def route_video_record(cmd):
if cmd == "start":
video_system.start_record()
elif cmd == "stop":
video_system.stop_record()
return flask.Response("ok")
def run(self, params):
# resetting init values
self.in_trial = False
self.got_detection = False
self.time = 0.0
self.cancel_trials = None
self.cancel_logic_trial = None
self.prev_det = None
self.prev_trial_detection = False
self.stim_cancel = None
self.consq_end = False
self.data_dir = session_state["data_dir"]
self.cur_trial = params["$num_trials"]
self.consecutive = params["consecutive"]
# determining the experiment type
self.ex_type = ("consecutive" if self.consecutive else
("continuous" if params["continuous"] else "regular"))
# logging yolo detections
self.yolo_log = data_log.QueuedDataLogger(
columns=[
("time", "timestamptz not null"),
("x1", "double precision"),
("y1", "double precision"),
("x2", "double precision"),
("y2", "double precision"),
("confidence", "double precision"),
],
csv_path=session_state["data_dir"] / "head_bbox.csv",
table_name="bbox_position",
)
self.yolo_log.start()
# detecting Aruco squares within arena
self.detectAruco()
if params["record_all"]: # record start at init
video_system.start_record()
if not self.consecutive: # no need to schedule next trials if consecutive
self.cancel_trials = schedule.repeat(
self.period_call,
params.get("exp_interval", 100),
params.get("num_of_exp", 1) - 1,
) # schedule the next trials
# starting image observers
yolo = exp.image_observers["head_bbox"]
yolo.on_detection = self.on_yolo_detection
yolo.start_observing()
self.log.info("Start Observing")
monitor.change_color("white")
def run(self):
self.rng = np.random.default_rng()
self.left_feeding_pos = None
self.right_feeding_pos = None
params = exp.get_params()
for a in self.aruco_markers:
if a["id"] == params["left_aruco_id"]:
self.left_feeding_pos = a["center"]
elif a["id"] == params["right_aruco_id"]:
self.right_feeding_pos = a["center"]
if self.left_feeding_pos is None or self.right_feeding_pos is None:
raise ValueError(
"Could not find left and/or right feeding positions")
else:
self.log.info(f"Left feeding position: {self.left_feeding_pos}")
self.log.info(f"Right feeding position: {self.right_feeding_pos}")
self.radius = params["feeding_radius"]
if self.aruco_img is not None:
img = np.copy(self.aruco_img)
else:
img, _ = image_sources[params["image_source_id"]].get_image()
for a in self.aruco_markers:
img = cv.circle(
img,
tuple(a["center"]),
radius=self.radius,
color=(0, 255, 0),
thickness=5,
)
now_str = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
area_image_path = session_state[
"data_dir"] / f"feeding_areas_{now_str}.jpg"
self.log.info(f"Saving feeding areas image to {area_image_path}")
cv.imwrite(str(area_image_path), img)
if params["record_video"]:
video_system.start_record()
self.bbox_collector.run(self.on_bbox_detection)
self.shaping_mode = params["shaping_mode"]
session_state["state"] = "idle"
def run(self):
self.find_reinforced_location()
self.bbox_collector.run(self.on_bbox_detection)
session_state["is_in_area"] = False
self.in_out_time = None
session_state.add_callback("is_in_area", self.is_in_area_changed)
session_state["cooldown"] = False
session_state["reward_scheduled"] = False
self.cancel_cooldown = None
self.cancel_reward_delay = None
self.cancel_blink = None
self.using_stochastic_delay = None
if exp.get_params()["record_video"]:
video_system.start_record()
def run_trial(self):
if exp.get_params()["record_video"]:
video_system.start_record()
if exp.get_params()["media_url"] is not None:
schedule.once(
lambda: mqtt.client.publish_json(
"event/command/init_media", {"url": exp.get_params()["media_url"]}
),
exp.get_params()["start_delay"],
)
else:
schedule.once(
lambda: mqtt.client.publish_json(
"event/command/init_bugs", exp.get_params()
),
exp.get_params()["start_delay"],
)
def run(self):
self.paths = list(
Path(exp.get_params()["stimuli_path"]).rglob("*.jpg")) + list(
Path(exp.get_params()["stimuli_path"]).rglob("*.JPG"))
random.shuffle(self.paths)
exp.session_state["image_list"] = self.paths
self.log.info(f"Loaded {len(self.paths)} images.")
intervals = [exp.get_params()["preseq_delay"]] + [
exp.get_params()["stimuli_duration"],
exp.get_params()["interstimuli_duration"],
] * len(self.paths)
self.cur_index = 0
self.clear(exp.get_params()["interstimuli_color"])
self.cancel_sequence = schedule.sequence(self.display_stimuli,
intervals)
video_system.start_record()
def run_trial(self, params):
self.in_trial = True
if (params.get("record_exp", True)
and not params["record_all"]): # recording trials only
video_system.start_record()
self.log.info("Trial " + str(params["$num_trials"] - self.cur_trial) +
" started " + str(datetime.datetime.now()))
if not self.consecutive:
self.stim() # present stimulus at trials start
# giving a reward if bypass_detection enabled
if params["bypass_detection"] and params["reward_detections"]:
self.dispatch_reward()
self.end_logic_trial()
else: # scheduling the logical end of trial
self.cancel_logic_trial = schedule.once(
self.end_logic_trial, params["trial_length"])
self.log.info("run trial procedure finished")
def on_day_start(self):
self.bbox_collector.start(self.on_bbox_detection)
arena.start_trigger()
video_system.start_record()
arena.run_command("set", "AC Line 2", [1])
arena.run_command("set", "AC Line 1", [1])
def start_record(log):
log.info("Starting video recording.")
video_system.start_record()
def run(self):
monitor.set_color(exp.get_params()["background_color"])
if exp.get_params()["record_video"]:
video_system.start_record()
def run(self):
self.cur_trial = exp.get_params()["$num_trials"]
if exp.get_params()["record_exp"]: # record start at init
video_system.start_record()
