def get_features(params):
#Obrim el fitxer que conte les ID de les imatges d'entrenament
ID=open(os.path.join(params['root'],params['database'],'train','ImageIDs.txt'), 'r')
#Extraccio de les caracteristiques de la imatge de la primera linia del ImageIDs.txt
nom=str(ID.readline()).replace('\n','')
desc_train=get_local_features(params,os.path.join(params['root'],params['database'],'train','images',nom + '.jpg'))
#Extraccio de les caracteristiques per a la resta de les imatges d'entrenament
dictrain=dict()
dictrain[nom]=desc_train
for line in ID:
nom=str(line).replace('\n','')
x=get_local_features(params,os.path.join(params['root'],params['database'],'train','images',nom + '.jpg'))
#Concatenar les caracteristiques de cada imatge en una numpy array
desc_train=np.concatenate((desc_train,x))
dictrain[nom]=x
#Tanquem el fitxer
ID.close()
#Entrenament del KMeans nomes per a les imatges d'entrenament amb 1024 paraules
paraules=1024
codebook=train_codebook(params,desc_train,paraules)
#Obrim el fitxer que conte les ID de les imatges d'entrenament
ID=open(os.path.join(params['root'],params['database'],'train','ImageIDs.txt'), 'r')
for line in ID:
nom=str(line).replace('\n','')
#Calculem les assignacions per les imatges d'entrenament
assignments=get_assignments(dictrain[nom],codebook)
#Creacio del BoW per les imatges d'entrenament i emplenament del diccionari
dictrain[nom]=build_bow(assignments,codebook,paraules)
#Tanquem el fitxer
ID.close()
#Guardem el diccionari amb el BoW de les imatges d'entrenament en l'arxiu "Features.txt"
bow_train = open (os.path.join(params['root'],params['database'],'train','Features.txt'), 'w')
pk.dump(dictrain,bow_train)
bow_train.close()
#Obrim el fitxer que conté les ID de les imatges de validacio
ID = open(os.path.join(params['root'],params['database'],'val','ImageIDs.txt'), 'r')
#Creacio del diccionari de les imatges de validacio
dicval=dict()
for line in ID:
#Extraccio de les caracteristiques per a les imatges de validacio
x=get_local_features(params,os.path.join(params['root'],params['database'],'val','images',str(line).replace('\n','') + '.jpg'))
#Calculem les assignacions per les imatges de validacio
assignments=get_assignments(x,codebook)
#Creacio del BoW per les imatges de validacio i emplenament del diccionari
dicval[str(line).replace('\n','')]=build_bow(assignments,codebook,paraules)
#Tanquem el fitxer
ID.close()
#Guardem el diccionari amb el BoW de les imatges de validacio en l'arxiu "Features.txt"
bow_val = open (os.path.join(params['root'],params['database'],'val','Features.txt'), 'w')
pk.dump(dicval,bow_val)
bow_val.close()
def extract_features(params):
feats_dic = {}
with open(os.path.join(params['root'],params['root_save'],params['image_lists'],params['split'] + '.txt'),'r') as f:
image_list = f.readlines()
# Get trained codebook
code = pickle.load(open(os.path.join(params['root'],params['root_save'],
params['codebooks_dir'],'codebook_train_val_'
+ str(params['descriptor_size']) + "_"
+ params['descriptor_type']
+ "_" + params['keypoint_type'] + '.cb'),'rb'))
for img in image_list:
im = cv2.imread(os.path.join(params['root'],params['database'],params['split'],'images',img.rstrip()))
im = resize_image(params,im)
des = get_local_features(params,im)
assign = get_assignments(code,des)
feats_dic[img] = build_bow(assign,code)
# Save dictionary to disk with unique name
save_file = os.path.join(params['root'],params['root_save'],params['feats_dir'],
params['split'] + "_" + str(params['descriptor_size']) + "_"
+ params['descriptor_type'] + "_" + params['keypoint_type'] + '.p')
pickle.dump(feats_dic,open(save_file,'wb'))
def extract_features(params):
Train_or_Val_dic = {}
with open(os.path.join(params['root'],params['root_save'],params['image_lists'],params['split'] + '.txt'),'r') as f:
