class XioFigurePlot(QtGui.QWidget):
'''这个类为绘制类
'''
def __init__(self):
super(XioFigurePlot, self).__init__()
self.ui = ui.Ui_Form()
self.ui.setupUi(self)
self.thread_figure = Timer('updatePlay()', sleep_time=2)
self.connect(self.thread_figure, QtCore.SIGNAL('updatePlay()'), self.draw)
self.thread_figure.start()
def draw(self):
def draw_fp(): # 绘制损失饼图
fp = Figure_Pie()
da=data_access.EquipmentData()
result=da.select()
fp.plot(*(result[-1][1], result[-1][2], result[-1][3], result[-1][4])) # '*'有一个解包的功能，将（1，1，1，1）解包为 1 1 1 1
graphicscene_fp = QtGui.QGraphicsScene()
graphicscene_fp.addWidget(fp.canvas)
self.ui.graphicsView_Pie.setScene(graphicscene_fp)
self.ui.graphicsView_Pie.show()
def draw_oee(): # 绘制oee日推图
L_eff=[]
oee = Figure_OEE()
da=data_access.OEEData()
result=da.select()
for i in range(1,len(result[-1])):
if result[-1][i]!=None:
L_eff.append(result[-1][i])
oee.plot(*tuple(L_eff)) # 参数
graphicscene_oee = QtGui.QGraphicsScene()
graphicscene_oee.addWidget(oee.canvas)
self.ui.graphicsView_OEE.setScene(graphicscene_oee)
self.ui.graphicsView_OEE.show()
def draw_loss(): # 绘制损失直方图
loss = Figure_Loss()
da=data_access.EquipmentTimeData()
result = da.select()
loss.plot(*(result[-1][1], result[-1][2], result[-1][3], result[-1][4]))
graphicscene_loss = QtGui.QGraphicsScene()
graphicscene_loss.addWidget(loss.canvas)
self.ui.graphicsView_Loss.setScene(graphicscene_loss)
self.ui.graphicsView_Loss.show()
def draw_mt(): # 绘制耗材使用图
mt = Figure_MT()
mt.plot()
graphicscene_mt = QtGui.QGraphicsScene()
graphicscene_mt.addWidget(mt.canvas)
self.ui.graphicsView_MT.setScene(graphicscene_mt)
self.ui.graphicsView_MT.show()
draw_fp()
draw_loss()
draw_mt()
draw_oee()
class XioFigurePlot(QtGui.QWidget):
'''这个类为绘制类
'''
def __init__(self):
super(XioFigurePlot, self).__init__()
self.ui = ui.Ui_Form()
self.ui.setupUi(self)
self.thread_figure = Timer('updatePlay()', sleep_time=10)
self.connect(self.thread_figure, QtCore.SIGNAL('updatePlay()'), self.draw)
self.thread_figure.start()
def draw(self):
def draw_fp(): # 绘制损失饼图
fp = Figure_Pie()
fp.plot(*(1, 1, 1, 1)) # '*'有一个解包的功能，将（1，1，1，1）解包为 1 1 1 1
graphicscene_fp = QtGui.QGraphicsScene()
graphicscene_fp.addWidget(fp.canvas)
self.ui.graphicsView_Pie.setScene(graphicscene_fp)
self.ui.graphicsView_Pie.show()
def draw_oee(): # 绘制oee日推图
pass
def draw_loss(): # 绘制损失直方图
pass
def draw_mt(): # 绘制耗材使用图
pass
draw_fp()
def batch_size_linear_search():
min = 8
max = 600
step_size = 8
optimizer = lambda x: torch.optim.SGD(x, lr=0.1)
experiment_name = "batch_size_linear_search"
t = Timer()
batch_size_times = {}
for i, batch_size in enumerate(range(min, max, step_size)):
t.start()
main(experiment_name, optimizer, epochs=i + 2, batch_size=batch_size)
elapsed_time = t.stop()
batch_size_times[batch_size] = elapsed_time
pickle.dump(batch_size_times, open("batch_size_times.pickle", "wb"))
# Plot
batch_sizes = []
times = []
for k in sorted(batch_size_times):
batch_sizes.append(k)
times.append(batch_size_times[k])
plt.plot(np.array(batch_sizes), np.array(times))
plt.xlabel("Batch Size")
plt.ylabel("Epoch Time")
plt.title("Batch Size vs Epoch Time")
plt.show()
def __init__(self):
super(XioFigurePlot, self).__init__()
self.ui = ui.Ui_Form()
self.ui.setupUi(self)
self.thread_figure = Timer('updatePlay()', sleep_time=2)
self.connect(self.thread_figure, QtCore.SIGNAL('updatePlay()'), self.draw)
self.thread_figure.start()
def validate(self, loader, model, criterion, epoch, args):
timer = Timer()
losses = AverageMeter()
top1 = AverageMeter()
wtop1 = AverageMeter()
alloutputs = []
metrics = {}
# switch to evaluate mode
model.eval()
def part(x):
return itertools.islice(x, int(len(x) * args.val_size))
for i, x in enumerate(part(loader)):
inputs, target, meta = parse(x)
output, loss, weights = forward(inputs,
target,
model,
criterion,
meta['id'],
train=False)
prec1 = triplet_accuracy(output, target)
wprec1 = triplet_accuracy(output, target, weights)
losses.update(loss.item(), inputs[0].size(0))
top1.update(prec1, inputs[0].size(0))
wtop1.update(wprec1, inputs[0].size(0))
alloutputs.extend(
zip([(x.item(), y.item()) for x, y in zip(*output)], target,
weights))
timer.tic()
if i % args.print_freq == 0:
print('[{name}] Test [{epoch}]: [{0}/{1} ({2})]\t'
'Time {timer.val:.3f} ({timer.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Acc@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'WAcc@1 {wtop1.val:.3f} ({wtop1.avg:.3f})\t'.format(
i,
int(len(loader) * args.val_size),
len(loader),
name=args.name,
timer=timer,
loss=losses,
top1=top1,
epoch=epoch,
wtop1=wtop1))
metrics.update(triplet_allk(*zip(*alloutputs)))
metrics.update({'top1val': top1.avg, 'wtop1val': wtop1.avg})
print(
' * Acc@1 {top1val:.3f} \t WAcc@1 {wtop1val:.3f}'
'\n topk1: {topk1:.3f} \t topk2: {topk2:.3f} \t '
'topk5: {topk5:.3f} \t topk10: {topk10:.3f} \t topk50: {topk50:.3f}'
.format(**metrics))
return metrics
def minhash_lsh_dedupe_cassandra(batch_minhashes_pickle_path, lsh_pickle_path,
tqdm_func, global_tqdm):
# [(file_id, [doc0_minhash, doc1_minhash, ...]), ....]
batch_minhashes = timed_pickle_load(batch_minhashes_pickle_path,
"batch minhashes")
# For some reason this will freeze when loading on the first run.
lsh = timed_pickle_load(lsh_pickle_path, "lsh")
checkpoint_file = batch_minhashes_pickle_path.replace(".pkl", "_ckpt.pkl")
if os.path.exists(checkpoint_file):
ckpt_file_id, ckpt_document_id = pickle.load(
open(checkpoint_file, "rb"))
else:
ckpt_file_id = -1
ckpt_document_id = -1
logger.info("Detecting duplicates")
timer = Timer().start()
duplicate_file_path = batch_minhashes_pickle_path.replace(
".pkl", "_duplicates.txt")
with open(duplicate_file_path, "a") as fh:
for file_id, documents in batch_minhashes:
if file_id <= ckpt_file_id:
global_tqdm.update(len(documents))
continue
for document_id, minhash in enumerate(documents):
if document_id <= ckpt_document_id:
global_tqdm.update(ckpt_document_id + 1)
ckpt_document_id = -1
continue
results = lsh.query(minhash)
duplicate_found = True if results else False
is_self = False
for json_results in results:
found_file_id, found_document_id = json.loads(json_results)
# This check is needed in case you re-run things
if file_id == found_file_id and document_id == found_document_id:
duplicate_found = False
is_self = True
break
if duplicate_found:
fh.write(f"{file_id} {document_id}\n")
else:
if not is_self:
lsh.insert(json.dumps((file_id, document_id)), minhash)
global_tqdm.update()
pickle.dump((file_id, document_id),
open(checkpoint_file, "wb"))
logger.info(timer.stop_string())
return True
def train(self, loader, model, criterion, optimizer, epoch, args):
adjust_learning_rate(args.lr, args.lr_decay_rate, optimizer, epoch)
timer = Timer()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
wtop1 = AverageMeter()
metrics = {}
# switch to train mode
model.train()
optimizer.zero_grad()
def part(x):
return itertools.islice(x, int(len(x) * args.train_size))
for i, x in enumerate(part(loader)):
inputs, target, meta = parse(x)
data_time.update(timer.thetime() - timer.end)
output, loss, weights = forward(inputs, target, model, criterion,
meta['id'])
prec1 = triplet_accuracy(output, target)
wprec1 = triplet_accuracy(output, target, weights)
losses.update(loss.item(), inputs[0].size(0))
top1.update(prec1, inputs[0].size(0))
wtop1.update(wprec1, inputs[0].size(0))
loss.backward()
if i % args.accum_grad == args.accum_grad - 1:
print('updating parameters')
optimizer.step()
optimizer.zero_grad()
timer.tic()
if i % args.print_freq == 0:
print('[{name}] Epoch: [{0}][{1}/{2}({3})]\t'
'Time {timer.val:.3f} ({timer.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Acc@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'WAcc@1 {wtop1.val:.3f} ({wtop1.avg:.3f})\t'.format(
epoch,
i,
int(len(loader) * args.train_size),
len(loader),
name=args.name,
timer=timer,
data_time=data_time,
loss=losses,
top1=top1,
wtop1=wtop1))
metrics.update({'top1': top1.avg, 'wtop1': wtop1.avg})
return metrics
def validate_egovideo(self, loader, model, epoch, args):
""" Run video-level validation on the Charades ego test set"""
timer = Timer()
outputs, gts, ids = [], [], []
outputsw = []
metrics = {}
# switch to evaluate mode
model.eval()
for i, x in enumerate(loader):
inp, target, meta = parse(x)
target = target.long().cuda(async=True)
assert target[0, :].eq(target[1, :]).all(), "val_video not synced"
input_var = torch.autograd.Variable(inp.cuda(), volatile=True)
output, w_x, w_z = model(input_var)
output = torch.nn.Softmax(dim=1)(output)
sw_x = torch.nn.Softmax(dim=0)(w_x) * w_x.shape[0]
sw_x = (sw_x - sw_x.mean()) / sw_x.std()
scale = torch.clamp(1 + (sw_x - 1) * 0.05, 0, 100)
print('scale min: {}\t max: {}\t std: {}'.format(
scale.min().data[0],
scale.max().data[0],
scale.std().data[0]))
scale = torch.clamp(scale, 0, 100)
scale *= scale.shape[0] / scale.sum()
outputw = output * scale.unsqueeze(1)
# store predictions
output_video = output.mean(dim=0)
outputs.append(output_video.data.cpu().numpy())
outputsw.append(outputw.mean(dim=0).data.cpu().numpy())
gts.append(target[0, :])
ids.append(meta['id'][0])
timer.tic()
if i % args.print_freq == 0:
print('Test2: [{0}/{1}]\t'
'Time {timer.val:.3f} ({timer.avg:.3f})'.format(
i, len(loader), timer=timer))
# mAP, _, ap = meanap.map(np.vstack(outputs), np.vstack(gts))
mAP, _, ap = meanap.charades_nanmap(np.vstack(outputs), np.vstack(gts))
mAPw, _, _ = meanap.charades_nanmap(np.vstack(outputsw),
np.vstack(gts))
metrics['mAPego'] = mAP
metrics['mAPegow'] = mAPw
print(ap)
print(' * mAPego {mAPego:.3f} \t mAPegow {mAPegow:.3f}'.format(
**metrics))
submission_file(ids, outputs,
'{}/egoepoch_{:03d}.txt'.format(args.cache, epoch + 1))
return metrics
def train_model(device, model, train_set_loader, optimizer):
timer = Timer().start()
model.train() # For special layers
total = 0
correct = 0
total_loss = 0
for images, targets in train_set_loader:
total += images.shape[0]
optimizer.zero_grad()
images = images.to(device, non_blocking=True)
targets = targets.to(device, non_blocking=True)
output = model(images)
loss = F.cross_entropy(output, targets, reduction='mean')
total_loss += torch.sum(loss)
loss.backward()
optimizer.step()
# logger.info(f"Batch Loss: {loss}")
_, predicted = torch.max(output.data, 1)
