def GetDataset(replay_memory: ReplayMemory, mem_lock: Lock,
batchsize: int) -> DataLoader:
datapoints = []
mem_lock.acquire()
for move_datapoint in replay_memory.memory:
player = move_datapoint.player
player_stone_map = GetPlayerStoneMap(move_datapoint.board_list, player)
opponent_stone_map = GetPlayerStoneMap(move_datapoint.board_list,
1 - player)
network_input = torch.Tensor([player_stone_map, opponent_stone_map])
value = torch.Tensor([move_datapoint.confidence])
policy = torch.Tensor(move_datapoint.policy)
datapoints.append((network_input, value, policy))
mem_lock.release()
return DataLoader(datapoints, batch_size=batchsize, shuffle=True)
def ReceivePlayouts(worker: socket.socket, worker_id: int,
replay_memory: ReplayMemory, mem_lock: Lock):
worker.setblocking(True)
while True:
try:
msg = communication.Receive(worker)
except Exception as err:
print(f"Error with worker {worker_id}, ending connection")
worker.close()
return
playout: List[MoveDatapoint] = pickle.loads(msg)
mem_lock.acquire()
for move_datapoint in playout:
replay_memory.push(move_datapoint)
mem_lock.release()
print(f"{len(playout)} new datapoints added from worker {worker_id}")
def main():
args = parse_args()
categories = parse_categories(parse_data(args.data)['names'])
cap = cv2.VideoCapture(0)
frame_queue = Queue()
preds_queue = Queue()
cur_dets = None
frame_lock = Lock()
proc = Process(target=detect,
args=(frame_queue, preds_queue, frame_lock, args))
proc.start()
try:
while (True):
ret, frame = cap.read()
frame_lock.acquire()
while not frame_queue.empty():
frame_queue.get()
frame_queue.put(frame)
frame_lock.release()
if not preds_queue.empty():
cur_dets = preds_queue.get()
if cur_dets is not None and len(cur_dets) > 0:
frame = draw_detections_opencv(frame, cur_dets[0], categories)
cv2.imshow('frame', frame)
cv2.waitKey(1)
except KeyboardInterrupt:
print('Interrupted')
proc.join()
cap.release()
cv2.destroyAllWindows()
class MultimodalPatchesCache(object):
def __init__(self,
cache_dir,
dataset_dir,
dataset_list,
cuda,
batch_size=500,
num_workers=3,
renew_frequency=5,
rejection_radius_position=0,
numpatches=900,
numneg=3,
pos_thr=50.0,
reject=True,
mode='train',
rejection_radius=3000,
dist_type='3D',
patch_radius=None,
use_depth=False,
use_normals=False,
use_silhouettes=False,
color_jitter=False,
greyscale=False,
maxres=4096,
scale_jitter=False,
photo_jitter=False,
uniform_negatives=False,
needles=0,
render_only=False,
maxitems=200,
cache_once=False):
super(MultimodalPatchesCache, self).__init__()
self.cache_dir = cache_dir
self.dataset_dir = dataset_dir
#self.images_path = images_path
self.dataset_list = dataset_list
self.cuda = cuda
self.batch_size = batch_size
self.num_workers = num_workers
self.renew_frequency = renew_frequency
self.rejection_radius_position = rejection_radius_position
self.numpatches = numpatches
self.numneg = numneg
self.pos_thr = pos_thr
self.reject = reject
self.mode = mode
self.rejection_radius = rejection_radius
self.dist_type = dist_type
self.patch_radius = patch_radius
self.use_depth = use_depth
self.use_normals = use_normals
self.use_silhouettes = use_silhouettes
self.color_jitter = color_jitter
self.greyscale = greyscale
self.maxres = maxres
self.scale_jitter = scale_jitter
self.photo_jitter = photo_jitter
self.uniform_negatives = uniform_negatives
self.needles = needles
self.render_only = render_only
self.cache_done_lock = Lock()
self.all_done = Value('B', 0) # 0 is False
self.cache_done = Value('B', 0) # 0 is False
self.wait_for_cache_builder = Event()
