def respond_role_menu(call, user):
role = Role.get(Role.role_name == call.data)
prev_value = RoleSelectionTracker.get(
RoleSelectionTracker.user == user,
RoleSelectionTracker.role == role).checked
new_value = not prev_value
RoleSelectionTracker.update(checked=new_value).where(
RoleSelectionTracker.user == user,
RoleSelectionTracker.role == role).execute()
if new_value:
answer_callback(f"نقش {role.role_name} اضافه شد.", call.id)
else:
answer_callback(f"نقش {role.role_name} حذف شد.", call.id)
# update role team keyboard
if role.team == "مافیا":
message_id = Tracker.get(Tracker.id == user.id).mafia_message_id
else:
message_id = Tracker.get(Tracker.id == user.id).citizen_message_id
# edit reply keyboard with new values
edit_message_reply_markup(
create_role_selection_menu(user, role.team),
chat_id=user.id,
message_id=message_id,
)
# send the new list of roles to every player
players = get_players(user, include_god=True)
num_players = len(players) - 1
for player in players:
send_current_roles(player, num_players, edit=True)
async def add_shape(sid: int, data: Dict[str, Any]):
pr: PlayerRoom = game_state.get(sid)
if "temporary" not in data:
data["temporary"] = False
floor = pr.active_location.floors.select().where(
Floor.name == data["shape"]["floor"])[0]
layer = floor.layers.where(Layer.name == data["shape"]["layer"])[0]
if pr.role != Role.DM and not layer.player_editable:
logger.warning(
f"{pr.player.name} attempted to add a shape to a dm layer")
return
if data["temporary"]:
game_state.add_temp(sid, data["shape"]["uuid"])
else:
with db.atomic():
data["shape"]["layer"] = layer
data["shape"]["index"] = layer.shapes.count()
# Shape itself
shape = Shape.create(**reduce_data_to_model(Shape, data["shape"]))
# Subshape
type_table = get_table(shape.type_)
type_table.create(
shape=shape,
**type_table.pre_create(
**reduce_data_to_model(type_table, data["shape"])),
)
# Owners
for owner in data["shape"]["owners"]:
ShapeOwner.create(
shape=shape,
user=User.by_name(owner["user"]),
edit_access=owner["edit_access"],
movement_access=owner["movement_access"],
vision_access=owner["vision_access"],
)
# Trackers
for tracker in data["shape"]["trackers"]:
Tracker.create(**reduce_data_to_model(Tracker, tracker),
shape=shape)
# Auras
for aura in data["shape"]["auras"]:
Aura.create(**reduce_data_to_model(Aura, aura), shape=shape)
for room_player in pr.room.players:
is_dm = room_player.role == Role.DM
for psid in game_state.get_sids(player=room_player.player,
active_location=pr.active_location):
if psid == sid:
continue
if not is_dm and not layer.player_visible:
continue
if not data["temporary"]:
data["shape"] = shape.as_dict(room_player.player, is_dm)
await sio.emit("Shape.Add",
data["shape"],
room=psid,
namespace=GAME_NS)
def GetTracker(key):
"""Get a tracker, or create one if it dosent exist"""
tracker = Tracker.all().filter('name =',key).get()
if tracker==None:
tracker = Tracker(name=key)
tracker.put()
return tracker
def next_tracking():
global _tracker
if not session.get('vid'):
abort(401)
previous_frame = request.args.get('previous_frame', 0, type=int)
current_frame = request.args.get('current_frame', 0, type=int)
left = request.args.get('left', 0, int)
top = request.args.get('top', 0, int)
width = request.args.get('width', 0, int)
height = request.args.get('height', 0, int)
is_new_box = request.args.get('new_box', 0, int)
if not _tracker:
_tracker = Tracker(Video.query.get(session.get('vid')).json,
previous_frame, left, top, width, height)
elif is_new_box:
_tracker.start_new_tracking(previous_frame, [(left, top, left+width, top+height)])
new_left, new_top, new_width, new_height = _tracker.get_next_box(current_frame)
return jsonify(left=new_left,
top=new_top,
width=new_width,
height=new_height)
async def add_shape(sid, data):
sid_data = state.sid_map[sid]
user = sid_data["user"]
room = sid_data["room"]
location = sid_data["location"]
if "temporary" not in data:
data["temporary"] = False
floor = location.floors.select().where(
Floor.name == data["shape"]["floor"])[0]
layer = floor.layers.where(Layer.name == data["shape"]["layer"])[0]
if room.creator != user and not layer.player_editable:
logger.warning(f"{user.name} attempted to add a shape to a dm layer")
return
if data["temporary"]:
state.add_temp(sid, data["shape"]["uuid"])
else:
with db.atomic():
data["shape"]["layer"] = layer
data["shape"]["index"] = layer.shapes.count()
# Shape itself
shape = Shape.create(**reduce_data_to_model(Shape, data["shape"]))
# Subshape
type_table = get_table(shape.type_)
type_table.create(shape=shape,
**reduce_data_to_model(type_table,
data["shape"]))