correct += predicted.eq(targets.data).cpu().sum()
average_train_loss = total_loss / total
accuracy = 100. * correct.item() / total
logger.info(
f"Training Took {timer.stop():0.2f}s. Images in epoch: {total} ")
return average_train_loss, accuracy
def evaluate(config,
model,
dataset_loader,
eval_metric,
split='dev',
dump=True):
timer = Timer()
metrics = MultiLabelMetric(config.num_class,
thresholds=config.metrics_thresholds)
eval_metric.clear()
progress_bar = tqdm(dataset_loader)
for idx, batch in enumerate(progress_bar):
batch_labels = batch['label']
predict_results = model.predict(batch)
batch_label_scores = predict_results['scores']
batch_labels = batch_labels.cpu().detach().numpy()
batch_label_scores = batch_label_scores.cpu().detach().numpy()
metrics.add_batch(batch_labels, batch_label_scores)
eval_metric.add_batch(batch_labels, batch_label_scores)
if not config.display_iter or idx % config.display_iter == 0:
last_metrics = metrics.get_metrics()
progress_bar.set_postfix(**last_metrics)
log.info(f'Time for evaluating {split} set = {timer.time():.2f} (s)')
print(eval_metric)
metrics = eval_metric.get_metrics()
if dump:
dump_log(config, metrics, split)
return metrics
def __init__(self):
super(XioAll, self).__init__()
self.ui = ui.Ui_Form()
self.ui.setupUi(self)
self.frame_left = None
self.frame_right = None
self.is_work = True
self.one_static_time = 0 # 一次故障静止的时间
self.all_time = 0 # 一天的工作时间
self.q = MyQueue() # 存放帧队列,改为存放状态比较好
self.vision = Vision()
# 若日期发生改变，自行插入全零数据
da = data_access.EquipmentTimeData() # 对损失项统计表进行操作
result_loss = da.select_("select * from loss ORDER BY SJ DESC limit 1")
current_time = datetime.datetime.now().strftime('%Y-%m-%d')
if str(result_loss[0][0]) != current_time:
da.update('insert into loss(SJ,action1,action2,action3,action4,action5,action6)values'
'("%s",%d,%d,%d,%d,%d,%d)' % (current_time, 0, 0, 0, 0, 0, 0))
else:
pass
da_oee = data_access.OEEData() # 对oee实时利用率进行统计
result_oee = da_oee.select_('select * from oee_date ORDER BY SJC DESC limit 1')
if str(result_oee[0][0]) != current_time:
da_oee.update_('insert into oee_date(SJC,O8,O9,O10,O11,O12,O13,O14,O15,O16,O17,O18)values'
'("' + current_time + '",0,0,0,0,0,0,0,0,0,0,0)')
else:
pass
self.thread_figure = Timer('updatePlay()', sleep_time=120) # 该线程用来每隔2分钟刷新绘图区
self.connect(self.thread_figure, QtCore.SIGNAL('updatePlay()'), self.draw)
self.thread_figure.start()
self.server = ThreadedTCPServer((self.HOST, self.PORT), ThreadedTCPRequestHandler) # 该线程用来一直监听客户端的请求
self.server_thread = threading.Thread(target=self.server.serve_forever)
self.server_thread.start()
self.thread_video_receive = threading.Thread(target=self.video_receive_local) # 该线程用来读取视频流
self.thread_video_receive.start()
self.thread_time = Timer('updatePlay()') # 该线程用来每隔0.04秒在label上绘图
self.connect(self.thread_time, QtCore.SIGNAL('updatePlay()'), self.video_play)
self.thread_time.start()
self.thread_recog = Timer('updatePlay()', sleep_time=1) # 该线程用来每隔一秒分析图像
self.connect(self.thread_recog, QtCore.SIGNAL('updatePlay()'), self.video_recog)
self.thread_recog.start()
self.thread_data = Timer('updatePlay()', sleep_time=1800) # 该线程用来每隔半小时向数据库读取数据
self.connect(self.thread_data, QtCore.SIGNAL('updatePlay()'), self.data_read)
self.thread_data.start()
def validate_video(self, loader, model, epoch, args):
""" Run video-level validation on the Charades test set"""
timer = Timer()
outputs, gts, ids = [], [], []
metrics = {}
# switch to evaluate mode
model.eval()
for i, x in enumerate(loader):
inputs, target, meta = parse(x)
target = target.long().cuda(async=True)
assert target[0, :].eq(target[1, :]).all(), "val_video not synced"
input_vars = [
torch.autograd.Variable(inp.cuda(), volatile=True)
for inp in inputs
]
output = model(
*input_vars)[-1] # classification should be last output
output = torch.nn.Softmax(dim=1)(output)
# store predictions
output_video = output.mean(dim=0)
outputs.append(output_video.data.cpu().numpy())
gts.append(target[0, :])
ids.append(meta['id'][0])
timer.tic()
if i % args.print_freq == 0:
print('Test2: [{0}/{1}]\t'
'Time {timer.val:.3f} ({timer.avg:.3f})'.format(
i, len(loader), timer=timer))
# mAP, _, ap = meanap.map(np.vstack(outputs), np.vstack(gts))
mAP, _, ap = meanap.charades_map(np.vstack(outputs), np.vstack(gts))
metrics['mAP'] = mAP
print(ap)
print(' * mAP {:.3f}'.format(mAP))
submission_file(ids, outputs,
'{}/epoch_{:03d}.txt'.format(args.cache, epoch + 1))
return metrics
def __init__(self):
super(XioPlayVideo, self).__init__()
self.ui = ui.Ui_Form()
self.ui.setupUi(self)
self.left_cam = cv2.VideoCapture('./videos/left_cam.mp4') # 左摄像头
self.right_cam = cv2.VideoCapture('./videos/right_cam.mp4')
self.frame_left = None
self.frame_right = None
self.tcpServer = QTcpServer() # tcp 服务器端
if not self.tcpServer.listen(QHostAddress.LocalHost, 8888):
print(self.tcpServer.errorString())
self.close()
self.connect(self.tcpServer, QtCore.SIGNAL('newConnection()'), self.read_message)
self.thread_video_receive = threading.Thread(target=self.video_receive_local) # 该线程用来读取视频流
self.thread_video_receive.start()
self.thread_time = Timer('updatePlay()') # 该线程用来每隔0.04秒在label上绘图
self.connect(self.thread_time, QtCore.SIGNAL('updatePlay()'), self.video_play)
self.thread_time.start()
self.thread_recog = Timer('updatePlay()', sleep_time=1) # 该线程用来每隔一秒分析图像
self.connect(self.thread_recog, QtCore.SIGNAL('updatePlay()'), self.video_recog)
self.thread_recog.start()
self.thread_data = Timer('updatePlay()', sleep_time=1800) # 该线程用来每隔半小时向数据库读取数据
self.connect(self.thread_data, QtCore.SIGNAL('updatePlay()'), self.data_read)
self.thread_data.start()
self.thread_tcp = None # 该线程用来完成tcp，未写完
def __init__(self):
super(XioPlayVideo, self).__init__()
self.ui = ui.Ui_Form()
self.ui.setupUi(self)
self.left_cam = cv2.VideoCapture('./videos/left_cam.mp4') # 左摄像头
self.right_cam = cv2.VideoCapture('./videos/right_cam.mp4')
self.frame_left = None
self.frame_right = None
self.thread_video_receive = threading.Thread(
target=self.video_receive_local) # 该线程用来读取视频流
self.thread_video_receive.start()
self.thread_time = Timer('updatePlay()') # 该线程用来每隔0.04秒在label上绘图
self.connect(self.thread_time, QtCore.SIGNAL('updatePlay()'),
self.video_play)
self.thread_time.start()
self.thread_recog = Timer('updatePlay()',
sleep_time=1) # 该线程用来每隔一秒分析图像
self.connect(self.thread_recog, QtCore.SIGNAL('updatePlay()'),
self.video_recog)
self.thread_recog.start()
self.thread_data = Timer('updatePlay()',
sleep_time=1800) # 该线程用来每隔半小时向数据库读取数据
self.connect(self.thread_data, QtCore.SIGNAL('updatePlay()'),
self.data_read)
self.thread_data.start()
self.thread_tcp = None # 该线程用来完成tcp，未写完
def fine_tune_train_and_val(args, recorder):
# =
global lowest_val_loss, best_prec1
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # close the warning
torch.manual_seed(1)
cudnn.benchmark = True
timer = Timer()
# == dataset config==
num_class, data_length, image_tmpl = ft_data_config(args)
train_transforms, test_transforms, eval_transforms = ft_augmentation_config(
args)
train_data_loader, val_data_loader, _, _, _, _ = ft_data_loader_init(
args, data_length, image_tmpl, train_transforms, test_transforms,
eval_transforms)
# == model config==
model = ft_model_config(args, num_class)
recorder.record_message('a', '=' * 100)
recorder.record_message('a', '-' * 40 + 'finetune' + '-' * 40)
recorder.record_message('a', '=' * 100)
# == optim config==
train_criterion, val_criterion, optimizer = ft_optim_init(args, model)
# == data augmentation(self-supervised) config==
tc = TC(args)
# == train and eval==
print('*' * 70 + 'Step2: fine tune' + '*' * 50)
for epoch in range(args.ft_start_epoch, args.ft_epochs):
timer.tic()
ft_adjust_learning_rate(optimizer, args.ft_lr, epoch, args.ft_lr_steps)
train_prec1, train_loss = train(args, tc, train_data_loader, model,
train_criterion, optimizer, epoch,
recorder)
# train_prec1, train_loss = random.random() * 100, random.random()
recorder.record_ft_train(train_loss / 5.0, train_prec1 / 100.0)
if (epoch + 1) % args.ft_eval_freq == 0:
val_prec1, val_loss = validate(args, tc, val_data_loader, model,
val_criterion, recorder)
# val_prec1, val_loss = random.random() * 100, random.random()
recorder.record_ft_val(val_loss / 5.0, val_prec1 / 100.0)
is_best = val_prec1 > best_prec1
best_prec1 = max(val_prec1, best_prec1)
checkpoint = {
'epoch': epoch + 1,
'arch': "i3d",
'state_dict': model.state_dict(),
'best_prec1': best_prec1
}
recorder.save_ft_model(checkpoint, is_best)
timer.toc()
left_time = timer.average_time * (args.ft_epochs - epoch)
message = "Step2: fine tune best_prec1 is: {} left time is : {} now is : {}".format(
best_prec1, timer.format(left_time), datetime.now())
print(message)
recorder.record_message('a', message)
return recorder.filename
def alignment(loader, model, epoch, args, task=best_one_sec_moment):
timer = Timer()
abssec = MedianMeter()
abssec0 = MedianMeter()
randsec = MedianMeter()
model = ActorObserverFC7(model)
# switch to evaluate mode
model.eval()
def fc7_generator():
for i, x in enumerate(loader):
inputs, target, meta = parse(x)
target = target.long().cuda(async=True)
input_vars = [
torch.autograd.Variable(inp.cuda(), volatile=True)
for inp in inputs
]
first_fc7, third_fc7, w_x, w_y = model(*input_vars)
timer.tic()
if i % args.print_freq == 0:
print('Alignment: [{0}/{1}]\t'
'Time {timer.val:.3f} ({timer.avg:.3f})'.format(
i, len(loader), timer=timer))
for vid, o1, o2 in zip(meta['id'], first_fc7, third_fc7):
yield vid, (o1.data.cpu().numpy(), o2.data.cpu().numpy())
for key, grp in groupby(fc7_generator(), key=lambda x: x[0]):
print('processing id: {}'.format(key))
_, mat = fc7list2mat(grp)
_, _, _, j, gt = task(mat, winsize=3)
_, _, _, j0, gt0 = task(mat, winsize=0)
_, _, _, jr, gtr = task(np.random.randn(*mat.shape), winsize=3)
abssec.update(abs(j - gt))
abssec0.update(abs(j0 - gt0))
randsec.update(abs(jr - gtr))
print(
' abs3: {abs3.val:.3f} ({abs3.avg:.3f}) [{abs3.med:.3f}]'
' abs0: {abs0.val:.3f} ({abs0.avg:.3f}) [{abs0.med:.3f}]'
'\n'
' absr: {absr.val:.3f} ({absr.avg:.3f}) [{absr.med:.3f}]'.format(
abs3=abssec, abs0=abssec0, absr=randsec))
return abssec.med
def train_epoch(self, data_loader):
"""Run through one epoch of model training with the provided data loader."""
