# May still need some teaking for the lower limit of x, for hour,
# day, year changes. Probably not worth the effort though.
#print("x:", x, "y:", y, "w:", w, "h:", h)
new_state = State.ACTIVE
# Draw the bounding box on the frame, and update the text.
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
# Update the last_active_time to now (keep the recording going).
last_active_time = datetime.datetime.now()
if new_state == State.ACTIVE:
# Motion has been detected, so the scene is now ACTIVE.
if state == State.IDLE:
# Transitioning from IDLE to ACTIVE, so we need to start recording.
VideoRecorder.start()
state = State.ACTIVE
elif new_state == State.RECORDING:
state = State.RECORDING
elif new_state == State.IDLE:
if state != State.IDLE:
# Transitioning from RECORDING to IDLE. Stop the recording.
state = State.IDLE
VideoRecorder.stop()
# draw the text on the frame
if state == State.IDLE:
text = "Idle."
elif state == State.RECORDING:
class App:
def __init__(self, preview=False, max_video_length=MAX_VIDEO_LENGTH):
log.info("booting up..")
self.final_dir = self._setup_dirs()
self.max_video_length = max_video_length
self.video_recorder = VideoRecorder(preview=preview)
self.audio_recorder = AudioRecorder()
time.sleep(2)
log.info("ready!")
def _setup_dirs(self):
final_dir = os.path.expanduser('~/media/')
if (os.path.isdir(final_dir) == False):
os.mkdir(final_dir)
return final_dir
def _make_filename(self):
return datetime.datetime.now().strftime("%Y-%m-%d-%H:%M:%S")
def has_space(self):
statvfs = os.statvfs("/")
megabytes_available = int(statvfs.f_frsize * statvfs.f_bavail / 1024 /
1024)
log.info(f"Still {megabytes_available}MB left on device")
return megabytes_available > MIN_DISK_SPACE_MB
def on_keyboard_release(self, key):
if key == keyboard.Key.enter:
if lock.locked():
self.stop_recording()
elif self.has_space():
self.start_recording()
else:
return False
if key == keyboard.Key.esc:
if lock.locked():
self.stop_recording()
return False
def timer(self, seconds, current_video):
log.info(f"going to sleep for {seconds}s and then stop recording")
for i in range(seconds):
if not lock.locked():
log.info("looks like recording has ended before timeout")
return
elif current_video != self.file_name:
log.info("there is a different ongoing recording")
return
time.sleep(1)
log.info("time's up!, stopping recording")
self.stop_recording()
def start_recording(self):
lock.acquire()
self.start_datetime = datetime.datetime.now()
self.file_name = self._make_filename()
timer_thread = threading.Thread(target=self.timer,
args=(self.max_video_length,
self.file_name))
timer_thread.start()
self.tmp_dir = tempfile.mkdtemp()
log.info("starting threads...")
self.video_recorder.start(self.file_name, self.tmp_dir)
self.audio_recorder.start(self.file_name, self.tmp_dir)
def stop_recording(self):
log.info("stopping threads...")
if not self.audio_recorder.stop():
return
if not self.video_recorder.stop():
return
now = datetime.datetime.now()
video_length = (now - self.start_datetime).seconds
if video_length > MIN_VIDEO_LENGTH:
log.info("starting mux...")
cmd = (
f"ffmpeg -i {self.tmp_dir}/{self.file_name}.wav -i {self.tmp_dir}/{self.file_name}.h264 "
f"-c:v copy -c:a aac -strict experimental {self.final_dir}/{self.file_name}.mp4"
)
subprocess.run(cmd, capture_output=True, shell=True)
log.info(f"{self.file_name}.mp4 is ready!")