# Owners
ShapeOwner.create(shape=shape, user=user)
# Trackers
for tracker in data["shape"]["trackers"]:
Tracker.create(**reduce_data_to_model(Tracker, tracker),
shape=shape)
# Auras
for aura in data["shape"]["auras"]:
Aura.create(**reduce_data_to_model(Aura, aura), shape=shape)
if layer.player_visible:
for room_player in room.players:
for psid in state.get_sids(user=room_player.player, room=room):
if psid == sid:
continue
if not data["temporary"]:
data["shape"] = shape.as_dict(room_player.player, False)
await sio.emit("Shape.Add",
data["shape"],
room=psid,
namespace="/planarally")
for csid in state.get_sids(user=room.creator, room=room):
if csid == sid:
continue
if not data["temporary"]:
data["shape"] = shape.as_dict(room.creator, True)
await sio.emit("Shape.Add",
data["shape"],
room=csid,
namespace="/planarally")
def __init__(self,
update_facebank=False,
show_results=True,
dataset_path=None,
save=False):
"""
:param update_facebank: Calculate new facebank or use old one
:param show_results: Draw bboxes and ident info at frame
:param videoPath: Path for processing the video
:param save: Save processed video
"""
self.dataset_path = dataset_path
self.video_path = f'{self.dataset_path}/{self.find_video()}'
self.face_identificator = FaceIdentificator(
update_facebank, f'{os.getcwd()}/{self.dataset_path}')
self.tracker = Tracker()
self.unique_facebank_names = list(
self.face_identificator.saved_embeddings.keys())
# TODO Create smth more smarter for scores initialization
self.saved_scores = dict(
zip(self.unique_facebank_names, [
self.face_identificator.threshold
for name in self.unique_facebank_names
]))
self.mapped_tracks = {}
self.show_results = show_results
self.treshold = 0.4
self.save = save
def get(self):
if os.environ.get('HTTP_HOST'):
url = os.environ['HTTP_HOST']
else:
url = os.environ['SERVER_NAME']
self.response.headers['Content-Type'] = 'text/plain'
self.response.out.write(
"""OpenTrack\n
Extreamly Basic tracking system, for use with p2p programs
To use the tracker visit
http://%s/trk/<tracker name>
The tracker does not need to be setup beforhand
It will return a list of IP adresses that have visited
that tracker and add yours to the list
Optionally add "?tick=<seconds>" to the url where seconds
is how recently it has been visited the default tick is 60 seconds
NEW: http://%s/tick/<tracker name>
provides a way of refreshing yourself in a tracker without retrieveing a list
\nCurrent Trackers (activity last 24 hours)\n"""%(url,url))
trackers = Tracker.all()
for tracker in trackers:
self.response.out.write(" - %s\n"%tracker.name)
async def create_tracker(sid: str, data: TrackerDelta):
pr: PlayerRoom = game_state.get(sid)
shape = get_shape_or_none(pr, data["shape"], "Tracker.Create")
if shape is None:
return
model = reduce_data_to_model(Tracker, data)
tracker = Tracker.create(**model)
tracker.save()
owners = [*get_owner_sids(pr, shape, skip_sid=sid)]
for psid in owners:
await sio.emit(
"Shape.Options.Tracker.Create", data, room=psid, namespace=GAME_NS,
)
if tracker.visible:
for psid in game_state.get_sids(
active_location=pr.active_location, skip_sid=sid
):
if psid in owners:
continue
await sio.emit(
"Shape.Options.Tracker.Create", data, room=psid, namespace=GAME_NS,
)
def register_user(message):
user = User.get_or_none(User.id == message.chat.id)
if user is not None:
game = Game.get_or_none(Game.user == user)
if game is None:
send_message(message.chat.id,
f"Hi again <b>{user.name}</b>!",
reply_markup=keyboards.main)
else:
text = f"Hi again <b>{user.name}</b>!\n\n"
text += ":game_die: Note that you're in the middle of a game."
send_message(
message.chat.id,
text,
)
return False
send_message(message.chat.id,
f"Hi <b>{message.chat.first_name}</b>!",
reply_markup=keyboards.main)
# tracker and user information
Tracker.replace(id=message.chat.id).on_conflict_replace().execute()
User.replace(
id=message.chat.id,
name=message.chat.first_name,
username=message.chat.username,
).on_conflict_replace().execute()
# default game settings for each user
user = User.get(User.id == message.chat.id)
GameSettings.insert(user=user).execute()
# insert selected roles tracker
roles = Role.select().where(Role.is_default == True)
data = []
for r in roles:
data.append((r, user, False))
RoleSelectionTracker.insert_many(data,
fields=[
RoleSelectionTracker.role,
RoleSelectionTracker.user,
RoleSelectionTracker.checked
]).execute()
return True
def get_user_location(self, request):
"""Get tracker location of user"""
entity = User.get_by_id(request.id)
if entity:
entity = Tracker.get_trackers(request)
return entity
else:
raise endpoints.NotFoundException()
