def accuPerPlate(nom, c_list, n_big_fold, n_fold, tdict, plate, sol, newFrameLot):
r = np.zeros(shape=(len(c_list),len(EVENTS), 2), dtype=np.int)
r2 = np.zeros(shape=(len(c_list), len(EVENTS), len(EVENTS)), dtype=np.int)
compteur = np.zeros(shape=(len(c_list),len(EVENTS),2))
j=0
for p in c_list:
#ii. predire et changer la forme de new_sol pour qu'elles soient exploitables
if tdict is not None:
sol = filtre2(sol, tdict, plate)
new_sol = tracking.sousProcessClassify(sol, loadingFolder, loadingFile= None, i=p, n_f=n_fold, n_big_f=n_big_fold)
#iii. construire le doublet source, target pour chaque frame, avec uniquement les hypotheses concernant les individus du training set
acc, acc2, compt, visu = calcAccuracy(new_sol, newFrameLot, tdict)
r[j]+=acc ; r2[j]+=acc2; compteur[j] +=compt
j+=1
del sol ;del newFrameLot ; del new_sol ; del tdict; gc.collect()
# print>>outputTotal, "n fold", n_fold, "plate", plate, "c list :", c_list
# for k in range(len(c_list)):
# print>>outputTotal, r[k]
# #print>>outputTotal, d
# outputTotal.close()
return r, r2, compteur
def accu(nom, c_list, nb_fold = -1):
global nb_folds
#i: charger les data
r = [np.zeros(shape=(len(EVENTS), 2), dtype=np.int) for x in range(len(c_list))]
r2 = [np.zeros(shape=(len(EVENTS), len(EVENTS)), dtype=np.int) for x in range(len(c_list))]
compteur = [np.zeros(shape=(len(EVENTS),2)) for x in range(len(c_list))]
#ACCURACY POUR TOUT LE MONDE
if nb_fold == -1:
listD = os.listdir('/media/lalil0u/New/workspace2/Tracking/data/raw')
first = True
for plate in listD:
output2=open(nom+plate+'.pkl', 'w')
sol, newFrameLot = gettingSolu(first, plate)
fichier = open(loadingFolder+"minMax_data_all.pkl", "r")
minMax = pickle.load(fichier)
fichier.close()
sol.normalisation(minMax)
first = False
j=0
for p in c_list:
#ii. predire et changer la forme de new_sol pour qu'elles soient exploitables
print loadingFolder
new_sol = tracking.sousProcessClassify(sol, loadingFolder, integers=True, loadingFile= None, i=p)
dicTraj =tracking.trajBuilder(new_sol)
tracking.outputTrajXml(dicTraj, loadingFolder+'/predictions/')
#iii. construire le doublet source, target pour chaque frame, avec uniquement les hypotheses concernant les individus du training set
acc, acc2, c, visu = calcAccuracy(new_sol, newFrameLot)
#pickle.dump(visu, open('visu'+plate+'_'+str(p)+'.pkl', 'w'))
#ecrireXml(visu, p)
print acc, acc2, c
r[j]+=acc; r2[j]+=acc2; compteur[j] +=c
j+=1
sol = None ;newFrameLot = None ; new_sol = None
pickle.dump(postProcessing(r, r2, compteur[0]), output2)
output2.close()
elif nb_fold == nb_folds:
first = True
outputTotal = open(nom+"TOTALE.txt", 'w')
output2=open(nom+'TOTALE.pkl', 'w')
listD = os.listdir('/media/lalil0u/New/workspace2/Tracking/data/raw')
for plate in listD:
sol, newFrameLot = gettingSolu(first, plate)
first = False; firstFold=True
for n_fold in range(nb_fold):
fichier = open("../results/data_TEST_fold"+str(n_fold)+".pkl", 'r')
tdict = pickle.load(fichier); fichier.close()
if plate not in tdict:
continue
if not firstFold:
sol.denormalisation(minMax)
print "normalization"
fichier = open(loadingFolder+"minMax"+str(n_fold)+".pkl", "r")
minMax = pickle.load(fichier)
fichier.close()
sol.normalisation(minMax)
firstFold=False
print "TIME TIME TIME after normalization", time.clock()
acc, acc2, compt = accuPerPlate(nom, c_list, n_fold, tdict, plate, sol, newFrameLot)
for pou in range(len(r)):
r[pou]+=acc[pou]; r2[pou]+=acc2[pou]
compteur[pou]+=compt[pou]
outputPlate = open(nom+str(plate)+'.pkl', 'w')
pickle.dump(postProcessing(r, r2, compteur[0]), outputPlate)
outputPlate.close()
print>>outputTotal, "c list :", c_list
for k in range(len(c_list)):
print>>outputTotal, r[k], compteur[k]
#print>>outputTotal, d
outputTotal.close()
pickle.dump(postProcessing(r, r2, compteur[0]), output2)
output2.close()
return r, r2, compteur