本文整理汇总了Python中pybrain.datasets.ClassificationDataSet.clear方法的典型用法代码示例。如果您正苦于以下问题：Python ClassificationDataSet.clear方法的具体用法？Python ClassificationDataSet.clear怎么用？Python ClassificationDataSet.clear使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pybrain.datasets.ClassificationDataSet的用法示例。
在下文中一共展示了ClassificationDataSet.clear方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __init__
# 需要导入模块: from pybrain.datasets import ClassificationDataSet [as 别名]
# 或者: from pybrain.datasets.ClassificationDataSet import clear [as 别名]
#.........这里部分代码省略.........
cv.Rectangle (image, self.pt1, self.pt2, (0, 255, 0), 1)
cv.SetImageROI(image, currentrect)
st = time.time()
out = self.ClassifyWindow(grayscale, currentrect)
print out, '|time', time.time() - st
cv.ResetImageROI(image)
if out == 1:
cv.Rectangle (image, (currentrect[0], currentrect[1]), (currentrect[2] + currentrect[0], currentrect[3] + currentrect[1]), (0, 255, 255), 5)
self.pt1, self.pt2 = PatchBoundary(tracks, self.pt1, self.pt2)
tmp = self.ImageFromRect(grayscale, currentrect)
features = FeaturesFromImg(grayscale, self.ninsize)
self.additionslds.addSample(features, [1])
for i in xrange(2):
badwin = self.ImageFromRect(grayscale, RandomRect(currentrect))
features = FeaturesFromImg(badwin, self.ninsize)
self.additionslds.addSample(features, [0])
print 'len of new dataset', len(self.additionslds)
'''
else:
rect = self.SearchObject(grayscale, currentrect)
if rect:
self.pt1, self.pt2 = RectToPoints(rect)
'''
if self.stage > 0:
tmp = self.ImageFromRect(grayscale, currentrect)
features = FeaturesFromImg(grayscale, self.ninsize)
self.additionslds.addSample(features, [1])
for i in xrange(2):
badwin = self.ImageFromRect(grayscale, RandomRect(currentrect))
features = FeaturesFromImg(badwin, self.ninsize)
self.additionslds.addSample(features, [0])
print 'len of new dataset', len(self.additionslds)
if len(self.additionslds) > 20:
self.additionslds._convertToOneOfMany( )
self.additionslds.outdim = self.net.outdim
#net = buildNetwork(self.ninsize[0] * self.ninsize[1], 96, self.additionslds.outdim, outclass=SoftmaxLayer)
trainer = RPropMinusTrainer(
self.net, dataset=self.additionslds,
momentum=0.1, verbose=True, weightdecay=0.01)
'''trainer = BackpropTrainer(
self.net, dataset=self.additionslds,
momentum=0.1, verbose=True, weightdecay=0.01)'''
trainer.trainEpochs( 3 )
self.additionslds.clear()
self.numoflearning += 1
if self.key == 113:
cv.SaveImage('img.bmp', image)
self.key = 255
if self.key == 119:
self.stage = 1
self.key = 255
if self.stage == 1 and self.numoflearning > 2:
self.stage = 2
'''
for item in tracks:
cv.Circle(image, (item[-1][0], item[-1][1]), 2, (0, 255, 0), -1)
'''
return image
def run(self):
DEVICE = 0 #/dev/video0
# create windows
cv.NamedWindow('Camera')
# create capture device
device = 0 # assume we want first device
capture = cv.CreateCameraCapture(DEVICE)
k = ''
while k !='q' :
frame = cv.QueryFrame(capture)#cv.RetrieveFrame(capture)
if frame is None:
break
cv.Flip(frame, None, 1)
frame = self.work(frame)
# display webcam image
cv.ShowImage('Camera', frame)
k = cv.WaitKey(10) % 0x100
if k !=255 :
self.key = k
print 'pressed', k