# prepare for wait until initial cache is built
self.wait_for_cache_builder.clear()
self.cache_builder_resume = Event()
self.maxitems = maxitems
self.cache_once = cache_once
if self.mode == 'eval':
self.maxitems = -1
self.cache_builder = Process(target=self.buildCache,
args=[self.maxitems])
self.current_cache_build = Value('B', 0) # 0th cache
self.current_cache_use = Value('B', 1) # 1th cache
self.cache_names = ["cache1", "cache2"] # constant
rebuild_cache = True
if self.mode == 'eval':
validation_dir = os.path.join(
self.cache_dir,
self.cache_names[self.current_cache_build.value])
if os.path.isdir(validation_dir):
# we don't need to rebuild validation cache
# TODO: check if cache is VALID
rebuild_cache = False
elif cache_once:
build_dataset_dir = os.path.join(
self.cache_dir,
self.cache_names[self.current_cache_build.value])
if os.path.isdir(build_dataset_dir):
# we don't need to rebuild training cache if we are training
# on limited subset of the training set
rebuild_cache = False
if rebuild_cache:
# clear the caches if they already exist
build_dataset_dir = os.path.join(
self.cache_dir,
self.cache_names[self.current_cache_build.value])
if os.path.isdir(build_dataset_dir):
shutil.rmtree(build_dataset_dir)
use_dataset_dir = os.path.join(
self.cache_dir, self.cache_names[self.current_cache_use.value])
if os.path.isdir(use_dataset_dir):
shutil.rmtree(use_dataset_dir)
os.makedirs(build_dataset_dir)
self.cache_builder_resume.set()
self.cache_builder.start()
# wait until initial cache is built
# print("before wait to build")
# print("wait for cache builder state",
# self.wait_for_cache_builder.is_set())
self.wait_for_cache_builder.wait()
# print("after wait to build")
# we have been resumed
if self.mode != 'eval' and (not self.cache_once):
# for training, we can set up the cache builder to build
# the second cache
self.restart()
else:
# else for validation we don't need second cache
# we just need to switch the built cache to the use cache in order
# to use it
tmp = self.current_cache_build.value
self.current_cache_build.value = self.current_cache_use.value
self.current_cache_use.value = tmp
# initialization finished, now this dataset can be used
def getCurrentCache(self):
# Lock should not be needed - cache_done is not touched
# and cache_len is read only for cache in use, which should not
# been touched by other threads
# self.cache_done_lock.acquire()
h5_dataset_filename = os.path.join(
self.cache_dir, self.cache_names[self.current_cache_use.value])
# self.cache_done_lock.release()
return h5_dataset_filename
def restart(self):
# print("Restarting - waiting for lock...")
self.cache_done_lock.acquire()
# print("Restarting cached dataset...")
if self.cache_done.value and (not self.cache_once):
cache_changed = True
tmp_cache_name = self.current_cache_use.value
self.current_cache_use.value = self.current_cache_build.value
self.current_cache_build.value = tmp_cache_name
# clear the old cache if exists
build_dataset_dir = os.path.join(
self.cache_dir,
self.cache_names[self.current_cache_build.value])
if os.path.isdir(build_dataset_dir):
shutil.rmtree(build_dataset_dir)
os.makedirs(build_dataset_dir)
self.cache_done.value = 0 # 0 is False
self.cache_builder_resume.set()
# print("Switched cache to: ",
# self.cache_names[self.current_cache_use.value]
# )
else:
cache_changed = False
# print(
# "New cache not ready, continuing with old cache:",
# self.cache_names[self.current_cache_use.value]
# )
all_done_value = self.all_done.value
self.cache_done_lock.release()
