fy = []
f1_ = []
for x in X:
if x is None:
fy.append(math.inf)
continue
if int(caseFile["iee_version"]) == 2:
imgPath, F = generateHuman(x, caseFile)
else:
imgPath, F = generateAnImage(x, caseFile)
imgPath += ".png"
dists = []
fy_ = math.inf
f1 = math.inf
if F:
N, DNNResult, P, L, D, _ = doImage(imgPath, caseFile, CH)
f1 = D / CR
else:
f1 = math.inf
f1_.append(f1)
fy.append([fy_])
# print("F1:", f1_)
return f1_
#SEDE
def HUDDevaluate_Pop1(X, caseFile, Y):
xl = caseFile["xl"]
xu = caseFile["xu"]
CR = caseFile["CR"]
CH = caseFile["CH"]
fy = []
f1_ = []
for x in X:
if x is None:
fy.append(math.inf)
continue
if int(caseFile["iee_version"]) == 2:
imgPath, F = generateHuman(x, caseFile)
else:
imgPath, F = generateAnImage(x, caseFile)
imgPath += ".png"
dists = []
fy_ = math.inf
f1 = math.inf
if F:
N, DNNResult, P, L, D, _ = doImage(imgPath, caseFile, CH)
f1 = D / CR
if f1 <= 1:
# Compare current vs. previous population, get closest and 2nd closest
if Y is not None:
# print("Comparing Current vs. Prev., F=1-2ndClosest")
for x2 in Y:
if int(caseFile["iee_version"]) == 2:
imgPath2, F2 = generateHuman(x2.X, caseFile)
else:
imgPath2, F2 = generateAnImage(x2.X, caseFile)
imgPath2 += ".png"
if x2.get("F")[0] <= 1:
N2, _, _, _, _, layersHM = doImage(imgPath2, caseFile, CH)
_, _, _, _, D2, _ = doImage(imgPath, caseFile, layersHM[layer])
fp = doParamDist(x, x2.X, xl, xu)
dists.append(fp)
else:
dists.append(math.inf)
# if x2.get("F")[0] <= 1:
# dists.append(fp) #F=1-fp 0.7
# else: #face not found #image 2 outside cluster
# dists.append(math.inf) #F=1-inf (-inf)
# else
closestDist = min(dists)
dists.remove(closestDist)
closestDist = min(dists)
fy_ = 1 - closestDist
else:
# print("Comparing Current vs. Current, F=1-2nClosest")
# print("Previous Population not found..")
# if previous population is None, closestDist is based on current population
dists_2 = []
for x3 in X:
if int(caseFile["iee_version"]) == 2:
imgPath, F3 = generateHuman(x3, caseFile)
else:
imgPath, F3 = generateAnImage(x3, caseFile)
imgPath += ".png"
if F3:
N, DNNResult, P, L, D3, _ = doImage(imgPath, caseFile, CH)
f3 = D3 / CR
fp = doParamDist(x, x3, xl, xu)
if f3 <= 1:
dists_2.append(fp)
if len(dists_2) > 1:
closestDist = min(dists_2)
dists_2.remove(closestDist)
closestDist = min(dists_2)
fy_ = 1 - closestDist
else:
fy_ = f1
else:
fy_ = f1
f1_.append(f1)
fy.append([fy_])
# print("F1:", f1_)
return fy
def HUDDevaluate_Pop2(X, caseFile, Y):
print("POP2")
xl = caseFile["xl"]
xu = caseFile["xu"]
CR = caseFile["CR"]
CH = caseFile["CH"]
fy = []
f1_ = []
for x in X:
j = len(fy)
l_D = []
if int(caseFile["iee_version"]) == 2:
for j in range(0, len(Y)):
l_D.append(doParamDist(x, Y[j].X, setX_2(1, "L"), setX_2(1, "U")))
else:
for j in range(0, len(Y)):
l_D.append(doParamDist(x, Y[j].X, setX(1, "L"), setX(1, "U")))
closestIndex = np.argsort(np.array(l_D))[0]
fyy = []
fy_ = math.inf
if x is None:
for x2 in Y:
fyy.append(fy_)
continue
if int(caseFile["iee_version"]) == 2:
imgPath, F = generateHuman(x, caseFile)
else:
imgPath, F = generateAnImage(x, caseFile)
imgPath += ".png"
if F:
N, DNNResult, P, L, D, _ = doImage(imgPath, caseFile, CH)