image_list = f.readlines()
#Train_files= os.listdir(r'C:\Users\Albert\Documents\UNI\Q-5\GDSA\Projecte\TerrassaBuildings900\Train\images')
#Val_files= os.listdir(r'C:\Users\Albert\Documents\UNI\Q-5\GDSA\Projecte\TerrassaBuildings900\Val\images')
descriptors = []
if params['split'] == 'train':
for img in image_list:
im = cv2.imread(os.path.join(params['root'],params['database'],params['split'],'images',img.rstrip()))
# Resize image
im = resize_image(params,im)
des=get_local_features(params,im)
if len(descriptors) == 0:
descriptors = des
else:
descriptors = np.vstack((descriptors,des))
code=train_codebook(params,descriptors)
# Save to disk
pickle.dump(code,open(os.path.join(params['root'],params['root_save'],
params['codebooks_dir'],'codebook_'
+ str(params['descriptor_size']) + "_"
+ params['descriptor_type']
+ "_" + params['keypoint_type'] + '.cb'),'wb'))
# Get trained codebook
code = pickle.load(open(os.path.join(params['root'],params['root_save'],
params['codebooks_dir'],'codebook_'
+ str(params['descriptor_size']) + "_"
+ params['descriptor_type']
+ "_" + params['keypoint_type'] + '.cb'),'rb'))
for img in image_list:
im = cv2.imread(os.path.join(params['root'],params['database'],params['split'],'images',img.rstrip()))
# Resize image
im = resize_image(params,im)
des=get_local_features(params,im)
assign=get_assignments(code,des)
Train_or_Val_dic[img] = build_bow(assign,code)
# Save dictionary to disk with unique name
save_file = os.path.join(params['root'],params['root_save'],params['feats_dir'],
params['split'] + "_" + str(params['descriptor_size']) + "_"
+ params['descriptor_type'] + "_" + params['keypoint_type'] + '.p')
pickle.dump(Train_or_Val_dic,open(save_file,'wb'))
def get_features(params, pca=None, scaler=None):
# Read image names/Llegim les imatges
with open(
os.path.join(params['root'], params['root_save'],
params['image_lists'], params['split'] + '.txt'),
'r') as f:
image_list = f.readlines()
# Initialize keypoint detector and feature extractor/ Inicialitzem els detectors i els extractors
detector, extractor = init_detect_extract(params)
# Initialize feature dictionary/Inicialitzem el diccionari
features = {}
# Get trained codebook /Llegim el codebook entenat
km = pickle.load(
open(
os.path.join(
params['root'], params['root_save'], params['codebooks_dir'],
'codebook_' + str(params['descriptor_size']) + "_" +
params['descriptor_type'] + "_" + params['keypoint_type'] +
'.cb'), 'rb'))
for image_name in image_list:
image_name = image_name.replace('\n', '')
# Read image/Llegim l'imatge
im = cv2.imread(
os.path.join(params['root'], params['database'], params['split'],
'images', image_name))
# Resize image
im = resize_image(params, im)
# Extract local features
#f_orb = image_local_features(im)
#f_sift=sift_features(im)
# posem f_i que sigui igual que la dimensió f_o
#f_sift.resize(f_orb.shape)
#feats=np.concatenate((f_sift,f_orb))
feats = image_local_features(params, im)
#f_orb.resize(f_root.shape)
#feats=np.concatenate((f_root,f_orb))
if feats is not None:
if params['normalize_feats']:
feats = normalize(feats)
# If we scaled training features
if scaler is not None:
scaler.transform(feats)
# Whiten if needed
if pca is not None:
pca.transform(feats)
# Compute assignemnts
assignments = get_assignments(km, feats)
# Generate bow vector
feats = bow(assignments, km)
else:
# Empty features
feats = np.zeros(params['descriptor_size'])
'''
print f_sift[1]
print f_orb[1]
print len(f_orb)
print len(f_sift)
'''
#print len(feats)
# Add entry to dictionary
features[image_name] = feats
# Save dictionary to disk with unique name
save_file = os.path.join(
params['root'], params['root_save'], params['feats_dir'],
params['split'] + "_" + str(params['descriptor_size']) + "_" +