def update_user_location(self, request):
"""Update current location of user"""
entity = User.get_by_id(request.id)
if entity:
entity = Tracker.put_from_message(request)
return entity
else:
raise endpoints.NotFoundException()
async def update_tracker(sid: str, data: TrackerDelta):
pr: PlayerRoom = game_state.get(sid)
shape = get_shape_or_none(pr, data["shape"], "Tracker.Update")
if shape is None:
return
tracker = Tracker.get_by_id(data["uuid"])
changed_visible = tracker.visible != data.get("visible", tracker.visible)
update_model_from_dict(tracker, data)
tracker.save()
owners = [*get_owner_sids(pr, shape, skip_sid=sid)]
for psid in owners:
await sio.emit(
"Shape.Options.Tracker.Update",
data,
room=psid,
namespace=GAME_NS,
)
for psid in game_state.get_sids(active_location=pr.active_location,
skip_sid=sid):
if psid in owners:
continue
if changed_visible:
if tracker.visible:
await sio.emit(
"Shape.Options.Tracker.Create",
{
"shape": shape.uuid,
**tracker.as_dict()
},
room=psid,
namespace=GAME_NS,
)
else:
await sio.emit(
"Shape.Options.Tracker.Remove",
{
"shape": shape.uuid,
"value": tracker.uuid
},
room=psid,
namespace=GAME_NS,
)
else:
await sio.emit(
"Shape.Options.Tracker.Update",
data,
room=psid,
namespace=GAME_NS,
)
async def remove_tracker(sid: str, data: ShapeSetStringValue):
pr: PlayerRoom = game_state.get(sid)
shape = get_shape_or_none(pr, data["shape"], "Tracker.Remove")
if shape is None:
return
tracker: Tracker = Tracker.get_by_id(data["value"])
tracker.delete_instance(True)
await sio.emit(
"Shape.Options.Tracker.Remove",
data,
skip_sid=sid,
room=pr.active_location.get_path(),
namespace=GAME_NS,
)
async def update_shape_tracker(sid: str, data: TrackerUpdateData):
pr: PlayerRoom = game_state.get(sid)
if data["_type"] == "tracker":
tracker = Tracker.get_by_id(data["uuid"])
else:
tracker = Aura.get_by_id(data["uuid"])
tracker.value = data["value"]
tracker.save()
await sio.emit(
"Shapes.Trackers.Update",
data,
room=pr.active_location.get_path(),
skip_sid=sid,
namespace=GAME_NS,
)
async def move_tracker(sid: str, data: TrackerMove):
pr: PlayerRoom = game_state.get(sid)
new_shape = get_shape_or_none(pr, data["new_shape"],
"Tracker.Options.Tracker.Move")
if new_shape is None:
return
tracker = Tracker.get_by_id(data["tracker"])
tracker.shape = new_shape
tracker.save()
await sio.emit(
"Shape.Options.Tracker.Move",
data,
skip_sid=sid,
room=pr.active_location.get_path(),
namespace=GAME_NS,
)
def get(self):
## remove any old peers
plist = Peer.all().filter('datetime <',datetime.now() - timedelta(days=1))
peercount = plist.count()
for p in plist:
p.delete()
## remove any empty trackers
trackers = Tracker.all()
trackercount = 0
for tracker in trackers:
p = Peer.all().filter('tracker =',tracker).order('-datetime').get()
if p == None:
tracker.delete()
trackercount += 1
## some simple stats
self.response.headers['Content-Type'] = 'text/plain'
self.response.out.write("Cleaned %d trackers and %d peers"%(trackercount,peercount))
if trackercount and peercount:
logging.info("Cleaned %d trackers and %d peers"%(trackercount,peercount))
def get(self):
user = User.get(id=int(get_jwt_identity()))
if user.account_type != "admin":
return {}, 403
week_ago = datetime.date.today() - datetime.timedelta(days=7)
base = Tracker.select().where((Tracker.timestamp >= week_ago)
& (Tracker.page_type == "car"))
last_7days_view = base.count()
diff_users = base.select(Tracker.ip).group_by(Tracker.ip).count()
top_10pages = base.select(Tracker.title, fn.Count(
Tracker.id)).group_by(Tracker.title).order_by(
fn.Count(Tracker.id).desc()).tuples()[:10]
for track in top_10pages:
print track
return {
"data": {
"last_7days": last_7days_view,
"top_10": top_10pages
},
"message": "",
"status": "success"
}
def update_state(user, state):
Tracker.update(state=state).where(Tracker.id == user.id).execute()
async def update_shape(sid, data):
sid_data = state.sid_map[sid]
user = sid_data["user"]
room = sid_data["room"]
location = sid_data["location"]
shape, layer = await _get_shape(data, location, user)
if not await has_ownership(layer, room, data, user, shape):
return
# Overwrite the old data with the new data
if not data["temporary"]:
with db.atomic():
data["shape"]["layer"] = Layer.get(location=location,
name=data["shape"]["layer"])
# Shape
update_model_from_dict(shape,
reduce_data_to_model(Shape, data["shape"]))
shape.save()
# Subshape
type_table = get_table(shape.type_)
type_instance = type_table.get(uuid=shape.uuid)
# no backrefs on these tables
update_model_from_dict(type_instance,
data["shape"],
ignore_unknown=True)
type_instance.save()
# Owners
old_owners = {owner.user.name for owner in shape.owners}