else:
log.info(f"Video was to short: {video_length}, removing it")
shutil.rmtree(self.tmp_dir)
log.info(f"{self.tmp_dir} removed")
lock.release()
def run(self):
def on_release(button):
if lock.locked():
self.stop_recording()
elif self.has_space():
self.start_recording()
else:
return False
button = Button(2)
button.when_released = on_release
listener = keyboard.Listener(on_release=self.on_keyboard_release)
listener.start()
pause()
def __init__(self):
super(MainWindow, self).__init__()
self.ui = Ui_dialog()
self.ui.setupUi(self)
self.setFixedSize(self.width(), self.height())
self.onBindingUI()
##
self.record_header_labels = [
'who', 'what', 'when', 'where', 'speaking'
]
## Create detail speaking information window
self.speaking_ui_cam1 = SpeakingMainWindow()
self.speaking_ui_cam2 = SpeakingMainWindow()
## For audio and video analysis from camera
global video_thread_cam1
global video_thread_cam2
global audio_thread_cam1
global audio_thread_cam2
## For showing Quadro W record
global nobody_record
global chung_chih_record
global yu_sung_record
global chang_yu_record
global i_hsin_record
global tzu_yuan_record
global chih_yu_record
global other_record
## For Q Learing
global nobody_q_table
global chung_chih_q_table
global yu_sung_q_table
global chang_yu_q_table
global i_hsin_q_table
global tzu_yuan_q_table
global other_q_table
## Create video and audio proces thread and process them to get Quadro W info
audio_thread_cam1 = AudioRecorder(
cam_ip='rtsp://*****:*****@192.168.65.66:554/stream1',
audio_spath='temporarily_rtsp_data/for_speaking/cam1',
audio_stime=5,
pos_path='nlp_recognize/pos.json',
dict_path='nlp_recognize/extra_dict.txt')
video_thread_cam1 = VideoRecorder(
cam_ip='rtsp://*****:*****@192.168.65.66:554/stream1',
record_path='record/',
qtable_path='q_table/',
compress_result=True,
fps=15,
analysis_sec=1,
prefix_name="cam1",
audio_thread=audio_thread_cam1,
human_model_path=
"trans_model/model_simulated_RGB_mgpu_scaling_append.0024.pb",
face_model_path='trans_model/face_new3.pb',
place_model_path='trans_model/place_new3.pb',
object_model_path=
'object_recognize/code/workspace/training_demo/model/pb/frozen_inference_graph.pb',
face_category_path='face_recognize/categories_human_uscc.txt',
place_category_path='places_recognize/categories_places_uscc.txt',
object_category_path=
'object_recognize/code/workspace/training_demo/annotations/label_map.pbtxt'
)
audio_thread_cam2 = AudioRecorder(
cam_ip='rtsp://*****:*****@192.168.65.41:554/stream1',
audio_spath='temporarily_rtsp_data/for_speaking/cam2',
audio_stime=5,
pos_path='nlp_recognize/pos.json',
dict_path='nlp_recognize/extra_dict.txt')
video_thread_cam2 = VideoRecorder(
cam_ip='rtsp://*****:*****@192.168.65.41:554/stream1',
record_path='record/',
qtable_path='q_table/',
compress_result=True,
fps=15,
analysis_sec=1,
prefix_name="cam2",
audio_thread=audio_thread_cam2,
human_model_path=
"trans_model/model_simulated_RGB_mgpu_scaling_append.0024.pb",
face_model_path='trans_model/face_new3.pb',
place_model_path='trans_model/place_new3.pb',
object_model_path=
'object_recognize/code/workspace/training_demo/model/pb/frozen_inference_graph.pb',
face_category_path='face_recognize/categories_human_uscc.txt',
place_category_path='places_recognize/categories_places_uscc.txt',
object_category_path=
'object_recognize/code/workspace/training_demo/annotations/label_map.pbtxt'
)