# returns true if no more items are available to be loaded
# this object should be destroyed and new dataset should be created
# in order to start over.
return cache_changed, all_done_value
def buildCache(self, limit):
# print("Building cache: ",
# self.cache_names[self.current_cache_build.value]
# )
dataset = MultimodalPatchesDatasetAll(
self.dataset_dir,
self.dataset_list,
rejection_radius_position=self.rejection_radius_position,
#self.images_path, list=train_sampled,
numpatches=self.numpatches,
numneg=self.numneg,
pos_thr=self.pos_thr,
reject=self.reject,
mode=self.mode,
rejection_radius=self.rejection_radius,
dist_type=self.dist_type,
patch_radius=self.patch_radius,
use_depth=self.use_depth,
use_normals=self.use_normals,
use_silhouettes=self.use_silhouettes,
color_jitter=self.color_jitter,
greyscale=self.greyscale,
maxres=self.maxres,
scale_jitter=self.scale_jitter,
photo_jitter=self.photo_jitter,
uniform_negatives=self.uniform_negatives,
needles=self.needles,
render_only=self.render_only)
n_triplets = len(dataset)
if limit == -1:
limit = n_triplets
dataloader = DataLoader(
dataset,
batch_size=self.batch_size,
shuffle=False,
pin_memory=False,
num_workers=1, # self.num_workers
collate_fn=MultimodalPatchesCache.my_collate)
qmaxsize = 15
data_queue = JoinableQueue(maxsize=qmaxsize)
# cannot load to cuda from background, therefore use cpu device
preloader_resume = Event()
preloader = Process(target=MultimodalPatchesCache.generateTrainingData,
args=(data_queue, dataset, dataloader,
self.batch_size, qmaxsize, preloader_resume,
True, True))
preloader.do_run_generate = True
preloader.start()
preloader_resume.set()
i_batch = 0
data = data_queue.get()
i_batch = data[0]
counter = 0
while i_batch != -1:
self.cache_builder_resume.wait()
build_dataset_dir = os.path.join(
self.cache_dir,
self.cache_names[self.current_cache_build.value])
batch_fname = os.path.join(build_dataset_dir,
'batch_' + str(counter) + '.pt')
# print("ibatch", i_batch,
# "___data___", data[3].shape, data[6].shape)
anchor = data[1]
pos = data[2]
neg = data[3]
anchor_r = data[4]
pos_p = data[5]
neg_p = data[6]
c1 = data[7]
c2 = data[8]
cneg = data[9]
id = data[10]
if not (self.use_depth or self.use_normals):
#no need to store image data as float, convert to uint
anchor = (anchor * 255.0).to(torch.uint8)
pos = (pos * 255.0).to(torch.uint8)
neg = (neg * 255.0).to(torch.uint8)
anchor_r = (anchor_r * 255.0).to(torch.uint8)
pos_p = (pos_p * 255.0).to(torch.uint8)
neg_p = (neg_p * 255.0).to(torch.uint8)
tosave = {
'anchor': anchor,
'pos': pos,
'neg': neg,
'anchor_r': anchor_r,
'pos_p': pos_p,
'neg_p': neg_p,
'c1': c1,
'c2': c2,
'cneg': cneg,
'id': id
}
try:
torch.save(tosave, batch_fname)
torch.load(batch_fname)
counter += 1
except Exception as e:
print("Could not save ",
batch_fname,
", due to:",
e,
"skipping...",
file=sys.stderr)
if os.path.isfile(batch_fname):
os.remove(batch_fname)
data_queue.task_done()
if counter >= limit:
self.cache_done_lock.acquire()
self.cache_done.value = 1 # 1 is True
self.cache_done_lock.release()
counter = 0
# sleep until calling thread wakes us
self.cache_builder_resume.clear()
# resume calling thread so that it can work
self.wait_for_cache_builder.set()
data = data_queue.get()
i_batch = data[0]
#print("ibatch", i_batch)
data_queue.task_done()
self.cache_done_lock.acquire()
self.cache_done.value = 1 # 1 is True
self.all_done.value = 1
print("Cache done ALL")
