def predict(data_type, seq_length, saved_model, image_shape, video_name, class_limit, config):
model = load_model(saved_model)
feature_file_path= config.featureFileName
work_dir = config.workDir
classlist= config.classes
# Get the data and process it.
if image_shape is None:
data = DataSet(seq_length=seq_length, class_limit=class_limit,
feature_file_path = feature_file_path,
repo_dir = config.repoDir,
work_dir=work_dir, classlist=classlist)
else:
data = DataSet(seq_length=seq_length, image_shape=image_shape,
class_limit=class_limit,
feature_file_path = feature_file_path,
repo_dir = config.repoDir,
work_dir=work_dir, classlist=classlist)
# Extract the sample from the data.
sample = data.get_frames_by_filename(video_name, data_type)
# Predict!
prediction = model.predict(np.expand_dims(sample, axis=0))
print(prediction)
data.print_class_from_prediction(np.squeeze(prediction, axis=0))
def predict(data_type, seq_length, saved_model, image_shape, video_name, class_limit):
model = load_model(saved_model)
# Get the data and process it.
if image_shape is None:
data = DataSet(seq_length=seq_length, class_limit=class_limit)
else:
data = DataSet(seq_length=seq_length, image_shape=image_shape, class_limit=class_limit)
# Extract the sample from the data.
sample = data.get_frames_by_filename(video_name, data_type)
# Predict!
prediction = model.predict(np.expand_dims(sample, axis=0))
print(prediction)
data.print_class_from_prediction(np.squeeze(prediction, axis=0))
def predict(data_type, seq_length, saved_model, image_shape, video_name, class_limit):
model = load_model(saved_model)
# Get the data and process it.
if image_shape is None:
data = DataSet(seq_length=seq_length, class_limit=class_limit)
else:
data = DataSet(seq_length=seq_length, image_shape=image_shape,
class_limit=class_limit)
# Extract the sample from the data.
sample = data.get_frames_by_filename(video_name, data_type)
# Predict!
prediction = model.predict(np.expand_dims(sample, axis=0))
print(prediction)
data.print_class_from_prediction(np.squeeze(prediction, axis=0))
def predict(data_type, seq_length, saved_model, image_shape, video_name,
class_limit):
print("**********************************")
print("\nstart loading model...")
print(datetime.datetime.now())
print("**********************************")
global model
if not model:
model = load_model(saved_model)
print("**********************************")
print("model loaded successfully...")
print(datetime.datetime.now())
print("**********************************")
# Get the data and process it.
if image_shape is None:
data = DataSet(seq_length=seq_length, class_limit=class_limit)
else:
data = DataSet(seq_length=seq_length,
image_shape=image_shape,
class_limit=class_limit)
# Extract the sample from the data.
sample = data.get_frames_by_filename(video_name, data_type)
# Predict!
print("**********************************")
print(datetime.datetime.now())
print("**********************************")
prediction = model.predict(np.expand_dims(sample, axis=0))
print("**********************************")
print(datetime.datetime.now())
print("**********************************")
#print(keras.np_utils.probas_to_classes(prediction))
print(prediction)
print("**********************************")
print(datetime.datetime.now())
print("**********************************")
return data.print_class_from_prediction(np.squeeze(prediction, axis=0))
def predict(seq_length, class_limit, feature_length, saved_model, video_name):
# 获取数据
data = DataSet(seq_length=seq_length, class_limit=class_limit)
# 利用视频名称提取样本的序列特征值
sample = data.get_frames_by_filename(video_name)
sample = np.reshape(sample, [-1, seq_length, feature_length])
# 确定训练的类数量
if class_limit is None:
class_num = 101
else:
class_num = class_limit
# 设置输入
input_x = tf.placeholder(tf.float32, [None, seq_length, feature_length],
name="input-x")
# 前向传播的计算
_, output_y = lstm_inference.inference(input_x,
class_num,
None,
train=False)
saver = tf.train.Saver()
with tf.Session() as sess:
tf.global_variables_initializer().run()
model = tf.train.get_checkpoint_state(saved_model)
if model and model.model_checkpoint_path:
saver.restore(sess, model.model_checkpoint_path)
prediction = sess.run(output_y, feed_dict={input_x: sample})
print("the prediction of the video %s is:" % video_name)
data.print_class_from_prediction(np.squeeze(
prediction, axis=0)) # 删除prediction中第1维(并且该维大小为1)
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