f1 = D / CR
if f1 <= 1:
j = 0
for x2 in Y:
if int(caseFile["iee_version"]) == 2:
imgPath2, F2 = generateHuman(x2.X, caseFile)
else:
imgPath2, F2 = generateAnImage(x2.X, caseFile)
imgPath2 += ".png"
N2, _, _, _, _, layersHM = doImage(imgPath2, caseFile, CH)
_, _, _, _, D2, _ = doImage(imgPath, caseFile, layersHM[layer])
fp = doParamDist(x, x2.X, xl, xu)
if j == closestIndex:
if not DNNResult:
fy_ = fp
fyy.append(fy_)
print("Eval-OK:", closestIndex, fy_)
else:
fy_ = 2 - N
fyy.append(fy_)
print("Eval-NO:", closestIndex, fy_)
else:
fy_ = 2 + fp
fyy.append(fy_)
j += 1
else:
for x2 in Y:
fy_ = 3 + f1
fyy.append(fy_)
else:
for x2 in Y:
fyy.append(fy_)
fy.append(fyy)
return fy
def HUDDevaluate_Pop3(X, caseFile, Y):
print("POP3")
xl = caseFile["xl"]
xu = caseFile["xu"]
CR = caseFile["CR"]
CH = caseFile["CH"]
fy = []
f1_ = []
for x in X:
j = len(fy)
fy_ = math.inf
fyy = []
l_D = []
if caseFile["iee_version"] == 2:
for j in range(0, len(Y)):
l_D.append(doParamDist(x, Y[j].X, setX_2(1, "L"), setX_2(1, "U")))
else:
for j in range(0, len(Y)):
l_D.append(doParamDist(x, Y[j].X, setX(1, "L"), setX(1, "U")))
closestIndex = np.argsort(np.array(l_D))[0]
if x is None:
for x2 in Y:
fyy.append(fy_)
continue
if caseFile["iee_version"] == 2:
imgPath, F = generateHuman(x, caseFile)
else:
imgPath, F = generateAnImage(x, caseFile)
imgPath += ".png"
if F:
N, DNNResult, P, L, D, _ = doImage(imgPath, caseFile, CH)
f1 = D / CR
j = 0
for x2 in Y:
if caseFile["iee_version"] == 2:
imgPath2, F2 = generateHuman(x2.X, caseFile)
else:
imgPath2, F2 = generateAnImage(x2.X, caseFile)
imgPath2 += ".png"
N2, _, _, _, _, layersHM = doImage(imgPath2, caseFile, CH)
_, _, _, _, D2, _ = doImage(imgPath, caseFile, layersHM[layer])
fp = doParamDist(x, x2.X, xl, xu)
if j == closestIndex:
if DNNResult:
fy_ = fp
fyy.append(fy_)
print("Eval-OK:", closestIndex, fy_)
else:
fy_ = 1 + N + abs(1 - f1)
fyy.append(fy_)
print("Eval-NO:", closestIndex, fy_)
else:
fy_ = 2 + fp
fyy.append(fy_)
j += 1
else:
for x2 in Y:
fyy.append(math.inf)
fy.append(fyy)
return fy
def HUDDevaluate_N(x, caseFile, prevEN, y):
xl = caseFile["xl"]
xu = caseFile["xu"]
CR = caseFile["CR"]
CH = caseFile["CH"]
if caseFile["iee_version"] == 2:
imgPath, F = generateHuman(x, caseFile)
else:
imgPath, F = generateAnImage(x, caseFile)
imgPath += ".png"
f2_ = None
fy = math.inf
fy_ = math.inf
fp = None
ND = None
D2 = None
if F:
N, DNNResult, P, L, D, _ = doImage(imgPath, caseFile, CH)
f1 = D / CR
fp = doParamDist(x, x, xl, xu)
if DNNResult:
AN = N
else:
AN = 1
if f1 <= 1:
f2 = 1 - AN
f1_ = 1 - f1
else:
f2 = 1
f1_ = f1
if prevEN is not None:
if not DNNResult:
f2_ = 0
else:
f2_ = 1
if y is not None:
for y1 in y:
if caseFile["iee_version"] == 2:
imgPath2, F2 = generateHuman(y1.X, caseFile)
else:
imgPath2, F2 = generateAnImage(y1.X, caseFile)
imgPath2 += ".png"
if F2:
N2, _, _, _, _, layersHM = doImage(imgPath2, caseFile, CH)
_, _, _, _, D2, _ = doImage(imgPath, caseFile, layersHM[layer])
ND = D2 / CR
fp = doParamDist(x, y1.X, xl, xu)
ND = fp
if math.isnan(fp):
ND = 0
if f1 < 1:
if not DNNResult:
fy = ND
else:
fy = 1 + (1 - N)
else:
fy = 2 + f1
if DNNResult:
fy_ = ND
else:
fy_ = 1 + N
