class Predict():
def __init__(self,
epoch=25,
val_loss=1.174,
model_type='lstm',
seq_length=40,
data_type='features'):
# model can be one of lstm, lrcn, mlp, conv_3d, c3d
self.model_type = model_type
self.seq_length = seq_length
self.data_type = data_type
filepath = os.path.join(
'..', 'data', 'checkpoints', model_type + '-' + data_type +
'.{:03d}-{:.3f}.hdf5'.format(epoch, val_loss))
# print('Loading the model:', filepath)
# model = load_model(filepath)
# Get the data and process it.
self.data = DataSet(seq_length=seq_length, class_limit=None)
# Get the model.
print("Model Type:", model_type)
self.rm = ResearchModels(len(self.data.classes), self.model_type,
self.seq_length, filepath)
# read the video IDs
# self.all_video_ids = sorted([os.path.basename(name).split('.webm')[0] for name in glob.glob('../*/*/*.webm')])
self.all_video_ids = sorted([
os.path.basename(name).split('.webm')[0]
for name in glob.glob('../*/videos_safe_viewing/*.webm')
])
assert (len(self.all_video_ids) != 0)
remove_list = read_remove_list()
# Not needed any more
# for item in remove_list:
# try:
# self.all_video_ids.remove(item)
# except:
# print(item, 'not in list')
self.showing_ids = []
print('Ready to accept ReST calls!')
def get_videos_ids(self, count=12):
# pick some at random to display
self.showing_ids = random.sample(self.all_video_ids, count)
# self.showing_ids = self.all_video_ids[2000:2000+count]
return self.showing_ids
def predict_video_id(self, video_id, top_N=2):
sample = self.data.get_frames_by_filename(video_id, self.data_type)
if sample is not None:
prediction = self.rm.model.predict(np.expand_dims(sample, axis=0))
return (self.data.get_top_N_from_prediction(
np.squeeze(prediction, axis=0)))
else:
return ([])
def predict_all_showing_ids(self):
predictions = []
for video_id in self.showing_ids:
prediction = self.predict_video_id(video_id)
predictions.append({"id": video_id, "label": prediction})
return predictions
def get_all_video_ids(self):
return self.all_video_ids
def get_GT_from_id(self, video_id):
return video_id.split('_')[1] # sample format, v_RopeClimbing_g01_c01