self.cache_done_lock.release()
# resume calling thread so that it can work
self.wait_for_cache_builder.set()
preloader.join()
preloader = None
data_queue = None
@staticmethod
def loadBatch(sample_batched, mode, device, keep_all=False):
if mode == 'eval':
coords1 = sample_batched[6]
coords2 = sample_batched[7]
coords_neg = sample_batched[8]
keep = sample_batched[10]
item_id = sample_batched[11]
else:
coords1 = sample_batched[6]
coords2 = sample_batched[7]
coords_neg = sample_batched[8]
keep = sample_batched[9]
item_id = sample_batched[10]
if keep_all:
# requested to return fill batch
batchsize = sample_batched[0].shape[0]
keep = torch.ones(batchsize).byte()
keep = keep.reshape(-1)
keep = keep.bool()
anchor = sample_batched[0]
pos = sample_batched[1]
neg = sample_batched[2]
# swapped photo to render
anchor_r = sample_batched[3]
pos_p = sample_batched[4]
neg_p = sample_batched[5]
anchor = anchor[keep].to(device)
pos = pos[keep].to(device)
neg = neg[keep].to(device)
anchor_r = anchor_r[keep]
pos_p = pos_p[keep]
neg_p = neg_p[keep]
coords1 = coords1[keep]
coords2 = coords2[keep]
coords_neg = coords_neg[keep]
item_id = item_id[keep]
return anchor, pos, neg, anchor_r, pos_p, neg_p, coords1, coords2, \
coords_neg, item_id
@staticmethod
def generateTrainingData(queue,
dataset,
dataloader,
batch_size,
qmaxsize,
resume,
shuffle=True,
disable_tqdm=False):
local_buffer_a = []
local_buffer_p = []
local_buffer_n = []
local_buffer_ar = []
local_buffer_pp = []
local_buffer_np = []
local_buffer_c1 = []
local_buffer_c2 = []
local_buffer_cneg = []
local_buffer_id = []
nbatches = 10
# cannot load to cuda in batckground process!
device = torch.device('cpu')
buffer_size = min(qmaxsize * batch_size, nbatches * batch_size)
bidx = 0
for i_batch, sample_batched in enumerate(dataloader):
# tqdm(dataloader, disable=disable_tqdm)
resume.wait()
anchor, pos, neg, anchor_r, \
pos_p, neg_p, c1, c2, cneg, id = \
MultimodalPatchesCache.loadBatch(
sample_batched, dataset.mode, device
)
if anchor.shape[0] == 0:
continue
local_buffer_a.extend(list(anchor)) # [:current_batches]
local_buffer_p.extend(list(pos))
local_buffer_n.extend(list(neg))
local_buffer_ar.extend(list(anchor_r))
local_buffer_pp.extend(list(pos_p))
local_buffer_np.extend(list(neg_p))
local_buffer_c1.extend(list(c1))
local_buffer_c2.extend(list(c2))
local_buffer_cneg.extend(list(cneg))
local_buffer_id.extend(list(id))
if len(local_buffer_a) >= buffer_size:
if shuffle:
local_buffer_a, local_buffer_p, local_buffer_n, \
local_buffer_ar, local_buffer_pp, local_buffer_np, \
local_buffer_c1, local_buffer_c2, local_buffer_cneg, \
local_buffer_id = sklearn.utils.shuffle(
local_buffer_a,
local_buffer_p,
local_buffer_n,
local_buffer_ar,
local_buffer_pp,
local_buffer_np,
local_buffer_c1,
local_buffer_c2,
local_buffer_cneg,
local_buffer_id
)
curr_nbatches = int(np.floor(len(local_buffer_a) / batch_size))
for i in range(0, curr_nbatches):
queue.put([
bidx,
torch.stack(local_buffer_a[:batch_size]),
torch.stack(local_buffer_p[:batch_size]),
torch.stack(local_buffer_n[:batch_size]),
torch.stack(local_buffer_ar[:batch_size]),
torch.stack(local_buffer_pp[:batch_size]),
torch.stack(local_buffer_np[:batch_size]),
torch.stack(local_buffer_c1[:batch_size]),
torch.stack(local_buffer_c2[:batch_size]),
torch.stack(local_buffer_cneg[:batch_size]),
torch.stack(local_buffer_id[:batch_size])
])
del local_buffer_a[:batch_size]