else:
print("face 2 not found")
else:
f1 = math.inf
f1_ = math.inf
f2 = math.inf
f2_ = math.inf
fy = math.inf
fp = math.inf
N = math.inf
D = math.inf
D2 = math.inf
P = None
L = None
DNNResult = None
AN = None
problemDict = {"Face": F, "Prediction": P, "Label": L, "N_": N, "Adjusted_N": AN, "FY_": fy_,
"Medoid-Distance": D, "Y-Distance": D2, "DNNResult": DNNResult,
"F1": f1, "F1_": f1_, "F2": f2, "F2_": f2_, "FY": fy, "FP": fp, "ND": ND}
return problemDict
def exportResults_2(PF, txt1, outPathPOP, popNum, dirPath, outFile, caseFile, prev_en, prev_pop, csvFlag):
outPathPOP = dirPath + outPathPOP
if not exists(outPathPOP):
makedirs(outPathPOP)
SimDataPath = caseFile["SimDataPath"]
clusterImages = list()
txt1 = dirPath + txt1
file = open(txt1, "a")
# if popNum == 1:
# F = HUDDevaluate_Pop1(PF.get("X"), caseFile, None)
# if popNum == 2:
# F = HUDDevaluate_Pop2(PF.get("X"), caseFile, PF.get("X"))
# if popNum == 3:
# F = HUDDevaluate_Pop3(PF.get("X"), caseFile, PF.get("X"))
dub = 0
for i in range(0, len(PF)):
X = PF[i].X
problemDict = HUDDevaluate(X, caseFile, None, None)
if problemDict["Face"]:
imgName = processX(X) + ".png"
if not isfile(join(outPathPOP, imgName)):
shutil.copy(join(SimDataPath, imgName), join(outPathPOP, imgName))
else:
shutil.copy(join(SimDataPath, imgName), join(outPathPOP, processX(X) + "_" + str(dub) + ".png"))
dub += 1
if problemDict["F1"] <= 1:
if popNum == 0 or popNum == 1:
clusterImages.append(imageio.imread(join(SimDataPath, imgName)))
clusterImages.append(imageio.imread(join(SimDataPath, imgName)))
if popNum == 2 and not problemDict["DNNResult"]:
clusterImages.append(imageio.imread(join(SimDataPath, imgName)))
clusterImages.append(imageio.imread(join(SimDataPath, imgName)))
if popNum == 3 and problemDict["DNNResult"]:
clusterImages.append(imageio.imread(join(SimDataPath, imgName)))
clusterImages.append(imageio.imread(join(SimDataPath, imgName)))
file.write(imgName + "\n")
file.write(str(PF[i].F) + "\n")
for nameX in problemDict:
file.write(str(nameX) + ": " + str(problemDict[nameX]) + "\n")
if csvFlag:
toCSV_N(PF, outFile, caseFile, prev_en, prev_pop, popNum)
if len(clusterImages) > 1:
imageio.mimsave(outPathPOP + "_" + str(len(clusterImages)) + '.gif', clusterImages)
def exportResults(PF, txt1, outPathPOP, popNum, dirPath, outFile, caseFile, prev_en, prev_pop, csvFlag):
outPathPOP = dirPath + outPathPOP
if not exists(outPathPOP):
makedirs(outPathPOP)
SimDataPath = caseFile["SimDataPath"]
clusterImages = list()
txt1 = dirPath + txt1
file = open(txt1, "a")
if popNum == 0 or popNum == 1 or popNum == 2 or popNum == 3:
dub = 0
for i in range(0, len(PF)):
X = PF[i].X
print(X)
problemDict = HUDDevaluate(X, caseFile, None, None)
if problemDict["Face"]:
imgName = processX(X) + ".png"
if not isfile(join(outPathPOP, imgName)):
shutil.copy(join(SimDataPath, imgName), join(outPathPOP, imgName))
else:
shutil.copy(join(SimDataPath, imgName), join(outPathPOP, processX(X) + "_" + str(dub) + ".png"))
dub += 1
clusterImages.append(imageio.imread(join(SimDataPath, imgName)))
file.write(imgName + "\n")
file.write(str(PF[i].F) + "\n")
for nameX in problemDict:
