def make_global(seq_path, frame_path, out_path):
glob_dict = clean_dict(get_global_img(seq_path))
seq_dict = clean_dict(imgs.read_seqs(frame_path))
def frame_fun(frame_j, name_i):
return np.concatenate([frame_j, glob_dict[name_i]], axis=0)
new_seqs = {
name_i: [frame_fun(frame_j, name_i) for frame_j in seq_i]
for name_i, seq_i in seq_dict.items()
}
imgs.save_seqs(new_seqs, out_path)
def add_mode(old_path, new_path, out_path):
old_modes = imgs.read_seqs(old_path)
new_modes = imgs.read_seqs(new_path)
def add_helper(name_i):
old_i = old_modes[name_i]
new_i = new_modes[name_i]
new_i = preproc.rescale.scale(new_i, 64, 64)
return np.concatenate([old_i, new_i], axis=1)
unified = {name_i: add_helper(name_i) for name_i in list(new_modes.keys())}
imgs.save_seqs(unified, out_path)
def unify_agum(paths, ae_model, out_path):
img_dict = [imgs.read_seqs(path_i) for path_i in paths]
img_dict = [files.clean_dict(dict_i) for dict_i in img_dict]
agum_set = img_dict[0]
for i, dict_i in enumerate(img_dict[1:]):
for name_j, seq_j in dict_i.items():
if (in_train(name_j)):
name_j = "%s_%d" % (name_j, i)
agum_set[name_j] = seq_j
files.make_dir(out_path)
seq_path = "%s/%s" % (out_path, "frames")
imgs.save_seqs(agum_set, seq_path)
simple_agum(out_path, ae_model)
def reconstruct(in_path, model_path, out_path=None, diff=False):
frames = imgs.read_seqs(in_path)
model = load_model(model_path)
frames = {
name_i: data.format_frames(seq_i)
for name_i, seq_i in frames.items()
}
rec_frames = {}
for name_i, seq_i in frames.items():
rec_seq_i = model.predict(seq_i)
rec_seq_i = [np.vstack(frame_j.T) for frame_j in rec_seq_i]
rec_frames[name_i] = rec_seq_i
imgs.save_seqs(rec_frames, out_path)
def agum_template(raw_path,agum_path,agum,n_iters=10):
raw_data=imgs.read_seqs(raw_path)
train,test=data.split(raw_data.keys())
train_data={ name_i:raw_data[name_i] for name_i in train}
agum_dict={}
for name_i,seq_i in list(train_data.items()):
agum_seq_i = agum(images=seq_i)
for j in range(n_iters):
new_name_i=name_i+'_'+str(j)
print(new_name_i)
agum_dict[new_name_i]=agum_seq_i
new_dict={**raw_data,**agum_dict}
imgs.save_seqs(new_dict,agum_path)
def outliner_transform(in_path,out_path):
seqs=imgs.read_seqs(in_path)
seqs={ name_i:outliner(seq_i)
for name_i,seq_i in seqs.items()}
imgs.save_seqs(seqs,out_path)
def unify_datasets(in_path, agum_path, out_path): #for data agumentation
data1, data2 = imgs.read_seqs(in_path), imgs.read_seqs(agum_path)
train, test = data.split(data2.keys())
new_data = {name_i + "_1": data2[name_i] for name_i in train}
unified = {**data1, **new_data}
imgs.save_seqs(unified, out_path)