def get_feats(number_of_features):
path = './image_features/*.npy'
files = glob.glob(path)
feats = []
count = 0
bar_len = 60
total = number_of_features * 7
for name in files:
# print(name)
img = Image(name, npy=True)
if (img.number <= number_of_features):
count += 1
lib_pat.progress(count, total, name)
feats_image = []
feats_image.append(
np.load("./image_features/face_" + str(img.group).zfill(3) +
"_" + str(img.number).zfill(5) + ".npy"))
feats_image.append(
np.load("./eyebrowL/lbp/face_" + str(img.group).zfill(3) +
"_" + str(img.number).zfill(5) + ".npy"))
feats_image.append(
np.load("./eyebrowR/lbp/face_" + str(img.group).zfill(3) +
"_" + str(img.number).zfill(5) + ".npy"))
feats_image.append(
np.load("./nose/lbp/face_" + str(img.group).zfill(3) + "_" +
str(img.number).zfill(5) + ".npy"))
feats_image.append(
np.load("./eyeL/lbp/face_" + str(img.group).zfill(3) + "_" +
str(img.number).zfill(5) + ".npy"))
feats_image.append(
np.load("./eyeR/lbp/face_" + str(img.group).zfill(3) + "_" +
str(img.number).zfill(5) + ".npy"))
feats_image.append(
np.load("./mouth/lbp/face_" + str(img.group).zfill(3) + "_" +
str(img.number).zfill(5) + ".npy"))
feats_image.append(
np.load("./eyebrowL/tas/face_" + str(img.group).zfill(3) +
"_" + str(img.number).zfill(5) + ".npy"))
feats_image.append(
np.load("./eyebrowR/tas/face_" + str(img.group).zfill(3) +
"_" + str(img.number).zfill(5) + ".npy"))
feats_image.append(
np.load("./nose/tas/face_" + str(img.group).zfill(3) + "_" +
str(img.number).zfill(5) + ".npy"))
feats_image.append(
np.load("./eyeL/tas/face_" + str(img.group).zfill(3) + "_" +
str(img.number).zfill(5) + ".npy"))
feats_image.append(
np.load("./eyeR/tas/face_" + str(img.group).zfill(3) + "_" +
str(img.number).zfill(5) + ".npy"))
feats_image.append(
np.load("./mouth/tas/face_" + str(img.group).zfill(3) + "_" +
str(img.number).zfill(5) + ".npy"))
feat = np.concatenate(feats_image, axis=1)
feats.append([feat[0]])
feats = np.concatenate(feats, axis=1)
feats = feats[0]
print("")
return (feats)
def extract(start, end):
path = './faces/*.png'
files = glob.glob(path)
number_of_features = end - start + 1
lbp_params = ((1, 1, 2, 2, 5), (5, 10, 8, 15, 6))
har_params = ((1, 1, 1, 2, 5), (1, 10, 20, 11, 8))
gab1_params = (1, 2, 5, 10)
gab2_params = (1, 2, 5, 10)
count = 0
bar_len = 60
total = number_of_features * 7
for name in files:
img = Image(name)
if (img.number >= start and img.number <= end):
image = cv2.imread(name, 0)
lib_pat.progress(count, total, name)
extraction_routine(
"./image_features/face_" + str(img.group).zfill(3) + "_" +
str(img.number).zfill(5), [image], lbp_params[0],
lbp_params[1], har_params[0], har_params[1], gab1_params,
gab2_params)
count += 1
# filled_len = int(round(bar_len * count / float(total)))
# percents = round(100.0 * count / float(total), 1)
# bar = '=' * filled_len + '-' * (bar_len - filled_len)
# sys.stdout.write('[%s] %s%s ...%s\r' % (bar, percents, '%', name))
# sys.stdout.flush()
print("")
def get_feats(number_of_features):
path = './image_features/*.npy'
files = glob.glob(path)
feats = []
count = 0
bar_len = 60
total = number_of_features * 7
for name in files:
# print(name)
img = Image(name, npy=True)
if (img.number <= number_of_features):
count += 1
lib_pat.progress(count, total, name)
feats_image = []
feats_image.append(
[np.load("./image_features/face_" + str(img.group).zfill(3) + "_" + str(img.number).zfill(5) + ".npy")[0]])
for i in range(len(feats_image)):
while len(feats_image[i][0]) == 1:
feats_image[i] = feats_image[i][0]
try:
feat = np.concatenate(feats_image, axis=1)
feats.append([feat])
except ValueError:
raise
feats = np.concatenate(feats, axis=1)
feats = feats[0]
print("")
return (feats)
def extraction_routine(arr_name, images, lbp_grids, lbp_dists, har_grids, har_dists, gab_grids1, gab_grids2):
"""
Saves to arr_name .npy the array of features extracted.