new_owners = set(data["shape"]["owners"])
for owner in old_owners ^ new_owners:
if owner == "":
continue
delta_owner = User.by_name(owner)
if owner in new_owners:
ShapeOwner.create(shape=shape, user=delta_owner)
else:
ShapeOwner.get(shape=shape,
user=delta_owner).delete_instance(True)
await send_client_initiatives(room, location, delta_owner)
# Trackers
for tracker in data["shape"]["trackers"]:
tracker_db = Tracker.get_or_none(uuid=tracker["uuid"])
reduced = reduce_data_to_model(Tracker, tracker)
reduced["shape"] = shape
if tracker_db:
update_model_from_dict(tracker_db, reduced)
tracker_db.save()
else:
Tracker.create(**reduced)
# Auras
for aura in data["shape"]["auras"]:
aura_db = Aura.get_or_none(uuid=aura["uuid"])
reduced = reduce_data_to_model(Aura, aura)
reduced["shape"] = shape
if aura_db:
update_model_from_dict(aura_db, reduced)
aura_db.save()
else:
Aura.create(**reduced)
await sync_shape_update(layer, room, data, sid, shape)
'init_beta':
1,
'init_v':
np.diag([4, 2]),
'init_nu':
5,
}
from models import GgiwTracker as Tracker
#cell 8
### Run Tracker
#cell 9
# tracker definition
tracker = Tracker(dt=dt, **config)
pr = cProfile.Profile()
for i in range(steps):
scan = measurements[measurements['ts'] == i]
pr.enable()
tracker.step(scan)
pr.disable()
estimates, log_lik = tracker.extract()
bboxes = tracker.extrackt_bbox()
s = io.StringIO()
ps = pstats.Stats(pr, stream=s).sort_stats('cumulative')
ps.print_stats(10)
def main(args):
cap = cv2.VideoCapture(args.video_input)
frame_rate = int(round(cap.get(cv2.CAP_PROP_FPS)))
video_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
video_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
demo_images_path = os.path.join(args.demo_output, 'demo_images')
if not os.path.exists(demo_images_path):
os.makedirs(demo_images_path)
device = torch.device(args.device)
model, _, postprocessors = build_tracktest_model(args)
model.to(device)
model.eval()
tracker = Tracker(score_thresh=args.track_thresh)
checkpoint = torch.load(args.resume, map_location='cpu')
_, _ = model.load_state_dict(checkpoint['model'], strict=False)
print("Model is loaded")
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
color_list = colormap()
print("Starting inference")
count = 0
tracker.reset_all()
pre_embed = None
res, img = cap.read()
while res:
count += 1
resized_img, nh, nw = resize(img)
rgb_img = cv2.cvtColor(resized_img, cv2.COLOR_BGR2RGB)
tensor_img = F.normalize(F.to_tensor(rgb_img), mean, std)
samples = nested_tensor_from_tensor_list([tensor_img]).to(device)
outputs, pre_embed = model(samples, pre_embed)
orig_sizes = torch.stack(
[torch.as_tensor([video_height, video_width])], dim=0).to(device)
results = postprocessors['bbox'](outputs, orig_sizes)
if count == 1:
res_track = tracker.init_track(results[0])
else:
res_track = tracker.step(results[0])
for ret in res_track:
if ret['active'] == 0:
continue
bbox = ret['bbox']
tracking_id = ret['tracking_id']
cv2.rectangle(img, (int(bbox[0]), int(bbox[1])),
(int(bbox[2]), int(bbox[3])),
color_list[tracking_id % 79].tolist(),
thickness=2)
cv2.putText(img, "{}".format(tracking_id),
(int(bbox[0]), int(bbox[1])), cv2.FONT_HERSHEY_SIMPLEX,
0.8, color_list[tracking_id % 79].tolist(), 2)
cv2.imwrite(
os.path.join(demo_images_path, "demo{:0>6d}.png".format(count)),
img)
print('Frame{:d} of the video is done'.format(count))
res, img = cap.read()
print('Lenth of the video: {:d} frames'.format(count))
print("Starting img2video")
img_paths = gb.glob(os.path.join(demo_images_path, "*.png"))
size = (video_width, video_height)
videowriter = cv2.VideoWriter(
os.path.join(args.demo_output, "demo_video.avi"),
cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), frame_rate, size)
for img_path in sorted(img_paths):
img = cv2.imread(img_path)
img = cv2.resize(img, size)
videowriter.write(img)
videowriter.release()
print("img2video is done")
def post(self):
data = parser.parse_args()
data['request'] = request
print "[*] Request: ", data["request"]
Tracker.create_from_request(**data)
return {}
async def update_shape(sid: int, data: Dict[str, Any]):
pr: PlayerRoom = game_state.get(sid)
if data["temporary"] and not has_ownership_temp(data["shape"], pr):
logger.warning(
f"User {pr.player.name} tried to update a shape it does not own.")
return
# todo clean up this mess that deals with both temporary and non temporary shapes
shape, layer = await _get_shape(data, pr)
# Overwrite the old data with the new data
if not data["temporary"]:
if not has_ownership(shape, pr):
logger.warning(
f"User {pr.player.name} tried to update a shape it does not own."
