def Catchvideo(WindowName, CameraId):
CNN = Model(64 * 64 * 1, 2)
cv2.namedWindow(WindowName)
cap=cv2.VideoCapture(CameraId)
sess = tf.Session()
pnet, rnet, onet = detect_face.create_mtcnn(sess, None)
minsize = 40 # minimum size of face
threshold = [0.6, 0.7, 0.9] # three steps's threshold
factor = 0.709 # scale factor
while cap.isOpened():
ok,frame=cap.read()
if not ok:
break
#灰度转换
img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
bounding_boxes, points = detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)
nrof_faces = bounding_boxes.shape[0]
for b in bounding_boxes:q
img = frame[int(b[1]):int(b[3]), int(b[0]):int(b[2])]
resule=CNN.mode(False,img)
resule=resule[0]
if(resule[0]>resule[1]):
cv2.rectangle(frame, (int(b[0]), int(b[1])), (int(b[2]), int(b[3])), (0, 255, 0))
cv2.putText(frame, 'me', (int(b[0]) + 5, int(b[1]) + 5), cv2.FONT_HERSHEY_SIMPLEX, 1, (225, 0, 225), 4)
cv2.imshow(WindowName, frame)
if cv2.waitKey(10) & 0xff==ord('q'):
break
def Catchvideo(Windowname, cameraId, NumPic, Path):
cv2.namedWindow(Windowname)
cap = cv2.VideoCapture(cameraId)
sess = tf.Session()
pnet, rnet, onet = detect_face.create_mtcnn(sess, None)
num = 0
minsize = 40 # minimum size of face
threshold = [0.6, 0.7, 0.9] # three steps's threshold
factor = 0.709 # scale factor
while cap.isOpened():
ok, frame = cap.read()
if not ok:
break
bounding_boxes, points = detect_face.detect_face(
frame, minsize, pnet, rnet, onet, threshold, factor)
nrof_faces = bounding_boxes.shape[0]
if (len(bounding_boxes) > 1):
continue
for b in bounding_boxes:
img = frame[int(b[1]):int(b[3]), int(b[0]):int(b[2])]
if (len(img) < 60):
continue
imgname = Path + "/" + "%d.jpg" % (num)
num += 1
print(imgname)
cv2.imwrite(imgname, img)
cv2.rectangle(frame, (int(b[0]), int(b[1])),
(int(b[2]), int(b[3])), (0, 255, 0))
cv2.imshow(Windowname, frame)
if num > NumPic:
break
if cv2.waitKey(10) & 0xff == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
w, h = img.shape
ret = np.empty((w, h, 3), dtype=np.uint8)
ret[:, :, 0] = ret[:, :, 1] = ret[:, :, 2] = img
return ret
print('Creating networks and loading parameters')
gpu_memory_fraction = 1.0
with tf.Graph().as_default():
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=gpu_memory_fraction)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options,
log_device_placement=False))
with sess.as_default():
pnet, rnet, onet = detect_face.create_mtcnn(sess, None)
#restore facenet model
print('建立facenet embedding模型')
tf.reset_default_graph()
sess = tf.Session()
model_checkpoint_path = './facenet/model_check_point/model-20170512-110547.ckpt-250000'
saver = tf.train.import_meta_graph(
'./facenet/model_check_point/model-20170512-110547.meta')
saver.restore(sess, model_checkpoint_path)
embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
phase_train_placeholder = tf.get_default_graph().get_tensor_by_name(
"phase_train:0")
print('facenet embedding模型建立完毕')