## Create thread to show record of Quadro W info
nobody_record = RecordUpdata(csv_path="record/Nobody_record.csv")
chung_chih_record = RecordUpdata(
csv_path="record/chung-chih_record.csv")
yu_sung_record = RecordUpdata(csv_path="record/yu-sung_record.csv")
chang_yu_record = RecordUpdata(csv_path="record/chang-yu_record.csv")
i_hsin_record = RecordUpdata(csv_path="record/i-hsin_record.csv")
tzu_yuan_record = RecordUpdata(csv_path="record/tzu-yuan_record.csv")
chih_yu_record = RecordUpdata(csv_path="record/chih-yu_record.csv")
## Create thread to calculate Q value and show
nobody_q_table = QLearningUpdata(
in_record_path="record/Nobody_record.csv",
out_table_path="q_table/Nobody_qtable.csv",
where_pool=[[2], [1], []],
where_category_path="places_recognize/categories_places_uscc.txt",
care_number=100,
decay_reward=0.98,
base_reward=100,
lower_limit=1,
decay_qvalue=0.9,
learning_rate=0.1)
chung_chih_q_table = QLearningUpdata(
in_record_path="record/chung-chih_record.csv",
out_table_path="q_table/chung-chih_qtable.csv",
where_pool=[[2], [1], []],
where_category_path="places_recognize/categories_places_uscc.txt",
care_number=100,
decay_reward=0.98,
base_reward=100,
lower_limit=1,
decay_qvalue=0.9,
learning_rate=0.1)
yu_sung_q_table = QLearningUpdata(
in_record_path="record/yu-sung_record.csv",
out_table_path="q_table/yu-sung_qtable.csv",
where_pool=[[2], [1], []],
where_category_path="places_recognize/categories_places_uscc.txt",
care_number=100,
decay_reward=0.98,
base_reward=100,
lower_limit=1,
decay_qvalue=0.9,
learning_rate=0.1)
chang_yu_q_table = QLearningUpdata(
in_record_path="record/chang-yu_record.csv",
out_table_path="q_table/chang-yu_qtable.csv",
where_pool=[[2], [1], []],
where_category_path="places_recognize/categories_places_uscc.txt",
care_number=100,
decay_reward=0.98,
base_reward=100,
lower_limit=1,
decay_qvalue=0.9,
learning_rate=0.1)
i_hsin_q_table = QLearningUpdata(
in_record_path="record/i-hsin_record.csv",
out_table_path="q_table/i-hsin_qtable.csv",
where_pool=[[2], [1], []],
where_category_path="places_recognize/categories_places_uscc.txt",
care_number=100,
decay_reward=0.98,
base_reward=100,
lower_limit=1,
decay_qvalue=0.9,
learning_rate=0.1)
tzu_yuan_q_table = QLearningUpdata(
in_record_path="record/tzu-yuan_record.csv",
out_table_path="q_table/tzu-yuan_qtable.csv",
where_pool=[[2], [1], []],
where_category_path="places_recognize/categories_places_uscc.txt",
care_number=100,
decay_reward=0.98,
base_reward=100,
lower_limit=1,
decay_qvalue=0.9,
learning_rate=0.1)
chih_yu_q_table = QLearningUpdata(
in_record_path="record/chih-yu_record.csv",
out_table_path="q_table/chih-yu_qtable.csv",
where_pool=[[2], [1], []],
where_category_path="places_recognize/categories_places_uscc.txt",
care_number=100,
decay_reward=0.98,
base_reward=100,
lower_limit=1,
decay_qvalue=0.9,
learning_rate=0.1)
## Start all thread
video_thread_cam1.start()
video_thread_cam2.start()
audio_thread_cam1.start()
audio_thread_cam2.start()
nobody_record.start()
chung_chih_record.start()
yu_sung_record.start()
chang_yu_record.start()
i_hsin_record.start()
tzu_yuan_record.start()
chih_yu_record.start()
nobody_q_table.start()
chih_yu_q_table.start()
yu_sung_q_table.start()
chang_yu_q_table.start()
i_hsin_q_table.start()
tzu_yuan_q_table.start()
chang_yu_q_table.start()
self.timer = QTimer(self)
self.timer.timeout.connect(self.start_webcam)
self.timer.start(0)