del local_buffer_p[:batch_size]
del local_buffer_n[:batch_size]
del local_buffer_ar[:batch_size]
del local_buffer_pp[:batch_size]
del local_buffer_np[:batch_size]
del local_buffer_c1[:batch_size]
del local_buffer_c2[:batch_size]
del local_buffer_cneg[:batch_size]
del local_buffer_id[:batch_size]
bidx += 1
remaining_batches = len(local_buffer_a) // batch_size
for i in range(0, remaining_batches):
queue.put([
bidx,
torch.stack(local_buffer_a[:batch_size]),
torch.stack(local_buffer_p[:batch_size]),
torch.stack(local_buffer_n[:batch_size]),
torch.stack(local_buffer_ar[:batch_size]),
torch.stack(local_buffer_pp[:batch_size]),
torch.stack(local_buffer_np[:batch_size]),
torch.stack(local_buffer_c1[:batch_size]),
torch.stack(local_buffer_c2[:batch_size]),
torch.stack(local_buffer_cneg[:batch_size]),
torch.stack(local_buffer_id[:batch_size])
])
del local_buffer_a[:batch_size]
del local_buffer_p[:batch_size]
del local_buffer_n[:batch_size]
del local_buffer_ar[:batch_size]
del local_buffer_pp[:batch_size]
del local_buffer_np[:batch_size]
del local_buffer_c1[:batch_size]
del local_buffer_c2[:batch_size]
del local_buffer_cneg[:batch_size]
del local_buffer_id[:batch_size]
ra = torch.randn(batch_size, 3, 64, 64)
queue.put([-1, ra, ra, ra])
queue.join()
@staticmethod
def my_collate(batch):
batch = list(filter(lambda x: x is not None, batch))
return default_collate(batch)
class lazy_array_loader(object):
"""
Arguments:
path: path to directory where array entries are concatenated into one big string file
and the .len file are located
data_type (str): Some datsets have multiple fields that are stored in different paths.
`data_type` specifies which of these fields to load in this class
mem_map (boolean): Specifies whether to memory map file `path`
map_fn (callable): Fetched strings are passed through map_fn before being returned.
Example of lazy loader directory structure:
file.json
file.lazy/
data_type1
data_type1.len.pkl
data_type2
data_type2.len.pkl
"""
def __init__(self, path, data_type='data', mem_map=False, map_fn=None):
lazypath = get_lazy_path(path)
datapath = os.path.join(lazypath, data_type)
#get file where array entries are concatenated into one big string
self._file = open(datapath, 'rb')
self.file = self._file
#memory map file if necessary
self.mem_map = mem_map
if self.mem_map:
self.file = mmap.mmap(self.file.fileno(), 0, prot=mmap.PROT_READ)
lenpath = os.path.join(lazypath, data_type+'.len.pkl')
self.lens = pkl.load(open(lenpath, 'rb'))
self.ends = list(accumulate(self.lens))
self.dumb_ends = list(self.ends)
self.read_lock = Lock()
self.process_fn = map_fn
self.map_fn = map_fn
self._tokenizer = None
def SetTokenizer(self, tokenizer):
"""
logic to set and remove (set to None) tokenizer.
combines preprocessing/tokenization into one callable.
"""
if tokenizer is None:
if not hasattr(self, '_tokenizer'):
self._tokenizer = tokenizer
else:
self._tokenizer = tokenizer
self.map_fn = ProcessorTokenizer(tokenizer, self.process_fn)
def GetTokenizer(self):
return self._tokenizer
def __getitem__(self, index):
"""
read file and splice strings based on string ending array `self.ends`
"""
if not isinstance(index, slice):
if index == 0:
start = 0
else:
start = self.ends[index-1]
end = self.ends[index]
rtn = self.file_read(start, end)
if self.map_fn is not None:
return self.map_fn(rtn)
else:
# if slice, fetch strings with 1 diskread and then splice in memory
chr_lens = self.ends[index]