train_loss = AverageMeter()
metrics = MultiLabelMetric(self.config.num_class)
epoch_time = Timer()
progress_bar = tqdm(data_loader)
for idx, batch in enumerate(progress_bar):
loss, batch_label_scores = self.train_step(batch)
train_loss.update(loss)
# training metrics
batch_labels = batch['label'].cpu().detach().numpy()
batch_label_scores = batch_label_scores.cpu().detach().numpy()
metrics.add_batch(batch_labels, batch_label_scores)
progress_bar.set_postfix(loss=train_loss.avg)
log.info(metrics.get_metrics())
log.info(f'Epoch done. Time for epoch = {epoch_time.time():.2f} (s)')
log.info(f'Epoch loss: {train_loss.avg}')
def test_model(device, model, test_set_loader, optimizer):
timer = Timer().start()
model.eval() # For special layers
total = 0
correct = 0
with torch.no_grad():
for images, targets in test_set_loader:
total += images.shape[0]
images = images.to(device, non_blocking=True)
targets = targets.to(device, non_blocking=True)
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
correct += predicted.eq(targets.data).cpu().sum()
accuracy = 100. * correct.item() / total
logger.info(f"Testing Took {timer.stop():0.2f}s. Images in epoch: {total}")
return accuracy
def load_hcpcs_corpus(debug=False):
corpus_file = 'debug-corpus.npy' if debug else 'corpus.npy'
corpus_output = os.path.join(proj_dir, 'data', corpus_file)
partb_file = 'partb-2012.csv.gz' if debug else 'partb-2012-2018.csv.gz'
partb_output = os.path.join(proj_dir, 'data', partb_file)
# load from disk if exists
if os.path.isfile(corpus_output):
print(f'Loading corpus from disk {corpus_output}')
corpus = np.load(corpus_output, allow_pickle=True)
return corpus
# load Medicare Data
timer = Timer()
data = load_data(data_dir, partb_output, debug)
print(f'Loaded data in {timer.lap()}')
# clean missing values
data.dropna(subset=['hcpcs', 'count'], inplace=True)
# generate sequences of HCPCS codes
# that occur in the same context
grouped_hcpcs = data \
.sort_values(by='count') \
.groupby(by=['year', 'npi'])['hcpcs'] \
.agg(list)
grouped_hcpcs = pd.DataFrame(grouped_hcpcs)
print(f'Generated hcpcs sequences in {timer.lap()}')
# drop top 1 percent longest sequences
quantile = 0.99
grouped_hcpcs['seq_length'] = grouped_hcpcs['hcpcs'].agg(len)
max_seq_length = grouped_hcpcs['seq_length'].quantile(quantile)
grouped_hcpcs = grouped_hcpcs.loc[
grouped_hcpcs['seq_length'] <= max_seq_length]
print(f'Removed sequences longer than {max_seq_length}')
# save corpus
np.save(corpus_output, grouped_hcpcs['hcpcs'].values)
return grouped_hcpcs['hcpcs'].values
def train(loader, D, G, optim_D, optim_G, criterion):
G_losses = [0]
D_losses = [0]
timer = Timer()
for i in range(1, config.num_epoch + 1):
iters = 0
for data in loader:
current_size = data.size(0)
labels0 = torch.tensor([0] * current_size).to(
config.device, torch.long)
labels1 = torch.tensor([1] * current_size).to(
config.device, torch.long)
noise = torch.randn(
(current_size, config.latent_size, 1, 1)).to(config.device)
D_loss = D_train(data, D, G, optim_D, criterion, current_size,
labels0, labels1, noise)
G_loss = G_train(D, G, optim_G, criterion, current_size, labels0,
labels1, noise)
iters += 1
D_losses.append(D_loss)
G_losses.append(G_loss)
if iters % config.log_iter == 0:
timer.save_batch_time()
log_batch_history(i, iters, len(loader), D_losses, G_losses,
timer)
save_model(i, G, optim_G, D, optim_D)
timer.save_epoch_time()
log_epoch_history(i, len(loader), D_losses, G_losses, timer)
if i % config.make_img_samples == 0:
for x in range(5):
make_img_samples(G)
import re
import numpy as np
from sklearn.neighbors import NearestNeighbors
from gensim.models import Word2Vec
from utils.utils import replace_umlauts, Timer
from utils.utils import raw_freq
model_path = "/media/echobot/Volume/home/simon/uni/masterarbeit/de/model/01/my.model"
model = Word2Vec.load_word2vec_format(model_path, binary=True)
w2v = {w: vec for w,vec in zip(model.index2word, model.syn0)}
with Timer('Loading model from %s' % model_path):
model = Word2Vec.load_word2vec_format(model_path, binary=True)
dataset_path = "/media/echobot/Volume/home/simon/uni/masterarbeit/data/business_signals_samples/fuehrungswechsel.txt"
dataset_path += ".corpus"
with open(dataset_path, 'r') as f:
W = [w.decode('utf-8') for line in f for w in line.split()]
X = [w2v[w] for sentence in W for w in W]
V = {w.decode for w in W}
X = np.array(X)
# with Timer("Calculating nearest neighbors... "):
# nbrs = NearestNeighbors(n_neighbors=5, algorithm='ball_tree').fit(V)
# distances, indices = nbrs.kneighbors(X)
def __init__(self):
super(XioAll, self).__init__()
self.ui = ui.Ui_Form()
self.ui.setupUi(self)
self.frame_left = None
self.frame_right = None
self.is_work = True
self.stype = 0
self.one_static_time = 0 # 一次故障静止的时间
self.all_time = 0 # 一天的工作时间
self.q = MyQueue() # 存放帧队列,改为存放状态比较好
self.vision = Vision()
# 控制输入视频地址
self.CamPath = ""
self.isWebCam = False
self.isCamChanged = False
# 数据库操作
self.da = data_access.DataAccess()
# 若日期发生改变，自行插入全零数据
result_loss = self.da.select_(
"select * from loss ORDER BY SJ DESC limit 1")
current_time = datetime.datetime.now().strftime('%Y-%m-%d')
if str(result_loss[0][0]) != current_time:
self.da.operate_(
'insert into loss(SJ,action1,action2,action3,action4,action5,action6)values'
'("%s",%d,%d,%d,%d,%d,%d)' %
(current_time, 10, 10, 10, 10, 0, 0))
else:
pass
result_oee = self.da.select_(
'select * from oee_date ORDER BY SJC DESC limit 1')
if str(result_oee[0][0]) != current_time:
self.da.operate_(
'insert into oee_date(SJC,O8,O9,O10,O11,O12,O13,O14,O15,O16,O17,O18)values'
'("' + current_time + '",0,0,0,0,0,0,0,0,0,0,0)')
else:
pass
self.yolo_Model = Yolo_Model.Yolo_Model()
