def test_BQE_CrossDataset_Similarity(Dataset):
if Dataset == 'DukeFromMarket':
DirCNN = '..\\FeatureCNN\\DukeMTMC'
#Cross Dataset
DirCross = '..\\FeatureCNN\\CrossDataset\\DukeFromMarket_feature.pkl'
DirBayes = '..\\Bayes_Market_trained.pkl'
elif Dataset == 'MarketFromDuke':
DirCNN = '..\\FeatureCNN\\Market-1501'
#Cross Dataset
DirCross = '..\\FeatureCNN\\CrossDataset\\MarketFromDuke_feature.pkl'
DirBayes = '..\\Bayes_Duke_trained.pkl'
else:
print('ERRORE')
print('START')
#Feature CNN
testData, queryData, trainingData = loadCNN(DirCNN)
#Load BayesianModel gia addestrato
Bayes = loadFile(DirBayes)
test_cams, test_feature, test_id, test_desc = testData
query_cams, query_feature, query_id, query_desc = queryData
train_cams, train_feature, train_id, train_desc = trainingData
#Cross dataset
f = open(DirCross, 'rb')
test_feature, query_feature = pickle.load(f)
f.close()
gallery, gallery_id, gallery_cams = test_feature, test_id, test_cams
query, query_id, query_cams = query_feature[0::], query_id[
0::], query_cams[0::]
start = time.time()
print('START TEST')
n, k = 3, 5
vettori_cmc, ranks, mAP_list = [], [], []
for i in range(n + 1):
ranks_index, ranks_probability, ranks_label = calculateRanks_Similarity(
query, gallery, gallery_id, Bayes)
ranks.append(ranks_index)
print('Ranks calcolato')
#Calcolo la cmc
cmc_vector = calculateCmcFromRanks(ranks_index, ranks_label, query_id,
gallery_cams, query_cams)
vettori_cmc.append(cmc_vector)
#Calcolo mAP
mAP = calculate_mAP(ranks_index, ranks_label, query_id, gallery_cams,
query_cams)
mAP_list.append(mAP)
#Calcolo la nuova query
query = queryExpansion(ranks_index,
ranks_probability,
gallery,
query,
k,
AQE=False,
soglia=0.5)
print('Nuova query calcolata')
#Cmc e mAP
results = [len(set(query_id)), query_id]
k_n_cmc_mAP = [k, n, vettori_cmc, mAP_list]
results.append([k_n_cmc_mAP])
#Solo i ranks
results_ranks = [len(set(query_id)), query_id]
k_n_ranks = [k, n, ranks]
results_ranks.append([k_n_ranks])
f = open(Dataset + '_test_complete_BQE_Similarity.pkl', 'wb')
pickle.dump(results, f)
f.close()
f = open('Ranks-' + Dataset + '_test_complete_BQE_Similarity.pkl', 'wb')
pickle.dump(results_ranks, f)
f.close()
def test300_BQE(Dataset):
if Dataset == 'Duke':
DirCNN = '..\\FeatureCNN\\DukeMTMC'
DirBayes = '..\\Bayes_Duke_trained.pkl'
elif Dataset == 'Market':
DirCNN = '..\\FeatureCNN\\Market-1501'
DirBayes = '..\\Bayes_Market_trained.pkl'
else:
print('ERRORE')
print('START')
#Feature CNN
testData, queryData, trainingData = loadCNN(DirCNN)
test_cams, test_feature, test_id, test_desc = testData
query_cams, query_feature, query_id, query_desc = queryData
train_cams, train_feature, train_id, train_desc = trainingData
#Load BayesianModel gia addestrato
Bayes = loadFile(DirBayes)
print('TRAINING COMPLETE')
gallery, gallery_id, gallery_cams = test_feature, test_id, test_cams
query, query_id, query_cams = query_feature[0:300], query_id[
0:300], query_cams[0:300]
query_first = query
print('START TEST')
#Calcolo primo rank
first_ranks_index, first_ranks_probability, first_ranks_label = calculateRanks(
query, gallery, gallery_id, Bayes)
#Calcolo la prima cmc
first_cmc_vector = calculateCmcFromRanks(first_ranks_index,
first_ranks_label, query_id,
gallery_cams, query_cams)
#Calcolo il primo mAP
first_mAP = calculate_mAP(first_ranks_index, first_ranks_label, query_id,
gallery_cams, query_cams)
n = 10
results, results_Ranks = [], []
risultatiTest = [len(set(query_id)), query_id]
risultatiTest_Ranks = [len(set(query_id)), query_id]
for k in [5, 15, 25, 35, 45, 55]:
query = query_first
ranks_index, ranks_probability, ranks_label = first_ranks_index, first_ranks_probability, first_ranks_label
vettori_cmc, ranks, mAP_list = [], [], []
ranks.append(first_ranks_index)
vettori_cmc.append(first_cmc_vector)
mAP_list.append(first_mAP)
print(k)
for i in range(n):
query = queryExpansion(ranks_index,
ranks_probability,
gallery,
query,
k,
soglia=0.5)
print('Nuova query calcolata')
ranks_index, ranks_probability, ranks_label = calculateRanks(
query, gallery, gallery_id, Bayes)
print('Ranks calcolato')
ranks.append(ranks_index)
#Calcolo la cmc
cmc_vector = calculateCmcFromRanks(ranks_index, ranks_label,
query_id, gallery_cams,
query_cams)
vettori_cmc.append(cmc_vector)
#Calcolo mAP
mAP = calculate_mAP(ranks_index, ranks_label, query_id,
gallery_cams, query_cams)
mAP_list.append(mAP)
k_n_cmc_mAP = [k, n, vettori_cmc, mAP_list]
results.append(k_n_cmc_mAP)
k_n_ranks = [k, n, ranks]
results_Ranks.append(k_n_ranks)
print('####################')
risultatiTest.append(results)
risultatiTest_Ranks.append(results_Ranks)
f = open(Dataset + '_300pics_k_n_BQE.pkl', 'wb')
pickle.dump(risultatiTest, f)
f.close()
f = open('Ranks-' + Dataset + '_300pics_k_n_BQE.pkl', 'wb')
pickle.dump(risultatiTest_Ranks, f)
f.close()