def img_dataset(in_path):
img_dict={}
for path_i in files.top_files(in_path):
name_i=files.clean_str(path_i.split("/")[-1])
img_i=cv2.imread(path_i, cv2.IMREAD_GRAYSCALE)
img_i=np.expand_dims(img_i,-1)
img_dict[name_i]=img_i
print(img_dict.keys())
return img_dict
def read_frames(seq_path_i, as_dict=False):
if (as_dict):
return {
files.clean_str(path_j): cv2.imread(path_j, cv2.IMREAD_GRAYSCALE)
for path_j in files.top_files(seq_path_i)
}
return [
cv2.imread(path_j, cv2.IMREAD_GRAYSCALE)
for path_j in files.top_files(seq_path_i)
]
def read_frame_feats(in_path):
seq_dict = {}
for path_i in files.top_files(in_path):
postfix = in_path.split(".")[-1]
if (postfix == "npy"):
seq_i = np.loadtxt(path_i, delimiter=',')
else:
seq_i = np.load(path_i)
name_i = files.clean_str(path_i.split('/')[-1])
seq_dict[name_i] = seq_i
return seq_dict
def read_single(in_path, as_dict=True):
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.clean_str(info_i)
feat_dict[info_i] = np.fromstring(data_i, sep=',')
if (as_dict):
return feat_dict
return from_dict(feat_dict)
def ts_plot(in_path, out_path):
seqs = {
files.clean_str(path_i): np.load(path_i)
for path_i in files.top_files(in_path)
}
files.make_dir(out_path)
for name_i, seq_i in seqs.items():
out_i = "%s/%s" % (out_path, name_i)
files.make_dir(out_i)
for j, ts_j in enumerate(seq_i.T):
out_ij = "%s/%d" % (out_i, j)
print(out_ij)
fig = plt.figure()
# plt.clf()
ax = plt.axes()
x = range(ts_j.shape[0])
ax.plot(x, ts_j)
plt.savefig(out_ij)
plt.close()
def get_len_dict(seq_dict):
return {
files.clean_str(name_i): len(seq_i)
for name_i, seq_i in seq_dict.items()
}