file.write(str(nameX) + ": " + str(problemDict[nameX]) + "\n")
if csvFlag:
toCSV_N(PF, outFile, caseFile, prev_en, prev_pop, popNum)
if len(clusterImages) > 1:
imageio.mimsave(outPathPOP + "_" + str(len(clusterImages)) + '.gif', clusterImages)
else:
dub = 0
for i in range(0, len(PF)):
clusterImages = list()
if prev_pop is not None:
problemDict = HUDDevaluate2(PF[i].X, caseFile, prev_en, prev_pop[i])
else:
problemDict = HUDDevaluate2(PF[i].X, caseFile, prev_en, None)
for j in range(0, indvdSize):
X = getI(PF[i].X, j)
if problemDict[processX(X)]["Face"]:
imgName = processX(X) + ".png"
clusterImages.append(imageio.imread(join(SimDataPath, imgName)))
if not isfile(join(outPathPOP, imgName)):
shutil.copy(join(SimDataPath, imgName), join(outPathPOP, imgName))
else:
shutil.copy(join(SimDataPath, imgName), join(outPathPOP, processX(X) + "_" + str(dub) + ".png"))
dub += 1
file.write(imgName + "\n")
file.write(str(PF[i].F) + "\n")
for nameX in problemDict[processX(X)]:
file.write(str(nameX) + ": " + str(problemDict[processX(X)][nameX]) + "\n")
if len(clusterImages) > 1:
imageio.mimsave(outPathPOP + str(i) + "_" + str(len(clusterImages)) + '.gif', clusterImages)
if csvFlag:
toCSV_N(PF[0], outFile, caseFile, prev_en, prev_pop[0], popNum)
file.close()
def toCSV_N(pop, outFile, CF, prevEN, prev_pop, probNum):
ID = CF["ID"]
counter = 1
problemDict = HUDDevaluate_N(pop[0].X, CF, prevEN, prev_pop)
if probNum == 2:
strW = "idx,clusterID"
for _x_ in problemDict:
if "Face" in problemDict:
if problemDict["Face"]:
for nameX in problemDict:
if nameX != "FY":
strW += "," + str(nameX)
break
#strW += ",cam_dir0,cam_dir1,cam_dir2,cam_loc0,cam_loc1,cam_loc2,lamp_loc0," \
# "lamp_loc1,lamp_loc2," \
# "head_pose0,head_pose1,head_pose2,pose,imgPath\r\n"
strW += ",head0,head1,head2,lampcol0,lampcol1,lampcol2,lamploc0," \
"lamploc1,lamploc2," \
"lampdir0,lampdir1,lampdir2,cam,age,hue,iris,sat,val,freckle,oil,veins,eyecol,gender,imgPath\r\n"
outFile.writelines(strW)
if probNum == 3:
ID = 0
for j in range(0, len(pop)):
problemDict = HUDDevaluate_N(pop[j].X, CF, prevEN, prev_pop)
X = pop[j].X
if problemDict["Face"]:
if ((not problemDict["DNNResult"]) and (probNum == 2)) or ((problemDict["DNNResult"]) and (probNum == 3)):
imgPath = join(CF["filesPath"], "Pool", processX(X) + ".png")
strMerge = str(counter) + "," + str(ID)
for nameX in problemDict:
if nameX != "FY":
strMerge += "," + str(problemDict[nameX])
for j in range(0, len(X)):
strMerge += "," + str(X[j])
#strMerge += "," + str(math.floor(X[len(X) - 1]))
strMerge += "," + str(imgPath)
strMerge += "\r\n"
outFile.writelines(strMerge)
counter += 1
def HUDDevaluate(x, caseFile, prevEN, y):
xl = caseFile["xl"]
xu = caseFile["xu"]
CR = caseFile["CR"]
CH = caseFile["CH"]
imgPath, F = generateAnImage(x, caseFile)
imgPath += ".png"
f2_ = None
fy = math.inf
fp = None
ND = None
D2 = None
HN = None
LN = False
if F:
N, DNNResult, P, L, D, _ = doImage(imgPath, caseFile, CH)
f1 = D / CR
if DNNResult:
AN = N
else:
AN = 1
if f1 <= 1:
f2 = 1 - AN
f1_ = 1 - f1
else:
f2 = 1
f1_ = f1
if prevEN is not None:
if AN >= max(prevEN):
HN = True
else:
HN = False
if (not DNNResult) or HN:
f2_ = 0
