def main_loop():
frame, face_data = acquire_frame(detector, embedder, vs, recognizer, le,
0.5, 0.65)
for item in face_data:
frame = draw_frame(frame, item)
show_frame(frame)
def main_core(detector, embedder, recognizer, le, frame_queue, pframe_queue):
print('[INFO] Starting:', mp.current_process().name)
# time.sleep(1.0)
# initialize the video stream, then allow the camera sensor to warm up
## Set Threding to start filming
video_getter = VideoGet(frame_queue=frame_queue,
src=0,
name='Video Getter')
time.sleep(1.0)
# print('[INFO] Starting VideoGet...')
video_getter.start()
time.sleep(1.0)
cpt = 0
exitbool = False
fps_count = FPS(2).start()
while True:
frame = video_getter.frame.copy()
fps_count.istime(
) # Calculate if enough time has passed for process the frame
if fps_count.boolFrames:
fps_count.updateactualtime() # Update the comparison time
face_data = acquire_frame(detector, embedder, frame, recognizer,
le, 0.5, 0.65) #,fa)
pframe_queue.put(face_data)
for item in face_data:
# print(item[2:])
frame = draw_frame(frame, item)
fps_count.update()
# cpt +=1
exitbool = show_frame(frame)
if exitbool or cpt == 80:
#
fps_count.stop()
print("[INFO] elasped time fps processed: {:.2f}".format(
fps_count.elapsed()))
print("[INFO] approx. processed FPS: {:.2f}".format(
fps_count.fps()))
time.sleep(1)
video_getter.stop()
time.sleep(2)
print('[INFO] Exiting :', mp.current_process().name)
break
for item in face_data:
frame = draw_frame(frame, item)
show_frame(frame)
## INICIO ##
# initialize the video stream, then allow the camera sensor to warm up
# print("[INFO] starting viif key == ord('q') or key == 27: self.stopped = Truedeo stream...")
# vs = VideoStream(src=0).start()
video_getter = VideoGet(src=0, name='Video Getter')
time.sleep(1.0)
video_getter.start()
time.sleep(1.0)
while True:
# main_loop()
frame = video_getter.frame
face_data = acquire_frame(detector, embedder, frame, recognizer, le, 0.5,
0.65, fa)
for item in face_data:
frame = draw_frame(frame, item)
exitbool = show_frame(frame)
# exitbool = show_frame(frame);
if exitbool:
video_getter.stop()
break
def main_core(args, frame_queue, pframe_queue):
print('[INFO] Starting:', mp.current_process().name)
print("[INFO] loading face detector...")
protoPath = os.path.sep.join([args["detector"], "deploy.prototxt"])
modelPath = os.path.sep.join(
[args["detector"], "res10_300x300_ssd_iter_140000.caffemodel"])
detector = cv2.dnn.readNetFromCaffe(protoPath, modelPath)
#predictor = dlib.shape_predictor(args["shape_predictor"])
#fa = FaceAligner(predictor, desiredFaceWidth=256)
# load our serialized face embedding model from disk
print("[INFO] loading face recognizer...")
embedder = cv2.dnn.readNetFromTorch(args["embedding_model"])
# load the face embeddings
print("[INFO] loading face embeddings...")
data = pickle.loads(open(args["embeddings"], "rb").read())
recognizer, le = train(data)
time.sleep(1.0)
# initialize the video stream, then allow the camera sensor to warm up
## Set Threding to start filming
video_getter = VideoGet(frame_queue=frame_queue,
src=0,
name='Video Getter')
time.sleep(1.0)
# print('[INFO] Starting VideoGet...')
video_getter.start()
time.sleep(1.0)
cpt = 0
exitbool = False
fps_count = FPS().start()
while True:
frame = video_getter.frame.copy()
face_data = acquire_frame(detector, embedder, frame, recognizer, le,
0.5, 0.65) #,fa)
# pframe_queue.put(face_data)
for item in face_data:
# print(item[2:])
frame = draw_frame(frame, item)
fps_count.update()
cpt += 1
# exitbool = show_frame(frame)
if exitbool or cpt == 80:
#
fps_count.stop()
print("[INFO] elasped time fps processed: {:.2f}".format(
fps_count.elapsed()))
print("[INFO] approx. processed FPS: {:.2f}".format(
fps_count.fps()))
time.sleep(1)
video_getter.stop()
time.sleep(2)
print('[INFO] Exiting :', mp.current_process().name)
break