# Load the jpg files into numpy arrays

file_list = os.listdir(fpath)

try:

file_list.remove('.DS_Store')

except:

pass

names = []

known_faces = []

for f in file_list:

names.append(f.split('.')[0])

file = os.path.join(fpath, f)

image_np = face_recognition.load_image_file(file, pixel=2000)

face_loc = face_recognition.face_locations(image_np, model=model)

face_encoding = face_recognition.face_encodings(image_np, known_face_locations=face_loc)[0]

known_faces.append(face_encoding)

print('faces loaded.')

file_list = []

for home, dirs, files in os.walk(fpath):

for filename in files:

fullname = os.path.join(home, filename)

file_list.append(fullname)

unknown_images = get_file_path(fpath)

count = 0

total = len(unknown_images)

for i in unknown_images:

count += 1

try:

im = Image(i)

except:

print('Pass ' + i)

continue

# im_xmp = im.read_xmp()

# if 'Xmp.xmp.Keywords' in im_xmp.keys():

# continue

final_labels = ''

xmp_labels = dict()

xmp_labels.setdefault('Xmp.xmp.Keywords', '')

unknown = face_recognition.load_image_file(i, pixel=1000)

face_loc = face_recognition.face_locations(unknown, model=model)

unknown_face_encoding = face_recognition.face_encodings(unknown, known_face_locations=face_loc)

for f in unknown_face_encoding:

dis =face_recognition.face_distance(known_faces, f)

min_dis = min(dis)

if min_dis <= 0.5:

idx = dis.tolist().index(min_dis)

label = names[idx] + ','

xmp_labels['Xmp.xmp.Keywords'] = xmp_labels['Xmp.xmp.Keywords'] + label

im.modify_xmp(xmp_labels)

final_labels = im.read_xmp()['Xmp.xmp.Keywords']

print(str(count) + '/' + str(total) + ' ' + i + ' added labels: ' + str(final_labels))