def q1_c():
model = load_facenet()
files = os.listdir("saved_faces")
data = []
paths = []
for path in files:
image = cv.imread("saved_faces/" + path)
v = img_to_encoding(image, model)
data.append(v)
paths.append(path)
with open("saved_data.pickle", "wb") as handle:
pickle.dump([data, paths], handle, protocol=pickle.HIGHEST_PROTOCOL)
def get_embedding_path():
"""
q1(c)
"""
model = load_facenet()
files = os.listdir("saved_faces")
embedding, paths = [], []
for path in files:
image = cv.imread("saved_faces/" + path)
encode = img_to_encoding(image, model)
embedding.append(encode)
paths.append(path)
return embedding, paths
def loading_input():
"""
q1(h) helper function, return the (embedding, paths) of the input file
"""
model = load_facenet()
input_img = os.listdir("input_faces")
embedding, paths = [], []
for path in input_img:
image = cv.imread("input_faces/" + path)
image = cv.resize(image, (96, 96))
encode = img_to_encoding(image, model)
embedding.append(encode)
paths.append(path)
return embedding, paths