# self.displayMessage("...加载YOLO模型成功...")
self.thread_figure = Timer('updatePlay()',
sleep_time=120) # 该线程用来每隔2分钟刷新绘图区
self.connect(self.thread_figure, QtCore.SIGNAL('updatePlay()'),
self.draw)
self.thread_figure.start()
# 按钮功能
self.connect(self.ui.fileSelectButton, QtCore.SIGNAL('clicked()'),
self.fileSelect)
self.connect(self.ui.mailSenderButton, QtCore.SIGNAL('clicked()'),
self.mailSend)
self.connect(self.ui.confirmDateButton, QtCore.SIGNAL('clicked()'),
self.displayMonthData)
self.connect(self.ui.WebCamButton, QtCore.SIGNAL('clicked()'),
self.webCamInput)
self.server = ThreadedTCPServer(
(self.HOST, self.PORT),
ThreadedTCPRequestHandler) # 该线程用来一直监听客户端的请求
self.server_thread = threading.Thread(target=self.server.serve_forever)
self.server_thread.start()
self.thread_video_receive = threading.Thread(
target=self.video_receive_local) # 该线程用来读取视频流
self.thread_video_receive.start()
self.thread_time = Timer('updatePlay()') # 该线程用来每隔0.04秒在label上绘图
self.connect(self.thread_time, QtCore.SIGNAL('updatePlay()'),
self.video_play)
self.thread_time.start()
self.thread_recog = Timer('updatePlay()',
sleep_time=1) # 该线程用来每隔一秒分析图像
self.connect(self.thread_recog, QtCore.SIGNAL('updatePlay()'),
self.video_recog)
self.thread_recog.start()
self.thread_data = Timer('updatePlay()',
sleep_time=1800) # 该线程用来每隔半小时向数据库读取数据
self.connect(self.thread_data, QtCore.SIGNAL('updatePlay()'),
self.data_read)
self.thread_data.start()
self.thread_shumei = threading.Thread(target=self.shumeiDeal)
self.thread_shumei.start()
self.thread_control = Timer('updatePlay()',
sleep_time=10) # 该线程用来每隔半小时向数据库读取数据
self.connect(self.thread_control, QtCore.SIGNAL('updatePlay()'),
self.control_judge)
self.thread_control.start()
# 12-25
self.thread_recogtiaoshi = Timer('updatePlay()',
sleep_time=0.3) # 该线程用来每隔0.3秒分析图像
self.connect(self.thread_recogtiaoshi, QtCore.SIGNAL('updatePlay()'),
self.video_recogtiaoshi)
self.thread_recogtiaoshi.start()
self.thread_recogzhuangji = Timer('updatePlay()',
sleep_time=0.3) # 该线程用来每隔0.3秒分析图像
self.connect(self.thread_recogzhuangji, QtCore.SIGNAL('updatePlay()'),
self.video_recogzhuangji)
self.thread_recogzhuangji.start()
self.X_l = 0
self.Y_l = 0
self.type_l = ""
self.flag = 0
self.a = 0
self.tiaoshi_back = False
self.tiaoshi_forward = False
self.X_r = 0
self.Y_r = 0
self.type_r = ""
self.firstFrame = None
self.chaiji_left = False
self.chaiji_right = False
self.cltime = 0
self.crtime = 0
self.totaltime = 0
# 用于面板进行输出
self.work_time = 0
self.tf_time = 0
self.tb_time = 0
self.Ldown = [0] * 10
self.Lup = [0] * 10 # 队列操作
self.Lhandsdown = [0] * 10
self.Lhandsup = [0] * 10
self.isJudgeMachineT = True
# 装机操作
self.mask_right = cv2.imread(
"E:/projects-summary/xiaowork/maindo/images/zhuangjiimages/right.jpg"
)
self.mask_left = cv2.imread(
"E:/projects-summary/xiaowork/maindo/images/zhuangjiimages/maskleft.jpg"
)
self.left_base = cv2.imread(
"E:/projects-summary/xiaowork/maindo/images/zhuangjiimages/left_base.jpg",
0)
self.redLower = np.array([26, 43, 46])
self.redUpper = np.array([34, 255, 255])
self.Lright = [0] * 10
self.Lleft = [0] * 10
self.is_JudgeRL = True
self.isRightStart = False
self.isLeftStart = False
self.zhuangjitime = 0
# 调试操作
self.status_LUP = [0] * 8
self.status_LDOWN = [0] * 8
self.isActionStartUP = False
self.isActionStartDOWN = False
self.x1UP, self.y1UP, self.x2UP, self.y2UP = [0, 0, 0, 0]
self.X1DOWN, self.Y1DOWN, self.X2DOWN, self.Y2DOWN = [0, 0, 0, 0]
# 定时投入文字
self.putTextStart_time = None
self.putTextEnd_time_left = None
self.putTextEnd_time_right = None
self.putTextEnd_time_up = None
self.putTextEnd_time_down = None
class XioAll(QtGui.QWidget):
'''这个类为主程序类
'''
HOST = 'localhost'
PORT = 8081
TOTAL = 0
isStatic = True
Shumei = None
action = None
pre_action = None
action_video = None # 视频内能识别
pre_action_video = None
def __init__(self):
super(XioAll, self).__init__()
self.ui = ui.Ui_Form()
self.ui.setupUi(self)
self.frame_left = None
self.frame_right = None
self.is_work = True
self.stype = 0
self.one_static_time = 0 # 一次故障静止的时间
self.all_time = 0 # 一天的工作时间
self.q = MyQueue() # 存放帧队列,改为存放状态比较好
self.vision = Vision()
# 控制输入视频地址
self.CamPath = ""
self.isWebCam = False
self.isCamChanged = False
# 数据库操作
self.da = data_access.DataAccess()
# 若日期发生改变，自行插入全零数据
result_loss = self.da.select_(
"select * from loss ORDER BY SJ DESC limit 1")
current_time = datetime.datetime.now().strftime('%Y-%m-%d')
if str(result_loss[0][0]) != current_time:
self.da.operate_(
'insert into loss(SJ,action1,action2,action3,action4,action5,action6)values'
'("%s",%d,%d,%d,%d,%d,%d)' %
(current_time, 10, 10, 10, 10, 0, 0))
else:
pass
result_oee = self.da.select_(
'select * from oee_date ORDER BY SJC DESC limit 1')
if str(result_oee[0][0]) != current_time:
self.da.operate_(
'insert into oee_date(SJC,O8,O9,O10,O11,O12,O13,O14,O15,O16,O17,O18)values'
'("' + current_time + '",0,0,0,0,0,0,0,0,0,0,0)')
else:
pass
self.yolo_Model = Yolo_Model.Yolo_Model()
# self.displayMessage("...加载YOLO模型成功...")
self.thread_figure = Timer('updatePlay()',
sleep_time=120) # 该线程用来每隔2分钟刷新绘图区
self.connect(self.thread_figure, QtCore.SIGNAL('updatePlay()'),
self.draw)
self.thread_figure.start()
# 按钮功能
self.connect(self.ui.fileSelectButton, QtCore.SIGNAL('clicked()'),
self.fileSelect)
self.connect(self.ui.mailSenderButton, QtCore.SIGNAL('clicked()'),
self.mailSend)
self.connect(self.ui.confirmDateButton, QtCore.SIGNAL('clicked()'),
self.displayMonthData)
self.connect(self.ui.WebCamButton, QtCore.SIGNAL('clicked()'),
self.webCamInput)
self.server = ThreadedTCPServer(
(self.HOST, self.PORT),
ThreadedTCPRequestHandler) # 该线程用来一直监听客户端的请求
self.server_thread = threading.Thread(target=self.server.serve_forever)
self.server_thread.start()
self.thread_video_receive = threading.Thread(
target=self.video_receive_local) # 该线程用来读取视频流
self.thread_video_receive.start()
self.thread_time = Timer('updatePlay()') # 该线程用来每隔0.04秒在label上绘图
self.connect(self.thread_time, QtCore.SIGNAL('updatePlay()'),
self.video_play)
self.thread_time.start()
self.thread_recog = Timer('updatePlay()',
sleep_time=1) # 该线程用来每隔一秒分析图像
self.connect(self.thread_recog, QtCore.SIGNAL('updatePlay()'),
self.video_recog)
self.thread_recog.start()
self.thread_data = Timer('updatePlay()',
sleep_time=1800) # 该线程用来每隔半小时向数据库读取数据
self.connect(self.thread_data, QtCore.SIGNAL('updatePlay()'),
self.data_read)
self.thread_data.start()
self.thread_shumei = threading.Thread(target=self.shumeiDeal)
self.thread_shumei.start()
self.thread_control = Timer('updatePlay()',
sleep_time=10) # 该线程用来每隔半小时向数据库读取数据
self.connect(self.thread_control, QtCore.SIGNAL('updatePlay()'),
self.control_judge)
self.thread_control.start()
# 12-25
self.thread_recogtiaoshi = Timer('updatePlay()',
sleep_time=0.3) # 该线程用来每隔0.3秒分析图像
self.connect(self.thread_recogtiaoshi, QtCore.SIGNAL('updatePlay()'),
self.video_recogtiaoshi)
self.thread_recogtiaoshi.start()
self.thread_recogzhuangji = Timer('updatePlay()',
sleep_time=0.3) # 该线程用来每隔0.3秒分析图像
self.connect(self.thread_recogzhuangji, QtCore.SIGNAL('updatePlay()'),
self.video_recogzhuangji)
self.thread_recogzhuangji.start()
self.X_l = 0
self.Y_l = 0
self.type_l = ""
self.flag = 0
self.a = 0
self.tiaoshi_back = False
self.tiaoshi_forward = False
self.X_r = 0
self.Y_r = 0
self.type_r = ""
self.firstFrame = None
self.chaiji_left = False
self.chaiji_right = False
self.cltime = 0
self.crtime = 0
self.totaltime = 0
# 用于面板进行输出
self.work_time = 0
self.tf_time = 0
self.tb_time = 0
self.Ldown = [0] * 10
self.Lup = [0] * 10 # 队列操作
self.Lhandsdown = [0] * 10
self.Lhandsup = [0] * 10
self.isJudgeMachineT = True
# 装机操作
self.mask_right = cv2.imread(
"E:/projects-summary/xiaowork/maindo/images/zhuangjiimages/right.jpg"
)
self.mask_left = cv2.imread(
"E:/projects-summary/xiaowork/maindo/images/zhuangjiimages/maskleft.jpg"
)
self.left_base = cv2.imread(
"E:/projects-summary/xiaowork/maindo/images/zhuangjiimages/left_base.jpg",
0)
self.redLower = np.array([26, 43, 46])
self.redUpper = np.array([34, 255, 255])
self.Lright = [0] * 10
self.Lleft = [0] * 10
self.is_JudgeRL = True
self.isRightStart = False
self.isLeftStart = False
self.zhuangjitime = 0
# 调试操作
self.status_LUP = [0] * 8
self.status_LDOWN = [0] * 8
self.isActionStartUP = False
self.isActionStartDOWN = False
self.x1UP, self.y1UP, self.x2UP, self.y2UP = [0, 0, 0, 0]
self.X1DOWN, self.Y1DOWN, self.X2DOWN, self.Y2DOWN = [0, 0, 0, 0]
# 定时投入文字
self.putTextStart_time = None
self.putTextEnd_time_left = None
self.putTextEnd_time_right = None
self.putTextEnd_time_up = None
self.putTextEnd_time_down = None
def fileSelect(self):
absolute_path = QFileDialog.getOpenFileName(self, '视频选择', '.',
"MP4 files (*.mp4)")
if absolute_path is not "":
self.reFlushDetection()
self.CamPath = absolute_path
self.isWebCam = False
self.isCamChanged = True
else:
self.displayMessage("...未进行选择，视频源路径不变...")
def webCamInput(self):
webCamDict = {"address": "", "status": ""}
webCamBox = WebCamBox("网络摄像头管理", webCamDict)
# 处理主动关闭输入框
if webCamBox.exec_():
return
if webCamDict["status"] == "":
return
ret = False
try:
cap = cv2.VideoCapture(webCamDict["address"])
ret, frame = cap.read()
except Exception as e:
raise e
finally:
if ret is True:
self.CamPath = webCamDict["address"]
self.isWebCam = True
self.isCamChanged = True
self.reFlushDetection()
self.displayMessage("...更换网络摄像头成功...")
else:
if webCamDict["status"] != "WrongPassword":
self.displayMessage("...IP地址错误，请重新输入...")