)
return
with db.atomic():
# Shape
update_model_from_dict(shape,
reduce_data_to_model(Shape, data["shape"]))
shape.save()
# Subshape
type_instance = shape.subtype
# no backrefs on these tables
type_instance.update_from_dict(data["shape"], ignore_unknown=True)
type_instance.save()
# Trackers
old_trackers = {tracker.uuid for tracker in shape.trackers}
new_trackers = {
tracker["uuid"]
for tracker in data["shape"]["trackers"]
}
for tracker_id in old_trackers | new_trackers:
remove = tracker_id in old_trackers - new_trackers
if not remove:
tracker = next(tr for tr in data["shape"]["trackers"]
if tr["uuid"] == tracker_id)
reduced = reduce_data_to_model(Tracker, tracker)
reduced["shape"] = shape
if tracker_id in new_trackers - old_trackers:
Tracker.create(**reduced)
continue
tracker_db = Tracker.get(uuid=tracker_id)
if remove:
tracker_db.delete_instance(True)
else:
update_model_from_dict(tracker_db, reduced)
tracker_db.save()
# Auras
old_auras = {aura.uuid for aura in shape.auras}
new_auras = {aura["uuid"] for aura in data["shape"]["auras"]}
for aura_id in old_auras | new_auras:
remove = aura_id in old_auras - new_auras
if not remove:
aura = next(au for au in data["shape"]["auras"]
if au["uuid"] == aura_id)
reduced = reduce_data_to_model(Aura, aura)
reduced["shape"] = shape
if aura_id in new_auras - old_auras:
Aura.create(**reduced)
continue
aura_db = Aura.get_or_none(uuid=aura_id)
if remove:
aura_db.delete_instance(True)
else:
update_model_from_dict(aura_db, reduced)
aura_db.save()
# Labels
for label in data["shape"]["labels"]:
label_db = Label.get_or_none(uuid=label["uuid"])
reduced = reduce_data_to_model(Label, label)
reduced["user"] = User.by_name(reduced["user"])
if label_db:
update_model_from_dict(label_db, reduced)
label_db.save()
else:
Label.create(**reduced)
old_labels = {
shape_label.label.uuid
for shape_label in shape.labels
}
new_labels = set(label["uuid"]
for label in data["shape"]["labels"])
for label in old_labels ^ new_labels:
if label == "":
continue
if label in new_labels:
ShapeLabel.create(shape=shape, label=Label.get(uuid=label))
else:
ShapeLabel.get(label=Label.get(uuid=label),
shape=shape).delete_instance(True)
await sync_shape_update(layer, pr, data, sid, shape)
async def update_shape(sid, data):
sid_data = state.sid_map[sid]
user = sid_data["user"]
room = sid_data["room"]
location = sid_data["location"]
shape, layer = await _get_shape(data, location, user)
if not await has_ownership(layer, room, data, user, shape):
return
# Overwrite the old data with the new data
if not data["temporary"]:
with db.atomic():
data["shape"]["layer"] = Layer.get(location=location,
name=data["shape"]["layer"])
# Shape
update_model_from_dict(shape,
reduce_data_to_model(Shape, data["shape"]))
shape.save()
# Subshape
type_instance = shape.subtype
# no backrefs on these tables
type_instance.update_from_dict(data["shape"], ignore_unknown=True)
type_instance.save()
# Owners
old_owners = {owner.user.name for owner in shape.owners}
new_owners = set(data["shape"]["owners"])
for owner in old_owners ^ new_owners:
if owner == "":
continue
delta_owner = User.by_name(owner)
if owner in new_owners:
ShapeOwner.create(shape=shape, user=delta_owner)
else:
ShapeOwner.get(shape=shape,
user=delta_owner).delete_instance(True)
await send_client_initiatives(room, location, delta_owner)
# Trackers
old_trackers = {tracker.uuid for tracker in shape.trackers}
new_trackers = {
tracker["uuid"]
for tracker in data["shape"]["trackers"]
}
for tracker_id in old_trackers | new_trackers:
remove = tracker_id in old_trackers - new_trackers
if not remove:
tracker = next(tr for tr in data["shape"]["trackers"]
if tr["uuid"] == tracker_id)
reduced = reduce_data_to_model(Tracker, tracker)
reduced["shape"] = shape
if tracker_id in new_trackers - old_trackers:
Tracker.create(**reduced)
continue
tracker_db = Tracker.get(uuid=tracker_id)
if remove:
tracker_db.delete_instance(True)
else:
update_model_from_dict(tracker_db, reduced)
tracker_db.save()
# Auras
old_auras = {aura.uuid for aura in shape.auras}
new_auras = {aura["uuid"] for aura in data["shape"]["auras"]}
for aura_id in old_auras | new_auras:
remove = aura_id in old_auras - new_auras
if not remove:
aura = next(au for au in data["shape"]["auras"]
if au["uuid"] == aura_id)
reduced = reduce_data_to_model(Aura, aura)
reduced["shape"] = shape
if aura_id in new_auras - old_auras:
Aura.create(**reduced)
continue
aura_db = Aura.get_or_none(uuid=aura_id)
if remove:
aura_db.delete_instance(True)
else:
update_model_from_dict(aura_db, reduced)
aura_db.save()
# Labels
for label in data["shape"]["labels"]:
label_db = Label.get_or_none(uuid=label["uuid"])
reduced = reduce_data_to_model(Label, label)
reduced["user"] = User.by_name(reduced["user"])
if label_db:
update_model_from_dict(label_db, reduced)
label_db.save()
else:
Label.create(**reduced)
shape_label_db = ShapeLabel.get_or_none(shape=shape,
label=label_db)
old_labels = {
shape_label.label.uuid
for shape_label in shape.labels
}
new_labels = set(label["uuid"]
for label in data["shape"]["labels"])
for label in old_labels ^ new_labels:
if label == "":
continue
if label in new_labels:
ShapeLabel.create(shape=shape, label=Label.get(uuid=label))
else:
ShapeLabel.get(label=Label.get(uuid=label),
shape=shape).delete_instance(True)
await sync_shape_update(layer, room, data, sid, shape)
def respond_message(message):
if message.text.startswith('/start'):
register_user(message)
message.text = emoji.demojize(message.text)
t = Tracker.get_or_none(Tracker.id == message.chat.id)
user = User.get_or_none(id=message.chat.id)
if not t or not user:
send_message(
message.chat.id,
":cross_mark: Not a registered user. Please click on /start.")