if index.start == 0 or index.start is None:
start = 0
else:
start = self.ends[index.start-1]
stop = chr_lens[-1]
strings = self.file_read(start, stop)
rtn = split_strings(strings, start, chr_lens)
if self.map_fn is not None:
return self.map_fn([s for s in rtn])
return rtn
def __len__(self):
return len(self.ends)
def file_read(self, start=0, end=None):
"""read specified portion of file"""
# atomic reads to avoid race conditions with multiprocess dataloader
self.read_lock.acquire()
# seek to start of file read
self.file.seek(start)
# read to end of file if no end point provided
if end is None:
rtn = self.file.read()
#else read amount needed to reach end point
else:
rtn = self.file.read(end-start)
self.read_lock.release()
#TODO: @raulp figure out mem map byte string bug
#if mem map'd need to decode byte string to string
rtn = rtn.decode('utf-8', 'ignore')
# rtn = str(rtn)
if self.mem_map:
rtn = rtn.decode('unicode_escape')
return rtn
class Player(QWidget):
def __init__(self, external_mutex, parent=None):
super(Player, self).__init__(parent)
self.init_UI()
self.init_data()
self.init_process()
self.init_connect()
self.init_offline()
self.external_mutex = external_mutex
def init_UI(self):
self.layout1 = QtWidgets.QGridLayout() # 创建主部件的网格布局
self.label_screen = QtWidgets.QLabel(self) # 用于展示图片的label
self.btn_start = QtWidgets.QToolButton()
self.btn_pause = QtWidgets.QToolButton()
self.btn_close = QtWidgets.QToolButton()
self.btn_start.setIcon(QIcon('GUI/resources/icons/play.png'))
self.btn_start.setIconSize(QSize(30, 30))
self.btn_pause.setIcon(QIcon('GUI/resources/icons/pause.png'))
self.btn_pause.setIconSize(QSize(30, 30))
self.btn_close.setIcon(QIcon('GUI/resources/icons/close.png'))
self.btn_close.setIconSize(QSize(30, 30))
self.setStyleSheet('''
QToolButton{border:none;color:red;}
QToolButton#left_label{
border:none;
border-bottom:1px solid white;
font-size:18px;
font-weight:700;
font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
QToolButton#left_button:hover{border-left:4px solid red;font-weight:700;}
''')
self.label_info = QtWidgets.QLabel()
self.label_info.setText("[空闲]")
pe = QPalette()
pe.setColor(QPalette.WindowText, Qt.white)
self.label_info.setPalette(pe)
self.label_info.setFont(QFont("Microsoft YaHei", 10, QFont.Bold))
self.progressBar = QtWidgets.QSlider()
self.progressBar.setOrientation(QtCore.Qt.Horizontal)
self.layout1.addWidget(self.label_info, 0, 0, 1, 7)
self.layout1.addWidget(self.btn_close, 0, 7, 1, 1)
self.layout1.addWidget(self.label_screen, 1, 0, 7, 8)
self.layout1.addWidget(self.btn_start, 8, 0, 1, 1)
self.layout1.addWidget(self.btn_pause, 8, 1, 1, 1)
self.layout1.addWidget(self.progressBar, 8, 2, 1, 6)
self.bottom_widgets = [
self.btn_start, self.btn_pause, self.progressBar
]
self.label_screen.setScaledContents(True)
self.label_screen.setPixmap(QPixmap("GUI/resources/helmet.jpg"))
self.setLayout(self.layout1) # 设置窗口主部件布局为网格布局
def init_data(self):
self.is_working = False
self.semaphore = True
self.is_change_bar = Value(
c_bool, False) #whether user has dragged the slider,default: False
self.frame_index = Value('i', 0)
self.share_lock = Lock() #shared lock for frame_index
self.share_lock2 = Lock() # shared lock for frame_index
self.mutex = threading.Lock()
self.timer = QTimer(self) # used for the updating of progress bar
self.temp_timer = QTimer(
self) #used for detecting whether the frame_total is given.
