def __init__(self, data_path='people', model_dir='model/20180402-114759'):
self.data_path = data_path
self.model_dir = model_dir
self.alignMTCNN = AlignMTCNN()
self.images_placeholder = None
self.embeddings = None
self.phase_train_placeholder = None
self.embedding_size = None
self.image_size = 160
self.threshold = [0.8, 0.8, 0.8]
self.factor = 0.7
self.minsize = 20
self.margin = 44
self.detect_multiple_faces = False
class FaceEmbedding:
def __init__(self, data_path='people', model_dir='model/20180402-114759'):
self.data_path = data_path
self.model_dir = model_dir
self.alignMTCNN = AlignMTCNN()
self.images_placeholder = None
self.embeddings = None
self.phase_train_placeholder = None
self.embedding_size = None
self.image_size = 160
self.threshold = [0.8, 0.8, 0.8]
self.factor = 0.7
self.minsize = 20
self.margin = 44
self.detect_multiple_faces = False
def convert_to_embedding(self, single=False, img_path=None):
extracted = []
with tf.Graph().as_default():
with tf.Session() as sess:
self.sess = sess
# Load the model
facenet.load_model(self.model_dir)
# Get input and output tensors
images_placeholder = tf.get_default_graph().get_tensor_by_name(
"input:0")
self.images_placeholder = tf.image.resize_images(
images_placeholder, (self.image_size, self.image_size))
self.embeddings = tf.get_default_graph().get_tensor_by_name(
"embeddings:0")
self.phase_train_placeholder = tf.get_default_graph(
).get_tensor_by_name("phase_train:0")
self.embedding_size = self.embeddings.get_shape()[1]
if not single:
for filename in os.listdir(self.data_path):
img = cv2.imread(self.data_path + "/" + filename, 1)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
bounding_boxes, points = self.alignMTCNN.get_bounding_boxes(
image=img)
faces = self.get_faces(img, bounding_boxes, points,
filename)
extracted.append(faces)
with open('extracted_embeddings.pickle', 'wb') as f:
pickle.dump(extracted, f)
return None
else:
img = cv2.imread(img_path, 1)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
bounding_boxes, points = self.alignMTCNN.get_bounding_boxes(
image=img)
faces = self.get_faces(img, bounding_boxes, points,
img_path)
return faces
def get_faces(self, img, bounding_boxes, points, filename):
faces = []
nrof_faces = bounding_boxes.shape[0]
print("No. of faces detected: {}".format(nrof_faces))
if nrof_faces > 0:
det = bounding_boxes[:, 0:4]
det_arr = []
img_size = np.asarray(img.shape)[0:2]
if nrof_faces > 1:
if self.detect_multiple_faces:
for i in range(nrof_faces):
det_arr.append(np.squeeze(det[i]))
else:
bounding_box_size = (det[:, 2] - det[:, 0]) * (det[:, 3] -
det[:, 1])
img_center = img_size / 2
offsets = np.vstack([
(det[:, 0] + det[:, 2]) / 2 - img_center[1],
(det[:, 1] + det[:, 3]) / 2 - img_center[0]
])
offset_dist_squared = np.sum(np.power(offsets, 2.0), 0)
index = np.argmax(
bounding_box_size - offset_dist_squared *
2.0) # some extra weight on the centering
det_arr.append(det[index, :])
else:
det_arr.append(np.squeeze(det))
for i, det in enumerate(det_arr):
det = np.squeeze(det)
bb = np.zeros(4, dtype=np.int32)
bb[0] = np.maximum(det[0] - self.margin / 2, 0)
bb[1] = np.maximum(det[1] - self.margin / 2, 0)
bb[2] = np.minimum(det[2] + self.margin / 2, img_size[1])
bb[3] = np.minimum(det[3] + self.margin / 2, img_size[0])
cropped = img[bb[1]:bb[3], bb[0]:bb[2], :]
resized = cv2.resize(cropped,
(self.image_size, self.image_size),
interpolation=cv2.INTER_CUBIC)
prewhitened = facenet.prewhiten(resized)
faces.append({
'name': filename,
'rect': [bb[0], bb[1], bb[2], bb[3]],
'embedding': self.get_embedding(prewhitened)
})
return faces
def get_embedding(self, processed_img):
reshaped = processed_img.reshape(-1, self.image_size, self.image_size,
3)
feed_dict = {
self.images_placeholder: reshaped,
self.phase_train_placeholder: False
}
feature_vector = self.sess.run(self.embeddings, feed_dict=feed_dict)
return feature_vector
def ecuclidean_distance(self, emb_list, embedding):
for emb in emb_list:
dist = np.sqrt(
np.sum(
np.square(
np.subtract(emb[0]['embedding'],
embedding[0]['embedding']))))
print(emb[0]['name'])
print(' %1.4f ' % dist, end='')
print("\n")
def load_pickle(self):
embeddings = pickle.load(open("extracted_embeddings.pickle", "rb"))
return embeddings