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)