return
# update the username with every message
# this is important as it is the only way to find out the user identity
User.update(username=message.chat.username, ).where(
User.id == message.chat.id).execute()
# ------------------------------------
# HOST a game
# ------------------------------------
if t.state == 'start' and message.text == ":desktop_computer: Host a Game":
host_start(message, user)
# end game
elif t.state == 'host_game' and message.text == ":cross_mark: Leave":
host_leave(message, user)
# select from a list of roles
elif t.state == 'host_game' and message.text == ":right_arrow: Next":
# FIXME: poll is deactivated. inline keyboard is used now.
# host_select_roles_with_poll(message, user)
host_select_roles(message, user)
# select from a list of roles
elif t.state == 'host_game' and message.text == ":envelope: Send Roles":
host_send_roles(message, user)
# ------------------------------------
# New Game
# ------------------------------------
join_code_pattern = r"^/start (?P<code>\w{4})$"
match = re.match(join_code_pattern, message.text)
if match:
code = match.group("code")
message.text = code
if t.state == 'start' and message.text == ":game_die: Join a Game":
player_leave(message, user)
elif t.state == 'join_game':
player_start(message, user)
elif t.state == 'start' and match:
player_start(message, user)
# ------------------------------------
# Change Name
# ------------------------------------
if t.state == 'start' and message.text == ":bust_in_silhouette: Change Name":
update_state(user, 'change_name')
text = f":bust_in_silhouette: Current name: <b>{f2p(user.name)}</b>\n\n"
text += ":input_latin_letters: Enter your new name:"
send_message(user.id,
text,
reply_markup=create_keyboard([":cross_mark: Discard"]))
elif t.state == 'change_name' and message.text == ":cross_mark: Discard":
update_state(user, 'start')
send_message(user.id,
":cross_mark: Discard",
reply_markup=keyboards.main)
elif t.state == 'change_name':
if len(message.text) > 100:
send_message(user.id,
"Name length must be less than 100 characters.")
return
User.update(name=message.text).where(User.id == user.id).execute()
update_state(user, 'start')
send_message(
user.id,
f":white_heavy_check_mark: Your Name is updated now to: <b>{message.text}</b>",
reply_markup=keyboards.main)
# ------------------------------------
# Settings
# ------------------------------------
if t.state == 'start' and message.text == ":gear_selector: Settings":
edit_game_settings(message, user)
def main(args):
utils.init_distributed_mode(args)
print("git:\n {}\n".format(utils.get_sha()))
if args.frozen_weights is not None:
assert args.masks, "Frozen training is meant for segmentation only"
print(args)
device = torch.device(args.device)
# fix the seed for reproducibility
seed = args.seed + utils.get_rank()
torch.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
scaler = torch.cuda.amp.GradScaler(enabled=args.fp16)
if args.det_val:
assert args.eval, 'only support eval mode of detector for track'
model, criterion, postprocessors = build_model(args)
elif args.eval:
model, criterion, postprocessors = build_tracktest_model(args)
else:
model, criterion, postprocessors = build_tracktrain_model(args)
model.to(device)
model_without_ddp = model
n_parameters = sum(p.numel() for p in model.parameters()
if p.requires_grad)
print('number of params:', n_parameters)
dataset_train = build_dataset(image_set=args.track_train_split, args=args)
dataset_val = build_dataset(image_set=args.track_eval_split, args=args)
if args.distributed:
if args.cache_mode:
sampler_train = samplers.NodeDistributedSampler(dataset_train)
sampler_val = samplers.NodeDistributedSampler(dataset_val,
shuffle=False)
else:
sampler_train = samplers.DistributedSampler(dataset_train)
sampler_val = samplers.DistributedSampler(dataset_val,
shuffle=False)
else:
sampler_train = torch.utils.data.RandomSampler(dataset_train)
sampler_val = torch.utils.data.SequentialSampler(dataset_val)
batch_sampler_train = torch.utils.data.BatchSampler(sampler_train,
args.batch_size,
drop_last=True)
data_loader_train = DataLoader(dataset_train,
batch_sampler=batch_sampler_train,
collate_fn=utils.collate_fn,
num_workers=args.num_workers,
pin_memory=True)
data_loader_val = DataLoader(dataset_val,
args.batch_size,
sampler=sampler_val,
drop_last=False,
collate_fn=utils.collate_fn,
num_workers=args.num_workers,