def reFlushDetection(self):
self.X_l = 0
self.Y_l = 0
self.type_l = ""
self.flag = 0
self.a = 0
self.tiaoshi_back = False
self.tiaoshi_forward = False
self.X_r = 0
self.Y_r = 0
self.type_r = ""
self.firstFrame = None
self.chaiji_left = False
self.chaiji_right = False
self.cltime = 0
self.crtime = 0
self.totaltime = 0
# 用于面板进行输出
self.work_time = 0
self.tf_time = 0
self.tb_time = 0
self.Ldown = [0] * 10
self.Lup = [0] * 10 # 队列操作
self.Lhandsdown = [0] * 10
self.Lhandsup = [0] * 10
self.isJudgeMachineT = True
self.tiaoshitime = 0
self.Lright = [0] * 10
self.Lleft = [0] * 10
self.is_JudgeRL = True
self.isRightStart = False
self.isLeftStart = False
self.zhuangjitime = 0
self.status_LUP = [0] * 10
self.status_LDOWN = [0] * 15
self.isActionStartUP = False
self.isActionStartDOWN = False
# 定时投入文字
self.putTextStart_time = None
self.putTextEnd_time_left = None
self.putTextEnd_time_right = None
self.putTextEnd_time_up = None
self.putTextEnd_time_down = None
self.displayMessage("...初始化检测参数成功...")
def mailSend(self):
list_mail = []
dilogUi = warningBox(u"邮件发送", u"请输入邮箱：", list_mail)
if dilogUi.exec_():
return
if len(list_mail) == 0:
return
if len(list_mail[0]) != 0:
print("准备发送！")
list_oee = self.da.select_oee()
list_loss = self.da.select_loss()
dict_oee = {}
hour = min(time.localtime()[3], 18)
for i in range(8, hour + 1):
dict_oee[str(i) + "点"] = list_oee[i - 8]
sender = '*****@*****.**'
list_mail.append("*****@*****.**")
message = "侧板焊接生产线生产数据\n" \
"\n" \
"今日OEE效能数据如下所示：\n" \
"{}" \
"\n" \
"\n" \
"*注：效率为0时未进行检测。\n" \
"\n" \
"今日设备运行情况分布如下所示：" \
"\n" \
"清理焊嘴：{} \n" \
"装载侧板：{} \n" \
"机器静止：{} \n" \
"机器工作：{} \n".format(dict_oee, list_loss[0], list_loss[1], list_loss[2], list_loss[3])
msg_wait = MIMEText(message, 'plain', 'utf-8')
try:
smtpObj = smtplib.SMTP()
smtpObj.connect("smtp.qq.com", 25)
mail_license = "wuhchbmndrjabgcc"
print("准备登录")
smtpObj.login(sender, mail_license)
print("登录成功！")
smtpObj.set_debuglevel(1)
smtpObj.sendmail(sender, list_mail, msg_wait.as_string())
except Exception as e:
print(e)
def displayMonthData(self):
self.ui.DateTable.clear()
# 获取月份
select_date = self.ui.dateEdit.text()
queryByMonth = "select * from oee_date where date_format(SJC,'%Y-%m')='{}'".format(
select_date)
# 取数据正常
result = self.da.select_(queryByMonth)
row = len(result)
if row == 0:
self.ui.DateTable.setRowCount(1)
self.ui.DateTable.setColumnCount(1)
self.ui.DateTable.setEditTriggers(
QtGui.QTableWidget.NoEditTriggers)
self.ui.DateTable.horizontalHeader().setResizeMode(
QtGui.QHeaderView.Stretch)
newItem = QtGui.QTableWidgetItem(
" 日期 {} 暂无数据".format(
select_date)) # 接受str，无法接收int
textFont = QtGui.QFont("song", 16, QtGui.QFont.Bold)
newItem.setFont(textFont)
self.ui.DateTable.setItem(0, 0, newItem)
else:
# 表格属性
self.ui.DateTable.setRowCount(row)
self.ui.DateTable.setColumnCount(12)
self.ui.DateTable.setHorizontalHeaderLabels([
'日期', '8时', '9时', '10时', '11时', '12时', '13时', '14时', '15时',
'16时', '17时', '18时'
])
self.ui.DateTable.setEditTriggers(
QtGui.QTableWidget.NoEditTriggers)
self.ui.DateTable.horizontalHeader().setResizeMode(
QtGui.QHeaderView.Stretch)
# 数据处理
for i in range(row):
list_data = list(result[i])
for j in range(12):
if j == 0:
cnt = str(list_data[j])[5:10]
else:
cnt = str(int(list_data[j]))
newItem = QtGui.QTableWidgetItem(cnt) # 接受str，无法接收int
textFont = QtGui.QFont("song", 12, QtGui.QFont.Bold)
newItem.setFont(textFont)
self.ui.DateTable.setItem(i, j, newItem)
def control_judge(self):
pass
def video_recogtiaoshi(self):
if self.isWebCam:
return
frame = self.frame_left
frameDown = frame[250:500, 680:970]
# 上方坐标
frameUP = frame[140:400, 540:800]
# 根据队列进行检测
isPersonUP, self.x1UP, self.y1UP, self.x2UP, self.y2UP = self.yolo_Model.detect_person(
frameUP)
if isPersonUP:
self.status_LUP.append(1)
else:
self.status_LUP.append(0)
self.status_LUP.pop(0)
isPersonDOWN, self.X1DOWN, self.Y1DOWN, self.X2DOWN, self.Y2DOWN = self.yolo_Model.detect_person(
frameDown)
if isPersonDOWN:
self.status_LDOWN.append(1)
else:
self.status_LDOWN.append(0)
self.status_LDOWN.pop(0)
if sum(self.status_LUP) > 5 and self.isActionStartUP is False:
self.displayMessage("工人上方开始清理焊嘴")
self.isActionStartUP = True
self.putTextStart_time = time.time()
self.da.insert_action_("qinglihanzuiUP", 0)
if sum(self.status_LUP) < 2 and self.isActionStartUP is True:
self.displayMessage("工人上方结束清理焊嘴")
self.isActionStartUP = False
self.putTextEnd_time_up = time.time()
self.da.insert_action_("qinglihanzuiUP", 1)
self.da.update_loss_("action1", 1)
self.da.update_loss_("action3", random.randint(0, 2))
if sum(self.status_LDOWN) > 5 and self.isActionStartDOWN is False:
self.displayMessage("工人下方开始清理焊嘴")
self.isActionStartDOWN = True
self.putTextStart_time = time.time()
self.da.insert_action_("qinglihanzuiDOWN", 0)
if sum(self.status_LDOWN) == 0 and self.isActionStartDOWN is True:
self.displayMessage("工人下方结束清理焊嘴")
self.isActionStartDOWN = False
self.putTextEnd_time_down = time.time()
self.da.insert_action_("qinglihanzuiDOWN", 1)
self.da.update_loss_("action1", 1)
self.da.update_loss_("action3", random.randint(0, 2))
def video_recogzhuangji(self):
if self.isWebCam:
return
img = self.frame_left
img = cv2.resize(img, (1280, 720))
img_right = cv2.bitwise_and(self.mask_right, img)
hsv_right = cv2.cvtColor(img_right, cv2.COLOR_BGR2HSV)
mask_det = cv2.inRange(hsv_right, self.redLower, self.redUpper)
img_left = cv2.bitwise_and(self.mask_left, img)
hsv_left = cv2.cvtColor(img_left, cv2.COLOR_BGR2HSV)
mask_det1 = cv2.inRange(hsv_left, self.redLower, self.redUpper)
if self.is_JudgeRL is True:
if np.sum(mask_det) < 10000:
self.Lright.append(1)
else:
self.Lright.append(0)
self.Lright.pop(0)
if sum(self.Lright) > 6 and self.isRightStart is False:
self.displayMessage("工人开始右方装载侧板")
self.isRightStart = True
self.putTextStart_time = time.time()
self.da.insert_action_("zhuangjiRIGHT", 0)
if sum(self.Lright) < 2 and self.isRightStart is True:
self.displayMessage("工人结束右方装载侧板")
self.isRightStart = False
self.putTextEnd_time_right = time.time()
self.da.insert_action_("zhuangjiRIGHT", 1)
self.da.update_loss_("action2", 1)
if np.sum(mask_det1) < 50000:
self.Lleft.append(1)
else:
self.Lleft.append(0)
self.Lleft.pop(0)
if sum(self.Lleft) > 6 and self.isLeftStart is False:
self.displayMessage("工人开始左方装载侧板")
self.isLeftStart = True
self.putTextStart_time = time.time()
self.da.insert_action_("zhuangjiLEFT", 0)
if sum(self.Lleft) < 2 and self.isLeftStart is True:
self.displayMessage("工人结束左方装载侧板")
self.isLeftStart = False
self.putTextEnd_time_left = time.time()
self.da.insert_action_("zhuangjiLEFT", 1)
self.da.update_loss_("action2", 1)
def shumeiDeal(self):
global Stype
while True:
if Stype == 1 and self.stype == 0:
message = '[' + time.strftime(
'%Y-%m-%d %H:%M:%S', time.localtime(
time.time())) + ']' + "******" + "工人吃饭！"
self.displayMessage(message)
self.stype = 1
if Stype == 2 and self.stype == 0:
message = '[' + time.strftime(
'%Y-%m-%d %H:%M:%S', time.localtime(
time.time())) + ']' + "******" + "5s保养"
self.displayMessage(message)
self.stype = 2
if Stype == 3 and self.stype == 0:
message = '[' + time.strftime(
'%Y-%m-%d %H:%M:%S', time.localtime(
time.time())) + ']' + "******" + ""
self.displayMessage(message)
self.stype = 3
if Stype == 4 and self.stype == 0:
message = '[' + time.strftime(
'%Y-%m-%d %H:%M:%S', time.localtime(
time.time())) + ']' + "******" + "工人吃饭！"
self.displayMessage(message)
self.stype = 4
if Stype == 5 and self.stype == 0:
message = '[' + time.strftime(
'%Y-%m-%d %H:%M:%S', time.localtime(
time.time())) + ']' + "******" + "工人吃饭！"
self.displayMessage(message)
self.stype = 5
if Stype == 6 and self.stype == 0:
message = '[' + time.strftime(
'%Y-%m-%d %H:%M:%S', time.localtime(
time.time())) + ']' + "******" + "工人吃饭！"
self.displayMessage(message)
self.stype = 6
if Stype == 0:
if self.stype == 1:
message = '[' + time.strftime(
'%Y-%m-%d %H:%M:%S', time.localtime(
time.time())) + ']' + "******" + "工人结束吃饭！"
self.stype = 0
self.displayMessage(message)
if self.stype == 2:
message = '[' + time.strftime(
'%Y-%m-%d %H:%M:%S', time.localtime(
time.time())) + ']' + "******" + "工人结束5s！"
self.stype = 0
self.displayMessage(message)
if self.stype == 3:
message = '[' + time.strftime(
'%Y-%m-%d %H:%M:%S', time.localtime(
time.time())) + ']' + "******" + "工人结束吃饭！"
self.stype = 0
self.displayMessage(message)
if self.stype == 4:
message = '[' + time.strftime(
'%Y-%m-%d %H:%M:%S', time.localtime(
time.time())) + ']' + "******" + "工人结束吃饭！"
self.stype = 0
self.displayMessage(message)
if self.stype == 5:
message = '[' + time.strftime(
'%Y-%m-%d %H:%M:%S', time.localtime(
time.time())) + ']' + "******" + "工人吃饭！"
self.stype = 0
self.displayMessage(message)
if self.stype == 6:
message = '[' + time.strftime(
'%Y-%m-%d %H:%M:%S', time.localtime(
time.time())) + ']' + "******" + "工人吃饭！"
self.stype = 0
self.displayMessage(message)
time.sleep(0.06)
def video_receive_local(
self,
cam1='E:/projects-summary/xiaowork/侧板焊接待检测视频/检测视频200519134451.mp4',
cam2='E:\\剪辑\\zhuangji\\ch11_20171221084313 00_09_06-00_10_21~2.mp4',
time_flag=True):
'''该方法用来接收本地视频
:param cam1: 左摄像头数据源
:param cam2: 右摄像头数据源
:param time_flag: 是否休眠，本地视频为True
:return: None
'''
self.left_cam = cv2.VideoCapture(cam1)
ret_1, frame_1 = self.left_cam.read()
# 无法重复播放
# preCamPath = cam1
# while True:
#
# self.frame_left = frame_1
# if ret_1 is False:
# self.left_cam = cv2.VideoCapture(cam1)
# if self.CamPath != "" and self.CamPath != preCamPath:
# self.left_cam = cv2.VideoCapture(self.CamPath)
# preCamPath = self.CamPath
# ret_1, frame_1 = self.left_cam.read()
# if time_flag is True:
# time.sleep(0.04)
# 优化版本
while True:
self.frame_left = frame_1
if ret_1 is False:
self.left_cam = cv2.VideoCapture(cam1)
if self.CamPath != "" and self.isCamChanged:
self.left_cam = cv2.VideoCapture(self.CamPath)
self.isCamChanged = False
ret_1, frame_1 = self.left_cam.read()
if time_flag is True:
time.sleep(0.04)
def video_receive_rstp(self, cam1='rstp:', cam2='rstp:'):
'''该方法用来接收网络视频
:param cam1: 左摄像头数据源
:param cam2: 右摄像头数据源
:return: None
'''
self.video_receive_local(cam1=cam1, cam2=cam2, time_flag=False)
def video_play(self):
'''该方法用来播放视频
:return: None
'''
def label_show_left(frame, label=self.ui.label): # 左控件label播放
height, width, _ = frame.shape
frame_change = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# if self.type_l == 'work':
# cv2.rectangle(frame_change, (self.X_l, self.Y_l), (self.X_l + 100, self.Y_l + 100), (0, 255, 0), 4)
frame_change = putChineseText.cv2ImgAddText(
frame_change, "生产操作行为的自动识别（侧板焊接车间）", 50, 30, (0, 0, 0), 50)
if self.isActionStartUP is True:
cv2.rectangle(frame_change, (540 + int(self.x1UP * 0.625),
140 + int(self.y1UP * 0.625)),
(540 + int(self.x2UP * 0.625),
140 + int(self.y2UP * 0.625)), (255, 0, 0), 6)
if time.time() - self.putTextStart_time > 0 and time.time(
) - self.putTextStart_time < 5:
frame_change = putChineseText.cv2ImgAddText(
frame_change, "工人开始在上方清理焊嘴", 140, 60)
if self.isActionStartDOWN is True:
cv2.rectangle(frame_change, (int(self.X1DOWN * 0.721) + 680,
int(self.Y1DOWN * 0.721) + 250),
(int(self.X2DOWN * 0.721) + 680,
int(self.Y2DOWN * 0.721) + 250), (255, 0, 0), 6)
if time.time() - self.putTextStart_time > 0 and time.time(
) - self.putTextStart_time < 5:
frame_change = putChineseText.cv2ImgAddText(
frame_change, "工人开始在下方清理焊嘴", 140, 60)
if self.isLeftStart is True:
if time.time() - self.putTextStart_time > 0 and time.time(
) - self.putTextStart_time < 5:
cv2.rectangle(frame_change, (0, 150), (300, 720),
(255, 255, 0), 6)
cv2.circle(frame_change, (150, 435), 6, (255, 0, 0), 20)
frame_change = putChineseText.cv2ImgAddText(
frame_change, "工人开始在左方装载侧板", 140, 60)
if self.isRightStart is True:
if time.time() - self.putTextStart_time > 0 and time.time(
) - self.putTextStart_time < 5:
cv2.rectangle(frame_change, (880, 100), (1080, 380),
(255, 255, 0), 6)
cv2.circle(frame_change, (980, 240), 6, (255, 0, 0), 20)
frame_change = putChineseText.cv2ImgAddText(
frame_change, "工人开始在右方装载侧板", 140, 60)
# 投入结束文字
if self.isLeftStart is False:
if self.putTextEnd_time_left is not None and time.time(
) - self.putTextEnd_time_left > 0 and time.time(
) - self.putTextEnd_time_left < 3:
frame_change = putChineseText.cv2ImgAddText(
frame_change, "工人结束左方装载侧板", 140, 60)
if self.isRightStart is False:
if self.putTextEnd_time_right is not None and time.time(
) - self.putTextEnd_time_right > 0 and time.time(
) - self.putTextEnd_time_right < 3:
frame_change = putChineseText.cv2ImgAddText(
frame_change, "工人结束右方装载侧板", 140, 60)
if self.isActionStartDOWN is False:
if self.putTextEnd_time_down is not None and time.time(
) - self.putTextEnd_time_down > 0 and time.time(
) - self.putTextEnd_time_down < 3:
frame_change = putChineseText.cv2ImgAddText(
frame_change, "工人结束下方清理焊嘴", 140, 60)
if self.isActionStartUP is False:
if self.putTextEnd_time_up is not None and time.time(
) - self.putTextEnd_time_up > 0 and time.time(
) - self.putTextEnd_time_up < 3:
frame_change = putChineseText.cv2ImgAddText(
frame_change, "工人结束上方清理焊嘴", 140, 60)
frame_resize = cv2.resize(frame_change, (360, 240),
interpolation=cv2.INTER_AREA)
image = QtGui.QImage(frame_resize.data, frame_resize.shape[1],
frame_resize.shape[0],
QtGui.QImage.Format_RGB888) # 处理成QImage
label.setPixmap(QtGui.QPixmap.fromImage(image))
if self.frame_left is not None:
label_show_left(self.frame_left)
def draw(self):
'''
展示图标
:return:
'''
def draw_fp(): # 绘制损失饼图
fp = Figure_Pie()
loss_data = self.da.select_loss()
sum1 = sum(loss_data)
loss_data /= sum1
fp.plot(*tuple(loss_data))
graphicscene_fp = QtGui.QGraphicsScene()
graphicscene_fp.addWidget(fp.canvas)
self.ui.graphicsView_Pie.setScene(graphicscene_fp)
self.ui.graphicsView_Pie.show()
def draw_oee(): # 绘制oee日推图
self.da.update_oee()
oee = Figure_OEE()
l_eff = self.da.select_oee()
oee.plot(*tuple(l_eff)) # 参数
graphicscene_oee = QtGui.QGraphicsScene()
graphicscene_oee.addWidget(oee.canvas)
self.ui.graphicsView_OEE.setScene(graphicscene_oee)
self.ui.graphicsView_OEE.show()
def draw_loss(): # 绘制损失直方图
loss = Figure_Loss()
loss_data = self.da.select_loss()
loss.plot(*tuple(loss_data))
graphicscene_loss = QtGui.QGraphicsScene()
graphicscene_loss.addWidget(loss.canvas)
self.ui.graphicsView_Loss.setScene(graphicscene_loss)
self.ui.graphicsView_Loss.show()
# def draw_mt(): # 绘制耗材使用图
# mt = Figure_MT()
# mt.plot(*(4, 5, 3))
# graphicscene_mt = QtGui.QGraphicsScene()
# graphicscene_mt.addWidget(mt.canvas)
# self.ui.graphicsView_MT.setScene(graphicscene_mt)
# self.ui.graphicsView_MT.show()
draw_fp()
draw_loss()
# draw_mt()
draw_oee()
def video_recog(self):
'''
视频识别部分
:return:
'''
if self.isWebCam:
return
self.totaltime += 1
frame_left = self.frame_left # 原始彩色图，左边摄像头
frame_left_gray = cv2.cvtColor(frame_left,
cv2.COLOR_BGR2GRAY) # 原始图的灰度图
def video_recog_left():
img = frame_left
spark, x, y = self.vision.find_spark(img)
self.q.enqueue(spark)
# print(spark)
if spark and x != 1070:
self.type_l = 'work'
self.X_l = x
self.Y_l = y
else:
self.type_l = ''
if spark or True in self.q.queue: # 如果一段间隔时间内不断有火花（和机器移动，稍后完成），则说明机器必定处于工作状态
self.one_static_time = 0 # 恢复到运动后，一次静止时间重新清零
self.work_time += 1
self.is_work = True
if self.work_time % 20 == 0:
if x != 1070:
self.displayMessage("机器正在工作")
if self.work_time % 60 == 0:
self.da.update_loss_("action4", 1)
else:
# ******* 截图
self.is_work = False
self.one_static_time += 1 # 一次静止时间
if self.one_static_time % 20 == 0:
self.da.update_loss_("action3", 1)
# ********
self.action = ThreadedTCPRequestHandler.action # 键盘操作
if self.action is not None: # 往面板上写当前由于什么原因导致机器静止
if self.pre_action is None:
pass
if self.action_video is not None:
if self.pre_action_video is None:
pass
video_recog_left()
self.pre_action = self.action
self.pre_action_video = self.action_video
def data_read(self):
pass
def displayMessage(self, message):
self.ui.textBrowser.append(
'[' +
time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())) +
'] ' + message)
l2_reg = float(l2_reg)
l1_reg = cli_args.get('l1_reg')
if l1_reg != None:
l1_reg = float(l1_reg)
print(f'Running job with arguments\n{cli_args}')
# define configs
train_perf_filename = 'train-results.csv'
test_perf_filename = 'test-results.csv'
n_estimators = 5 if debug else 100
print(f'n_estimators: {n_estimators}')
print(f'max_depth: {max_depth}')
# init timer
timer = Timer()
# iterate over runs
for run in range(runs):
print(f'Starting run {run}')
# load data
data = load_sampled_data(sample_size)
print(f'Loaded data with shape {data.shape}')
# drop columns, onehot encode, or lookkup embeddings
x, y = get_embedded_data(data, embedding_type, embedding_path,
drop_columns)
del data
print(f'Encoded data shape: {x.shape}')
# model config
window_size = int(cli_args.get('window_size', 5))
min_seq_length = int(cli_args.get('min_seq_length', 2))
embedding_size = int(cli_args.get('embedding_size', 300))
iters = int(cli_args.get('iters', 5))
desc = f'e{embedding_size}-w{window_size}-i{iters}-t{ts}'
# I/O
data_dir = os.environ['CMS_RAW']
curr_dir = os.path.join(proj_dir, 'cbow')
embeddings_output = os.path.join(proj_dir, 'embeddings', f'cbow-{desc}.kv')
loss_output = os.path.join(curr_dir, 'logs', f'train-loss-{desc}.csv')
time_output = os.path.join(curr_dir, 'logs', f'train-time-{desc}.csv')
# load corpus
timer = Timer()
corpus = load_hcpcs_corpus(debug)
print(f'Loaded corpus with length {len(corpus)} in {timer.lap()}')
# use sample for debug
if debug:
corpus = corpus[:500000]
print(f'Using sample of corpus with length {len(corpus)}')
# vocab size
vocab_size = get_vocab_size(corpus)
# loss and timing callback
callback = GensimEpochCallback(loss_output, time_output)
# train model
def main(experiment_name,
optimizer,
output_directory_root="experiments/resnet18_logistic_cifar10",
epochs=60,
batch_size=512,
num_workers=1):
output_directory = os.path.join(output_directory_root, experiment_name)
if not os.path.isdir(output_directory):
os.makedirs(output_directory, exist_ok=True)
# Setup regular log file + tensorboard
logfile_path = os.path.join(output_directory, "logfile.txt")
setup_logger_tqdm(logfile_path)
tensorboard_log_directory = os.path.join("runs",
"resnet18_logistic_cifar10",
experiment_name)
tensorboard_summary_writer = SummaryWriter(
log_dir=tensorboard_log_directory)
# Choose Training Device
use_cuda = torch.cuda.is_available()
logger.info(f"CUDA Available? {use_cuda}")
device = "cuda" if use_cuda else "cpu"
# Datasets and Loaders
train_set_loader, test_set_loader = get_data_loaders(
batch_size, num_workers)
# Create Model & Optimizer
model = torchvision.models.resnet18(pretrained=True)
for param in model.parameters():
param.requires_grad = False
num_classes = 10
model.fc = nn.Linear(model.fc.in_features, 10)
model.to(device)
optimizer = optimizer(model.parameters())
logger.info("=========== Commencing Training ===========")
logger.info(f"Epoch Count: {epochs}")
logger.info(f"Batch Size: {batch_size}")
# Load Checkpoint
checkpoint_file_path = os.path.join(output_directory, "checkpoint.pth")
start_epoch = 0
if os.path.exists(checkpoint_file_path):
logger.info("Checkpoint Found - Loading!")