Params MUST be tuples, use (1,) for single values
:param lbp_grids: tuple
:param lbp_dists: tuple, must be the same size than ibp_grids
:param har_grids: idem
:param har_dists: idem
:param gab_grids1: idem
:param gab_grids1: idem
:return: void
"""
count = 0
total = len(lbp_grids) + len(har_grids) + len(gab_grids1) + len(gab_grids2)
lbps = []
for i in range(len(lbp_grids)):
# print('LPB Extraction, Iteration {}/{}'.format((i + 1), len(lbp_grids)))
with mp.Pool() as p:
lbp = p.starmap(lib_pat.get_LBP, [(images[j], lbp_dists[i], lbp_grids[i], j) for j in range(len(images))])
lbps.append(lbp)
count += 1
lib_pat.progress(count, total, up=False)
lbp_feats = np.concatenate(lbps, axis=1)
hars = []
for i in range(len(har_grids)):
# print('Haralick Extraction, Iteration {}/{}'.format((i + 1), len(har_grids)))
with mp.Pool() as p:
har = p.starmap(lib_pat.get_Haralick,
[(images[j], har_dists[i], har_grids[i], j) for j in range(len(images))])
hars.append(har)
count += 1
lib_pat.progress(count, total, up=False)
har_feats = np.concatenate(hars, axis=1)
gabs1 = []
for i in range(len(gab_grids1)):
# print('Gabor Extraction, Iteration {}/{}'.format((i + 1), len(gab_grids1)))
with mp.Pool() as p:
gab = p.starmap(lib_pat.get_Gab, [(images[j], gab_grids1[i], j) for j in range(len(images))])
gabs1.append(gab)
count += 1
lib_pat.progress(count, total, up=False)
gab_feats1 = np.concatenate(gabs1, axis=1)
gabs2 = []
for i in range(len(gab_grids2)):
# print('Gabor Extraction, Iteration {}/{}'.format((i + 1), len(gab_grids2)))
with mp.Pool() as p:
gab = p.starmap(lib_pat.get_Gab_real_im, [(images[j], gab_grids2[i], j) for j in range(len(images))])
gabs2.append(gab)
count += 1
lib_pat.progress(count, total, up=False)
gab_feats2 = np.concatenate(gabs2, axis=1)
feats = np.concatenate((lbp_feats, har_feats, gab_feats1, gab_feats2), axis=1)
np.save(arr_name, feats)
return feats
def get_feats_test():
path = './image_features_test/*.npy'
files = glob.glob(path)
feats = []
count = 0
bar_len = 60
total = len(files)
for name in files:
count += 1
lib_pat.progress(count, total, name)
feats_image = []
feats_image.append([np.load(name)[0]])
for i in range(len(feats_image)):
while len(feats_image[i][0]) == 1:
feats_image[i] = feats_image[i][0]
try:
feat = np.concatenate(feats_image, axis=1)
feats.append([feat])
except ValueError:
raise
feats = np.concatenate(feats, axis=1)
feats = feats[0]
print("")
return (feats)