def read_frame_seqs(in_path, n_split=1):
frame_seqs = FrameSeqs()
for i, path_i in enumerate(files.top_files(in_path)):
name_i = files.Name(path_i.split('/')[-1]).clean()
if (len(name_i) == 0):
name_i = files.Name(str(i))
frames = [
read_frame(path_j, n_split) for path_j in files.top_files(path_i)
]
frame_seqs[name_i] = frames
return frame_seqs
def split_each_label(in_path,out_path,train_size=0.8):
frames=data.imgs.read_frame_seqs(in_path)
new_frames=data.imgs.FrameSeqs()
for name_i,seq_i in frames.items():
split_size= int(len(seq_i) *(1.0-train_size))
n_split=int(len(seq_i) /split_size)
for j in range(n_split-1):
seq_j=seq_i[j*split_size:(j+1)*split_size]
name_j=files.Name("%d_1_%d" % (int(name_i),j))
new_frames[name_j]=seq_j
print(name_j)
seq_j=seq_i[(n_split-1)*split_size: ]
name_j=files.Name("%d_2_%d" % (int(name_i),0))
new_frames[name_j]=seq_j
new_frames.scale()
new_frames.save(out_path)
def read_actions(in_path):
actions = ActionImgs()
for path_i in files.top_files(in_path):
name_i = files.Name(path_i.split('/')[-1])
name_i = name_i.clean()
actions[name_i] = cv2.imread(path_i, cv2.IMREAD_GRAYSCALE)
return actions
def read_seqs(in_path):
seqs=Seqs()
for path_i in files.top_files(in_path):
data_i=np.loadtxt(path_i, delimiter=',')
name_i=path_i.split('/')[-1]
name_i=files.Name(name_i).clean()#clean(name_i)
seqs[name_i]=data_i
return seqs
def read_seqs(in_path):
paths = files.top_files(in_path)
seq_dict = Seqs()
for path_i in paths:
data_i = np.load(path_i)
name_i = path_i.split('/')[-1]
name_i = files.Name(name_i).clean()
seq_dict[name_i] = data_i
return seq_dict
def read_single(in_path):
lines = open(in_path, 'r').readlines()
feat_dict = {}
for line_i in lines:
raw = line_i.split('#')
if (len(raw) > 1):
data_i, info_i = raw[0], raw[-1]
info_i = files.Name(info_i).clean() #files.clean_str(info_i)
x_i = np.fromstring(data_i, sep=',')
x_i = np.nan_to_num(x_i, nan=0.0, posinf=0.0, neginf=0.0)
feat_dict[info_i] = x_i
return Feats(feat_dict)
def read_feats(in_path):
if (type(in_path) == list):
all_feats = [read_feats(path_i) for path_i in in_path]
return concat_feats(all_feats)
lines = open(in_path, 'r').readlines()
feat_dict = Feats()
for line_i in lines:
raw = line_i.split('#')
if (len(raw) > 1):
data_i, info_i = raw[0], files.Name(raw[-1])
info_i = info_i.clean()
feat_dict[info_i] = np.fromstring(data_i, sep=',')
return feat_dict
def read_pairs(in_path):
if (type(in_path) == list):
return [read_pairs(path_i) for path_i in in_path]
raw_i = pairs.read(in_path)
dict_i = {files.Name(name_i): data_i for name_i, data_i in raw_i.items()}
return pairs.DTWpairs(dict_i)
def read_rename(id):
rename = json.load(open("%s.json" % id))
return {
files.Name(name_i): files.Name(rename_i)
for name_i, rename_i in rename.items()
}
def rename(self, name_dict):
new_feats = Feats()
for name_i, name_j in name_dict.items():
new_feats[files.Name(name_j)] = self[name_i]
return new_feats