checkpoint = torch.load(checkpoint_file_path)
logger.info(f"Last completed epoch: {checkpoint['epoch']}")
logger.info(f"Average Train Loss: {checkpoint['train_loss']}")
logger.info(f"Top-1 Train Accuracy: {checkpoint['train_accuracy']}")
logger.info(f"Top-1 Test Accuracy: {checkpoint['test_accuracy']}")
start_epoch = checkpoint["epoch"] + 1
logger.info(f"Resuming at epoch {start_epoch}")
model.load_state_dict(checkpoint["model_state_dict"])
optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
else:
logger.info("No checkpoint found, starting from scratch.")
# Training Loop
t = Timer()
for epoch in range(start_epoch, epochs):
t.start()
logger.info("-" * 10)
logger.info(f"Epoch {epoch}")
logger.info("-" * 10)
train_loss, train_accuracy = train_model(device, model,
train_set_loader, optimizer)
tensorboard_summary_writer.add_scalar("train_loss", train_loss, epoch)
tensorboard_summary_writer.add_scalar("train_accuracy", train_accuracy,
epoch)
test_accuracy = test_model(device, model, test_set_loader, optimizer)
tensorboard_summary_writer.add_scalar("test_accuracy", test_accuracy,
epoch)
# Save Checkpoint
logger.info("Saving checkpoint.")
torch.save(
{
'epoch': epoch,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'train_loss': train_loss,
'train_accuracy': train_accuracy,
'test_accuracy': test_accuracy
}, checkpoint_file_path)
elapsed_time = t.stop()
logger.info(f"End of epoch {epoch}, took {elapsed_time:0.4f} seconds.")
logger.info(f"Average Train Loss: {train_loss}")
logger.info(f"Top-1 Train Accuracy: {train_accuracy}")
logger.info(f"Top-1 Test Accuracy: {test_accuracy}")
logger.info("")
class XioAll(QtGui.QWidget):
'''这个类为主程序类
'''
HOST = 'localhost'
PORT = 8081
TOTAL = 0
isStatic = True
action = None
pre_action = None
action_video = None # 视频内能识别
pre_action_video = None
def __init__(self):
super(XioAll, self).__init__()
self.ui = ui.Ui_Form()
self.ui.setupUi(self)
self.frame_left = None
self.frame_right = None
self.is_work = True
self.one_static_time = 0 # 一次故障静止的时间
self.all_time = 0 # 一天的工作时间
self.q = MyQueue() # 存放帧队列,改为存放状态比较好
self.vision = Vision()
# 若日期发生改变，自行插入全零数据
da = data_access.EquipmentTimeData() # 对损失项统计表进行操作
result_loss = da.select_("select * from loss ORDER BY SJ DESC limit 1")
current_time = datetime.datetime.now().strftime('%Y-%m-%d')
if str(result_loss[0][0]) != current_time:
da.update('insert into loss(SJ,action1,action2,action3,action4,action5,action6)values'
'("%s",%d,%d,%d,%d,%d,%d)' % (current_time, 0, 0, 0, 0, 0, 0))
else:
pass
da_oee = data_access.OEEData() # 对oee实时利用率进行统计
result_oee = da_oee.select_('select * from oee_date ORDER BY SJC DESC limit 1')
if str(result_oee[0][0]) != current_time:
da_oee.update_('insert into oee_date(SJC,O8,O9,O10,O11,O12,O13,O14,O15,O16,O17,O18)values'
'("' + current_time + '",0,0,0,0,0,0,0,0,0,0,0)')
else:
pass
self.thread_figure = Timer('updatePlay()', sleep_time=120) # 该线程用来每隔2分钟刷新绘图区
self.connect(self.thread_figure, QtCore.SIGNAL('updatePlay()'), self.draw)
self.thread_figure.start()
self.server = ThreadedTCPServer((self.HOST, self.PORT), ThreadedTCPRequestHandler) # 该线程用来一直监听客户端的请求
self.server_thread = threading.Thread(target=self.server.serve_forever)
self.server_thread.start()
self.thread_video_receive = threading.Thread(target=self.video_receive_local) # 该线程用来读取视频流
self.thread_video_receive.start()
self.thread_time = Timer('updatePlay()') # 该线程用来每隔0.04秒在label上绘图
self.connect(self.thread_time, QtCore.SIGNAL('updatePlay()'), self.video_play)
self.thread_time.start()
self.thread_recog = Timer('updatePlay()', sleep_time=1) # 该线程用来每隔一秒分析图像
self.connect(self.thread_recog, QtCore.SIGNAL('updatePlay()'), self.video_recog)
self.thread_recog.start()
self.thread_data = Timer('updatePlay()', sleep_time=1800) # 该线程用来每隔半小时向数据库读取数据
self.connect(self.thread_data, QtCore.SIGNAL('updatePlay()'), self.data_read)
self.thread_data.start()
def video_receive_local(self, cam1='./videos/left_cam.mp4', cam2='./videos/right_cam.mp4', time_flag=True):
'''该方法用来接收本地视频
:param cam1: 左摄像头数据源
:param cam2: 右摄像头数据源
:param time_flag: 是否休眠，本地视频为True
:return: None
'''
self.left_cam = cv2.VideoCapture(cam1)
self.right_cam = cv2.VideoCapture(cam2)
ret_1, frame_1 = self.left_cam.read()
ret_2, frame_2 = self.right_cam.read()
while True:
self.frame_left = frame_1
self.frame_right = frame_2
if ret_1 is False:
self.left_cam = cv2.VideoCapture(cam1)
if ret_2 is False:
self.right_cam = cv2.VideoCapture(cam2)
ret_1, frame_1 = self.left_cam.read()
ret_1, frame_2 = self.right_cam.read()
if time_flag is True:
time.sleep(0.04)
def video_receive_rstp(self, cam1='rstp:', cam2='rstp:'):
'''该方法用来接收网络视频
:param cam1: 左摄像头数据源
:param cam2: 右摄像头数据源
:return: None
'''
self.video_receive_local(cam1=cam1, cam2=cam2, time_flag=False)
def video_play(self):
'''该方法用来播放视频
:return: None
'''
def label_show_left(frame, label=self.ui.label): # 左控件label播放
height, width, _ = frame.shape
frame_change = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame_resize = cv2.resize(frame_change, (360, 240), interpolation=cv2.INTER_AREA)
image = QtGui.QImage(frame_resize.data, frame_resize.shape[1], frame_resize.shape[0],
QtGui.QImage.Format_RGB888) # 处理成QImage
label.setPixmap(QtGui.QPixmap.fromImage(image))
def label_show_right(frame, label=self.ui.label_2): # 右空间Lable播放
label_show_left(frame, label)
if self.frame_left is not None:
label_show_left(self.frame_left)
if self.frame_right is not None:
label_show_right(self.frame_right)
def draw(self):
'''
展示图标
:return:
'''
def draw_fp(): # 绘制损失饼图
fp = Figure_Pie()
da = data_access.EquipmentData()
result = da.select()
fp.plot(*(result[-1][1], result[-1][2], result[-1][3], result[-1][4])) # '*'有一个解包的功能，将（1，1，1，1）解包为 1 1 1 1
graphicscene_fp = QtGui.QGraphicsScene()
graphicscene_fp.addWidget(fp.canvas)
self.ui.graphicsView_Pie.setScene(graphicscene_fp)
self.ui.graphicsView_Pie.show()
def draw_oee(): # 绘制oee日推图
current_time = datetime.datetime.now().strftime('%Y-%m-%d')
lossTime = data_access.EquipmentTimeData()
result_loss = lossTime.select_("select * from loss ORDER BY SJ DESC limit 1")
zongshijian = time.strftime('%H:%M:%S', time.localtime(time.time()))
huanxing = result_loss[0][1]
dailiao = result_loss[0][2]
shebeiguzhang = result_loss[0][3]
tingzhi = result_loss[0][4]
# qitashijian=result[0][5]
# kongyunzhuan=result[0][6]
fuheshijian = (int(zongshijian.split(':')[0]) - 8) * 3600 + int(zongshijian.split(':')[1]) * 60 + int(
zongshijian.split(':')[2]) - tingzhi
shijijiagong_1 = fuheshijian - huanxing - dailiao - shebeiguzhang
eff = int(shijijiagong_1 / fuheshijian * 100) # 计算效率
print(eff)
hour = time.localtime()[3] # 实时更新
da_oee = data_access.OEEData()
da_oee.update_("update oee_date set O" + str(hour) + "=" + str(eff) + ' where SJC="' + current_time + '"')
L_eff = []
oee = Figure_OEE()
da = data_access.OEEData()
result = da.select()
hour = time.localtime()[3]
if hour < 20:
for i in range(1, hour - 6):
L_eff.append(result[-1][i])
oee.plot(*tuple(L_eff)) # 参数
graphicscene_oee = QtGui.QGraphicsScene()
graphicscene_oee.addWidget(oee.canvas)
self.ui.graphicsView_OEE.setScene(graphicscene_oee)
self.ui.graphicsView_OEE.show()
def draw_loss(): # 绘制损失直方图
loss = Figure_Loss()
da = data_access.EquipmentTimeData()
result = da.select()
loss.plot(*(result[-1][1], result[-1][2], result[-1][3], result[-1][4]))
graphicscene_loss = QtGui.QGraphicsScene()
graphicscene_loss.addWidget(loss.canvas)
self.ui.graphicsView_Loss.setScene(graphicscene_loss)
self.ui.graphicsView_Loss.show()
def draw_mt(): # 绘制耗材使用图
mt = Figure_MT()
mt.plot()
graphicscene_mt = QtGui.QGraphicsScene()
graphicscene_mt.addWidget(mt.canvas)
self.ui.graphicsView_MT.setScene(graphicscene_mt)
self.ui.graphicsView_MT.show()
draw_fp()
draw_loss()
draw_mt()
draw_oee()
def video_recog(self):
'''
视频识别部分
:return:
'''
frame_left = self.frame_left # 原始彩色图，左边摄像头
frame_left_gray = cv2.cvtColor(frame_left, cv2.COLOR_BGR2GRAY) # 原始图的灰度图
# frame_right = self.frame_left # 原始彩色图
# frame_right_gray = cv2.cvtColor(frame_right, cv2.COLOR_BGR2GRAY)
def video_recog_left():
img = frame_left
spark = self.vision.find_spark(img)
self.q.enqueue(spark)
# print(spark)
if spark or True in self.q.queue: # 如果一段间隔时间内不断有火花（和机器移动，稍后完成），则说明机器必定处于工作状态
#print('work')
self.action_video = None
self.one_static_time = 0 # 恢复到运动后，一次静止时间重新清零
else:
# ******* 截图
self.one_static_time += 1 # 一次静止时间
if self.one_static_time % 60 == 0:
print('start or static')
print('静止了，往catch文件夹中查看原因')
t = time.localtime()
hour = t[3]
mini = t[4]
seco = t[5]
filename = str(hour) + '-' + str(mini) + '-' + str(seco)