params['descriptor_type'] + "_" + params['keypoint_type'] + '.p')
pickle.dump(features, open(save_file, 'wb'))
def get_features(params, pca=None, scaler=None):
# Read image names
with open(
os.path.join(params['root'], params['root_save'],
params['image_lists'], params['split'] + '.txt'),
'r') as f:
image_list = f.readlines()
# Initialize keypoint detector and feature extractor
detector, extractor = init_detect_extract(params)
# Initialize feature dictionary
features = {}
# Get trained codebook
km = pickle.load(
open(
os.path.join(
params['root'], params['root_save'], params['codebooks_dir'],
'codebook_' + str(params['descriptor_size']) + "_" +
params['descriptor_type'] + "_" + params['keypoint_type'] +
'.cb'), 'rb'))
for image_name in image_list:
# Read image
im = cv2.imread(
os.path.join(params['root'], params['database'], params['split'],
'images', image_name.rstrip()))
# Resize image
im = resize_image(params, im)
# Extract local features
feats = image_local_features(im, detector, extractor)
if feats is not None:
if params['normalize_feats']:
feats = normalize(feats)
# If we scaled training features
if scaler is not None:
scaler.transform(feats)
# Whiten if needed
if pca is not None:
pca.transform(feats)
# Compute assignemnts
assignments = get_assignments(km, feats)
# Generate bow vector
feats = bow(assignments, km)
else:
# Empty features
feats = np.zeros(params['descriptor_size'])
# Add entry to dictionary
features[image_name] = feats
# Save dictionary to disk with unique name
save_file = os.path.join(
params['root'], params['root_save'], params['feats_dir'],
params['split'] + "_" + str(params['descriptor_size']) + "_" +
params['descriptor_type'] + "_" + params['keypoint_type'] + '.p')
pickle.dump(features, open(save_file, 'wb'))
dir = path.dirname(__file__)
#dir = sys.path.insert(0,__file__)
terrassa_buildings = '../TB2016/'
def build_bow(assignments, n):
# Inicialitzem a zeros un vector de mida dels clusters
descriptor = np.zeros((n,))
# Construim un vector de repeticions.Cada assignments li atribuim un cluster
for n_assig in assignments:
descriptor[n_assig] += 1
# L2 normalize
descriptor = normalize(descriptor)
return descriptor
# Comprovem que funciona
descriptor1 = get_local_features(path.join(terrassa_buildings, "../train/images/aaeoeolbth.jpg"))
codebook = train_codebook(5, descriptor1)
descriptor2 = get_local_features(path.join(terrassa_buildings, "../val/images/aalfirydrf.jpg"))
assig = get_assignments(codebook, descriptor2)
# Crea un vector ordenat amb els descriptors que equival a cada regio (k=5)
asdf = build_bow(assig, 50)
print asdf
print ("Numero de regiones diferentes: " + str(len(asdf)))
dictrain[nom] = x
#Tanquem el fitxer
ID.close()
#Entrenament del KMeans nomes per a les imatges d'entrenament amb 1024 paraules
paraules = 1024
codebook = train_codebook(params, desc_train, paraules)
#Obrim el fitxer que conte les ID de les imatges d'entrenament
ID = open(
os.path.join(params['root'], params['database'], 'train', 'ImageIDs.txt'),
'r')
for line in ID:
nom = str(line).replace('\n', '')
#Calculem les assignacions per les imatges d'entrenament
assignments = get_assignments(dictrain[nom], codebook)
#Creacio del BoW per les imatges d'entrenament i emplenament del diccionari
dictrain[nom] = build_bow(assignments, codebook, paraules)
#Tanquem el fitxer
ID.close()
#Guardem el diccionari amb el BoW de les imatges d'entrenament en l'arxiu "Features.txt"
bow_train = open(
os.path.join(params['root'], params['database'], 'train', 'Features.txt'),
'w')
pk.dump(dictrain, bow_train)
bow_train.close()
#Obrim el fitxer que conté les ID de les imatges de validacio
ID = open(
os.path.join(params['root'], params['database'], 'val', 'ImageIDs.txt'),