else:
f2_ = 1
if y is not None:
if caseFile["iee_version"] == 2:
imgPath2, F2 = generateHuman(y, caseFile)
else:
imgPath2, F2 = generateAnImage(y, caseFile)
imgPath2 += ".png"
if F2:
N2, _, _, _, _, layersHM = doImage(imgPath2, caseFile, CH)
N1, _, _, _, D2, _ = doImage(imgPath, caseFile, layersHM[layer])
if N1 <= N2:
LN = True
HN = False
ND = D2 / CR
fp = doParamDist(x, y, xl, xu)
ND = fp
if DNNResult and LN:
fy = ND
elif (not DNNResult) or (not LN):
if ND != 0:
# fy = 1 + (1 / f1)
fy = 1 + (N / f1)
else:
fy = math.inf
else:
print("face 2 not found")
else:
f1 = math.inf
f1_ = math.inf
f2 = math.inf
f2_ = math.inf
fy = math.inf
fp = math.inf
N = math.inf
D = math.inf
D2 = math.inf
LN = False
HN = True
P = None
L = None
DNNResult = None
AN = None
problemDict = {"Face": F, "Prediction": P, "Label": L, "N_": N, "Adjusted_N": AN, "Low_N": LN, "High_N": HN,
"Medoid-Distance": D, "Y-Distance": D2, "DNNResult": DNNResult,
"F1": f1, "F1_": f1_, "F2": f2, "F2_": f2_, "FY_": fy, "FP": fp, "ND": ND}
return problemDict
def compareX(x1, x2):
equal = True
for i in range(0, len(x1)):
if x1[i] != x2[i]:
equal = False
return equal
def prepareINIT(PF, caseFile, dirPath, probNum, pop_size):
print("Preparing Initial Population", probNum)
CP = join(caseFile["GI"], str(caseFile["ID"]), "init" + str(probNum) + ".pop")
N, _ = getEntropy(PF, probNum, caseFile)
if isfile(CP):
initpop = loadCP(CP)
print("Loaded Initpop:", initpop)
else:
initpop = initPOP_Pop(PF, probNum, pop_size, N, caseFile)
saveCP(initpop, CP)
exportResults(initpop, "/Initial/" + str(probNum) + ".txt", "/Initial/" + str(probNum), probNum, dirPath,
None, caseFile, N, initpop, False)
print("Initial population Size:", len(initpop))
return initpop, N
def getEntropy(PF, probNum, caseFile):
N = []
PF2 = []
for member2 in PF:
problemDict = HUDDevaluate_N(member2.X, caseFile, None, None)
if problemDict["N_"] is not None:
N.append(problemDict["N_"])
return N, PF2
def initPOP_N(PF, probNum, popLen, N, caseFile):
pop2 = Population(popLen)
for i in range(0, len(PF)):
F_list = []
if probNum == 2:
key = "FY"
elif probNum == 3:
key = "FY_"
for j in range(0, len(PF)):
problemDict = HUDDevaluate_N(PF[i].X, caseFile, N, PF[j].X)
F_list.append(problemDict[key])
pop2[i] = Individual(X=PF[i].X, CV=PF[i].CV, feasible=PF[i].feasible, F=np.array(F_list))
return pop2
def initPOP_Pop(PF, probNum, popLen, N, caseFile):
pop2 = Population(popLen)
X_list = []
for ind in PF:
X_list.append(ind.X)
if probNum == 1:
F = HUDDevaluate_Pop1(X_list, caseFile, PF)
elif probNum == 2:
F = HUDDevaluate_Pop2(X_list, caseFile, PF)
else:
F = HUDDevaluate_Pop3(X_list, caseFile, PF)
for i in range(0, len(PF)):
pop2[i] = Individual(X=PF[i].X, CV=PF[i].CV, feasible=PF[i].feasible, F=np.array(F[i]))
return pop2
def getPF(pop1, CP):
if isfile(CP):
PF = loadCP(CP)
else:
print("Extracting Pareto Front")
length = 0
PF_ = []
for i in range(0, len(pop1)):
if pop1[i].get("rank") == 0:
PF_.append(pop1[i])
length += 1
PF = Population(len(PF_))
for i in range(0, len(PF)):
PF[i] = PF_[i]
print("Pareto Front #individuals:", length)
saveCP(PF, CP)
return PF
def cleanX(x, caseFile):
goodIndex = []