pin_memory=True)
# lr_backbone_names = ["backbone.0", "backbone.neck", "input_proj", "transformer.encoder"]
def match_name_keywords(n, name_keywords):
out = False
for b in name_keywords:
if b in n:
out = True
break
return out
for n, p in model_without_ddp.named_parameters():
print(n)
param_dicts = [{
"params": [
p for n, p in model_without_ddp.named_parameters()
if not match_name_keywords(n, args.lr_backbone_names)
and not match_name_keywords(n, args.lr_linear_proj_names)
and p.requires_grad
],
"lr":
args.lr,
}, {
"params": [
p for n, p in model_without_ddp.named_parameters() if
match_name_keywords(n, args.lr_backbone_names) and p.requires_grad
],
"lr":
args.lr_backbone,
}, {
"params": [
p for n, p in model_without_ddp.named_parameters()
if match_name_keywords(n, args.lr_linear_proj_names)
and p.requires_grad
],
"lr":
args.lr * args.lr_linear_proj_mult,
}]
if args.sgd:
optimizer = torch.optim.SGD(param_dicts,
lr=args.lr,
momentum=0.9,
weight_decay=args.weight_decay)
else:
optimizer = torch.optim.AdamW(param_dicts,
lr=args.lr,
weight_decay=args.weight_decay)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, args.lr_drop)
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.gpu], find_unused_parameters=True)
model_without_ddp = model.module
if args.dataset_file == "coco_panoptic":
# We also evaluate AP during panoptic training, on original coco DS
coco_val = datasets.coco.build("val", args)
base_ds = get_coco_api_from_dataset(coco_val)
else:
base_ds = get_coco_api_from_dataset(dataset_val)
if args.frozen_weights is not None:
checkpoint = torch.load(args.frozen_weights, map_location='cpu')
model_without_ddp.detr.load_state_dict(checkpoint['model'])
output_dir = Path(args.output_dir)
if args.resume:
if args.resume.startswith('https'):
checkpoint = torch.hub.load_state_dict_from_url(args.resume,
map_location='cpu',
check_hash=True)
else:
checkpoint = torch.load(args.resume, map_location='cpu')
missing_keys, unexpected_keys = model_without_ddp.load_state_dict(
checkpoint['model'], strict=False)
unexpected_keys = [
k for k in unexpected_keys
if not (k.endswith('total_params') or k.endswith('total_ops'))
]
if len(missing_keys) > 0:
print('Missing Keys: {}'.format(missing_keys))
if len(unexpected_keys) > 0:
print('Unexpected Keys: {}'.format(unexpected_keys))
if not args.eval and 'optimizer' in checkpoint and 'lr_scheduler' in checkpoint and 'epoch' in checkpoint:
import copy
p_groups = copy.deepcopy(optimizer.param_groups)
optimizer.load_state_dict(checkpoint['optimizer'])
for pg, pg_old in zip(optimizer.param_groups, p_groups):
pg['lr'] = pg_old['lr']
pg['initial_lr'] = pg_old['initial_lr']
print(optimizer.param_groups)
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
# todo: this is a hack for doing experiment that resume from checkpoint and also modify lr scheduler (e.g., decrease lr in advance).
args.override_resumed_lr_drop = True
if args.override_resumed_lr_drop:
print(
'Warning: (hack) args.override_resumed_lr_drop is set to True, so args.lr_drop would override lr_drop in resumed lr_scheduler.'
)
lr_scheduler.step_size = args.lr_drop
lr_scheduler.base_lrs = list(
map(lambda group: group['initial_lr'],
optimizer.param_groups))
lr_scheduler.step(lr_scheduler.last_epoch)
args.start_epoch = checkpoint['epoch'] + 1
# check the resumed model
# if not args.eval:
# test_stats, coco_evaluator, _ = evaluate(
# model, criterion, postprocessors, data_loader_val, base_ds, device, args.output_dir
# )
if args.eval:
assert args.batch_size == 1, print("Now only support 1.")
tracker = Tracker(score_thresh=args.track_thresh)
test_stats, coco_evaluator, res_tracks = evaluate(model,
criterion,
postprocessors,
data_loader_val,
base_ds,
device,
args.output_dir,
tracker=tracker,
phase='eval',
det_val=args.det_val,
fp16=args.fp16)
if args.output_dir:
# utils.save_on_master(coco_evaluator.coco_eval["bbox"].eval, output_dir / "eval.pth")
if res_tracks is not None:
print("Creating video index for {}.".format(args.dataset_file))
video_to_images = defaultdict(list)
video_names = defaultdict()
for _, info in dataset_val.coco.imgs.items():
video_to_images[info["video_id"]].append({
"image_id":
info["id"],
"frame_id":
info["frame_id"]
})
video_name = info["file_name"].split("/")[0]
if video_name not in video_names:
video_names[info["video_id"]] = video_name
assert len(video_to_images) == len(video_names)
