def process():
sess = tf.Session()
# read pnet, rnet, onet models from align directory and files are det1.npy, det2.npy, det3.npy
pnet, rnet, onet = mtcnn_detect.create_mtcnn(sess, 'models')
cropped_im = []
save_im = []
directory = "./dataImg/" + inputValue + "/"
if not os.path.exists(directory):
os.makedirs(directory)
for i in range(len(im_arr)):
frame = im_arr[i]
bounding_boxes, _ = mtcnn_detect.detect_face(frame, minsize, pnet,
rnet, onet, threshold,
factor)
nrof_faces = bounding_boxes.shape[0]
if nrof_faces == 1:
det = bounding_boxes[:, 0:4]
img_size = np.asarray(frame.shape)[0:2]
cropped = []
scaled = []
scaled_reshape = []
bb = np.zeros((nrof_faces, 4), dtype=np.int32)
for i in range(nrof_faces):
emb_array = np.zeros((1, embedding_size))
bb[i][0] = det[i][0]
bb[i][1] = det[i][1]
bb[i][2] = det[i][2]
bb[i][3] = det[i][3]
cropped = frame[bb[i][1]:bb[i][3], bb[i][0]:bb[i][2], :]
cropped = cv2.resize(cropped, (image_size, image_size),
interpolation=cv2.INTER_CUBIC)
save_im.append(cropped)
#cropped = prewhiten(cropped)
#cropped = flip(cropped, False)
cropped_im.append(cropped)
print(directory)
for i in range(len(save_im)):
cv2.imwrite(directory + str(i) + '.jpg', save_im[i])
print('Extracted: %d' % len(cropped_im))
calSVM(cropped_im, inputValue)
from scipy import misc
import time
from scipy.spatial import distance
from keras.models import load_model
import pickle
import facenet
# some constants kept as default from facenet
minsize = 20
threshold = [0.6, 0.7, 0.7]
factor = 0.709
margin = 44
image_size = 160
sess = tf.Session()
# read pnet, rnet, onet models from align directory and files are det1.npy, det2.npy, det3.npy
pnet, rnet, onet = mtcnn_detect.create_mtcnn(sess, 'models')
with tf.Graph().as_default():
with tf.Session() as sess:
np.random.seed(seed=1324)
facenet.load_model('./models')
images_placeholder = tf.get_default_graph().get_tensor_by_name(
"input:0")
embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
phase_train_placeholder = tf.get_default_graph().get_tensor_by_name(
"phase_train:0")
embedding_size = embeddings.get_shape()[1]
emb_array = np.zeros((1, embedding_size))
(model, class_names) = pickle.load(open('svm_classifier.pkl', 'rb'))
frame = cv2.imread('.datas/1.jpg')