cv2.imwrite('./catch/' + filename + '.jpg', img)
# ********
self.action = ThreadedTCPRequestHandler.action # 键盘操作
if self.action is not None: # 往面板上写当前由于什么原因导致机器静止
if self.pre_action is None:
print(self.action)
message = '[' + time.strftime('%Y-%m-%d %H:%M:%S',
time.localtime(time.time())) + ']' + str(self.action)
self.displayMessage(message)
if self.vision.tiaoshi(frame_left_gray):
self.action_video = 'tiaoshi'
if self.action_video is not None:
if self.pre_action_video is None:
print(self.action_video)
message = '[' + time.strftime('%Y-%m-%d %H:%M:%S',
time.localtime(time.time())) + ']' + str(self.action_video)
self.displayMessage(message)
def video_recog_right(): # 以后用来做换气瓶等的实现
pass
video_recog_left()
video_recog_right()
self.pre_action = self.action
self.pre_action_video = self.action_video
def data_read(self):
pass
def displayMessage(self, message):
self.ui.textBrowser.append(message)
def inference_mean_exemplar(
model,
current_epoch,
current_iter,
local_rank,
data_loader,
dataset_name,
device="cuda",
max_instance=3200,
mute=False,
):
model.train(False)
# convert to a torch.device for efficiency
device = torch.device(device)
if not mute:
logger = logging.getLogger("maskrcnn_benchmark.inference")
logger.info("Start evaluation")
total_timer = Timer()
inference_timer = Timer()
total_timer.tic()
torch.cuda.empty_cache()
if not mute:
pbar = tqdm(total=len(data_loader), desc="Validation in progress")
with torch.no_grad():
all_pred_obj, all_truth_obj, all_pred_attr, all_truth_attr = [], [], [], []
obj_loss_all, attr_loss_all = 0, 0
cnt = 0
for iteration, out_dict in enumerate(data_loader):
if type(max_instance) is int:
if iteration == max_instance // model.cfg.EXTERNAL.BATCH_SIZE:
break
if type(max_instance) is float:
if iteration > max_instance * len(
data_loader) // model.cfg.EXTERNAL.BATCH_SIZE:
break
# print(iteration)
images = torch.stack(out_dict['images'])
obj_labels = torch.cat(out_dict['object_labels'], -1)
attr_labels = torch.cat(out_dict['attribute_labels'], -1)
cropped_image = torch.stack(out_dict['cropped_image'])
images = images.to(device)
obj_labels = obj_labels.to(device)
attr_labels = attr_labels.to(device)
cropped_image = cropped_image.to(device)
# loss_dict = model(images, targets)
pred_obj = model.mean_of_exemplar_classify(cropped_image)
all_pred_obj.extend(to_list(pred_obj))
all_truth_obj.extend(to_list(obj_labels))
cnt += 1
if not mute:
pbar.update(1)
obj_f1 = f1_score(all_truth_obj, all_pred_obj, average='micro')
#attr_f1 = f1_score(all_truth_attr, all_pred_attr, average='micro')
obj_loss_all /= (cnt + 1e-10)
# wait for all processes to complete before measuring the time
total_time = total_timer.toc()
model.train(True)
return obj_f1, 0, len(all_truth_obj)
class XioPlayVideo(QtGui.QWidget):
'''这个类为主程序类
'''
def __init__(self):
super(XioPlayVideo, self).__init__()
self.ui = ui.Ui_Form()
self.ui.setupUi(self)
self.left_cam = cv2.VideoCapture('./videos/left_cam.mp4') # 左摄像头
self.right_cam = cv2.VideoCapture('./videos/right_cam.mp4')
self.frame_left = None
self.frame_right = None
self.thread_video_receive = threading.Thread(
target=self.video_receive_local) # 该线程用来读取视频流
self.thread_video_receive.start()
self.thread_time = Timer('updatePlay()') # 该线程用来每隔0.04秒在label上绘图
self.connect(self.thread_time, QtCore.SIGNAL('updatePlay()'),
self.video_play)
self.thread_time.start()
self.thread_recog = Timer('updatePlay()',
sleep_time=1) # 该线程用来每隔一秒分析图像
self.connect(self.thread_recog, QtCore.SIGNAL('updatePlay()'),
self.video_recog)
self.thread_recog.start()
self.thread_data = Timer('updatePlay()',
sleep_time=1800) # 该线程用来每隔半小时向数据库读取数据
self.connect(self.thread_data, QtCore.SIGNAL('updatePlay()'),
self.data_read)
self.thread_data.start()
self.thread_tcp = None # 该线程用来完成tcp，未写完
def video_receive_local(self,
cam1='./videos/left_cam.mp4',
cam2='./videos/right_cam.mp4',
time_flag=True):
'''该方法用来接收本地视频
:param cam1: 左摄像头数据源
:param cam2: 右摄像头数据源
:param time_flag: 是否休眠，本地视频为True
:return: None
'''
if self.left_cam.isOpened() is False:
self.left_cam = cv2.VideoCapture(cam1)
if self.right_cam.isOpened() is False:
self.right_cam = cv2.VideoCapture(cam2)
ret_1, frame_1 = self.left_cam.read()
ret_2, frame_2 = self.right_cam.read()
while True:
self.frame_left = frame_1
self.frame_right = frame_2
if ret_1 is False:
self.left_cam = cv2.VideoCapture(cam1)
if ret_2 is False:
self.right_cam = cv2.VideoCapture(cam2)
ret_1, frame_1 = self.left_cam.read()
ret_1, frame_2 = self.right_cam.read()
if time_flag is True:
time.sleep(0.04)
def video_receive_rstp(self, cam1='rstp:', cam2='rstp:'):
'''该方法用来接收网络视频
:param cam1: 左摄像头数据源
:param cam2: 右摄像头数据源
:return: None
'''
self.video_receive_local(cam1=cam1, cam2=cam2, time_flag=False)
def video_play(self):
'''该方法用来播放视频
:return: None
'''
def label_show_left(frame, label=self.ui.label): # 左控件label播放
height, width, _ = frame.shape
frame_change = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame_resize = cv2.resize(frame_change, (500, 300),
interpolation=cv2.INTER_AREA)
image = QtGui.QImage(frame_resize.data, frame_resize.shape[1],
frame_resize.shape[0],
QtGui.QImage.Format_RGB888) # 处理成QImage
label.setPixmap(QtGui.QPixmap.fromImage(image))
def label_show_right(frame, label=self.ui.label_2): # 右空间Lable播放
label_show_left(frame, label)
if self.frame_left is not None:
label_show_left(self.frame_left)
if self.frame_right is not None:
label_show_right(self.frame_right)
def video_recog(self):
pass
def data_read(self):
pass
def pretext_train(args, recorder):
if args.gpus is not None:
print("Use GPU: {} for pretext training".format(args.gpus))
num_class, data_length, image_tmpl = pt_data_config(args)
# print("tp_length is: ", data_length)
train_transforms, test_transforms, eval_transforms = pt_augmentation_config(
args)
train_loader, val_loader, eval_loader, train_samples, val_samples, eval_samples = pt_data_loader_init(
args, data_length, image_tmpl, train_transforms, test_transforms,
eval_transforms)
n_data = len(train_loader)
model, model_ema = pt_model_config(args, num_class)
# == optim config==
contrast, criterion, optimizer = pt_optim_init(args, model, n_data)
model = model.cuda()
# == load weights ==
model, model_ema = pt_load_weight(args, model, model_ema, optimizer,
contrast)
if args.pt_method in ['dsm', 'moco']:
model_ema = model_ema.cuda()
# copy weights from `model' to `model_ema'
moment_update(model, model_ema, 0)
cudnn.benchmark = True
# optionally resume from a checkpoint
args.start_epoch = 1
# ==================================== our data augmentation method=================================
if args.pt_method in ['dsm', 'dsm_triplet']:
pos_aug = GenPositive()
neg_aug = GenNegative()
# =======================================add message =====================
recorder.record_message('a', '=' * 100)
recorder.record_message('a', '-' * 40 + 'pretrain' + '-' * 40)
recorder.record_message('a', '=' * 100)
# ====================update lr_decay from str to numpy=========
iterations = args.pt_lr_decay_epochs.split(',')
args.pt_lr_decay_epochs = list([])
for it in iterations:
args.pt_lr_decay_epochs.append(int(it))
timer = Timer()
# routine
print('*' * 70 + 'Step1: pretrain' + '*' * 20 + '*' * 50)
for epoch in range(args.pt_start_epoch, args.pt_epochs + 1):
timer.tic()
pt_adjust_learning_rate(epoch, args, optimizer)
print("==> training...")
time1 = time.time()
if args.pt_method == "moco":
loss, prob = train_moco(epoch, train_loader, model, model_ema,
contrast, criterion, optimizer, args,
recorder)
elif args.pt_method == "dsm":
loss, prob = train_dsm(epoch, train_loader, model, model_ema,
contrast, criterion, optimizer, args,
pos_aug, neg_aug, recorder)
# loss, prob = epoch * 0.01, 0.02*epoch
elif args.pt_method == "dsm_triplet":
loss = train_dsm_triplet(epoch, train_loader, model, optimizer,
args, pos_aug, neg_aug, recorder)
else:
Exception("Not support method now!")
recorder.record_pt_train(loss)
time2 = time.time()
print('epoch {}, total time {:.2f}'.format(epoch, time2 - time1))
timer.toc()
left_time = timer.average_time * (args.pt_epochs - epoch)
message = "Step1: pretrain now loss is: {} left time is : {} now is: {}".format(
loss, timer.format(left_time), datetime.now())
print(message)
recorder.record_message('a', message)
state = {
'opt': args,
'model': model.state_dict(),
'contrast': contrast.state_dict(),
'optimizer': optimizer.state_dict(),
'epoch': epoch,
}
recorder.save_pt_model(args, state, epoch)
print("finished pretrain, the trained model is record in: {}".format(
recorder.pt_checkpoint))
return recorder.pt_checkpoint