# save mot results.
save_track(res_tracks, args.output_dir, video_to_images,
video_names, args.track_eval_split)
return
print("Start training")
start_time = time.time()
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
sampler_train.set_epoch(epoch)
train_stats = train_one_epoch(model,
criterion,
data_loader_train,
optimizer,
device,
scaler,
epoch,
args.clip_max_norm,
fp16=args.fp16)
lr_scheduler.step()
if args.output_dir:
checkpoint_paths = [output_dir / 'checkpoint.pth']
# extra checkpoint before LR drop and every 5 epochs
if (epoch + 1) % args.lr_drop == 0 or (epoch + 1) % 5 == 0:
checkpoint_paths.append(output_dir /
f'checkpoint{epoch:04}.pth')
for checkpoint_path in checkpoint_paths:
utils.save_on_master(
{
'model': model_without_ddp.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'epoch': epoch,
'args': args,
}, checkpoint_path)
log_stats = {
**{f'train_{k}': v
for k, v in train_stats.items()}, 'epoch': epoch,
'n_parameters': n_parameters
}
if epoch % 10 == 0 or epoch > args.epochs - 5:
test_stats, coco_evaluator, _ = evaluate(model,
criterion,
postprocessors,
data_loader_val,
base_ds,
device,
args.output_dir,
fp16=args.fp16)
log_test_stats = {
**{f'test_{k}': v
for k, v in test_stats.items()}
}
log_stats.update(log_test_stats)
if args.output_dir and utils.is_main_process():
with (output_dir / "log.txt").open("a") as f:
f.write(json.dumps(log_stats) + "\n")
# for evaluation logs
# if coco_evaluator is not None:
# (output_dir / 'eval').mkdir(exist_ok=True)
# if "bbox" in coco_evaluator.coco_eval:
# filenames = ['latest.pth']
# if epoch % 50 == 0:
# filenames.append(f'{epoch:03}.pth')
# for name in filenames:
# torch.save(coco_evaluator.coco_eval["bbox"].eval,
# output_dir / "eval" / name)
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))
"""
link = "https://github.com/patillacode/twallery"
sys.stderr.write('\nerror: {0}\n\n'.format(message))
self.print_help()
sys.stderr.write('\nPlease check the README or go to {0}\n\n'.format(
link))
sys.exit(2)
if __name__ == '__main__':
try:
parser = TrackParser()
mandatory = parser.add_argument_group("mandatory arguments")
mandatory.add_argument('--hashtags',
required=True,
nargs='*',
help="")
args = parser.parse_args()
# Create Tracker with given hashtags
tracker = Tracker(args.hashtags)
stream = tracker.authenticate()
# Capture data by the keywords
stream.filter(track=tracker.hashtags)
except (KeyboardInterrupt, SystemExit):
logger.debug("Farewell my friend!")
except:
logger.error(traceback.format_exc())
async def update_shape(sid, data):
sid_data = state.sid_map[sid]
user = sid_data["user"]
room = sid_data["room"]
location = sid_data["location"]
# We're first gonna retrieve the existing server side shape for some validation checks
if data["temporary"]:
# This stuff is not stored so we cannot do any server side validation /shrug
shape = data["shape"]
layer = location.layers.where(Layer.name == data["shape"]["layer"])[0]
else:
# Use the server version of the shape.
try:
shape = Shape.get(uuid=data["shape"]["uuid"])
except Shape.DoesNotExist:
logger.warning(f"Attempt to update unknown shape by {user.name}")
return
layer = shape.layer
# Ownership validatation
if room.creator != user:
if not layer.player_editable:
logger.warning(
f"{user.name} attempted to move a shape on a dm layer")
return
if data["temporary"]:
if user.name not in shape["owners"]:
logger.warning(
f"{user.name} attempted to move asset it does not own")
return
else:
if not ShapeOwner.get_or_none(shape=shape, user=user):
logger.warning(
f"{user.name} attempted to move asset it does not own")
return
# Overwrite the old data with the new data
if not data["temporary"]:
with db.atomic():
data["shape"]["layer"] = Layer.get(location=location,
name=data["shape"]["layer"])
# Shape
update_model_from_dict(shape,
reduce_data_to_model(Shape, data["shape"]))
shape.save()
# Subshape
type_table = get_table(shape.type_)
type_instance = type_table.get(uuid=shape.uuid)
# no backrefs on these tables
update_model_from_dict(type_instance,
data["shape"],
ignore_unknown=True)
type_instance.save()
# Owners
old_owners = {owner.user.name for owner in shape.owners}
new_owners = set(data["shape"]["owners"])
for owner in old_owners ^ new_owners:
if owner == "":
continue
delta_owner = User.by_name(owner)
if owner in new_owners:
ShapeOwner.create(shape=shape, user=delta_owner)
else:
ShapeOwner.get(shape=shape,
user=delta_owner).delete_instance(True)
await send_client_initiatives(room, location, delta_owner)
# Trackers
for tracker in data["shape"]["trackers"]:
tracker_db = Tracker.get_or_none(uuid=tracker['uuid'])
reduced = reduce_data_to_model(Tracker, tracker)
reduced['shape'] = shape
if tracker_db:
update_model_from_dict(tracker_db, reduced)
tracker_db.save()
else:
Tracker.create(**reduced)
# Auras
for aura in data["shape"]["auras"]:
aura_db = Aura.get_or_none(uuid=aura['uuid'])
reduced = reduce_data_to_model(Aura, aura)
reduced['shape'] = shape
if aura_db:
update_model_from_dict(aura_db, reduced)
aura_db.save()
else:
Aura.create(**reduced)
# Send to players
if layer.player_visible:
for room_player in room.players:
for psid in state.get_sids(user=room_player.player, room=room):
if psid == sid:
continue
if not data["temporary"]:
data["shape"] = shape.as_dict(room_player.player, False)
await sio.emit("Shape.Update",
data,
room=psid,
namespace="/planarally")
# Send to DM
for csid in state.get_sids(user=room.creator, room=room):
if csid == sid:
continue
if not data["temporary"]:
data["shape"] = shape.as_dict(room.creator, True)
await sio.emit("Shape.Update",
data,
room=csid,
namespace="/planarally")