def proj_template(in_path,out_path,proj_funcs):
files.make_dir(out_path)
paths=files.top_files(in_path)
for seq_path_i in paths:
print(seq_path_i)
seq_i=imgs.read_frames(seq_path_i,as_dict=False)
proj_seq_i=[proj_i(seq_i) for proj_i in proj_funcs]
new_imgs=np.concatenate(proj_seq_i,axis=1)
out_i="%s/%s" % (out_path,seq_path_i.split("/")[-1])
imgs.save_frames(out_i,new_imgs)
def extract(model_path, out_path, frame_path, img_type="binary"):
action_frames = imgs.read_frames(frame_path, True, img_type)
model = load_model(model_path)
extractor = Model(inputs=model.input,
outputs=model.get_layer("hidden").output)
X = action_format(action_frames)
# raise Exception(X.shape)
X_feats = model.predict(X)
names = list(action_frames.keys())
feat_dict = {names[i]: feat_i for i, feat_i in enumerate(X_feats)}
single.save_ts_feats(feat_dict, out_path)
def make_model(in_path,
out_path,
n_epochs=5,
i=None,
nn_type="basic",
img_type="binary"):
action_frames = imgs.read_frames(in_path, True, img_type)
# raise Exception(list(action_frames.values())[0].shape)
train, test = data.split_dict(action_frames)
assert (equal_dims(train))
X = action_format(train)
y = np.array(
[int(name_i.split("_")[0]) - 1 for name_i in list(train.keys())])
dims = X.shape
params = {"input_shape": (dims[1], dims[2], dims[3])}
if (nn_type == "sim"):
model = sim_model(X, y, i, params, n_epochs)
else:
model = basic_model(X, y, i, params, n_epochs)
if (out_path):
model.save(out_path)
def action_read(in_path):
return imgs.read_frames(in_path,True)
def action_shape(in_path):
frames = imgs.read_frames(in_path)
for frame_i in frames:
print(frame_i.shape)