self.frame_total = Value('i', -1)
self.playable = Value(c_bool, True)
self.is_working = Value(c_bool, False)
manager = Manager()
self.play_src = manager.Value(c_char_p, '0') #用于记录播放的视频地址
self.mode = None # 'online' or 'offline'
def init_connect(self):
self.btn_pause.clicked.connect(self.pause)
self.btn_close.clicked.connect(self.close)
self.btn_start.clicked.connect(self.play)
def init_offline_connect(self):
self.timer.timeout.connect(self.update_progressBar)
self.timer.start(50) # update the progressbar value every 50ms
self.temp_timer.timeout.connect(self.set_MaxValue)
self.temp_timer.start(50)
#progressbar
self.progressBar.sliderPressed.connect(
self.lockBar
) # when the user is dragging the slider, stop updating the value
self.progressBar.sliderReleased.connect(self.change_progressBar)
def init_online_connect(self):
pass
def init_process(self):
self.origin_img_q = mp.Queue(maxsize=2)
self.result_img_q = mp.Queue(maxsize=4)
self.p_detector = Process(target=detector,
args=(self.origin_img_q, self.result_img_q))
self.p_detector.start()
self.img_fetcher = Process(target=play,
args=(self.origin_img_q, self.frame_index,
self.share_lock, self.frame_total,
self.is_change_bar, self.playable,
self.is_working, self.play_src))
self.img_fetcher.start()
def lockBar(self):
self.share_lock.acquire()
self.mutex.acquire()
self.semaphore = False
self.mutex.release()
def change_progressBar(self):
self.frame_index.value = self.progressBar.value()
self.is_change_bar.value = True
self.share_lock.release()
self.mutex.acquire()
self.semaphore = True
self.mutex.release()
def set_MaxValue(self):
if self.frame_total.value is not -1: #只执行一次
self.progressBar.setMaximum(self.frame_total.value)
self.temp_timer.disconnect()
def update_progressBar(self):
self.mutex.acquire()
if self.semaphore:
self.progressBar.setValue(self.frame_index.value)
self.mutex.release()
def close(self):
self.is_working.value = False
self.label_screen.setPixmap(QPixmap("GUI/resources/helmet.jpg"))
time.sleep(0.1)
self.label_info.setText('空闲')
def play(self):
self.playable.value = True
temp_str = self.label_info.text()
if '[暂停]' in temp_str:
temp_str = temp_str.replace('[暂停]', '')
self.label_info.setText(temp_str)
def pause(self):
self.playable.value = False
temp_str = self.label_info.text()
if '[暂停]' not in temp_str:
temp_str = '[暂停]' + temp_str
self.label_info.setText(temp_str)
def display(self):
while True:
start_time = time.time()
if self.is_working.value:
self.external_mutex.acquire()
if not self.result_img_q.empty():
prev = time.time()
show = self.result_img_q.get()
post = time.time()
# print(datetime.timedelta(seconds=post - prev))
showImage = QImage(show.data, show.shape[1], show.shape[0],
QImage.Format_RGB888) # 转换成QImage类型
self.label_screen.setScaledContents(True)
self.label_screen.setPixmap(
QPixmap.fromImage(showImage)) #
print("FPS: ",
1.0 / (time.time() -
start_time)) # FPS = 1 / time to process loop
self.external_mutex.release()
else:
time.sleep(0.1)
def init_offline(self, video_path=None):
"""
:param video_path: 要离线播放的video地址
:return:
"""
self.init_offline_connect()
self.offline_video_thread = threading.Thread(target=self.display)
self.offline_video_thread.start()
def start_online(self, play_src):
self.play_src.value = play_src #path of online camera
play_src = '0'
self.label_info.setText('[在线模式]:' + play_src)
self.is_working.value = True
self.playable.value = True
# for i in self.bottom_widgets: # delete all the left_widgets and then replace them with new ones
# i.setVisible(False)
# self.layout1.removeWidget(i)
# self.layout1.addWidget(self.btn_start, 8, 2, 1, 1)
# self.layout1.addWidget(self.btn_pause, 8, 4, 1, 1)
# self.btn_start.setVisible(True)
# self.btn_pause.setVisible(True)
self.progressBar.setVisible(False)
def restart_online(self, play_src):
self.play_src.value = play_src
play_src = '0'
self.is_working.value = False
self.playable.value = False
self.label_info.setText('[在线模式]:' + play_src)
self.progressBar.setVisible(False)
time.sleep(0.1)
self.is_working.value = True
self.playable.value = True
def start_offline(self, play_src):
self.play_src.value = play_src # path of online camera
self.label_info.setText('[离线模式]:' + play_src)
self.is_working.value = True
self.playable.value = True
self.progressBar.setVisible(True)
def restart_offline(self, play_src):
self.play_src.value = play_src
self.is_working.value = False
self.playable.value = False
self.label_info.setText('[离线模式]:' + play_src)
self.progressBar.setVisible(True)
time.sleep(0.1)
self.is_working.value = True
self.playable.value = True
