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 codebook(params):
with open(os.path.join(params['root'],params['root_save'],params['image_lists'],'train.txt'),'r') as f:
train_image_list = f.readlines()
with open(os.path.join(params['root'],params['root_save'],params['image_lists'], 'val.txt'),'r') as f:
val_image_list = f.readlines()
descriptors = []
#if params['split'] == 'train':
for img in train_image_list:
im = cv2.imread(os.path.join(params['root'],params['database'],'train','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))
print "descriptors train: "
print np.shape(descriptors)
for img in val_image_list:
im = cv2.imread(os.path.join(params['root'],params['database'],'val','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))
print "descriptors train + val: "
print np.shape(descriptors)
code=train_codebook(params,descriptors)
# Save to disk
pickle.dump(code,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'),'wb'))
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'))
