#check output dir
logger.debug("Create new model dir...")
if os.path.isdir(args.model_dir):
new_name = args.model_dir+time.strftime("%Y-%m-%d-%H-%M-%S", time.gmtime())
logger.debug("backup old model-dir in %s" %new_name )
os.rename(args.model_dir, new_name )
os.makedirs(args.model_dir)
#list classes
list_classes = os.listdir(args.train_dir)
logger.info("classes are : %s" %list_classes)
#use descriptor
desc=None
if args.descriptor:
desc = descriptors.get(args.descriptor)
logger.info("using descriptor %s" %args.descriptor)
else:
logger.info("No descriptor is used")
#create dataframe
logger.info("create data frame for training...")
train_df = pd.DataFrame(columns=["path", "desc", "class"])
for cls in list_classes:
list_images_paths = glob.glob(os.path.join(args.train_dir, cls, "*.*"))
for id in xrange(len(list_images_paths)):
descvect=None
if desc is not None:
descvect = desc(list_images_paths[id]).run()
os.rename(args.output_dir, args.output_dir+time.strftime("%Y-%m-%d-%H-%M-%S", time.gmtime()))
os.makedirs(args.output_dir)
#model infos
with open(os.path.join(args.model_dir,"model.json"),'r') as json_file:
json_data = json.load(json_file)
descriptor_name = json_data["descriptor"]
model_name = json_data["model"]
normalizer_name = json_data["normalizer"]
model = joblib.load(os.path.join(args.model_dir, "model", "model.pkl"))
logging.info("descriptor = %s, model = %s" %(descriptor_name, model_name))
#use descriptor
desc=None
if descriptor_name is not None:
desc = descriptors.get(descriptor_name)
logger.info("using descriptor %s" %desc.__name__)
#create dataframe
test_df = pd.DataFrame(columns=["path", "desc", "class"])
list_images_paths = glob.glob(os.path.join(args.test_dir, "*.*"))
for id in xrange(len(list_images_paths)):
descvect=None
if desc is not None:
descvect = desc(list_images_paths[id]).run()
test_df.loc[len(test_df)+1] = [list_images_paths[id], str(descvect) , -1]